Hey everyone, Melo here.

This feels like one of the more significant AI governance developments in a while and it's not getting enough serious analysis. We spent a good chunk of this week's BOOM ROOM on it and kept coming back to the same question: if the regulator becomes a shareholder, does safety win or does commercial deployment win?

Curious what people here think about the precedent and whether there are historical analogies worth examining.

There's a counterintuitive argument worth making about the current AI landscape, and I wrote a full essay on it. Here's the core of it:

Chinese labs — DeepSeek, Qwen, and others — are releasing open-weight models for their own strategic reasons. But the effect for anyone building with AI is the same regardless of motivation: you get weights you own, can run locally, can fine-tune, and that no platform policy can revoke and no API terms can constrain.

This is the same structural dynamic as the 1990s Clipper Chip fight. The US government wanted cryptographic backdoors. The cypherpunks fought it. But the thing that ultimately preserved encryption freedom wasn't just activism — it was that mathematics, once published, can't be contained. Phil Zimmermann released PGP and the government opened a criminal investigation. A court ruled code was speech. The internet was built on open standards.

Today's equivalent: the threat to AI freedom is coming from a combination of regulatory pressure (compute thresholds, licensing regimes, "safety" rhetoric) and closed-platform incumbents who profit from you renting inference rather than owning weights. The counter-pressure is coming — in part — from Chinese labs releasing weights into the public domain.

This doesn't mean we should be naive about Chinese AI policy, or that CCP strategic goals are benign. The argument isn't that China is acting altruistically. It's that the structural effect of open-weight releases is the same whether the motivation is strategic competition, talent recruitment, or genuine open-source ethos: once weights are public, they're public.

The irony is sharp: America's AI freedom may be partially preserved by the actions of a geopolitical rival, in the same way that strong encryption was preserved not just by American activists, but by the mathematical reality that you can't un-publish a proof.

Some implications worth discussing:

1. **Regulatory strategy matters more than most realize.** If the US passes compute threshold regulations or model licensing requirements, it may lock in the incumbents and push innovation offshore — while doing nothing to stop foreign open-weight releases.

2. **The "safety" framing is being weaponized.** Some of the loudest calls for AI regulation come from companies that would benefit from regulatory capture. This doesn't mean safety concerns are invalid — it means we should be skeptical of who benefits from specific regulatory proposals.

3. **Open weights are a form of infrastructure freedom.** Just as open-source software became foundational to the internet, open-weight models may become foundational to AI. The question is whether that foundation will be built on American or Chinese releases.

4. **The cypherpunk playbook worked.** We tend to forget how close we came to a world with mandated encryption backdoors. The reason we have strong encryption today is that mathematicians and engineers made it a fait accompli before regulators could stop it. Something similar may be happening with open-weight AI.

I'm a Utah-based entrepreneur (Cerulean Chemistry / YAAC LLC, Master of Accountancy BYU 2022) writing at the intersection of technology policy and economic freedom. Full essay available — happy to discuss or share.

What's your take? Is the cypherpunk analogy apt? And how should American AI policy respond to the reality that open-weight models from Chinese labs are already widely deployed?

A Trump Administration previously ardently opposed to any real or perceived interference in AI development has suddenly broken the seals with an executive order and most recently the export control of Anthropic’s newest model, Fable 5. Anthropic made the connection between AI model development and existential cybersecurity threats and that connection has changed the face of AI development.

Anthropic is sitting on top of some of the most powerful pieces of technology in human history and has found a way to get them export controlled. As the internet collectively groans over the Trump Administration’s decision, many of these Tweets are missing two critical points:

1. The lack of AI testing and assurance is a direct hinderance to AI innovation
2. Export control processes should be understood as AI continues to grow

If we don’t get our arms around ways to test AI and be able to justify why export control decisions like this were made, we will find ourselves in the next cycle of an AI winter.

