It comes as a folded mailer, the kind that lands between the grocery circular and the dentist reminder and is easy to throw away. Unfolded, in the flat cadence of a statute, the Department of Water Resources informs me that my home sits in a Levee Flood Protection Zone; that the levee between us and the river can fail, overtop, or erode; that if it does, this is roughly where the water would go; and that I should consider purchasing flood insurance.

The state is required to send it, to every property owner behind a state-federal levee across seventeen counties, every year, under a law passed two years after Katrina. The same page that explains the program notes, almost in passing, that in many of these places the risk of flooding is greater than the risk of fire. A strange sentence to read in California, yet a true one.

Sacramento exists because of levees. Two rivers meet here, and more than 1,600 miles of state-federal levee hold the Central Valley's water where the valley would prefer it. Every one of those miles is a shared thing. My house does not have its own levee. Which way my neighbor's yard slopes, whether the storm drains were cleared before the first big rain, how much the levee district could afford to repair this year, how much snow fell in the Sierra last winter, these decide whether the notice is just a formality or a forecast.

And yet the letter’s single instruction, the one act the law requires of the state each year, is addressed to me alone: go and buy a product (insurance).

I have kept it on my desk while writing this because it’s what prompted me to go down this rabbit hole of collective risk & how we price it. The state knows the risk is collective. It says so, with a map. Then it hands the remedy to individual households… the remedy (post-disaster), but not as a preventative solution.

It was not always arranged this way.

On the evening of December 7, 1736, twenty-odd Philadelphians signed a set of articles agreeing that whenever fire broke out in the city, each would arrive with at least half his buckets and bags and work to save whatever could be saved. The fourth article is the whole idea in a sentence: that we will, all of us, upon hearing of fire breaking out, immediately repair to the same (the full articles are preserved at Founders Online). Benjamin Franklin was the seventh name on the list. The Union Fire Company was modeled on Boston’s mutual fire societies, and unlike most of those, its members resolved to help anyone in distress, paying member or otherwise. Within sixteen years Philadelphia had eight volunteer companies, and a city built of wood rarely lost more than a house or two to a single blaze.

Notice the sequence. The brigade came first. Only in 1752 did Franklin convene the same men to found the Philadelphia Contributionship for the Insurance of Houses from Loss by Fire — the oldest insurer in America, still writing policies today. Its emblem was four clasped hands. It surveyed every building before agreeing to cover it, and in 1769 stopped insuring wooden structures altogether.

Insurance, in its founding American form, was the financial appendix to a physical mutual. The pooling of labor preceded the pooling of money. We have since kept the appendix and amputated the preceding body.

The unit of loss and the system of risk

We have designed disaster insurance around the unit that experiences the loss, the household, rather than the system that produces the risk.

A house floods. But the probability of its flooding is a function of:

• upstream land use,

• drainage,

• levees,

• wetlands,

• the neighboring parcel’s grading,

• municipal culvert maintenance,

• building codes,

• and watershed management

A house burns. But whether the ember that lands on its roof finds a fuel path depends on:

• vegetation across property lines,

• prescribed burning on public land,

• utility infrastructure,

• road width,

• water pressure

• and what the surrounding five hundred homes did last spring

Asking the homeowner to independently insure against these risks is structurally strange. The principle underneath the strangeness is simple:

Risk is individual at the moment of loss, but collective in its creation and prevention.

Today’s insurance system enters almost entirely at the first half of that sentence. It prices the moment of loss with enormous actuarial sophistication and organizes society around the second half hardly at all. The result is a market that, when the collective risk rises past what individual premiums can carry, does the only thing a loss-financing institution can do: it leaves.

California is the live demonstration. Homeowners premiums rose 84% between late 2020 and March 2026. The FAIR Plan, the state’s insurer of last resort, saw enrollment jump 43% between September 2024 and December 2025 after the Los Angeles fires, now carries roughly $750 billion in exposure, and levied a $1 billion assessment on member insurers, the first in three decades, to pay Palisades and Eaton claims. A Bloomberg analysis found 14% of its policies now sit in largely urban, lower-fire-risk areas. The retreat has spilled out of the wildland-urban interface and into the suburbs.

This is what happens when you pool only the loss. The pool fills faster than anyone is draining it.

I wrote last year about where this goes if nothing else changes. In The Coming Climate Financial Crisis, I traced what fills the vacuum when carriers retreat: catastrophe bonds, weather derivatives, bundled climate-risk securities… instruments that relocate the risk across balance sheets without reducing a single unit of it, in a pattern uncomfortably close to the mortgage securitization that preceded 2008. That essay asked who steps in when the insurers leave. This one is the other half of the answer. The question is what to do so that fewer of them have to.

Two jobs, one wrapper

I would not eliminate insurance. I would unbundle the multi-faceted aspect its attempting to do.

Insurance finances residual loss. Resilience collectively reduces the probability and severity of that loss. These are different jobs, requiring different institutions and we have jammed them into a single product that can only do the first.

The Dutch never made that mistake, because they could not afford to. Around 1122, farmers along the Rhine near Utrecht organized to build and maintain a dam; by 1248 the Count of Holland had chartered the Hoogheemraadschap van Rijnland to coordinate flood protection across a district. These waterschappen are the oldest form of democratic governance in the Netherlands, older than the provinces, older than the States General and twenty-one of them still operate today as a fourth layer of government with their own elections and their own taxes. Their founding logic was that dikes were built and maintained by those directly benefiting from them: every landholder contributed labor and levy, and the motto was he who does not contribute is not protected.

The water board is a resilience institution. It holds no reserves against a breach. Its entire product is that the breach does not happen.

Sacramento, where I live, sits on a living fossil of the same form. The Delta’s levees were mostly built by landowners and are maintained by roughly a hundred reclamation districts, the oldest special districts in Sacramento County, most formed before 1900, that tax themselves through per-acre assessments proportional to the benefit each parcel receives. 65% of the Delta’s 1,115 miles of levee are non-project levees, built and kept by island landowners rather than the Army Corps. The median district runs on about $370,000 a year. When a project qualifies for state cost-share, the district still fronts the money and goes into debt waiting for reimbursement.

These are imperfect, underfunded and often invisible institutions. They are also proof that the form works: a legally constituted body, governed by the people behind the levee, whose job is prevention rather than payout.

What already exists and where it falls short

The closest thing the modern insurance system has to a water board is FEMA’s Community Rating System. A community that goes beyond minimum floodplain standards earns points across nineteen creditable activities:

• drainage maintenance,

• open-space preservation,

• public outreach,

• warning systems

and every National Flood Insurance Program (NFIP) policy in that community is discounted from 5% up to 45%.

Philadelphia, Franklin’s own city, entered CRS this year as a Class 7, earning residents a 15% cut.

California has done something similar for fire. The Safer from Wildfires regulation requires every admitted insurer that prices wildfire risk to file discounts for twelve mitigation measures, two of which are community-level: recognition as a Firewise USA site, or designation as a Fire Risk Reduction Community. Filed Firewise discounts run from under 1% to 20% of the wildfire portion of the premium. The FAIR Plan itself offers an additional 10% for Firewise neighborhoods.

So the bridge between collective action and individual premium has been half-built. Look at what it credits, though. CRS rewards municipal programs and documentation… the floodplain manager’s binder. Firewise rewards a neighborhood’s plan and its annual reporting. Neither has a line item for organized neighbor labor. Nobody is counting the hours.

Nobody has built the layer that says: 500 verified volunteer hours clearing a shared ember corridor, or reinforcing a levee toe, or restoring a creek edge, equals a measurable reduction in expected loss, equals a community-wide credit, equals a carrier funding the materials because it beats paying the claim.

That layer is the missing institution.

The Resilience Commons

Call it a Resilience Commons: neighborhood-scale civic infrastructure in which residents, governments, insurers, utilities, lenders, nonprofits, contractors and philanthropies all participate because every one of them benefits when systemic risk falls.

The difference from a tax-funded public works program is participation. Imagine Sacramento saying: here are the 30 things a neighborhood can do to reduce heat, flood, fire and outage risk. We supply the engineering standards, the tools, the materials, the training and the permits. You supply some of the labor, the coordination and the community stewardship.

Then, every weekend, something that looks like Habitat for Humanity for Resilience. Here are some examples of ‘community activities’:

• Community days for rain gardens and bioswales.

• Clearing drainage routes before the first atmospheric river.

• Planting shade trees along the corridors that run hottest.

• Replacing combustible fencing where it touches the house.

• Cutting defensible-space corridors across property lines, because embers do not read deeds.

• Installing ember-resistant vents.

• Mapping which neighbors are elderly or medically fragile before the outage, not during it.

• Inspecting culverts.

• Deploying shared water storage.

• Standing up a neighborhood battery.

• Keeping the evacuation road passable.

Some of this requires licensed contractors. A great deal of it does not.

An enormous share of resilience is coordination, maintenance, landscaping, observation, and simple retrofits… exactly the categories Franklin’s articles assigned to men with buckets.

People are no longer told to “prepare for climate change” (or El Niño, this year). They are handed something concrete to do together. That is the difference between a public and a population.

The resilience dividend and who owes it

The feedback loop is the economic heart of this initiative:

Which raises the financing question in its correct form. If everyone benefits, everyone should be able to contribute.

Today a homeowner is asked to spend $8,000 hardening a property alone. In a Commons, the homeowner contributes time or money; the city contributes materials, permitting and technical expertise (with AI & technology as an accelerant to enabling the technical expertise to be distributed & permitting approval); the insurer contributes because claims fall; the utility contributes because grid-ignition exposure falls; FEMA and the state contribute because future disaster outlays fall; the lender contributes because collateral risk falls; the health system contributes where heat and smoke resilience reduces admissions; and philanthropy covers the households that cannot cover themselves.

The numbers are not speculative. The National Institute of Building Sciences’ Mitigation Saves study, peer-reviewed across seventy organizations, found that federally funded mitigation since 1995 cost $27 billion and will save $160 billion, $6 per $1.

• Modern building codes return 11:1.

• Riverine flood mitigation runs as high as 7:1.

• National Institute of Building Sciences (NIBS) titles one of its own briefing sheets “Everybody Saves: Multiple Stakeholders”, which is the point precisely.

If a dollar today avoids six of expected future loss, those six dollars have several beneficiaries. The institutional innovation is pulling some of that avoided future cost forward to finance the prevention. You could, quite literally, calculate a resilience dividend and write the cap table.

This starts to look less like property insurance and more like preventive medicine for places. We do not want health insurance that merely pays for the heart attack. We want a health system that helps prevent it.

Property insurance today essentially says: we will become deeply involved once your neighborhood burns down. The Contributionship in 1769 said something different… it sent a surveyor first to reduce risk for all beforehand.

Neighboring as Infrastructure

There is a philosophical dimension here that matters more than the finance.

Modern resilience policy imagines citizens as customers of government. Government builds the levee; the homeowner buys the policy; the contractor installs the vent. Each transaction is clean and each party is alone.

Historically, communities maintained vast amounts of common infrastructure themselves, through institutions built for the purpose:

• The water boards.

• The volunteer companies.

• The barn raising.

• The irrigation district.

• The drainage association.

• The mutual aid society.

Also, there is precedent in our electric infrastructure… in 1934, only 11% of American farms had electricity because investor-owned utilities found the countryside unprofitable; the Rural Electrification Act of 1936 offered low-cost loans, the utilities declined them and farmer-owned cooperatives took the money and strung the lines. By 1950 close to 80% of farms had power. The state supplied capital and standards. The cooperatives supplied everything else.

That is the formula climate adaptation needs to rediscover: state capacity x community capacity. Government brings expertise, coordination, financing and heavy infrastructure. People bring distributed intelligence, stewardship, labor and the social coordination no agency can manufacture. And insurers become something more interesting than actuarial casinos sitting downstream of catastrophe. They become risk-reduction investors, with a balance-sheet reason to fund the Commons.

The hard edges

It would be dishonest to leave the Dutch motto out. He who does not contribute is not protected is the enforcement mechanism and a Commons without one becomes a free-rider problem in a fleece vest. The reclamation districts solve this with assessment liens; the water boards solve it with taxation and elections.

A Resilience Commons needs an answer… most likely a hybrid of a modest baseline levy and a labor credit that can be bought out and the answer cannot be that working families are expected to spend weekends on levees while their wealthier neighbors write a check. The credit for labor and the credit for money have to be fungible and the philanthropic layer has to sit under the households for whom neither is easy.

Verification is the second edge. An insurer will not price a credit it cannot audit. Volunteer hours are easy to inflate and hard to map to reduced expected loss. This is where the Commons stops being a nostalgic institution and becomes a data institution: parcel-level risk baselines, interventions logged and geotagged, outcomes measured against synthetic controls, credits issued against demonstrated deltas rather than good intentions. The measurement layer is the product.

And the third edge is the one the Contributionship found in 1769. An insurer that learns which buildings burn can respond by reducing risk or by refusing to cover the buildings. Both lower its losses. Only one lowers the fire. A Commons must be structured so that the cheaper path for the carrier is funding the mitigation, not exiting the ZIP code… which means the credit has to be large enough and the exit costly enough, that the actuary and the neighbor want the same thing.

Begin before there is anything to insure

‘Natural disaster’ insurance should begin before there is anything to insure.

Franklin understood this in 1736 and formalized it in 1752. The Dutch understood it in 1122 and have been re-electing the same institution for nine centuries. The farmers of the Delta understood it well enough to build 1,100 miles of levee before the Corps of Engineers arrived.

We have the pieces… the CRS shows premiums can move with community action, Safer from Wildfires shows regulators can compel carriers to credit neighborhoods, and NIBS’ Mitigation Saves shows the dividend is real and large.

What is missing is the layer between insurance, municipal infrastructure and civic participation: the body that measures neighborhood risk, identifies interventions, organizes residents, finances improvements, and proves the reduction in expected loss to everyone who benefits from it.

That layer is a fund, a data platform, and a Saturday morning with buckets, all at once. It is also, I suspect, an investable institution.

Let us Pool the Prevention.

A month later OpenAI published its own position on biology, defining a model of “High” capability as one that could give meaningful help to novices with only basic training, enough to create a biological or chemical threat. Its head of safety systems had already said the company expected the successors to its o3 reasoning model to reach that level. Within months all three leading labs had shipped models under heightened biological safeguards, and the International AI Safety Report, chaired by Yoshua Bengio, written by more than a hundred experts, backed by some thirty governments, recorded the reason without embellishment: pre-deployment testing could not rule out that the models would help someone build a weapon.

The same report found that one AI system now outperforms 94% of virology experts at troubleshooting laboratory work, and that of 375 biological AI tools surveyed, only 3% carry any safeguards at all.

Then the question stopped being what these systems might say and became what they can design. In October 2025 a Microsoft-led team published in Science the result of a two-year confidential project. Using freely available protein design tools, they redesigned dangerous proteins so that the new versions slipped past the screening software DNA synthesis companies rely on to catch dangerous orders, 76,080 sequences, generated to test a suspicion. They borrowed a term from computer security and called it a “zero day”: a hole nobody knew was there. Publication was withheld until patches had been written and distributed worldwide.

None of that is a fringe position. It is the hedged, lawyered, publicly filed consensus of the people with the most commercial reason to be relaxed about it.

The wall was never information

The argument for taking AI seriously as a biosecurity problem rests on a single observation about what the barrier has always been.

The barrier is know-how… the accumulated, unwritten, failure-derived judgment that lives in the hands of people who have done a thing many times and cannot fully explain how. Which batch of reagent behaves badly. Why the yield collapsed on Tuesday. What the written protocol leaves out because everyone in the lab already knows it. Information was never the constraint: the genetic sequences of dangerous pathogens have been public for two decades, and having DNA made to order is now a service you can buy. Know-how is expensive, slow, and passed hand to hand, and it has been the load-bearing wall of biological arms control for fifty years.

Large models dissolve that wall. That is close to a description of what they are for. A system trained on the entire published record of methods, failed experiments, appendices and lab-forum troubleshooting can compress a decade of bench apprenticeship into something that answers at three in the morning and never condescends. Every legitimate use of that capability, and there are many, and they are enormous, runs on the same mechanism as the illegitimate one. The frontier labs know this. It is why testing models for biological uplift is the one area where the industry’s safety apparatus carries real weight.

Take that seriously and you arrive somewhere uncomfortable. The number of actors who can attempt something ambitious in biology is going up, the skill required is going down, and the deterrence that governed nuclear weapons: visible facilities, detectable materials, tests that announce themselves, has no equivalent here.

What would they actually build?

So consider what an ambitious actor would actually build, given the choice.

Let’s begin by ruling out the obvious. A respiratory virus is the nightmare everyone has already prepared for, it spreads too indiscriminately to be useful to anyone with a border to defend and the entire post-2020 surveillance system is pointed straight at it.

Consider instead something mosquito-borne. A dengue-equivalent: a virus causing a fever that looks like a hundred other illnesses, that produces no symptoms at all in roughly three out of four people it infects, that kills rarely enough to avoid triggering emergency protocols and often enough to fill hospital beds. Dengue has a further property that matters here: the severe form appears disproportionately on a person’s second infection, with a different strain. The damage arrives years late, in a population that has already declared the first wave survived.

Pair it with a mosquito chosen for one quality: the ability to survive winter where the models say it cannot.

I am not going to sketch how any of that would be done, and the omission is not squeamishness. The engineering is the least interesting part of this and the part with the least policy leverage. What matters is choosing the target, and choosing the target requires no laboratory at all, only a map of warm sheltered places and a list of countries whose doctors have no reason to suspect the disease.

Dryness was never the protection

Target selection here has nothing to do with hostility. The logic selects for absence — for places with no prior exposure to the disease, wherever they happen to fall on anyone’s list of friends and enemies. That is the uncomfortable part. The qualifying characteristics are not weaknesses. Several of them are the marks of a well-run country.

Picture a hot, dry city. Extreme summer heat, almost no standing water in nature, a health system genuinely excellent at the diseases it expects. It has no history of the disease, so its population has no prior immunity and no partial protection from an earlier strain. Its doctors have never seen the illness outside a textbook and will record the first two hundred cases as fever of unknown origin. It has no mosquito control agency, because it never needed one. And it is a city built on water infrastructure: irrigation for landscaping that should not exist, condensation dripping from air conditioners running five months a year, construction water, stored containers, ornamental fountains. The dryness that looks like protection on a climate map is undone by the built environment at the scale of a single apartment block. Cities of this description exist on four continents.

Then pick somewhere cold. Somewhere the phrase “no mosquitoes” appears in the tourist brochures. The same logic runs in reverse: the weather outdoors is hostile and the weather indoors is not. Cellars, barns, geothermal heating pipes, car parks, service tunnels. A city is a lattice of warm, stable hiding places, and no national risk model treats it that way.

The attack, if you can call it that, leaves nothing to trace. No launch, no plume, no seismic signature. A shipping container and a season. The first sign is a cluster of odd fevers in a hospital with no reason to test for what it is looking at. By the time the blood work comes back, the mosquitoes have bred three generations. There is no proportionate response, because there is no identified aggressor and by the time genetic analysis could name one, the population is established and the question is academic.

The strategic payoff is not measured in deaths. It is the permanent conversion of a health system into a chronic-cost system, the repricing of its tourism and its insurance, and the quiet discovery that a country with no mosquito control apparatus needs years and a new agency to build one.

Who do you retaliate against?

Here is the part that should actually worry you.

Every element of that scenario except the intent is already in the record.

Dengue virus has been found in Aedes aegypti mosquitoes in Dubai — a species that, along with locally acquired dengue, had not been reported in the UAE in recent decades, in a Gulf region the mosquito had largely never colonised until urbanisation changed the arithmetic. Local cases followed the heavy rains of April 2024. The UAE’s own Ministry of Climate Change and Environment has named this exact risk, identifying rising temperatures and shifting rainfall as drivers of mosquito-borne disease including dengue. The warning was correct, specific and published. It did not produce a mosquito control agency.

Los Angeles County sustained a seven-week chain of locally acquired dengue in the autumn of 2024, in a state where the mosquito arrived in 2013 and reached more than half of all counties within a decade.

Modelling published in The Lancet Regional Health now projects dengue risk expanding across California as the climate and the landscape change. This July, Aedes aegypti turned up in Vacaville, forty minutes from my desk. The US has passed five hundred dengue cases this year.

Europe’s disease agency has stopped calling this unusual and now describes record chikungunya and West Nile seasons as “the new normal”, with the Asian tiger mosquito established in 369 European regions, up from 114 a decade ago.

And in October 2025, Iceland, one of exactly two places on earth without mosquitoes, recorded its first: a cold-tolerant species that survives winter in sheltered buildings and which entomologists expect to stay.

That same autumn, Britain’s health agency published the first-ever detection of Aedes aegypti eggs in the UK, found in a freight storage facility near Heathrow, alongside tiger mosquito eggs at a motorway service station in Kent. The list of mosquito-free regions on this planet now has one entry, and it is Antarctica.

Nobody engineered any of this. The mosquitoes rode freight, as they have for four centuries. The climate decided whether they stayed.

Which is to say: the adversary in section III is doing, at enormous cost and enormous legal risk, a slightly accelerated version of what the atmosphere is doing for free, continuously, to every country at once, with no attribution problem because there is nobody to attribute it to.

A threat with no department

The engineered-disease scenario deserves to be taken seriously on its own terms. It is also the most useful device available for a much larger argument, because it is the version of the problem national security institutions are structurally capable of hearing. Give a defence ministry a hostile actor and it will find budget. Give it a trend line and it will find a working group.

Climate is the trend line that behaves like a hostile actor.

It is energy security. Whether the grid holds through back-to-back heatwaves, the geopolitics of refined lithium and rare earths, the exposure of any country whose energy transition depends on a single supplier.

It is biosecurity. Mosquitoes moving into new territory, ancient pathogens surfacing from thawing permafrost, livestock disease shifting geography, and more contact between people and wildlife as habitat shrinks, a continuous, unattributable, non-negotiable expansion of the disease surface.

It is financial and insurance security. Insurers withdrawing from California and Florida, mortgage markets that cannot price thirty-year risk on twenty-year assets, government borrowing costs that have already begun to reflect physical exposure, and city budgets resting on actuarial tables that assume the climate stands still.

It is food, water and migration security, and each of those has its own agency, its own budget cycle, and its own reason to believe someone else is handling it.

The failure is architectural. Every one of those propositions is already well documented inside the relevant department; the analysis is not what is missing. Climate is a whole-system input broken up across a dozen institutions that share no clock, no budget line, and no common risk register. Each treats it as someone else’s problem on someone else’s balance sheet, which is a precise description of how a system full of individually competent parts produces collectively stupid behaviour.

A multi-sector climate security strategy is the recognition that a threat arriving simultaneously through energy, disease, capital, food and population cannot be defended against by any institution organized around one of them.

I made a version of this argument in last year, August 2025, in a piece called In Defense of Defense, two months before Iceland recorded its first mosquito. The line I keep returning to:

Imagine the collapse of pollinators, or the hijacking of mosquitoes by deadly disease vectors, not as biodiversity issues, but as catastrophic defense failures.

I wrote that as a provocation then, the record has now made it a description of what is happening.

Four cities, one summer

I have spent this summer across four cities: Toronto, London, Sacramento & Madrid. I have been bitten by mosquitos in all four.

I mention it because I went looking afterwards, and every one of those cities has either mosquito-borne disease circulating this season or an invasive mosquito that was absent a generation ago.

• Toronto confirmed its first human West Nile case of the year on 30 July.

• Sacramento County confirmed its first in early July, alongside thirty-one dead birds and twenty mosquito samples testing positive.

• Madrid, inland, continental, on a plateau 650 metres up, first picked up the tiger mosquito in the late 2010s and has watched it settle in, municipality by municipality, ever since.

• And southeast England, Heathrow included, is now a place where the eggs of both invasive Aedes species have turned up in traps.

Four cities, two continents, one indifferent summer and every one of them outside the latitude where any of this was supposed to be my problem. None of these places required an adversary or AI to create this growing health threat.

Author’s note/remark: funnily the biosecurity safeguards in these AI systems triggered when I asked them to review my first draft write-up as they deemed the research & writing warranted a trigger & blocked output!

Several companies in our portfolio have begun scaling with public and corporate stakeholders, and their paths are starting to rhyme. This is a first in a series of works of what I see is the similarities they share.

To begin, I’m starting further back than finance. Before asking what such a mechanism should contain, I wanted to understand what kind of object a first-of-a-kind project actually is… and biology has been working on that problem for four billion years.

What follows is an exploration for reading projects as living systems & for why most first-of-a-kind projects are built to suppress deviation rather than learn from it.

Consciousness is the interface through which we operate the most complex system any of us will ever meet… our body.

The interface mostly runs in the background. Breath, temperature, hunger, balance, the coordination happens silently, and we notice it only when it fails. Homeostasis is the mode in which we exist. The thin surface we call attention sits over an enormous machinery of feedback loops that remains politely out of view. When the machinery is intact, the body appears to us as a single coherent thing. When it fails, we discover that it was a system all along, held together by an operating consciousness we never had to see.

Large projects have their own version of this arrangement. They too are systems maintained by feedback loops most participants never notice. They too can appear coherent for years, for decades, before revealing that no operating consciousness was ever really keeping them alive. And they too, like bodies, tend to be understood only when they begin to fail…

In 1881, the French began work in Panama under Ferdinand de Lesseps, who carried the surest reputation in nineteenth-century infrastructure. He had come from successfully building the Suez Canal.

He had the capital, the engineering talent of France and the political backing of a nation that wanted very much for him to be right. He spent the next eight years building the wrong thing, in the wrong way, with the wrong sensing apparatus, and bankrupted the better part of a generation of French savers in the process.

Twenty-two thousand workers died. The company collapsed in scandal. The mosquito had been named as the vector of yellow fever in 1881. Nobody believed it until long after the French had lost. The hypothesis sat unaccepted until long after they had lost.

The Americans, arriving two decades later, had broadly similar engineers. What they brought was a different architecture for iterating on First-of-a-Kind project development.

This essay concerns the architecture of these unique project types and why, more than a century later, most first-of-a-kind projects are still being built the French way.

The grammar of failure

What undid de Lesseps lived underneath the engineering. He imported his Suez model, a sea-level canal cut through sand, into a jungle where geology, hydrology, and disease all behaved differently. When reality diverged from the model, his organization suppressed the divergence. Casualty reports were softened. Geological surveys were dismissed.

The financial structure, built on retail bond issuance to French households, made every admission of error politically catastrophic. The project could not afford to learn.

The American effort that followed reorganized around a single different premise: the model would have to change. Under John Stevens, the second chief engineer, the sea-level canal became a lock canal.

The sanitation department was made an independent bureau and empowered to act on William Gorgas’s mosquito programme over the objections of a commission that thought him a crackpot. The capital structure was sovereign, which meant it could absorb iteration. The engineering, medical, and political layers ran on partially independent feedback loops, each allowed to be surprised without taking down the others.

This is, at root, a story about the architecture of identity under surprise. The French project could not deviate from its founding model without ceasing to be itself. The American project was built so that deviation was the design.

Identity is the discipline of selective absorption.

What biology already knew

Biology has been solving this problem for four billion years. The cleanest formulation comes from neuroscience, by way of the psychiatrist and neuroscientist Karl Friston, who has spent two decades arguing that any system which maintains its identity over time must do so by minimizing a quantity called variational free energy. The mathematics is forbidding; the intuition is simple.

A living system carries a model of its world and receives a continuous stream of sensory evidence about that world. Free energy is, roughly, the discrepancy between the two: the surprise the system encounters when reality refuses to match its predictions. The system has two ways to reduce this discrepancy. It can update its model so that future predictions are better. It can act on the world to bring reality back inside the predicted range. Perception and action, in this view, are the same activity seen from two sides.

The cell does this perpetually. It samples its environment, predicts what comes next, and acts when prediction errors exceed tolerance. It defends a small number of variables ferociously: pH, temperature, oxygen. Nearly everything else is allowed to flex.

Crucially, it maintains a statistical boundary, a Markov blanket, between its interior states and the world. The blanket is what makes the cell a cell. It permits exchange while preserving identity.

A project, viewed honestly, is the same kind of object. It carries a model of itself. It has sensors. It has interior states and exterior pressures. It has, or fails to have, a coherent boundary between the two. The project that persists under stress is the one whose architecture knows where its blanket runs, what is permitted to cross it, and which interior variables must be defended.

What persists is what predicts.

On the consciousness of coalitions

There is a further move available worth making. The same body of theory that explains how a cell persists has a great deal to say about what it means for a system to be aware of itself.

Two strands of consciousness research matter here. The first, Giulio Tononi’s integrated information theory, holds that consciousness is a measure of how much a system’s information is irreducibly integrated across its parts, a quantity Tononi labels Φ.

A high-Φ system is one where you cannot understand any part without simultaneously understanding many others. A low-Φ system is a collection of mostly independent components that happen to share a label. The second strand, global workspace theory, treats consciousness as a broadcast layer: local processing becomes globally available, and the system becomes capable of acting as a whole.

Most project coalitions are low-Φ structures. They share documents but not models. Each stakeholder holds information that is largely inaccessible to the others. They have been bound together by contract, which is a weak form of integration. When stress arrives, the coalition fragments along its original seams, because there is no coherent whole to which any part can speak.

The coalitions that survive surprise, Panama under Stevens, Apollo under Webb, the better corners of the modern semiconductor supply chain, share a property the contracting documents never name. Their parts run each other’s models.

• The lender understands what the engineer is solving for.

• The engineer understands the regulator’s constraint set.

• The community knows what the financier is exposed to.

What binds them is harder to put in a contract: mutual prediction, shared interior life, the practical capacity to model one another under pressure.

A coalition is conscious in proportion to how well its parts can model each other.

The default mode

Where does that shared interior life actually form? Not, as it turns out, on the agenda.

The brain has two large-scale networks that toggle against each other. The task-positive network attends to immediate stimuli: the spreadsheet, the meeting, the deliverable. The default mode network activates when the brain is ostensibly idle, and engages in autobiographical memory, future scenario construction, and the modeling of other minds.

The default mode is where the brain integrates its days into a coherent self, rehearses possible futures, and develops theory of mind about the people it is going to meet tomorrow. Brains that lose default mode function lose, in measurable ways, the capacity to be themselves.

Project coalitions have a default mode, though we tend to call it something else… the dinner before the term sheet, the walk between sessions at the offsite, the long thread that wanders off-topic, the unscheduled call where someone says what they actually think. We treat these as overhead or relationship-building. They are the substrate on which coalitional intelligence is built. The default mode of a coalition is where its parts come to run each other’s models well enough that the contractual layer can hold.

Quarterly reports, structured updates, formal governance. A coalition that runs only on these has no default mode network.

It will look efficient on the dashboard but it will fragment at the first deviation, particularly if they haven’t pre-defined what resilience looks like for their respective organization.

No coalition becomes conscious of itself on the agenda.

Three Quiet Errors

Most first-of-a-kind projects fail along three predictable lines that follow directly from this architecture.

The frozen prior. Stakeholders enter with mental models that are never formally updated. When reality diverges, the response is renegotiation under duress rather than recalibration by design. The Panama Company could not update its model and remain solvent. Most modern infrastructure vehicles inherit the same defect, dressed in modern documents.

Note: The Panama Company’s yellow fever was not an unknown unknown. It was a phenomenon everyone had seen, whose mechanism no one had modeled, what we call in futurist scnearios, a black jellyfish, the risk that arrives wearing the face of something familiar.

