Daniel Pryor (@DanielPryorr) header
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Research Assistant @GovAIOrg. Views my own.

London, England
Joined April 2011
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OH MY GOD 😂😭 – at Camberwell, London
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Daniel Pryor retweeted
KIMI ANTONELLI IS THE 2026 F1 WORLD CHAMPION!!! 🏆 The youngest to ever do it at 20 years and 68 days old! #F1 #MexicoGP
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Daniel Pryor retweeted
Six months in and the tribunal system is already gumming up. There’s basically zero reason not to challenge any rent increase (apart from a lingering reluctance to take the piss), and as more tenants cotton on, practically the entire rental market in England will grind to a halt.
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Daniel Pryor retweeted
A ‘hello world’ milestone for Project Suncatcher 🚀 After launching our prototype satellite a few weeks ago, we’ve now successfully sent a command up to orbit, booted up the TPU, ran the prompt, and received our first complete response back on earth - a haiku!
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Daniel Pryor retweeted
The meme is true!
Replying to @AIRiskExplorer
Open-weight models, led by DeepSeek, appeared in 21 reported operations in Q3 2026, more than in all previous quarters combined (14). In H2 2026, they appear in 46% of the operations where we know the model type.
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Daniel Pryor retweeted
Apply for GovAI's 2027 Summer Fellowship, Research and Applied tracks! 📍 Location: London, UK 📅 Deadline: 23:59 GMT Sunday, 22 November 2026 Fellows will join us in London to conduct a project on a topic of their choice, participate in an expert Q&A and seminar series aimed at developing a broad understanding of the field, and spend time forging connections with researchers and practitioners. Learn more below. 🔗 Learn more about the Research Track: governance.ai/post/uk-summer… 🔗 Learn more about the Applied Track: governance.ai/post/uk-summer…
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Daniel Pryor retweeted
I’m an avid Economist reader, but this article is wide off the mark for me. It claims that distillation makes China’s AI progress dependent on America. ASML’s dominance of advanced lithography machines hasn’t exactly led to China giving up on its semiconductor indigenisation push, has it? News over the summer of the U.S. raising concerns that an EUV machine had been smuggled into China is but one example. We heard just yesterday that Chinese companies allegedly distilled from Astra amidst an Anthropic clamp down. If these companies weren’t getting value from distillation, why would they continue, despite having now been called out by the USG for doing it? China will never accept dependence on America, especially when it comes to AI. The article further claims that cutting off distillation would “set these companies back months, but no more.” The point is not the length of time but how much of a lead in capabilities this translates to. Recent IFP analysis suggests that distillation accounts for half of the capability gap and the U.S. lead would be 13 months without it. Picture the AI capabilities we had 13 months ago versus now (and that doesn’t even account for what is available internally) and then think about where we could be 13 months from now. The article further states that: “safeguards to restrain American models on risky subjects, such as bioweapons, already make Chinese models safer.” This assumes that the only way Chinese models can have dangerous capabilities is by copying them from American ones. It is trivial to fine-tune models on additional data which can enhance their dual-use capabilities conditional on the data being available. This also doesn’t account for custom harnesses which can be developed to surround these models which can enhance their capabilities further even without fine-tuning. Finally, the claim that “Distillation […] reduces the chance of catastrophe” fails to account for the evidence we already have of how models developed by Chinese companies using adversarial distillation are popping up in the PLA. We also know (thanks to Anthropic) that PLA-affiliated surveillance activity was detected because they queried a Chinese model that silently routed their query to Claude as part of a distillation program! If we had a war between the U.S. and China, how does having highly capable models integrated into PLA systems that benefitted from adversarial distillation reduce the chance of catastrophe? Yes distillation benefits the consumer but the downsides of China doing this in an industrial way clearly outweigh the benefits in my view. anthropic.com/threat-intelli… bloomberg.com/news/articles/… reuters.com/technology/deeps… anthropic.com/news/distillat… openai.com/index/estimating-… cset.georgetown.edu/publicat… jamestown.org/chinese-resear… dset.tw/en/research/distilla… ifp.org/how-to-increase-amer… cisa.gov/news-events/cyberse…
In praise of distillation. We (@kuhhbin) argue in @TheEconomist this week against efforts to ban Chinese models trained in part on American ones. OpenAI and Anthropic may dislike distillation, but: — Price competition is good for the ecosystem — Paradoxically, distillation may be good for national security, both in propagating US developers' safeguards on cyber/bio risk to Chinese models, and in keeping China in fast-follower mode — The US developers' own IP claims here are pretty flimsy, since distillation isn't all that different to the 'fair use' defense they're using for the training data they hoover up. economist.com/leaders/2026/1…
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Daniel Pryor retweeted
A recent set of posts by @ramez on the current state of recursive self improvement highlighted that OpenAI's report on their own research automation shows that models struggle to do their R&D work — the models' 80% time horizon is only 15 minutes: 🧵
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I distinctly remember lots of people saying this wouldn’t happen, because it wasn’t the intention. That’s not how laws work!
