Blockchain Capital: Lessons from the Crypto Market Worth Re-reading for AI Investors

Author: @jonah_b, Researcher at Blockchain Capital

Compiled by: Jiahua, ChainCatcher

It is hard not to notice the striking similarities between the current AI frenzy and previous rounds of excitement in the crypto market.

The crypto market serves as a great observation point for how people act in the face of a major technological wave: there are good sides, bad sides, and the most unbearable sides. This is because the cycles in the crypto market progress rapidly, allowing early projects to gain liquidity that other markets struggle to provide, and market behavior is inherently public since blockchain data is open.

We have learned a lot from this market, and these lessons are equally applicable to AI. If you are investing in AI, this article is for you.

Lottery-style Betting

Huge successes trigger follow-ups and create FOMO. Once a certain asset produces a groundbreaking winner, investors rush in, trying to replicate its success. However, the second wave of projects rarely reaches the heights of the first wave, and many projects ultimately turn out to be castles in the air, resulting in significant losses of capital.

Bitcoin became a trillion-dollar asset. Subsequently, Ethereum became a hundred-billion-dollar asset, and Solana also reached a multi-billion-dollar valuation, proving that this market can produce more than one massive winner.

Thus, a wave of investment in new public chains arose. Venture capital firms treated these projects like lottery bets, where hitting just one could yield multiples of the entire fund size.

Today, many emerging AI labs' valuations are also built on lottery-style expectations.

OpenAI and Anthropic are both approaching trillion-dollar valuations. The formula behind this is simple: first, there is an extremely sought-after market, followed by a proven successful latecomer. Thus, every new entrant is seen as the next lottery ticket to wealth.

Back then, many L1 projects achieved valuations of billions of dollars based almost solely on a white paper and a founding team. The stories they told were enticing:

What if the global economy operated on our chain?

Similarly, emerging AI labs have raised billions based on a set of research ideas and founding teams poached from OpenAI, Anthropic, or Google DeepMind.

What if they could really create the "God Machine"?

However, many times, this investment merely bets on the next higher-priced buyer appearing.

Many early investors are not necessarily evaluating the company's current fundamentals and future prospects. They know that the addition of a star talent or a partnership with a massive cloud provider could attract investors to push the company's valuation higher once again. Coupled with the increasing liquidity in the secondary market, they assume that the next buyer will always appear.

Market Chaos

As BCAP GP @CremeDeLaCrypto mentioned on the Bankless program, when large amounts of capital flow into the market, there will always be "a group of speculators and fraudsters chasing quick money, rushing wherever it is hot."

For over a decade, crypto investors have witnessed this phenomenon firsthand: batch after batch of projects claiming that "tokens are products" have been pushed to market under dubious market-making tactics, investor-unfriendly high FDV/low circulation structures, unfavorable SAFT agreements, and a plethora of other market tricks.

Today, the AI industry is also playing out a similar scene. For example, those structures composed of three layers of SPVs with exorbitant fees...

However, there is a key difference between the two: in the crypto market, prices are public, and tokens can be traded openly. The prices and valuations of AI companies, on the other hand, are formed in opaque, illiquid secondary markets.

Even without discussing speculative behavior, AI investors can see from the crypto market that changes in market structure will determine where profits ultimately flow.

For instance, what AI investors are heavily betting on now may eventually turn into a standardized commodity.

Case from the Crypto Market: Block Space Became a Commodity

Block space was once scarce and expensive, leading capital to flood in to try to expand supply.

However, the industry later went a bit overboard: more and more L1 chains launched, and Ethereum also added L2. Ultimately, block space transitioned from scarcity to abundance, even excess.

This is good for technological development, but not necessarily for investors. Today, many alternative L1s still generate very limited revenue.

The early market generally bet on the "fat protocol" theory, but ultimately, the "fat application" theory prevailed.

As block space became cheaper, users paid less for the underlying infrastructure, while spending more on upper-layer applications. Projects like Tether, Hyperliquid, Aave, and Polymarket at the application layer ended up capturing the bulk of the revenue.

AI Case: Models May Become Commoditized

AI may be replaying the early experiences of the crypto market.

Chinese AI labs are continuously making increasingly powerful model weights public. I have explained the incentives behind their actions.

If model weights become standardized commodities, and model prices continue to decline, value will shift upstream and downstream of the tech stack.

Applications will become the main beneficiaries: if the marginal cost of using a model gradually approaches the marginal cost of running the model, applications will no longer have to pay high profits at the model layer.

OpenAI and Anthropic may not be directly hit by this change, as they already have user bases, enterprise customer relationships, and distribution channels aimed at developers. But they are the exceptions.

In this sense, they resemble Hyperliquid in the AI field rather than just providing underlying infrastructure like L1.

In other words, OpenAI and Anthropic have achieved vertical integration. Other AI labs that cannot directly reach end users may find themselves in a more difficult position.

Another result of declining model profit margins is that the energy and hardware at the bottom of the tech stack have the opportunity to achieve higher profit margins. This trend may be even more pronounced when the supply of computing power is constrained by physical conditions.

In short, the hardware layer and application layer may take more profits, while the model layer will be squeezed. Yet, a significant amount of investment capital is flowing precisely into the model layer.

None of This is New

From a broader cyclical perspective, this is not surprising.

Carlota Perez argues in "Technological Revolutions and Financial Capital" that such technological revolutions go through several stages: infrastructure laying period, frenzy period, crash period, and deployment period.

Financial capital tends to over-invest in infrastructure during the frenzy period, but these overbuilt infrastructures, now priced cheaply, will support the development of the next generation of applications.

This helps explain why there was overbuilding of block space in the crypto market. The expansion of models in the AI field may become the next case.

Investing in emerging AI labs requires believing that they can yield substantial returns on R&D investments. The history of the crypto market and alternative L1s should at least make us question this assumption.

Of course, if AGI emerges, the above judgments may not hold. Because we have no idea what the economy will look like after AGI arrives.

This content is provided for general informational purposes only and doesn't constitute financial, investment, legal, or tax advice. Any events, rewards, online promotions, or related information mentioned herein should not be considered a recommendation, solicitation, or invitation to purchase, sell, trade, or otherwise deal in any crypto assets. Crypto assets are highly volatile and may result in loss. The availability of WEEX services, products, and related events may vary by region. You are responsible for ensuring that your participation is in accordance with applicable local laws and regulations.