Welcome to the 8th edition of the 2026 season of Drift Signal. I’m Nicolas, Head of Research at Vsquared Ventures. This is my personal newsletter, which examines macro and markets through the lens of late-cycle investment theory, and the views expressed are my own.

Here is some recently published content that could be of interest:

• In Currency of Power, Marieke Flament and I speak with Izabella Kaminska on the eurodollar system, stablecoins, and the long arc of money moving beyond the reach of the state. From eurodollar markets and the LIBOR regime to the rise of Tether, Izabella shows how today’s stablecoins echo earlier offshore dollar structures rather than break from them. We also discuss her idea of stablecoins as a form of stabilisation — a way to manage global imbalances — and what this means for the future of dollar power and financial control. Watch, listen or read here: The Dollar’s Parallel Life.

Now on to today’s edition. The conventional story of our present moment begins in 2016. Brexit, Trump, the sense of rupture: two votes that seemed to announce a new era. The rupture of 2016 was real, but the ground had shifted two years earlier, reported as separate stories, and largely forgotten under later dates.

In 2014, Russia annexed Crimea and ended the post-Cold War settlement. China began building a parallel economic and financial order. American shale unwound the petrodollar system from the inside. Alibaba revealed a second digital civilisation. And in Gamergate and the US midterms, the infrastructure of populist politics was assembled before anyone knew what it would be used for. This edition traces the six threads that converged that year — and have not diverged since.

1/ 2014 sets the stage

Some time ago I listened to a Hidden Forces episode in which Demetri Kofinas speaks with Jacob Siegel of Tablet Magazine.

• Jacob describes returning to America after a tour of duty and finding his country dramatically changed. The event he uses to illustrate this is the 2014 misogynistic harassment campaign known as Gamergate. He also mentioned ISIS founding its caliphate in the Middle East — a separate but equally striking 2014 marker, notable for the beheading videos and a new kind of information warfare that nobody had a ready answer to.

I fully understand Jacob’s point on ISIS. In fact, I remember that in 2015 I was studying a piece in the New York Review of Books, published anonymously, and thinking that these people were operating with a startup founder’s logic and that we had no playbook for it. ISIS felt like a turning point. But it then faded, and although it reshaped the Middle East in a major way, people rarely discuss it now.

What else was moving in 2014 that we have since forgotten filed under later dates?

The answer, on reflection, is quite a lot — enough to frame an entire newsletter edition! And that challenges the conventional story of our time, which tends to begin in 2016. Brexit in June, Trump in November: two votes that seemed to announce a new era. In an essay I wrote at the time, titled Brexit: Doom or Europe’s Polanyi Moment?, I framed the stakes this way:

One could say that 9/11 and the Iraq war were like World War I, having planted the seeds for the global political turmoil that’s led us to the faltering of the Syrian regime… and the migrant crisis that triggered Brexit. The equivalent of World War II may be looming in the form of ISIS and widespread terrorism: not battles waged by massive, professional armies powered by high technology, but destruction brought about by isolated groups of amateur individuals interconnected throughout a global cultural, financial and technological network.

If that is right, the rupture did not begin in 2016. Across geopolitics, energy, technology, and politics, the architecture of the present decade looks to have been largely in place by the end of 2014.

2/ Russia crosses a line

The post-Cold War order rested on the following assumption: as of 1989, territorial war in Europe was finished. Liberal democracy had won, markets would bind former adversaries together, and international norms would hold the peace. Francis Fukuyama’s famous (and widely misunderstood) concept of the “End of History” became the Western elite’s intellectual compass, grounded in the remarkable events of 1989.

The problem, notably from a European perspective, was a failure of historical reasoning. As historian Timothy Garton Ash argues, the West looked at nearly two decades of progress after 1989 and assumed the trend would continue. It took a contingent, fragile, hard-won outcome and read it as evidence that history moved inevitably toward freedom and liberal democracy. That left no room for reversals, for the agency of hostile powers, or for the possibility that the Soviet collapse was an event to be managed rather than a victory to be celebrated.

The warning signs were there early. As Garton Ash recalls in his book Homelands, he met Putin in St Petersburg in 1994 — then the city’s deputy mayor — who was already insisting on Russia’s need to protect Russian speakers abroad and recover territories that had “historically always belonged to Russia.” Crimea was mentioned by name. The West filed this away and moved on.

• What followed was two decades of mistaking economic interdependence for hard security. Germany deepened its reliance on Russian gas even as it let its armed forces atrophy. NATO’s 2008 Bucharest summit produced the worst of both worlds: a communiqué promising Ukraine eventual membership without any concrete steps to guarantee its security, increasing Russia’s threat perception while offering Ukraine nothing real in return.

Then in late 2013, Ukraine’s pro-Russian government fell following the Maidan Revolution. The country pivoted decisively toward the West. Russia’s answer came in early 2014: the annexation of Crimea, the first forcible redrawing of a European border in decades. Western sanctions followed, and a long economic confrontation began to take shape.

My friend Moritz Müller-Freitag, reviewing Garton Ash’s book, puts it like this in his beautiful text Quo Vadis, Europa?:

In the decade that followed, Europe wilfully turned a blind eye to Russia, first in 2008 and then again in 2014. Garton Ash aptly calls the annexation of Crimea ‘the turning point at which the West failed to turn.

In 2014, the annexation of Crimea closed the post-1989 chapter. Great power rivalry was back on European soil. The rules that had governed the continent for twenty-five years no longer held.

3/ The West sanctions Russia, China takes note

In response to the 2014 annexation of Crimea, the US and its allies imposed sanctions on Russia. Their immediate target was Moscow, but their most important audience was Beijing.

What the sanctions demonstrated was that the US could use financial infrastructure as a precision geopolitical tool. SWIFT access, dollar clearing, treasury holdings were leverage, and any country sufficiently integrated into the dollar system was, in principle, exposed to the same pressure.

• China’s exposure at the time was considerable. It held vast quantities of US treasuries, conducted the bulk of its trade in dollars, and had no parallel system to fall back on. The conclusion Beijing drew that year was the following: this integration was a strategic vulnerability, and reducing it was a matter of national security.

The response came on three fronts. First, China slowed its accumulation of US treasuries and began diversifying its reserves. Second, it launched the Belt and Road Initiative, deploying capital into infrastructure across Asia, Africa, and Europe — seeking returns outside the dollar system and building relationships beyond Western financial architecture. As I wrote in 2017 in When China Rules the Global Digital World:

The Belt and Road Initiative is a process by which Chinese tech giants will become able to expand their operations throughout Asia, Africa, and Europe. There are already ways through which popular Chinese applications such as WeChat and Alipay are adopted beyond the Chinese domestic market: mainly the diaspora and the growing number of Chinese tourists travelling abroad. But once the same applications are adopted in Central Asia, Africa and Eastern Europe, with the experience curve that comes from expanding to new countries and adapting to their particular customs, how long do you think it will take for Western Europeans to get interested in WeChat and Alipay, too?

Third, China began constructing alternative financial plumbing. The Cross-Border Interbank Payment System, launched in 2015, was designed to process renminbi settlements outside SWIFT.

The dollar’s power as a sanctions weapon rests on the absence of a credible alternative. Later events — US semiconductor controls on ZTE and Huawei in 2018 and 2019, the freezing of $300 billion in Russian sovereign reserves in 2022 — only confirmed the original calculation and accelerated the work. The decision, however, was taken by the CCP as early as in 2014.

4/ Xi Jinping runs a vulnerability audit

2014 produced two Chinese decisions that are rarely discussed together:

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Welcome to the 7th edition of the 2026 season of Drift Signal. I’m Nicolas, Head of Research at Vsquared Ventures. This is my personal newsletter, which examines macro and markets through the lens of late-cycle investment theory, and the views expressed are my own.

Here is some recently published content that could be of interest:

• In The Deep Brief, Vsquared’s deep tech newsletter, my colleague Herbert Mangesius and I argue that the defence innovation playbook has always been the same — urgent demand, freedom to iterate, and an ecosystem that emerges as the byproduct — and that two active wars in Ukraine and Iran are now making that playbook impossible to ignore for Europe: The Byproduct Theory of Defense Innovation.

• In Currency of Power, my colleague Marieke Flament and I argue that the war in Iran did not merely disrupt oil markets — it severed the financial architecture that Washington was building to replace the petrodollar, and handed China a ten-year head start on the alternative it has been quietly constructing: Petrodollars vs. Electroyuans.

• Also in Currency of Power, Marieke and I interview Sean Neville — co-founder of Circle, inventor of USDC, and now founder of Catena — on why AI agents are the next customers for stablecoin infrastructure, and what it actually takes to build a financial institution for a world where machines move the money: Stablecoins Were Just the Beginning.

• Finally, Marieke and I also examine the de-dollarisation debate, arguing that it misses the point because it treats the dollar as one thing when it is really two — a reserve currency and a trade currency — with different sources of power and very different prospects, and that the real contest is not whether the dollar survives but who controls the settlement layer of the next world economy: The Dollar’s Many Lives.

Now, in today’s edition, I want to explain why America’s innovation economy is not a cultural phenomenon, not a regulatory one, and not simply a matter of having more capital or talent than everyone else. Instead, it is the product of two forces: deliberate state engineering over three decades of genuine urgency, and a macroeconomic structure that continuously floods the US with foreign capital. When those two forces combined, in the 1980s and 1990s, they created something no other country has been able to replicate.

I call the mechanism at the heart of it the overflow mechanism: the process by which foreign capital, flooding into US Treasuries and blue-chip stocks, displaces domestic institutional capital up the risk curve and into venture, financing innovation at a scale that no deliberate policy could produce. Understanding it also explains why China took a radically different path, why Europe spent thirty years competing on the wrong terrain, and why that may be about to change.

1/ America races ahead, and almost nobody understands why

For 17 years I have worked in tech, and for 17 years I have heard the same lament: Europe is falling behind the US, and something must be done about it. The explanations on offer tend to fall into three categories, none of which, on close inspection, holds up.

• The first is cultural: America rewards risk-taking while Europe rewards prudence and caution. But Europe cannot be treated as a single unit in this way. Entrepreneurial culture exists all across the continent, in pockets large and small, and the fact that it does not translate into racing ahead in tech has nothing to do with attitudes toward risk.

• The second explanation is about freedom. Too much regulation in Europe, the argument goes. But Europe is not less free than the US — it is more fragmented, which creates friction where none exists in a single unified market, but that is a different matter. The American business world is at least as regulated, if not more so. What entrepreneurs in the US have, and their European counterparts largely lack, is two things: a Common Law system that lets you move forward with confidence that disputes will eventually be settled in court in an equitable manner, and an enormous supply of lawyers whose expensive and abundant work — what my friend Dan Wang calls a “lawyerly society” — absorbs the regulatory burden so that founders rarely have to think about it directly.

• The third explanation is resource-based: more capital, more talent in the US. But by most measures, Europe has more of both. Talent is more abundant here, and Europe as a whole is a net saver while the US is a net borrower, which means the raw capital exists in abundance on this side of the Atlantic.

What actually explains America’s lead is something else entirely, and it has two parts:

• The first is deliberate state engineering: successive waves of public investment, driven by genuine urgency, that built an entrepreneurial ecosystem from the ground up over three decades.

• The second is macroeconomic structure: the dollar’s role as the world’s reserve currency turns the US into a magnet for foreign capital, generating a structural surplus of risk capital that no other country can manufacture by policy alone.

Together, these two forces created a flywheel that has been spinning faster and faster since the 1980s — and that other countries, whether they copy Silicon Valley’s surface features or pour public money into accelerators, cannot replicate simply by wanting to. China found an alternative path, running the opposite logic with its own coherent model. Europe, for 30 years, competed on the wrong terrain entirely. But the current wave of innovation — hardware, energy, defence, industrial systems — may be the moment when Europe’s particular strengths finally come into their own.

2/ The reason America races ahead is that the dollar makes it a magnet for foreign capital

How the dollar became the world’s reserve currency is a story of war, pragmatism, and raw power.

• The reserve currency foundation was laid at Bretton Woods in 1944, when forty-four nations agreed to anchor the global monetary system to the dollar, pegged to gold at $35 per ounce.

• That foundation cracked in August 1971, when President Nixon unilaterally ended the dollar’s convertibility to gold, freeing the currency from physical constraints and launching a profound reconstruction of global financial markets.

• Then to replace the gold anchor, the Nixon administration struck an agreement with Saudi Arabia in 1974: oil would be priced in dollars, and surplus revenues would be recycled into US Treasury bonds—something known as the petrodollar recycling system. Because the world needed oil, it needed dollars to buy oil, and the self-reinforcing loop was complete.

The system, however, comes with a genuine burden. To supply the world with the dollar liquidity it needs, the US must run persistent trade deficits, importing far more than it exports. The continuous influx of foreign capital keeps the dollar strong, which makes American exports expensive and American manufacturing structurally uncompetitive. As a result, American manufacturing hollowed out steadily from the 1980s onward, and the communities that depended on it paid the price for a privilege they never asked for and rarely benefited from.

But the same mechanism turns the US into what I call America Capital Partners: a vast investment platform that absorbs the world’s excess savings and redeploys them into the most dynamic business ecosystem on earth. Foreign capital flows back into US Treasuries, stocks, and real estate in such volumes that it effectively saturates every conventional asset class. The domestic investors who would otherwise hold those assets — pension funds, university endowments, insurance companies, foundations — find themselves crowded out of the safe end of the market.

• Venture capital is at the far end of that curve. Foreign capital does not typically flow directly into Silicon Valley; it flows into Treasuries and blue-chip stocks, where it crowds out domestic institutions and sends them looking for yield elsewhere. This is the mechanism I call the overflow: foreign capital, by saturating the safe end of America’s financial markets, creates pressure that travels all the way up the risk curve — spilling, finally, into venture capital.

3/ The overflow mechanism explains why only the US has a venture capital industry

To understand exactly how that displacement works in practice, you need to follow the domestic money one level down — to the institutional allocators who actually deploy it:

• The rulebook for those was written by David Swensen at Yale, whose endowment model redefined how large pools of capital should be deployed: a broad allocation between equity and fixed income, then within equity a further split between public markets and private markets, with each bucket sized according to expected returns and liquidity needs.

• Venture capital sits awkwardly within that framework, for three reasons. First, it is too small to model with any precision. Second, it does not follow the normal rule whereby more capital flowing into an asset class compresses returns across the board. Third, as Andy Rachleff has observed, returns are so skewed toward a handful of top firms — which remain largely inaccessible to most allocators — that any quantitative return expectation is essentially fictional for the majority of LPs. You cannot build a serious portfolio model around an asset class where the returns you are counting on are, in practice, out of reach.

• What this means in practice is that venture capital is not really part of a quantitative allocation framework at all. It is, instead, a small discretionary pocket: capital that a large allocator can deploy freely in pursuit of outsized returns, without worrying too much about modelling the outcome. And in normal circumstances, that pocket stays small, because there are plenty of conventional assets to absorb the bulk of the portfolio.

Now apply this to America Capital Partners as a whole. With the US being a magnet for foreign capital due to the dollar’s status, Foreign capital floods into US Treasuries, equities, and real estate, driving up prices and compressing yields. Domestic pension funds and endowments, already crowded out of their preferred safe assets, find that their conventional buckets are overallocated and underperforming. As a result, that tiny discretionary pocket — venture capital — becomes one of the few places left where the return expectations, however uncertain, still justify the allocation.

At the level of any individual institution, that pocket remains small of course. But at the scale of the entire US economy, with trillions of dollars of domestic institutional capital all making the same calculation simultaneously, the aggregate discretionary pocket becomes enormous. And because the expected returns never quite materialise for most of the capital deployed into venture, that surplus remains flexible and versatile, not locked into any particular commitment. It sits ready to meet the moment when a large innovation wave begins to gather — the dotcom boom, the shale revolution, the mobile and cloud era, the current AI build-out.

• This is the overflow mechanism, seen from the inside: every institutional allocator, crowded out of the safe assets they would prefer to hold, finds the same discretionary pocket waiting at the top of the risk curve — and fills it.

4/ Financialisation in the 1970s created the conditions for venture capital to emerge

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Welcome to the sixth edition of the 2026 season of Drift Signal. As a reminder, I serve as Head of Research at Vsquared Ventures. This is my personal newsletter, which examines macro and markets through the lens of late-cycle investment theory, and the views expressed are my own.

Here is some recently published content that could be of interest:

• In Currency of Power, Marieke Flament and I examine how China is open-sourcing its intelligence while America is open-sourcing its currency, and why the two are converging into a new version of an old trap: Wide Open, Locked In.

• Also in Currency of Power, Marieke and I interview Kurt Hemecker, CEO of Gold Token SA at MKS PAMP, on tokenising gold, the lessons of Libra, and why privacy and anonymity are not the same thing: Not Every Gold Token Is the Same.

• My Vsquared colleagues Thomas Oehl and Benedikt von Schoeler report from our recent deep tech ecosystem event in Munich, where the conversation shifted from whether frontier science works to whether it can be industrialised at scale: New Horizons: The Next Chapter.

Now, in today’s edition, I examine OpenAI’s valuation problem through the lens of business history. Starting from Aswath Damodaran’s diagnosis that Sam Altman pitches like a trader rather than a CEO, I draw a parallel with Ray Kroc’s McDonald’s in the early 1960s: a business with genuine scale and a proven system, but a financial structure that could not support its own ambitions.

