How AI-Crypto Will Lead to a Hyper-Financialized Future

We are moving into an era of hyper-financialization, in which anything that can become financialized will be. And it will culminate at the intersection of AI and crypto.

Through a wider use of markets, we are likely to see more seamless coordination across all aspects of society. The reason we can’t use markets in a broad set of society today is because there’s so much overhead in interacting with markets, which are valuable coordination mechanisms if the returns from interacting with it outweigh the expense and overhead.

Markets incentivize participation through potential financial gains, which makes them an effective system for coordinating economic activity. But if the cost of producing those incentives and executing on those incentives is greater than the return from those incentives, then an asset is essentially incompatible with markets, or the markets created for them are too inefficient to be viable.

Crypto has laid the groundwork for more autonomous, efficient financial markets, and enables us to codify critical market functions – like market making, settlement, debt creation, and more – such that the cost of leveraging markets with this infrastructure goes down. That significantly expands the universe of asset types compatible with markets.

This crypto market infrastructure gets us closer to the ultimate state of hyper-financialization.

Though crypto has introduced more efficient market infrastructure, inefficiencies still persist around human participation. Interacting with markets still requires manual effort, introduces individual biases, and relies on limited mental processing relative to AI capabilities. This friction increases the overhead of interacting with markets and leads to suboptimal coordination.

The final unlock to maximize market efficiency is AI: the most expressive technology we have. AI can act as highly capable, deflationary actors that reduce the inefficiencies of human actors in the market – capabilities like prediction, automation, and personalization at enormous scale and sophistication. This further chips away at the overhead of interacting with markets and slowly expands the scope of what markets can be used for.

The result of the convergence of these two technologies will be an explosion of novel financial markets for a wider range of societal functions, allowing us to use markets, the greatest coordination mechanism we have, for a broader set of society’s functions.

What exactly will this future entail, and why should we embrace it?

Hyper-financialization allows markets to become more efficient at eliciting granular, asymmetric insights – enabled by the convergence of AI and crypto – and as the AI agents that interact with markets become more capable, the scope of valuable asymmetric information we can access broadens. This allows markets to distinguish true signals from noise more adeptly.

That’s why long-tail financial markets will be among the earliest beneficiaries of hyper-financialization.

That might mean, for example, being able to trade not just in company stocks, but in niche markets around every subdivision within a company. Perhaps a person possesses asymmetric knowledge about the truck division of a car company, but no other aspect of it. Investing in the whole company creates extraneous noise, because the investor has to account for the uncertainty they have around the rest of the company.

However, if one could invest in only the truck division, which they know more about, the information revealed through that investment has higher signal-to-noise. In short, granularity incentivizes people to reveal more targeted information, leading to greater market efficiency, less noise, and lower overhead.

To become a more efficient society, we must integrate market-based mechanisms into more critical operations. But most existing financial infrastructure has too much friction and overhead to support such granular markets.

Before blockchain technology, we had to move through bureaucratic, inefficient legacy institutions to participate in markets. The main benefit crypto provides for hyper-financialization is the codification of critical market functions. Codifying these functions makes them more autonomous and brings down overhead costs. This is why crypto can provide the rails for markets around a wider range of niche, long-tail assets.

Moreover, AI actors can interpret complex market data more efficiently than human counterparts. They possess the analytical capabilities to process information faster and more accurately than any individual. By combining highly efficient AI actors with autonomous crypto market infrastructure, overhead costs plummet considerably across an expanding range of niche markets.

Prediction markets enable detailed forecasts on niche, time-boxed topics by aggregating dispersed insights across populations. However, they face a fundamental liquidity challenge: as markets become more specific, fewer traders possess the specialized knowledge to participate. This can result in anemic trading volume and market liquidity.