Mismatched precision. The lender watches debt coverage. The developer watches schedule. The offtaker watches reliability. The community watches the externality nobody priced. They are looking at the same project and weighting different signals within it. Conflict surfaces at the threshold breach, never at the drift, and by the time the dashboards agree the coalition has already broken.

Passive monitoring. Covenant reporting is a financial fossil, a residue of the era when data was expensive. The instrumentation now exists technically; the governance has not caught up. We have built digital twins and operate them as if they were quarterly newsletters.

The biological correction in each case is the same: continuous prediction, selective surprise absorption, and a clear architectural decision about which layers are permitted to learn and which must hold.

Allostasis

There is a deeper conceptual move available, from homeostasis to allostasis. Homeostasis defends a fixed set-point. Allostasis adjusts the set-point in anticipation of change. Living systems do not sit in equilibrium with their environments; they move with them, shifting their targets as the world shifts.

Most project structures defend fixed equilibria: fixed prices, fixed covenants, fixed milestones. They are then surprised when the world refuses to hold still.

The allostatic version writes recalibration into the structure itself: indexed pricing, milestone-gated capital, planned governance reviews, contingent tranches.

The body has done this for several hundred million years and we are slowly importing the vocabulary and this understanding.

The choice underneath

Every first-of-a-kind financing hides a deeper design question that most term sheets refuse to name. Are you building this project to survive surprise, or to learn from it?

The survival-mode project is over-collateralized, fixed-offtake, conservatively covenanted. It minimizes free energy by suppressing variance. It is brittle under deviation, and legible to traditional capital. Most of what gets built lives here.

The learning-mode project is staged, instrumented, milestone-unlocked, contingent. It minimizes free energy by improving the model. It is antifragile under deviation, and largely illegible to capital trained on the older paradigm.

The interesting commercial space, the one some of us are quietly working in, sits in the instruments that make learning-mode projects legible to institutional capital. The translation layer is the sense-making infrastructure itself. It is what allows learning to be underwritten by capital that does not yet know how to underwrite learning.

Coda

The systems that persist in nature have evolved architectures for absorbing the right amount of surprise in the right places. They run continuous prediction. They defend the variables that must hold. They flex the ones permitted to move. They share interior life across their parts, integrated tightly enough that the whole can speak when the world demands a response. They develop these capacities in the rest periods between tasks, where the integration work is quietly done.

First-of-a-kind project finance is, structurally, an identity-preservation problem under high uncertainty. Its biological analogue is fully specified, four billion years old, and freely available in the substrate of every living thing.

De Lesseps built a static model he could not revise. Stevens, whose name almost nobody remembers, built something that could learn.

The work still to be done is to make the second kind contractible, at the rate the world is now producing surprises.

Some are black swans, genuinely unforeseeable. Most are black jellyfish, familiar phenomena that turn uncontrollable at scale and tipping points we watched approach. Most of what will break the next decade of infrastructure is already visible to someone.

The outlier was once the thing a structure survived. It is becoming the thing a structure is for.

The result is usually filed under “wisdom of crowds” and left there. But the interesting question was never that the crowd was accurate. The interesting question is why? and under what conditions the accuracy disappears.

The crowd at Plymouth worked because it disagreed:

• The farmer estimated from years of hauling feed;

• the butcher worked backward from carcass yields;

• each entrant drew on a different contact with the animal world, and each brought independent errors.

Aggregate enough independent errors and they cancel; what remains is signal. The magic ingredient was never the size of the crowd, but in fact was the statistical independence of its mistakes.

This condition is fragile: though the failure mode is specific. Talking, by itself, can help a crowd when what circulates is information. What corrupts a crowd is the circulation of anchors: a posted number, a confident voice, a shared almanac that everyone consults before guessing. Then the errors correlate and a correlated crowd is confidently, collectively wrong. Diversity of information, not diversity of appearance, is what makes many minds smarter than one.

Hold that thought, because we are currently running the largest correlated-crowd experiment in history…

The Consensus Machine

A large language model is, at its core, a lossy compression of the statistical distribution of human text. Train on the corpus of everything written, learn the conditional probability of the next word, and what emerges is something remarkable: a fluent approximation of what the crowd of all authors would most plausibly say next. This is a genuine achievement, and I use these systems daily.

But notice what the architecture is. An LLM is Galton’s crowd… pre-computed, compressed, and frozen into weights. When you query it, you are asking the consensus a question. Repeated sampling deserves a fair hearing here: asking the model many times and taking the majority does improve performance on bounded reasoning problems, because different samples wander down different search paths.

What repeated sampling cannot do is manufacture independent evidence. The farmer, asked ten times, may check his arithmetic; he cannot acquire the butcher’s eyes. A blind spot shared by the whole training distribution survives every draw from it.

The industry’s other instinctive fix, ensemble several frontier models, inherits the same flaw for the same reason.

The major models share training corpora, share architectures, share the same human-feedback conventions. And the correlation is no longer conjecture: a study spanning more than 350 models found their errors substantially correlated… on one benchmark, when two models both erred, they gave the same wrong answer 60 percent of the time, and the largest, most accurate models were the most correlated of all, even across distinct providers. Plymouth’s condition fails at the foundation.

The crowd of models is a crowd that has read the same almanac.

And the condition is deteriorating. Model output is now flowing back into the training corpus at scale, and researchers have documented the failure mode this produces, models trained on their predecessors’ output lose the tails of the distribution first, the rare judgments before the common ones.

Collapse is a hazard rather than a destiny: it bites hardest where generated text replaces real data indiscriminately, and careful curation, accumulation of human data, and output filtering can slow it. But the economics lean toward the hazard. Synthetic text is nearly free; provenance is expensive; and every incentive in the pipeline points toward more of the former.

The crowd is collapsing, gradually and by default, into a single voice. The almanac is now being written by the crowd that reads it. I gestured at this loop over a year ago in Influencing the Shoggoth… the observation that everything we write becomes the priors of tomorrow’s machines; what I underestimated was how quickly the loop would begin consuming itself.

Emerging ‘Distillation Hazard’

There is a second mechanism accelerating the collapse, and it deserves a plain-language explanation because it will shape the economics of this industry for the next decade.

The practice is called distillation. A frontier model, the kind that costs billions of dollars and years of compute to train, can be queried millions of times, and its answers harvested. Train a much smaller model on those harvested answers, and the small model absorbs a startling fraction of the large one’s capability at a rounding-error fraction of the cost. The student never studies the world. The student studies the teacher’s answer key.

The dispute this has ignited is usually framed in legal and moral terms, and the irony there is genuine. The frontier labs trained on the accumulated writing of the internet: books, forums, journalism, code, without asking anyone, and called it fair use. Now the same maneuver is being run against them, and they call it theft.

Their defense rests mostly on terms-of-service contracts, which are notoriously difficult to enforce against a determined actor in another jurisdiction. And beneath the legal question sits a colder economic one: if 80% of a billion-dollar model’s value can be extracted for a few million dollars, the moat that justified the billion was never a moat. That thread: who captures the value of intelligence when intelligence can be photocopied, deserves its own essay, and I am deliberately parking it here.

Because from the vantage point of this one, distillation matters for a different reason entirely. The hazardous form, worth naming precisely, is black-box distillation: training on a teacher’s outputs alone, with no independent data and no external verification.

Done that way, a distilled model is a copy of a copy of the average. The frontier model already compressed the crowd into a single voice; the student compresses that voice again, and each pass of the photocopier tends to strip away more of the tails… the rare judgments, the minority readings, the residual variance that survived the first compression. (A student trained with its own data, or checked against the world, can escape this and can even beat its teacher on narrow tasks. The escape routes all run through the same door: fresh contact with reality.) What emerges from a distillation economy is a proliferating ecosystem of models that looks like diversity: hundreds of systems, dozens of vendors, a healthy competitive landscape by any conventional map. But trace the lineage and most of them descend from the same two or three teachers. Many bodies, one mind.

Galton’s crowd degrades in stages. First everyone reads the same almanac. Then, cheaper still, everyone reads somebody’s book report of the almanac. The distillation hazard is that the second stage arrives disguised as competition… the crowd appears to be growing precisely as its independent information approaches zero. Epistemic diversity is a property of lineage: of where the data came from, what objective was optimized, what contact with reality was maintained. A census of interfaces measures none of it.

Monoculture as Systemic Risk

Why should an investor care? Because the failure mode scales with adoption.

An adapted framework I have been developing borrows from condensed-matter physics, a phenomenon called Anderson localization, for which Philip Anderson shared the 1977 Nobel Prize. The physics, compressed: electrons move through a crystal as waves. In a perfectly regular crystal, those waves propagate freely. Introduce enough disorder: random impurities, irregularity in the lattice and something counterintuitive happens. The waves stop traveling. They become trapped in small pockets, and conduction ceases entirely. Disorder, which intuition says should merely slow things down, instead acts as a containment system.

Let’s map this onto economies… as analogy and hypothesis, offered in that spirit; economies are not quantum crystals, and heterogeneity can occasionally transmit risk as well as trap it and the implication still inverts a decade of systemic-risk thinking.

Model firms as lattice sites, supply relationships as the pathways between them, and the heterogeneity of firm behavior, differences in how firms substitute inputs, hold inventory, write contracts, as the disorder.

In a heterogeneous economy, shocks hit a firm and die out locally; every neighbor responds differently, and the shock finds no coherent path to propagate. In a homogeneous economy, where firms optimize identically, a local shock travels freely and aggregates into macro volatility. Homogeneity, on this view, is the systemic risk factor. Diversity of behavior, usually dismissed as inefficiency, is the immune system.

Now watch what happens as underwriting, credit, capital allocation, and policy analysis all converge on the same handful of foundation models and, one tier down, on the swarm of distilled descendants those models have spawned.

Every institution consulting the consensus machine is a lattice site behaving identically, and the apparent variety of vendors offers no protection: a bank running a distilled model and a hedge fund running its teacher are two sites with the same potential, however different the logos.

We are engineering the disorder out of the decision layer of the economy, polishing the crystal at precisely the moment we should be protecting it. This concern has now reached the institutions charged with watching for it: the Bank of England’s financial-stability committee has warned that widespread reliance on a small number of common AI models could push firms into correlated positions and amplify shocks. The errors will not be larger. They will be shared, arriving everywhere at once, correlated across every balance sheet that outsourced its judgment to the average. A monoculture of reasoning fails the way a monoculture crop fails: all at once, to the same pathogen.

The view from the edge

Internally we test opportunities for emrgnce against a framework we call the Proximity Inversion: the observation that in most institutional systems, the people closest to a problem hold the richest signal but the least capital and authority to act on it, while capital and authority sit furthest from the signal. Every layer between the edge and the center: reports, codes, standards, committee summaries, abstracts reality to make it legible, and destroys information in the process. By the time the signal reaches the people empowered to act on it, the part that mattered is gone.

Re-run the LLM through that lens and the diagnosis sharpens. The language model is the ultimate instrument of distance. It ingests the world only after the world has been flattened into text… described, summarized, published, averaged. Everything the vessel operator knows and never wrote down, everything the farmer senses before the almanac prints it, is invisible to the machine by construction. The consensus machine is legibility’s final form: an intelligence built entirely from the abstractions, with the proximate signal boiled away at every step. It does not merely sit far from the edge. It is made of distance.

Where variance returns

So the question worth asking about the next decade of machine intelligence: what would restore the Plymouth condition… independent errors, productive disagreement, a mechanism for settling it?

The answer, I suspect, lies in systems that model mechanisms rather than word frequencies.

A mechanism claim: this cause produces this effect, through this pathway, unless this condition intervenes, has a property that a statistical compression lacks: it is falsifiable at the level of the claim.

An honest caveat first: mechanism-based models can inherit each other’s mistakes too; several teams can encode the same wrong causal structure, and often do. Their advantage is narrower and more valuable than automatic independence. Two mechanism-based models that disagree do so legibly, thus we can locate the dispute at a specific causal claim and adjudicate it against data, or against the world. Two consensus machines that disagree do so opaquely; there is no ledger of why and hence nothing to settle.

Disagreement between mechanisms is information.
Disagreement between compressions is noise.

Which points at the deeper conclusion. Variance returns wherever intelligence is forced back into contact with independently observed reality… proprietary measurement, sensors at the edge, tacit expertise, local feedback loops that never passed through the published corpus. The mechanism is the vehicle; the contact is the cargo. An intelligence that must reconcile its causal claims against observations nobody else holds is, by construction, capable of independent error and independent error, Plymouth taught us, is the raw material of collective truth.

There is an old optical principle buried in this. Depth perception requires two eyes set apart, two structurally different vantage points on the same scene. A single averaged view, however sharp, is flat. Parallax is the reward for maintaining genuinely separate perspectives, and it is the only way any observer, biological or artificial, has ever perceived depth.

The economics follow. Every marginal dollar allocated by the consensus machine makes the consensus more crowded, and makes the independent estimator more valuable.

Correlation-driven intelligence competes away its own edge with each new adopter; mechanism-driven intelligence appreciates as the field homogenizes around the average. In markets, in underwriting, in any domain where being right matters more when others are wrong together, the returns to variance are rising exactly as fast as the supply of variance is falling.

Galton’s crowd was wise because nobody in it had read the same book.

We are now training every mind on the same book, the cheaper minds on a summary of it… and calling the result intelligence.

The ox, for the record, weighed 1,197 pounds; the mean of the 787 tickets came to within a pound of it. Somewhere in that pile, at least one entrant had written down the true weight exactly, one independent judgment, indistinguishable from the noise around it until the aggregation surfaced it.

The lesson was never that crowds are wise. The lesson is that independence is precious, rare, and at this particular moment in the history of machines… for sale at a discount.

The heat had become an imposition. Not a trendline, not a scenario, not a panel topic, but a discomfort that reached into sleep and pulled her out of it. She was feeling the thing itself, while I had spent the week in events and roundtables talking about everything that follows from it.

I lay there after she’d gone back down, listening to the Dyson push warm air around a warm room and thought about how far the heat had travelled to reach her... out of the charts and the scenario decks and the panels I’d spent the week inside, across the city, through the window, into the bed of someone who has never heard the word “anomaly.”

The climate crisis becomes real when it stops being a chart and starts waking up your child.

Fluent in Relief

We were in London for Climate Action Week, the largest climate gathering in Europe, and the city was breaking records while we talked about it. On the 25th, Heathrow hit 36.7°C, the hottest June day Britain had ever logged.

Somewhere on the schedule, a session on extreme heat was cancelled on account of the extreme heat. I have been to a lot of these weeks. This was the first where I watched the subject matter walk into the room and stay... the future, arrived early, a specter haunting the very week we had gathered for.

Underneath the panels, one question kept surfacing all week: how did we get so fluent in expensive relief and so clumsy with cheap prevention?

We have learned how to pay for the damage. We have not learned how to pay for the avoided damage.

Cold, Without Making It Hotter

It started at the World Economic Forum dinner gathering. I was seated next to `Aulani Wilhelm, who runs Nia Tero and has spent a career on Indigenous guardianship of land and water. She has a phrase, becoming an ancestor, building the thing whose benefit outlives your claim on it.

We talked our way into resilience and what she kept returning to was that resilience, in the traditions she works with, is not a feature you bolt on. It is inherent. The old systems were resilient because the people who built them had no way to be anything else.

This made me think of the wisdom that exists within our etymology, for instance in Farsi, the yakhchāl is simply the word for refrigerator. The older meaning is better: yakh, ice; chāl, pit. A domed mud-brick structure that made and kept ice in the desert without electricity... thermal mass, evaporative cooling, a baad-gir, windcatcher, drawing air over qanat water, and at night a clean radiative dump of heat to a dry sky. It made cold while sending its heat somewhere that cost no one... up to the dry night sky, a sink that asks nothing back.

That is the part worth keeping... a compressor moves heat rather than losing it, warming the air outside to cool the air within; multiply that across a dense city and all those rooms cooling themselves nudge up the temperature everyone shares. The desert gave the yakhchāl an open sky to throw heat into that a crowded city can’t, so the two are not a fair fight. But the principle outlasts the mismatch: the cooling worth wanting sheds its heat to a sink no neighbour is asked to pay for.

Good infrastructure does not make its comfort someone else’s problem.

I recall seeing parts of this idea a few years back, an updated blend of old knowledge & new tech, at the House of the Future competition in Dubai, a government-run search for what a desert home should be.

One entry stayed with me. It took the baad-gir and wired it into the newest hardware on the market: an atmospheric water generator, solar PVT panels, a heat pump and earth and palm-fibre walls for thermal mass. Old geometry, frontier equipment, almost fossil-fuel-free and close to self-sufficient.

Old geometry, frontier equipment morphed into a passive home for the same desert. The windcatcher kept its place because it still reads the wind better than anything we have built to replace it. These solutions were almost always multi-functional, for the same unglamorous reason: their makers lived downstream of every choice and could not externalise a single one.

By midweek I was muttering my daughter’s line myself, crossing a baking city between venues. It was, in fact, so hot.

From Nowhere in Particular

This is where I become unreasonable about air conditioners...

The room I sat in stayed cool the way most rooms do now: a unit on the wall moving heat from inside to out, buying us a bearable meeting by making the street worse, warming the city in aggregate to do it. It is the same box, sold into every climate on earth... Phoenix, Lagos, London, indifferent to the place it lands in. A solution from nowhere in particular, shipped everywhere in general.

The box scales because it ignores place. Resilience works because it does not.

The yakhchāl was the opposite kind of object: designed from inside one place, almost unintelligible from a continent away. Its intelligence was local because its inputs were: the wind it caught, the sky it radiated to, the water it drew. The most durable solutions are of a place; the exported ones are merely for it. (I’ll save my views on: Sovereign Intelligence is also Resilience Intelligence for another write-up)

We have grown very good at the second kind and impatient with the first. The reflex now is to find one answer and scale it to the world, which might be a fine instinct for software, a costly one for resilience, which is built of soils and slopes and watersheds that refuse to standardise. And the knowledge that fits a place is usually already in that place, held by the people who have lived its failures. We keep flying over it on the way to a product.

A first-principles look says a great many of these placeless solutions should not have to exist, or could be answered by something drawn from the ground they stand on. And somewhere, I will come back to it, there is a solution that runs the other way, rooted so deeply in its place that it earns more the more it prevents.

Does Prevention Pay?

The banker’s objection arrived the next day and it was a fair one. Tim Lord runs climate and energy for HSBC UK; before the bank, he ran the country’s decarbonisation strategy. His point was not philosophical and rather poignant: Prevention has to be bankable.

It needs revenue certainty: a predictable line of cash a lender can underwrite, and a financing ladder patient enough for returns that arrive slowly. The trap is obvious once you see it... the placeless product already has the certainty. The same air conditioner sells into every climate on earth, a clean standardized line item and the hotter it gets the more of them ship.

A solution cut to a single place carries no such guarantee... it is viewed as bespoke by definition and bespoke is hard to underwrite. We finance what scales identically, so we keep buying the box from nowhere, again and again.

Capital likes repetition. Nature pays for fit.

The Cheapest Thing We Own

The answer, or the start of one, came in at an intimate United Nations Office for Disaster Risk Reduction (UNDRR) event. The frame that organized the room: nature is the cheapest infrastructure we have and we keep buying the expensive version anyway. It came with receipts, each a case of prevention made bankable.

• Thames Tideway — London’s super-sewer, built as a separate regulated company and paid for, slowly, by the people of the city through their water bills. The city funds it because the city is the beneficiary.

• DC Water’s environmental impact bond in Washington, where the payment between the utility and its investors flexes with the outcome: the return rises or falls on how well green infrastructure holds stormwater.

• The Greater Cape Town Water Fund, which found that the cheapest way to give the city more water was to clear thirsty invasive pine and eucalyptus from the catchment; trees drinking the supply before it reached a tap. Restoration, run as a water utility.

• Quito’s FONAG, an endowment that pays upstream to protect the watershed that waters the city below, and has done for a quarter of a century.

Every one of them is the same move: an intervention that stacks benefits, financed by handing each benefit to the party that values it: the city, the ratepayer, the insurer who no longer has to pay the claim.

The taxonomy of how you wire that together: the credit enhancement, the resilience monoline, the floor-and-upside structures, is for the notes. The headline is that prevention can be made to pay and the banker’s objection has an answer.

What the Stewards Know

And here is the solution I promised, the one that runs the other way. ʻAulani had handed it to me back at dinner and it took the rest of the week to see why it mattered.

Take a hillside that slips and potentially devastates a whole town. The engineered answer is a retaining wall: one function, pure cost, depreciating from the day it is poured. The answer the land’s stewards carry is older and more productive: terrace the slope into small plots and farm them along the contour.

The same earthwork that holds the hill grows food: subsistence in one place, a high-value market crop in another. One act, doing prevention and production at once. The slope holds, the plot earns, the place feeds itself.

It is the exact inverse of the air conditioner: an answer grown from the specific hill and the hands that work it, worth more the more disaster it quietly prevents.

That is the whole argument of the week, that could be seen from atop this ‘hill’.

The Operating Envelope

It’s important to remember that every asset has an ideal operating envelope. For instance, solar output derates in heat. Batteries behave differently across temperature ranges. Data centers throttle when their cooling fails. People sit inside an envelope too. Human performance is infrastructure, studies cluster the peak of cognitive and physical work in a narrow band, roughly 21 to 23°C, with output falling away as a building runs too hot or too cold. Read this way, comfort is a resilience input and cooling is part of the envelope that keeps the system running.

Comfort is not softness... it is the condition under which people and systems perform.

As volatility turns heatwaves from anomaly into operating condition, the live question is whether we are willing to let our buildings, grids, data centers and people quietly derate. Scarcity thinking rations cooling, accepts the lower performance and dresses it up as virtue.

Resilience thinking designs the system so that clean, abundant energy and efficient, place-fitted cooling keep high performance available to everyone, and not only to the people on the top floor. The place-based designs do half the work here: a yakhchāl, a windcatcher, an earth wall lowers the load before a single watt is spent and clean capacity covers the rest. Build enough of both and human performance stays protected as the climate gets harder to operate in. That is the whole game: enough clean headroom that productivity and the planet stop being a choice anyone has to make.

2040, Again

This past week, I was reminded of a moment I have written about before. Years back, I was setting up a custodial account for the same little girl who had awoken to the heat this past week, I filled in her birth date, and the screen returned the year she turns eighteen and the account becomes hers: 2040. It landed like an alarm bell.

At that time, 2040 was the year I noticed the IPCC and the net-zero reports keep circling, the one they flag as a critical point, drawn, if anything, by models that stay shy of tipping points, the feedbacks that do not move in straight lines. In that moment it stopped being a figure in a deck and became a date inside my own family: the maturity on a child’s brokerage account and a climate horizon at once, a number I will be underwriting against for the rest of my working life. London handed it back to me a second time, in a hot room, in a hot week.

The week did not hand me a new conviction. It reached back, found the one already sitting with me and refreshed it with a new sense of urgency. The cheapest infrastructure we have has been here all along... in the catchment, the watershed, the terraced hill, the mud-brick dome that made ice in the desert. We keep declining to buy it and reaching for the box that ships anywhere instead, because we never built the instrument that pays for everything prevention does at once. This week I watched that instrument get assembled in pieces, in separate rooms, by people who mostly had not met.

Built to Hold

This is the work we are now trying to make investable at emrgnce: not resilience as a side benefit, but resilience as the underwrite. The first of these initiatives have begun modeling their systemic potential with our friends at Anthos, where we have been early design partners and first investors.

Our goal is to catalyze a portfolio of platforms across sectors, geographies and industries that hold prevention and production together, where resilience and growth are underwritten as a single position rather than traded against each other.

The underwriting is causal, it looks upstream, for the one place where a single intervention pays into several markets at once: the slope that holds and earns, the watershed that supplies and stores. It prices the whole bundle, instead of the one slice a single desk can see.

What pulls the strategy together is the length of the assets. Most of what matters here has to last. A ship is a thirty-year asset; a port, a grid, a coastline, a watershed, longer still. You are underwriting it against four decades in which the climate, the map, and the rulebook all keep moving... heat and storm rewriting the operating envelope, geopolitics redrawing routes around chokepoints and sanctions, decarbonization rules and carbon pricing arriving on a schedule of their own. For an asset like that, resilience is the underwrite itself, the question the model opens with.

A long-lived asset is a bet on the world it will have to survive.

So you stop forecasting a single future and underwrite across several, holding the position that keeps its value whichever one arrives. The place-based logic scales straight into this.

The hillside held because it was matched to its slope; a fleet holds because it is matched to its routes and its regimes, built and run with the operators closest to them, the people who have read those waters and those rules for a living. Maritime is where the thesis cuts sharpest, because a ship meets all three forces at once, every voyage, for thirty years, and where the partners nearest the asset are most worth listening to.

There is a horizon hidden in all of this. A thirty-year asset bought today is still working in the 2050s. The horizon I underwrite against and the one I parent against turned out to be the same horizon. That is the quiet alignment the week in London kept handing me: place-based resilience, underwritten across futures, built with the people already closest to each coastline, grid and community.

The horizon I underwrite against and the horizon I parent against are the same horizon.

The table is small and still being set. I find that the people who belong at it tend to recognize the shape of the problem a few sentences before I finish describing it.

This post originally appeared on LinkedIn.

This past week, SpaceX filed its S-1 (and had its first successful Starship mission)… and as I worked through the 685-page prospectus (or at least had Claude assist me in doing so), I was pulled back to the piece I wrote almost two years ago, Why Being Late Can Still Mean Capturing 90% of the Upside. At the time, I was reflecting on Starship’s first “Mechzilla” catch and revisiting why I had originally invested in SpaceX… not for Mars, but for the galactic telecom company I believed it was quietly becoming.

That thesis is no longer a private bet. It’s now sitting in audited financials, on its way to what looks likely to be a $1.75 trillion debut on the Nasdaq under SPCX.

And the trade is in front of me.

I came in at a ~$40 billion valuation years ago (thinking it was rather ‘rich’ then), a private placement secured through my network (before all these crazy multi-tier SPVs craze we are seeing around Anthropic et al.), when SpaceX still felt like a difficult deal to access. At today’s secondary marks, I’m sitting on something close to 3,000% on paper. At the IPO target, that’s closer to 43x.

By any disciplined definition, this is a “take some chips off the table” moment.

So I want to think out loud about why I’m leaning to not doing that… also, it should be said that none of this is financial advice. It is a personal reflection on my own position and decision-making. Many of my private placement & angel investments are made with the assumption that they could go to zero and I size them accordingly. As the saying goes, past performance is not indicative of future results.

The 94x problem

The headline number that everyone is fixated on is the multiple: 94x 2025 revenue, with a $4.94 billion GAAP net loss in 2025 and a $4.28 billion loss in Q1 2026 alone. Nvidia trades in the single-to-low-double-digit revenue multiples. Tesla sits around 8–10x. Even the most aggressively priced recent IPOs have topped out between 20x and 40x. There is no P/E to anchor to, there are no profits to put a P over. Musk retains 85% of the voting power through a super-voting share class.

This is a real list of risks. I want to take it seriously rather than wave it away.

But the question that actually matters, the one I keep coming back to, is whether the 90% of future value I originally bought into is still ahead, or whether it’s mostly behind me.

After reading the S-1, I believe most of it is still ahead. And that conviction isn’t a feeling, but rather the output of re-running the framework I’ve been writing about: the Three Futures Test.

The multiple, reframed

Before getting to the test itself, I want to sit with the 94x number for a moment because it’s the number that does the most work in the bear case, and I think it’s also the most misunderstood.

The 94x is calculated on 2025 revenue. SpaceX 2025 revenue is, mechanically, the past. The Anthropic-Colossus offtake is $1.25B per month, $15B per year, north of $40B contracted through 2029, which is essentially zero in the 2025 number and effectively a full year of run-rate by mid-2026.

If you take a more conservative post-listing valuation of $1.5T and overlay the Anthropic-annualized revenue, you’re closer to ~47x on a forward run-rate basis. Still rich. But a very different conversation than 94x.

Now compare across the AI-infra premium tier. Cursor’s potential $60B acquisition offer from SpaceX on roughly $2B ARR, with $6B forward ARR projected by year-end, somewhere in the 10-30x revenue range depending on how forward you go.

Cursor is software-only and is being valued by growth investors on a forward basis. If a code editor at $2B ARR clears 30x current, an orbital connectivity utility + launch monopoly + AI infrastructure stack at $20B run-rate clearing ~47x forward is not absurd.

There is a layer to this that also points to the xAI/SpaceX teams in being able to spin up a data center (or even a Starlink fab plant) at a speed and scale others in the industry haven’t delivered in the past. Nvidia CEO Jensen Huang famously praised Elon Musk’s construction of the xAI Colossus supercomputer (initially a 100,000-GPU cluster) as “superhuman,” noting it took just 19 days to get the first servers running workloads and 122 days for the initial full build… a feat that normally takes other companies four years. (Just this internal capability to spin up data center capacity at the speeds Anthropic and the AI market growth demand, is itself a skill/experience MOAT that can grow a whole new industry/revenue arm that has nothing to do with the space-play alpha that SpaceX dominates).

What gets missed in the 94x discourse is what’s actually historically rare about expensive companies… they almost always grow into the multiple rather than have it compressed down to them.

A few worth remembering:

• Amazon, 1999–2010: traded north of 30x revenue at the dot-com peak. The stock collapsed. The company didn’t. Revenue went from $1.6B to $34B over the decade… a >20x revenue compound. The “expensive on revenue” company became the cheapest infrastructure asset on the public market because the denominator did the heavy lifting.

• NVIDIA, 2016–2024: I bought NVDA in 2016 at a cost basis of ~$1.51 (post-split), as I wrote in Why Being Late. Through that period, the company traded at “too rich” P/S multiples repeatedly. Revenue compounded from ~$5B to ~$60B… 12x… while the stock did much more. The multiple compressed and the price ran. Both happened.

• Tesla, 2013–2020: skeptics screamed about 15-20x revenue valuations every other quarter. Revenue went from $2B to $31.5B. The multiple compressed by way of denominator growth, not equity decline.

• Cisco, 1990–2000: the internet’s plumbing went from $69M in revenue to $18.9B over the decade, a 273x revenue compound. Even after the 2000 correction, the business had compounded the entire decade. (The cautionary lesson there is what happens when an expensive multiple meets a slowing compound… relevant, but not the case for SpaceX in 2026.)

The point isn’t that expensive always works. It’s that for a category-defining infrastructure leader at the beginning of a structural compound, not the end, the revenue multiple is a snapshot of a moving denominator. The denominator is what changes the picture, and the denominator is what compounds.

Now let’s do the actual math on the compound. Elon’s personal wealth has compounded at roughly 50% per year since 2012 by the public estimates, that’s the empirical track record across Tesla, SpaceX and now xAI.

If you extend that same compound to SpaceX revenue and just halve it to a much more conservative ~25% growth assumption, SpaceX gets to ~$25B in 2026 from $18.6B in 2025. Layer the Anthropic deal on top and you’re at $40B run-rate. That’s a 2x revenue compound in twelve months, without a single new contract beyond what’s already announced. The 94x quietly walks itself down to the mid-40s before anyone touches the stock price.

And the war chest is the part that should make a bear pause. SpaceX has $15.9B in cash today. The IPO is targeting up to $75B of fresh primary capital. That’s a ~$90B war chest going into a moment where the entire AI infrastructure market is supply-constrained, terrestrial data centers are running into siting and grid limits and SpaceX is the only operator on Earth that controls every layer from launch to compute to connectivity.