We have nationwide rent controls now. This tribunal didn’t just stop a rent rise within a tenancy, it cut the rent to below what had been agreed between the landlord and the tenant at the start of the tenancy based on a judgement of what the property is worth.
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Daniel Pryor retweeted
Big new IFP report from @GeorgiaCAdamson, @KonstantinPilz, and me on the US-China AI competition. We spent the last 6 months digging into how Chinese AI companies’ access to compute and distillation of American models translate into the current US-China capabilities gap. The result is a 23k-word research report, including an interactive analytical model that visualizes changes in the US AI lead under different policy interventions. Our findings: 1. The US AI lead over China is currently only around 6 months. 2. The US could roughly double this lead, to over a year, using authorities already available to the federal government. 3. On chips: China’s access to AI chips will roughly double over the next year, with chips housed in data centers overseas accounting for roughly half of China’s 2027 compute. 4. On distillation: the current ~16x US training compute advantage would normally yield a 13-month lead. But we estimate adversarial distillation halves this to around 6.5 months. 5. Increasing the lead would have some obvious benefits: - Give the US broadly a greater cyberdefensive advantage against Chinese threat actors. - Give US AI companies breathing room to spend more R&D resources on making their systems safer and more secure, without fear of being overtaken by less safety-conscious Chinese competitors. 6. Provide the US government more leverage in negotiations with China about the future of AI. 7. The two most important interventions are: - For BIS: Create a new end-use control to restrict China’s remote access to training clusters abroad — China’s largest and fastest-growing source of frontier-scale compute. - For DOJ/FTC: Create a narrow safe harbor to allow US companies to coordinate on countering Chinese adversarial distillation. 8. Both interventions (and all the others we discuss in the report) are things the federal government could do today. 9. To estimate the effects of each policy intervention, we model the US lead as a function of its compute advantage over China and China’s ability to adversarially distill the capabilities of the US frontier. 10. The model makes many simplifying assumptions, but provides a useful way to assess and rank the impact of different policy interventions at a high level. Read the full report here: ifp.org/how-to-increase-amer…
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"The driver of Britain’s water shortages isn’t data centres, it is a 30 year failure to build the reservoirs we need to meet demand" open.substack.com/pub/samdum… @Sam_Dumitriu
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Daniel Pryor retweeted
Exclusive: The White House has reinstated the UK’s access to frontier AI models for safety testing after lobbying from ministers. The US government reversed its decision to restrict access to the latest models from Anthropic, OpenAI and Google to stop the UK’s AI Safety Institute (AISI) from testing them before release. Kanishka Narayan, the AI minister, has been involved in lobbying efforts to restore access. Ministers feared that without the models, Britain’s attempt to become a world leader in AI regulation would be dead in the water. telegraph.co.uk/news/2026/10…
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Daniel Pryor retweeted
This is a slide from Terence Tao's 'Math 2.0' public lecture yesterday evening at Caltech. I am highly confident that, if the cure actually worked, almost any Stage 4 cancer patient would tell you that human comprehension of its mechanism is completely irrelevant to them.
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Published today in PNAS Nexus: Conversational AI increases political knowledge as effectively as self-directed internet search. AISI’s Human Influence team finds that conversational AI's effects on political knowledge generalise across multiple topics, political perspectives, and model families, suggesting that the shift toward AI-assisted political information-seeking may not lead to increased public belief in political misinformation. You can access the paper here: academic.oup.com/pnasnexus/a…
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Daniel Pryor retweeted
This is, IMO, a pretty miserable calculus. I don’t want to have to beg for access to economically critical technologies. This is one reason to build a lot of compute now so that we have some leverage if it comes to that.
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Daniel Pryor retweeted
Closer alignment with the EU, especially on security and defence, seems to now come with a major trade-off of making it harder to join whatever AI umbrella the US creates for aligned countries. If we end up in a timeline where the US has much better models than the rest of the world, then having the option of adopting whatever measures it demands to get access to them (eg around national security, tech regulation, and defence) will be very important.