AMMs were invented to address liquidity shortcomings in long-tail markets. But prediction markets are hyper-long-tail, so they struggle to attract sufficient liquidity. These issues are further exacerbated because contemporary prediction market traders are often inefficient humans compared to AI agents, resulting in lower trade volumes and less incentive for liquidity providers.

Without sophisticated actors like AI agents to improve trading efficiency, many niche markets remain too shallow for practical uses. AI increases market efficiency through its superior data processing and decision-making, directly translating into higher trading volumes. Subsequently, greater transaction volume incentivizes more market makers to offer their capital, which tightens spreads in the market and enables further activity to reveal more accurate forecasts.

This process facilitates the expansion of prediction markets to reveal truth and aggregate information on more topics:

Essentially, the frictionlessness of crypto transactions and the precision of AI allow prediction markets to function as coordination mechanisms at scale for new sectors. This expands the range of subjects that can leverage prediction market infrastructure to reveal accurate forecasts.

As social choice theory and Kenneth Arrow’s Impossibility Theorem shows, no voting system can be optimally designed to aggregate a population’s preferences. However, a hyper-financialized society could enable the use of efficient markets to compensate for imperfect voting outcomes.

Arrow showed this in his paper “A Difficulty in the Concept of Social Welfare” that, when faced with multiple choices, no voting system can perfectly aggregate individual preferences into a fair, rational collective decision.

With advanced financial infrastructure, voting processes that were previously political in nature could be decided by market outcomes instead. We could use something called Futarchy, proposed by economist Robin Hanson, where prediction markets determine policies instead of direct votes.

In a Futarchy, participants vote on proposals by placing bets. The system implements the policy with the highest market price. Successful bettors who supported effective policies would subsequently receive payouts. [For more on this concept, see Vitalik’s Futarchy piece here.]

People would govern countries, states, cities, and more not based on consensus preferences, but by betting on which policies will accomplish agreed-upon goals. This will push society closer to market-based decision-making, which is able to overcome more cognitive biases.

In an increasingly specialized world, signals like academic credentials or employment history often fail to reflect the nuanced skills and expertise individuals accumulate. Such affiliations risk becoming noisy indicators, struggling to comprehensively capture competence across niche areas.

As society grows more financialized, the credentials and affiliations we use to judge people’s abilities can lose meaning. Prestigious schools and employers serve as noisy signaling mechanisms when assessing someone’s nuanced competence in various arenas.

However, markets have the opportunity to become a superior signal mechanism. If granular markets can better reveal asymmetric insights, they could surface specifics around a person’s ability in their field.

For example, imagine an AI researcher who excels at a particular niche subfield, but didn’t attend a top program or work at a famous lab. Granular markets could allow shares representing that researcher’s specific expertise to be traded separately from the researcher’s primary work. This would provide a precise, market-based signal of the researcher’s capabilities in that niche.

You can get much more precise, less noisy signals by using cleverly designed market mechanisms to speculate on people’s value. These could better serve as signaling methods than our current blunt metrics.

A state of hyper-financialization allows us to create more niche hedging vehicles: as prediction markets grow to cover more improbable events, we can trade assets as a means of mitigating our exposure to nearly any uncertainty.

For example, luck insurance could exist to hedge against the unpredictability in business outcomes, career trajectories, artistic success, and more. To balance out life’s randomness, you could purchase:

In these ways, the exponential growth of financial markets inches us closer to Kenneth Arrow’s hypothetical vision of a riskless society: one where markets can insure against virtually any probabilistic event.

Ultimately, the logical end-state of hyper-financialization is moving towards Kenneth Arrow’s vision of a “riskless society,” enabled by the fusion of blockchain technology and artificial intelligence.

Crypto provides the efficient, autonomous infrastructure to facilitate markets for a broader set of niche assets. Meanwhile, AI agents possess the analytical power required to rapidly process signals and insights across specialized niches. Their combined capabilities allow more granular markets to form and run efficiently.

Together, the intersection of crypto and AI transforms markets into viable coordination mechanisms across more of society.