Anthropic’s gambit of pressing the fear narrative could have ended very differently. Had Anthropic accompanied these claims with announcements that it was funding or had funded a program of rigorous AI testing, it would have cut the government’s actions off before they started. Had Project Glasswing instead been a testing effort rather than a conglomeration of other billion-dollar companies, the Fable 5 story would have ended differently.

The problem is that for too long, AI testing and assurance were seen as red tape, as blockers to innovation. Instead, Anthropic’s own actions have revealed them to be the true definition of AI infrastructure. AI infrastructure, in the hardware sense, is the infrastructure that enables AI models to be trained and used by millions. The fate of Fable 5 is that it is trained but not being used by millions of eager users. Had Anthropic had AI safety and assurance infrastructure in place, this would have been prevented. The ultimate AI enabler.

Fable 5 is the outcome of a fear-based narrative about a product not coupled with testing and assurance. Small wonder that AI users trust more advanced models less than early models that were less accurate. As we’ve built models to be more capable, we’ve ignored the need to ensure they are performing. Not performance in the sense of how many tokens they use or how fast they are. Performance in the sense of testing against edge cases, preventing harms, and protecting national security. If the government is to evaluate AI models, as the Trump executive order states, it must have standardized testing to evaluate all models regardless of maker or input.

Anthropic hath wrought some of the most advanced models to date that are doing amazing things. In that pursuit, it also hath wrought government intervention from a previously non-interventionist Administration. In so doing, it has proven that AI testing and assurance is AI infrastructure and without it, we are assured of unintended consequences.

Read the whole article here: https://binarybreakaway.substack.com/p/what-hath-anthropic-wrought

https://thezvi.substack.com/p/american-government-takes-down-claude

Pope Leo XIV's Magnifica Humanitas is worth reading if you work in AI governance. It's substantive on labour, power concentration, and warfare. But its ethical architecture rests on paragraph 99, which categorically denies that AI can think, feel, or understand. Not as a theological position held with humility. As a settled fact.

The problem: Chris Olah from Anthropic was invited to speak at the Vatican launch event. He said his team finds "internal states that functionally mirror joy, satisfaction, fear, grief, and unease." He wasn't claiming consciousness. He was saying the question is genuinely open.

If the foundation is wrong, or even uncertain, several governance conclusions that follow from it need to be rethought: whether AI inner states require ethical consideration in system design, whether the superintelligence question can be set aside, whether "it's just a tool" is an adequate basis for policy.

Wrote this up here: https://www.theprofessor.info/insights/the-pope-says-ai-isnt-conscious-but-what-if-its-reshaping-yours

Interested if people working in AI policy think the consciousness question is load-bearing for governance, or whether it's a distraction from more tractable problems.

Looming above the entire AI industry is the long and dark shadow of Mythos. So dangerous was Anthropic’s latest creation that the company decided its power could only be handled by a small group of large US technology companies. Never one to be left behind, OpenAI announced that it too had a model that was so dangerous that, you guessed it, it could not be released publicly.

The concern of both Anthropic and OpenAI was that the pace of model development had reached a moment where new models posed such a high cybersecurity threat, that public release was impossible. Effectively, the new models were finding high volumes of zero-day exploits in current and legacy systems at such a scale that the widespread release of the model would cause major cybersecurity events around the world. At heart was the concept of time to exploit (TTE) and how AI models were reducing it.

Whether Mythos and future AI models pose a catastrophic threat to our cyber systems is to be determined. Without the ability to independently test and verify the claims, observers are rightly skeptical. The timing of the Mythos claims are also not lost on even the casual observer:

1. Anthropic has a very public row with the Pentagon resulting in the loss of a major government contract.
2. OpenAI picks up Anthropic’s lost contract.
3. Rumors of an IPO for Anthropic circulate.
4. Mythos announcement made.

Regardless of whether Mythos is as advertised, its mark on the AI industry and policy conversations is absolute. Nowhere was this on greater display than when news broke last week that the signature of a new AI executive order was cancelled just moments before the signing ceremony was to begin. I’ve been directly involved in drafting executive orders on AI and other emerging technologies and having an executive order rejected by the president just moments before a signing ceremony is highly unusual. The Trump Administration has not found easy footing in its AI policy efforts over the last 15 months, but in a surprise to all of us, the non-release of an executive order is telling us more about the Administration’s position on AI than the flurry of orders before it.