• The solution, as McDonald’s CFO Harry J. Sonneborn understood, was never about selling more hamburgers. It was about securing a second, structural asset beneath the entire operation, one that every participant in the ecosystem depended on and could not bypass.

I then apply that logic to the AI economy, show why owning infrastructure is not the right answer for OpenAI specifically, and propose that building the global compute market is the asset that could finally give Sam Altman a story worth telling to serious investors.

1/ Sam Altman cannot explain OpenAI’s valuation

10 days ago, I listened to Aswath Damodaran’s recent appearance on the Meb Faber podcast. I highly recommend it because it captures the growing unease surrounding the AI boom. Discussing OpenAI, the “dean of valuation” argues that the company’s valuation cannot be justified by any conventional financial model. In Damodaran’s view, when Sam Altman defends this premium, he pitches like a trader spinning a narrative rather than a CEO explaining a sustainable economic mechanism.

• To Damodaran’s point, here are some numbers. OpenAI generated around $13 billion in revenue in 2025, yet its projected cash burn is rising toward $47 billion by 2028. Despite this capital drain, OpenAI’s valuation has recently crossed the $700 billion mark, implying a price-to-revenue multiple that has absolutely no precedent in the history of software.

AI enthusiasts are quick to dismiss such challenges as luddism. But Damodaran’s discomfort does not stem from a denial of the AI opportunity as a whole. Just as many of us, he recognises the importance of the technological shift. Rather, his concern is rooted in the absence of a credible path from the company’s current, negative cash flows to its stated valuation. The narrative told by OpenAI management relies on the promise of future abundance in general, but the story is missing a mechanism that explains how value will actually be captured and returned to OpenAI shareholders.

This leaves us with an interesting financial puzzle. The question is not whether OpenAI is a valuable company, but rather how it could ever become as valuable as Altman claims.

2/ McDonald’s Ray Kroc once had the same problem

In the early 1960s, Ray Kroc, the man who built McDonald’s into a global brand, found himself in a bind similar to Altman’s. He had built an operation with enormous influence, a proven system (the legendary “Speedee System”, pioneered by the McDonald brothers), and a rapidly growing network of franchisees.

Yet beneath the surface of this apparent success, his financial structure was quietly failing. The McDonald’s brand and its detailed operating manual—rules so precise that, as Paul Graham once wrote, they function like a piece of software—were real and valuable assets. However, Kroc soon discovered that extracting thin commissions from the sale of cheap hamburgers could not generate the cash flow required to fund a sprawling empire.

We can understand Kroc’s predicament through the framework of a business’s two distinct financial loops, which I introduced in an edition published in December 2020:

• What Kroc had built was what I call a “trading loop”: the franchise commissions on burger sales. High in frequency, thin in margin, and wholly dependent on the daily performance of individual franchisees, this stream of cash kept the lights on but could not fund the expansion of McDonald’s at the scale that Kroc envisioned.

• What McDonald’s lacked was a “capitalist loop”: a capital-heavy asset inserted into the production process for the long term, one that would generate increasing returns as the network scaled and give McDonald’s a structural grip over every franchisee in the system. Without it, most of the value Kroc created accrued to others rather than to McDonald’s itself.

As I listened to Damodaran discussing OpenAI’s valuation, I could not help but see a parallel with McDonald’s. Under Altman’s management, the company has successfully built a trading loop—subscriptions, API charges, and a growing base of paying users—on top of a large language model, a software experience and a whole ecosystem that together have captured the global imagination. But under the current subscription model, software and algorithms alone cannot fund the staggering capital requirements needed to sustain them. The question we must ask now is what the much-needed capitalist loop actually looks like in the AI economy.

3/ Capitalism requires more than scale

To understand OpenAI’s current problem, it helps to look to the French historian Fernand Braudel and his three-layer framework of the economy.

At the base sits material life, the informal transactions of daily existence. Above it is the market economy, where merchants engage in relentless, margin-crushing competition. And at the top lies capitalism—an ensemble of processes that allows an enterprise to escape the rigours of the market economy by inserting capital into the production process to pursue increasing returns to scale.

• As the economist W. Brian Arthur defined them in his landmark 1996 article published by the Harvard Business Review, increasing returns represent “the tendency for that which is ahead to get further ahead”. They are the mechanisms of positive feedback that reinforce success, and mastering them is what separates a true capitalist enterprise from a mere business trapped in the competition-driven market economy.

Yet increasing returns alone are not sufficient. For such returns to compound and translate into sizable profits, the business needs those two financial loops that fit together. The trading loop generates a constant stream of cash flow from recurring transactions: burgers sold, subscriptions charged, APIs called. The capitalist loop is slower and heavier: capital inserted into a structural asset for a long period, generating increasing returns that compound over time.

• One clarification matters here. The trading loop is not the same as revenue. It is the high-frequency, market-economy side of the business: real, but thin on margin and constrained by competition. At McDonald’s, the franchise commissions were the trading loop. By contrast, the brand and the franchise system were assets with the potential to generate increasing returns as the network scaled. But Kroc could not realise that potential without a second, structural asset to anchor McDonald’s capitalist loop.

OpenAI faces the exact same problem. Its subscriptions and API charges are the trading loop: the hamburger commission of the AI economy. The model and the developer ecosystem built around it are assets capable of generating increasing returns. But as Damodaran argues, possessing such assets is not enough. Without a capitalist loop to convert them into durable, compounding returns, they remain a story rather than a valuation. That is precisely what is missing from Sam Altman’s pitch.

4/ Harry Sonneborn understood what Ray Kroc did not

Harry Sonneborn, who would go on to become McDonald’s first chief financial officer (and later its CEO), is remembered as the person who first saw the flaw in Ray Kroc’s original model.

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Welcome to the fifth edition of the 2026 season of Drift Signal. As a reminder, this is a newsletter that examines macro and markets through the lens of late-cycle investment theory.

Before we jump into today’s edition, I am delighted to share that I have recently joined Vsquared Ventures as Head of Research. Vsquared is a European deep tech venture capital firm, founded in Munich and operating across the continent, that backs what we call the “New Industrials”—companies combining breakthrough science with next-generation software and physical systems manufactured at scale: batteries, chips, robots, satellites, and more. The firm thinks in systems, across technology risk, capital structure, industrial policy, and sovereign strategy. That combination is rare, and it is precisely where I want to be working as the late cycle unfolds.

• I will continue to publish Drift Signal, and the views expressed here remain my own. The synergies with my work at Vsquared are obvious—late-cycle investment theory is, in many ways, a framework for understanding exactly the industrial and energy transitions that Vsquared backs—but this newsletter remains an independent voice.

A small format adjustment: From this edition onward, the header will carry a brief note on what I am working on, along with links to recent publications worth your time. Today I want to point you to two pieces I published with Marieke Flament for our joint newsletter Currency of Power:

• How Much Does a Higgendorfus Weigh? is a conversation with Michael Green, Chief Strategist at Simplify, on gold, Bitcoin’s fatal contradictions, passive investing, and why tokenisation is a genuine revolution that has nothing to do with crypto.

• The Tokenized Money Stack maps how America, China, the UK, and the EU are each building a different piece of the future monetary architecture—and each blind to the layers it most urgently needs.

Now, in today’s edition, I will examine the war in Iran, its impact on global markets, and its possible consequences through a late-cycle lens.

• A brief disclaimer: I write this letter for investors, mainly in tech, but with a wider macro perspective. The market and economic effects of the conflict are what concern me here. I will not address the nature of the Iranian regime, the political or religious disputes tied to the conflict, the relationship between the US and Israel, or broader questions of religion, politics, and influence. Those topics sit outside the scope of this publication.

My aim here is straightforward. As I argued throughout last year’s editions, we are living through the modern equivalent of the 1970s. What happened in that decade offers the clearest available guide to what may unfold in the coming years across Europe, America, China, and the wider world.

1/ History is rhyming again

On the surface, the US attack on Iran looks like a show of strength, or an attempt at one. Through the lens of my late-cycle investment theory, however, it looks like something else: a mature paradigm leader under stress, reaching for military force because its economic tools are running out.

• It is hard for all of us economic history wonks not to make the parallel with 1979. That year, the Iranian revolution and the foundation of the Islamic Republic struck a global economy already weakened by stalled productivity, rising debt, and the collapse of the Bretton Woods system. Oil prices doubled, then doubled again. The result was stagflation—inflation and unemployment rising together, defying every conventional remedy. US President Jimmy Carter paid the political price and badly lost his reelection bid, to Republican Ronald Reagan.

Today a similar pattern is assembling, piece by piece. The recent US attack on Iran has disrupted the world’s most sensitive energy corridor. Insurers have pulled cover on supertankers transiting the region. Oil above $90 a barrel is adding inflationary pressure to an economy already strained by tariffs, a softening labour market, and the electricity demands of the AI buildout. In America, February payrolls fell by 92,000, with unemployment having risen to 4.4%. Healthcare and social services accounted for the entirety of recent job gains; every other sector contracted.

• Unlike in the 1970s, the Federal Reserve cannot act. With US debt at 122% of GDP and annual debt service approaching $1 trillion, the Volcker option—massively raising interest rates to crush inflation—would trigger a sovereign debt crisis before it brought prices under control. The Fed is paralysed.

This edition argues that the attack on Iran is not an isolated event. It is a late-cycle shock landing on a late-cycle economy—and the consequences will follow the same logic as 1979, albeit with some important differences which investors and decision-makers should keep in mind.

2/ The late-cycle framework points to what comes next

To understand why the attack on Iran matters beyond the immediate headlines, we once again need to revisit Carlota Perez’s model of technological revolutions.

As Carlota wrote in her landmark Technological Revolutions and Financial Capital, every 50 to 70 years, a new techno-economic paradigm follows a predictable arc: a period of chaotic installation, a financial bubble, a phase of broad productive deployment, and finally maturity.

In the maturity phase, the fog of uncertainty lifts. Markets saturate. Productivity gains slow. The institutions built around the paradigm begin to crack under the weight of accumulated imbalances. We are in that final phase now, for the age of semiconductors, computing, and networks.

This is what my late-cycle investment theory means by our living “the 1970s of tech.” In the 1970s, the previous paradigm—the age of oil, automobiles, and mass production—exhausted its underlying productivity gains. Growth stalled. Debt rose. Institutions built for earlier eras stopped delivering. Then several exogenous shocks arrived, including one in the form of the Iranian revolution, and a system already under pressure broke and was gradually replaced by something else entirely.

The structure today is identical. Enormous AI investment is driving electricity costs higher without yet producing broad productivity gains. Public debt sits at levels that remove the standard monetary response. Global supply chains are fragmenting. Institutional coordination is failing.

• When a shock arrives in a late-cycle economy, it finds a system already primed to amplify it. Our being in the late cycle not only explains the consequences, it also explains the decision itself. The same institutional decay that makes the system fragile makes reckless decisions more likely. A leader orders a strike without a plan, without congressional authorisation, without apparent concern for the aftermath, and genuinely believes it will work out. The decay is self-reinforcing: it produces both the action and the steep bill that follows.

The 1970s ended one order and opened another. The same transition is now underway—and the country best positioned to lead in the future might not be the one doing the bombing.

3/ The United States has misread Iran before

The attack on Iran did not arrive without precedent. The US has a long record of misreading this country at precisely the moments that matter most.

In the late 1970s, the Carter administration backed the Shah long after his position had become untenable, maintained almost no contact with the opposition, and then—when his fall became inevitable—convinced itself that Khomeini was a manageable transitional figure. Carter’s UN ambassador called him a “saint”. His ambassador to Tehran compared him to Gandhi.

• The Carter administration, after all, officially promoted human rights as a cornerstone of America’s foreign policy—and by that point, the Shah’s regime had come to represent the opposite. (And as Sarah Paine noted in a Dwarkesh podcast on the Cold War, Carter’s global promotion of human rights also contributed, indirectly, to the collapse of the Soviet Union.)

The French dimension adds another layer. Khomeini spent 112 days in Neauphle-le-Château, a village west of Paris, between October 1978 and February 1979. He arrived there by elimination: deported from Iraq by Saddam Hussein under pressure from the Shah, refused entry by Kuwait, he landed in France because it was the one country an Iranian could enter without a visa.

• President Giscard d’Estaing then permitted him to stay, against the advice of his own intelligence services, calculating (as did others in the West) that political Islam would serve as a counterweight to Soviet influence and that French commercial contracts—including deals with Airbus and Thomson—would be protected. From that modest house, Khomeini recorded the speeches that were copied onto cassettes and smuggled into Iran. And so the Iran revolution was partly organised from a small village in the Perche, a rural, quiet, and picturesque region of France where many Parisians keep their country homes.

As we know now, none of this went according to plan. Within a year, the senior Iranian military had been executed and American diplomats were being held hostage in Tehran. Carter never recovered. His failure to read the Iranian revolution correctly, and then to manage its consequences, handed Reagan the presidency and reshaped American politics for a generation.

The current administration shows the same pattern of strategic blindness, in a different register. The Trump strikes on Iran appear driven more by domestic political pressure than by any coherent strategic calculus. There doesn’t seem to be a plan beyond the bombing itself. Bruno Maçães wrote about it in a recent edition of his newsletter World Builders: Trump loves the spectacle of decisive strength but has no appetite for the consequences of genuine political action in the real world.

The recent strikes on Iran have underscored how asymmetric modern warfare really is, with low‑cost drones, missiles, and proxy actions forcing expensive, high‑tech defences to respond and shifting the battlefield beyond traditional fronts. US operations depend on expensive, high-value assets—F-15s at $97 million each, Patriot interceptors in limited supply. Iran fights with drones that cost $25,000. The economics favour Iran, which just has to wait it out, over America.

More generally, political leaders caught in a late cycle such as ours share a tendency to mistake military reach for strategic clarity. Carter paid for that mistake with his presidency. But the story did not end there. Reagan course-corrected, Volcker finally broke inflation, and the US went on to reap the rewards of a prolonged maturity phase for three more decades, in addition to taking the lead in the next technological revolution—that is, the age of semiconductors, computing and networks.

Put differently, late cycles produce crises, but they also create the conditions for what comes next. The question today is whether the recovery—when it arrives—will accelerate the race toward the next paradigm or, as in the 1970s, merely add more coins to the machine, prolonging the current techno-economic paradigm by a few decades with temporary expedients.

World Builders

A crime and a tragedy: the Iran war

You pounce when the enemy is weak and that is what Israel and the US did. There was no immediate security threat from Iran. Rather the opposite. The threat had never been smaller or more negligible. But the absence of a threat also made the possibility of war a lot more attractive…

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2 months ago · 27 likes · Bruno Maçães

4/ Iran’s nuclear programme began as a French commercial deal

Before asking what comes next, it is worth asking how Iran acquired the capabilities now under attack. The answer, as it were, runs through Paris—not Pyongyang, Moscow, or Beijing.

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Late-cycle investment theory holds that we are living through the 1970s of tech. The age of semiconductors, computing, and networks is reaching maturity, and the symptoms are now harder to ignore: slowing growth, sticky inflation, institutional breakdown, and a concentration of capital in the hands of a shrinking number of incumbents.

• When I introduced this framework in June last year, it was still possible to treat it as a hypothesis. That is less true today. The data arriving from Q4 2025 make the case on their own terms. US GDP growth came in well below expectations. Inflation is running above target. Consumer spending is softening. The stagflation I flagged in August is no longer a prediction—it is now a reading on the instrument panel.

But the framework is not only about what is breaking down. It is also about what is being built in the gaps. The 1970s were not just a decade of crisis; they were the decade in which lean production, financial innovation, and the early personal computer laid the ground for everything that followed. Something similar is happening now, though the geography of that construction has shifted in ways that most Western analysis is still not equipped to see.

China, in particular, demands a different kind of attention. Standard economic comparisons—growth rates, debt ratios, trade balances—typically apply between economies of a certain scale. China, however, has long exceeded that scale, and the frameworks have not kept up. What is taking shape there, in electricity, batteries, robotics, and the industrial base that underpins them, looks less like a conventional emerging market story and more like the early US viewed from mid-19th-century Europe: a mass so large that it is generating its own rules.

• Meanwhile, the financial system is undergoing its own restructuring. Tokenisation is moving from experiment to infrastructure. The boundary between venture capital and credit is blurring as the economy shifts back toward hard assets. And the monetary order is fracturing along a clear fault line: dollar-denominated digital networks on one side, physical gold and parallel settlement systems on the other.

As with the previous progress report, I have organised this edition around ten snapshots. Some are data points, some are structural arguments, and some are uncomfortable signals—including one that Karl Polanyi would have recognised immediately. I welcome your feedback.

1/ Economic data points to emerging stagflation in the US

The latest economic data from the US suggest that the stagflationary pressures I predicted in August last year are beginning to manifest. US Q4 2025 GDP growth came in at a disappointing 1.4%, well below the 3% expectation, with final sales at 1.2% versus 2.6%. Consumer spending remains modest at 2.4%, signalling that households are already feeling the squeeze.

