tylermjohn's Quick takes

I'd be excited to see 1-2 opportunistic EA-rationalist types looking into where marginal deregulation is a bottleneck to progress on x-risk/GHW, circulating 1-pagers among experts in these areas, and then pushing the ideas to DOGE/Mercatus/Executive Branch. I'm thinking things like clinical trials requirements for vaccines, UV light, anti-trust issues facing companies collaborating on safety and security, maybe housing (though I'm not sure which are bottlenecked by federal action). For most of these there's downside risk if the message is low fidelity, the issue becomes polarized, or priorities are poorly set, hence collaborating with experts. I doubt there's that much useful stuff to be done here, but marginal deregulation looks very easy right now and looks good to strike while the iron is hot. 

AIxBio looks pretty bad and it would be great to see more people work on it

• We're pretty close to having a country of virologists in a data center with AI models that can give detailed and accurate instructions for all steps of a biological attack — with recent reasoning models, we might have this already
• These models have safeguards but they're trivial to overcome — Pliny the Liberator manages to jailbreak every new model within 24 hours and open sources the jailbreaks
• Open source will continue to be just a few months behind the frontier given distillation and amplification, and these can be fine-tuned to remove safeguards in minutes for less than $50
• People say it's hard to actually execute the biology work, but I don't see any bottlenecks to bioweapon production that can't be done by a bio undergrad with limitless scientific knowledge; on my current understanding, the bottlenecks are not manual dexterity bottlenecks like playing a violin which require years of practice, they are knowledge bottlenecks
• Bio supply chain controls that make it harder to get ingredients aren't working and aren't on track to work
• So it seems like we're very close to democratizing (even bespoke) bioweapons. When I talk to bio experts about this they often reassure me that few people want to conduct a biological attack, but I haven't seen much analysis on this and it seems hard to be highly confident.

While we gear up for a bioweapon democracy it seems that there are very few people working on worst-case bio, and most of the people working on it are working on access controls and evaluations. But I don't expect access controls to succeed, and I expect evaluations to mostly be useful for scaring politicians, due in part to the open source issue meaning we just can't give frontier models robust safeguards. The most likely thing to actually work is biodefense.

I suspect that too many people working on GCR have moved into working on AI alignment and reliability issues and too few are working on bio. I suspect there are bad incentives, given that AI is the new technology frontier and working with AI is good career capital, and given that AI work is higher status.

When I talk to people at the frontier of biosecurity, I learn that there's a clear plan and funding available, but the work is bottlenecked by entrepreneurial people who can pick up a big project and execute on it autonomously — these people don't even need a bio background. On my current guess, the next 3-5 such people who are ambivalent about what to do should go into bio rather than AI, in part because AI seems to be more bottlenecked by less generalist skills, like machine learning, communications, and diplomacy.

Just Compute: an idea for a highly scalable AI nonprofit

Just Compute is a 501c3 organization whose mission is to buy cutting-edge chips and distribute them to academic researchers and nonprofits doing research for societal benefit. Researchers can apply to Just Compute to get access to the JC cluster, which supports research in AI safety, AI for good, AI for science, AI ethics, and the like, through a transparent and streamlined process. It's a lean nonprofit organization with a highly ambitious founder who seeks to raise billions of dollars for compute. 

The case for Just Compute is fairly robust: it supports socially valuable AI research and creates opportunities for good researchers to work in AI for social benefit and without having to join a scaling lab. And because frontier capabilities are compute constrained, it also slows down the frontier by using up a portion of the total available compute. The sales case for it is very strong, as it attracts a wide variety of donors interested in supporting AI research in the academy and at nonprofits. Donors can even earmark their donations for specific areas of research, if they'd like, perhaps with a portion of the donations mandatorily allocated to whatever JC sees as the most important area of AI research.

If a pair of co-founders wanted to launch this project, I think it could be a very cool moonshot!

AI swarm writers:

Comms is a big bottleneck for AI safety talent, policy, and public awareness. Currently the best human writers are better than the best LLMs, but LLMs are better writers than 99% of humans and much easier to align to a message and style than human employees. In many venues (particularly social media) factors other than writing and analytical quality drive discourse. This makes a lot of comms a numbers game. And the way you win a numbers game is by scaling a swarm of AI writers. 

I'd like to see some people with good comms taste and epistemics, thoughtful quality control, and the diligence to keep at it experiment with controlling swarms of AI writers producing and distributing lots of decent quality content on AI safety. Probably the easiest place to get started would be on social media where outputs are shorter and the numbers game is much starker. As the swarms got good, they could be used for other comms, like blogs and op eds. 4o is good at designing cartoons and memes, which could also be utilized.

To be clear, there is a failure mode here where elites associate AI safety with spammy bad reasoning and where mass content dilutes the public quality of the arguments for safety, which are at the limit are very strong. But at the moment there is virtually zero content on AI safety, making the bar for improving discourse quality relatively low.

I've found some AI workflows that work pretty well, like recording long voice notes, turning them into transcripts, and using the transcript as context for the LLM to write. I'd be happy to walk interested people through this or, if helpful, write something public. 

I want to see a bargain solver for AI alignment to groups: a technical solution that would allow AI systems to solve the pie cutting problem for groups and get them the most of what they want, for AI alignment. The best solutions I've seen for maximizing long run value involve using a bargain solver to decide what ASI does, which preserves the richness and cardinality of people's value functions and gives everyone as much of what they want as possible, weighted by importance. (See WWOTF Afterwards, the small literature on bargaining-theoretic approaches to moral uncertainty.) But existing democratic approaches to AI alignment seem to not be fully leveraging AI tools, and instead aligning AI systems to democratic processes that aren't empowered with AI tools (e.g. CIPs and CAIS'S alignment to the written output of citizens' assemblies.) Moreover, in my experience the best way to make something happen is just to build the solution. If you might be interested in building this tool and have the background, I would love to try to connect you to funding for it. 

For deeper motivation see here.