Markets as mind 🧠

The phrase artificial intelligence didn’t appear until 1956, when computer scientist John McCarthy gathered researchers at Dartmouth to imagine “thinking machines.” Already, ENIGMA-cracker Alan Turing had posed the question Can machines think? in 1950 and primitive learning programs, like checkers engines, were beginning to emerge.

But the world may have already been living with a proto-AI for centuries. The modern stock market has been processing vast streams of information since at least the 17th century Amsterdam Exchange. By the time the New York Stock Exchange opened in 1792, markets were already digesting information and spitting out signals.

The mechanics of collective intelligence – inputs, processing, outputs, and feedback – were there long before the term AI was coined. What if the stock market wasn’t just an early computing machine for capital, but humanity’s first large-scale AI?

The market as black box

AI refers to digital systems trained on data to produce outputs. By that standard, markets may qualify. Macroeconomic data, corporate reports, policy shifts, human sentiment, algorithmic trades all count as inputs. Pricing mechanisms, arbitrage, liquidity flows, feedback loops fulfil the processing aspect. Prices, volatility, capital allocation, the outputs. And, of course, markets evolve after crises and regulatory interventions, as well as innovating with new financial instruments.

Both AI and markets are black boxes. We see the outputs but rarely understand the hidden states or joined-up processes. Both scale beyond human comprehension. And both adapt under pressure, sometimes in unpredictable ways. The 2010 Flash Crash, when markets lost nearly $1 trillion in minutes, was a reminder of just how black-box those dynamics can be.

A key difference is centralisation. AI models like ChatGPT are trained with curated data and aligned objectives (at least in theory). Markets, by contrast, emerge spontaneously from distributed incentives.

The market that thinks

The Market Mind Hypothesis, set out in 2023 by Patrick Schotanus, is a theory that blends cognitive economics and complexity science, which studies – you guessed it – complex systems and their emergent phenomena.

Schotanus suggests that markets aren’t just nuts-and-bolts systems of supply and demand but instead adaptive, thinking entities that learn from shocks, digest narratives, and generate emergent intelligence.

In a crash, markets don’t simply record falling prices, they amplify panic and transmit fear, reorganising capital flows with a speed and intensity beyond the reach of any one trader or institution. Think of the meme-stock frenzy. A few viral posts triggered cascades of optimism, pulling in waves of new participants, inflating valuations far beyond fundamentals.

The market as collective brains

Recent research has begun to model markets explicitly on the brain. A 2025 paper, The Financial Connectome, outlined a brain-inspired framework for modeling market dynamics in order to forecast crises.

Latent dynamics – the hidden, low-dimensional processes that shape complex behaviours – have their parallels in finance. Liquidity, volatility, and sentiment act like the brain’s underlying states, invisible directly but mirrored in the spongy circuitry of neural networks.

This resonates with earlier work in cognitive economics, an emerging field analysed in Minds and Markets as Complex Systems. Cognitive economics borrows from psychology, neuroscience, and complexity theory to study how real people make choices when faced with uncertainty. Instead of assuming perfectly rational agents, it looks at how attention, memory, bias, and emotion shape economic behaviour. 

Both minds and markets are complex adaptive systems. They evolve through feedback, nonlinearity, and emergence. No single neuron or trader grasps the whole, but together they produce patterns that look like cognition.

The market mind hypothesis

Schotanus’ Market Mind Hypothesis goes further, suggesting a two-way link. Not only do markets behave like minds, but our own cognition can be understood in market terms – trading beliefs, bidding for attention, arbitraging between goals.

This reflexivity, famously described by George Soros, makes markets unique. Beliefs about markets often feed back into market behaviour. Traders act on expectations of others’ expectations. Price isn’t just a reflection of fundamentals, it’s also a mirror of collective psychology.

In markets, perception is reality. Narratives can move billions of dollars because belief itself becomes the input.

The market as consciousness 

In her 2022 talk The Market Mind, Vivienne Brown explores the idea that markets can be seen as forms of distributed consciousness. Not a single brain, but a networked mind. Like the internet or an ant colony, she argues, markets manifest collective intelligence. But unlike other collaborative systems, factors like money, risk, and reward intensify adaptation and sharpen reflexes in the system.

Cognitive economics suggests individuals offload computation onto the market, just as neurons offload computation onto networks. No one person alone can ascertain the ‘true’ value of Apple (AAPL). Instead, each investor contributes a fragment of a belief, a trade, a reaction. The aggregation of these fragments forms something greater than the sum of its parts. This is emergent phenomena.

The stock market is not just a mirror of human psychology but something like a cognitive prosthetic, a system we can lean on to compute what we as individual humans cannot. And like HAL, it has moods. Volatility and bubbles aren’t bugs in the machine but features of cognition. If the market is a brain, then just like us it is prone to overreaction, mania, and depressive crashes.

The market as intelligence squared

Like the human mind, markets rely heavily on storytelling to interpret complex events, shape perceptions, and drive behaviour. Narratives help investors and consumers make sense of uncertainty by providing a framework for understanding.

Robert Shiller showed how financial narratives spread like viruses. Andrew Lo described markets as evolutionary systems, adapting under pressure. Jean-Philippe Bouchaud urged economics to embrace complexity science. Each perspective nudges us away from the idea of markets as mechanical calculators, and toward markets as living, thinking entities.

Perhaps that is why markets remain so resistant to accurate prediction. We are not forecasting a machine, but a mind. So what happens when we connect them to machines that also think? Algorithmic trading already blurs this line. AI models now read filings, scan sentiment, and place trades at superhuman speed. The market mind is being connected to artificial minds.

The result of this intelligence² could be a hyper-efficient allocator of capital. Or flash crashes multiplied, narrative bubbles amplified, and systemic risks too fast to contain. Given this evolution in market intelligence,  who knows what markets will dream up next.

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