• Meanwhile, inflation remains elevated: the official PCE Price Index rose 2.9% year-on-year, with core PCE hitting 3%—both above expectations. Month-over-month, December PCE and core PCE climbed 0.4%, the highest since February 2025, suggesting renewed upward pressure on prices even as broader macroeconomic growth slows—and Trump seems determined to double down on tariffs, even after the landmark decision issued by the Supreme Court two days ago.

Real-time price tracking from Truflation paints an even sharper picture. Its PCE index reads 1.55%, while core PCE is 1.92%, highlighting that consumers are already paying more at the point of purchase than conventional monthly statistics capture. Some note that these figures precede anticipated price shocks in the second half of 2026, particularly as supply-chain disruptions—such as the memory chip shortage—propagate through consumer electronics and other sectors.

Taken together, these developments indicate the emergence of the stagflation I warned of: growth faltering while prices remain sticky. Unlike the 1970s, however, wage growth remains weak, offering little cushion for households, while energy and technology costs continue to rise. If the current trajectory persists, we may soon witness broad-based inflation without compensating wage gains, confirming that our entering the 1970s of tech is moving from theory toward reality.

2/ China is so large that it defies every analytical framework we have

Most Western analysis of China still looks at growth rates, debt ratios, trade balances. And when it comes to China, the numbers are impressive: a trade surplus heading toward $1.2 trillion in 2025, manufacturing output at nearly a third of the global total, 160 cities with over a million inhabitants. But these tools were built to compare economies of a certain scale, and China has long since exceeded that scale. It should make us wonder: how should we analyse China?

Adam Tooze published an essay last year that made me realise how much China stands in its own category. Look at the coal production curve across all of recorded human history, he argues, and you see three phases: the pre-industrial world, the industrial age from 1750 to 2000, and then something else entirely—a vertical break, within living memory… driven entirely by China’s urbanisation and industrialisation! Adam’s conclusion is as follows: China’s growth is not an instance of a general rule, nor an exception to one. It is, as he puts it, “the ball game”.

Balaji S. Srinivasan recently drew similar conclusions on X: “China is the exception to every rule.” At a billion-person civilisational scale, it can sustain nationalism, near-autarky, and centralised control in ways no ordinary nation-state can. As a result, standard categories do not apply.

• The right comparison, to me, is the US viewed from Europe in the mid-19th century. I wonder how European elites looked at America back then. They probably kept measuring America against frameworks built for older, smaller states, and missed the paradigm shift that sheer mass was making inevitable—that is, America taking the lead in the age of electricity, steel and heavy engineering, then completely reshaping the world in its image in the subsequent age of oil, automobiles, and mass production.

The same mistake is being made about China today. And I believe the late-cycle framework, with its emphasis on manufacturing depth and the seeds of the next technological revolution, is one of the few lenses large enough to take China seriously on its own terms.

Chartbook

Chartbook 402 Dual-circulation: travels through China in the summer of 2025.

Looking back, the first few months of this summer feel to me like a slow escape from the terrible vortex of US politics. This happened in July in China, not in Germany…

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9 months ago · 219 likes · 27 comments · Adam Tooze

3/ China is turning electricity into the foundation of the next technological revolution

As the current age of semiconductors, computing and networks enters its late-cycle maturity phase, I believe China—just like the US in the late 19th century—is laying the groundwork for the next technological revolution, and that revolution will lead us into the age of electrification.

• Historically, every “great surge of development” revolves around a new, pervasive, low-cost input. Today, that input is electricity—increasingly generated by renewables—and its emblematic products are electric vehicles, batteries, drones, and robotics.

Three analytical frameworks help make sense of this shift:

• First, Carlota Perez’s model of techno-economic paradigms reveals how the seeds of a new revolution are planted during the exhaustion of the old. While the West fixates on AI—which to me is an efficiency innovation extending the current computing paradigm—China is leveraging massive scale to trigger the “big bang” of the electrification era, transitioning from a fossil-dependent economy to the world’s first true “electrostate”.

• Second, the concept of “programmable electricity” helps us understand how the new grid will function. In the age of electrification, energy is no longer a static commodity; it is a software-defined network where generation, storage, and consumption are dynamically orchestrated by the most advanced version of software—that is, AI.

• Finally, Sam D’Amico and Noah Smith’s “Electric Tech Stack”—comprising lithium-ion batteries, electric motors, power electronics, and semiconductors—provides a lens for understanding the industrial base in the age of electrification. This stack serves as a universal, modular template for modern hardware, emerging as the modern equivalent of the American system of manufacturing. Also see a variant: the “Electro-industrial stack”.

Through these frameworks, it becomes clear that future geopolitical power belongs to those who master the physical generation and intelligent distribution of electricity.

4/ The AI bubble is real, but it is the wrong kind of bubble

A recent essay by Speedinvest’s Sameer Singh deserves attention from anyone following the late-cycle framework. In it, Sameer distinguishes between mid-cycle bubbles, which leave behind useful infrastructure, and late-cycle bubbles, which trigger overconsumption of the input that powered the whole surge. This, like my own work, maps onto Carlota Perez’s model.

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Welcome to the third edition of Drift Signal’s 2026 Season!

In the 1970s, the “Nixon Shock” proved that even the most ‘permanent’ institutions—like the gold standard—can vanish overnight when they no longer serve the stability of the global economy. Today, we find ourselves in a similar maturity phase, where institutions that seem to have been here forever are beginning to crack under the weight of their own imbalances.

The following edition explores why the Epstein scandal has resurfaced in 2026 not merely as a collection of horrific crimes, but as a “tool of demolition” for a public that has lost faith in traditional reform. By drawing parallels to the Watergate era, today I examine how institutional exhaustion and late-cycle hubris turn dormant scandals into the primary levers for systemic reset.

1/ The Epstein scandal exposes a broken system

The Epstein files now dominate media coverage. Their impact is arguably large. People from finance, business, politics, science, and the arts appear across the material—notably the emails. Yet almost none of them has had to answer in court. Apart from a few cases such as Ghislaine Maxwell in the US and the man formerly known as Prince Andrew in the UK, there has been no real public reckoning. This lack of reckoning partly explains why the scandal has been lingering for years and won’t go away, but it also suggests something deeper than failure. It’s no surprise that everyone concludes as follows: it’s all a cover up and a long effort to shield powerful people.

‘Why no reckoning?’ is a fair question. The facts have been known for years. Epstein was first convicted in 2008 for soliciting sex from a minor. He served a light sentence, returned to public life, and continued—not on his own but as part of a wide network of rich and powerful people. Then he was arrested again and died in custody in 2019. Seven years after his death, the silence over human trafficking and dozens of women, many of them underage, being paraded and raped by Epstein and his clique covered two Trump years and the full Biden term.

My (cynical) view is that the deeper reason for the current force of the scandal lies not in the facts themselves, however horrific, but in the state of institutions. As of 2026, public trust is badly eroded. Courts, media, and elite networks no longer command belief. And when people see evidence of abuse and protection at the highest levels, they no longer accept delay, process, or silence.

As a result, outrage now escapes control. The Epstein case has become proof that the system itself is rotten. It stands for decades of immunity for the powerful and neglect of the weak. The files expose what many already believed, and they do so at a moment when belief in restraint has collapsed.

2/ Economic cycles drive institutional collapse

My late-cycle investment theory is based on Carlota Perez’s model of techno-economic paradigms. According to Carlota, every 70 years or so, a technological revolution gives rise to a new paradigm. That paradigm drives growth, speculation, and eventually a financial bubble. When the bubble bursts, the state must step in to build social institutions aligned with the new system. Once in place, these support a golden age of growth. Eventually, the paradigm reaches maturity: markets saturate and imbalances accumulate. At that point, institutions once considered permanent begin to crack.

An example is the gold standard, a key institution initially set up in the 19th century. In 1944, as the Second World War was coming to an end, the damaged gold standard was deemed so critical that it was restored at the Bretton Woods Conference. For decades spanning the 19th and the 20th centuries, the logic of pegging currencies to gold went unquestioned; it was the “forever institution”, and dispensing with it was simply unthinkable for both policymakers and business people.

• Yet during the maturity phase of the age of oil, automobiles and mass production, global economic pressure pushed the Nixon administration in 1971 to end the dollar’s link to gold. The “Nixon Shock” ended a system that had shaped money for generations and seemed permanent. It had been set up in the 19th century, not during the current age of oil, automobiles and mass production, and had delivered great prosperity. When Nixon decided to pull the plug, the break was sudden and complete—but it ultimately allowed the global economy to thrive in the coming decades! Maturity phases bring exactly this kind of change. Institutions that seem eternal can vanish overnight once they start to undermine stability, and only then do we realise we can do without them.

Now we have again reached a maturity phase—that of the age of semiconductors, computing and networks. Like during the previous maturity phase (the 1970s), imbalances run deep, and society demands radical answers. Scarcity and conflict loosen political limits and fuel a drive to try new approaches. Society searches for ways to fix the failures of a mature and exhausted techno-economic paradigm, with consequences that will shape institutions, markets, and power for decades to come.

3/ Society turns to radical levers during maturity

In the late cycle, society looks for levers to release pressure. In particular, when pain rises and institutions fail to adapt, voters turn to radical options. The election of Barack Obama in 2008 reflected this demand for a break from the old order after the financial crisis. However, while he delivered some reforms such as the Affordable Care Act, he generally governed with caution. The system remained essentially intact, and the changes fell short of public expectations.

That disappointment led to a search for something more disruptive. Trump’s election in 2016 was a signal: many voters wanted a shock to the system and rejected careful reform as a result. Trump, a complete outsider, was meant to test the limits. However, his presidency failed to meet that hope. He mismanaged key moments, especially during the pandemic, and lost in 2020. Biden followed. His policies were less cautious than Obama’s, but his style was reassuring and familiar. For many, this again felt like too much continuity and too much compromise.

• That set the stage for Trump’s return in 2024. This time, expectations were higher. Voters wanted destruction of the old framework. Instead, however, victory produced hubris. Trump treated victory as proof of invincibility. He focused on personal gain, loyalty, and revenge. Rather than breaking the system, he ultimately looks like he benefits from it.

This brought us closer to the breaking point. People who had considered or accepted Trump as a tool for radical change now see him as an obstacle. The question then becomes how to strike at the system that protected him and many others.

• And this is where scandal enters. The Epstein files offer exactly what is needed. They point to crimes—human trafficking and pedophilia—that provoke universal anger and implicate elites across sectors. And if radical leaders such as Trump will not tear the system down after all, then such a universally appalling scandal will be used to do it instead.

It’s not the first time this has happened. In fact, the current breakdown follows a historical rhythm that became visible during the last great shift in the American institutional order.

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Welcome to the second edition of Drift Signal’s 2026 season!

So much is happening in the global economy right now that it can feel almost impossible to keep up. Every week brings new insights, striking analyses, and ideas from some of the sharpest writers and thinkers in economics, finance, and technology. As I myself struggle to absorb all of these perspectives, I realised that for this edition, I needed to step back and offer a broader synthesis.

The essay below is my attempt to do just that: a brief history of the global economy from 1908 to 2026, tracing the successive technological revolutions, the institutions that emerged around them, and the forces now shaping the transition into the next era. It draws on the insights of others but also actively builds on many threads of my own work over the past ten years. Concepts and frameworks I’ve developed in earlier editions and essays are woven throughout, providing continuity while connecting them to what I see as the next inflection point—the emerging age of electrification.

Think of this edition as both a reference and a narrative: a way to make sense of the overdriven, saturated global economy we inhabit today, while also tracing the structural patterns that will determine who leads, who adapts, and who falls behind in the coming technological revolution.

1/ America establishes industrial leadership through mass production

Carlota Perez describes long periods of economic transformation as “great surges of development” triggered by specific technological revolutions. In her framework, a technological revolution introduces a cluster of new technologies organised around a pervasive, low-cost input. This, in turn, gives rise to a new techno-economic paradigm—a shared logic that reshapes how goods and services are produced, firms are organised, and capital is allocated. When this paradigm eventually aligns with an appropriate socio-institutional framework, it can support a sustained surge of growth.

• In her landmark book Technological Revolutions and Financial Capital, Carlota identifies the fourth technological revolution—fuelled by oil—as beginning in 1908 with the introduction of the Ford Model T. In her view, this moment marked the transition to the age of oil, automobiles, and mass production. It displaced the earlier paradigm centred on electricity, steel, and heavy engineering, replacing it with Ford’s assembly line and standardised parts, often referred to as the American system of manufacturing.

By combining scientific management with interchangeable components, American firms achieved unprecedented returns to scale. Productivity gains became self-reinforcing: larger assembly lines lowered unit costs while expanding the consumer market, setting in motion the economic and institutional dynamics that would define the twentieth century.

America also pioneered the socio-institutional framework that transformed the new techno-economic paradigm into a sustained surge of growth. During the New Deal, the US developed the regulatory and social structures necessary to manage this new capacity. Henry Ford’s famous, early decision to pay workers five dollars a day reflected an emerging logic: employees had to be consumers as well. From 1933 onward, the US government under Democrat Franklin D. Roosevelt established institutions such as Social Security, unemployment benefits, and the Wagner Act of 1935, along with other New Deal measures. These reforms eventually played a key role in creating a radically new American middle class by providing steady income and labour protections.

This virtuous circle of high wages and mass consumption created the modern way of life and finally stabilised the economy, starting in America. Cheap petroleum was central to this process: it powered the factories that drove industrial production whilst fuelling the automobiles, heating, and energy needs that made mass consumption possible. Cities such as Detroit grew into major industrial hubs, linking energy, production, and everyday life in ways that reinforced economic expansion.

A few years later, World War II converted this manufacturing might into decisive military supremacy. As the “Arsenal of Democracy,” American factories pivoted to produce 300,000 aircraft and millions of vehicles, dwarfing the output of the Axis powers. By 1944, the US held 70% of the world’s gold reserves and, unlike European countries, possessed an intact, high-capacity industrial base. This dominance allowed the US to dictate the terms of the post-war order, shifting global trade toward American supply chains and establishing the dollar as the primary medium of international exchange.

2/ America then designs a new international economic order

The Bretton Woods Conference of 1944 sought to prevent a return to the chaotic currency devaluations and protectionism of the 1930s.

• Led by figures such as Harry Dexter White, the US used its industrial and financial leverage to establish the dollar as the anchor of the global economy. Under this system, the dollar was pegged to gold at $35 per ounce, while other currencies maintained fixed exchange rates against the dollar. This framework eliminated exchange rate uncertainty, providing the stability necessary for international trade and long-term investment to flourish. The creation of the International Monetary Fund and the World Bank provided the institutional oversight required to manage this new monetary hierarchy.

This vision was executed by a group of architects often called the “Wise Men” (after a book by Walter Isaacson and Evan Thomas)—political and financial figures such as W. Averell Harriman, Dean Acheson, and John McCloy—who under Harry Truman and his successors blended diplomatic strategy with economic pragmatism. Through the Marshall Plan, the US injected $13 billion into Western Europe to rebuild war-torn infrastructure and stabilise foreign markets.

This aid was more than a transfer of wealth: it also required Western European nations, including Germany, to adopt American-style market systems and use dollars for procurement. By making aid conditional on regional cooperation and on Europeans coming together in what would eventually become the EU, the US integrated a fragmented Europe into an “Allied Scale” (a concept coined by Rush Doshi), capable of supporting the Western security apparatus and coordinated trade policies.

• These interventions established the liberal international economic system that would dominate the post-war era, with the US acting as the ultimate provider of liquidity and security. By opening its domestic markets to exports from Western European countries and Japan, the US fostered a global environment of interdependence.

Ultimately, the alignment of fixed exchange rates, American oversight, and reconstructed trade partners created the ideal conditions for what Carlota calls the “synergy phase” of the fourth technological surge, in which financial capital, institutions, and production aligned to allow new technologies to diffuse widely. These foundations ensured that the ensuing decades were defined by strong growth and the expansion of the middle class across the Western alliance.

3/ Industrial expansion fuels a global golden age

With the post-war monetary and trade framework in place, the techno-economic paradigm of oil, automobiles and mass production expanded at full scale across the Western economies.

During this ‘Golden Age,’ or the Trente Glorieuses (as they’re known in France), growth was strong enough to double living standards within a single generation. Productivity and real wages rose together, driven by large, hierarchical firms organised around scale, stability, and long investment horizons. These corporations followed a “cathedral” model, combining vertically integrated production with institutionalised innovation in vast research laboratories such as Bell Labs. Governments reinforced this expansion by building the energy, transport, and communications networks required to sustain industrial scale.

• The economic logic of the period rested on a virtuous circle of mass production and mass consumption. To keep assembly lines running smoothly, firms entered a long-term compact with labour, offering high wages and job security in return for loyalty and discipline. This arrangement produced the “Great Compression,” when income inequality fell to its lowest level of the twentieth century. Middle-class growth spread across Western economies, supported by collective bargaining, social insurance, and a financial system oriented toward household stability through mortgages and limited instalment credit for cars and appliances.

As this model diffused, prosperity extended beyond the US. Western Europe and Japan adopted the American industrial system, adapting it to local conditions. Japan, in particular, combined state-directed capital with powerful trading houses to rebuild around heavy industry and electronics, achieving annual growth rates close to 9%.

Although multinational firms largely operated as “multi-domestic” entities, replicating production and management across national markets, they started establishing the trade links and capital flows that would later sustain a more volatile phase of global integration.