You don’t need genius capital allocation in that setup. You need one more Anthropic-sized offtake, for another $15B/year contract from any of the obvious counterparties (and Anthropic itself has explicitly signaled appetite for more), to add another ~50% to run-rate revenue. Most of that $90B is going to data center capacity.

And if Starship-enabled orbital data centers actually unlock a structural cost advantage on AI compute: cooling, energy, land, latency-bounded workloads all rethought from the orbital side then this entire conversation about revenue multiples becomes the wrong conversation. That isn’t 47x compressing to 20x. That’s a category being redefined in a way that doesn’t have a clean comparable.

When the category gets redefined by the leader, the multiple stops being the right unit of measurement.

That’s the part of the trade I’m not selling.

What the S-1 confirmed about my original thesis

Two years ago, I wrote that someone has to build the communications infrastructure for the emerging space economy and that SpaceX had quietly achieved a near-monopoly on the first critical piece. The S-1 is the first time we get to see what that thesis looks like with audited numbers underneath it.

Starlink generated $11.4B in 2025 revenue, up nearly 50% year-over-year, with $4.4B in operating income, a ~50% operating margin business at scale. Over 10 million subscribers across 164 countries as of early 2026. This is no longer a satellite story. It’s a scaled, profitable, recurring connectivity utility and most of its addressable market lies on the ~80% of global land mass that terrestrial networks don’t reach.

The proof point I keep returning to is John Deere. Deere’s RFP for connectivity ran over a year. They tested 40 providers. They chose Starlink. Deere has 600,000 connected machines today, is targeting 1.5 million, and has a stated goal of fully autonomous farming systems by 2030. The S-1 names Deere directly as a flagship industrial customer. This is what the connected edge looks like when it actually arrives and it arrives through SpaceX.

The launch business is misread by the market. SpaceX ran 165 Falcon 9 launches in 2025; only 43 were for outside customers. Nearly three-quarters of all launches were internal, they were Starlink. The launch business isn’t underperforming, as the 8% YoY growth suggests on the surface. It’s being run as cost-of-goods for the connectivity business.

The AI segment, via the xAI merger, is where the losses concentrate. It’s also where new optionality showed up that I had not underwritten when I originally invested. Earlier this month, Anthropic signed an agreement to take the full 300MW of compute capacity at Colossus 1 in Memphis, over 220,000 Nvidia GPUs, at approximately $1.25 billion per month through May 2029. That’s north of $40 billion of contracted revenue from a single offtake, on a data center that xAI was reportedly running at ~11% utilization. They turned a stranded compute asset into one of the largest take-or-pay style contracts the AI infrastructure build-out has produced. That is a structured-finance lesson, not a story about renting GPUs to a competitor.

Buried in the same announcement: Anthropic also expressed interest in working with SpaceX on multiple gigawatts of compute capacity in space.

Re-running the Three Futures Test

When I originally invested, years before either of my write-ups existed, the three futures I could envision for SpaceX were: commercial satellite services, deeper space exploration and Mars cargo. As I wrote in the original piece, I didn’t need all three to work… I needed one. Starlink at 5–10% of the global telecom market was enough to justify the bet on its own.

With the S-1 now in front of me, here is how I’d re-run the Three Futures Test today:

Future 1 — Starlink becomes the connectivity utility for the connected economy. Not the consumer broadband product. The non-terrestrial layer underneath everything that operates outside the ~20% of land that terrestrial networks actually cover, agriculture (Deere), maritime (something I look at constantly through Fifth Wave), energy infrastructure, defense (Starshield), MRV-grade climate sensing, autonomous mobility. Recurring, contracted, high-margin with a moat that compounds with every satellite generation. A few hundred billion of the IPO valuation is defensible on Starlink alone.

Future 2 — SpaceX becomes the infrastructure layer of the AI compute build-out. The Anthropic-Colossus deal is the template, a multi-year, gigawatt-scale, take-or-pay style contract that monetizes infrastructure most operators were treating as a sunk capital problem. As terrestrial data center siting gets harder (the Memphis pollution fights around Colossus are the leading indicator, not the exception), the option value of gigawatts of orbital compute becomes a real conversation. Only one company controls the launch cost curve that makes that conversation possible.

Future 3 — The space economy itself matures, and SpaceX is the launch monopoly underneath all of it. Starship reusability economics, if they materialize anywhere close to targeted, change the unit cost of putting mass in orbit by an order of magnitude. That makes lunar cargo, in-space manufacturing, and the wildcard I personally find most underrated: Space-Based Solar Power, actually pencil. Caltech’s MAPLE demonstrator successfully beamed power from orbit to a receiver on Earth in 2023. JAXA and Mitsubishi have transmitted multi-kilowatt power across hundreds of meters on the ground. China is building dedicated SBSP testing infrastructure in Bishan and has publicly stated its ambition to deploy the first commercial space-based solar facility. The skeptics on SBSP are the same skeptics who told me Starlink wouldn’t scale past hobbyists. I’m willing to take that side of the trade again.

Bonus Scenario 4 — the devil’s advocate. Even if AI capex normalizes, even if the orbital compute conversation gets pushed to the 2030s, even if Starship execution slips by another five years… the floor under SpaceX is Starshield, NASA and allied sovereign space programs. Government dependency on SpaceX for launch and secure connectivity is now structural, not contractual. In a flatter, less euphoric future, the launch monopoly and the defense connectivity layer alone produce a durable, sovereign-grade revenue base. The pessimistic scenario doesn’t look like a zero. It looks like a slow compounder.

That’s the test. Three viable futures where I’d be happy with the outcome, only one of which needs to play out and a pessimistic fourth that doesn’t break the position even if all three of the others stumble.

Sizing the $5T scenario

Here’s where I want to push the framework one step further than I usually do because the question I keep getting asked by friends and LPs this week is some version of “sure, but where could this actually go?“ The $1.75T IPO is the floor case for the bull thesis. What I find more interesting is the next leg: what would have to be true for SpaceX to be a $5 trillion company?

The number sounds absurd until you do the unit math. At the mature-leader revenue multiples that NVIDIA, Microsoft, and Apple trade at (15-20x), $5T implies somewhere between $250-330B of annual revenue. SpaceX exits 2025 at $18.6B and arrives in 2026 at roughly $35B run-rate including the Anthropic deal annualized. So the question becomes: what does it take to compound from $35B to ~$300B over the next decade?

The thing about applying the Three Futures Test at this scale is that no single future has to do all the work. The same logic that made me comfortable buying SpaceX at $40B applies here… three viable paths, only one needs to land. So let me size each one.

Future 1: The connectivity utility. What does Starlink-as-galactic-telecom look like at the scale that moves the needle? Global telecom services is a ~$1.5 trillion market. Verizon, AT&T, China Mobile, and Deutsche Telekom each generate $120-140B in revenue at industry-standard 15-25% operating margins. Starlink already runs at 50% operating margins, roughly twice the industry. To do $150B in connectivity revenue, SpaceX would need to capture ~10% of the global telecom market, roughly the equivalent of becoming the world’s largest single telecom operator. Or, more accurately for the segment Starlink actually competes in: own the non-terrestrial layer underneath every connected machine on the 80% of land terrestrial networks don’t reach, plus maritime, plus aviation, plus defense, plus IoT/edge. That’s not displacing Verizon. That’s building the layer Verizon doesn’t have. One Verizon-equivalent of revenue at twice the margin is one path.

Two Verizon-equivalents, at this margin profile, is most of the way to $5T on connectivity alone.

Future 2: The launch-monopoly-times-space-economy. This is the future that depends entirely on Starship economics. If reusability hits anywhere close to targeted unit cost, say $200-500/kg to LEO from current $1,500-3,000/kg, every existing market for space-based services repriced down by an order of magnitude becomes a new addressable market. Earth observation, in-space manufacturing, lunar logistics, defense constellations, Mars cargo, none individually a huge revenue line today, but collectively a category that doesn’t yet exist as a financial market.

To do $50-75B in launch + adjacent space services revenue, SpaceX needs to be the underlying infrastructure for roughly 80-90% of orbital activity over the next decade. They are already at 80%+ of global launch mass.

The harder question I ask isn’t whether they’ll dominate… it’s how big the category itself gets when launch stops being the bottleneck.

Future 3 (formerly the bonus): Becoming AI’s infrastructure utility — on land first, in space eventually. This is the one that surprised me when I worked through the math. The Anthropic-Colossus deal is $15B/year. AWS does $108B in cloud revenue. Azure does ~$80B. To become a top-3 AI compute provider by 2030, SpaceX would need to scale Colossus + sister facilities to 5-10 GW of deployed compute capacity, versus the current ~300 MW at Colossus 1. That’s roughly 5-10 more Anthropic-sized offtakes, against a hyperscaler capex pool that already exceeded $320B in 2025 and is forecast to clear $1T annually by 2030. The supply-side constraint is energy, land, water, and grid, exactly the bottlenecks SpaceX is uniquely positioned to route around. $100B in AI compute revenue by 2030 is not a stretch case. It’s roughly the trajectory the market is already underwriting for the existing hyperscalers, just transferred to a new operator with a structural moat the incumbents don’t have. And that’s all on Earth, before orbital compute enters the conversation.

The bonus option: Space-Based Solar Power. This is the wildcard I find most underrated and the hardest to size partly because the unit economics aren’t yet a financial market, and partly because the engineering trajectory is steepest. For context: 1 GW of continuous baseload generates ~8.76 TWh/year; premium clean firm power for data centers and industrial loads can clear $80-150/MWh. To do $50B in SBSP revenue at the premium end, SpaceX would need to deliver ~40-50 GW of continuous power-beamed capacity, roughly half the entire US installed nuclear fleet, beamed from orbit. That is a 10-15 year engineering ramp from where Caltech’s MAPLE demonstrator sits today. I’m not underwriting this as part of the base case. But it’s the only one of the four where the category itself gets redefined and where the structural cost advantage I keep referring to lives. If it works, it’s not a $50B line item. It’s the energy backbone for AI compute, and the multiple stops being the right framing entirely.

Blending the futures. Here’s the part the spreadsheet wants me to say out loud: I don’t need any one of these to fully play out to get to $5T.

A blended scenario looks something like — $150B connectivity + $50B launch/space services + $75B compute + $25B optional SBSP/other = $300B revenue at 15-20x = $4.5-6T.

That blend doesn’t require Starlink to displace Verizon, doesn’t require Starship to deliver Mars cargo by 2030, doesn’t require any of the more aggressive single-path assumptions.

It just requires each of the three futures to land somewhere between 30-60% of its ceiling. Which is, structurally, what the Three Futures Test is actually testing for. You don’t need any one bet to hit the moon. You need three bets where the floor of each is meaningful and the ceiling of any one is enormous.

The question I’d encourage anyone reading the S-1 to ask isn’t “is $5T possible?“ — it’s “which of these three paths am I willing to bet against simultaneously?“ Because that’s what shorting SpaceX from here implicitly does.

You have to be wrong on connectivity, wrong on launch economics, and wrong on AI infrastructure and also right that SBSP never materializes over a ten-year window where SpaceX has a $90B war chest, controls the launch cost curve and is the only operator that can actually run the experiment.

I’m not willing to take the other side of that trade.

SpaceX Flywheel in Motion: Where Market Structure Meets the Long View

There’s one more piece I want to put on the table, because if you spent any time watching $INTC over the last year you’ll already be familiar with the convention, the 🚩 flag for the largest asset allocators, when the market structure itself is about to do most of the work, separate from any fundamental view.

There are two unique mechanics around SPCX that I think most retail commentary is dramatically underweighting.

The first is the Nasdaq 100 “Fast Entry” rule, which went into effect on May 1, 2026. Under the new framework, companies whose market cap ranks within the top 40 of the index become eligible for inclusion just 15 trading days after IPO, down from a historic seasoning period of three to twelve months. SPCX at $1.75T will sail through that threshold. The Nasdaq 100 is tracked by roughly $600-800B in passive AUM across QQQ, QQQM, and the broader NDX-tracking mutual fund and institutional mandate universe. Inclusion forces all of them to take a benchmark-weight position within days, regardless of any view on the multiple, the lockup structure, or the underlying fundamentals.

The mechanic that makes this especially interesting is the low-float weighting that scales as float expands. SPCX is going to enter the index with a small free float, somewhere around 5%, which means a fractional initial weighting. But the insider lockup releases on a tiered schedule: 10% of insider shares unlock at day 15 if the stock is 30% above IPO, then 7% increments at days 70, 90, 105, 120, and 135. Each unlock expands float, which scales index weight, which triggers more forced passive buying. This is reflexivity engineered directly into the listing structure.

First passive bid lifts the stock through the unlock trigger → more float comes online → index weight rises → the next passive bid is larger. Each turn of the flywheel widens the AUM pool that has to chase.

The second mechanic, and the bigger one, is the S&P 500 rule change that’s under active consideration right now. S&P Dow Jones Indices opened a formal review in March of whether to fast-track mega-cap IPOs into the S&P 500, specifically halving the 12-month seasoning period and eliminating the GAAP profitability requirement for companies with market caps above $200B. The consultation period closes May 28. Changes could take effect June 8. The SPCX listing is targeted for June 12. The timing is not a coincidence.

If the rule passes in time, the consequences scale up by roughly an order of magnitude.

The S&P 500 has roughly $24 trillion in benchmarked AUM, about 30x the Nasdaq 100 pool. The forced passive buying alone would be a step-change. But the more important effect is on the active side: long-only managers benchmarked to the S&P 500 cannot stay underweight a 1-2% index name without taking real career risk. If SPCX enters at ~$1.75T (~0.5% of total S&P market cap) and runs toward $3T+ over the next 12-18 months on fundamentals, it becomes a 1-2% weight that every benchmark-aware active manager has to have a view on. Long-onlys will be forced to take a view. And that view, for most of them, will be some version of “we have to own at least benchmark weight.” Independent of fundamental conviction.

The combined effect of both rule changes, Nasdaq fast-entry plus S&P fast-track, is that the natural compounding I described in the multiples section gets a turbocharge from passive flows and forced active positioning. The multiple compression doesn’t have to wait for revenue to fully catch up. The buyer base catches up first.

Now, the honest version of this story includes the critics. Michael Burry has flagged the proposed changes publicly. George Noble called them a “shameless” manipulation of index methodology, arguing the traditional seasoning periods exist for a reason: they allow real price discovery and protect passive investors from being forced into untested, illiquid securities. I take that critique seriously. In any other moment, I’d be sympathetic to it.

But here’s the part that, for me, reframes the whole conversation: the only reason index providers are even contemplating these rule changes is that the existing rules cannot accommodate a generation of category-defining companies that stayed private at extraordinary scale. SpaceX, OpenAI, Anthropic… their market caps already exceed 95% of current index constituents before they list. The rules were designed for a world where companies IPO’d at $1-10B and grew into mega-cap status over decades. They don’t work for companies that arrive on the public market already at $1T+. The mechanics being engineered now are downstream of a structural reality: the private market kept these companies inaccessible to public-market investors for a generation. The rule changes are, in some sense, the public market catching up to the private one.

For me as a long-term holder, the index mechanics aren’t the reason to hold SpaceX, the Three Futures Test is the reason. But the mechanics do something important: they accelerate the discovery of the real multiple. Once the entire benchmark-aware active management complex has to have a view on SPCX, the public market valuation stops being a function of “what’s the smart money saying at this exact moment” and starts being a function of “what’s the structural floor of forced ownership at this benchmark weight.” That floor is what lets long-term holders like me stop checking the chart on any given day.

100% this is financial engineering and it’s worth saying that part out loud. But the engineering is structurally aligned with the long-view investment philosophy I’ve been running for a decade. When the index becomes the dominant buyer of a category-defining business, the daily volatility becomes noise around a slowly-rising benchmark-weighted floor. That is exactly the kind of structural setup that lets me do what I do best: sit still and let direction compound.

What I’m doing

I’m holding and specifically holding for the part of the return I haven’t earned yet.

The plan is to keep a meaningful core position for the next decade and revisit sizing thoughtfully over time. Not because I think the multiple is wrong, but because position-sizing discipline eventually matters more than directional conviction at this scale. Trimming because a position got large is a different question from whether the thesis still holds, in my opinion, the thesis still holds.

When I look back at my own public-market returns over the decade, 606.19% cumulative, 23.11% annualized, well ahead of the S&P 500 (15.59%), NASDAQ (19.42%), DJIA (12.85%), and Russell 2000 (9.75%) over the same window, almost none of that came from timing. It came from being directionally right and being still. The years where the spread between my portfolio and the indices opened up the widest were the years I did the least.

Personal public-market portfolio vs. major U.S. indices, Dec 31, 2016 – May 25, 2026. Source: Charles Schwab. 23.11% annualized over the past decade.

My own Schwab account return over the same period is less interesting to me as a scoreboard than as evidence of the behavior the essay is really about: being directionally right, sizing risk, and sitting still.

SpaceX, for me, is the most concentrated expression of that philosophy I’ve ever put on. A direction I bought into a decade ago, that’s about to become a public-market ticker, that, by my honest read of the S-1, still has most of its compound ahead of it. The trade isn’t to exit at the IPO. The trade is to keep doing what’s already worked: stay directional, stay still, and let the compound do the work.

Closing Thought

The 94x multiple is, at its core, a question about whether you’re paying for the past or the future. If you’re underwriting SpaceX on 2025 financials, the answer is obvious and you should pass. If you’re underwriting the connectivity layer of the connected economy, the structured offtake of AI infrastructure, and the Starship-enabled space economy, with orbital compute and power beaming sitting on top as call options, you stay in the seat.

When I wrote Why Being Late, the point was simple: when you can see multiple futures where you’d be happy with the outcome, the pressure of timing fades away. SpaceX is the cleanest example I have of that thesis playing out in my own portfolio.

I bought a direction, years before I ever sat down to write about it. A year and a half after I did write it down, and one S-1 later, that direction is more confirmed than I imagined at the time. The ticker and access is changing but my core thesis hasn’t.

In my view, there’s still 90% (or equivalently large %) of the journey ahead.

Over roughly a decade in and around place-based investing; first across the Gulf, more recently across California, one view has only strengthened for me: resilience is one of the easiest theses to admire and one of the hardest to underwrite cleanly.

California alone is the world’s fourth-largest economy, the most powerful regulatory leverage point on the planet and the front line of nearly every climate stress test the rest of the country is about to take in the coming decades. The dollars are large. The need is unambiguous. So in theory, the narrative writes itself.

But in this case, the scale narrative is not necessarily an advantage. And the importance of a question, even one as plainly important as this, is not the same as the investability of an answer to it.

I have, more than once, seen investors let a strong place-based narrative carry weight that only a balance sheet should carry, and I have done it myself. The error is not in believing the place matters. It does, and the rest of this piece argues that it does. The error is treating that belief as a substitute for the underwrite rather than an input to it. Place feeds the company’s economics through customers, regulation, labour, and physical risk. It does not do the work of underwriting how any one firm captures and compounds inside it.

The investor’s question is therefore not whether a place matters. It is where value accrues, who captures it, whether the advantage can compound at the company level, and in any portfolio built around resilience whether the return travels in the same direction as the resilience it claims to produce. A return that erodes the system that made it possible is not a return. A resilience play that does not compound is philanthropy.

That tension, return and resilience, compounding in the same direction, in a specific place, is the reason we built the Capital Valleys Forum.

One state. Many valleys.

The Forum’s name carries an argument in its plural. There is no California, singular. There are valleys. The valleys in question: Sacramento. Silicon. Santa Cruz. North Bay. San Joaquin. Tulare. Kern. Imperial. Shasta.

Each is its own economy, with its own tax base, its own labour market, its own climate-risk profile, and its own political weather.

Silicon Valley exports software and absorbs electrons. The San Joaquin and Tulare grow much of what the country eats and run a tax base well below the state average. Kern produces oil, solar, and warehouses, and is one of the most physically and fiscally exposed economies in the union. Sacramento is where the rules that govern all of them are written.

A useful way to think about California is therefore not as a single market but as a federation of valleys whose returns, vulnerabilities, and time horizons do not align, and whose only honest synthesis runs through the capital. That is the underwriting reality. A portfolio that buys “California resilience” without naming which valley, whose tax base, and whose physical risk is buying a slogan.

A city most people in climate do not talk about

When we announced the Capital Valleys Forum last month, I wrote that Sacramento may be the most important climate city in America that almost no one in climate is talking about. The case is structural, not sentimental.

CARB sets the tailpipe and clean-fuel standards that move every major automaker from Detroit to Stuttgart. The California Energy Commission and the CPUC shape how compute, transport and buildings consume power. CalSTRS deploys hundreds of billions in pension capital. SMUD remains one of the most ambitious municipal utilities in North America. The Central Valley grows roughly a quarter of the food the country eats. Silicon Valley sits ninety minutes west and consumes a rising share of the state’s electrons.

Sacramento is where those forces meet: the policy seat, the capital seat, the agricultural seat, and, increasingly, the regulatory laboratory whose rules quietly become the de facto national floor.

The mechanism is not subtle. California is now a $4.1 trillion economy, the world’s fourth largest, carrying roughly 14% of global GDP gravity. Above that threshold, regulating one jurisdiction is regulating the firm.

Last September, Baxter International announced it would remove DEHP from intravenous bags in all fifty states because of a single California law. There is precedent in California’s policy strength permeating into other states/nations:

• CARB tailpipe standards went national the same way in the 1970s. Prop 65 reformulations followed in the 1980s.

• The 2007 children’s-toy phthalate ban became federal CPSIA within a year.

• The 2020 Toxic-Free Cosmetics Act replicated across Oregon, Washington, and Vermont before the decade was out.

What looks from the outside like federal policy is often California policy the rest of the market chose not to fight.

The premise of the Forum was that this concentration of leverage: geographic, regulatory, fiscal, and physical, all converging in one capital city, is itself an investable bottleneck. If you want to underwrite resilience as an asset class rather than a theme, you have to do it where the rules, the infrastructure, the capital and the physical stress all converge on the same table.

The Emerging Cascade

The morning opened on the system everyone in the room had lived through. Thirty-six months that broke the assumptions much of US infrastructure was built on.

• The 2020 wildfires that burned 4.3 million acres. Dixie in 2021.

• The September 2022 heat dome. Nine atmospheric rivers in the winter of 2022–23.

• A second wave of atmospheric rivers in early 2024.

• The driest forty-four-year stretch on record for Southern California.

• And then Palisades and Eaton in January 2025: $131 billion in damages, the costliest urban wildfire in American history.

No single event in that timeline is unprecedented on its own. Taken together, they are the new operating environment.

The financial mirror is unforgiving:

• California’s FAIR Plan exposure has risen nine-fold since 2018 to $458 billion.

• 2.8 million policies were non-renewed between 2020 and 2022.

• Seven of the state’s twelve largest insurers have paused or limited new policies.

• Premiums in the Pacific Palisades zip codes rose 33 per cent above inflation between 2018 and 2022 and the policies that did exist paid out roughly 5,000 claims in the first month after the January fires alone.

As I’ve written before, property insurance is the canary. The mine is mortgages, municipal bonds and household wealth. A Senate Budget Committee report has openly warned of a systemic shock comparable to 2008, perhaps greater.

The corollary

The same state that is the laboratory of climate stress is also the most powerful single regulatory market in the world.

Let’s take DEHP IV Bags as an example:

• Roughly 70 per cent of California hospitals were using DEHP IV bags.

• Baxter is the largest US supplier.

• 60% of Baxter’s US IV-fluid output runs through a single plant in Marion, North Carolina.

California’s AB 2300: the Toxic-Free Medical Devices Act, shepherded through Sacramento by Breast Cancer Prevention Partners, phases the chemical out for California alone, bags by 2030 and tubing by 2035. Baxter recognized maintaining one product line for California and another for the other forty-nine is uneconomic, given California’s market size. So the free market does the rest.

The leverage is growing, not shrinking, as the federal floor recedes. The 2025 federal rollback inventory reads as a vacuum:

• thirty-one EPA rules under review, the Endangerment Finding rescinded

• four of six PFAS limits being cut

• three California waivers revoked

• a 52 per cent proposed cut to the EPA budget.

Sacramento’s response in the same four months:

• twenty-three lawsuits filed, AB 794 (PFAS drinking-water backstop)

• AB 1817 (PFAS textile ban)

• twenty-eight aligned state coalitions

• and more than 350 state-level bills introduced.

Federal abdication does not weaken the California Effect. It strengthens it.

California as the Climate Capital

The core premise we were exploring at the Capital Valleys Forum is that California truly has been the climate capital for some time. The question that frames the next decade, and that the rest of the day was built to interrogate, is whether the state is still up for the job? It is not a rhetorical question. The answer is contested inside the building, inside the Capitol two miles away and inside the boardrooms of the firms the policies affect.

Policy, Provocations, Prosperity

The day was structured around three movements: Policy, Provocations & Prosperity. We chose that frame for two reasons.

The first is that most climate convenings collapse the three: a panel about “policy” turns into a litany of what is broken, which turns into a wish-list, and nothing in particular gets built.

The second is that the three movements track the actual time horizons of the people in the room. Operators live in the present. Allocators live in the medium. Policymakers, when they are honest, live in both at once.

What the grammar of the day was

The day’s most consequential arguments did not stay inside the compute-and-grid conversation; it travelled across themes. The idea that an AI data centre is not one asset but four: a rack-layer hardware-and-networking problem, a chiller-layer water-and-thermodynamic problem, a generation-and-storage-layer utility problem, and across all three, a capital-stack problem; turned out to be a usable grammar for almost everything else on the agenda. A resilience premium, the additional return that accrues to assets engineered to absorb climate, regulatory, and load-shape volatility, is not a soft benefit at any of those layers. It is the thing that makes a four-layer underwrite hold together at the cost of capital these projects now command. Once that grammar was in the room, it stayed there.

Two of the organixations contributing to the conversations of the day: Cisco, whose networking, compute, and data-centre architecture sit at the operational rack and chiller layer of every hyperscale build and CalSTRS, one of the largest public pension funds in the world and one of the most consequential long-duration allocators of capital anywhere.

Between them is the entire underwriting span of the AI build-out: the operational firm at the rack and the institutional balance sheet at the bottom of the capital stack. What the panel made plain is that the AI-energy build-out is no longer a technology question, an infrastructure question, or a capital question. It is all three, simultaneously, and the firms that win in California are the ones that can underwrite across the stack rather than within a single layer of it.

The same insight surfaces, in a different vocabulary, on the systems side. Nothing California needs to build: clean energy, water resilience, affordable housing, equitable mobility, community well-being, builds itself.

Each requires four gears in mesh, all cogs driving the same systems machine: policy that sets direction, infrastructure that carries the load, capital that finances the build, and the commodities and operating layer that turn it from a balance-sheet entry into a real asset.

The human forces between those gears… public opinion, NIMBYism versus broader interests, trust and credibility, technology and innovation, labour, either speed them up or grind them. We developed this imagery as an operating description of why a state with the world’s fourth-largest economy, the most ambitious regulatory floor, and the deepest pool of climate venture capital still cannot, on its own, deliver the build-out.

The four gears do not mesh by accident. Our forum in Sacramento was the seeds of continued working sessions on how they are made to engage with one another.

Returns and resilience, in the same direction

The closing segment was that climate adaptation is no longer an overlay on the operating model. It is the operating model we all will have to engage with. Companies built for California’s emerging market and climate realities… efficient on water and electrons, resilient to disruption, low-carbon in their supply chain, embedded in their communities, earn higher revenue from preferred customers, lower operating costs, fewer disruptions, better financing terms, and a stronger licence to operate.

This breaks out of the ESG/checkbox KPI oriented view and transcends into the EBITDA architecture of growing inside a climate-constrained economy.

And it is showing up in the returns…

The comparative analysis we put on screen, S&P 500 ex-financials against a portfolio of resilient corporations and one of vulnerable corporations, runs from 2017 through early 2024. The resilient cohort outperforms the index. The vulnerable cohort underperforms. The spread is widest at the moments of greatest stress. Climate resilience is business resilience. Business resilience, in California, is the state’s resilience. The three compound in the same direction, in the same place. The whole argument for the Capital Valleys Forum sits inside that one chart.

The bet on Sac

There is a national conversation about California that runs on a single false binary. Either the state is too consequential to fail… its regulatory leverage, its capital, its scale and the rest of the country is along for the ride. Or it is in cascading decline… its insurance market, its housing, its capital flight and the rest of the country is wise to short it. Both readings treat California as a single market with a single set of vital signs, but as I’ve shared it’s actually a culmination of all the local economies and geographies of the state.

The Forum’s actual argument runs underneath that binary. California is not a single market. It is a federation of valleys whose returns, vulnerabilities, regulatory exposures, and capital structures do not align and whose only honest synthesis runs through Sacramento. Within each valley, every consequential build-out: compute, water, energy, food, mobility, is a four-layer underwrite: policy, infrastructure, capital, and the operating-and-commodities layer that turns a balance-sheet entry into a real asset. No layer compounds at the company level unless the other three are sequenced behind it. No valley compounds at the state level unless the others are coordinated with it.

The investable bottleneck, then, is not California. It is the coordination problem inside California across its valleys and across the layers of its build-out that everything else turns on. And that coordination problem has a physical location. It happens in Sacramento, by default, because Sacramento is the only city in the state where the regulator, the pension allocator, the utility planner, the agricultural producer, the technology operator, and the community organiser can sit at the same table before lunch. The Capital Valleys Forum is a working test of whether that coordination can be made deliberate rather than incidental.

The bet: California’s leverage compounds when the coordination is the asset. Returns and resilience travel in the same direction only when policy, capital, infrastructure, and operations are underwritten as one transaction, in one place, with one set of trade-offs on the table. Sacramento is the only California city positioned to host that transaction.

California will not solve this alone

California will not solve this alone, but it will set the terms. Returns and resilience compound in the same direction, but only in the places where capital is patient enough to learn the place. Sacramento, on May 8, looked like a place willing to learn and like a market that the rest of the country, whether it knows it yet or not, will end up trading against.

What follows is the a snippet of the programming on the day itself: the sessions, the speakers and some recaps of their days. The list below is a sampling rather than a complete recap; most of what was useful in the room was conversation rather than transcription and these parts that made it into this write-up are the parts I happened to capture.

Sessions from the day

The day ran thirteen sessions across the three blocks: keynotes, firesides, panels & lightning talks. A few moments from the room, captured below, are a sampling rather than a full account. What follows is the handful where I wasn’t too distracted to capture a shot.

Grateful to have had great sessions and panels exploring topics from:

Where the Goods Move, the Country Follows: Freight, Ports, and California’s Climate Playbook, a fireside moderated by David Hume from the Liquid Grid, with Christine Casey, PhD, Deputy Secretary for Freight Policy at CalSTA.

Building the Workforce, Supply Chains & Deployment Lanes for Electrification, a panel with Orville Thomas, CEO of CalEPIC, and Kara Demirjian Huss from DCCM, on what it actually takes to build the new-economy flywheel: deployed projects, trained technicians, local suppliers from innovation to deployment to workforce.

California’s Climate Corridor: Regional Ecosystems into a National Export Engine, a panel with Jason Anderson, President & CEO of Cleantech San Diego; Nuin-Tara Key, COO of California Forward; Andrew Chang, CEO of New Energy Nexus; and Teddy Hennes, Partner and Head of Platform at emrgnce / Cool Climate Collective, on how to connect California’s distributed innovation advantage, from San Diego’s 925 cleantech establishments, the Central Valley’s agri-tech corridors, the Bay Area’s venture-backed innovators into a unified export engine that scales beyond state borders without leaving the prosperity behind.