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Daniel Pryor retweeted
Could automating AI R&D radically accelerate AI progress in an “intelligence explosion”? Preliminary evidence suggests that it could. In a new paper with authors across academia, civil society, and frontier AI companies (including @dawnsongtweets @merettm @jackclarkSF @Yoshua_Bengio @geoffreyhinton ), we assess the evidence & offer policy recommendations 🧵
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Third-party evaluators mostly test frontier AI models through an API. That misses the risks that come from how companies build and use AI internally. Our new paper makes the case for embedded assessments. It was led by Jacob Charnock. The other co-authors are Sophie Williams, @zaheedkara, @Manderljung, Alejandro Tlaie Boria, @StephenLCasper, @AnkaReuel, and me. 🔗 Read the paper: governance.ai/research-paper…
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Daniel Pryor retweeted
I am increasingly frustrated about the state of conversation about AI in the UK. It feels like we are in never ending cycles of deluded thinking. Under current trends, Britain will be defenceless against the transformative effects of AI and have no leverage over development at the frontier. We are the most exposed economy to AI in the world. We have very little data centre capacity and few credible plans to expand – and our high energy costs mean we would struggle to even if we tried. I am publishing a paper today on what the UK should do to build that leverage, let the economy capture value from AI and prepare the state to adapt, however AI develops. The three strategies are as follows: 1. Build the infrastructure that gives us leverage. Fix the grid, fix planning, make Britain a place frontier AI can run. 2. Let the economy capture the value of AI. Reform the regulatory, labour and talent barriers that stop British firms adopting AI. 3. Build a state that can adapt to a changing world. Build a tax base that survives the labour market changing, public services that use AI to deliver better, and capacity to weather the upcoming geopolitical storms. Two years ago, we published a similar report on AI - many recommendations of which ended up in the action plan. I remember feeling hopeful, but sadly the fundamental problems facing the country on AI have not shifted. I truly hope that this week of momentum culminates in action. There’s still a narrow path for the UK, but everyday that path is narrowing.
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Daniel Pryor retweeted
Unless Britain sorts out its sky-high energy costs, it will abdicate whatever little remaining relevance it has to influence the future of AI. (H/t @jujulemons)
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Daniel Pryor retweeted
“A Transformative AI Strategy for Europe” is finally out. It argues that transformative AI could arrive before 2030, and that Europe isn’t ready. The strategy was written by independent experts in academia and civil society, including @vestager, @Yoshua_Bengio, and @DAcemogluMIT. It makes 77 recommendations over 206 pages. 🔗 Read the strategy: transformative-ai.eu 🔗 Download the PDF: transformative-ai.eu/downloa… Five objectives are flagged as immediate: ▶︎ Create a Member State Alliance for Supply Chain Security Europe has no privileged access to frontier AI models. But it does host chokepoints in the AI supply chain (e.g. ASML’s lithography machines and Zeiss optics). The Netherlands, Germany, and France should found an alliance to turn those assets into leverage, mainly to protect against economic coercion. ▶︎ Make Europe’s institutions ready to act The European Commission and Member State governments can’t compete for world-class frontier AI talent. The strategy asks for faster hiring and better pay, backed by ring-fenced funding. One estimate puts the cost of properly resourcing the AI Office’s supervisory work at around €50-60 million a year. The EU spends €278.8 million a year subsidizing banana growers in its outermost regions. ▶︎ Secure Europe’s share of global AI compute Compute will be as important as energy, and Europe is a net importer of it. Data centers in Texas or Shenzhen can lock out European customers overnight. Europe hosts around 5% of global AI compute and should aim for 15% by 2030. Getting there means making data centers much faster to permit and connect. ▶︎ Ensure resilience to AI crises Transformative AI would pose severe risks (e.g. from cyber attacks, biological weapons, and loss of control). Some of this is already happening. OpenAI, Anthropic, and the UK AI Security Institute have all lost control of AI agents in recent months. Europe needs hardened critical infrastructure and stockpiled crisis resources. ▶︎ Make Europe the global leader in AI assurance technology Assurance technology verifies claims about AI models and the infrastructure they run on. Europe can’t show that a hosted model is secure, or that a foreign model is free of hidden backdoors (e.g. “secret loyalties”). So developers may be reluctant to host unreleased frontier models on EU soil. The same technology could verify whether countries keep to any future agreement to pace AI development. Kudos to @MonikaSchnitzer and @privitera_ for initiating and steering the project. Glad I was able to contribute.
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