Crypto Encounters AI: A Comprehensive Breakdown of The Space

Over the years, crypto as a vertical has seen several waves of technological developments, enabling new potential use cases. The most notable one that comes to mind is the surfacing of alternative L1s, having been developed to make up for the perceived shortcomings of both Bitcoin and Ethereum. In hindsight, as long as one was early enough to the narrative and remembered to materialize gains, a position in almost any project within the alt-L1 vertical would have paid off handsomely.

Forwarding to the present day, almost all alt-L1s are down massively compared to their all-time highs. This can, of course, largely be attributed to the worsening macroeconomic outlook back in 2022 and the ensuing interest rate hikes, causing token prices to drop across the board. Still, it is not far-fetched to state that dying narratives also had a large effect on prices and the popping of the bubble. Having said that, it is not as though some of these alt-L1s have not re-emerged from the ashes, as it has become clearer which chains are differentiated with product-market fit through sustainable technological implementations bringing about tangible improvements. One of the best examples of the aforementioned is undoubtedly Solana, which is again well-positioned to benefit from a more favorable market outlook.

Now, following the initial release of OpenAI’s large language model (“LLM”) chatbot, ChatGPT, in November 2022 and the resulting hype around artificial intelligence, a new sector within crypto has emerged, or more accurately, has been discovered by market participants. This sector revolves around the usage of blockchain technology in combination with some forms of offchain AI and decentralized compute and data, which has led to an explosion in the valuations of many AI-related protocols.

As is often the case with new verticals, trying to value these protocols is more as though a guessing game than an economical analysis, and it is impossible to anchor prices based on fundamentals since offered products are in their early stages. Not to mention the information inefficiency the crypto market frequently displays. The size of the potential customer base and resulting demand are theorized and estimated, leading to narratives such as the aforementioned alt-L1 craze during the previous cycle. In other words, this often works in an investor's favor because it becomes easier for the market to speculate around the new vertical, and narratives become stronger, causing people to front-run each other, finally investing at valuations that would make most TradFi value/income investors faint.

The best teams and designs are prone to winning in the long run, but the point is that product-market fit tends to result from the combination of great technology, marketing, and price appreciation in underlying native tokens as a result of speculators, not to mention flawless market timing. The merging of crypto and AI increasingly resembles one of the next subsectors with large rewards across the board, chiefly driven by difficult-to-quantify potential and narrative-building. To be clear, this is not to say that there are no AI protocols building highly innovative and value-add solutions.

A handful of AI projects already exist that are quite highly valued, but there is definitely more potential left, especially if the market continues to turn increasingly bullish overall. Even more interestingly, there are still plenty of projects that have not yet been launched and found by the market, and it is more certain than not that several of these will produce jaw-dropping price charts as investors continue looking for the next paradigm-shifting protocols.

Beyond the price action, the intersection of crypto and AI prompts numerous intriguing considerations. For example, how does one know that the output of an AI model is correct and without bias? Consider a scenario where one asks an LLM, “Who shot JFK?” or “How many coups did the U.S. influence in the last century?” Some third parties might be keen to tell history from only their perspective. Further, how does one keep AI accessible for all, regardless of regulatory entities who may have an interest in limiting the general population’s access to knowledge? Thus, verifiability via blockchains couples perfectly with emerging issues in AI where the solutions are not yet clear.