The Resilience Premium: Finding Returns in Places That Last, a panel with Nile Garritson, CFA, Portfolio Manager at CalSTRS; Peter Ferguson, HSBC’s lead for VC-backed energy transition and climate tech in Northern California; Brian Wayne from CurvePoint Capital; and Susan Su from Climate Money, on how debt & equity markets price resilience via various financing vehicles and how sponsors unlock cheaper, longer-term capital by underwriting the resilience layer.

Powering AI Without Breaking the Grid, a panel moderated by David Siap, energy subject-matter expert at CREO Syndicate, with Elias Habbar-Baylac, who leads Strategic Energy and Sustainability Investments and Business Development in Cisco’s Chief Sustainability Office; Matt Price, Founder of Hayzel; and Steve Severance, Co-founder of Modern Energy, on whether AI can be scaled without scaling its footprint and what an honest accounting of inference and training data-center intensity looks like beyond the headline PUE and WUE ratios.

Where the Money Meets the Molecule, a fireside with Robert Schuetzle from Infinium and Peter Ferguson from HSBC, on how a Sacramento-grown company and a global bank turned waste CO₂ and clean electrons into bankable molecules… for aviation, shipping, trucking, and, this year, the immersion fluids cooling the AI data-centre build-out.

The End of Stationarity: What Scales in Water?, a fireside with Garrett Rapp, Senior Engineer at West Yost, and Kyle Cordova from AquaPoro, walked us through what it actually takes for water solutions to scale when the hydrologic patterns the infrastructure was built for no longer exist.

A policy x data-oriented highlight for me was when we had our friends at Anthos showcasing what the future of dynamic modeling can look like when we can interrogate multi-model causal interactions and variables in a world where spreadsheets alone can’t map the complexity.

Voices from the Room

A few of the day’s participants have since published their own reflections, each from a different vantage on the stack and each is worth reading.

Orville Thomas, CEO of CalEPIC and moderator on Building the Workforce, Supply Chains & Deployment Lanes for Electrification, has written about a week of climate conversations, on the road and at home in Sacramento, and what they amount to for the on-the-ground work of building the region’s clean-economy flywheel: the technicians, the suppliers, the deployment lanes that turn the climate transition from a federal-policy talking point into a working regional economy.
👉Read it here.

David Siap, who moderated Powering AI Without Breaking the Grid and leads the energy research agenda at CREO Syndicate, the leading network of private family offices investing into sustainable solutions, has written about the five weeks he spent in the field between California’s compute, energy, and capital nodes, in the lead-up to the Forum. His piece is one of the more honest accounts of what the AI-energy build-out actually looks like from a deployment lens, rather than a deck.
👉Read it here.

Teddy Hennes, our Platform Partner at emrgnce, moderated California’s Climate Corridor: Regional Ecosystems into a National Export Engine, has written about California’s early-stage startup scene from the California-Forward perspective — what holds the state’s distributed climate ecosystems back from compounding into a unified export engine, and what the next decade of platform-building has to do about it.
👉Read it here.

Devin Young, who with his co-founder DJ Passey delivered When Your Model Breaks and demo, shared his take.
👉Read it here.

Victor Hugo wrote that nothing is more powerful than an idea whose time has come. What he didn’t write, but history has shown repeatedly, is that the idea rarely announces its arrival. It doesn’t come with a press release, but often comes with a crisis.

The pattern is consistent enough to be a law. The Great Smog of London in December 1952 killed an estimated 12,000 people in five days. The science of air pollution had existed for decades. Activists had been raising alarms for years. None of it moved policy, but the smog did. The Clean Air Act was passed by British Parliament four years later. The idea’s time had come, not because the argument got better or the underlying facts changed but because the cost of the old arrangement became undeniable.

The 1973 OPEC oil embargo is the more directly relevant precedent. The U.S. had understood its oil dependence for years. Energy economists had been publishing papers. Politicians had commissioned reports. When the embargo hit and gas lines stretched around city blocks, something shifted, not in the analysis, but in whose problem it was. The Department of Energy was created. Corporate Average Fuel Economy (CAFE) standards were enacted by the U.S. Congress in 1975 via the Energy Policy and Conservation Act, once again largely in response to the 1973 OPEC oil embargo. The first modern solar installations got federal backing. Energy efficiency ceased to be an environmentalist preference and became a national security imperative.

The idea was the same before and after the embargo. What changed was the world’s willingness to act on it.

I’ve been thinking about this pattern this past week while at SF Climate Week, partially also continuing a stream of thought from my prior week in DC. Because we are living, right now, in one of those inflection moments and the evidence suggests that most markets haven’t priced it yet.

The pattern is visible in two places at once. In developed markets, data centers are forcing energy procurement to move at the speed of compute. In emerging markets, fossil volatility is turning renewables from a climate preference into fiscal defense. Different markets, same mechanism: when energy becomes the bottleneck, speed beats ideology.

Climate as Resilience: Last Year’s Language

If you attended climate conferences over the past year, you heard a particular phrase repeated like a mantra: climate as resilience. It was the reframe designed to make climate action legible to finance… to shift the conversation from moral obligation to risk management, from idealism to fiduciary duty.

It was the right argument. And for much of the audience, it still hasn’t been enough. Because resilience is abstract until the thing you’re supposed to be resilient against actually arrives.

That’s what’s different now. The geopolitical and energy disruptions of the past year: the commodity volatility, the supply chain fragmentation, the tanker route risk, the re-weaponization of energy as a geopolitical instrument, have turned the resilience argument from a conference slide into a live market condition.

Climate action is no longer a forward-looking hedge against possible disruption. It is the response to present disruption. The revelation is happening. It is just still making its way through markets, still being absorbed by capital allocators, still being translated into the regulatory and financing frameworks that will shape the next thirty years.

That lag, between the revelation and the repricing, is where the opportunity lives.

Samuel Insull and the Utilization Thesis

Before we get to the frontier, it’s worth pausing on the mechanism. Because the same economic logic that unlocked electricity for the masses is the one that will determine whether the energy transition reaches everyone or stalls at the edge of affordability.

In the 1890s, Samuel Insull inherited a chaotic, expensive, and deeply skeptical electricity market. Thomas Edison had built direct current systems that were brilliant but difficult to scale economically, each neighborhood needed its own generator, costs were astronomical, and electricity was a luxury for the wealthy. The public remained unconvinced.

Insull’s insight wasn’t technical, but purely economic. He recognized that the cost of electricity was almost entirely fixed, from the plant, the wires, the infrastructure and that every additional unit of consumption spread that fixed cost across a larger base. Utilization was the variable that determined whether electricity was a luxury or a utility.

So he went to work on utilization. He offered cheap off-peak rates to industrial customers who could shift load to overnight hours. He built the interconnected grid specifically to average out demand peaks across geographies. He pushed relentlessly into residential markets not because it was profitable immediately but because density of consumption was the mechanism through which electricity became affordable for everyone.

By 1920, Chicago had some of the cheapest electricity in the world. Once again, not because the technology got dramatically better, but because Insull understood that the path from expensive and exclusive to cheap and universal ran through maximum utilization of existing infrastructure.

This is the ACE framework, Average Cost Economics, applied to a network. And it is still the most under-appreciated dynamic in energy transition today.

Prosperity isn’t a byproduct of cheap energy, it’s inseparable from it. Insull did more than just wire Chicago, he had engineered the material conditions for a middle class.

The Data Center Version

There’s a hierarchy that nobody in the energy industry says out loud but everybody operates by.

At a session hosted by Google, HSBC and Sightline Climate, Kim Zou finally said it: speed, speed, speed — then cost — then climate.

That’s the procurement stack for hyperscale data centers. A truly honest take to the current demand requirements. When you’re trying to bring a gigawatt of compute capacity online, energy availability is an existential constraint. Climate is a preference. The industry’s credibility gap, the distance between what it says in sustainability reports and what it actually optimizes for, lives in that sentence.

But here is what Insull would recognize immediately: that priority stack isn’t a values statement. It’s a phase. The early electricity industry also prioritized availability over everything else. You couldn’t sell efficiency to customers who didn’t yet have power. Once availability was solved, the economics of utilization took over and efficiency became the profit motive, not just the preference.

The hyperscale operators, whether Google, Meta, Microsoft or Amazon, are all publishing PUE (Power Usage Effectiveness) metrics and making serious commitments to clean power. But the underlying logic is the same as Insull’s. The three variables that determine the climate profile of your compute are: the cleanliness of your power source, on-premise versus cloud trade-offs, and how efficiently your actual workloads run. All three are utilization problems in disguise.

PUE is a ratio: total energy consumed by the facility divided by the energy delivered to IT equipment. Google’s average hovers around 1.10. Most enterprise data centers run between 1.5 and 2.0. That gap is wasted energy and wasted spend… the same gap Insull spent his career closing in the power grid.

The emerging frontier is self-powered data centers: facilities that own their energy supply chain rather than purchasing from a utility. Pair that with demand response assets and you’re looking at the architecture of a grid that behaves more like Insull’s integrated system than the fragmented utility model of the 20th century.

The phrase I heard: BYONCE, “Bring Your Own New Clean Energy”, sounds like a joke, but it captures the posture perfectly. It presents a structural shift in how large energy users relate to the grid. The implication is that speed, cost and climate stop being a priority stack and start being a simultaneous optimization problem. When your clean energy is co-located, dispatchable and yours, you don’t have to choose.

Forced Transitions

But the data center story, important as it is, wasn’t my main highlight of SF Climate Week.

It was when, our emrgnce ecosystem and Reciprocal, convened a room of friends for our Emerging Markets: The Next Climate Frontier event.

We opened with a provocation we called Forced Transitions: the argument that geopolitical shocks don’t merely disrupt energy markets, they accelerate adoption curves in economies with the least legacy infrastructure to protect.

The question on the table: as oil and gas volatility reshapes global capital flows, what role do emerging markets play? Are they recipients of climate finance & typecast as philanthropic playgrounds or are they the next proving grounds?

The answer the room kept arriving at: proving grounds. By a wide margin.

The Leapfrog Dividend

There’s a pattern in technology adoption that development economists call leapfrogging, the phenomenon by which countries that missed an earlier infrastructure wave skip it entirely and adopt the successor technology faster than incumbents encumbered by the old one.

The canonical example is mobile telephony. Sub-Saharan Africa never built landline infrastructure at scale. When mobile networks arrived, there was nothing to cannibalize, no installed base to protect, no regulatory regime built around legacy systems. The continent went from low telephone penetration to near-universal mobile access in under a decade. M-Pesa, the mobile money platform built on top of that infrastructure, emerged in Kenya in 2007 and processed more transactions than Western Union within five years.

Once again, this pattern is structural.

Incumbency is a liability when the technology generation turns.

We are at a technology generation turn in energy. The successor technology: distributed solar, storage, smart grid, demand response and the software layer that coordinates all of it, does not require the legacy grid architecture that incumbent utilities were built to protect. In many cases, legacy infrastructure actively impedes deployment.

Emerging economies, in this light, are not behind. They are unencumbered. And unencumbered, in a transition, is an asymmetric advantage.

The 1973 embargo is instructive again here. Japan, almost entirely import-dependent for oil, responded faster and more systematically than any other developed economy, cutting energy intensity per unit of GDP by nearly 40% over the following decade. The country most exposed had the most to gain from moving first and the fewest vested interests defending the old arrangement. Constraint, again, as catalyst.

Crisis-as-a-Catalyst

What I named in my talk as “Crisis-as-a-Catalyst” is the mechanism by which the leapfrog dynamic converts from latent opportunity to active imperative.

Oil and gas volatility is not abstract. When energy prices spike, the countries most exposed are net importers and most of the world’s emerging economies are net importers. Risk premiums, tanker route disruptions, OPEC+ production decisions: these are not geopolitical abstractions to a grid operator in West Africa or Southeast Asia. They are budget crises. They are the conditions under which governments that have resisted energy transition on cost grounds suddenly find the cost calculus has inverted.

This is precisely what happened in Europe following 2022. Germany had spent a decade hedging on energy transition, protected by cheap Russian gas and resistant to the speed that the moment required. The disruption of that supply didn’t just accelerate the renewable buildout, it restructured the entire political economy of energy transition on the continent. REPowerEU mobilized capital and political will in months that had been unavailable for years. The idea was the same, the crisis made it feasible and timely.

Now that dynamic is playing out across emerging markets… more slowly, less visibly, but with larger long-term consequences. Energy sovereignty is the frame that unlocks the investment thesis. A country that builds domestic renewable capacity isn’t making a climate bet. It’s making a national security bet. The climate co-benefit is real but secondary to the decision-maker.

Climate finance has struggled for years with the gap between the cost of capital in developed markets and the risk perceptions of emerging ones. The spread is real but often overstated and the narrative around it gets the causality backwards. The question isn’t: how do we reduce perceived risk enough to attract capital? The question is: what conditions make the investment inevitable regardless of climate preference?

Energy sovereignty answers that question. And the geopolitical conditions that create that imperative are not easing, but rather deepening. The revelation is not complete. It is arriving and the data is there if you start plugging in the right causal relationships, which is what I set out to explore.

Slide from my Emerging Markets presentation on Crisis-as-a-Catalyst

The Causal Model: What the Numbers Actually Say

The vulnerability of fossil-dependent emerging markets is not a hypothesis. It is a measurable, compounding mechanism and we mapped it.

The entry point is LCOE: the levelized cost of energy. Utility-scale solar is now regularly benchmarked in the high-$30s to low-$40s per MWh in leading markets, while solar-plus-storage remains higher and depends heavily on storage duration, financing costs, grid conditions and local irradiance.

The long-term cost curve for solar continues to move downward, even if annual costs can stall or rise with interest rates, tariffs, interconnection delays or supply-chain shifts.

Diesel generation, by contrast, is structurally exposed to every crisis that touches the Strait of Hormuz, global fuel markets, shipping costs, insurance and dollar liquidity.

In many import-dependent or off-grid settings, diesel already runs several times more expensive than utility-scale solar. During acute crisis episodes, that gap can widen dramatically. The crossover has already happened in the places where fuel logistics and FX exposure matter most. Every new crisis makes the gap harder to ignore, because each shock strengthens the case for deploying the cheaper, less geopolitically exposed alternative.

But the LCOE crossover is only the beginning. The more important mechanism is fiscal transmission, the way an energy shock drains state capacity through four simultaneous pathways.

Let’s take Egypt as the case study:

When energy prices spike, subsidies expand (political cost of letting prices through to consumers), FX reserves drain from higher import bills, dollar debt service rises as the currency weakens against that import pressure, and monetary tightening to fight imported inflation further squeezes fiscal space. The total fiscal drain roughly doubles from baseline during a crisis episode. Every gigawatt of solar deployed reduces the subsidy burden and the FX reserve bleed simultaneously. This is not at all an environmental investment, it is a fiscal defense.

Pakistan makes this concrete.

Pakistan’s solar boom has been driven less by climate policy than by affordability pressure. Households and businesses are not waiting for a perfect transition plan; they are arbitraging against an expensive, unreliable, fossil-dependent system.

Recent estimates suggest Pakistan’s rapid solar adoption has already avoided roughly $12 billion in fossil-fuel import costs. In a crisis scenario where fuel costs spike, that avoided exposure becomes even more valuable. The same solar panels do not just produce cheaper electrons; they become a hedge against FX depletion, imported inflation, and geopolitical volatility.

The structural argument runs deeper than cost. Transit risk, insurance disruption, refinery concentration, dollar exposure, subsidy drain, and price volatility hit fossil fuels directly and quickly.

Solar-plus-storage has vulnerabilities too. Chinese module concentration, critical minerals, grid integration and financing costs but these are slower-moving deployment and supply-chain risks, but they are not instant cascade risks.

Once again, this is not a climate argument, it is a national security argument expressed in the language of supply chain risk management. The countries that recognize that soonest will move fastest. The capital that understands the causal model, not just the LCOE crossover, but the full fiscal transmission chain, will be positioned at the right moments in the right markets.

The map of the ten most exposed emerging market states tells the rest of the story.

Bangladesh, Lebanon, Jordan, Kenya, Tunisia, Nepal and Egypt each sit at a different point between fossil vulnerability and transition readiness. The gap between those two scores is the opportunity space: in gigawatts deployed, import bills avoided, FX reserves preserved and fiscal headroom restored.

It is the political economy of governments that can no longer afford the alternative.

Second and Third Order Effects

The first order effect is obvious: faster clean energy deployment in markets that represent the majority of future energy demand growth.

The second order is more interesting: the companies and technologies that win in emerging markets are building for constraint, not abundance. They are optimizing for intermittency, limited grid infrastructure, mobile payment rails, distributed ownership models and communities (and construction sites) that have never had reliable power at all. That is a more rigorous design requirement.

My belief is that the technologies and business models that emerge from those constraints will flow back into developed markets, just as mobile money infrastructure pioneered in Kenya became the template for fintech innovation globally, just as the fuel efficiency innovations catalyzed by the 1973 embargo eventually raised global standards across the industry. The edge innovates under pressure. The center adopts when it’s convenient.

The third order is the one worth building toward: a global energy system where the geography of production and the geography of consumption are realigned. For a century, energy has been produced where hydrocarbons exist and consumed everywhere else. That asymmetry underwrote the geopolitical order of the 20th century… the petrostates, the tanker routes, the military presences, the currency arrangements, all of it downstream of the geology of oil.

The transition to renewables decouples production from geology. Solar irradiance, wind corridors, tidal and geothermal energy… these are distributed unevenly but vastly more evenly than oil reserves. The countries that have been energy-dependent for a century are, in many cases, among the best-positioned for renewable production.

That is a reordering of geopolitical power. The full implications are not yet priced into markets. They are not yet reflected in foreign policy. They are not yet articulated in the capital allocation frameworks of most institutional investors. But they are directional. And they are inexorable.

The Capital Implication

Volatility is a filter. The capital and operators that find their way into these markets through this cycle will define the next decade of climate infrastructure.

Insull’s lesson applies here too. He didn’t wait for demand to materialize organically. He created the conditions for it… through pricing, through interconnection, through relentless focus on utilization as the variable that made the economics work. The result was a network that created its own demand by making electricity too cheap and too available to ignore. Prosperity followed infrastructure. Infrastructure was a choice made in advance of the demand it generated.

That is the bet on offer in emerging markets today. The infrastructure that gets built now: the grid architecture, the ownership models, the digital layer, the regulatory frameworks, will determine what’s possible at scale for the next thirty years. The window between revelation and repricing is where patient, catalytic capital earns its return. This is not done by picking winners in established verticals, instead done by shaping the systems those verticals will operate within.

The fastest path to the energy transition isn’t the obvious one.

It runs through the places nobody’s watching… through constraints, through forced transitions, through the unencumbered edge, through the slow-moving recognition of an idea whose time, unmistakably, has come.

This piece originally appeared on Mehrad’s LinkedIn, a reflection of ideas from SF Climate Week 2026 & the prior week in DC

Earlier this month, I was in Washington DC attending what I can only describe as a five-day production of The Greatness of American Capitalism, staged at Semafor World Economy.

With an ensemble cast with 400+ Fortune 500 CEOs, cabinet secretaries, central bankers and finance ministers. From headline takes, it clearly was the ‘the Davos in DC’, but as the week unfolded, it proved to truly be a ‘festival of news’ as Ben Smith, Editor-in-Chief of Semafor, put it.

One could feel the invisible hand of Adam Smith himself guiding every panel and discussion. American optimism descended over the proceedings like a benediction, the kind of communion only IMF Spring Meetings week can summon in this city.

The American Dream, the American Ideal and the American Balance Sheet, all converging on the 3 main stages.

The themes were timely as ever: risk, resilience and the redemptive power of innovation. Speakers across every track, from Cabinet to boardroom, arrived at the same revelation: the future is uncertain, but human ingenuity will meet it.

There are always, at gatherings of this caliber, ideas whose time has come. This year’s quartet was clear: GLP-1s, energy growth, interwoven geopolitics, AI, and most movingly, the emerging consensus that in an agentic commerce world, consumer experiences will matter more than ever. One’s agent, it was gently clarified, cannot experience consumerism on one’s behalf. That pleasure, mercifully, remains ours to perform.

A few of my observations from the week worth holding onto:

Digging is the ultimate resilience

The most memorable aphorism of the week came during a discussion on energy security. Dig for tunnels, dig for geothermal, dig for resources, dig deeper than your adversary can reach. The principle generalizes: capital, like geology, rewards depth. The strategic assets of the next decade: energy, compute, minerals, sovereign capacity, will all reward whoever was willing to dig earliest and deepest. Surface-level positioning is increasingly cheap; depth is the moat.

Energy resilience is becoming a political premium

Refineries and terminals were struck somewhere offstage in the recent regional escalation; flow was disrupted, infrastructure is repricing, capacity will take years to rebuild.

Dr. Fatih Birol, the IEA’s executive director, captured it on stage with an unusually direct line: [the Iran war] will redraw the global energy map in real terms. It is an apt description of what is already underway. The conversation has rightly shifted from infrastructure to flow, but the deeper point is that energy resilience is no longer just an economic variable. It has become a political one, particularly in emerging markets. A leader who can credibly own, not merely subscribe to, their nation’s energy supply now commands a premium of trust from their citizenry.

Predictability and sovereignty are being repriced.

A thought on frontier models: East and West

One thread I found myself exploring on my own, prompted by the week’s AI conversations, was why the frontier looks the way it does between the two leading model ecosystems. The standard answers: compute, data volume, capital... felt incomplete. What kept surfacing for me was epistemological density.

The English-language corpus carries centuries of adversarial reasoning baked in: legal argumentation, scientific peer review, Socratic philosophy, financial analysis under fire. The chain-of-thought that makes frontier models good at hard reasoning is, in some real sense, downstream of that tradition. Where corpora are structured differently, or where state filtering systematically removes precisely the domains in which high-stakes reasoning lives… law, finance, dissent, science under pressure, the gap may be architectural rather than something that closes with more tokens. A working hypothesis worth pressure-testing: the moat is in the library, not the GPU.

The bit-watt spread is the next refinery margin

Energy and AI were discussed on separate panels all week. The synthesis was within reach and never quite gripped: it almost never makes sense to curtail inference. A watt unspent clears at a few cents; a watt spent on tokens clears at orders of magnitude more.

Whoever owns the cheapest marginal watt closest to demand owns the bit-watt spread... the refinery margin of the digital economy. This is the thesis hiding inside the week’s two biggest themes, and it is where I expect the most consequential capital formation of the next cycle to concentrate.

A note on consumer markets:

PMI’s chief executive Jacek Olczak delivered the line of the week: cigarettes belong in a museum, alongside the rotary dial phone. A powerful sentiment, offered with the conviction of a man who has personally led his industry’s reinvention. I had the chance for a brief one-on-one with him after... what stayed with me was less the headline reinvention story and more the granularity of his thinking on the supply side.

He spoke about topsoil health, plant health, and the financial logic that connects them to the business, not as an ESG layer or a sustainability talking point, but as basic operating economics. It is genuinely refreshing to hear a Fortune 500 CEO at that altitude move fluently between soil biology and ROI on the same breath, and it left me with a more grounded read on what serious reinvention actually looks like from the inside.

Consumer markets provided the elegant interlude. Luxury, we were reminded, no longer means the $4,000 handbag. It means inclusive luxury: a $12 sock or a $300,000 watch, depending on how the guest arrives at the home. As Patrice Louvet of Ralph Lauren explained, Ralph Lauren does not sell clothes; he sells dreams. Ralph’s Coffee has become its own sub-brand.

Beyond the main stage... happenings across DC

The week was richer for what surrounded it. A lecture at George Washington University on foreign direct investment was a useful reset (thanks Karim for the invite), a reminder that the slower-moving architecture of global capital flows still does most of the actual work, even as the headlines chase faster signals.

Several World Bank Spring Meetings side events hosted by the Atlantic Council added the geopolitical layer that the main stage tends to abstract away. The conversations there moved more honestly between economic policy and security policy than the convening format usually allows.

And a few congressional fundraisers brought some of the most candid exchanges of the week, particularly on energy security and the geopolitics of supply. There is a different register of conversation that happens when the room is small and the cameras are off, and it tends to be where the actual operating assumptions of policy reveal themselves.

I will admit one of these conversations updated my thinking. Many have long been skeptical of the framing of Iran as an “imminent” threat at the surface... the rhetoric has been used too cynically, too often, and the surface case has rarely held up. But what was laid out, more eloquently than I had heard before, was the harder version of the argument: that the regime’s strike-hardening of its nuclear program was approaching a depth at which Western and regional strike capability would no longer reach it.

The window was not measured in years. It was measured in weeks to months. Combined with the regime’s recent strikes on GCC nations and infrastructure, a clear demonstration of how it behaves when it judges itself unaccountable, the case for action becomes harder to dismiss, even granting that some of the regime’s posture was provoked by earlier escalation. I left less certain than I arrived. That is what serious rooms & conversations are supposed to do.

The takeaway I walked out with...

If I had to compress the week into a single thought, it would be this: instability is a routing problem, not a containment one.

The assumption underneath much of the week’s optimism was that the developed world’s economic insulation is structural. It is not. It is conditional. Higher energy costs will hit oil-importing emerging economies first, and many lack the financial muscle to weather a sustained shock. What follows, currency stress, political turnover, migration pressure, supply disruption, security spillovers, does not stay local but re-routes. The developed economies absorb it in different forms, sometimes years later, and rarely in the shape they were modeling for.

In a tightly coupled global system, the parties closest to a shock who lack the capacity to absorb it become the parties who export it onward. The ones who thought distance was their hedge discover that distance was a routing assumption, not a barrier.

Resilience analysis that stops at one’s own borders is incomplete. The right question is not only can we weather this, but can the most exposed economy on the chain weather this, because if it cannot, the shock does not disappear. It merely changes form and manifests across the rest of our interconnected systems.

That, more than any single panel, was what the week left me with.

Every dissonance resolved, as great drama demands, in the same hopeful crescendo. Capital markets, efficient. CEOs, bullish on AI. Productivity, soon to be reimagined beyond its rearview-mirror measure of hours clocked, though no one specified by what, or measured by whom.

I left deeply impressed. It is rare to witness so many powerful people agree so fully on what the problems are, who the protagonists are and what paths the story can take.

Kudos to the brilliant Semafor editors who put it altogether through curation and conversation. Truly a “festival of news”.

This piece originally appeared on Mehrad’s LinkedIn.

There is a certain kind of room that changes how you think. The ideas inside it may not be entirely new, but the people holding them are, for once, in the same place at the same time, and the friction of that proximity produces something no white paper or conference panel can replicate.

Villars-sur-Ollon sits in the Swiss Alps, roughly equidistant from the frictions of Davos and the comforts of Geneva. The Villars Institute convenes there each year with a mandate that sounds almost quaint until you see it in practice: bring together the people who think in systems, not sectors, and see what they build. This year’s gathering drew sovereign finance architects and coral reef scientists, indigenous land rights leaders and luxury brand CSOs, structured finance architects and deep tech founders… the kind of intellectual cross-section that makes you realize how siloed your individual conversations have actually been.

I was invited to present as part of a working group on emerging innovations and their pathway from proof-of-concept to market relevance. What I brought was a framework. What I left with was considerably more.

These are my notes & the ideas that sharpened my thinking, as well as the provocations I am still working through and directions I am now actively pursuing. In keeping with the Chatham House rules under which the summit operates, no remarks are attributed. The ideas are what matter here.

The Animating Paradox

Every substantive conversation at Villars eventually arrived at the same clearing: nature has value, we just failed to finance it.

This sounds simple, but clearly it is not. It represents a significant maturation in how serious people are framing the problem… away from moral obligation and toward something far more tractable: a financing failure. A structural one, with identifiable causes and, in principle, identifiable remedies.

The societal consensus that nature has economic value is no longer contested ground. The World Economic Forum also recently released their latest report, which quantified over $44 trillion in economic value, more than half of global GDP, as moderately or highly dependent on nature. Governments, corporations, and institutional investors have signed enough frameworks and pledges to paper several conference halls. The bottleneck is belief no longer, but architecture.

For decades, the dominant approach treated nature protection as a government function, funded by taxation and administered through regulation. That model produced real outcomes: national parks, marine protected areas, species recovery programs, but failed to scale anywhere near the rate required. The private market has been handed the baton and it is standing at the line without a clear lane to run in. You cannot finance what you cannot measure, and you cannot measure what you have not yet decided to value on a balance sheet.

What the Villars conversations made clear is that we are now in the architecture phase. Climate and nature have moved from environmental policy issues to global capital allocation issues. The question is how…? That is a very different, and much harder, problem than the one we spent the last decade answering.

We’ve been treating nature like a charity case when it’s always been a compounding asset.

The Archetype the Room Was Asking For

If there was a single phrase that echoed across sessions, it was this: “We need system leadership.”

The framing offered was deceptively simple: think holistically, feel empathetically, act with purpose. What made it land rather than float was the specificity of what it was pointing at. The problems that persist in nature finance, the gap between pilot and scale, the failure of instruments to aggregate into markets, the inability of good science to translate into investable propositions, are coordination failures. Coordination failures require a different kind of leadership than execution challenges do.

The systems leader is the person who holds the boundary itself. The place where the ecologist’s model meets the banker’s spreadsheet, where the regulator’s mandate meets the community’s livelihood, where the startup’s technology meets the institution’s procurement process. These are the inflection points where value is either created or lost, and they require someone whose mental model is wide enough to see all of them simultaneously. The expert goes deep in one domain and hands off at the edge. The systems leader lives at the edge.

This is, not incidentally, the animating idea behind the Bottlenecks Institute. The hypothesis is that the most leveraged interventions in complex systems are almost never the obvious ones, they are the small, structural constraints that, once removed, allow everything downstream to accelerate. Finding those constraints requires exactly the kind of systems view the room kept calling for and rarely found.

Nature as Infrastructure… and Why the Distinction Matters

One of the cleanest reframes to emerge from the summit was also one of the most practically consequential: nature is infrastructure.

The distinction matters enormously for how capital engages with it. Infrastructure carries assumptions that charity does not. It is essential, it requires ongoing maintenance, it generates returns over long time horizons, it can support public-private financing structures, and its degradation is a cost, measurable and attributable. A bridge requires inspection, repair, and eventually replacement. A power grid requires continuous investment to remain reliable. A mangrove system protecting a coastline from storm surge, a watershed providing clean water to a city, a kelp forest sustaining a fishery, these are assets with operating costs, performance metrics, and quantifiable returns. They have been consistently underpriced because we chose not to read them that way. That choice is now expensive.

The 30x30 framework: protecting thirty percent of land and ocean by 2030, was cited frequently as a policy anchor. The more interesting conversation was about which thirty percent. Protecting the easiest or most politically tractable areas produces a number, not an outcome. Biodiversity hotspots, the concentrated zones of irreplaceable endemic species, represent a small fraction of total land area but a disproportionate share of ecological function. The targeting problem is as important as the financing problem, and the two are inter-connected: a financial instrument built around a precisely defined and measurable asset is categorically more bankable than one built around a diffuse, politically determined one.

Infrastructure isn’t built once and forgotten. Neither is a forest.

The Food System Is Where Theory Meets Urgenc

If there is one data point from Villars I expect to be quoting for years, it is this: in the Holocene, the climatically stable period in which human civilization developed, breadbasket crop failures for staples like corn and maize occurred roughly once every sixteen years. At 1.5 degrees of warming, that becomes once every three years. At two degrees, it is every other year.

Read that again slowly. A system designed by agriculture, supply chains, commodity markets, food policy to handle a failure event once in a generation will face that same event in consecutive years within the lifetime of infrastructure being built today.