4/ The Nixon Shock accelerates the rise of global value chains

In August 1971, President Richard Nixon unilaterally suspended the convertibility of the US dollar into gold, effectively dismantling the Bretton Woods system. Faced with rising costs from the Vietnam War and expanding domestic social programmes, Nixon nonetheless chose not to devalue the dollar. Instead, by suspending its convertibility into gold, he effectively unpegged the global economy from any hard asset. The shift transformed the international monetary order from a gold-backed system into a fiat regime underpinned by American economic and military power.

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Welcome to the first edition of Drift Signal for 2026!

• This year, I am committing to two deeply researched and thought-provoking editions each month, or roughly twenty-five across the year. If there is a topic you would like me to dig into later this year, feel free to reply with ideas 🤗

A few weeks ago, I was invited as a guest on Demetri Kofinas’s Hidden Forces podcast. Here’s the link to the full episode in case you would like to listen 🎧 One highlight of my conversation with Demetri was our discussion on the social contract in the age of computing and networks. It is a subject I wrote about often in the late 2010s (even a whole book!), but one I have not explored in depth since 2020.

That conversation made me realise that this question has not gone away. If anything, it has sharpened. There is this growing view that current progress in computing and networks (that is, AI) will make large parts of the workforce redundant while pushing wealth into ever fewer hands. This view is now common, and it is voiced not only by critics but by prominent tech leaders themselves.

My first reaction to this idea is…deep skepticism. The claim that technology drives labour toward irrelevance, destroys demand, and leads to feudalism echoes Marx’s theory of technological determinism. Marx argued that the development of productive forces under capitalism would eventually create a conflict with existing social relations, concentrating wealth and displacing labour. Yet history has not borne this out in the simple way he predicted. While technology has indeed transformed work and displaced certain jobs, new industries, markets, and forms of labour have always emerged instead of rendering work irrelevant or returning society to feudal structures.

• Still, the more I thought about it, the more I realised why this idea feels convincing. It’s not that the logic is inevitable—it’s that we don’t know where to look for an exit. That uncertainty makes the whole system seem unavoidable, even if it isn’t.

This edition follows that line of thought. It seeks to show where the limits on wealth concentration now lie and why capitalism will always correct itself, regardless of our current outlook. In short, the system must still reach a limit—and I expect the change to come through a mix of currency moves, a sharp fall in consumption, and ultimately a (potentially brutal) political backlash.

1/ Capitalism rewards those who escape competition

Capitalism, in its purest form, is about escaping the market. While standard economics praises perfect competition, the historian Fernand Braudel defined capitalism as “attenuating the rigorous competition in the market economy by ‘inserting’ capital into the production process”. Many later echoed this view. Peter Thiel famously summed it up in his book Zero to One when he wrote that “competition is for losers”. The aim of any ambitious capitalist firm is to reach ‘escape velocity’, moving beyond price wars and thin margins into a position of full control.

To understand the capitalist journey, we must read the economic landscape through two opposing faces, each shaped by distinct unit economics:

• The ‘Southern Side’ is the domain of intangible assets such as software, money, and intellectual property. The unit economics rest on high fixed costs and near-zero marginal costs. The key feature here is increasing returns, where output grows faster than inputs. Value can rise through supply-side scale and/or demand-side network effects, as early spending on research and infrastructure spreads across a vast and growing base of users or transactions. The further a firm pulls ahead, the faster profits and value grow.

• The Northern Side is the world of physical assets and institutions: factories, labour, logistics, and regulation. The unit economics combine high fixed costs with heavy variable costs. Although there are increasing returns in manufacturing (Wright’s law), each new site, such as a warehouse or plant, adds complexity, risk, and expense. This is why, beyond a certain point, these activities face diminishing returns, as the cost of managing size and physical flow eventually exceeds the gains from further expansion.

It is rare to operate on the Southern Side alone, but capitalist power emerges when gains from the Southern Side more than offset the weight of the Northern Side. This success, however, cuts both ways. Nations that host these Southern Side giants, such as the US with Silicon Valley’s tech, Wall Street’s financial services, and Hollywood’s IP, often end up in a hegemonic dead end. By favouring the export of bits and financial claims over atoms, they drive a structural hollowing out of domestic manufacturing, whose economic returns cannot compete with those of Southern Side businesses.

2/ The global thermostat is failing

Standard economic theory promises a ‘self-correcting’ world, where exchange rate discipline acts as a global thermostat. In a textbook economy, success at doing capitalism triggers capital inflows, which appreciate the currency, make exports more expensive, force a rebalance, and ultimately impose hard limits on wealth accumulation in any given place.

But this logic doesn’t apply to today’s America. When a nation’s primary exports are no longer tons of steel but lines of code and claims on global income, the thermostat breaks.

• This “Dutch Disease” of the age of computing and networks explains why the US will have a hard time reindustrialising. Because the world requires massive dollar liquidity—the dollar still being the dominant reserve currency—demand for it is inelastic. When the US tries to reduce outflows through tariffs for instance, it only creates a global dollar shortage. This strengthens the currency further, making American atoms (manufacturing) permanently uncompetitive while reinforcing the dominance of its bits (Southern Side assets).

As I wrote last year, the US now operates less like a nation-state and more like a global investment firm—America Capital Partners. Thanks to the dollar’s “exorbitant privilege”, America captures fees through the erosion of the dollar’s value (= inflation) and carried interest through asset appreciation (= the booming stock market). Just as 19th-century Britain maintained dominance through “invisible exports” while its industrial base declined, the US now relies largely on extracting value from the rest of the world through businesses—tech, finance, and IP—concentrated on the Southern Side.

• And this explains the current accumulation of wealth by the American few. In this winner-takes-most US economy so concentrated on the Southern Side, increasing returns easily bypass any distributive promises left. Wealth no longer spills over through markets or the institutions designed to curb its excesses, such as collective bargaining—which mostly exists on the factory floor, not elsewhere in the economy. Instead, wealth concentrates within the American ‘Owner’ class, those who own and operate business on the Southern Side, creating a system where success drives structural distortion rather than stability.

3/ Manufacturing has a natural ceiling

In the physical world of atoms, currency still rules and acts as a balancing mechanism. Manufacturing provides the clearest example of where textbook economic discipline still functions because it deals in tangible goods, where price competition is fierce and margins are thin. In this sector, a 10% swing in the exchange rate can instantly erase a decade of productivity gains.

This creates a natural ceiling: if a nation becomes too successful at exporting goods, its currency appreciates until its factories become uncompetitive. Maintaining a prosperous industrial engine is like running a race in an airtight room. If you run too fast, you consume the oxygen (the currency rises) and eventually collapse (exports slow down). This, in turn, imposes a limit on the concentration of wealth in the hands of industrialists.

Manufacturing powerhouses like Germany and China have survived not by stopping the race but by installing secret vents to keep oxygen levels artificially high:

• Germany is a freerider in the eurozone. By sharing the euro with less competitive neighbours, Germany keeps its currency weaker than a standalone Deutsche Mark would be. It pairs this with active wage restraint, keeping domestic labour costs below productivity gains.

• China implements widespread financial repression and currency management. Artificially low interest rates transfer wealth from household savers to subsidise strategic manufacturing. In addition, strict capital controls and an undervalued renminbi anchor this export-led model by preventing domestic wealth from fleeing.

For the US, meanwhile, the manufacturing train has long left the station. As the issuer of the global reserve currency, America must supply dollar liquidity to the world. This monetary setup, however advantageous, makes defending America’s domestic manufacturing sector impossible: any attempt to shield Northern Side industries operated by American ‘Makers’ triggers dollar scarcity, strengthens the currency, and accelerates the hollowing out of American atoms. Hence the system leaves the US reliant on its current Southern Side dominance—software, finance, and IP—because the structural conditions forbid a return to industrial self-sufficiency. And as a result, the only way forward for America is to double down on the Southern Side and thus to serve the interests of its Owners.

4/ Owners control the system

To be clear, ‘Makers’ in this edition are those who operate the Northern Side of physical production and are tied to factories, labour, and the limits of tangible goods (think: Boeing and Detroit). By contrast, ‘Owners’ dominate the Southern Side of software, financial services and IP, where scale, mobility, and legal control allow wealth to grow largely unconstrained (think: Silicon Valley)1.

This distinction is crucial because it explains why some actors can accumulate extraordinary power while others remain trapped in competitive, low-margin markets.

Owners operate in a space where the usual constraints of currency, local regulation, and industrial cycles barely apply. While a Maker is a prisoner of manufacturing’s unforgiving unit economics, an Owner is a landlord of their own network. Controlling a global software platform, a leading AI standard, or a top-tier luxury brand gives them advantages no Maker can match. Their wealth is embedded in assets that generate returns anywhere in the world, and their position compounds over time.

The key features of the Owners’ advantage are:

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In January 2025, I rebranded this publication from European Straits to Drift Signal, opening a new chapter with the edition titled From Startups to What’s Next.

I chose the name ‘Drift’ to reflect how the world appears to be moving sideways or even unravelling in an increasingly uncertain environment. ‘Signal’ represents my commitment to cutting through the noise to provide actionable insights for those ready to move in business, finance, and policy. With this change, I shifted away from a primarily European focus to adopt a global perspective, as the most critical developments today often transcend regional boundaries.

• Since that reset, I have used Late-Cycle Investment Theory to explore a world that I believe is living through the structural equivalent of the 1970s for the age of computing and networks. My essays have traced the emergence of a New Industrial Order, where value is moving away from pure code toward the tangible systems that make productio…

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Last week, I dissected the American operating system—a high-variance machine that attracts extraordinary talent and capital but now traps them in dysfunction.

My goal was to explain how the mechanisms that drive US innovation also create a “lock-in” that blocks industrial capacity, leaving the country reliant on crises to force necessary change.

Next week, I will turn to Europe, exploring how a continent defined by borders and deep cultural differences might finally learn to turn its fragmentation into a strategic asset.

But today, we land in the middle to examine the most complex and misunderstood player of them all: China. This is the second installment of my end-of-year trilogy on the world’s great powers.

Western observers often fail to grasp China because they judge it by the wrong metrics—quarterly GDP growth or stock market efficiency. This kind of misses the point. As we will see, China is not a market economy with a government attached; it is a managed engineering state (echoing Dan Wang) optimised for survival. From the trauma of the Opium Wars to the dominance of the electric vehicle supply chain, the Chinese system is built to ensure that the Century of Humiliation never returns. It prioritises control over efficiency, and national stability over individual freedom.

Here are ten systemic lenses to help you see the machine as it actually is 🇨🇳

1/ China acts to prevent a return to humiliation

To understand modern China, start with loss. From the Opium Wars through the Japanese invasion, China once lost land, wealth, and control. Foreign powers forced drugs on its people, seized ports, burned palaces, and carved out legal rights on Chinese soil.

This history sits at the centre of how both the Chinese state and Chinese society as a whole see risk, power, and time. The shared aim since the 19th century has been simple: never again accept weakness in front of others. Strength, unity, and control follow from that aim. The fact that China is, at least facially, a communist regime is almost irrelevant. Any government in charge of the country today would pursue exactly the same objectives.

This lens also cuts through the tired question of whether China is socialist or capitalist. China works through something else:

• China is highly entrepreneurial. New firms form fast. People copy, adapt, and push ideas into the market with speed. Both legendary investors Paul Graham and Michael Moritz note that the drive to start and build in China rivals, and may exceed, that of the US. Historian Stephen Kotkin makes an even sharper point: to him, the economic gains since the late 1970s came from Chinese people, not the Party. Deng Xiaoping’s key move was to step back after the disastrous Mao era, and let China’s entrepreneurial drive work and deliver.

• China is also deeply pragmatic. Policies are tools, not beliefs. Leaders test, keep what works, and drop what fails. Labels matter less than results. This is probably Deng’s most important contribution to what China has achieved over the past 60 years.

All of this serves a deeper motive. China seeks to restore control and standing after a long period of enforced weakness. Seen this way, its behaviour is coherent, persistent, and rooted in memory. This is the first fact to grasp if you want to understand China today.

2/ China solved economic development step by step

When Mao died in 1976, China was poor, inward, and stalled. It lagged far behind the world it faced. The Party knew this. Deng Xiaoping’s response from 1978 was not a grand plan, but a change in method. He kept party rule, then solved the economic problem one step at a time:

• The first step was agrarian reform. The state broke up collective farming and let families keep the surplus they produced. Output rose fast and hunger eased, delaying labour moving off the land and toward the yet-to-be-industrialised coastal cities. Joe Studwell shows this move as the base of every successful Asian development story.

• Next came industry, but only in small spaces. Deng set up special zones, with Shenzhen the best known. These zones let firms export, hire, and price with far less control. The rest of the country stayed under tight rule. What worked spread. What failed stopped. Access to rich markets forced firms to meet price and quality tests. Copying came first, improvement followed. This path, which Studwell calls “export discipline”, built skill fast.

• Then there was the financial repression (about which I wrote in greater detail in an earlier edition). Money stayed under state grip. Banks paid savers little and lent cheap to favoured firms. The currency stayed weak. Capital flowed to factories, not homes or finance. Under financial repression, returns mattered less than scale and learning.

The Party also changed how it judged itself. Cadres rose by hitting growth targets, not by quoting doctrine—all under a regime providing longer-term perspectives through the succession of five-year plans and a clearly laid-out succession plan as to who would become paramount leader (which, to be accurate, current paramount leader Xi Jinping has consistently dismantled).

3/ China’s size shapes everything it does

China is not just a big country. It is closer to a world of its own. Back in the day, my source for such statements was Martin Jacques’s masterful When China Rules the World, but more recently Adam Tooze also wrote about it in a riveting edition of his newsletter Chartbook.

Indeed, the scale of modern China is unprecedented. It has 160 urban centres with more than a million people, 18 megacities over ten million, and nearly all of its housing stock built since the 1980s. By comparison, the US has ten cities above a million and only two megacities (New York City, and Los Angeles). China’s sheer population and urban mass reshape everything: the way cities are planned, factories built, and energy consumed.

Scale extends to industry and energy. BYD alone employs over 900,000 people. China produces nearly 30% of global electricity and dominates solar panel manufacture. Adam notes that China’s rapid industrialisation and energy use have “blown away every conceivable alternative centre of relevance in the last twenty-five years.” The size of the population and domestic market gives the state leverage to experiment, invest, and scale at a speed and scope impossible elsewhere.

• This mass is paired with a unique, state-driven approach. China’s “engineering state” (to quote Dan Wang again) blends strong oversight with private initiative, applies rigorous export discipline, and channels savings into strategic sectors. For Chinese leaders, policy is “a process rather than a program,” as Henry Kissinger once analysed in his book On China—incremental and iterative. The Party maintains control while capturing real-time knowledge of the economy and making sure to respond when it can.

China’s scale is exceptional in every sense. Its size defies conventional statistical models and resists classification within any preexisting category. Understanding China requires recognising that its industrial, urban, and economic strategies are shaped first and foremost by its sheer magnitude. In short, China is not just “big”—its vastness is the framework through which all of its development must be interpreted. This perspective is often unfamiliar to the West, where analyses tend to rely on models and categories designed for much smaller nations.

4/ China is building the world’s first electrostate

I met the China analyst Arthur Kroeber, head of research at Hong Kong-based asset manager Gavekal, in New York in May. At the very end of our conversation he said (quoting from memory):

By the way, there’s a topic we haven’t discussed, but which I think is the most important trend in the world right now: China has just passed France in terms of electricity’s share of final energy consumption… and that will change everything.

A few days earlier I had read venture capitalist Jonas Janssen’s essay on electricity becoming programmable infrastructure. And days later the Financial Times published an article describing China as “the first electrostate.”

Hearing the same point three times in a week convinced me: Electrification, starting in China, is absolutely the defining technological trend of our time.

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The Trump administration’s recently released National Security Strategy has surprised both commentators and allies. It names Europe as a primary threat, sets out a sweeping agenda to reshape allies’ politics and societies, and signals ambitious goals. Yet, as Henry Farrell noted in an essay published yesterday, the US government lacks the institutional capacity to implement these plans. The priorities outlined cannot be executed coherently, and attempts to do so risk contradiction, confusion, and strategic misfires.

This disconnect between ambition and execution is not unique to foreign policy. It reflects a deeper feature of the American system: the mechanisms that once attracted and concentrated talent and capital in support of innovating and building now trap them in place, while the structures necessary to coordinate and build effectively remain underdeveloped or broken. Over time, this has produced extraordinary innovation, wealth, and global influence—but it has also created a “lock-in” that impedes adaptation, industrial expansion, and coherent national action.

In this week’s edition, I explore ten ways to think about America’s structural dynamics—from its role as the ultimate destination for people and capital to the constraints on industry, energy, and finance, and the policy and fiscal pressures that define its current era. Taken together, these lenses show why the US continues to innovate, why it struggles to build simultaneously, and why strategies for much-needed change are often blocked by coordination failures at the system’s core.

1/ America is the ultimate destination for people

Back in 2020, as I was dedicating myself to understanding the essence of America, I wrote an edition of this newsletter framing the US as the “final destination.” Unlike Europe, where people always retain the option to leave and return, the US is a place you move to with the intent—or necessity—of staying. Bridges are burned, and the system expects permanence.

