Smoke signals, not foundations. That’s what I read when news broke that Anthropic and OpenAI are partnering with the incoming Trump administration to develop an AI model evaluation plan. The headlines are breathless, painting this as a responsible pivot toward safety. But as someone who has spent two decades peeling back the layers of financial and technological systems, I see something far more calculated—a strategic capture of the regulatory narrative, designed to shield incumbents and tilt the playing field. And for the crypto industry, this isn’t just a side note. It’s a mirror.
Context: The Macro Liquidity Map of Power Let’s step back. The global liquidity map isn’t just about dollars and Treasury yields. It’s about influence. The incoming Trump administration has signaled a protectionist stance on technology, particularly against China. Pair that with AI’s exponential growth, and you get a perfect storm of nationalism and innovation. Anthropic and OpenAI, two of the most prominent AI labs, are now positioning themselves as the “responsible players” willing to submit to government oversight. But why now?
Historically, when a new technology threatens to disrupt existing power structures, the incumbents don’t fight the regulator—they join them. In 2017, I watched ICOs promise decentralization only to be co-opted by venture capital whales. In 2020, DeFi protocols bragged about permissionless yields while their multisig wallets held the keys to liquidity. The pattern is consistent: the loudest advocates of “trustlessness” are often the first to shake hands with authority when it suits their bottom line.
Anthropic and OpenAI are no different. Their collaboration with the Trump administration isn’t about AI safety. It’s about defining what “safe” means—and ensuring their own models are the benchmark. This is a classic regulatory moat: set the standard, then charge everyone else admission. For crypto, this is a live rehearsal of what could happen when governments decide to “regulate” digital assets through technical certification.
Core: Systemic Interconnectedness—Why AI Standards Will Leak Into Crypto Based on my audit experience examining 15 Layer-1 whitepapers in 2017, I learned that technical standards are never neutral. They embed the assumptions and biases of their creators. The same is true for AI evaluation plans. If the Trump administration mandates that all government-procured AI systems must pass a specific red-team test or meet certain transparency thresholds, that becomes a de facto technical barrier. Companies that can’t afford the compliance cost—or whose models don’t fit the approved architecture—are frozen out.
Now, let’s trace the flow of funds. The US government is the world’s largest buyer of technology. If AI contracts require compliance with this new evaluation plan, then AI companies will demand similar guarantees from their upstream suppliers—cloud providers, chipmakers, even data sources. And where does much of that data reside? On blockchains. A government-approved AI model might need to prove it wasn’t trained on “unapproved” data, which could mean requiring on-chain verification of training datasets. Suddenly, crypto infrastructure becomes an audit tool, not a value innovation.
Thesis broken. Capital preserved. But which capital? The trillions of dollars in AI-related equities and private placements will flow toward companies that can achieve this new stamp of approval. Crypto projects that ignore this signal risk becoming marginalized, seen as “unregulated wild west” compared to the “safe and compliant” AI systems.
Contrarian Angle: The Decoupling Thesis Is a Myth Many in crypto believe that digital assets will decouple from traditional finance and government influence. They point to Bitcoin’s censorship resistance or DeFi’s borderless nature as proof. But this is a dangerous delusion. The AI evaluation partnership shows that governments are learning how to co-opt emerging tech by setting the terms of discourse. “Safety” becomes the new “Know Your Customer.” “Transparency” becomes the new “Licensing.”
High APY is just delayed pain. The same goes for the illusion that crypto can operate in a parallel regulatory universe. When AI models become the gatekeepers of identity verification, credit scoring, and even property rights—all of which are being built on blockchains—the standards those models follow will dictate who gets access. The Anthropic-OpenAI deal is a trial run for a future where a small set of approved algorithms control the rails of the digital economy. If you think crypto is immune because it’s decentralized, you haven’t audited the governance tokens or looked at who sits on the foundation boards.
Takeaway: Cycle Positioning—Don’t Wait for the Hammer to Fall Systemic risk doesn’t take weekends off. The AI standards plan may take months to materialize, but its signal is already propagating. For crypto project leads: start engaging with standards bodies now. Submit feedback on technical specifications. Build relationships with policymakers, not just influencers. The era of ignoring government and hoping for the best is over.
For investors: re-evaluate portfolios. Projects that can demonstrate alignment with emerging AI and data standards—through verifiable on-chain audits, transparent governance, and open-source security—will survive the coming integration. Those that rely solely on hype and “community vibes” will be left behind.
And for the industry as a whole: recognize that the AI-crypto convergence is not just about compute markets or ZK proofs for ML. It’s about who writes the rules. The smoke signals are here. Don’t mistake them for foundations.
— Grace Taylor, Digital Asset Fund Manager & Former Cryptography Researcher, Austin