The battle over open-source artificial intelligence is starting to mirror the early regulatory war over Bitcoin. $BTC was once branded a dangerous currency by lawmakers; today open AI models face similar calls for restriction. If the pattern holds, decentralized AI could be the next big asymmetry.
In July 2023, Anthropic CEO Dario Amodei told Congress that open source is generally a good thing and that risks from released open models had been relatively limited. But he warned that scaling open models was heading down a very dangerous path. The subtext, critics argue, is that closed models are safe and open ones should be curbed.
That playbook is familiar. In 2014, Rep. Jared Polis bought the first Bitcoin on Capitol Hill. Sen. Joe Manchin later called for a ban on what he called a dangerous currency. By 2023, regulators were accused of cutting crypto off from banking in what became known as Operation Choke Point 2.0. The industry survived, and Washington is now moving toward clearer rules via the passed GENIUS Act and the pending CLARITY Act.
Decentralized AI, or DeAI, is now in the same fight. A U.S. export ban on Anthropic's latest release is expected to push the company toward permissioned access that verifies a user's identity before granting a model. OpenAI has restricted its GPT-5.6 rollout to trusted partners. Identity requirements are likely to spread.
The fear driving these moves is national security. NSA chief Joshua Rudd, cited by Sen. Mark Warner, described how Anthropic's Mythos model broke into almost all classified systems in hours, not weeks. Yet open source is closing the gap. GLM-5.2 recently scored on par with Anthropic's Sonnet 4.6 from February, leaving open models roughly three to four months behind the frontier. An open rival to Mythos and GPT-5.6 is predicted by fall.
The bigger unlock may be decentralized training on peer-to-peer networks that mirror $BTC and $ETH. Distributed training has grown from sub-1-billion parameters to 100 billion in two years. Early projects include Dark Bloom for low-cost private inference on idle Macs, c0mpute for decentralized inference, and Pluralis for training across distributed consumer GPUs. More are expected to launch tokens and reward users for contributing compute.
Governments will likely try to ban open models, and they will likely fail. For investors, backing the space could feel like buying $BTC in 2014, when it was still considered dangerous. The timing and details of token launches remain unconfirmed.
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