The actuarial math of feeding humanity has already changed yet most of the financial models have not caught up.

The food system conversation moved quickly from the statistical to the structural. Monoculture at global scale, optimized for yield and commodity price, is elegant in its efficiency and catastrophic in its fragility. More than 60% of global calories currently come from just three crops. The system was not designed for redundancy; it was designed for throughput.

The alternative requires rethinking the entire economic architecture of agriculture: what is subsidized, what is insured, what is exported, what is consumed locally, and whose cuisine gets treated as heritage versus commodity.

This provoked an idea I am still developing: the localization of food systems may be primarily a cultural story, not an agricultural one. Regenerative, polyculture farming genuinely rooted in local terroir and culinary tradition creates a cuisine that cannot be replicated at industrial scale and that rarity is precisely what makes it valuable. The high-end restaurant in Tokyo or London serving a dish anchored in a specific microregion’s biodiversity is simultaneously a conservation finance story, a rural development story, and a sovereign identity story. The export premium funds the local ecology that makes the product possible. The cuisine is the business model.

The transition cost question was put simply in the room: who will pay for growers to change? The answer that resonated most was not governments or philanthropists but the value chain itself… spreading transition risk across every party that benefits from a more resilient food system. That logic connects directly to the financial architecture discussed later in this piece. And separately: agricultural subsidies in most major economies are weight-based and crop-specific, which means the most effective reform strategy may be to redirect their intent rather than challenge their existence.

If an enhanced or alternative crop meets the qualifying criteria on fewer acres and generates more economic value per unit of land, the subsidy program effectively becomes a transition mechanism. The government doesn’t change the policy. The farmer changes the crop and the system shifts.

There are no non-radical food futures. The question is only which kind of radical we choose.

The Financial Architecture: What’s Emerging

The summit’s finance conversations were the most technically dense and, in places, the most generative. Several structural ideas surfaced for me that deserve wider circulation.

Natural Asset Companies represent perhaps the most architecturally interesting instrument. The concept: bundle all the credits, revenue potential, and ecosystem services associated with a natural asset into a share structure, something that trades and behaves like equity, with all the liquidity and transparency implications that brings. The asset issues shares. Investors buy exposure to the compounding value of a living system, not just a static credit. The critical design constraint is cash flow: a natural asset company without embedded revenue streams is conservation philanthropy in new clothing. The ones that will work are those where the protection of the asset is operationally necessary for a downstream commercial activity — fisheries, tourism, watershed services, raw material supply chains.

Digital shares with layered yields extend this logic to the instrument level. Assign a credit to a digital share that yields a carbon token and a biodiversity token simultaneously. Co-benefits bundled at the instrument, not the project. Most current instruments force investors to choose a primary benefit: you buy a carbon credit or a biodiversity unit, when the underlying asset produces both. Separating the benefits at the instrument level may reduce the value of each, while bundling them more accurately reflects the real economics of a healthy ecosystem.

Jurisdictions as demand signals was one of the more practically actionable ideas to emerge. We spend enormous energy persuading individual corporations to voluntarily create demand for nature-positive instruments, with modest results. Jurisdictions such as cities, regions & nations can mandate that demand at scale, with immediate effect. The example discussed: a sovereign integrating nature-based credits into corporate tax obligations, so that a defined percentage of tax liability can be discharged through qualifying nature finance instruments. Suddenly the question of whether a market for biodiversity credits exists becomes very straightforward.

Subsidy as equity inverts the typical public finance logic. Rather than structuring government support as grants or transfers, which exit the system at project close, structure them as equity stakes. The government remains on the cap table. If the asset performs, public capital appreciates alongside private. This aligns incentives over the full life of the asset and gives governments a legitimate basis for long-term governance involvement that is economic rather than purely regulatory.

The Turning Point was defined with unusual precision: systemic change becomes self-sustaining when a negative externality is both priced and communicated. Pricing alone changes the economics for sophisticated actors. Communication changes the behavior of everyone else. A carbon price with no narrative is a compliance mechanism. A carbon price embedded in a legible consumer story is a market signal. Both matter. The sequence matters too.

Price the externality. Then tell the story. In that order.

The Demand Problem Nobody Wants to Own

Across every session on nature finance, the same structural gap reappeared: the supply of nature-positive outcomes is constrained by the absence of demand signals strong enough to justify investment.

The compliance market has created a floor, but a fragile one, vulnerable to political reversal, methodology disputes, and the reputational contagion that has periodically collapsed voluntary carbon markets. What is missing is demand that is structural: baked into operating agreements, procurement contracts, regulatory frameworks, and financial covenants, rather than contingent on a CSO’s budget cycle or a CEO’s tenure.

The clearest diagnosis I heard across the narrative sessions was blunt: we are good at stats, but not at stories. And because of that, we are not winning. The cleanest structural articulation: “Transactions need to become transitions.”

Individual deals at pilot scale, however well-designed, do not build markets. Markets emerge when transactions aggregate into a repeatable pattern, when the deal structure becomes a template, when the template becomes a standard, when the standard becomes an expectation. That aggregation requires deliberate architecture. More pilots will not produce it.

The analogy that surfaced for me goes back more than a decade… to my time working with the Dubai Future Accelerators and early meetings at the Prime Minister’s Office, rooms where the question was always the same: how do you finance an outcome that doesn’t yet exist?

The instrument we kept returning to was the income share agreement. A student defers payment and repays from future earnings as a percentage of income, aligning incentives across student, institution, and lender around a single outcome: productive employment. The beauty of the structure is what it assumes: that the outcome is real, that it is measurable, and that the financial instrument can be built around the delta between where someone starts and where they end up.

That logic travels. The question worth asking seriously is whether you can build the equivalent for natural capital, an outcome-based agreement where the end buyer commits to a price for a defined ecological outcome, and the delta between current state and that target is what gets financed.

The mechanism exists. It is the Advance Market Commitment architecture, which successfully accelerated vaccine development by pre-committing to purchase at scale before supply existed. Applied to natural capital: a defined buyer commits to purchase a verified ecological outcome at a set price; that commitment is treated as a receivable by the project developer; the receivable supports project finance at commercial terms without subsidy. The Catalytic Cashback Facility framework we have been developing across our portfolio is one expression of this logic — applied initially to operational efficiency gains in maritime and agricultural settings, but pointing at something considerably wider.

An idea without a way to finance it is just aspiration dressed up as a plan.

The Mountain Notices

Between sessions, the summit took us out onto the mountain. The hikes were stunning in the way the Alps always are… the kind of landscape that makes you feel briefly and usefully small. But something was off, and it took a few minutes to name it.

We were walking on ski runs. In March. The snow had already gone.

It happened to fall on the vernal equinox, which for Persians marks Nowruz, the new year, the return of spring, the oldest celebration of nature’s renewal I know. I have spent enough Nowruzes in my life to carry a felt sense of what the season should feel like when it arrives. This did not feel like spring arriving. It felt like winter having already left.

The alpine village of Villars has built its economy, its identity, its entire sense of place around snow. The hotels, the lifts, the restaurants, the families who have run the same chalets for three generations, all of it predicated on a seasonal certainty that is quietly becoming unreliable. We were inside spending three days discussing biodiversity finance and planetary boundaries, and outside the mountain was making the same argument without PowerPoint slides or panel discussions. It was simply showing us.

I asked the question in one of our sessions and it landed harder than I expected: what happens to the ski instructor, the lift operator, the hotel owner, the village economy, when the snow stops coming on schedule? These are not abstract future risks; they are present-tense disruptions to real livelihoods, in a place that happens to host one of the world’s most sophisticated conversations about climate and nature. The irony is not subtle. The mountain is not waiting for the policy frameworks to catch up.

This is the human narrative that tends to escape the technical science of the predicament. The data on crop failure frequencies and biodiversity loss is important and necessary. But the moment that actually lands, the moment that moves from information to understanding, is standing on a bare ski run in the middle of March, in a place that has known snow for as long as anyone can remember, realizing that the transition is not coming. It is here. It arrived while we were still debating the architecture.

What I Brought, and What It Became

My presentation at Villars was built around a simple provocation: early-stage venture investing is foresight, applied to capital. And when that foresight is trained on systems rather than sectors, venture transcends being just a financial returns mechanism and starts to act as a catalyst.

I spent my professional formation in Dubai, working on public-private initiatives designed to catalyze new industries from relatively thin air to attract companies and capital into sectors that did not yet exist in the region and to build the regulatory and commercial conditions that would make them stay. The core lesson from that decade: policy does not create industries. It creates permission structures. Innovation fills them.

That reframe has shaped everything about how we approach venture investing in climate systems. We are looking for the moment before a market discovers what something is actually worth; the inflection point where the scientific evidence is clear, the regulatory trajectory is legible, and the commercial infrastructure is just beginning to form. That is where early capital is most catalytic, and most asymmetrically rewarded. Foresight-driven venture is, in this sense, financial alchemy: the transformation of an undervalued future into a present-day instrument.

The Collective VC model we have been evolving is what makes this possible, and what distinguishes it from the corporate venture architecture that dominates the field. A chemicals company’s CVC finds chemistry. A food company’s CVC finds food technology. Each is structurally confined to the perimeter of its parent’s capability map. Our collective model that is now expanding into new domains, carries no such constraint. We follow the bottleneck wherever it leads.

My presentation used seaweed cultivation as the worked example, chosen deliberately because it illustrates a dynamic that sits at the heart of our investment thesis. Seaweed farming itself is not venture-backable; it is a regenerative practice, ancient and relatively low-margin. But the moment you apply innovation to what that cultivation produces, feed additives that measurably reduce livestock methane emissions, alternative packaging feedstocks, marine habitat restoration, aquaculture inputs… you have transformed a biological process into a platform. The same organism, viewed through a systems lens, becomes simultaneously a maritime play, an agricultural play, a packaging play, and a biodiversity finance instrument. A corporate investor, constrained to their own capability map, sees one of those. Collective venture holds the whole prism. It is, at its core, a formalized method for executing systems-level solutions through the mechanism of venture risk and reward.

What I Brought, and What It Became

My presentation at Villars was built around a simple provocation: early-stage venture investing is foresight, applied to capital. And when that foresight is trained on systems rather than sectors, venture stops just being a returns mechanism and becomes a catalytic one.

I spent my professional formation in Dubai, working on public-private initiatives designed to catalyze new industries from relatively thin air to attract companies and capital into sectors that did not yet exist in the region and to build the regulatory and commercial conditions that would make them stay. The core lesson from that decade: policy does not create industries. It creates permission structures. Innovation fills them.

That reframe has shaped everything about how we approach venture investing in climate systems. We are looking for the moment before a market discovers what something is actually worth; the inflection point where the scientific evidence is clear, the regulatory trajectory is legible, and the commercial infrastructure is just beginning to form. That is where early capital is most catalytic, and most asymmetrically rewarded. Foresight-driven venture is, in this sense, financial alchemy: the transformation of an undervalued future into a present-day instrument.

The Collective VC model is what makes this possible, and what distinguishes it from the corporate venture architecture that dominates the field. A chemicals company’s CVC finds chemistry. A food company’s CVC finds food technology. Each is structurally confined to the perimeter of its parent’s capability map. Our collective model, developed at Cool Climate Collective and now expanding into new domains, carries no such constraint. We follow the bottleneck wherever it leads.

My presentation used seaweed cultivation as the worked example, chosen deliberately because it illustrates a dynamic that sits at the heart of our investment thesis. Seaweed farming itself is not venture-backable; it is a regenerative practice, ancient and relatively low-margin. But the moment you apply innovation to what that cultivation produces, feed additives that measurably reduce livestock methane emissions, alternative packaging feedstocks, marine habitat restoration, aquaculture inputs… you have transformed a biological process into a platform. The same organism, viewed through a systems lens, becomes simultaneously a maritime play, an agricultural play, a packaging play, and a biodiversity finance instrument. A corporate investor, constrained to their own capability map, sees one of those. Collective venture holds the whole prism. It is, at its core, a formalized method for executing systems-level solutions through the mechanism of venture risk and reward.

Where I Am Exploring Next

The summit clarified several directions I am actively developing, and where I am genuinely interested in collaboration.

The Bio Happiness Index. Dr. Soumya Swaminathan’s distinguished lecture at the summit offered one of the most precise definitions I encountered across the three days: bio-happiness is the state of wellbeing and fulfillment that arises when biodiversity is conserved and utilized in ways that enhance human health, nutrition, and livelihoods, creating harmony between people and nature. Her corollary was equally sharp: healthier choices for people are also healthier choices for the planet. This is one of the most important measurement frameworks currently underway. Our work on a causal intelligence platform is designed precisely for this kind of work: a permissionless, locally-pluggable index infrastructure where communities and researchers can input their own data and generate a standardized output without requiring a centralized authority to validate or publish it. We are actively exploring what that collaboration looks like.

Jurisdiction as demand signal, practically applied. The Singapore model, corporate tax liability partially dischargeable via nature-based credits, is one expression. The underlying logic applies at city scale, at regional scale, and at the level of development finance conditions. I am interested in working with municipalities and regional governments that want to structure themselves as demand anchors for nature-positive instruments, and in building the financial architecture that makes that practical.

Catalytic Cashback Facility, expanded. The CCF is a framework we’ve been developing and exploring applications in maritime and agricultural context, but has wider applicability than we have yet publicly articulated. Any operational setting where a nature-positive input generates a measurable efficiency delta: energy, water, materials, logistics, is potentially a candidate. If you are working on the measurement side, the structured finance side, or the commercial offtake side of this kind of instrument, I want to hear from you.

The food system inversion. The localized polyculture-as-cuisine-as-conservation thesis is early and underdeveloped, but pointing at something real. Connecting regenerative agriculture to premium culinary identity and export positioning requires collaborators not typically in the room for nature finance conversations: chefs, cultural institutions, tourism boards, premium food retailers. If you are working at that intersection, reach out.

A Closing Observation

The week at Villars produced more concrete follow-up conversations, partnership explorations, and collaborative commitments than any summit I have attended in recent memory. Some of that is the caliber of the room. Some of it is the Chatham House format, which allows people to speak with a candor that on-the-record settings rarely permit. Most of it, I think, is the result of spending several days in the presence of people who have accepted that the problems we are working on require more than any individual organization can deliver and who have therefore arrived ready to collaborate.

That orientation is the actual shift. The field does not lack capital, science, or ambition. It has lacked and is beginning to develop, the willingness to treat coordination itself as the work, and systems thinking as the discipline it genuinely is. The shorthand that emerged from one session said it best: beyond silos, beyond ego. Stop fragmentation, start convergence.

If any of the threads in this piece resonate with work you are doing on measurement, financial architecture, food systems, demand signal design, or causal intelligence; I would genuinely like to hear from you. The conversations already emerging from this week are among the most substantive I have had in years.

The problem was never that we didn’t care enough. It was that we hadn’t built the structures that let caring become investable.

This piece originally appeared on Mehrad’s LinkedIn.

On the morning of March 9th, the Dow dropped 900 points before the opening bell had finished echoing. By the close, it had clawed back every point and finished up 239.

A 1,100-point intraday swing, one of the largest in market history, triggered not by a change in fundamentals, but by a single comment from the President suggesting the Iran conflict might be winding down. (Clearly not the case, as the past 2 weeks of escalations have shown).

Within 400 milliseconds of the first strike on February 28th, trading algorithms had already processed the news, adjusted risk models, and begun executing thousands of sell orders. By the time a human trader read the headline, equity futures had dropped 2% and oil had spiked 7%. The algorithms didn’t wait for analysis, context or a measured assessment of what the strike actually meant for global supply chains.

They saw war. They sold. Then they saw a tweet. They bought.

This is the information diet the modern market runs on… and it has the nutritional value of a gas station hot dog.

Overfed and Undernourished

The best analogy for what AI is doing to information comes from food science, I’ve heard some calling it: ultra-processed information.

The analogy is more precise than it sounds. Over the last century, we re-engineered the food system. We replaced nutrient-dense crops with synthetic fertilizers and products engineered for shelf life, palatability and scale. The result is a population swimming in calories and starving for minerals. People are overfed and undernourished simultaneously… in a way we are full, but not nourished. The body registers satiation while the cells quietly deteriorate.

AI is doing the same thing to information. It industrializes production; generating analysis that tastes like understanding, that has the texture and confidence of insight, but has been stripped of the contextual nutrients that would make it actually useful. We are swimming in takes. We are drowning in commentary.

We are mentally full, saturated with signal that turns out to be noise while remaining profoundly undernourished for lack of real, contextualized understanding.

“War is bad for markets” is ultra-processed information take. It’s the equivalent of asking a base model LLM a generic question and getting a technically correct but contextually useless answer. Yes, wars create volatility. Yes, the Strait of Hormuz closure disrupted 20% of global seaborne oil. Yes, airlines got hit, defense stocks rallied. Every financial media outlet on earth ran some version of this story this week, and every version said roughly the same thing: uncertainty is bad, volatility is up, here are the sectors to watch.

That’s not analysis. That’s pattern-matching on headlines. And it’s the investing equivalent of what some in AI call “slop”… outputs that are fluent, confident and almost entirely devoid of the contextual depth that would make them actionable. A gas station hot dog of insight. It fills you up but fails to feed you.

The Dow’s Monday swing wasn’t a market processing information. It was a market reacting to the absence of information. It is filling the void with sentiment, momentum and algorithmic reflexes trained on historical patterns that may or may not apply to a situation with no precedent.

The View From a Room Full of Allocators

I spent part of SxSW with a gathering of allocators in Texas. The mood in that room was not panic. It wasn’t even particularly bearish. It was recalibration.

These are people who don’t trade on headlines. They trade on fundamentals: production data, reserve reports, offtake agreements, infrastructure capacity, unit economics. They have access to the underlying data about the businesses they invest in, not the algorithmically amplified sentiment about the sectors those businesses happen to occupy. And what they were seeing was not a uniform story of destruction. It was a landscape of differentiated opportunity hiding behind a generalized narrative of fear.

Here’s what I mean. “War in the Gulf is bad for energy” is the ultra-processed take. The textured take requires you to hold multiple variables simultaneously:

US LNG exporters are capacity-constrained and suddenly commanding premium pricing. European gas storage is only 30% full and Qatar, which supplies roughly 20% of global LNG, just shut down Ras Laffan, the world’s largest LNG export facility, after an Iranian drone strike. That gap has to be filled by someone. US producers with existing export terminal access just had their strategic position repriced overnight.

Domestic energy producers in the Permian are looking at a demand environment that structurally improved. Not just because oil went up… oil always goes up in a crisis and then comes back down. But because the reliability premium on non-Gulf supply just became tangible in a way that no amount of policy advocacy could have achieved. Buyers who previously optimized purely on price are now optimizing on price plus supply security. That’s a structural shift, not a trade.

Pipeline operators with non-Gulf routes… the East-West Pipeline, the Fujairah bypass, the potential Yanbu rerouting that Saudi Arabia is already negotiating with Pakistan, are becoming critical infrastructure overnight. These were second-tier assets a month ago. Now they’re the only arteries still flowing.

Battery storage, distributed energy, domestic manufacturing, anything that reduces a nation’s or a company’s exposure to a chokepoint, just had its strategic value repriced. Not by a policy paper, but by a war.

I recently wrote about how renewables function as sovereign defense infrastructure, the technology that prevents the conditions for conflict by eliminating energy dependency. The same logic applies at the portfolio level. The companies and technologies that reduce chokepoint exposure aren’t just “ESG plays” or “climate tech.” They’re resilience infrastructure and in the past month, resilience just got repriced from a nice-to-have to a prerequisite.

Context Windows: The Real Parallel Between AI and Investing

A large language model without context is just a pattern-matching engine. Ask it a generic question, you get a generic answer… fluent, plausible and potentially useless for your specific situation.

Give it a rich context window: your documents, your data, your specific constraints and the same model produces outputs that are genuinely useful because they’re grounded in your reality, not the averaged reality of its training data.

The public market, in its current form, operates like a zero-context LLM. It ingests headlines, processes sentiment, and produces outputs: buy/sell signals, sector rotations, price movements, based on pattern-matching against historical precedent. “War in Middle East means short airlines, long oil, buy gold.” It’s not wrong, exactly. It’s just operating without a context window.

The 1,100-point swing is what happens when a market runs on zero context. The algorithms that sold within 400 milliseconds of the Iran strike weren’t processing the specific implications of the Strait closure for Qatar’s LNG contracts, or for the Shandong teapot refineries, or for the relative competitive position of US shale versus Gulf crude.

They were matching a pattern: military conflict → risk off → sell.
Then when Trump suggested de-escalation: diplomatic signal → risk on → buy.

Lacking real substantive context and reacting on pure reflex.

Private markets operate fundamentally differently. When you’re underwriting a direct investment into a battery storage company, a domestic SAF producer, an infrastructure play with locked-in offtake agreements… you’re not trading on sentiment. You’re operating inside a rich context window. You have the unit economics. You have the contract structures. You have the capacity constraints. You can see that a specific company’s competitive position just drastically improved not because its technology got better, but because its competitor’s supply chain now runs through a war zone.

That distinction, between investing on generalized signal and investing on contextualized fundamentals, is the distinction between ultra-processed information and actual analysis. It’s the same distinction that separates useful AI output from slop.

The Opportunity That Generalized Takes Often Miss

Let me be specific about what I think the noise is obscuring.

✈️“War is bad for airlines” is the headline take.
The contextual take: airlines that locked in substantial domestic SAF offtake agreements or hedged fuel with non-Gulf-exposed positions are in a fundamentally different situation than those running unhedged on conventional jet fuel routed through Hormuz.

The war didn’t change the unit economics of sustainable aviation fuel. It changed the relative value of not being exposed to a chokepoint. That’s not a sentiment trade, it’s a structural repricing of supply chain architecture that the public market hasn’t yet differentiated.

🛢️“Oil prices go up in a war” is the headline take.
The contextual take: the insurance market for Gulf transit is in the early stages of a structural repricing that will outlast the conflict itself. Lloyd’s war risk premiums don’t snap back overnight. The companies and routes that can operate outside the risk zone, pipelines bypassing the Strait, terminals on the Red Sea or Atlantic coast, domestic production with no maritime exposure, aren’t just benefiting from a temporary spike. They’re benefiting from a permanent increase in the risk premium applied to their competitors.

🪖“Defense stocks rally” is the headline take.
The contextual take: the nature of this conflict, drones hitting desalination plants, missiles striking civilian infrastructure, cyber disruptions to port operations is accelerating demand not for traditional munitions but for distributed resilience systems: drone defense, grid hardening, critical infrastructure protection, maritime surveillance. The second-order defense opportunity looks nothing like the first-order headline.

But the example that best illustrates why context depth matters and why generalized takes are so expensive is what’s happening right now in shipping.

🚢“Tanker rates spike in a war” is the headline take. And it’s true, spectacularly so. VLCCs that were earning roughly $35,000 a day in Q3 2025 crossed $100,000 by late November. After the strikes on February 28th, Saudi shipper Bahri signed the DHT Jaguar at $208,000 a day. By early March, the benchmark Middle East Gulf-to-China route hit $423,736 a day, the highest ever recorded. Some fixtures reportedly approached $537,000 a day, with analysts projecting $800,000 in a full blockade scenario. A vessel that barely covered operating costs six months ago is now generating more daily revenue than most startups earn in a quarter.

That trade is now visible to everyone.
It’s the ultra-processed version: war ➡ tankers ➡ rates up ➡ buy shipping stocks.

Every financial media outlet has run the story.

Here’s the second-order trade that almost nobody is watching:

The Gulf crisis has simultaneously disrupted LNG markets, driving up gas prices across Asia right before summer demand season. Price-sensitive nations, India chief among them, are responding the way they always do when gas gets expensive: they burn more coal.

India’s maritime coal imports rose roughly 9% in February alone, to 19 million tonnes. Its 9% steel production expansion in 2026 is creating simultaneous metallurgical coal demand on top of the thermal substitution. That’s a double demand shock on bulk carriers.

Now here’s where it compounds. The extreme premiums in the tanker market are pulling vessel owners toward VLCC and product tanker routes, effectively removing tonnage from the dry bulk trade. Meanwhile, war-risk insurance withdrawals and Cape of Good Hope rerouting are adding weeks to voyage durations, shrinking effective fleet capacity across the board. One market participant described losing a bulk carrier to a competitor who paid 50% above typical rates to carry coal from Indonesia to India’s west coast, precisely because vessel owners were uncertain about Gulf routes.

So bulk carriers face a pincer: new coal demand pulling volumes up, while the tanker crisis pulls vessels toward higher-paying routes, tightening available supply. That’s a supply-demand squeeze in dry bulk that most people aren’t watching because it doesn’t show up in the headline “war is good for tankers.”

The investor who understood coal’s role here not as an energy thesis but as a shipping arbitrage, triggered by a geopolitical shock in an entirely different commodity class, cascading through fuel substitution patterns in a third market, that’s the pattern worth paying attention to. And it’s the kind of pattern that only surfaces when you have enough context depth to trace cause through consequence across three interconnected systems simultaneously.

Every one of these distinctions requires context that the public market’s zero-context processing can’t access. It requires knowing…
…which SAF producer has which feedstock agreement.
…which pipeline operator has which capacity.
…which defense company is positioned for which threat vector.
That’s not information you get from a headline. It’s information you get from being in the room or from having access to the underlying data to connect the dots.

And Now Double-Click on the Region Everyone Just Wrote Off

Here’s where the ultra-processed take might be at its most damaging….

“The Middle East is in turmoil. Avoid.” That’s the generalized signal. It’s the take that gets your capital allocation committee to nod along and move to the next agenda item. It’s also, for anyone who actually understands the structural mechanics of the Gulf economies, almost exactly wrong.

Start with the counterintuitive fact that experienced Gulf investors have understood for years: when regional tensions rise, oil prices rise with them. Saudi Arabia is one of the world’s largest oil exporters. Every time the price moves up, government revenues increase significantly and quickly. And those revenues do not sit in a drawer. They flow directly into the infrastructure programs, giga-projects, sector development budgets, and economic reforms that form the backbone of Vision 2030. The geopolitical premium that frightens investors in London or Frankfurt is, in structural economic terms, often the same force that accelerates government spending on the very projects those investors are considering.

The thing they fear is simultaneously the thing that funds what they want to participate in. That is counterintuitive, but it is empirically true.

Then consider the sovereign wealth funds. Saudi Arabia and the wider GCC hold sovereign wealth valued in the trillions. These funds were built precisely to absorb external shocks, that is their design purpose, not a happy accident. This past week, the UAE re-affirmed $1.4 Trillions US Investments Plan Amid Iran Conflict.

When global markets tighten, when borrowing costs rise, when uncertainty spreads across other asset classes, these funds don’t retreat, they actually deploy. The capacity to inject liquidity, backstop major projects, stabilize banking systems, and sustain investment programs through periods of regional volatility is a structural feature of the Gulf economies. For foreign investors, this changes the risk calculation fundamentally, you’re investing alongside a government that has both the incentive and the balance sheet to keep building through the storm.

Now layer on the infrastructure reality that the generalized take completely ignores. The India-Middle East-Europe Economic Corridor, the Iraq Development Road, the expansion of non-Hormuz pipeline and port capacity — these aren’t speculative. They’re under construction. Saudi Arabia is already negotiating with Pakistan to reroute oil exports through Yanbu on the Red Sea coast. The UAE has the Fujairah pipeline bypass operational. The trade architecture of the region is being physically rewired to reduce Strait dependency, which is not just in response to this week’s crisis, but as a long-term strategic build that this crisis now accelerates.

The demand for foreign capital, foreign knowledge and foreign institutional partnership across the Gulf is not passive. It is explicit government policy, backed by regulatory reform, licensing incentives, sector-by-sector opening, and direct ministerial engagement with international operators. That appetite is genuine, substantial, and if anything, intensified by the current moment because the current moment makes the case for diversification and resilience infrastructure louder than any policy paper ever could.

The real risk in the Middle East isn’t the one everyone is talking about. The real risk is the one the global investment community has run for decades: waiting for certainty. Waiting for the political risk to resolve, the regulatory framework to be fully established, the market to be proven, the pioneers to have already succeeded. And by the time all of that is visible, the strategic positions are taken, the government partnerships are locked, and the early movers have captured the value that the latecomers spent years waiting to feel comfortable about.

“The Middle East is in turmoil” is the ultra-processed take.
The contextual take: a region with trillions in sovereign capital, accelerating diversification spend, infrastructure being built to bypass the exact chokepoints that just failed, and an explicit policy appetite for international partnership, is probably not the place you want to be leaving right now. It might be the place you want to be arriving (timing it right).

The Investing Version of “Context is Everything”

There’s a broader principle here that extends well beyond this week’s crisis.

We’re entering a period where the gap between generalized market intelligence and contextualized fundamental analysis is going to widen dramatically. AI is accelerating this in both directions simultaneously.

On one side, AI-powered algorithmic trading is making public markets faster, more reflexive, and more sentiment-driven, more prone to the kind of 400-millisecond pattern-matching that produces 1,100-point swings.

On the other side, AI applied to private market due diligence: parsing complex datasets, modeling scenario-specific outcomes, identifying structural shifts that headline analysis misses, is making fundamental analysis deeper and more accessible than ever.

But here’s what most people in both the AI and investing worlds haven’t fully internalized yet: LLMs gave us the language interface, the ability to interpret and generate human-readable analysis at scale (and speed). That was the breakthrough of the last three years. What they didn’t give us, and what the next frontier demands, is context depth. The ability to reason not just from language patterns but from localized knowledge, domain-specific data and real-time conditions that no training corpus contains.

Think about what that means in practice. Localized context: understanding that a specific pipeline operator in Oman has a specific capacity constraint that makes it a chokepoint beneficiary this week, not just that “pipeline stocks might go up.”
Domain context: knowing that Lloyd’s war risk repricing follows a specific actuarial pattern that persists 18-24 months after a conflict, not just that “insurance costs rise in wars.”
Real-time context: processing that Qatar’s Ras Laffan shutdown this morning creates a specific LNG supply gap that maps to a specific set of US export terminals with specific available capacity, not just that “LNG prices are volatile.”

None of that comes from generalized language models trained on the open internet. It comes from systems that can hold complexity, that can see the second and third-order consequences of a specific event cascading through a specific supply chain affecting a specific set of companies with specific contractual positions. That’s not pattern-matching. That’s something closer to structural foresight and the ability to trace cause through consequence across interconnected systems in real time.

Some of the work we're pursuing at Cool Climate Collective sits exactly at that intersection: building intelligence systems that don’t just summarize what happened, but model what happens next, across domains, with the kind of causal depth that generalized takes can’t touch. We all agree that the LLM gave us the interface, however I firmly believe that the context layer, which is: localized, domain-rich, temporally aware, is where the real value gets created. And it’s barely been built or even capitalized on (yet).

The investors who will outperform in this environment are the ones who understand which mode they’re operating in. If you’re in the public market reacting to headlines, you’re competing with algorithms that are faster than you and just as context-free. If you’re in private markets with access to underlying fundamentals and increasingly, with AI tools that can process those fundamentals at depth rather than at surface, you’re in a different game entirely. One where the noise that drives public market volatility is actually your edge, because it creates mispricings that only contextual analysis can identify.

“War is bad for markets” is what you get when you ask the market for a take without giving it any context. The answer isn’t wrong. It’s just useless for making actual decisions. The same crisis, processed through a rich context window, the specific companies, the specific supply chains, the specific structural shifts, produces a completely different output. Not “this is bad.” But “this is bad here, neutral here, and a generational opportunity here… and here’s why.”