• The scale of America helps explain why this worked for so long. As an old English teacher of mine once said, the key to understanding the US is that it’s a very large country. Specifically, there is physical and cultural space for people to live apart from one another, forming small communities of like-minded individuals, all under the same flag.

As always, Albert Hirschmann’s famous framework of Exit, Voice, and Loyalty is a useful lens for this dynamic: Americans can rely on voice—speak up, protest, or dissent—without blocking others from living their lives. Loyalty is deeply embedded in national life through symbols, institutions, and rituals, from the pledge of allegiance to civic narratives of shared destiny. Exit, by contrast, is unnecessary and therefore made difficult: the country is large enough, and opportunities plentiful enough, that most people never need to leave.

The Godfather, a story about immigrants in America, illustrates this well. Young Vito arrives seeking a new life. Later, Michael goes to Sicily, briefly reconnecting with the motherland, but the scale of opportunity in the US makes going back pointless. For the Corleone family, as for anyone else, America is the land where you stay, where you build, and where you are pulled in—sometimes into paths you did not expect, but paths you cannot escape.

Over time, this American lock-in became institutionalised—deeply embedded in law and policy. US taxation exemplifies it: citizens owe taxes no matter where they live, and formally renouncing citizenship is costly and rare. Historically, this generated immense benefits: immigrants who treated America as a final stop worked hard, took risks, founded businesses, and staffed new industries.

Today, however, the system is under strain. As Noah Smith has been discussing recently, social media has collapsed the buffers that once allowed Americans to coexist relatively peacefully, turning voice into constant conflict. Loyalty has weakened as a result, yet exit remains almost impossible because lock-in is a pillar of American society. The result is today’s America: a society designed to be a final destination, now struggling under the pressures of dissent and division, unable to find an escape.

2/ America is also the ultimate destination for capital

Just as America is a final destination for people, it also functions as the ultimate destination for global capital. As I wrote earlier this year, the US operates less like a conventional nation-state and more like the world’s largest investment firm—what I then called “America Capital Partners”.

Trade deficits, often seen as a liability, are in fact a deliberate feature. When Americans import goods, dollars flow abroad. Foreign entities—in China, Germany, or Japan—must then decide where to store those dollars—and they invest them back into US Treasuries, stocks, and real estate, effectively recycling capital into America’s economy. In this system, the US captures value much like a private equity firm: the “management fee” comes from mild dollar depreciation and inflation, while the “carried interest” emerges through asset appreciation and dominance in finance and technology.

This creates a powerful feedback loop. Capital inflows make the US the world’s investment destination of last resort, pushing domestic investors toward riskier ventures that fuel innovation and growth. Foreign nations, acting as America’s “Limited Partners,” gain safe returns while the US secures the funding necessary for government operations and new industries at low cost.

Yet the system is fragile: credibility, transparency, and continued trust in the dollar are essential. As with America as a destination for people, the US must remain the most attractive place to invest—otherwise, global capital could find an exit, and the model collapses.

3/ Talent and capital make the US a factory for innovation

Put these two features together—America as the final stop for people and for capital—and you get a system built to produce new things. The country draws in ambitious newcomers who expect (and are expected) to stay. It draws in global savings that need a home. Most places only get one of these flows. The US gets both, and they reinforce each other. People arrive ready to take risks. Money arrives looking for returns. The result is a place where trying something new feels normal.

• Paul Graham captured this dynamic back in 2006 when he wrote that to build a real technology hub “you only need two kinds of people… rich people and nerds.” His point was that these two groups are the limiting factor. If they meet in the same place, “startups condense,” and the rest of the entrepreneurial ecosystem forms around them.

America scaled that idea far beyond any single region. Immigration brings in people who have already taken the biggest risk of all by moving. The financial system channels foreign capital into domestic assets, which pushes investors toward ventures with higher upside. Universities pull in young talent from around the world, and labour mobility lets ideas and skills move around fast.

• Innovation still clusters, but it clusters in a country built as a final destination. People come and stay. Capital comes and stays. Founders start companies, successful founders fund new ones, and each cycle attracts more talent. This is the chain reaction Paul pointed to, except powered by global inflows of both labour and capital. What that means is that America’s edge is structural: a system that locks in people and money will produce more attempts at new things, and enough of them will work and turn into wealth and power.

4/ The dollar as reserve currency and financialisation constrain what America can build

America’s system produces innovation at a global scale, yet it struggles to turn ideas into physical production. The US excels at generating new companies, technologies, and financial returns in general, but its ability to rebuild factories or expand manufacturing appears constrained. Why?

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This edition is the second instalment in my ongoing exploration of the future of the West. Last week, I focused on the concept of “allied scale”—the capacity of America and Europe to convert their combined mass into coordinated, effective leverage. I wrote about how historical institutions, postwar leadership, and deliberate investment once enabled the West to marshal resources far beyond the sum of its parts. I also noted how that ability has weakened: internal fragmentation, misaligned incentives, and financial extraction have left Europe underutilised, the US distracted, and China in the position to benefit by default. Allied scale, I argued, is the discipline of coordination, long-term planning, and strategic vision—one that transforms size and wealth into power.

This week, I turn to a closely connected challenge: fiscal sustainability and the structural pressures confronting Western economies. Debt is no longer a marginal concern but a central constraint on policy and growth. Europe struggles under eurozone imbalances, idle capacity, and fiscal rigidity. The US faces a debt wall, a dollar-dependent global role, and rising interest obligations that limit monetary freedom. Policy choices—whether austerity, conventional borrowing, or digital finance—carry far-reaching consequences for growth, wealth distribution, and strategic autonomy.

By examining history, contemporary policy, and structural dynamics, this edition shows how fiscal strategy intersects with allied scale. Mobilising idle capacity, coordinating investment, and managing debt wisely are economic imperatives that help preserve the West’s influence, resilience, and cohesion in a rapidly shifting global order. The choices made now will shape whether the West navigates this period safely or faces prolonged stagnation and systemic vulnerability as it confronts China as a challenger.

1/ The state can (and must) borrow in a way no private actor can

Here is the key lesson I once taught my public finance students at Sciences Po: a state holds a kind of credit that no business or household can match. A business can go under. A household can default. Both can vanish from the economy. A state does not do so in normal times. It can fall only if it is invaded or split apart, which is rare.

• Lenders assume the state will outlive them and keep the tools it needs to raise tax. This single fact shapes how public debt works. As long as the tax base stays broad, the economy keeps some pace, and leaders avoid gross errors, lenders expect the state to honour its word. This is why the state can issue debt that runs for many decades or more.

It is also vital to see the different roles of interest and principal. The state does not have to pay down the principal on its debt. It can roll it over and replace old debt with new debt. It can do so because lenders trust the state to endure and to keep the power to tax.

• What the state must pay is the interest. If it funds interest with new debt year after year, the debt stock climbs at a rising rate. At some point the interest bill grows too large to meet through tax or through new loans on fair terms. This is the point where debt runs out of control. To avoid that case, the state must keep economic growth firm, collect tax in a steady way, and avoid interest rates that sit above the growth of its own income. GDP is a sound guide to the tax base, and when GDP grows faster than the interest rate, the debt load falls over time even if the principal stays the same.

There is another reason why the state must act in a way no business or household can copy. Over the last century we learnt from Keynes and those who used his work that private actors cannot keep the economy stable and growing by themselves. When demand drops, firms cut and households save more. If the state did the same, the slump would deepen. Therefore the state’s function is to act in a countercyclical way: it must spend when demand weakens and invest when private investment is low. Only by doing so, and by funding it with debt, does it support growth and protect its own credit.

2/ Wartime borrowing set the ground for post-war growth and the fall of public debt

The strongest case for the special place of public debt comes from the mid-20th century. The Second World War pushed the fiscal limits of the major Western states. America and Britain, in particular, raised spending to levels once seen as out of reach. Their debt ratios climbed to heights that would alarm most policymakers today. Yet these debts did not lead to default. On the contrary, they became the base for the long boom that followed.

The war forced states to use all idle labour and capital. This ended the long stretch of under-use that marked the interwar years. In the US, the depression held on until military outlays cut joblessness close to zero. In the UK, the scale of wartime production built state capacity and made large post-war budgets possible. When peace came, these states did not return to the small budgets of the pre-war years. They replaced military outlays with welfare systems and with active fiscal policy. This formed the ground for what later became known as Keynesian demand management.

Debt ratios then fell because growth surged. From the late 1940s to the early 1970s, the West saw a long and strong expansion. Productivity and real wages rose at a rate that doubled living standards within a generation. High growth lifted tax income and pushed up nominal GDP. Debt stocks stayed large in cash terms but shrank compared with the size of the economy. From the late 1960s onward, inflation helped as well. It reduced the real value of fixed-rate debt and, when paired with steady growth, cut the real weight of public debt without the need for large primary surpluses.

• States also used tight financial rules to keep the process steady. They steered domestic savings into government bonds at low yields, which held down borrowing costs and reduced the risk of capital flight. This mix let states carry high debts while the economy grew faster than the interest rate. By the 1970s, the US and other Western countries had brought their debt ratio down to levels that made wartime borrowing a distant concern.

3/ Currency devaluation doesn’t work every time

Monetary policy is the other main pillar of macroeconomic management and works alongside a sound fiscal stance. In that area, currency devaluation has long helped indebted states regain competitiveness and absorb shocks. A weaker currency makes exports cheaper abroad and gives domestic producers room to grow. It acts as an automatic stabiliser.

It does not always work, however. For devaluation to succeed, trading partners must be expanding, and wage growth must remain contained so the cost advantage is not lost. When these conditions hold, a cheaper currency can help close external deficits, rebuild output, and restore confidence:

• Post-war Germany used currency reform to remove old claims, cut purchasing power, and reset relative prices, giving firms space to expand exports when foreign demand was strong.

• In the 1980s, small open economies like Canada, Ireland, Denmark, and Australia combined fiscal cuts with large devaluations and wage restraint. These worked because their trading partners were growing, and devaluation allowed exports to rise even as spending tightened.

• A key success factor in both periods was steady US growth. America was doing so well both in the immediate post-war period and the 1980s that it could reward its trade partners’ devaluations with strong and steady demand for their products. By contrast, major economies trying deflation alone in the interwar years failed: not all can devalue simultaneously, and attempts to shift adjustment onto others contract the whole system.

That said, modern developed economies face limits that make these tools weaker:

• Eurozone members cannot devalue individually because they share the same currency. Therefore the only way for them to regain competitiveness is through internal wage and spending cuts. These depress demand and raise unemployment if exports do not pick up soon. When all governments cut spending at the same time, growth stalls across the bloc.

• The US faces a different trap. The dollar’s global role forces large trade deficits. Attempts to curb imports often strengthen the dollar, hurting exporters, while new dollar-based digital instruments increase global demand for outflows.

Both the eurozone and the US could ease their respective constraints, but such strategies require long-term commitment and political agreement. At present, neither shows the capacity to make these choices, and they face unprecedented fiscal stress as a result.

4/ Cutting spending does not reduce debt and can deepen economic problems

Austerity—the deliberate reduction of public spending to restore fiscal balance—is politically appealing but economically flawed. As we saw with the ill-designed, Elon Musk-led DOGE effort earlier this year (make sure to read Michael Cembalest’s takedown on that one), austerity’s appeal is simple: governments signal responsibility, and voters see “less debt” as safer. Therefore elected officials often adopt austerity as a low-cost show of competence, believing cuts will restore confidence and encourage private investment. Yet history shows the logic is mistaken.

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The Western alliance stands at a critical inflection point. For decades, America and Europe have held unmatched economic, industrial, and military resources, yet these capabilities are increasingly misaligned. Allied scale—the ability to convert collective mass into coordinated, actionable power—was the foundation of Western strength after World War II. It enabled NATO and the European project to transform a continent fractured by war into a US-sponsored platform for sustained leverage. Today, however, that capacity is under strain. Rising global competition, internal dysfunction in Europe, and strategic missteps under Trump threaten to leave the West unable to act cohesively, even as China consolidates its own industrial and technological advantages.

This edition examines the erosion of allied scale and the forces behind it, from the postwar leadership of the “Wise Men” who worked with the EU’s founding father Jean Monnet to translate US resources into durable European capacity, to the financial and strategic predation that now diverts allied capital toward America. It explores the consequences of eurozone structural flaws, the weakening of transatlantic coordination, and the growing misalignment between financial inflows and industrial potential. By integrating history, economics, and strategy, my goal is to show how Europe and the US have gradually undermined the very leverage that once defined their alliance.

Finally, it’s important to look to the future. Despite the challenges, Europe possesses latent industrial capacity and strategic potential that could restore allied scale if mobilised effectively. Defence buildouts, advanced manufacturing, and digital finance offer catalysts for revitalising transatlantic coordination, while selective American engagement could transform Europe’s structural weaknesses into opportunities for partnership.

• Allied scale is less a measure of size or resources than a discipline of coordination, investment, and shared, long-term strategic vision. For Europe and America, mastering that discipline once again will determine whether the West can sustain its global influence and collective security in the decades ahead.

1/ Russia’s invasion of Ukraine forces a rethink of our ideas about war and peace

People of my generation, born in the 1970s, grew up believing that military campaigns for territorial gain in Europe would never happen again. Previous conflicts—the Napoleonic Wars, World War I, and World War II—had ended in massive destruction and ultimately a territorial status quo. Moreover, post-World War II security arrangements, first established by America and later reinforced during the Cold War, seemed to guarantee that no such reckless ventures would occur again. War still existed, of course, but Europe no longer felt at risk of territorial uncertainty.

Since 2022, however, that certainty has vanished, and one particularly striking development has been the daily expert discussions that refine our understanding of war. Earlier this year, an interview of military historian Phillips O’Brien by Paul Krugman caught my attention. O’Brien emphasises that the outcomes of war depend less on individual tactical victories, such as the battles of Kursk or Stalingrad, and more on the sustained capacity to endure conflict through collective strength. In other words, industrial power and reliable alliances ultimately determine the outcome of war.

• Historically, major wars are won by coalitions capable of sustaining long campaigns. O’Brien notes that in both world wars, Germany often had superior armies but lost because it faced stronger coalitions. Its efforts were further hampered by weak partners such as Italy in World War II and Austria-Hungary in World War I. This leads to the enduring conclusion that wars are decided in factories and alliance meetings as much as on battlefields.

This insight directly informs current assessments of global power. The Russia-Ukraine war demonstrates that manufacturing capacity and strong alliances matter more than legacy arsenal size or territorial extent. Ukraine’s access to Western support for ammunition and supply lines allows it to withstand Russia’s aggression. And as long as this alliance holds, Ukraine’s resilience will continue.

2/ Allied scale is one of the key ideas of our time

A helpful way to think about alliances in the post-Cold War world comes from China analyst Rush Doshi, who served on the National Security Council in the Biden administration and wrote the book The Long Game. His work draws on the wider practice of “net assessment”, a long-term review of strengths and gaps that helps planners judge how great-power rivalry may unfold.

To grasp Rush’s idea of “allied scale”, it helps to start with the split between scale and mass:

• Mass is the total size, capacity, or resources of a system. It is the plain weight of what exists, whether people, land, or infrastructure. Mao-era China had great mass in its huge population and territory, even though its output and reach stayed low.

• Scale is the ability to act well at a certain level or to have an effect that goes beyond the raw mass. It reflects how well a country organises itself and how far it can reach. Countries like Germany or Japan show scale without mass. They have modest populations and land, yet they achieve strong economic results through high output, new ideas, and global trade.

In short: mass is size and scale is leverage.

• Now, it is not enough for several nations to join and call themselves allies for them to gain allied scale. To gain scale, they must work out their aims, their plans, and the part each state plays. Rush’s view is that this, allied scale, is America’s strongest edge over China. When taken together, the US and its allies hold roughly THREE TIMES China’s nominal GDP and about twice its GDP adjusted for purchasing power. No single state in the world, even China, can match the West’s mass or the scale that comes from it when it is well used.

Conversely, Rush warns that a leading power can lose its lead if it fails to turn mass into scale. In particular, Europe, in his view, must face its habit of having relative mass but not turning that mass into scale and then into power. How well the West works through these issues will decide how much leverage it has in world affairs, whether in war, industry, or any other field.

3/ Allied scale after World War II was built through NATO and the European project

In a masterful Twitter thread published in 2018, journalist and author Claire Berlinski explained how allied scale was achieved by the US in the Cold War era. Her insight is that the US did not merely create NATO to deter Soviet aggression; it also built a broader framework that made Europe manageable and stable. NATO provided a security umbrella, but it was the European project—the construction européenne, as the French call it—that turned European size into usable scale. By encouraging cooperation, trade, and shared institutions, the Americans ensured that Europe could never again fall into the destructive rivalries that had defined its past.

• A central figure in this effort was Frenchman Jean Monnet. Monnet was not a career politician or a conventional statesman; he was a practical organiser, a builder of institutions, and a firm Atlanticist. His early years selling his family’s cognac in America had shaped his worldview: he understood American resources, culture, and organisational capacity, and he believed Europe’s security and prosperity depended on a close partnership with the US. During and after the war, Monnet cultivated close relationships with influential Americans, including the legendary W. Averell Harriman and other “Wise Men” of US policy circles. Through these connections, he was able to translate US strategic interests into European institutions.

