The foundations of public blockchains were laid by cypherpunks. Even though the crypto industry was always destined to harbor a variety of ideas and practices, it’s the principles of decentralization, open source software, cryptographic security, privacy, and self-sovereignty that underpin its most disruptive outputs. But there’s a problem. Absent a regulatory framework that favors innovation and legitimizes blockchains as administrative infrastructure with unique capabilities, crypto entrepreneurs have been facing a difficult choice: stay true to the purist ethos and thereby significantly complicate their project’s structure and operations, or compromise on the original ideals in return for regulatory blessings and a more conventional path to mainstream success. I call this the Cryptopreneur’s Dilemma.

The compute and training layers of the AI-industrial-complex are currently dominated by centralized Web2 giants that have unparalleled access to capital, state-of-the-art hardware, and vast datasets. While this will likely remain the case for the most powerful general ML models, mid-tier and bespoke models may increasingly source their compute resources from more affordable and accessible Web3 networks. Similarly, for inference needs that exceed the capabilities of personal edge devices, some consumers may turn to Web3 networks for less censored, more diverse outputs. Rather than attempting to overhaul the entire AI stack, Web3 challengers should focus on these niche use cases, and lean more heavily into their unique value proposition around censorship-resistance, transparency, and social verifiability.