Context isn’t just everything in AI, it’s everything in investing and both are still in the earliest stages of learning that lesson.

So where does this all lead us…?

I'll end with this note on an old parable, often attributed to a Chinese farmer. I've explored a version of this story before through a climate lens, but it lands differently this week. It goes like this…

The farmer’s horse runs away.
The neighbors say, “Such bad luck.” The farmer says, “We’ll see.”
The horse returns, bringing wild horses with it. “Such good luck!” We’ll see.
His son breaks his leg taming one. “Such bad luck!” We’ll see.
The army comes to conscript young men, but passes over the son because of his broken leg. “Such good luck!” We’ll see.

War is bad for markets. We’ll see.
War is good for energy. We’ll see.
The Strait closing is a disaster. We’ll see.

I might be an optimist trying to find the opportunity that lies before us, as they say, never waste a good crisis. But from the context window I have, there is something real to act on here. A longer-term structural shift toward the futures we actually want to inhabit… more sovereign, more resilient, more locally powered.

Maybe the farmer wasn’t being evasive after all. He was the only one in the village with a particular larger context window.

The farmer had context. Most of the market doesn’t.

In 1941, Japan imported over 80% of its petroleum from the United States. When Roosevelt froze Japanese assets and embargoed oil exports that July, in response to Japan’s occupation of French Indochina, he didn’t just impose a sanction. He severed a lifeline. Japan’s navy had 18 months of fuel reserves and its army had 12. The country faced a binary: withdraw from its empire and negotiate, or go to war to secure the oil fields of the Dutch East Indies before the reserves ran dry.

They chose war. Not out of madness, but out of math.

This is the pattern that almost never gets named in energy policy debates: oil dependency doesn’t just create economic vulnerability, it creates the conditions for conflict. The resource you can’t generate at home becomes the chokepoint someone else controls. And when that chokepoint tightens, the options collapse to diplomacy or force. Pearl Harbor wasn’t an aberration. It was the logical endpoint of a nation whose industrial economy was tethered to a supply chain it didn’t own.

What Does Sovereignty Look Like From the Other Side?

This isn’t an observation from the armchair or the doomscroll. I spent the better part of a decade in Dubai, building companies and navigating the innovation ecosystem of a city that, like much of the Gulf, was built on the momentum of hydrocarbon wealth, but has been quietly, methodically, constructing a post-oil identity for longer than most Western observers realize.

The UAE is often caricatured in Western media: hyperwealth, artificial islands, performative sustainability. But underneath the spectacle is something more interesting: a sovereign state that understood, earlier than most, that oil isn’t just a commodity. It’s a dependency. And dependency, for a small nation flanked by larger, more volatile neighbors, is an existential risk.

Living there, you feel the geometry of it in ways that don’t register from Washington or London. The Strait of Hormuz is not an abstraction, it’s the waterway you fly over, the bottleneck your neighbors’ economies flow through, the reason the conversation at every geo-political policy talk eventually turned to Iran. You learn quickly that the Gulf states don’t think about energy the way Western commentators do, as a climate issue or an economic input. They think about it the way small countries flanked by large, unpredictable neighbors think about anything that determines whether they survive: as a question of sovereignty, full stop.

And the uncomfortable truth that rarely surfaces in Western analysis is this: everyone in the region knows that a wider war with Iran has been a plausible scenario for decades. The question was never if the Strait could close, it was when, and whether you’d built the architecture to endure it. The UAE’s answer has been to build that architecture, methodically, for over a decade.

The Mohammed bin Rashid Solar Park, one of the world’s largest single-site solar installation wasn’t just a PR exercise. The UAE’s investments in nuclear energy (Barakah), green hydrogen, and long-duration storage are the actions of a government that looked at the same map Japan’s war planners studied in 1941 and drew a different conclusion: the way out of dependency isn’t more supply, it’s by diversification -- including a different kind of generation entirely

What the UAE recognized, and what this week has made viscerally clear to every energy-importing nation watching the Strait of Hormuz close, ports take missile fire, and flight paths reroute across an entire region, is that energy security isn’t a trade policy. It’s a sovereignty architecture. And the only architecture that holds when chokepoints fail is the one you build at home.

What Happens When Nations No Longer Need Each Other’s Oil?

Now step back from the Gulf. Widen the lens.

The deeper structural argument isn’t about any single region or any single conflict. It’s about what happens to the global order when the energy dependency graph starts to rewire.

For most of the 20th century, geopolitical alliances were, at root, energy alliances.

• The US-Saudi relationship wasn’t built on shared values… it was built on shared supply.

• Europe’s relationship with Russia wasn’t a partnership… it was a pipeline.

Japan’s post-war alignment with the West was, in significant part, the price of guaranteed energy imports. The architecture of international relations has been load-bearing on a simple premise: nations that can’t generate their own power must align with nations that can supply it.

Renewables break that premise.

Not immediately, not completely, but structurally. When a nation can generate baseload electricity from the sun hitting its own soil, from the wind crossing its own coastline, from nuclear plants on its own territory and when batteries can store that energy at grid scale, the compulsion to maintain alliances of necessity begins to weaken. What replaces it isn’t isolationism. It’s choice. Nations that no longer depend on supply chains running through contested straits or adversarial territories can begin to form alliances based on something other than who controls the oil.

This is the geopolitical shift that almost nobody in the energy transition conversation talks about: renewables don’t just decarbonize the grid, they disincentivize the reasons nations go to war. Supply chains will always matter. Resources will always shape alliances. But energy has occupied a unique position in that architecture, the one input so fundamental that its absence doesn’t just create economic pain, it creates existential crisis. As energy dependencies give way to domestic generation, that singular leverage begins to erode.

Deeper into the 21st century, alliances will increasingly be strengthened and weakened by different measures: shared governance frameworks, technology standards, cultural alignment, institutional trust. Not because supply chains stop mattering, but because the one supply chain that could start a world war gradually loses that power.

The implications are profound and uncomfortable for incumbents on every side. For petrostates, it means the commodity that buys alliances loses leverage. For importers, it means the relationships maintained out of energy desperation become optional. For the US itself, long the guarantor of Middle Eastern energy flows, it means the strategic rationale for forward-deployed naval power in the Gulf begins to erode when the nations it protects can power themselves.

Why Isn’t China Going to War Over This?

For decades, the specter of a war with Iran carried an unspoken assumption: it wouldn’t stay contained. Russia and China would back Tehran. The Strait would close. Oil would spike. Alliances would activate. The great powers would be drawn in, and the world would spiral toward something no one could control. This was the implicit logic behind every argument for restraint in the Gulf… not that Iran couldn’t be struck, but that striking Iran meant risking the kind of cascading escalation that pulls everyone in. A wider war. Maybe the wider war.

That isn’t what happened.

Russia, bogged down in Ukraine, diplomatically isolated, and hemorrhaging economic capacity, is in no position to open a second front of confrontation with the West. And China, the actor whose response matters most, has done something that would have been unthinkable a decade ago: it chose the Gulf corridor over its own oil supplier.

Consider what’s just happened through Beijing’s eyes. In the span of three months, the United States has intervened in Venezuela, seizing its president and redirecting its oil exports and launched strikes on Iran that have, among other consequences, effectively closed the Strait of Hormuz.

China imported roughly 11.6 million barrels of crude per day in 2025. Of that, roughly a fifth came from sanctioned sources, primarily Iran, Venezuela, and relabeled barrels transiting through Malaysia and Indonesia. Iran alone accounted for roughly 13% of China’s seaborne crude imports. Venezuela added another 4-5%. These weren’t strategic partnerships built on shared ideology. They were arbitrage plays, discounted barrels from pariah states, purchased at $8-10 below Brent through shadow fleets, forged documentation and ship-to-ship transfers in international waters.

Both supply lines are now under severe, simultaneous pressure. Venezuela’s crude is being redirected to the United States under “authorized channels.” Iran’s export infrastructure, Kharg Island, the Strait itself, is in the crosshairs of an active military campaign. China’s independent refiners, the “teapot” refineries of Shandong province that depend on these discounted barrels to survive, face a margin crisis with no quick fix.

This should be Beijing’s nightmare. And five years ago, it would have been.

But here’s the distinction that changes everything: for China in 2025, the loss of Iranian and Venezuelan crude is not an existential question. And that single fact, that it is not existential, is what makes rational cost-benefit analysis possible at all.

This matters more than it might seem. When energy is existential, you don’t get to weigh costs and benefits. Japan in 1941 didn’t evaluate Pearl Harbor the way you’d evaluate a strategic trade-off. They were in survival mode, the amygdala of the state, not the prefrontal cortex. The options collapsed to fight or die. When energy is not existential, the entire decision-making apparatus shifts. The question moves from “how do we survive?” to “what’s the smartest play?” And that shift is what makes all the cascading calculations that follow even possible.

What moved the needle? Two decades of methodical investment on two fronts.

The first is energy. China invested over $625 billion in clean energy in 2024 alone. It hit its official 2030 wind-and-solar capacity target six years early — in 2024. Electric and hybrid vehicles reached 51% market penetration in China by March 2025. The country’s demand for transport fuels: gasoline, diesel, jet fuel, has plateaued.

Growth in oil demand has shifted almost entirely to petrochemical feedstocks, not combustion. The renewable transition and battery buildout have done something that no amount of diplomatic maneuvering or shadow-fleet logistics could do: they’ve turned an existential supply risk into a manageable cost pressure.

The second is geopolitics, and the lesson was written in real time across Ukraine.

For two years, the world watched what happens when a major power backs a pariah state past the point of return. Russia’s invasion of Ukraine didn’t just trigger sanctions, it triggered a wholesale rewiring of European energy infrastructure, a deepening of NATO, and the effective isolation of Moscow from Western capital markets, technology supply chains, and institutional legitimacy. China watched all of this. And it learned.

The question Beijing faces with Iran isn’t whether it sympathizes with Tehran’s position. It’s whether signaling support for another bad actor on the global stage, one now absorbing direct American military strikes is worth putting the rest of China’s economic architecture at risk.

The trade relationships, the technology access, the capital flows, the Belt and Road partnerships that depend on at least nominal alignment with international norms. And those Belt and Road partnerships cut particularly close: the UAE, Saudi Arabia, Oman, the very nations on the other side of Iran’s aggression, are critical nodes in China’s infrastructure corridor strategy, a pattern, incidentally, as old as China itself; the Zhidao road and Lingqu Canal of 212 BCE were the Qin Dynasty’s version of the same play: “civil works” that were really high-frequency logistics lanes for troops and grain across hostile terrain. Beijing doesn’t sacrifice civilizational corridor infrastructure for a discount on crude. Backing Tehran doesn’t just risk Western sanctions. It risks alienating the Gulf states that China needs for everything other than oil.

And as of this past week, we can see the math playing out in real time. Bloomberg reports that Beijing is actively pressuring Iranian officials to keep the Strait of Hormuz open, urging Tehran not to attack oil and LNG tankers transiting the waterway, and specifically not to disrupt Qatari gas exports, which account for roughly 30% of China’s LNG imports. China buys 90% of Iran’s oil. It is Tehran’s most important economic lifeline. And yet, when forced to choose between protecting its Iranian supplier and protecting its broader Gulf corridor… the Qatari gas, the Saudi and Emirati trade relationships, the infrastructure partnerships, Beijing chose the corridor without hesitation. That’s not diplomatic ambiguity. That’s the cost-benefit analysis of a nation that no longer depends on any single supplier for survival.

Two decades ago, cheap Iranian crude might have been worth all of it. Today, after years of reducing the existential weight of oil in its energy mix, the math doesn’t pencil.

This is the compounding logic that single-variable analysis misses. China is not yet free of oil dependency. But it is no longer structurally vulnerable to oil dependency in the way Japan was in 1941, or in the way it itself would have been even a decade ago. And because the energy question is no longer existential, every other strategic consideration gets to register — the Ukraine precedent, the Belt and Road dependencies, the cost of global isolation. None of those factors would have mattered if China still faced a survival-level energy crisis. All of them matter now, precisely because it doesn’t.

The loss of Iranian and Venezuelan discount barrels hurts China’s teapot refiners. It does not threaten China’s grid, its transportation network, or its industrial base. And defending those barrels would threaten something far more valuable than the discount itself. That compound distinction, between inconvenience and existential threat on one axis, between cheap oil and global standing on the other — is the strategic dividend of the energy transition. And it’s the reason China’s response to this week will be economic displeasure, not military escalation.

What If the Best Defense Technology Doesn’t Look Like a Weapon?

Sit with that for a moment. Because the implication is staggering.

The single most important reason that this week’s events are unlikely to cascade into a global war is that the nation with the most to lose from the disruption, the world’s largest oil importer, has spent twenty years building the energy infrastructure that makes restraint the rational choice. Renewables didn’t just give China cheaper electrons. They gave China the option not to fight. And in a world where the conditions for great-power conflict are accumulating on every front, the existence of that option may be the most consequential strategic fact of the decade.

This is the argument that neither the climate community nor the defense establishment has fully articulated, though the foundations are there. I’ve written before about why our definition of “defense tech” is dangerously narrow… confined to missiles, drones, and cyber, when the real vulnerabilities are systemic: ecological, biological, infrastructural. The same logic applies here, but with an even sharper edge. A solar farm doesn’t look like a defense asset. A battery factory doesn’t feel like a strategic deterrent.

But if the function of defense is to prevent the conditions under which war becomes rational, then the energy transition is the most effective defense infrastructure ever built, not because it wins wars, but because it removes the compulsion to start them.

Under rational decision-making parameters and the entire architecture of nuclear deterrence, game theory, and international relations rests on this assumption: renewables fundamentally alter the escalation calculus. They raise the threshold at which disruption becomes existential.

They widen the space between “this is costly” and “this is worth fighting over.” They give nations the room to absorb shocks, weigh options, and choose restraint. That is not a climate benefit. That is a deterrence benefit. And in deterrence terms, a technology that prevents the conditions for war is more valuable than a technology that prevails in one.

What Does the Cost of the Current System Actually Look Like?

None of this should be read as abstraction. The war unfolding right now is producing consequences that are immediate, visceral, and compounding.

Sunday morning, residents of Tehran woke to black skies. Israeli strikes hit four oil storage facilities overnight, and the resulting fires released toxic hydrocarbon compounds, sulfur, and nitrogen oxides into the air over a city of ten million people. Iran’s Red Crescent warned that the rainfall was highly acidic, capable of chemical burns to the skin and serious lung damage. Residents described oil-stained rain coating buildings, cars, and streets.

Meanwhile, the infrastructure that sustains basic human life is entering the target set. Bahrain accused Iran of striking a desalination plant on Sunday. Iran says the US hit one of theirs. In a region where entire populations depend on desalinated water to survive, the war is no longer just hitting military assets… it’s hitting the systems that keep cities alive.

The ripple effects extend far beyond the Gulf. Maritime traffic through the Strait has dropped to near zero. Maersk, Hapag-Lloyd and MSC have all suspended transits, rerouting around the Cape of Good Hope, adding two weeks and roughly a million dollars in fuel costs per voyage. Dubai and Doha airports shut down for days.

A significant share of Europe’s jet fuel supply normally transits Hormuz; those flows are now severed. Fertilizer shipments are disrupted ahead of Northern Hemisphere spring planting. The Middle East itself imports 85% of its food and military logistics are taking priority over perishable cargo.

And here’s where the structural argument meets the balance sheet: when jet fuel supply chains run through a war zone, the economics of sustainable aviation fuel invert overnight. SAF produced from local waste feedstocks: municipal solid waste, agricultural residue, forestry byproducts, is still more expensive on paper. But "cheaper" fuel that can't reach you isn't cheap… it's unavailable. And unavailable has no price — it has consequences: grounded fleets, cancelled routes, stranded passengers, and an industry burning cash while it waits for a strait to reopen.

“The premium you once paid for localized production turns out to be the discount you get on not being exposed to a chokepoint”

The same inversion applies across every sector watching this crisis: distributed energy over centralized imports, regional supply chains over global dependencies, domestic production capacity over just-in-time efficiency routed through contested straits.

The devastation isn’t a footnote to the energy transition argument. It is the argument. Every supply chain executive, airline CFO, and energy minister watching this week is learning the same lesson in real time: resilience isn’t a premium. It’s a prerequisite. And the systems that survive contact with reality are the ones you build at home.

Did a Fossil Fuel President Just Make the Best Case for Renewables?

That’s the irony nobody is naming.

An American president who has dismissed renewables, pulled out of climate agreements, and championed fossil fuel dominance has, through the unintended consequences of his own military actions, produced the most compelling strategic case for energy transition in a generation. The Strait of Hormuz closing isn’t an argument for more drilling. It’s an argument for never needing the Strait of Hormuz again.

Every nation watching this past week, in Asia, in Europe, in Africa, in South America, is recalculating the same equation Japan’s war planners faced in 1941. The math hasn’t changed. What’s changed is the menu of options. Since coal powered the industrial revolution, energy independence has been a geological lottery, you either had the reserves under your soil or you didn’t, and if you didn’t, you negotiated or you fought.

For the first time in industrial history, there is a scalable alternative that doesn’t require winning that lottery. Sun and wind aren’t deposits. They’re conditions. And nearly every nation on earth has them.

The counterargument is obvious: renewables create new dependencies — on lithium, cobalt, rare earths, on supply chains that today run disproportionately through China. But there’s a categorical difference between depending on a material you can mine in multiple geographies, stockpile, substitute, and recycle vs. depending on a fuel you combust once and must continuously re-import through a chokepoint someone else controls.

One is a manufacturing challenge. The other is a casus belli - the kind of dependency that starts wars.

Solar panels don’t get embargoed. Wind doesn’t flow through straits. Batteries don’t require shadow fleets. Nuclear reactors don’t care who controls Kharg Island.

The case for renewables isn’t just about carbon. It’s about sovereignty… and survival. This week made that legible.

Speaking of Legibility, you might also enjoy my prior write-up on The Cost of Legibility.

One year ago, we published our first Rfx as a signal and an invitation as a way to convert “interesting futures” into actionable filters.

That 2025 Rfx didn’t just serve as a write-up, we were able to convert it into workflow. It directly shaped four new investments into our portfolio for 2025 and it helped catalyze the incubation of two new initiatives in our growing emrgnce ecosystem.

If 2025 taught us anything, it’s this: we’re not living through a “shift.” We’re living through a compression of timelines: climate volatility, institutional brittleness and AI-driven capability jumps all arriving faster than our legacy decision systems can metabolize.

The future isn’t coming. It’s already here… just unevenly distributed, violently compounding and poorly governed.

Re-introducing Rfx: Request for X

As a reminder, Rfx is not our “predictions for the year” list. It’s a standing request: for startups, systems and strategies that deserve deeper exploration, especially the ones that look strange until they become obvious.

Think of it as a beacon, not to summon hype, but to attract builders, operators and aligned capital partners toward the same handful of leverage points.

Agency Under Compression

A Note: There is an underlying theme that connects 2026’s Rfx I’m about to share, a simple lens: when the old pathways to stability stop feeling real, people (and systems) reach for short-horizon agency, and the volatility that follows becomes the upstream context for everything else.

I still remember when fintech felt like adult infrastructure. Mint.com nudged you when you overspent. Wealthfront made it easy to route a slice of every paycheck into the slow, boring magic of compounding. The promise wasn’t adrenaline, it was stability & growth.

But the macro beneath it has shifted and the product layer evolved with the incentives.

When housing becomes a moving target for many, wages start chasing inflation and “the career ladder” feels like a museum exhibit, people don’t stop wanting agency… they start renting it in smaller, faster doses.

So the interfaces optimized for hope: tiny decisions, instant feedback, lottery-shaped payoffs. Robinhood didn’t invent that impulse, but it perfected the aesthetic: the Robinhood-ification of finance, where complexity is packaged as empowerment and “swinging for the fences” becomes a daily ritual.

Zoom out and you see the same pattern everywhere. Same-day (0DTE) options compress time horizons into hours. Sports betting turns live games into a stream of micro-wagers. Prediction markets move from niche to mainstream posture — Polymarket positioning for a regulated U.S. return, Kalshi expanding “event contracts,” and even mainstream brokers experimenting at the edges of this category.

I won’t opine on the morality on this casino-ification of fintech. For me this transcends the moral debate, but appears as a systems signal. When a society can’t offer a believable path to compounding stability, it manufactures volatility-as-a-service. The casino isn’t the disease; it’s a pressure valve. And pressure valves don’t reduce pressure, they redirect it.

Eventually, the demand for agency leaves the screen and shows up in the street. Iran feels like a live boundary condition in plain sight: a collapsing currency, persistent inflation and “modern life” getting harder to finance, intersecting with energy constraints and water stress, producing strikes and protests that have forced the question of legitimacy back onto the table these past three days.

This is the reality we’re exploring in Rfx 2026: human-made brittleness (affordability, debt, institutional fragility) increasingly overlapping with physics (heat, water, energy, food shocks). The second- and third-order effects are the real story, where “economic stress” becomes “political rupture,” and where policy whiplash becomes its own accelerant.

With that as the operating backdrop, here are the seven Rfx themes we are looking for in 2026.

1) Causal Actors: Gravity vs Butterfly

Not all causal forces are equal and not all are required forever.

Some causes sustain a system (gravity). Others initiate a chain reaction (butterfly). Some are horizontal and continual; others are vertical and hierarchical. Too many strategies fail because they treat all causes as interchangeable.

If you misidentify the kind of cause you’re fighting, you’ll win battles and lose the system.

We’re interested in frameworks and interventions that can name:

• initiating vs sustaining causes

• structural constraints vs sensitive triggers

• continual vs one-time causal actors

Question: What changes when we stop treating causality as a story… and start treating it as a design space?

Concept Exploration:
This is the distinction between triggers and load-bearing structure. The butterfly is real, small perturbations can reorder trajectories in complex systems but “sensitivity” is not the same thing as “importance.” Gravity is the deeper truth: enduring constraints, incentive gradients, and structural causes that keep the system behaving like itself even after the headlines change. The practical reference frame here is intervention thinking: don’t just ask “what correlates,” ask “what can be changed,” and then separate what starts a chain reaction from what sustains it. Strategy becomes less about reacting to sparks and more about redesigning what keeps catching fire.

The world doesn’t break evenly, it breaks along its load-bearing causes.

2) Causal Intelligence: Decision Traces as Infrastructure

We’re taking 21st-century problems and running them through decision systems that were never built for uncertainty.

LLMs, what Jensen Huang frames as “universal function approximators” can map inputs to outputs, but they don’t preserve the why; they risk designing the future by remixing the past. Whilst ‘cutting-edge VC’ attention is expanding LLMs to “world models” (see: the LAM Rfx we flagged in our Rfx 2025); what we see next is causal intelligence: decision systems that can explain, stress-test and update their reasoning.

Inside organizations and especially inside government, there’s a missing layer: the why behind decisions. The context. The precedent. The exceptions. The invisible architecture of judgment.

If you can’t replay the reasoning, you can’t improve the system.

A few months back, we began our first causal explorations as design partners to Anthos. As we dive deeper, we are looking for partners (operators, agencies, implementers) who can turn better causal thinking into operational reality: workflow changes, procurement designs, feedback loops and “learning systems” that don’t collapse into theater the moment uncertainty arrives.

Question: What becomes possible when institutions can remember causality, not just outcomes?

Concept Exploration:
A lot of institutional failure is epistemic, not moral: we demand linear plans in nonlinear worlds and we reward legibility over learning. The classic warning is the “legibility trap”, simplifying complex systems into neat categories until you erase the ecology that made them stable in the first place (the forestry parable is the clean metaphor everyone remembers). The modern version shows up in delivery: policies that look correct on paper but collapse in implementation because feedback loops are weak, incentives are miswired and uncertainty is treated like a defect rather than a condition. The most compelling examples of causal intelligence aren’t dashboards, they’re decision trails: capturing context, exceptions and precedent so institutions can learn without pretending certainty.

A society that can’t remember why becomes a society that can’t learn how.

3) Tipping Realities: Mapping the Cascades, Not the Headlines

We’re interested in systems that model non-linearity as a first-class citizen: feedback loops, compounding risks, correlation breakdowns, and brittle equilibria.

Not everything needs to be “predicted.” But it does need to be postured for.

The goal isn’t certainty. The goal is robustness when certainty is impossible.

This includes climate-driven supply chain cascades, food system fragility, grid instability, and social tipping dynamics that turn “economic stress” into “political rupture.”

Question: What does a tipping-aware operating system look like for a company, a city, or a portfolio?

Concept Exploration:
Tipping work gets serious when it stops being about “the one big cliff” and becomes about linked thresholds and cascades; how a shock in one domain turns into stress in another: heat → grid → water → food → prices → unrest, or flood → logistics → insurance retreat → municipal credit stress. The most useful mental models here look less like forecasts and more like failure choreography: what breaks first, what breaks second, and what containment looks like when multiple systems slip at once. In other words: the real value isn’t predicting the domino, it’s seeing the table it sits on.

The cliff rarely announces itself. It just becomes the new baseline.

4) Emerging Markets as Early Signals (Not Afterthoughts)

Emerging markets aren’t “later.” They’re first — first to feel extremes, first to confront broken infrastructure, first to invent under constraint. They are where solutions get stress-tested against reality, not theory.

The edge isn’t a dead end. It’s the proving ground.

We’re looking for businesses that can win on unit economics inside volatility: intermittent power, high heat, supply constraints, informal housing, climate migration, and policy uncertainty.

Question: What products become obvious when you design for instability as the default?

Concept Exploration:
Emerging markets are where the future shows up first because constraints arrive earlier: extreme heat that reshapes productivity, grid volatility that reshapes product design, supply fragility that reshapes unit economics, and policy uncertainty that reshapes distribution. The best reference points here aren’t “impact stories”, they’re constraint stories: humid heat thresholds (where cooling becomes survival, not comfort), internal displacement dynamics (where migration is not a surprise but a downstream physics-and-economics outcome), and informal infrastructure realities (where solutions win only if they are rugged, cheap to maintain, and culturally adoptable). If a solution thrives there, it often becomes globally exportable because it was forged in real conditions, not ideal ones.

The frontier is where the future stops being theoretical.

5) The Doomsday Belt: Bangladesh, Jakarta, New York (and the Next 20 Cities)

Some geographies are emerging as the world’s stress concentrators: where sea level, subsidence, heat, migration pressure, and institutional capacity collide. We’re especially interested in solutions that work in places where failure has nowhere to go.

When resilience fails in a megacity, it doesn’t fail locally, it exports instability.

This theme includes coastal defense and retreat logistics, salinity and water security, heat survivability, insurance and liability redesign, and any infrastructure that turns “inevitable stress” into “manageable strain.”

Question: What gets built when the customer is not a consumer but a city that can’t afford collapse?

Concept Exploration:
This “belt” becomes legible when you stack three kinds of maps:
(1) where water rises,
(2) where land sinks, and
(3) where institutions thin.
Bangladesh is the canonical story of slow violence: salinity intrusion turning fresh water, soils, and livelihoods into a long negotiation with the sea.
Jakarta is the stark reminder that sometimes the ground is the crisis: subsidence turning flooding into a structural condition rather than an emergency event.
New York is the mirror: wealth doesn’t delete exposure, it just delays acceptance, and once coastal flooding becomes “routine,” the real drama is insurance, liability, and who gets priced out of safety.

Some cities are where the planet collects its debts.

6) Doomsday Interventions: Buying Time Where Time Collapses

There are domains where “adaptation” is too polite a word. Some risks are not gradual, they’re phase changes. We’re interested in interventions that reduce the probability, speed, or blast radius of catastrophic outcomes, especially around the cryosphere and sea-level-linked cascades.

Doomsday isn’t a date on a calendar (unless referring to December 18, 2026). It’s a curve that quietly steepens until it becomes a cliff.

What we’re looking for: pragmatic, testable approaches that acknowledge real constraints (energy flows, boundary conditions, governance, maintenance) while still bending the trajectory.

Question: What does it look like to build a credible “time-buying” intervention — one that’s modular, measurable, and politically survivable?

Concept Exploration:
The “Doomsday Glacier” framing (Thwaites) is useful not because it’s cinematic, but because it forces a mechanical mindset: grounding lines, warm-water intrusion, buttressing loss, fracture dynamics: the kinds of failure pathways that don’t negotiate. From there, sea-level rise becomes less a number and more a systems problem: timelines, uncertainty bands and the gap between “planning-grade expectations” and “politically speakable truths.” The most illuminating work in this space tends to pair physics with legitimacy: what it takes for an intervention to be measurable, governable and deployable without becoming geopolitical theater.

When the timeline collapses, time becomes the scarcest asset on Earth.

7) C2B: Consumer-to-Business — Turning “Free Labor” into Public Good

The modern economy already runs on “free labor”: our clicks, our preferences, our data exhaust, our micro-decisions: all translated into training signal and monetized upstream.

C2B (consumer-to-business) asks: can we build systems where that surplus is intentionally harvested and routed toward communal resilience?

If value can be extracted from participation, then participation can fund resilience.

We’re exploring models where the value extracted (training data, behavioral signal, local intelligence) exceeds the cost of providing value — and the spread becomes a new kind of civic financing layer.

This is where the IKEA effect matters: people value what they help build. The participation doesn’t just produce signal, it produces ownership, affinity, and continuity (the psychological glue most “community” projects fake with branding).

Question: Can we decouple “cost of engagement” from “value created,” and redirect the surplus toward shared outcomes?

Concept Exploration:
Modern platforms already run a quiet arbitrage: millions of micro-actions (attention, preferences, behavior) become training signal and market power upstream. C2B is simply is asking whether we can route some of that surplus into resilience without turning participation into extraction. The investable leap is designing consent-first systems where participation produces business value and a community dividend… a flywheel, not a guilt trip.

If the economy already taxes attention, we should at least decide where the revenue goes.

A Call to Action

If you’re building something that aligns with any of the seven themes or you’re sitting on a weird, high-conviction insight that deserves to be pressure-tested, we want to hear from you.

Rfx is how we keep our foresight honest: make it legible, make it actionable, make it accountable to outcomes.

Last year, in Possibilities Come with Responsibilities, I wrote about the Dubai Future Forum as a place that stress-tests our assumptions about progress, prosperity, and climate. This year deepened that exploration, rather than offering another round of glossy “future trends”, the Forum felt like a place where our existing systems were put on the table and pulled apart: how we fund change, govern risk, teach young people, and deploy technology.

Across sessions, the question wasn’t just “What’s coming next?” but “What future are we willing to help bring into being?”.

Government policy teams, NGO leaders, corporate strategists, and capital allocators were all invited into that same conversation, not as spectators to someone else’s forecast, but as co-designers.

In that sense, DFF is less a conference and more a workshop on the wiring of the world: a space where the people who shape regulation, products, and portfolios are asked to rethink the defaults they operate within, and to recognise that the future is not a trend line to observe but a system we are already actively building.

For me, a single phrase from this year’s Foresight & Strategic Philanthropy symposium framed the week: we have an opportunity to earn a foresight dividend and avoid an urgency cost. At its core, a foresight dividend is what compounds when we invest in preparation rather than reaction; urgency cost is what we pay when we wait until a crisis is undeniable and then spend heavily just to stay upright. Almost every session, from philanthropy to AI to climate, was really about which side of that ledger we are on.

💹 The Foresight Dividend

In the Strategic Philanthropy sessions, the most interesting voices weren’t talking about “giving more”; they were talking about building capabilities. Critical thinking, creativity, and futures literacy came up again and again, as infrastructure that is needed. Foundations working in education, youth empowerment, and community development described a pivot from funding projects to funding social architecture: spaces where people can think together, prototype, and contest the future on their own terms.

