GovAI

We help decision-makers navigate the transition to a world with advanced AI, by producing rigorous research and fostering talent.

[GovAI @GovAIOrg]

We're looking for a Head of Hiring to join GovAI!
You'll lead a growing team, shape our recruitment strategy, and strengthen how we identify and assess exceptional talent as GovAI scales.

Help us build the next generation of AI governance talent. Apply by 22 November:
https://t.co/dt3ozKvhHl

[GovAI @GovAIOrg]

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: https://t.co/f6lul6qfHi

🔗 Learn more about the Applied Track: https://t.co/ixSB0pG1sC

[GovAI @GovAIOrg]

New technical report out today from GovAI researcher Jake Steckler: "General-Purpose AI and the Future of Drone Autonomy in Contested Environments"

Check out Jake's thread with key takeaways below, and read the full report here: https://t.co/ay4bOGPuwi

[Jake Steckler @JakeASteckler]

Rapid AI progress has many people imagining a world where general-purpose AI models autonomously control military drones at scale. But there’s little analysis on what this would look like in practice, and how soon it might be feasible.

My latest paper at @GovAIOrg asks what it would take to make this vision a reality, and what the path there means for AI policy.

Read it here: https://t.co/eiTWhtRHRQ

The AI-enabled drones widely fielded today use narrow AI, not general-purpose models like LLMs. AI is typically only used to handle limited portions of a flight when the link to a pilot is broken, and these models are brittle in unpredictable conditions on real-world battlefields. Because of this, today’s drones still require significant pilot involvement to be most effective.

General-purpose AI models might make it possible to field autonomous systems that can receive plain language commands and make complex decisions in unpredictable environments without human control. Many people see frontier LLMs as a path toward this end - and rapid progress in robotics capabilities by frontier models like GPT-6 Astra and most recently Claude Fable 5.2 supports this case.

But two things currently stand in the way:

- The biggest and most powerful models are still not capable enough even in controlled environments without compute and latency constraints
- Military drones will have to operate “on the edge” in contested environments without consistent cloud compute access - which makes these constraints even more severe

If and when general-purpose AI plays a major role in drone autonomy, it’ll likely be through models that are far smaller than today’s frontier, and those that militaries can rapidly modify. And, notably - when it comes to these types of small open-weight models, China currently dominates.

This has a variety of implications for the US military, frontier AI developers, and US policymakers.

Thread below with my key takeaways and recommendations.

1/

[GovAI @GovAIOrg]

Apply for GovAI's 2027 DC Summer Fellowship!

📍 Location: Washington, DC

📅 Deadline: Sunday, November 22nd, 2026 at 11:59 pm ET

The DC Summer Fellowship is a three-month bipartisan opportunity designed to accelerate or launch impactful careers in American AI governance and policy. Participants deepen their understanding of the field, connect with a network of experts, and build their skills and professional profile, all while conducting a research project of their choice under expert guidance.

🔗 Learn more: https://t.co/HVhrNHMTk0

[Jacob Charnock @charnock_jacob]

New GovAI post: I argue Frontier AI companies should publish their third-party evaluation contracts. This would show the conditions evaluators work under, make results easier to interpret and compare, and create incentives to improve evaluation arrangements.

More in thread 🧵

[GovAI @GovAIOrg]

Apply for GovAI's 2027 DC Summer Fellowship!

📍 Location: Washington, DC

📅 Deadline: Sunday, November 22nd, 2026 at 11:59 pm ET

The DC Summer Fellowship is a three-month bipartisan opportunity designed to accelerate or launch impactful careers in American AI governance and policy. Participants deepen their understanding of the field, connect with a network of experts, and build their skills and professional profile, all while conducting a research project of their choice under expert guidance.

🔗 Learn more: https://t.co/HVhrNHMTk0

[GovAI @GovAIOrg]

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: https://t.co/ByViwtygR6

🔗 Learn more about the Applied Track: https://t.co/2KBsNDngi0

[GovAI @GovAIOrg]

New research from our team and great collaborators out today ⬇️

You can read the full post on our website: https://t.co/gRhljJt9Qs

[Sharif Kazemi @SharifKazemi]

Understanding how governments use AI is important, so how are UK public servants using AI at work today?