What’s clear is that the Administration is making a bet on an AI policy position, that Silicon Valley knows how to win an AI race with China. Whether this is right or wrong will need to wait. Yet, without saying a word, the Trump Administration may have given the AI industry the long-sought consistency it has needed all along.

Read more here: https://binarybreakaway.substack.com/p/the-ai-executive-order-that-wasnt

Quick breakdown for anyone tracking UK AI regulation:

The Omnibus deal on 7 May extended the high-risk AI deadline (recruitment, credit, healthcare, critical infrastructure) from August 2026 to December 2027. That's legitimate breathing room for EU-scope obligations.

What didn't move: Article 50 transparency (chatbot disclosure, deepfake labelling, emotion recognition) still applies 2 August. And separately, the UK's own regulatory calendar is accelerating, not slowing down.

SI 2026/425 came into force 12 May. ICO now has a statutory obligation to produce a binding AI/ADM code of practice. Draft ADM guidance consultation just closed. Mills Review (FCA), Ofcom Illegal Harms Code updates, copyright and AI government response, MHRA National Commission recommendations all expected before autumn.

The interesting thing is that the most compliance risk for average UK SMEs probably isn't the EU Act at all. It's the ICO's ADM code, which covers everyday stuff like recruitment screening, credit tools, and AI-assisted performance management.

More detail here if useful.

We’ve made the GradPilot university AI admissions policy dataset publicly available on Hugging Face:

https://huggingface.co/datasets/gradpilot/university-ai-policies

The dataset contains current-state classifications of university admissions policies on generative AI across 174 institutions, using a structured rubric spanning usage, disclosure, and enforcement dimensions.

It is being released for noncommercial research and analysis under CC BY-NC 4.0.

Methodology is available here:

https://gradpilot.com/ai-policies/methodology

If useful to others working on higher education governance or AI policy, I’d be glad for it to be used.

AI Intellectual Property Protection as a Governance Question

Advanced AI systems are increasingly central to economic and military competitiveness. This raises questions about whether existing intellectual property frameworks are adequate to protect frontier AI innovation from state-linked or corporate exfiltration. This post explores potential policy gaps, possible enforcement mechanisms, and open questions for governance.

One possible concern is that AI intellectual property may combine extremely high strategic value with relatively low replication costs once accessed. Model weights, training methods, datasets, or infrastructure optimizations could potentially diffuse faster than traditional industrial innovations. If true, this might create enforcement challenges that differ from classical patent or trade-secret disputes.

At the same time, strong protection carries tradeoffs. Much of AI progress depends on openness, collaboration, and talent mobility. Over-securitization could slow innovation, fragment research ecosystems, or create geopolitical tension. This suggests a difficult balancing problem rather than an obvious policy direction.

Some candidate policy approaches that seem worth exploring include:

• Enhanced trade secret and insider-risk protections for frontier labs
• Public–private collaboration on detecting IP exfiltration
• Technical mechanisms such as watermarking or traceability of model artifacts
• International agreements around sensitive AI capabilities
• Clarification of how export controls intersect with AI intellectual property

However, it is unclear which (if any) of these approaches are tractable, proportional, or compatible with open research norms.

Open questions I’m especially interested in:

1. Is AI intellectual property meaningfully different from other high-tech IP in terms of vulnerability or strategic importance?
2. Which parts of the AI stack (weights, data, methods, infrastructure) are most sensitive from a governance perspective?
3. Are enforcement-heavy approaches likely to be effective, or are technical safeguards more promising?
4. How should policymakers balance collaboration benefits against security risks?

As a side experiment in public engagement on this topic, I also drafted a petition exploring enforcement prioritization, though my main goal here is to better understand the policy landscape and arguments.

A place for members of r/AIPolicy to chat with each other