Monnet’s approach perfectly reflected Rush’s logic of allied scale. He did not aim simply to gather European states under a common banner; he sought to make them act collectively, to turn fragmented national power into coordinated, effective action.

• The European Coal and Steel Community, the precursor to today’s European Union (and Monnet’s brainchild), exemplified this principle: by pooling resources critical to both industry and security, from 1954 onward Monnet and his collaborators created leverage that exceeded any single nation’s capacity. This, Claire notes, was a conscious American goal as much as a European initiative: by shaping institutions, the US converted European mass into scale, making the continent secure, prosperous, and politically predictable.

In short, allied scale after World War II was a dual project. NATO guaranteed defence and deterred aggression, while the European project enabled coordination, economic integration, and political stability. Together, they allowed Europe to become a dependable partner in a larger alliance system, one in which the US bore the burden of security while Europe delivered scale. Monnet’s vision, supported by American power, was the practical embodiment of this strategy.

4/ The Western alliance weakened from the 1970s onward

By the 1970s, the Western alliance that had been built with such care after World War II began to fray. Several converging forces undermined the structures that had converted European mass into effective scale:

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The venture capital industry stands at an inflection point. After two decades of pouring capital into software startups, investors are discovering that the easy wins have been claimed. Software’s promise of infinite scalability and minimal marginal costs has collided with brutal realities: low barriers to entry, fierce competition, and the emergence of AI tools that commoditise code itself. Meanwhile, AI, hailed as the next frontier, demands capital at a scale that favours megafunds and tech giants, leaving most investors facing uncertain returns in what many suspect is a bubble.

This shift has prompted a fundamental reallocation of capital towards deep tech—ventures that combine cutting-edge science with tangible, physical outputs. These companies build batteries that transform transport, materials that reshape manufacturing, and systems that modernise defence. Unlike software, which can be copied almost overnight, deep tech creates defensible moats through patents, process knowledge, and capital-intensive infrastructure. And unlike AI, where incumbents dominate, deep tech offers genuine opportunities for startups to create entirely new industries.

Yet deep tech challenges conventional venture wisdom. It demands founders with deep technical expertise, not just customer insight. It requires long-term vision paired with relentless pressure for early milestones—prototypes, operational metrics, and customer adoption that validate progress. It needs investors who understand both equity and debt, science and manufacturing, innovation and industrial scale, startups and big corporations. Most importantly, it shifts the primary risk from market acceptance to technological achievement—if you can build it, buyers are already waiting.

This edition examines why tech investors are abandoning traditional software for deep tech, how this transition reshapes risk assessment and capital deployment, and what it means for the future of innovation. Through ten interconnected arguments, it reveals that deep tech is not simply another investment category but a return to venture capital’s original purpose: funding breakthrough technologies that transform industries and create lasting competitive advantage.

For those willing to master its distinct challenges, deep tech offers what software no longer can—the opportunity to build companies that are both economically defensible and strategically vital. A reading list follows for those seeking to explore these themes further.

1/ Tech investors are abandoning traditional software and focusing on deep tech

For them, the era of deploying entire funds into SaaS startups is over because AI has upended the market. Investing in AI startups is costly—you are up against US megafunds and big tech incumbents, while everyone warns it is all a bubble anyway. As a result, like those who backed Hopin or Getir in 2020 and 2021, investors in AI today may never see their money again. As Jerry Neumann wrote in his landmark article, AI will not make you rich:

Investing in the maturity phase is… difficult… nothing much happens at all. The uncertainty about what will work and how customers and society will react is almost gone. Things are predictable, and everyone acts predictably.

All in all, traditional software is waning, and AI is high-risk (high beta). And so deep tech it is.

• The label is vague—some call it hard tech, frontier tech, industrial tech, or production tech—but its meaning is clear: (i) it is scientifically cutting-edge, achievable only through rigorous lab research, and (ii) it is tangible, producing outputs that require factories, clean rooms, wind tunnels, or other physical assets.

There is a logical explanation for deep tech’s rise. It touches domains that software has barely reached because software needed digestible entry points, like electric cars or smartphones. Before software can truly transform a car, the car itself must become electric. Otherwise, software is just a navigation tool measuring average gas consumption. Electric cars turn software into an operating system that controls everything—power, propulsion, and embedded features. Thus deep tech, for example in batteries, is technology’s gateway to dominating the multi-trillion-dollar car industry.

There is more. To paraphrase David Galbraith, software may have eaten the world, but now hardware is eating software back. Unit economics matter in a competitive environment. If your product is more than software—an entire car operated by software—then software is important, but it is not where your company gains an edge. Software has near-zero marginal cost, and open-source components make advantage hard to sustain. As David observes, the rational choice is to price software at zero and compete on reliability and yield on the hardware side, counting on superior manufacturing that operates at a larger scale. That’s exactly what the Chinese do 🇨🇳.

• Conversely, imagine an EV company cutting corners on hardware to save costs while charging more for premium software. The result will likely be a poor-quality car that constantly needs repair, paired with high-quality software that can never prove its worth because the underlying hardware is unreliable.

All in all, deep tech matters more than ever because it combines scientific breakthroughs with tangible execution, creating a defensible advantage where software alone cannot anymore. It is where true, long-term value in technology lies. This is why investors are into deep tech these days.

2/ Founders and investors face tougher challenges when building deep tech companies

From a founder’s perspective, deep tech is not for the faint of heart, because unlike software, it is harder to start from the customer’s needs:

• In software, you can be fresh out of university or a coding bootcamp, or even know little about software itself. All you need is the ability to spot a user’s need and build a tool to meet it (“Make something people want”)—by coding the MVP yourself, partnering with a competent technical co-founder, or using tools like Lovable or Cursor. If the business takes off, you can always hire stronger talent later to serve users at scale.

• In deep tech, it is different. You might imagine the perfect product, but bridging the gap between concept and a sustainable, working prototype is far harder without deep industry knowledge and years of lab or factory experience. Building Airbnb was about discovering what people wanted—turns out, borrowing someone else’s sofa was it. Building a deep tech product, by contrast, demands far more technical skill and discipline.

For investors, the picture looks even worse. Software used to be reassuring for VC firms because the path from inception to exit, however challenging, was clearly laid out. Deep tech, by contrast, is seen as too capital intensive, highly dilutive for early investors and founders, slow to exit, and incompatible with Silicon Valley’s standard model of 100%-equity funding through seed, Series A, B, and so on.

Yet the data tells a more nuanced story. Leo Polovets’s 2023 analysis of PitchBook data for US and Canadian companies shows that deep tech firms are sometimes less capital intensive than purely software-based ones. To see why, we need to remember that every company sits somewhere on the scalability–defensibility spectrum—and there is no single “optimal” position when it comes to capital intensity.

• A company must be scalable because capitalism rewards increasing returns, allowing firms to escape competitive pressure. But scalability alone is not enough if the business is not defensible. Consider airlines: they benefit from supply-side network effects, yet margins remain thin because competition persists at any scale. The same applies to oil: rising prices make drilling in expensive locations viable, which increases supply and ultimately stabilises the market.

Software faces a comparable dynamic. If a software company raises prices, competitors rush in because barriers to entry are so low in that space. As Jeff Bezos famously put it, “Your margin is my opportunity.” Low barriers to entry mean rivals will appear quickly, forcing companies to raise more capital to maintain an edge—sometimes exceeding the capital intensity of deep tech firms.

It takes exceptional characteristics, such as powerful network effects across multiple sides of the business (as Google enjoyed for years), to keep competitors at bay. For most software companies, though, life is not much easier than for deep tech ones. Success is far from guaranteed because they must raise significant capital, exits are uncertain, and competition remains fierce all the way up.

3/ Markets reward companies that combine defensibility with capital efficiency

Mostly, it’s because most competitors crash into the funding wall. If Company A has raised a lot but hasn’t tightened the bolts or improved its unit economics, it remains dependent on new capital to subsidise demand. When that capital dries up—whether due to a shift in the macro environment or investors simply losing patience—the prize doesn’t go to the company that raised the most. It goes to Company B, which resisted overreliance on funding and achieved the highest capital efficiency.

As Kai-Fu Lee wrote in AI Superpowers:

From the outside, venture-funded battles for market share look to be determined solely by who can raise the most capital and thus outlast their opponents. That’s half-true: while the amount of money raised is important, so is the burn rate and the “stickiness” of the customers bought through subsidies. Startups locked in these battles are almost never profitable at the time, but the company that can drive its losses-per-customer-served to the bare minimum can outlast better-funded competitors. Once the bloodshed is over and prices begin to rise, that same ruthless efficiency will be a major asset on the road to profitability...

Wang Xing [founder of Chinese delivery startup Meituan] embodied a philosophy of conquest tracing back to the fourteenth-century emperor Zhu Yuanshang, the leader of a rebel army who outlasted dozens of competing warlords to found the Ming Dynasty: “Build high walls, store up grain, and bide your time before claiming the throne.” For Wang Xing, venture funding was his grain, a superior product was his wall, and a billion-dollar market would be his throne.

More generally, defensibility acts as a buffer against endless fundraising. Sometimes it comes from true scale, which delivers better economics through efficiencies and allows companies to beat competition once and for all. In other cases, it stems from lock-in, access to key resources, superior branding, or other advantages. The list is long, but behind every successful company are two recipes: one for scaling the business, the other for making it defensible.

If you have scalability without defensibility, your company becomes a profitless capital sink and capital intensity soars. If, on the other hand, defensibility emerges early enough to reduce the need for additional funding, capital intensity stays under control, and investors can expect a return.

And this seems to be exactly what is happening with deep tech. The aforementioned Polovets analysis shows that deep tech companies, at least in North America, are often less capital intensive than less-defensible software companies. In other words, in software, markets can stay irrational longer than startups can stay solvent—but deep tech may be where rationality finally pays. And this dynamic—where capital efficiency and defensibility determine long-term survival—helps explain why deep tech may be less capital intensive than it seems.

But understanding capital needs is only part of the story. To evaluate deep tech properly, investors must also consider the full spectrum of risk: technological, market, financing, and management. That is where the profile of deep tech becomes truly distinctive.

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Venture capital has always been an American game played with American rules. Silicon Valley sets the terms, defines success, and draws the world’s best entrepreneurs into its orbit like a gravitational force. For European founders, this creates an existential question: do you stay local and risk irrelevance, or go global and risk losing everything that made you unique?

Martin Mignot understands this tension better than most. A partner at Index Ventures, he’s spent the last three years building their New York office after a career in London, helping European companies navigate the treacherous waters of US expansion. Index itself embodies this challenge—a firm born in Geneva that had to become truly transatlantic to compete at the highest level. Today, they’re arguably the best venture capital firm in the world, with recent exits including Figma, Scale AI, and Wiz.

This summer, Index published Winning in the US, a data-driven playbook for European and Israeli companies expanding to America. The book’s breakthrough is its recognition that there’s no single path to US success. Instead, it identifies four distinct archetypes—‘Telescope’, ‘Magnet’, ‘Pendulum’, and ‘Anchor’—each requiring radically different strategies. A gaming company can conquer America from Helsinki; an enterprise software startup must transplant its founders to San Francisco. Get the archetype wrong, and you’ll burn millions trying to force the wrong playbook.

But what struck me most in our conversation wasn’t the tactical advice about hiring or go-to-market strategies. It was Martin’s observation about the fundamental asymmetry between Europe and America: Europeans obsess about American politics, culture, and market dynamics, whilst Americans barely think about Europe at all. This indifference is both liberating and terrifying. It means European founders won’t face the political backlash they fear, but it also means they’re starting from absolute zero—unknown, unproven, and irrelevant until they prove otherwise.

This conversation with Martin, recorded in September, explores what it really takes for European companies to succeed in America, why so many fail by hiring the wrong “head of US,” and whether the current geopolitical tensions matter less than we think. The transcript has been edited for flow and clarity.

“It’s an overnight success a decade in the making”

Hi, Martin. Nice to see you.

Good to see you.

Martin, we go way back. We’ve known each other for twelve years now. You were already working at Index when they invested in my firm, The Family, back in 2013. You’re still at Index and have become a partner since then. You used to be in London but are now in New York, or between New York and San Francisco—perhaps you can tell us more about that.

Before we discuss the book, Winning in the US, which Index published in July, we should start with a few words about the firm. I won’t name names, but this summer I had breakfast with someone in the US who told me, between us, that Index Ventures has probably become the best venture capital firm in the world. That’s because you’ve had tremendous results recently: lots of portfolio companies going public, lots of visibility in the media, clearly very strong tailwinds, and a very strong presence both in Europe and in the US. You don’t have to comment on the praise, but tell us more about Index Ventures.

Well, first of all, it’s obviously very nice to hear that. I think it’s true that we’ve had a really strong start to the year with the IPO of Figma recently, where we were the largest shareholder; the investment of Meta into Scale AI, where we were the second largest shareholder; the offer by Google to Wiz, where we were the largest shareholder; and the Dream Games operation. So we’ve had a lot of very big outcomes, all in the past six months. But obviously, as you know, in venture it’s an overnight success a decade in the making. Most of these companies—we’ve been in Figma since we led the seed round when Dylan was nineteen. That was over twelve years ago. So these successes are a very long time coming. It just happened that a lot of these transactions are all happening at the same time, but they’ve been very long processes.

Regarding Index, we’ve been doing early stage tech investing for about three decades now. We’re actually celebrating our thirtieth anniversary next year in 2026. We started originally out of Geneva, which will be relevant to the conversation we have today. In a way, starting from a place like Geneva, where there is little to no homegrown tech mega-success, forced us to be global from day one. That was very much the original idea: to bring West Coast, Silicon Valley investment style to the rest of the world, with the simple idea that great entrepreneurs could come from anywhere where you had talent, infrastructure, and resources.

That forced us to look outside. Our first big successes were actually in places like Finland with MySQL, Estonia with Skype, and Sweden with King, which were not prime locations at the time for tech investment and tech entrepreneurship. Then from that strong starting point in Europe, we very quickly expanded back to the US. Today, we are a totally transatlantic fund. Half of the team is in the US, half is in Europe. We invest a little bit more in the US, but it’s roughly half and half. It’s a very balanced, fully transatlantic fund that operates as one team across all those geographies, which is a bit different from a lot of other funds.

“It’s much easier to be born global than to become global”

Index has been publishing various books over the past few years. I know the team who’s been working on it—it’s a lot of work because for each book, you go and interview dozens of portfolio founders and beyond, collecting testimonies and data. It’s all beautifully made. The book Winning in the US is a very nice object.

Tell us more about the book, and maybe we should spend some time discussing the “archetypes”. I think it’s really a breakthrough in the book because startups form such a diverse world. So many startups fail because they apply recipes they’ve read somewhere that don’t really apply to their particular context. You’ve made the effort to distinguish between different types of startups, which you call archetypes. There are four of them, but perhaps it’s worth spending a few minutes introducing each archetype and what playbook being in one bucket or another dictates for founding teams.

Absolutely. Before I get into the nitty-gritty of the archetypes, let me provide some context on the books and on Index Press. The initiative, which we call Index Press, can be found on the Index Ventures website under a section called Press. The driver behind these was that founders always ask us the same questions. Some questions are very specific, but many are not specific to a company—they touch on org design, compensation, option plans, and international expansion. We have a book called Scaling Through Chaos. We’ve done a lot around option plans and compensation in general, and international expansion. We’ve got two books: one is Winning in Europe for US companies expanding to Europe, and this latest book is Winning in the US for European and Israeli companies that want to succeed and expand into the US market.

All these books share the same characteristic and structure, mixing a quantitative approach with qualitative insights. For this book, we studied the expansion plans of hundreds of European VC-backed companies—when they expanded, how they expanded, what team they built, and so on. We coupled that with testimonials, interviewing more than forty top founders like Nikolay Storonsky, Ilkka Paananen, and Daniel Ek, as well as some senior executives, to bring the data to life.

We came up with four archetypes—actually five, but one is a bit different. The fifth one, which I’ll start with, is what we call the “transplant.” These are companies with European founders that started in the US and have been based in the US from day one. Examples include Datadog, Duolingo, and Stripe—not shabby successes, and it’s actually been a very successful archetype. But obviously, that’s not relevant for most European companies. These companies are typically US companies for all purposes, though they also tend to develop quite a bit of European presence over the long run.

What makes a company not a transplant? What stage do you need to wait for before going to the US to fall into the other archetypes?

Any company that was started in Europe, meaning either has a European legal entity or the founders are literally based in Europe with the team based in Europe, even if they incorporate in the US, we would still call them European companies. The transplants are very much US-registered companies whose founders and early team members are all based in the US. They are US companies for all purposes, though some of these founders may be dual citizens. That’s a different category.

The book focuses more on the four other archetypes, which are European-started companies that then expand to the US. These archetypes depend mostly on two things: the size and importance of the US market for the business in the long run, and the go-to-market approach—whether it’s marketing-led or sales-led.

Can you explain in concrete terms what it means to be marketing-led? People may confuse the two categories.

