Brownstone Research’s Ben Lilly argues that open-source AI is following Bitcoin’s early regulatory battle and says decentralized AI projects could offer investors a “Bitcoin in 2014” opportunity as governments attempt to restrict open models.
A new installment of Chain of Thought, the Brownstone Research newsletter written by Ben Lilly, argues that the battle over open-source artificial intelligence is following the same path Bitcoin walked a decade ago, and that investors who recognize the pattern stand to profit.
The note opens with testimony that Anthropic CEO Dario Amodei gave to Congress in July 2023. Amodei acknowledged that open source is “a good thing” in most scientific fields and that the risks of open models released so far were “relatively limited,” but he warned that the scaling of open-source models was heading “down a very dangerous path.”
Lilly reads the subtext plainly: if open models are dangerous, then the closed models sold by companies like Anthropic are the safe choice — and the policy that follows is to restrict the open and elevate the closed.
Bitcoin’s early skeptics mirror what AI is facing
That framing is one digital-asset investors know well.
He revisits Bitcoin’s early skeptics, from Rep. Jared Polis buying the first Bitcoin on Capitol Hill in 2014 to Sen. Joe Manchin’s call to ban a “dangerous currency,” through the 2023 accusations that regulators tried to cut crypto off from the banking system in what critics dubbed “Operation Choke Point 2.0.”
The industry survived, he notes, and Washington is now moving toward clearer rules through the passed GENIUS Act and the pending CLARITY Act.
Decentralized AI, which Lilly calls “DeAI,” is having that same fight now. He points to recent developments as evidence the walls are going up: a U.S. export ban on Anthropic’s latest release, which he says will push the company toward permissioned access that verifies a user’s identity before granting a model, and OpenAI’s decision to restrict its GPT-5.6 rollout to trusted partners.
He expects identity requirements to spread. “It’s for your protection, you see,” he writes. “It always is.”
The note leans on a national-security anecdote to explain the fear driving these moves. Lilly cites NSA chief Joshua Rudd, by way of Sen. Mark Warner, describing how Anthropic’s “Mythos” model broke into “almost all of our classified system, not in weeks, but in hours.”
Yet open source is closing the gap, according to the piece. Lilly says the recent GLM-5.2 scored on par with Anthropic’s Sonnet 4.6 from February, leaving open models roughly three to four months behind the frontier, and predicts an open rival to Mythos and GPT-5.6 by fall.
He argues the bigger unlock is decentralized training on peer-to-peer networks that mirror Bitcoin and Ethereum — swapping compute-for-network-security for compute-for-model-training. Distributed training, he notes, has grown from sub-1-billion parameters to 100 billion in two years.
He names three early projects — Dark Bloom, which enables low-cost private inference on idle Macs; c0mpute, a decentralized inference network; and Pluralis, which trains AI across distributed consumer GPUs — and expects more to launch tokens and reward users for contributing compute.
The note ends with the notion that governments will try to ban open models and they will fail. For him, investing in the space “will be like buying Bitcoin in 2014, back when it was still ‘dangerous.’”
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