This is where foresight stops being a niche discipline and becomes a form of risk management. When local organisations gain the skills to scan for weak signals, map systems, and design multiple futures, they are less dependent on distant donors’ priorities. Impact data and listening tools become shared assets rather than reporting burdens. The foresight dividend shows up as fewer firefights, more early interventions, and communities that are partners rather than beneficiaries. The urgency cost is everything we pay when we don’t make that shift in time.

🌍 Foresight for the Planet

That idea crystallized for me during the inaugural Foresight for the Planet awards.

We were honored to have His Highness Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum, Crown Prince of Dubai, present the inaugural Dubai Foresight Awards, and in the category of Foresight for the Planet, three very different “frameworks” were recognized side by side:

• Congo Basin Futures,

• UNDP’s Seeding the Future in Egypt’s Nile Delta,

• and our Cool Climate Collective’s Three Futures Test.

What struck me was the diversity of applications that emerged as the leading finalists for this category from the hundreds of projects & frameworks from over 50 countries.

Congo Basin Futures uses participatory foresight and indigenous knowledge to protect one of Earth’s most critical ecosystems.

Seeding the Future applies futures thinking to agriculture in a region where climate vulnerability is already a daily reality.

Our own Three Futures Test applies our foresight framework into climate finance, stress-testing early-stage ventures across multiple scenarios so capital can move earlier and smarter.

One category with three very different arenas: forest, food, finance, linked by a simple conviction: long-term thinking only matters if it turns into decisions that change local conditions.

Standing alongside projects that are delivering tangible impact across a river basin and a delta was a humbling reminder that our frameworks aren’t abstractions; they’re interfaces. If they’re useful, they help governments, NGOs, and private investors see the same system from different vantage points and still agree on what to do next.

🎯 Focus in an Age of Infinite Expertise

Another core theme that was anchored in Khalfan Belhoul’s opening keynote, was the idea that focus is becoming the scarce resource.

With AI at our fingertips, we live in a world where everyone and no one is an expert. Any question can be answered convincingly in seconds; the problem is deciding which answers deserve our finite attention. Corporate leaders from education technology, consumer goods, and finance all described the same tension: information is exploding; decision bandwidth is not.

In that context, foresight is less about producing more scenarios and more about curating the few that matter for a given decision. Focus becomes a governance question: which futures are we willing to spend time on, and which do we quietly rule out because they don’t fit our current incentives?

Organizations that protect deep focus, time for teams to work through uncertainty rather than skim across it, are effectively buying an asset others are giving away for free.

🤖 Generating Futures vs Owning Them

The “Business of Foresight” session, with leaders from Shell, PepsiCo, IKEA and AXA, began with a simple question: what role can foresight practitioners realistically play inside very large organizations?

As they compared where their teams sit in the hierarchy, how close they are to real decision rights, and when they’re brought into the process, a second tension emerged: how to use AI to do more of the analytical and narrative heavy lifting without outsourcing human judgment. Each company is experimenting with AI, using it to scan signals, generate narratives, and translate scenarios into the language of marketing, strategy, or risk. AI can now draft compelling future stories, sometimes faster and cleaner than humans. That is both a gift and a trap.

The gift is obvious: AI expands the option space. It can help teams see adjacencies, edge cases, and cross-sector analogies they would never have time to explore manually. The trap is more subtle: if we let the machine not just draft the story but also decide which stories are worth telling, we outsource our own discomfort.

Foresight is supposed to surface our biases and blind spots, not hide them behind fluent text. The most thoughtful practitioners in that room were clear: AI can amplify foresight work, but the responsibility for choosing a future and being accountable for it, remains stubbornly human.

Our own work to extend the Three Futures Test with AI is still evolving. We’re experimenting with bridges between structural, mechanistic models, like those used by central banks, and statistical approaches to causal inference grounded in real-world data. These are early steps toward a shared Causal Intelligence that, I hope, will become part of the common toolkit for foresight practitioners and climate investors in the years ahead.

I’ll be testing some of these ideas soon in a different arena. At the World Economic Forum session on “Adapting Capital for Volatile Markets”, I’ll be offering the introductory remarks, using that stage as a seeding ground to bring these foresight frameworks and Causal Intelligence explorations into conversation with some of the world’s largest funds exploring Nature Based Solutions.

The real opportunity is not for any single model or manager, but for capital at scale to start treating foresight as standard practice rather than a niche craft, so that, in a more volatile world, long-term responsibility and resilience become core features of how we allocate capital, not afterthoughts.

🪐 What Is Our Future as a species?

The Forum also nudged the conversation beyond sectors and balance sheets to a simpler question: What is our future? Not us as a society, not as a civilization, but as a species.

Nikku Madhusudhan reminded us that we now know thousands of planets orbiting other stars, each with its own atmosphere and potential chemistry for life. We are no longer speculating in a vacuum; we’re one data point in a growing catalogue of worlds. The discovery of exoplanets that could host life in forms very different from our own brings a broader question into view:

Are we prepared to find life as we don’t know it. And how does these discoveries shape our universal view and mental models?

Thinking as a species rather than a sector changes the emotional register of climate and biodiversity. It’s one thing to say “our emissions are too high”; it’s another to admit that on a planet teeming with life, a civilization capable of looking for life elsewhere is undermining the conditions for its own. That shift doesn’t give us policy prescriptions, but it does give us context. Climate finance, rights of nature, and strategic philanthropy become less about “impact portfolios” and more about whether we are behaving like a civilization that is thinking about our long term run.

🐙 The Rise of the Black Jellyfish

Risk language is evolving too. Most of us now know black swans (rare, high-impact surprises) and grey rhinos (obvious, lumbering risks we still manage to ignore), but a newer emerging metaphor that resonated with me at the Forum was the black jellyfish: risks we technically “know” about… the “unknown knowns”, we have the knowledge, but fail to grasp the complexity, feedback loops, and potential for systemic instability. Such as microplastics, PFAS, slow-moving shifts in oceans or food systems, that we still treat as background noise until they will likely (eventually) bloom.

Black jellyfish risks are particularly dangerous for NGOs and private markets because they sit in the gap between knowledge and action. The science is often clear enough; what’s missing is an institutional willingness to change course before the costs are undeniable.

In climate tech investing, we see them in sectors where externalities are well-documented but still lightly priced; in philanthropy, we see them in geographies we know are fragile but keep telling ourselves we’ll get to “after this grant cycle”. Foresight doesn’t eliminate black jellyfish, but it can at least move them from the background of the slide to the center of the decision.

⏳Time Is Not Neutral

Beneath all of this sits the question of time. During a panel on that very topic, Patrick Noack described time as the raw material of foresight and the “fourth, invisible dimension” we work in. But it became clear over the discussion that time is not neutral; it is shaped by culture and power. Western institutions tend to treat time as something to be optimized: quarterly earnings, electoral cycles, project milestones. Many Indigenous and Pacific worldviews treat time as cyclical and relational, woven through ancestry, seasonality, and place.

One mental model that frames this well, highlighted by Lisa Kay Solomon of the Long Now Foundation, is Stewart Brand’s “pace layers” view of civilization: instead of a single system running on one clock, it imagines stacked layers, each changing at its own speed, from the flicker of fashion to the deep drift of nature. When those layers stay in healthy relationships, the fast ones learn and adapt while the slow ones hold memory and meaning; when they fall out of sync, the friction shows up as crisis.

When we let fast layers experiment while slow layers stabilize, we get resilience. When we force the deeper layers (governance, culture, ecosystems) to move as fast as product cycles and stock prices, we get brittleness and revolt. Plastics and forever chemicals are, in that sense, temporal design failures: products whose use-time is measured in minutes but whose harm-time runs centuries.

The real work of foresight is learning to order change in ways that compound regeneration rather than lock-in harm.

Intention Over Reflex

Taken together, these threads, foresight dividends, planetary awards frameworks, AI for causal intelligence, black jellyfish, time, point to a simple, uncomfortable question:

Are we acting with intention about the futures we are building, or just reacting by reflex?

Dubai’s wager, through the Forum and the Foresight Awards, is that foresight can be treated as infrastructure: a capability shared across governments, NGOs, and markets to think longer, act earlier, and design with time instead of against it.

For those of us working at the intersection of climate, capital, and community, that means something practical: fund capability, not just projects; use AI to widen our field of view without delegating responsibility; bring local voices into the room before the crisis hits; and treat every “forever” decision as a last resort.

The future will not reward us for having the most beautiful scenarios or the largest conferences. It will, however, quietly reward those who earn the foresight dividend… by choosing preparation over urgency, regeneration over extraction, and intention over reflex, while there is still time to choose.

Earlier this month, I was back at UCSD’s main campus, after over a decade since I graduated, for Agile Electrification, a working convening of California’s Distributed Energy Resources (DER) practitioners: top solar installers, policy teams, utility innovators, founders, and investors.

The brief was ambitious: build a “New Deal for California Energy”, backcast from a High-DER home future, and run 3-month Designers-in-Residence sprints that feed a decision-support platform called GoldenGrid, all focused on engaging prosumer households as the grid’s flexible backbone.

The bet is that existing (& new) solar homes are the fastest bridge to the flexibility the grid now needs.

As we dove into various topics and themes, and while Andrew, one of our organizing conveners, was presenting the work they’ve been doing, one slide in particular stood out to me.

This is the foundational backcast that was presented at the event, from Standard Home to High-DER Home. The inflection points lined up cleanly: cost curves, technical specs, flexibility & policy levers, like footholds on a well-marked trail.

That slide was the invitation that nudged me to apply our Three Futures Test, overlay adjacent plausible futures, and test how the inflection points and our options shift.

A High-DER Future & Present Realities

Let’s take a minute to reflect on the present reality we are living in.

The High-DER future has to be built inside today’s constraints: tapering incentives, rising soft costs, interconnection queues that behave like black holes, and a demand curve that’s finally bending up after decades of flatline. Add in tariff re-writes, re-domesticating energy-intensive industries, transformer shortages, and permitting friction, and you get the operating conditions that innovative problem solvers thrive in.

This is the paradox: the same forces that slow projects also raise the premium value on distributed flexibility. As electrification compounds from EVs, heat pumps, induction, data centers, small-scale automation, the grid wants capacity and behavior. In other words, not just more megawatt-hours, but better timing, better coordination, and better recovery when things go wrong. DERs win when they show up as elastic, fast-to-deploy, and resilient, the very same traits centralized builds can’t deliver on short notice.

The reality is that… headwinds for traditional build-out & deployment are tailwinds for resilience.

The facts on the ground:

• Money isn’t doing all the work anymore. Subsidies are flattening while soft costs: design, permitting, interconnection studies, customer acquisition… keep climbing. Projects pencil less on policy and more on operational value.

• Queues, not kilowatts, are the bottleneck. Interconnection timelines stretch from months to years; distribution upgrades lag; turbine backlogs; transformer lead-times distort deployment calendars.

• Demand is back, structurally. After years of flat consumption, electrification, onshoring, heat pumps, EVs, and AI/data centers are driving a persistent upward slope in load.

• Risk is now routine. Climate volatility from wind, heat, drought, wildfire, floods, turns “rare events” into seasonal features. Reliability and recoverability are no longer add-ons; they’re requisites.

Disaster isn’t an outlier anymore… it’s the baseline, so resilience just became the grid’s uptime budget.

The line may squiggle, the direction doesn’t: we’re driving toward electric.

Put together, these graphs say what the room already feels: resilience is not optional, and electrification is not waiting. That is exactly why the backcast must recognize the world we’re operating within: the queues, the costs, the weather and why the strategy has to reward DERs that are elastic: able to cycle more, coordinate better, and recover faster.

The Limits of Single-Future Backcasting

Backcasting is a powerful strategic foresight method. For those not familiar, it’s a planning approach that starts by defining a specific, desirable future state and then works backward to map the policies, technologies, behaviors, investments, and milestones required to reach it. Unlike forecasting, which extrapolates today forward, backcasting treats the future as a design target, making explicit the constraints, bottlenecks, inflection points, and enabling conditions along the way. It produces concrete transition pathways (who does what, by when, under which assumptions) and clarifies trade-offs, sequencing, and dependencies across adjacent futures.

But any backcast is a story told from one plausible future looking inward. The inflection points along that path… what we fund, what we regulate, what we standardize… are constrained by the assumptions of that chosen future.

A core realization I’ve come to in my work is that: A backcast is faithful to one future, however the inflection points can be, & are often, manipulated by adjacent futures, typically outside the purview of those deep within an industry.

This is why we developed the Three Futures Test and have been refining it to extend beyond just our core investing practice at Cool Climate Collective. As we now use it for further strategic guidance and planning for founders, policymakers, and thought partners. In practice, we hold multiple plausible futures simultaneously, then map how bottlenecks and inflection points in one are interdependent on outcomes in an adjacent future. That interdependence is usually where the real leverage hides. The first backcast isn’t necessarily wrong but it is incomplete without its adjacencies.

Only after examining those adjacencies do the optimal investment, policy & commercialization decisions emerge.

Three Futures around Distributed Energy Resources

So what do these Three Futures look like when plotted against the initial future?

Well, first there’s the future in the slide deck: High-DER Homes, distributed solar, batteries, smart panels, Virtual Power Plants (VPPs) coordinating the evening peak.

There’s a second future standing in our peripheral vision: humanoid and home robotics, moving from warehouses into household routines within the next 10 years. I recently wrote a Virtual Foresight Scenario (VFS) on a plausible future accelerant strategy enabled by a recognizable brand.

A core insight into home robotics economics is that robots won’t sip power. A single useful household robot adds roughly the energy signature of an EV to daily household consumption. Think of one busy home robot as ~½ to 1 EV-day of energy; stack two robots plus light fabrication and you’re into 2–5 EV-days worth of load.

Note on Assumptions: EV ~30 miles/day at 0.30–0.40 kWh/mi (~9–12 kWh/day). Current humanoids ~0.3–0.6 kW while working; 6–10 hours/day ~2–6 kWh/day per robot, higher with tools or longer duty cycles.

That’s not a rounding error; it’s a fundamental shape change as the other reality as most EV owners can attest, we’re not typically charging overnight, every night unless we are doing long commutes daily… but if we assume the household robotics will be daily support for the home, from cleaning or childcare support, as well as the fact we could in theory have our robots work (or charge) overnight, midnight becomes prime time.

Viewed from inside the DER backcast alone, inflection points look like “more rooftop solar, better interconnection standards, one-cycle-per-day batteries, smarter time-of-use rates.”

Viewed from outside, with the robotics future in frame, those inflection points shift: storage specs pivot from capacity to throughput; orchestration shifts from optimization to necessity; modular energy stops being a clever option and becomes how a robot sustains itself.

And lastly, there’s a third future we’re starting to notice re-assert itself across our portfolio, the concept of Electrons-to-X: electrons flowing reliably into heat, mobility, materials, and autonomy because storage and control made them usable everywhere. I originally highlighted this concept in our Rfx (Request for X) at the end of last year.

The future rarely arrives alone, so let’s take a closer look at each adjacent future.

Future 1: High-DER Homes (The Foundational Future)

Inhabit only this future and the path is tidy: costs fall, permitting softens, smart panels proliferate, VPPs scale. Most homes that want distributed energy get solar plus a battery that shifts the evening peak. Backcasting from here, your levers are familiar—interconnection reform, incentives rewarding installed capacity, warranties optimized for roughly one cycle per day.

You can build successful companies for two decades within this frame.

But this backcast assumes the night remains quiet and the battery’s job is one elegant discharge after sunset. It’s the right story until its neighboring futures arrive.

Future 2: Robotics at Home (The Adjacency That Moves the Goalposts)

Pull the robotics future closer. The moment a useful robot becomes commonplace, the household gains a persistent overnight load… not a spike, a plateau.

Design DER from inside this reality and different requirements emerge:

Throughput as first principle. Storage operates at 1.5 to 2.0 cycles per day with deeper depth-of-discharge, not a single elegant evening discharge. Design for calendar life measured in kilowatt-hours cycled, not just years installed.

Modularity as infrastructure. Swappable 1-3 kWh packs that robots can dock and share; fixed storage built from standardized bricks, not monolithic slabs.

Orchestration as necessity. Robots, EVs, HVAC, and batteries don’t “schedule”—they negotiate. Without intelligent coordination, midnight becomes a transformer problem.

Here’s the critical insight: these constraints don’t just define the robotics market. They reach back and rewrite the DER backcast. The inflection points in the original High-DER Home slide charted: policy priorities, technical specifications, flexibility requirements—shift under the weight of an adjacent plausibility.

The home becomes a small 24/7 facility. The “right” battery stops being the one with the largest headline capacity and becomes the one optimized for sustained multi-cycle operation.

Future 3: Electrons-to-X (The Spillover That Makes It Systemic)

When storage and control become elastic enough to carry noon into evening and evening across the night shift, electrons stop being fragile. They can credibly flow into heat pumps, induction cooking, mobility, materials processing, and autonomy without overwhelming feeders or utility bills.

This electrostate future is what an Electrons-to-X future enables: DER as substrate, not accessory. I won’t relitigate our published Rfx framework here, nor fully unpack our Electron Stacks layering model, those warrant separate write-ups. But the foundational realization is that this unlocks a new world of possibility when we can transform electrons into other essentials and enable further utility in our localized spaces & communities. Electrons to water, at an efficient, non-humid required climate (well, our portfolio company Aquaporo is doing that).

When we recognize such adjacent futures exist and closely align in underlying requirements, the original backcast resolves into a larger system. The destination didn’t change; but the constraints, and bottlenecks to prioritize for, did.

This is where the Three Futures Test moves from methodology to capital allocation. Standing outside the futures, watching them illuminate each other, we saw an inflection point invisible from inside any single backcast.

An Elastic Energy Future

When we invested in Elastic Energy, we didn’t place a singular bet on building for the DER future, the robotics future, or the Electrons-to-X future. Elastic was building the connective tissue between all three without necessarily realizing it themselves (at first). That’s often where the best investments hide: founders solving the right problem slightly ahead of when the market can articulate why it matters.

Ben and Brandon built what they call a “micro-grid controller for the home”, a palm-sized Energy Router running local AI to orchestrate every high-load device through universal protocol support. On its surface, it solves today’s High-DER Home problem: your smart thermostat, EV charger, and battery don’t talk to each other, so they’re “grid dumb” even when individually intelligent. Elastic makes them grid smart.

But apply the Three Futures Test and the investment thesis deepens considerably:

In the High-DER future alone, Elastic is essential infrastructure. It abstracts much of California’s Smart-Inverter Operationalization Working Group’s eight years of standards work into a plug-and-play router that ships with SunSpec, IEEE 2030.5, and vendor-specific dialects pre-loaded and updates over-the-air. For solar installers dealing with ~$3,500+ customer acquisition costs and 25% soft-cost line items, adding Elastic’s Router that can pay back in under six months (in high-TOU markets with storage) while creating a new recurring monthly revenue stream. It’s the API layer that turns static installations into revenue-generating assets.

When the robotics future arrives, Elastic’s role shifts from valuable to existential. The moment a household robot becomes mundane, orchestration stops being about optimization and becomes about preventing midnight feeder stress and localized overloads. Elastic’s real-time negotiation engine, already designed to coordinate EVs, HVAC, batteries, and water heaters, simply adds robots to the participant pool. The platform that prevents today’s timer-synchronized EV charging from blowing transformers is the same platform that prevents tomorrow’s overnight robot fleets from doing the same. The architecture doesn’t need reinvention; the load profile just changes.

For Electrons-to-X, Elastic becomes the enablement layer. When storage and control are elastic enough to handle dynamic 24/7 demand, electrons can credibly flow into heat pumps, induction, mobility, materials, and autonomy without overwhelming feeders or bills. Elastic’s edge-AI orchestration, running models locally to keep latency low and privacy high, makes DER resilient enough to become substrate. It’s making energy manageable at the pace and complexity the broader electrification transition requires.

Here’s the asymmetric bet: Elastic Energy doesn’t need all three futures to materialize simultaneously. Any single future makes them successful… when we also ran our Three Futures Test related specifically to energy co-ordination, we also projected 3 plausible winning scenarios (within the confined bounds of the near-term future):

Consumer-led flex markets hitting mainstream? They win.

Enterprise pivot to B2B grid services? They win.

Regulation-driven mass adoption through FERC 2222 compliance mandates? They win decisively.

But the deeper thesis is catalytic: Elastic’s existence makes all three futures more achievable. It’s infrastructure that de-risks the very transitions it depends on. By solving whole-home orchestration before it becomes a bottleneck, they’re not just capturing value from the energy transition, they’re removing friction that would otherwise slow it.

The market is already pulling. After technical demos, major inverter OEMs and VPP operators say: “We didn’t know this was possible—if you can do this, I want 10,000 of them.” A utility capacity manager called it “probably the most compelling solution we’ve seen.”

We invested because the adjacent futures revealed something the DER backcast alone couldn’t show: whoever solves orchestration for the complexity that’s coming doesn’t just win a market, they enable multiple markets to exist. Elastic Energy is that rare infrastructure bet that becomes more valuable precisely because multiple plausible futures depend on it.

That’s the adjacent future opportunity in practice.

A Time-Traveler’s Observation Post

When one stands just outside the futures, we get to take in a systems view of the intersecting future timelines that span across industries that industry experts often don’t have the time or capacity to explore… part of the perks of being a ‘jack of all trades’ with the network to engage with the masters of one.

I often have a chance to engage with domain experts and institutions through my work with Bottlenecks Institute to map where each future’s constraints and inflection points intersect, and transfer those foresights to place capital and convene networks where small interventions can plausibly change trajectories (or accelerate said futures).

Systemic coordination across industries, experts and trends enables an enhanced data-driven foresight framework to guide investment and operational decision-making across the multi-stakeholders these concepts affect.

Backcasting, Reframed

If we are to expand on that High-DER Home future pathway, I’d add adjacent circles for Home Robotics and Electrons-to-X, as an observer, standing outside the future, looking in with a macro multi-future perspective.

From that vantage point, the backcast reads differently:

Today: Admit overnight demand. Spec DER for throughput, not just capacity.

Near-term: Modularize energy infrastructure; enable robots to carry their own operational slack; let homes compose capacity from standardized blocks.

Mid-term: Default to intelligent orchestration; prevent timer-synchronization problems before they form at scale.

Policy: Compensate for kilowatt-hour throughput and flexibility provision; intelligently cap exports and imports; reward demand-response citizenship.

Result: The DER transition doesn’t slow, it locks in more durably and unlocks Electrons-to-X.

We didn’t necessarily change the future that was already being projected, but we would adjust the constraints we designed for. For example, if an inevitability is to arrive via one of those adjacent futures, are certain aspects of policy work/strategy necessary or will they smooth themselves out based on the fundamental realities… or framed differently can we communicate that eventuality to the hold-out stakeholders and accelerate the inevitable?

Beyond the First Layer

But where does this go?

It doesn’t end with one set of plausible adjacencies. If we want to see what gets unlocked once the desired future is unlocked, we simply run the framework one layer out.

First, credit the adjacencies that made High-DER Homes real. EV adoption normalized big residential circuits and smart charging. Heat pumps and induction pulled daytime and shoulder loads into electricity. Installer tech & permitting reform will shave soft costs and cycle time. Financing & tariffs (TOU, NEM variants, VPP payments) taught households to value timing, not just kWh. And yes, home/warehouse robotics will begin shifting chore and micro-fab work into the night, demanding storage that is throughput-capable and orchestration by default. These futures didn’t compete with DER; they will accelerate it.

Now let’s lean out of that ring.

Once most homes are prosumer nodes, the system gains new moves. Local coordination stops being a pilot and starts being a market.

That next layer, an Emergent Future is the PolyGrid Economy: polycentric local markets where interoperable, grid-edge micro-utilities trade flexibility, capacity, and ride-through. Neighborhoods compose resources like Lego, a school’s canopy covers the dinner peak; a block of homes provides frequency response; a cold-storage business sells resilience during heat waves. The roles of “customer,” “asset,” and “operator” blur, by design.

Push one layer further to a Speculative Future and you reach a Distributed Resource Order (DRO): a ruleset where multi-resource micro-utilities (electricity, heat, water, ‘x’) are coordinated by markets and protocols, not one-off programs. Now the unit of action is the polygrid, not the single site. Policy pays for behavioral throughput and system reliability, not just installed capacity. The bulk grid’s job shifts from supplying everything to composing what’s already there.

When these parts interoperate, the system compounds.

Why This Matters

Our investment thesis is operational: build for the world that operates around the clock. Elastic Energy for example is both a bet on DER and infrastructure for DER, pivotal technology that makes distributed energy resilient when the robotics adjacency materializes.

The work is to steward preferable futures. Not by predicting when robots fold laundry, but by ensuring that when they do, homes can breathe and grids can, too.

Back at UCSD, the work Andrew & the Agile Electrification team presented was compelling. It still is. What changed was the vantage point. Stand outside the diagram and watch the futures illuminate each other. That’s where leverage concentrates. That’s where we’ll keep placing capital.

The original backcast illuminated one path forward. The adjacent futures showed which paths could compound and accelerate the inevitable.

Disney has never just sold entertainment; it has sold worlds to inhabit. From the earliest animated films to the intricate choreography of theme parks, Disney has been in the business of identity formation and integrated businesses.

Each medium, film, merchandise & streaming platforms today, functions as a node in a carefully designed system that embeds characters into the lives of families.

• Past: The VHS tape, the Disney Channel, and branded Happy Meal toys transformed entertainment into habit. Childhood rituals became a form of brand loyalty that lasted generations.

• Present: Disney+ and transmedia storytelling extend this legacy. It’s ecosystems of narrative IP, cross-sold across parks, games, and merchandise.

• Future: The next node as many can already see is about embedding this knowledge and understanding of their IP and viewer habits from streaming across the merchandise and live experiences. (See: Netflix also launching Netflix House)

But I posit, Disney is actually sitting upon some character IP that is multi-generational and with the arrival of AI, it can go beyond just ‘generative AI content’ strategy for entertainment but enter the world of utility with embodied technology robots that blur the boundary between toy and tool, companion and utility.

A foresight lens reframes the question: the robot is not just a gadget, but can be a cultural anchor in the household system. Its value isn’t in the tasks it performs but in how it shapes the identities, habits, and trust structures of families.

The Consumer Robotics Inflection Point

We are on the edge of the first true consumer robot moment. The Roomba showed utility; Alexa introduced voice as an interface. But both lacked character. Disney could deliver what Apple did for personal computing: fuse functionality with cultural resonance.

So why Disney? Why Now?

I typically apply my foresight work to the realm of investing, yet as I surveilled the landscape of the emergence of household robotics, I’m seeing the convergence of a few technological breakthroughs:

1. Energy density for household robotics (e.g. Unitree’s G1 claims to have 2hrs of active usage with a swappable battery)

2. Dexterity in their hand movements (e.g. The latest Tesla Optimus claims to have 22 Degrees of Freedom, which is a significant upgrade from earlier versions and approaches the complexity of a human hand, which has around 27 DoF).

3. Cognitive functionality with AI (e.g. NVIDIA’s Jetson enables the combining of vision-language-action neural networks at the edge to allow robots to understand, adapt, and execute tasks in real-world, dynamic environments without requiring extensive reprogramming)

   

… however the cultural unlock to market still remains a question mark.

At a high level, a home robot earns its keep only if it crosses a simple bar: it must replace paid chores with real savings or unlock new capabilities that compound a household’s time and skills.

Think “downloadable mastery” (e.g., precision carpentry to custom-fit a door frame) or supporting in various routine activities, whether cleaning, tidying or meal-prep assistance. I wrote about this last year.

The dividend is time: more attention for partners and kids, less cognitive load for everything else.

So now the question is how do we convey this social value to potential customers… what if we design a product that serves a dual function that piggy backs off the trust and entertainment value of an existing IP… enter Disney.

Oh Toodles…

First, Disney would have to make the idea of personalized AI show its value in a simpler form function (before jumping to more complex robotics). Beginning with a Toodles AI companion.

Some context for non-parents/non-watchers:

Toodles is a helpful companion from the Mickey Mouse Clubhouse show. Toodles’ main function is to carry and present “Mouseketools,” a selection of four tools (sometimes three) chosen at the beginning of each episode to solve specific tasks and challenges faced by the characters. Toodles is summoned by the call “Oh, Toodles!” at which point he appears onscreen so the characters (and the audience) can pick which tool is most helpful for the current situation.

Toodles serves not only as an interactive plot device to encourage problem-solving but also helps educate young viewers on critical thinking, sequencing, and teamwork. His positive, ever-helpful persona is integral to reinforcing the show’s educational and collaborative themes.

So how could Toodles be envisioned with AI? Part toy, part personalized tutor.

If you want families to accept a machine into their daily rituals, begin with a ritual, not a machine. Toodles already encodes a loop children trust: call, choose, solve together. Reinterpret that loop with AI and you have a gentle on-ramp with minimal new hardware (AI-enhanced existing toy), just a familiar face that makes bedtime smoother and mornings less fraught. The test is modest: can “Oh, Toodles” reliably convert five minutes of chaos into five minutes of calm? If yes, parents will ask for more.

The AI “Mouseketools” become a small set of context-aware skills: story endings that invite a parent, a tidy-up game that ends in a real-world handoff, a feelings check-in that names emotions without mining data. Guardrails are explicit: local edge processing, clear consent for anything cloud-assisted, and a visible off switch. The aim is not stickiness but relief. If the outcome is fewer tantrums and quicker bedtimes, trust accrues quickly.

The business model is dull by design: a fair-priced object and a modest subscription for curated skills, no ad tech, no numbing scroll. In exchange, families get predictable gains at the margins of the day.

Little Helper (the real unlock)

Disney’s 2025 refresh, Mickey Mouse Clubhouse+, brings back Toodles and introduces Little Helper to the Clubhouse world… an explicit bridge between “helpful character” and “household helper.”

In other words, Disney already has a robot archetype children recognize and parents trust; the only question is how far to let it step off the screen.

Toodles would just be the introductory AI-at-home piece offering simple bedtime stories and helpful child friendly AI companion, but the real utility robot would be the Little Helper adaptation.

Little Helper is the household’s third hand: a quiet articulating arm on a weighted base, closer in spirit to the Pixar lamp than to a humanoid. It does unfussy work people fumble when tired, holding the bowl while you stir, steadying the onion while you chop, keeping the toy bin at floor height so a child can finish the job.

So is this feasible? Well, we had the core mechanics of the ‘Pixar Lamp’ prototyped 13 years ago, over a decade before our ChatGPT/LLM awakening.

We would need to ensure that agency remains with the family. The robot offers; a human accepts. Movements are slow, joints are force-limited, and certain actions simply won’t happen without an adult present.

The value proposition is time and attention. Not grand automation, but fewer dropped plates, literal and cognitive, during the pressure windows of the day.

If Toodles can help restore order in five-minute slices, Little Helper extends that order into the physical world.

Together they form a ladder of trust: ritual first, embodiment second.

Cultural Anchor & Transmission Vectors

Disney’s superpower has always been cultural inheritance across generations. Older siblings, cousins, and parents pass down characters like Mickey, Moana, or Elsa as trusted guides to the next generation.

• Parents as transmission vectors: Today, elder siblings and parents recommend Mickey Mouse Clubhouse to nieces, nephews and younger children. Disney isn’t just targeting kids, it is co-opting parents as cultural curators. My wife and I were introduced to the show through a dual signal of parent friends & family (thanks Lisa & Farnaz!)