On one extreme, you have what we call the “telescope” companies. These are marketing-led companies where the US is a large part of their market. Examples include gaming companies like King, Supercell, and Voodoo in France. Because of the app store and marketing channels, especially performance marketing that are global, these companies can scale globally, including in the US. The US is their largest market but not the dominant market. They can do this all from one location, which can be anywhere—Paris, Stockholm, or Helsinki. These have been some very big successes. Anything in consumer mobile, with gaming being the biggest, follows this approach. These companies tend to keep most of their team in Europe. Everything is very Europe-driven. Over time, when they get bigger, they may hire a few senior folks in the US for product angles or business development relationships with Meta, Apple, Google, and TikTok.

Those are the companies you buy from to raise awareness and convert customers.

Exactly. They’re your distribution partners, so you want to be close to them. But apart from that, most of the team stays in Europe.

On the other extreme of the spectrum, very sales-driven companies where the US is the vast majority of your market, especially early on, you have what we call the “magnet.” These are typically enterprise software companies, especially if you’re selling infrastructure-related products or truly global solutions like cybersecurity. Examples from our portfolio include incident.io and Collibra. Most Israeli cybersecurity companies follow this model, and Datadog in France would be a good example. These companies start in Europe and build their first work there, but there’s such a pull from the US market that very quickly the founding team ends up having to spend most of their time in the US, moving there either part-time or full-time, building at least a go-to-market team, and eventually moving more and more senior leadership to the US.

That’s because in those segments, sales means presence at a very senior level. The CEO or very senior people need to be there to meet potential customers, convert them, and prove they mean business.

Exactly. That’s what we mean by sales-driven versus marketing-driven.

Then you have two others in between these extremes. One is the “pendulum”—companies that started in Europe. Index, in a way, is a pendulum. These are truly balanced companies where the US becomes close to half of the business, maybe slightly more or less, but Europe remains very important, as does the rest of the world. Examples include Spotify and Celonis. These companies build significant teams on the ground in the US but also have significant teams in Europe and never truly make a choice. If you look at Daniel Ek, he’s a really good example—he literally spent half his time in the US and half in Europe when scaling Spotify in the US. These businesses live on both feet and keep strong presence on both continents.

Finally, you have the “anchor” model, where Europe remains the dominant geography. The senior executive team remains in Europe, and the largest market remains European. But the US grows over time and there’s a pull, though it remains a smaller portion of the overall market. Adyen is a good example—the founding team is still in Europe, and most of the market is European, even though the US is growing very fast. Eventually, you can think of it as being served from Europe. Revolut is another good example where Europe remains the largest share of the market. These are typically in industries that are very big domestic sectors where you can build a hundred-billion-dollar company just in Europe, even though you will have some global presence. Fintech is obviously a case study of that.

There’s another framework I’ve been using a lot, which is to distinguish between “default global” and “default local.” You have to be aware of this. When you start, depending on the market you’re trying to address and the industry you’re part of, if you apply a local playbook to a business that’s meant to be global, it usually fails. Fintech is very much default local because you need to address a market that’s local due to regulations and culture—the relationship with money and banking—and you can’t really succeed otherwise.

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Europe has the talent, technology, and industrial history to lead in tech, yet it struggles to turn potential into impact. Its diversity—cultural, linguistic, and industrial—too often becomes a source of friction rather than advantage. The result is a continent that excels in niches but lacks systems.

Fragmentation costs Europe dearly. Startups face barriers to cross-border expansion. Industrial sectors remain insular, duplicating efforts instead of sharing knowledge. Governments and institutions struggle to coordinate strategy, leaving capital, ideas, and talent trapped. Without dense networks connecting people, companies, and technologies, European innovation cannot scale.

• Global competitors show what’s possible. China uses shared technological platforms, such as the Electric Tech Stack, to link industries and accelerate learning. The US relies on dense human networks and institutional continuity to amplify ideas. Europe has neither, yet it could build its own path: structured diversity, where differences generate innovation through connection rather than in spite of them.

In this context, venture capital has a unique role. European VCs are naturally positioned to become the connectors that bind the continent’s New Industrial ecosystem together—linking startups, legacy firms, investors, and governments.

By combining shared technology with human networks, Europe can turn its fragmentation into a competitive advantage rather than a handicap. This edition examines how.

1/ Europe struggles to turn diversity into a strength

In a meeting earlier this week in London, I found myself discussing New Industrials in Europe: the work needed for this segment of the market to take off and why Europe has a chance to outperform its results from the previous software-dominated cycle. My interlocutor immediately raised what is often cited as Europe’s main weakness: fragmentation.

• I have covered this topic extensively in this newsletter and usually stress a point many miss: European fragmentation is NOT primarily about regulations. In fact, given everything harmonised over the past four decades as part of building the single market, Europe is in many ways less fragmented legally than the US.

The reason the US often sees it differently is twofold. First, a shared language and culture make the differences in local regulations seem more legible and familiar. Second, the US has a culture of solving regulatory problems by throwing lawyers at them—made easier by a far higher number of lawyers per capita than anywhere else in the world.

• So, it is not regulations that make Europe fragmented. Instead, it is language, and above all, culture. A German and a French person already struggle to understand and respect each other when both speak imperfect English to communicate. Even if they manage that, the cultural gap between the high-context, irony-prone French and the structure-driven, clarity-seeking Germans can still make collaboration and business difficult.

In the end, Europe’s challenge is not to erase its diversity but to learn how to make it work as a strength rather than a source of friction.

2/ Industries close themselves off and block innovation

Then my interlocutor and I went on to discuss another form of fragmentation in Europe, one that is specific to the industrial sector. It is the fragmentation between industries themselves, which makes them insular and inward-looking, and prevents the collaboration and cross-pollination that could turn them into fertile ground for innovation.

What is an industry, really? It is not a group of companies doing the same thing and competing with one another. Rather, it is a network of companies that cooperate along the value chain—from raw material extraction at the top to serving the final customer at the bottom.

• From a distance, a design studio specialising in automobiles, a car manufacturer managing labour and logistics, and a car dealer focused on sales may seem to have little in common in terms of work culture or mindset. Yet because they belong to the same industry—automotive—they share a culture. They use a common jargon, follow the same customs, and operate under the same long-established regulations and best practices.

This shared knowledge and language make an industry competitive and efficient. But they also come at a cost: isolation. The industry becomes an ingroup; the rest of the economy becomes the outgroup, ignored at best, rejected at worst. The older and more optimised the industry, the stronger this isolation tends to be, as habits and best practices harden into barriers.

Meanwhile, innovation today depends on openness—on thinking and building in public. In the tech world, we take for granted the culture of sharing ideas through blogs, newsletters, and social media. But this is not how things work in the old industrial world. There, insularity still prevails. Industry players rarely share beyond their own circles, often confining communication to obscure trade publications, leaving the rest of the world unaware of what is happening inside.

3/ Fragmentation slows Europe and weakens competitiveness

All in all, by this point, we all see the problem. Europe is fragmented across so many dimensions—culture, language, regulations, and inward-looking industries. It is no wonder it struggles to compete in a fast-moving innovation economy that rewards scale and depth. We can try to mitigate some of these divides—by improving Europeans’ English, for instance, or by making the single market even more unified—but these efforts barely move the needle.

At this stage of the conversation, my interlocutor mentioned something intriguing: a January edition of Kyle Chan’s High Capacity newsletter, which focused on “China’s overlapping tech-industrial ecosystems”. It highlighted one striking fact. Many of China’s leading players may appear to belong to different industries—some building phones, others cars, others drones or batteries—yet in reality, they are deeply interconnected. They sell to one another, draw from the same talent pool, and maintain shareholding ties or other forms of partnership. Have a look at Kyle’s graph:

The key question is: why is it so different in China? And further, what does this mean for industrial competition on the global stage? Most importantly, how can Europe overcome its industrial fragmentation and build cross-industry connections that turn its legacy strengths into a platform for innovation and growth?

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Late-cycle investment theory suggests that we are living through the 1970s of tech.

Back then, the age of oil, automobiles, and mass production was reaching maturity, putting unbearable pressure on national economies that had expanded rapidly since the Second World War. The shift towards slower growth and stagnant markets pushed institutions—the Bretton Woods system and the welfare state—close to breaking point. To sustain growth for a few more decades, new approaches such as lean production, financialisation, and later free trade had to be embraced.

Today, it is another age—that of semiconductors, computing, and networks—that is reaching maturity. Most of the winners, the Microsofts, Googles, and Metas, emerged during the previous phase of synergy. Now they are the ones allocating vast amounts of capital to the lean production of our time: artificial intelligence. And as in the 1970s, we face a transition towards slower growth and stagnant markets, which puts institutions under severe strain. That said, I’m confident we will again find levers to perpetuate growth, and just as the 1980s and 1990s followed the 1970s, we may eventually see a new era of economic expansion. What form that will take, however, remains unclear.

Now that we are well into October and Q3 2025 is behind us, it is time to revisit the late-cycle investment theory. As before, I will do so by providing ten snapshots of developments that align, at least in part, with the late-cycle framework. As always, I welcome your feedback.

1/ Market concentration reaches historic levels

We keep being told that the US economy is doing well because the stock market keeps rising, regardless of the terrible events happening in the world and the underwhelming state of the global economy. But there are ways to relativise the impression given by this single economic indicator:

• First, the stock market may be climbing, but the dollar is weakening. That means U.S. assets are not performing particularly well from a foreign investor’s perspective.

• Second, much of the stock market’s growth is driven by passive investing. A recent study found that a $1 inflow into passive funds can create an additional $5 in market value. For now, passive investors such as BlackRock and Vanguard are mainly adding funds, but this could reverse as large generations reach retirement, turning inflows into outflows—with potentially serious consequences.

• Third, the stock market is often discussed as if it were homogeneous, when in fact the gains are concentrated in a handful of companies. The so-called Magnificent Seven, together with Broadcom, now represent 37% of the S&P 500.

Where is this headed? Four ideas:

• First, stock market concentration is not new. The last time it happened was during the Nifty Fifty era, when the post-war boom highlighted clear winners in the age oil, automobiles, and mass production. Investors then had no better option than to focus almost exclusively on these blue-chip, highly profitable companies.

• Second, this echoes Jerry Neumann’s observation that investing in the late cycle is difficult because outcomes are so predictable. We know Nvidia is dominating GPUs. Microsoft, Google, and Meta will endure. Apple, despite challenges in China, will remain strong because iPhones and MacBooks are not going anywhere. Where do you find alpha in such a landscape?

• Third, this concentration will not last forever. The Nifty Fifty eventually came back to earth during the 1973–1974 crash, triggered by rising inflation, the oil crisis, unreasonable valuations, and an economic recession. Could we be seeing a similar context today?

• Finally, as Gita Gopinath notes in The Economist, the consequences of a market correction today could be far more severe than during the Nifty Fifty or dot-com crashes, because a much larger share of global capital is now invested in US stocks:

Over the past decade and a half, American households have significantly increased their holdings in the stock market, encouraged by strong returns and the dominance of American tech firms. Foreign investors, particularly from Europe, have for the same reasons poured capital into American stocks, while simultaneously benefiting from the dollar’s strength. This growing interconnectedness means that any sharp downturn in American markets will reverberate around the world.

2/ China weaponises rare earth dominance

The main headlines over the past few days have focused on China imposing a stringent export control regime on rare earth materials—a segment in which Chinese players have patiently built a dominant position over the past few decades.

• “Rare earths” refers to a group of soft, heavy metals whose properties make them essential for certain electrical and electronic components, notably catalysts and magnets. These, in turn, are found in products as widespread as batteries, electric vehicles, wind turbines, weapons systems, and consumer electronics such as smartphones. In other words, rare earths are everywhere, and much of modern technology depends on them.

Contrary to what the name suggests, rare earths are not actually rare. They are relatively abundant but occur in very low concentrations, which means that transforming them into usable materials requires extensive and costly mining and refining operations. What China controls is not access to the raw resources but the capacity to refine and process them for industrial use.

As Yanmei Xie noted, commenting on an FT article published in August, it makes little sense for any Western private company to try to compete with this dominance. Only sustained state support could make such an effort viable over the long term—not only to bridge the path to profitability but also to shield operations from potential Chinese sanctions under the new regime. In the meantime, China has decided to play its hand, effectively doing for refined rare earths what the United States did under Biden for silicon chips, which are also made from an abundant material: sand.

This move not only takes Trump’s trade war in a new direction but also highlights the breakdown of international institutions and the resulting fragmentation of global markets. That fragmentation, in turn, creates opportunities for a new generation of intermediaries and traders to emerge.

Just as the nationalisation of oil reserves in the 1970s gave rise to Marc Rich, the inventor of the oil spot market, today’s export controls on silicon chips and rare earths may give birth to a new generation of commodity trading empires—the Glencores and Trafiguras of the modern age. These traders would act as crucial brokers of access and liquidity, navigating political barriers, securing alternative supply routes, and keeping critical industries supplied in an era of restricted flows.

3/ GPU trading creates new intermediaries

Another market that appears ready for the rise of new intermediaries in the mould of Marc Rich is that of GPUs—the dedicated chips, mostly made by Nvidia, used first for training AI models and then for processing inferences as millions of users access them.

The way the market works today is simple but inefficient. Companies that decide to build capacity often raise capital from the private credit market, place large orders with Nvidia, wait for delivery, and then install the GPUs in data centres that require vast amounts of energy. This setup is suboptimal in several respects. Capacity is being built without clear visibility on future usage. It is being built without securing the necessary energy supplies near data centres. The system favours large buyers with the ability to raise capital, thereby limiting opportunities for new entrants. In turn, this makes Nvidia highly dependent on a small group of customers, creating mutual reliance that constrains flexibility in the value chain.

To address these inefficiencies, some players are already exploring ways to create a market for trading GPUs on the spot—much as traders did with commodities in the 1970s. In time, such a market could enable the development of sophisticated financial products such as futures contracts, improve long-term matching of supply and demand, reduce the risk of bubble bursting, foster competition across the computing and energy stack, and further consolidate Nvidia’s market power.

This, to me, is another instance of the Marc Rich theory at work—especially since GPUs, as a specialised category of silicon chips, are also subject to export controls.

And I did not come up with the idea myself. I stumbled upon it twice within a short period, which is usually a sign that something is indeed taking shape—once in an essay by Felix Salmon in Bloomberg and again in an Odd Lots podcast featuring trader Don Wilson.

4/ AI suppliers form manufacturing partnerships

In an edition published earlier this year, titled Who Will Be the Japan of the AI Era?, I first argued that AI is essentially an efficiency innovation.

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Last week I argued that AI-induced economic surplus would flow differently across sectors: largely to consumers in services, except where regulatory capture enables rent-seeking, whilst AI-savvy manufacturers would be better positioned to retain surplus and reinvest it. The distinction, I suggested, ran along the manufacturing-services divide.

• That framing was too simple. Both situations coexist. Some service sectors exhibit increasing returns to scale that allow firms to retain surplus and compound value. Conversely, some manufacturing sectors face such intense competition that firms must cut prices, passing gains to consumers. Thus the real divide is not between manufacturing and services, but between businesses that can capture producer surplus and those that cannot. AI creates supply-side value only when producers retain part of the surplus—a dynamic that exists in some manufacturing contexts but not all, and in some service sectors but not most.

This edition refines last week’s argument. It examines how AI will reshape capitalism itself: strengthening some firms’ ability to escape the market economy’s competitive pressures, whilst trapping others within it. It explores three pathways for AI to enter legacy value chains, analyses why client-facing services cannot scale like software, and considers how AI might transform proximity services and the social contract. Finally, it assesses how these dynamics will play out across the three major economic blocks: the US, China, and Europe.

• The stakes are high. AI will reshape not just industries but the formula for generating prosperity itself.

1/ In last week’s edition, I tried to answer a set of simple questions

How will the deployment of AI translate into widespread prosperity? Through which sectors, and in which regions?

I received some pushback, mostly regarding my attempt to draw a line between services on one hand and manufacturing on the other. Specifically, the competitive analysis I outlined implied that the AI-induced economic surplus in services would largely flow to consumers—except where regulatory capture enables rent-seeking—whereas AI-savvy manufacturers would be better positioned to retain a significant share of that surplus, allowing them to reinvest and compound value creation.

In fact, both of those situations coexist:

• Some service sectors exhibit increasing returns to scale that confer a durable competitive advantage, enabling firms to retain most of the surplus and reinvest it.

• Conversely, some manufacturing sectors face such intense competition that firms must cut prices to maintain market share, passing nearly all the surplus to consumers.

In other words, the distinction is not as clear-cut as I initially suggested. The divide between high- and low-producer-surplus businesses is orthogonal to the manufacturing–services split. AI will create value on the supply side only when producers can capture part of the surplus—but this is not unique to manufacturing, and there are manufacturing contexts where it is not possible1.

What I am offering today is therefore a refinement of last week’s argument. The previous edition still stands as useful scaffolding, but this version presents a more definitive and self-contained analysis.

2/ At the highest level, this is a discussion about capitalism itself

My friend Bill Janeway, with whom I had lunch earlier this week, reminded me that Peter Thiel’s remark that “competition is for losers” ultimately traces back to Adam Smith and The Wealth of Nations. Yet the (more recent) framework I find most useful comes from the French historian Fernand Braudel, who divided the economy into three distinct layers:

• Material life, the largest and least measurable layer, composed of the countless informal transactions that make up daily existence.

• The market economy, where individuals and small firms trade goods and services, linking production to consumption. It corresponds today to the world of freelancers, artisans, and small family businesses who operate on razor-thin margins because competition is relentless. It is the domain of merchants who buy and sell, earning only modest spreads as they perform services and/or sell goods that merely pass through their hands.