To start getting a sense of this, we polled ~200 UK public servants from our network - in collaboration between @GovAIOrg, @B_I_Team, and @apoliticalco.

Results suggest many public servants today use AI daily for routine tasks, and that broader adoption might be held back by lack of trust in accuracy and security.

🧵below on key charts. A blog summary of our results can be found on the GovAI website: https://t.co/zsH58eaqXU

1) The vast majority (95%) of our respondents report using AI tools for work in the last month, and 6 out of 10 use it at least once per day.

[GovAI @GovAIOrg]

Welcome to the team, Bharat!

[Bharat Chandar @BharatKChandar]

Happy to share I am now Research Director for AI Economics and Policy at @DigEconLab. As part of new role I will be leading the AI Economic Indicators.

I am also now a Research Fellow at @GovAIOrg. I'm excited to work with them on most important questions in econ of AI.

[GovAI @GovAIOrg]

Interested to learn how Chinese companies talk about recursive self-improvement (RSI) and what that means?

Check out the new article by GovAI researchers Cheryl Wu and Zilan Qian.

→ https://t.co/WcTHBeA0WC

[Cheryl Wu @cherylwoooo]

[1/7] China’s AI labs are also talking about “recursive self-improvement” (RSI). A big question here is: Is Beijing coordinating a race to achieve RSI?

@ZilanQian and I looked at Chinese AI companies, investors, and policy docs.

We found a more bottom-up, commercial story.

[Markus Anderljung @Manderljung]

What's the gap between open-weight and closed models on biological tasks?

@GovAIOrg's Hanna Palya analysis suggests about 7 months.

[GovAI @GovAIOrg]

At GovAI, we have a growing team and focus on AI in China — so stay tuned for updates from us in this space!

For instance, how do Chinese AI firms make money?

GovAI researcher Cheryl Wu takes a look at this question in recent work collaborating with @EpochAIResearch.

[Epoch AI @EpochAIResearch]

How do Chinese AI firms make money? In a new report, @cherylwoooo and @ansonwhho identify five revenue sources and analyze how much each contributes. 🧵

[GovAI @GovAIOrg]

Read it here: https://t.co/dA2fjO0Bnj

[GovAI @GovAIOrg]

Does China think it's in an “AI race” with the US?

How worried is Beijing about AI-driven job loss?

Find out answers to these questions and many more in this explainer co-authored by GovAI researcher Zilan Qian.

[Zilan Qian @ZilanQian]

And wondering whether China thinks it is in an “AI Race” With the United States, whether Beijing is worried about AI-Driven job loss, or China's public opinion about AI? We also got answers here!

[GovAI @GovAIOrg]

Learn more in the recent paper led by GovAI researcher Alan Chan: https://t.co/VgFWKzMt1y

[GovAI @GovAIOrg]

How could using AI to automate the AI research and development process itself lead to a potentially rapid speed-up in capabilities? What's the current evidence for how much this is happening already?

[Alan Chan @_achan96_]

On current trends, it’s possible that AI research & development (R&D) will be fully automated within the next few years. We should take this seriously.

Although the existing evidence is preliminary and sometimes mixed, it suggests that this positive feedback loop could lead to an “intelligence explosion” that compresses years of AI progress into months or less.

[GovAI @GovAIOrg]

Here is the full paper: https://t.co/dktkO8uAlA

[GovAI @GovAIOrg]

You might have heard recently about third-party evaluation for frontier AI and the idea of "embedded assessments" — learn more about what this is, why it matters, and how it could work in practice in the recent paper from our team.

[Jonas Freund @jonasfreund_]

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: https://t.co/Ul5ayxyGwH

[GovAI @GovAIOrg]

More new work from GovAI researcher Cheryl Wu (@cherylwoooo), in collaboration with Epoch AI.

[Epoch AI @EpochAIResearch]

How do Chinese AI firms make money? In a new report, @cherylwoooo and @ansonwhho identify five revenue sources and analyze how much each contributes. 🧵

[GovAI @GovAIOrg]

Check out this recent work on recursive self-improvement (RSI) from GovAI researcher Cheryl Wu.

[Cheryl Wu @cherylwoooo]

[1/5] The 8 most valuable data points labs should share to help measure RSI:

First, RSI would likely accelerate growth in AI capabilities.

Thus, companies should report performance on diverse benchmarks for the latest internally deployed models.