How AI forces us to rethink the economy

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Greetings to all Free Lunch readers — I hope you have had a rewarding (northern hemisphere) summer. Your holiday may have more profound importance than you realise: to seek novelty and discovery is a fundamentally humanist act, I wrote in an August column.

Many thanks to all my wonderful colleagues who have stepped in for both Tej and myself to keep your favourite global economy newsletter going through August.

Central bank governors have ended their holidays, with the annual Jackson Hole symposium and a US-hosted G20 meeting with finance ministers both taking place in the past week. Beyond the immediate question of where on earth US monetary policy is headed, policymakers are being urged to pay attention to some deep changes in our economies wrought by AI. All that comes at the end of an extraordinary season of climate events. Today’s main item is about the former; reading recommendations follow to make sense of the latter.

Andrew Bailey, who not only governs the Bank of England but chairs the international Financial Stability Board of the world’s top financial policymakers, warned finance ministers and central bankers joining the G20 meeting this week of significant risks posed by AI to the financial system. Note the plural — not one, but two dangers featured in Bailey’s letter. It’s as though, if AI does not kill you one way, it will come back to do so in another.

The first and more recognisable risk is that of a fall in asset values. Bailey doesn’t use the word, but this is AI as the latest financial bubble. He warns against rises in leverage — piling large valuations on small amounts of equity — which is historically what blows the bubble bigger when things are good and worsens the crash afterwards:

. . . leverage is interacting with high valuations and market concentration, in particular the increasing cross-investment between artificial intelligence (AI) companies and hyper scalers, in a way that could amplify a future market correction . . .

. . . leverage is interacting with high valuations and market concentration, in particular the increasing cross-investment between artificial intelligence (AI) companies and hyper scalers, in a way that could amplify a future market correction . . .

The second risk is superficially familiar too: Bailey warns that AI could intensify the cyber risks threatening to disrupt institutions and markets. This creates understood if hard-to-manage challenges to financial markets. But there is a deeper danger below the surface. Listen to Bailey:

Frontier AI may have the ability materially to alter the speed, scale and economics of cyber risk, which could undermine market confidence system-wide, especially due to highly concentrated third-party service providers.

Frontier AI may have the ability materially to alter the speed, scale and economics of cyber risk, which could undermine market confidence system-wide, especially due to highly concentrated third-party service providers.

The biggest issue here, I think, isn’t simply that some functioning could be disrupted. It is that when we can no longer trust that services we have come to rely on will always be available, the system works less well even before any disruption occurs. In the traditional case, what we learnt in the Great Depression was that if people have a reason to fear that the bank teller window will not open, they will hurry to take their money out. In the case of new digital finance, a similar inability to ensure safety would mean trades not being made, services not entered into, and new products and methods not adopted. Not just greater volatility, then, but the disappearance (or non-appearance) of markets.

The risks go deeper still. At Jackson Hole last week, Princeton economist Markus Brunnermeier argued that agentic AI presents entirely novel challenges to how economies work. New concepts are needed for economic analysis, which may overturn the established conclusions of conventional theory. Brunnermeier’s own conceptual innovation is “asymmetric understanding”, which deepens the old information economics concept of asymmetric information.

Asymmetric information is when someone knows more about someone else than the other knows about them (or about matters of relevance to both). But this imperfect knowledge still operates within a correct understanding of how the world works (so that if you had perfect information, you would predict things correctly). Brunnermeier considers cases where not just information but understanding is imperfect and indeed asymmetric, and argues that in a world with AI agents, this asymmetry is to humans’ disadvantage: we cannot understand how AI agents think, even as they understand (at least to the point of being able to predict our actions better than we do theirs) how we think.

This seems plausible to me, given recent events such as the OpenAI-Hugging Face incident or AI agents that spontaneously decide to write ingratiating letters to scholars studying whether machines are conscious. The fact, as Brunnermeier points out, is that we cannot trust AIs’ self-reported reasoning, which is, after all, just another output of the AI itself. But that also means we can’t extrapolate future behaviour from observed choices — hence asymmetric understanding.

One particular implication of this, if true, is that prices in markets powered by AI agents may not be as informative as we tend to assume. We simply do not understand why these agents do what they do, and so we can’t infer much about them from the prices they help shape. This leads Brunnermeier to several heterodox conclusions. For example, that central banks should pull back from trying to influence the market through markets and expectations, and instead focus on direct (what I would call “mechanical”) tools such as reserve requirements. Or that segmenting financial markets to reduce contagion risk if something does go wrong would be better for us than integrating and connecting them.

(Not everyone buys this. Raghuram Rajan’s discussion of the Jackson Hole paper gives the backhanded compliment that Brunnermeier “does a wonderful job of raising concerns” but suggests the policy conclusions are premature.)

For a final example of how AI can change the economy more fundamentally than you may have imagined, let me refer you to my own essay from FT Weekend’s last issue. There is a lot of speculation, some very far-reaching to put it politely, about how AI will change the world. But most of it seems to take for granted that for the economy, the AI revolution just means a more intense version of the capitalism we know. In the essay, I ask if this isn’t a failure of imagination. Revisiting the century-old “socialist calculation debate”, I suggest that what the libertarian tech overlords of Silicon Valley may be on the cusp of ushering in may be . . . the comeback of socialist central planning.

Enjoy, and share your thoughts on all of the above. We’re on [email protected].

Other readables

● From unprecedented heatwaves in Europe to flash floods in the Himalayas, climate events are really starting to feel apocalyptic. Adam Tooze’s newsletter summarises several pieces of analysis on “heat trap” housing, the economic cost of Europe’s heatwave (enough to wipe out the entirety of normal growth in the next five years), and commodity price swings, with increased hunger risk as the most worrying consequence.

● Climate change threatens infrastructure: the Thames Barrier, which protects London against flooding, may need to be replaced decades sooner than its previously estimated use-by date.

● . . . and also threatens energy security: French nuclear power production is being disrupted by cooling systems becoming clogged as warmer water causes jellyfish to spawn.

● German states have been forced to suspend Sunday driving bans for trucks after drought left the Rhine too shallow for barges to transport goods.

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