• Capitalism, a set of processes that allow actors to rise above the day-to-day grind of commerce and pursue increasing returns to scale. Unlike the merchant’s world, capitalism is that of financiers and entrepreneurs who realise they can weaken market competition by deploying capital within production itself, thus generating those crucial increasing returns.

When increasing returns produce a surplus that a firm can retain—having freed itself from the pressures of the market economy—the result is compounding: more capital leads to a larger surplus, which can then be distributed among shareholders, workers, consumers, and the state. In turn, this enables further investment and greater value creation. Capitalism, in this sense, is the only true engine of prosperity. Its output is a rising return on invested capital, which drives higher labour productivity—the single economic indicator most closely correlated with long-term development2.

3/ The question then becomes: how will AI affect capitalism and reshape the formula for generating prosperity?

The first dimension concerns AI being used to strengthen existing industries. As Dan Wang, author of Breakneck, wrote this week in the Financial Times, we should expect this to be AI’s primary role over the coming years:

AI is not a simple “race”. What matters is not just creating the technology but what each country does with it. Silicon Valley has been obsessed with superintelligence, as if it were possible to build God in a box. Beijing has been interested less in treating AI as a supernatural goal, and more as a technology to be harnessed — Chinese academics and policymakers consistently talk about AI as a practical tool for enhancing existing industries.

AI will help both countries deepen their specialisations. Think about it this way: America is better in service sectors such as consulting and litigation; with AI, it might be able to generate even more lawsuits. China, which has far superior training data on manufacturing, might grow even better at producing electronics, drones and munitions.

This idea of AI deepening and entrenching existing positions suggests that some firms in sectors with high barriers to entry may further consolidate their position and capture an even larger share of economic surplus. In relatively stable industries where competition has been softened, AI will enable firms to reinvest retained surplus rather than being forced to cut prices.

• In contrast, in highly competitive merchant sectors, AI-induced gains are likely to flow immediately to consumers via lower prices, because relentless competition prevents firms from retaining much surplus.

It is notable that Dan uses professional services (dominant in the US) and manufacturing (China’s strength) as examples. This explains why, over the past few years, Western debates have focused on AI destroying white-collar jobs—lawyers and accountants first—while China has quietly applied AI to manufacturing, where there are fewer jobs to displace and substantial productivity gains to be secured. For China, that is even an advantage given the demographic challenges it faces.

The second dimension is whether AI will reshape the boundary between capitalism and the market economy. In some sectors, AI can shift the balance, enabling some firms to retain more surplus and reinvest, while others see their historical advantages eroded:

• Will AI allow firms in competitive markets to escape the constant pressure of price competition and enter capitalism’s compounding cycle?

• Conversely, will AI undermine entrenched capitalist advantages, eroding incumbents’ market power as new entrants leverage AI more effectively?

The third dimension concerns the geographic distribution of wealth and power. Several factors come into play: the availability of capital, access to the compute-energy stack—from electricity and data centres to chips—state support for capitalist expansion through industrial policy, the regulatory approach to capitalism’s potential dead ends (a race to the bottom where no firm profits, or dominance by a single firm that can exploit customers), and the broader institutional context. AI will shape which paths sectors and regions follow, depending on how it interacts with existing market and capital structures.

The next sections will examine these dimensions from different angles and conclude with a provisional assessment of where various regions stand within the emerging AI economy.

4/ There are three ways for AI to enter legacy value chains

About ten years ago, I designed a simple model to understand how technology penetrates legacy industries and showed how incumbents find new reasons to be in denial at every stage. The first stage is when incumbents do not care at all. For AI, this stage was brief, as the hype hit the market with full force from November 2022 onward. The second stage is where we are now: incumbents convince themselves they are experimenting with the new technology, while in reality they mostly run small-scale projects with consultants that have no impact on the core business3.

Even though it’s still early, I believe we have seen enough to be able to conclude that AI will penetrate industries in three different ways, depending on the industry’s economics as well as its regulatory and institutional context:

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Artificial intelligence is everywhere. Big tech is investing heavily, politicians are heralding a bright future, and venture capitalists are rushing to fund startups, convinced AI will deliver unprecedented productivity gains—especially in services, the part of the economy that has long resisted them.

But the reality may be more complex. AI’s impact depends not just on automation, but on who captures and reinvests the gains. Like lean production in the 1970s, AI often optimises existing capabilities rather than creating an entirely new technological revolution. Some sectors and regions are positioned to turn AI efficiency into lasting productivity and economic growth. Others see the benefits leak away as temporary consumer savings, financial speculation, or foreign dependence.

This edition argues that capturing AI’s economic value requires two conditions: the ability to generate and retain producer surplus, and reliable access to the compute-energy stack needed for large-scale deployment. By examining manufacturing and service sectors, along with China, America, and Europe, this edition shows how industrial capacity, infrastructure, and institutions decide who will prosper—and who will be left behind—in the AI economy.

Note (13 October 2025): The reasoning in this post was partly revised and significantly refined in a later edition, which you can read here:

1/ AI repeats the lean production pattern

As I’ve written in previous editions (see here, here and here), my view is that AI represents radical optimisation within the age of computing and networks rather than a new technological revolution. Like lean production in the 1970s, which marked the maturity of the oil and automobile age, AI fundamentally rethinks how to harness mature technologies rather than replacing them.

• Lean production reorganised existing assembly lines and standardised parts around flexibility, waste reduction, and continuous improvement. Japan became the main beneficiary through institutional differentiation (long-term employment, keiretsu ties between manufacturers and suppliers) and favourable geopolitics (containerisation, globalisation). The result was Japan’s ability to make better, cheaper products that reshaped global value chains.

Today, AI represents a similar efficiency breakthrough and, just like with lean production, success depends on institutional readiness, process knowledge, and strategic capital deployment at least as much as on the technology itself.

The key question becomes which industries and regions can best capture economic benefit from this lever: where will AI generate productivity gains and, in turn, result in widespread prosperity?

2/ AI drives economic growth through producer surplus

Economic development from AI depends on whether productivity gains flow to producers, who reinvest them, or to consumers, who spend them.

Capitalism, as historian Fernand Braudel defined it, pursues increasing returns to scale and escape from margin-destroying competitive pressure. When successful, it generates economic surplus with capitalists retaining significant shares for reinvestment. With capitalism done right, AI can expand producer margins and create new capital for productive capacity when applied strategically.

The market economy—the other category in Braudel’s general framework for analysing the economy—operates differently. Unlike with capitalism, competition drives prices down, eroding margins toward zero. When AI efficiency gains diffuse through competition, they create consumer surplus. This improves living standards but fails to generate capital for domestic expansion.

• This framework exposes a crucial misconception about AI’s economic impact. The technology’s ability to automate work, particularly in services, is widely celebrated. Yet automation itself tells us nothing about value flows. The critical question is whether replacing workers generates reinvestable surplus or merely redistributes existing value.

3/ Why replacing workers doesn’t equal productivity growth

The distinction between producer and consumer surplus is crucial when considering AI’s potential to transform services, the sector that has historically resisted productivity gains. The common narrative in the tech community presents AI as revolutionary because it can automate service work at scale and reduce the number of workers a firm needs. But this view rests on a misunderstanding: while cutting jobs can temporarily improve a company’s margins, it wrongly assumes that replacing workers with machines automatically increases labour productivity and drives economic growth.

• When AI replaces a customer service representative or a junior analyst, it lowers labour costs. Measured at the firm level, labour productivity rises because the same output is produced with fewer workers. But higher labour productivity alone does not guarantee wider economic prosperity. In sectors belonging to Braudel’s ‘market economy’, competitive pressures often force firms to pass efficiency gains to consumers through lower prices, leaving the firm with little or no extra value. Output may stay the same with fewer workers, but unit prices fall. And without retained earnings to fund expansion, new investment, or innovation, the productivity gain remains trapped at the firm level. In other words, efficiency improves, but the broader economy sees no lasting increase in productive capacity.

Services also face inherent limits in output, determined more by demand than by production capacity. A law firm using AI for document review does not necessarily handle more cases, if only because a senior partner can manage only so many clients; the firm may simply lower fees under competitive pressure. A bank automating loan processing does not necessarily issue more loans, because the regulator and the central bank prevent it from doing so; as a result, it may simply offer better rates. Likewise, a restaurant using AI wherever it can still faces real estate constraints, as it can host only so many patrons at lunch hour. In all these cases, the efficiency gains benefit consumers rather than increasing the sector’s productive capacity.

• Service automation also erodes the process knowledge that supports future productivity growth. When AI handles routine tasks, workers lose the learning opportunities that traditionally build expertise. Unlike manufacturing, where automation typically complements human work—allowing engineers and operators to monitor, adjust, and improve processes while machines handle repetitive tasks—service automation can replace much of the routine work that trains human expertise. Even if workers supervise or handle exceptions, they no longer perform the tasks that develop tacit skills. As a result, efficiency improves and costs fall, but the firm’s capacity to innovate and expand productivity in the future is weakened.

In short, in service sectors, replacing workers with AI can raise measured productivity and reduce costs, but without retained value, reinvestment, or capability-building, these gains rarely translate into sustained economic growth.

4/ Services leak value whilst manufacturing captures it

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China builds 232 times more ships than America by tonnage. It produces thirteen times more steel. Every year, it adds more solar capacity than America has ever installed. These numbers represent more than economic statistics—they signal a fundamental shift in global manufacturing power that poses an existential question for Europe: how do you compete when your rival operates at unprecedented scale?

The conventional answer is to match that scale. Build bigger factories, chase lower costs, copy successful models. But for Europe, this path leads nowhere. China’s manufacturing dominance rests on advantages Europe cannot replicate: a unified market of 1.4 billion people, state coordination across vast industrial ecosystems, and production volumes that trigger Wright’s Law across multiple sectors simultaneously.

Yet Europe possesses different strengths. It has skilled workforces, advanced institutions, and the financial sophistication to leverage technology in ways that China’s mass-production model cannot easily match. The question is not whether Europe can outproduce China—it cannot. The question is whether Europe can compete differently, using intelligence and integration to achieve the benefits of scale without requiring mass.

This edition argues that Europe can. But success demands abandoning comfortable assumptions about how manufacturing competition works. It requires focusing on critical technologies, building dense industrial clusters, developing new forms of patient capital, and using artificial intelligence to compress learning curves that traditionally require massive output. Most importantly, it requires connecting Europe’s emerging technology startups with its established manufacturers in ways that create competitive advantage rather than mere efficiency gains.

Each actor in this ecosystem has a role to play. For clarity, I use two terms throughout this essay. “New Industrials” (coined by Gregory Bernstein) are startups embedding advanced technology directly into production and supply chains. “Legacy Industrials” are established manufacturers with scale, infrastructure, and market experience. The argument that follows hinges on how these two groups interact, and how investors act as the bridge between them.

1/ China dominates global manufacturing

It has taken years, and the work of leading analysts and authors, to understand what makes China so dominant in large-scale manufacturing. The secret is not low labour costs—they are no longer particularly low. Instead, China’s advantage comes from rediscovering the old formula for manufacturing supremacy: the Marshallian Trinity. This combines a dense pool of skilled workers who share knowledge, ready access to capital and modern infrastructure, and a network of supportive institutions that coordinate firms, suppliers, and research. Together, these elements form a self-reinforcing ecosystem that drives efficiency, innovation, and scale.

And talk about scale! China controls 32% of global manufacturing while the US produces 15%, a gap projected to widen to 40% versus 11% by 2030. The scale differential spans multiple sectors: China produces 13 times the steel of the US, builds 232 times more ships by tonnage, and manufactures 60% of global electric vehicles. It consumes 30% of the world’s electricity and adds annual solar capacity exceeding total US installations. With 1.6 million engineering graduates each year—compared with America’s lawyer-heavy output—China has systematically built industrial capacity that converts manufacturing dominance into geopolitical leverage.

• Adam Tooze argues that China’s unprecedented scale and rapid development represent such a radical departure from all previous human history that conventional social science models, designed for smaller cases, cannot explain it; rather, it constitutes the central dynamic defining modern globalization and development.

When a nation produces at such scale, it benefits immensely from what is known as Wright’s law: costs decline predictably with cumulative production volume. Chinese manufacturers have achieved production scales across multiple sectors that create systematic cost advantages through learning curves, supplier networks, and capital efficiency.

• This concentration of industrial power sets the stage for a stark contrast with Europe, where smaller-scale production, dispersed supply chains, and different institutional structures produce very different economic outcomes.

2/ Europe struggles with manufacturing scale

Europe’s manufacturing remains strong yet fragile. German machinery exports reached €66.8 billion in 2023, while northern Italy hosts both luxury goods and a dense network of high-value industrial firms. Nordic countries lead in cleantech niches, and France maintains a nuclear technology presence. Manufacturing makes up about 24% of the EU economy, compared with 11% in the US.

Europe’s share of global manufacturing value added declined from 27% in 2000 to 16% in 2014—and even below that since then. Chinese steel output now exceeds European production sixfold. Where scale determines success, China wins decisively.

• Europe excels in markets where scale matters least: bespoke engineering, luxury goods, and specialised machinery. These sectors require intensive customer interaction. A German machine tool manufacturer does not just sell equipment; it provides months of consultation and years of service. Europe’s fragmentation turns this into a complex task, requiring navigation of multiple languages, regulations, and commercial practices. The history of France’s nuclear industry reveals the challenge: domestic standardisation once delivered 58 reactors efficiently, but exports demanded customisation, eroding economies of scale.

• The inverse relationship between scale and customisation hits Europe hard. Fragmentation magnifies the effect: each national market demands unique specifications, creating what Seth Godin calls client–customer confusion. European firms rely on a handful of demanding clients rather than broad customer bases like Chinese competitors.

Eurozone structural imbalances worsen the problem. Southern Europe’s unemployment represents idle capacity, while northern wage repression, especially in Germany, prevents natural adjustment. The shared currency burdens both sides: southern industrialists face an overvalued euro, while German workers see suppressed wages despite strong exports.

Finally, large-scale European projects face bureaucratic obstacles similar to the US. Europe has evolved into what Dan Wang, in his recent book Breakneck (now a New York Times best seller) calls a “lawyerly society” rather than an “engineering state” like China. Regulatory processes can take years while Chinese competitors break ground.

• All in all, Europe remains strong in specialised niches but struggles in mass markets, held back by fragmentation and institutional limits. In contrast, China’s scale-driven ecosystem exposes the structural gaps that shape global competitiveness.

3/ There’s no easy path to learning from the Chinese

Before considering how Europe should defend and expand its position in global manufacturing, let’s examine the idea of learning from the Chinese through joint ventures. After all, that is exactly what China did decades ago: they absorbed as much as they could from the West, welcoming Western manufacturers at home and forming joint ventures with knowledge-hungry local players.

In a June Bloomberg article, Dan Wang and Ben Reinhardt argued that America could follow the exact same playbook: invite Chinese manufacturers into joint ventures. For the Chinese, it would be an easy proposition—they could deploy their abundant dollar-denominated capital to produce closer to the American consumers they covet, while American manufacturers would gain a chance to learn and catch up. At first glance, it seems like a shortcut to competitiveness and industrial renewal.

I tested the idea with friends and colleagues: should Europe try the same approach, forming joint ventures with Chinese manufacturers? Three major objections emerged.

• First, two exporting powers struggle to align. Both Europe and China depend on exports. Any deep partnership such as promoting joint ventures in Europe would force one side to import more, upsetting its economic model—and China’s scale and state capacity would dominate such a relationship. A LinkedIn post I wrote recently highlights this structural tension: Europe cannot simply pivot to China without undermining its own economic base.

• Second, joint ventures succeed mostly in developing economies—less so in developed ones like Europe. They rely on a mindset willing to experiment, absorb knowledge, and scale fast. China from the 1990s onward exemplified this: disciplined learning, combined with long-term innovation and massive buildout, created a strong feedback loop toward manufacturing excellence. Europe lacks that mindset today. Its institutions, regulations, and high costs make learning from a foreign partner slower, more complex, and riskier. Attempting a joint-venture approach now risks what we might call structured dependency: gaining access to Chinese know-how without developing independent capabilities.

• Third, cultural and operational gaps are real. Anecdotes abound of Chinese firms buying European companies, mismanaging local workforces, and moving operations back to China. Even without geopolitical tension, aligning across such differences is difficult.

Europe can still learn from China, but trying to replicate their approach of inviting Chinese partners risks dependency rather than independence. The safer path lies in selective technology partnerships, knowledge exchanges, and domestic capacity-building. Europe needs to strengthen its own processes, workforce, and institutions rather than bet the whole house on a joint-venture shortcut.

• This is not a rejection of cooperation, but a note of caution: learning from China may require adaptation rather than straightforward imitation. Europe would do well to consider the potential pitfalls of handing over the keys to the Chinese through joint ventures as it explores ways to strengthen its own industrial base.

One way to do this is by mastering what writer Noah Smith and entrepreneur Sam D’Amico call the “Electric Tech Stack”—the set of core technologies that underpins modern manufacturing and strategic industries. By investing across batteries, motors, power electronics, and semiconductors, Europe can build an industrial base informed by China’s example while remaining independent.

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