• Robot as cultural anchor: By embedding the first lovable household robot in this lineage, Disney doesn’t just win market share, it colonizes the trust architecture of families. When AGI-capable assistants arrive, they will not be alien, they will already “live in Mickey’s world.”

Systems Thinking in Action

The opportunity lies at the right price point, where parents justify the purchase as both an educational toy and a household aid:

• An affordable companion tool that is Toodles from Mickey Mouse Clubhouse: playful, familiar, and loved by children - will break the initial surveillance paranoia.

• Following by a third-hand helper for parents: holding, fetching, tidying, extending household capacity in subtle ways whilst also being a companion to children.

Like the iPod moment for robotics: a device that starts as novelty but becomes infrastructure.

This is the market wedge. Once the first wave of families normalizes a Disney robot, the pathway to more advanced, AGI-enabled household companions becomes inevitable.

This is not speculation but an inevitable convergence: Disney as the first mover to make robots culturally acceptable, affordable, and desirable.

The Worldview Fork

Here lies the real fork in the road…

• Mass Consumer Everything: Robots as dopamine factories, another way to capture attention, monetize childhood, and deepen dependence on branded ecosystems.

• Freedom + Augmentation: Robots as genuine aids, designed to reduce parental load, extend children’s play and cultivate deeper bonds.

Disney can choose to build either the next attention trap or the first cultural gift of the AI & Robotic age.

Implications for Humanity

The question is not just what Disney could do, but what it would mean for humanity if they did it first. Systems thinking applied to this VFS forces us to look beyond the product launch to the broader feedback loops and externalities:

a) The Normalization of Robots in Childhood

The first Disney robot in a living room would not be a gadget, but a childhood companion. If children grow up with lovable, capable robots as normal, they will enter adulthood with different baselines of trust and expectation. Just as the iPhone rewired social behavior in a decade, a Disney robot could recalibrate our human-machine relationship in a single generation.

b) Consumer Tech as Cultural Infrastructure

When technology is introduced through culture, it bypasses skepticism. Parents may resist buying “robots” but they will happily buy “Disney toys.” This framing turns consumer tech into cultural infrastructure: invisible, trusted, and sticky. The household becomes both the adoption site and the training ground for AGI.

c) The Risk of Branded Childhoods

Here lies the danger. If the most formative interactions between humans and machines are owned by a single brand, then the architecture of childhood itself risks becoming corporate IP territory. The long arc of identity formation… stories, play, even moral lessons, could be shaped less by community or family and more by corporate design decisions.

d) The Fork Between Tool and Trap

The fundamental choice isn’t technical, it’s moral.

• Robots can become dopamine amplifiers, designed to monetize every interaction.

• Or they can become freedom machines, designed to free up parental bandwidth, create healthier play environments, and scaffold children’s learning.

Which path Disney (or whoever comes first) chooses will ripple far beyond consumer robotics, it will help set the normative expectations for how AGI enters society.

🎭 As a parent, this isn’t abstract. It’s my lived review of Mickey Mouse Clubhouse. Despite skepticism of mass dopamine factories, from junk food to social media, I still see the possibility for a company like Disney to seed something different: a robot that truly helps families flourish.

A wedding, a storm & a flight that never boarded

In the lead up to NY Climate Week, I was in Florence for my cousin’s wedding. The next day, I was meant to begin the chain of flights that would take me to New York for Climate Week. Bags in hand, rental car returned, we enter the terminal… and then: cancelled.

The Mediterranean this summer ran hotter than usual, loading the air with moisture like dry tinder. A late-September shift in the atmosphere struck the match, and storms bloomed out of clear skies. In today’s climate, overheated seas are the quiet accelerant, turning blue skies into cancellations in minutes.

That moment reminded me of something we rarely name but all depend on: continuity is the first thing we assume, and the last thing we insure.

• We flick a switch and expect the lights to come on.

• We turn the tap and expect water to flow.

• We book a flight and expect wheels to leave the runway.

Modern life is built on the unspoken assumption that tomorrow will look like today, that the system will keep humming. It’s the most valuable thing we own, and yet we treat it as free… until the weather, the grid or politics prove otherwise.

Water as a Foundational Economic Catalyst

One of my first stops in New York (once I finally made it into the city) was a session at Goals House with Matt Damon and Vedika Bhandarkar of Water.org. It’s easy to dismiss Damon as the celebrity face, but what struck me was the financial architecture behind their model and how fluently he articulated it. His grasp went well beyond advocacy; it was systems design, explained with clarity and conviction.

Water.org doesn’t just dig wells… although many still think that’s their work. Today, they’re a force in leveraging micro-loans for water resilience, delivered through local micro-finance institutions. And their repayment rates? ~98–99%.

Why? Because water is essential, and because access frees up & enables real value flows for household income or education.

It’s a masterclass in leverage: philanthropy as the catalytic layer, pulling in multiples of commercial capital. A design shift so $1 donated can crowd in $20 of lending.

Two lessons stuck with me:

• Access beats charity. Families repay not out of gratitude, but because water compounds into income, dignity and economic stability.

• Blended capital isn’t a bug. Philanthropy de-risks, investment scales, local lenders sustain.

In finance we call it crowding in capital. In human terms, it’s called futures.

From Leverage to Lived Practice: Financing Climate Deals

That insight from Damon’s session echoed in my own panel later in the week, where I spoke alongside peers on “How Climate Deals Really Get Financed.”

Our discussion unpacked the capital stack for hard-to-abate sectors — cement, chemicals, steel, etc. Exploring what it takes to move projects from pilot to portfolio:

• Public incentives (IRA credits, or what remains of them, state programs).

• Mission-driven capital (green banks, guarantees, philanthropy).

• Private markets (project finance, debt, insurance, securitization).

The lesson is the same as Damon’s microloan story: capital structure is destiny. Get it wrong, and breakthroughs stall. Get it right, and pilots scale into industries. Continuity is manufactured through design of contracts, incentives, and guarantees.

When the Well Runs Dry, So Do Options

The absence of water is not just thirst; it is the foreclosure of futures.

Water is the first credit line we all draw upon. Without it, no loan of education, health, or opportunity clears. A child walking hours with a bucket is not in school; her balance sheet of options never compounds.

A village without water doesn’t just lose its crops, it exports its children. Migration begins not with ambition, but with scarcity.

In financial terms, water is principal, not interest. Every other investment: microloans, schools, clinics, accrues only once water is secured. Scarcity turns continuity into a privilege, and the decision to stay or leave is often written in the aquifer.

The underlying reality is stark: mass migration will define the coming decades as the continuity of a stable climate unravels. That is why so much of our recent foresight work and research, has focused on the foundational resources that anchor stability and enable resilience in shifting futures.

Our latest investments span from cost-effective water technologies that can operate in energy-limited, arid regions, to innovations that expand global cooling capacity, from skyscrapers to data centers, where continuity of climate control is itself a survival constraint.

Comedy as a Constant

Beyond finance and industry, I make it a habit to immerse in another dimension of Climate Week, specifically exploring how entertainment and culture metabolizes crisis.

At one gathering, creatives and policymakers unpacked how shows like The Diplomat or The West Wing don’t just dramatize politics but seed expectations of what government can and should do.

Followed by a late-night event, From Monologue to Momentum, a program presented by the Hollywood Climate Summit and WGA East, in partnership with the Sustainable Entertainment Alliance, I listened to late-night veterans lay bare the mechanics of comedy as translation.

The comedically stacked panel with Emily Erotas (Late Night with Seth Meyers), and Sofía Manfredi (Last Week Tonight with John Oliver, Patriot Act), joined by Nicole Conlan (The Daily Show) and Rollie Williams (Climate Town).

Together they showed how satire can carry audiences across the gap between dread and dialogue, breaking down headlines that might otherwise paralyze us.

The insight was blunt: narratives are leverage too. Just as micro-loans multiply capital, jokes multiply comprehension. A one-liner can bypass defenses that a 40-page report never will. Policy writes rules; culture writes defaults.

And yet, even this continuity is precarious. With late-night shows under pressure, Jimmy Kimmel’s (temporary) suspension being only the latest reminder, comedy itself risks interruption.

Which brought to my mind the recurring Kimmel gag where Damon never gets his slot: “Sorry, Matt Damon, we ran out of time.”

At Climate Week, the irony cut deep: in a climate-shocked world, running out of time is the only punchline we can’t afford.

Continuity in a Changing Climate

Continuity of flights, harvests, elections, and balance sheets is the foundation we never price properly and the first thing climate disruption unravels.

An update to my worldview, sharpened by Climate Week:

1. Continuity will be the binding constraint. Not water, energy, or capital in isolation, but the unbroken sequence of small things that must go right.

2. Our models are now actors. Once published, forecasts shape politics and markets. Call it Schrödinger’s climate model: observation alters the outcome.

3. So model the modelers. Anticipate not just the climate curve, but the reactions of insurers, regulators, and investors.

4. Finance is the fastest lever. Physics unfolds over centuries, culture over generations, but capital moves tomorrow.

The lesson of Climate Week was that continuity is our most fragile asset, yet also our most overlooked form of wealth. Whether in a household tap, an industrial capital stack, or a late-night joke that metabolizes dread into dialogue, continuity is what allows everything else to compound.

Leverage isn’t only financial, it’s cultural.

Migration isn’t only geographic, it’s economic.

Resilience isn’t only infrastructure, it’s narrative.

If we design for continuity… of water, of capital, of stories… the storms will still come, but the futures they interrupt won’t collapse so easily. Continuity is the quiet scaffolding of modern life. And in a climate-shocked century, it may be the only constant worth insuring.

Continuity budgeting, treating resilience not as a footnote but as cost of goods sold, may be the most radical accounting shift of our age.

P.S. As if to underline the point, the day before my flight home from New York I received this email from JetBlue… ‘severe weather…. may experience delays or cancellations…’

This post originally appeared on LinkedIn.

Fast-forward to September: we’re launching Bottlenecks Institute, a research organization to map, conceptualize and help remove the constraints slowing progress across climate, energy, biotech, and AI. Think living “bottleneck cards,” expert interviews, and AI-assisted mapping, paired with the foresight backbone I’ve been refining inside Cool Climate Collective: the Three Futures Test to translate scenarios into investable narratives and coordinated action.

Why a Bottlenecks Institute (and why now)?

The April room made it clear: teams doing systems-level work face the deepest capital and adoption gaps, not because the tech is weak, but because rules, incentives, and institutions weren’t built for what they’re trying to do. When conventional models falter, catalytic capital and coordination are a way for us to correct the market where longer term negative externalities aren’t priced in.

BI operationalizes that insight:

• A living repository of regulatory, cultural, technical, and coordination constraints.

• AI-assisted mapping + structured expert input to prioritize where marginal dollars and attention can unlock the most progress.

• A media/insight layer (pod + briefs) to make the “what’s blocking what” legible to funders, policy, and operators.

Who’s behind Bottlenecks Institute (and why that matters)

We built BI to be multidisciplinary by design, each of us covering a different part of the bottleneck landscape.

James Gray — Hands-on AI Strategy × Operator Discipline
James and I originally met at the Califlorence Climate unconference where we both connected on the ideas of new models and concepts that can be applied to frontier climate technology; he’s an entrepreneur and expert on emerging climate & energy technologies. He’s also the founder of Seren Labs, a data platform that helps investors and corporations discover the climate solutions that are gaining traction and ready for scale. He brings years of expertise and insight into the challenges and opportunities in climate-related industries, and takes a special interest in novel mechanisms and frameworks related to talent, funding, research collaboration, and pilot projects.

Parnian Barekatain — AI for Science × Community Biotech
Parnian spans AI, RL, and synbio: previously at OpenAI, organizing their first OpenAI hackathon; visiting researcher with the MIT Media Lab’s Sculpting Evolution group; an Emergent Ventures grantee for synthetic biology at MIT. She’s also a community convener (recent BI meetups) and a translator between lab-bench ambition and deployment reality, perfect for keeping our “bottleneck cards” current and grounded.

Ian Derrington — Deep Science × Systems Clarity
A physicist and inventor whose early work helped pioneer nanopore DNA sequencing (MspA), Ian brings a rare combo of bench-level rigor and model-builder mindset. His publications in PNAS and related work seeded techniques that underpin modern sequencing; today he channels that pattern-recognition into mapping technical and institutional constraints and explaining them clearly to non-specialists.

and myself 👋, the team’s resident climate investor and foresight practitioner.

Together, we’re designing BI as a human–AI research loop: expert interviews and community input → causal/bottleneck mapping → Three Futures scenarios → who needs to do what & when with public artifacts others can adapt.

NOTE: You’ll also see Parnian & James hosting BI meetups to gather fresh constraints directly from builders.

From Forecast to Force-Multiplier

My contribution comes via a foresight discipline we’ve battle-tested across our early-stage investing: the Three Futures Test. We leverage the test as a way to map ventures or initiatives across plausible futures, score Impact × Revenue × Reach (IR³), and then share scenarios & insights to founders, collaborators and partners so we can coordinate around concrete unlocks, turning foresight from a private edge into a public good.

This is the same scaffolding we used to run Bottleneck Bingo in April, now formalized inside BI, more on that below...

To keep the work usable, not theoretical, we frame each bottleneck with three plausible paths that share the same system spine and diverge only where incentives or shocks differ:

• Future One (Momentum): what happens if today keeps happening.

• Future Two (Nudge): a concrete change—procurement clause, code update, channel, or proof point—re-weights decisions.

• Future Three (Stress): Gray Rhino and Black Swan pressure-tests that check posture and option value, not clairvoyance.

The goal isn’t prediction, it’s pre-decision. Each path ends with “who moves first and in what order,” so when signals blink, coalitions don’t debate, they act.

That’s why each card includes a compact Forecast tab (like the screenshot below):

If you want the deeper mechanics, over the Foresight Methodologies, shared them in a previous write-up, but the essence is simple: shared maps, legible futures, pre-decisions. That’s the foresight through-line from April’s catalytic convening event to today’s research engine.

An Example Card: Insufficient nuclear workforce & expertise

We’ll focus on the core two tabs in the screenshots:

1. Details — why this bottleneck matters, who it affects, and where it’s showing up.
   

2. Forecast — a compact view of plausible paths built off Three Futures Test Methodology, what would have to change, and the signals we’ll watch to know we’re on the right track. (We generate the Forecast after Details, Resources, and Efforts are published, our minimum inputs to run the Three Futures forecasting model for Bottlenecks Institute - if a card is missing a Forecast, likely it’s missing foundational inputs, designed as a safeguard to avoid hallucinogenic forecast modeling).

Think of these cards as a shared map. The goal is that a program officer, policymaker, operator, or founder can look once and know: what the obstacle is, which lever to pull, who should pull it, and what evidence would confirm we’re making progress.

What the Forecast is actually doing

• Future scenarios: brief, plausible paths (e.g., “Institutional Rebuild & Training Boom”).

  • Each Subsection:
    Shifts (what changes), Why it happens (drivers), What it means (implications), When (early signs vs. full effect), and a Likelihood rating.

• Indicators: a handful of measurable signals that tell us a scenario is materializing.

• Unlocks: written as if → then (“If fast-track licensing is adopted, then median time-to-hire falls from X → Y”).

• Pre-decisions: simple rules that say advance / pivot / pause based on the indicators, so coalitions can move without relitigating.

The card above is one brick in the system we’ve outlined.

We are beginning with problem selection & cause prioritization (via the cards), but as we expand categories; we will improve the Human ↔ AI workflow, to transform these cards into living assets with APIs to form Reports and Impact Metric agents that foundations and funders can use to track portfolio impact in real time (as per the broader market data and foresight projections about the problem area)... just because something is true today when we support it, doesn’t necessarily mean the same is true years from now even if the solution hasn’t changed.

How to plug in

• Founders/Operators: Submit the bottleneck you’re wrestling with; we’ll map it, model deltas, and suggest the most leverageable unlocks.

• Funders/Policy: Aim capital or policy attention where the modeled delta is highest; we’ll show the “who/what/when” and how to measure signal change. Check out our growing collection of Bottleneck Cards (with many more to be enhanced/under review in the coming weeks)

• Researchers/Communities: Join a BI meetup or read/listen to the BI substack.

A note: we are still adding/vetting bottlenecks so our collection of cards shall be growing in the coming months. Additionally what is going on behind the scenes/what can be done with them shall also be able to unlock a new frontier of value to partner organizations and foundations (thanks in large part to Ian & Parnian’s brilliant technical work).

Keep the conspiracy quiet, the coordination loud

If there is one thing we’ve been reminded over the past year, it’s that we don’t win alone. The future belongs to coalitions that can conspire wisely, invest patiently, and build resiliently, and do it with enough humor to stay human while we push against physics and institutions.

Let’s keep getting better at answering: What’s in the way?
And then, let’s move it.

The goal is to reframe our defense doctrine, moving from reactive fortifications to proactive campaigns, so we can outmaneuver cascading risks and secure the high ground of a livable future.

Listen to the overview:

When most people hear “defense tech,” they picture missiles, drones, jet fighters, tanks… hardware built to counter human adversaries. Sometimes the definition expands to cybersecurity, still framed as humans targeting human infrastructure, now with AI in the mix. Biosecurity might get a fleeting mention, engineered pathogens, weaponized viruses, but it rarely moves beyond a soundbite.

What’s missing? The silent vulnerabilities. The slow failures. The domains where neglect, not an enemy soldier, is the real threat.

The broader trends of divestment, coupled with a lack of meaningful action against known vulnerabilities in these adjacent domains, tell the real story… one I’ll unpack in this series.

This is the defense doctrine we’ve inherited, a narrative shaped almost entirely by visible, and invisible, threats to manmade infrastructure. But it’s a broken record on repeat, each cycle sounding worse, the collateral damage potential growing as both weapons and infrastructure scale in reach and destructive capacity.

In the Traditional Defense view, I’ve written about how we’re drifting into a strategic deadlock: part stalemate, part accelerated arms race, all under the shadow of an emerging 21st-century MADD Doctrine.

However, I ask… what if the next theater of defense is not the battlefield between nations, but the front lines of the biosphere itself?

To put it plainly: an all-encompassing “defense tech” doctrine must safeguard our ecological and biological systems. The very systems that make Earth habitable are the same ones that allowed us to thrive here. As they begin to fracture, we must awaken the same instinctive urgency that traditional defense concepts evoke: a defense of our families, their health, their well-being, and their future prosperity.

Imagine the collapse of pollinators, or the hijacking of mosquitoes by deadly disease vectors, not as biodiversity issues, but as catastrophic defense failures. These aren’t side notes in an environmental brief; they are active combat zones in a new kind of war, where the adversary is systemic imbalance itself.

Beyond Us vs. Them

Once we accept the first layer of this premise, that ecological and biological systems must be integrated into our concept of defense, we run headlong into a second, harder truth: this isn’t an easy narrative to sell. It refuses to fit the traditional “us versus them” mold. There’s no single adversary to demonize, no flag to rally against. The threat is diffuse, systemic, and, most unsettling of all, woven into the very systems we ourselves maintain.

When we think about defense, we’ve historically anchored it to nation-states, tribal identities, and clear adversaries. We’ve built our systems to protect “us” from a defined “them.”

Today, the reality of that threat is more abstract. It’s not about human adversaries alone; it's about how destabilized natural systems can become a kind of adversary if we don’t act collectively.

Nature isn’t at war with us, though through our pollution, we may be waging war on it. What looks like “pushback” is simply consequence: destabilized systems expressing the physics of imbalance.

Massive floods, wildfires, and intensifying hurricanes aren’t acts of vengeance; they’re the predictable chain reactions of a planetary system out of equilibrium. The solution isn’t outcompeting another nation… it’s defending the boundaries we all share.

This framework came vividly to life for me last year in Seattle, when I attended TED Countdown and watched Johan Rockström present it in his TED Talk. (I highly recommend a watch.) It’s a rude awakening, but with it an alarm that we need to take things seriously… as seriously as we would any other existential threat to our family, society and way of life.

Prosperity without sustainability is an illusion. There is only so much productivity gain or GDP growth we can squeeze from an extractive economy that’s quietly compounding its own negative externalities.

In military terms, it’s like winning a string of tactical skirmishes while bleeding out strategically…

Our Pyrrhic Victory over Nature

In 279 BCE, at the Battle of Asculum, Pyrrhus of Epirus defeated the Romans but suffered such heavy losses that the victory became famously known as a "Pyrrhic victory," indicating it was won at too great a cost to be truly beneficial.

Pyrrhus reportedly said, "If we are victorious in one more battle with the Romans, we shall be utterly ruined," highlighting that the toll on his forces, including many of his commanders and soldiers, was unsustainable.

There’s also another lesson in Pyrrhus’ end. Years later, in the chaos of a night assault on Argos, he wasn’t brought down by a general’s sword but by the hand of a poor woman defending her home. From her rooftop, she hurled a roof tile that smashed into the king’s neck, stunning him just long enough for enemy soldiers to close in. Even the most celebrated commander can be stopped cold by the resentment and desperation of ordinary lives left in ruin. Strategic brilliance means little if the campaign leaves behind only embittered civilians and shattered communities because they become the very terrain that turns against you.

Our current model is a Pyrrhic campaign: we push for GDP gains, resource extraction, and quarterly wins, but each “success” erodes the very logistical base that is foundational to life on Earth: our climate, our ecosystems, our human health, that makes long-term victory possible. Armies that neglect their supply lines inevitably face collapse. Nations & leaders that neglect planetary boundaries face the same fate.

The battlefield has shifted, but the lesson is ancient: we can’t outmaneuver an enemy if we’re destroying the ground we’re standing on.

Like Pyrrhus, we can’t keep claiming short-term wins that leave us weaker for the next fight. In the modern theater, those hollow victories show up in public health: the climate conditions we’ve created are now enabling disease vectors to turn our own environments into hostile ground.

Zika as a Cost-of-Failure Case Study

Heat and drought aren’t abstractions; they reshape disease logistics. During the 2015–16 El Niño, climate anomalies created unusually favorable conditions for Aedes mosquitoes, amplifying Zika transmission across the Americas. Modeling of that event found El Niño–linked temperature and rainfall shifts helped drive the surge, as published in Nature.

Transmission is temperature sensitive: a recent 2023 study estimates Zika’s basic reproduction number R0​ peaks near ~30°C, implying that warming pushes more regions into the risk band and lengthens transmission seasons.

The costs are tangible: the World Bank pegged 2016 Zika losses at ~$3.5 B, while another assessment estimated $7–$18 B in short-term regional costs. The U.S. alone appropriated $1.1 B in emergency funds (totaling $1.7 B with prior reprogrammed dollars) for response and preparedness. And each microcephaly case can carry lifetime direct medical costs on the order of $90k+ in Latin America before counting family income losses.

Looking ahead, more viruses and vector-borne diseases will increasingly enter a “Goldilocks” zone, with persistent 30 °C+ temperatures extending suitable transmission windows across the tropics and beyond. Global warming will likely exceed 1.5°C above pre-industrial levels in the near term, meaning many nations will regularly breach the Aedes-optimal threshold.

In security terms, that spells recurring, compounding drain on national capacity… every nation for itself, but all nations seeing their borders pushed into the heat-risk zone.

Heat as a Force Multiplier

Even without a pathogen, heat alone behaves like a force-multiplier: it drives peak electricity to the redline, triggers price spikes and regional blackouts, and floods emergency rooms… each effect compounding the next. It’s the blackout that hits as your grandmother’s oxygen concentrator shuts down. The food spoiling in your fridge during a three-day outage. The emergency call that never gets through because the cell towers overheated.

Translate that into security terms and you get a rolling drain on national capacity: stressed grids, strained hospitals, lost work hours, and political pressure from households who simply can’t keep cool.

• Records & outages are now routine: According to IEA, in 2024, 40+ countries representing ~70% of global electricity demand hit new peak-load records during heatwaves; many also suffered major outages or rolling blackouts.

• Cooling drives both demand and bills: In India, each +1°C adds >7 GW to peak demand (up from ~4 GW in 2019); in the Middle East and parts of the U.S., cooling exceeds 70% of residential peak load on the hottest days.

• Blackouts can span borders: A June 2024 heatwave triggered a multi-country blackout across the Balkans, confirmed by the regional grid operator’s incident report.

• Hospitals feel it immediately: During the 2021 Pacific Northwest heat dome, a report published by the CDC found that mean daily rate of heat-related Emergency Department visits was 69x higher than the same period in 2019; 79% of all May–June ED visits occurred in just six days.

• Heat already kills at continental scale: Summer 2022 saw an estimated 61,672 heat-related deaths in Europe, a mortality load large enough to strain health systems and social services, according to a report in Nature.

Why True Defense Is Collective

This is where principles-based thinking comes in. We need a Defense 2.0 that drills down to root causes, using first principles to ask how we can realign our systems to prevent ecological threats rather than scramble to contain them after impact.

In other words, let’s not treat our planet the way our healthcare system too often treats patients, trapped in a billing-driven cycle that rewards costly interventions over prevention. This isn’t a critique of doctors, who work to heal, but of the industrialized insurance and billing machinery that distorts priorities. If we can reframe defense tech to include safeguarding the biological systems that underpin human survival, we stand a far better chance of achieving long-term resilience.

Others are beginning to voice this too. Thinkers in regenerative agriculture, ecological economics, and allied fields are already hinting at a broader definition of security, one anchored in the stability of our natural systems. Their work reminds us that defending our ecological foundations is every bit as critical as securing our digital or physical infrastructure.

In the end, Defense Tech 2.0 is a shift in doctrine. It demands we evolve our defenses to protect not only the systems we’ve built, but the living systems that make our existence possible.

With the battle map now drawn, heat, water stress, disease vectors, grid strain… we move to the next phase: tactics, tools, and strategies for cities, nations, and individuals to protect what matters most. This is a standing order for the 21st century: secure the living systems, or lose the war for a livable future.

Last week, as I set out for a meeting in Davis on a route that typically takes me no more than 20 minutes, I received an alert from Google Map: "Expected travel time: 47 minutes."

A frustrating detour lay ahead, the result of yet another round of urgent infrastructure repairs, this time part of Sacramento's extensive Fix50 initiative, a $483M project that was supposed to be completed in July 2025 (now delayed an additional year).

As traffic ground to a crawl, I made a mental note to dig deeper into the issue, wondering if this was an isolated inconvenience or part of a much larger story unfolding across the globe.

Roads at Risk: Global Infrastructure Under Siege

Turns out, my experience was hardly unique. The Autobahn in Germany recently made headlines for cracking under record-breaking heat.

Closer to home, reports from Phoenix described road surfaces reaching temperatures upwards of 140°F, enough to buckle the roads and melt asphalt. It’s becoming clear that our roads, vital arteries carrying the lifeblood of our economy, were not designed for the climate we now face.

Every day, nearly 50 million tons of freight worth more than $52 billion travels across U.S. highways. Yet, these same roads are increasingly vulnerable to climate extremes.

The scale is staggering: In 2023 alone, the U.S. endured 28 separate billion-dollar weather disasters, each event punctuated by damaged or closed roads and bridges. Nebraska’s 2019 "bomb-cyclone" floods simultaneously shut down over 3,300 miles of state highways overnight, slicing vital supply lines and isolating communities.

A Debt to the Future: The Hidden Cost of Deferring Resilience

The heart of the problem lies in our outdated infrastructure standards, conceived in an era when weather patterns were predictable and temperatures stayed within manageable bounds. Climate change has shifted this baseline dramatically and our infrastructure (and maintenance of it) is no longer keeping pace.

For instance, potholes, a seemingly mundane issue. They’re proliferating at alarming rates due to more frequent freeze-thaw cycles. Winnipeg, Canada, saw pothole claims leap from a typical 360 per year to over 2,000.

Similarly, intense heat waves in places like Missouri have caused roads to buckle violently, at times even launching vehicles into the air.

The underlying costs are spiraling: California alone spends approximately $1 billion annually repairing climate-related road damage. This figure is only expected to rise as extremes intensify.

Specifically, consider Pew’s Analysis of state budget shortfalls, even with major federal efforts like the IRA (Pre-OBBBA), revealed that infrastructure spending alone isn’t enough to close the resilience gap:

• 33 states expect to miss at least some of their benchmarks for roadway conditions, preservation and maintenance funding, or both over the next decade.

• 24 states reported a combined $86.3 billion funding gap over 10 years.

If we care about national economic health, viewing infrastructure resilience through the same lens as national debt makes sense.

Every time we defer investing in resilient, adaptive infrastructure, we’re effectively borrowing against the future.

These hidden liabilities pile up, and soon the cost of repeated short-term fixes will exceed the economic benefits that the roads themselves provide. It's a high-interest loan we can’t afford to ignore.

Gridlock Ahead: The Looming Threat to Our Daily Lives

What’s at stake is more than economics; it's our quality of life. If current trends continue unchecked, every city will suffer its own equivalent of Los Angeles’ infamous 405 rush hour, not from population growth but from infrastructure contraction. Imagine routine daily commutes becoming multi-hour ordeals, supply chains snarled, and the basic reliability we take for granted evaporating.

But it doesn't have to be this way… promising solutions are already emerging.

Innovative materials, like self-healing asphalt infused with micro-capsules of rejuvenating oils or fibers, could extend road lifespans significantly.

Cities like Los Angeles and Phoenix are experimenting with reflective coatings that lower road temperatures and minimize heat damage. Real-time sensor networks embedded in roads and bridges are helping predict structural failures before they become disasters.

Forward-looking cities are proving resilience pays off. Rotterdam, for example, has entirely redesigned its urban landscape to coexist with water, turning flood risks into beautiful public spaces. Likewise, New York City’s comprehensive flood defenses post-Hurricane Sandy show what's possible with proactive investment.

Complacency: A Short-Term Mind Virus

Yet, complacency remains a dangerous short-term mindset, a kind of societal "mind virus." Allowing infrastructure to deteriorate doesn't just damage roads; it erodes trust in public institutions.

The very definition of our social safety net expands from financial and healthcare provisions to the basic guarantee of functional roads, bridges, and transit. When communities can no longer reliably provide these essentials, we edge closer to societal breakdown, extreme perhaps, but so are the realities we're facing in a post-1.5°C world.

Building for the Storms Ahead

Our roads are more than mere conduits for cars and trucks… they embody our collective investment in future prosperity. But their foundations are showing cracks, both literal and metaphorical.

Alexis de Tocqueville once noted that roads can be a marker of public wellbeing, just as vital in building trust as schools or places of worship:

“I know of no people who have established schools so numerous and efficacious, places of public worship better suited to the wants of the inhabitants, or roads kept in better repair.”

Tocqueville, in Democracy in America, celebrated public infrastructure, roads, in particular, as foundational to a healthy democracy. When roads begin to crumble, it’s not only the pavement that fails, it’s the trust that binds society together.

To safeguard our future, we must invest proactively in climate-resilient infrastructure. This is not an abstract call to action; it's a direct response to the daily realities confronting each of us, whether stuck in traffic or witnessing escalating maintenance bills.

It's time we recognize infrastructure resilience as an essential national priority (this applies to all nations, and some actually do it in that perspective). Otherwise, we're simply paving over today’s problems at the expense of tomorrow’s stability.

For those interested in exploring this further, our foundations_ research team published a deep-dive report titled, “When the Asphalt Cracks, So Does the Economy”. The report unpacks the climate-related damage and repair cycles that are increasingly straining our economy, through both reduced productivity and rising fiscal burdens.

It also outlines the long-term financial risks that emerge from deferring systemic upgrades in favor of patchwork repairs. If you're interested in the intersection of infrastructure, resilience, and economic foresight, it’s well worth a skim.