Quick takes

The Current Landscape: The current State-of-the-Art (SOTA) in function-based screening relies heavily on sophisticated machine learning models, such as Transformers and Sparse Autoencoders (SAEs), trained on massive genomic databases. These tools excel at analyzing sequence text to identify familiar structural homologies or dangerous functional motifs. By screening digital intent at the order stage, these models provide a highly effective defense against known biological threats and their immediate variants. The Frontier Challenge: However, as synthesis capabilities advance, the frontier of biosecurity faces a deeper challenge: predicting entirely novel, engineered mutations that do not exist in any historical training data. When an amino acid sequence is heavily modified, its digital text changes drastically, often allowing it to clear traditional pattern-matching filters as unclassified noise. Yet, if the altered sequence retains the ability to fold into the same functional three-dimensional shape, the underlying threat remains identical. To a purely computational framework, mapping these potential evolutionary trajectories feels like an intractable problem because the theoretical mutational space is nearly infinite. A Physics-Based Complement: A highly promising frontier lies in integrating these machine learning screens with principles of statistical mechanics to radically bound this problem space. In the physical world, an amino acid chain cannot simply adopt any arbitrary configuration; its survival and function are strictly governed by its thermodynamic energy landscape. Out of billions of theoretical sequence combinations, the vast majority are physically non-viable—they will naturally misfold, aggregate, or degrade due to energetic constraints. While calculating these landscapes from scratch remains a monumentally difficult computational challenge, leveraging thermodynamic stability models allows us to systematically filter out the non-physical noise. By

The recent work on SAEBER, which applies sparse autoencoders (SAEs) to the screening of dna synthesis printers marks a big step towards effective function based screening. This allows for printers to be monitored just as a lab technician uses computational gel electrophoresis to separate a messy mixture into clear, readable bands through the use of a specialized gel. SAEs happen to do the exact same thing by taking the muddied activation results of a neural network and projecting them out onto a higher dimensional space until the individual viral motifs can be seen clearly. This allows for the motifs to be tracked as they move through the system in real-time, rather than waiting for a final product. However, while SAEBER is undoubtedly an effective method, can we say for a fact that it is the best tool for function based screening? Would it be better to scan the digital thoughts of the AI responsible for guiding the system generating the product, or monitoring the stability of the system itself, given that we can model the printer's physical state at any given time step during the printer's run? While scanning the digital motifs helps provide an understanding of the AI's intent, it would be interesting to see if monitoring the physical state of the printer might provide a more resilient safety net. My intuition is that modelling the printer’s state as a physical landscape and understanding the implications of changes in the landscape might be more prone to false positives from natural noise, but it also has the potential to be better at detecting divergence much earlier than waiting to interpret a complex digital signal. Has there been much discussion on combining these—using the physics of the machine to flag a problem, and the AI’s internal motifs to figure out exactly what that problem is?

So... what's the general take on the hantavirus outbreak?

Consultancy Opportunities – Biological Threat Reduction 📢📢📢 The World Organisation for Animal Health (WOAH) is looking for two consultants to support the implementation of the Fortifying Institutional Resilience Against Biological Threats (FIRABioT) Project in Africa.  Supported by Global Affairs Canada's Weapons Threat Reduction Program, this high-impact initiative aims to support WOAH Members in strengthening capacities to prevent, detect, prepare, respond and recover from biological threats. The project also supports the implementation of the Signature Initiative to Mitigate Biological Threats in Africa, an initiative of the Global Partnership to Prevent the Spread of Weapons and Materials of Mass Destruction. WOAH is looking for one consultant to work with anglophone countries and another to work with francophone countries. 📍 Base: Nairobi (remote work possible) ✈️ Travel: Regular missions across Africa 📅 Duration: 6 months (March–August 2026) These consultants will work closely with WOAH regional and HQ teams to design, deliver, and report on an ambitious technical programme, including in-person workshops across WOAH Members in Africa. Focus areas include: • National contingency planning • Risk communication • Laboratory biological risk management • Management of high-consequence agents and toxins You’ll be work in collaboration with Veterinary Services and other agencies to strengthen institutional resilience against biological threats. 📌 Deadline to apply: 14 February 2026 👉 For full details and application guidelines, please click here: Anglophone consultancy : https://rr-africa.woah.org/en/events/call-for-applications-consultant-for-biological-threat-reduction-and-emergency-preparedness-english-french-an-asset/ Francophone consultancy: https://rr-africa.woah.org/en/events/call-for-applications-consultant-for-biological-threat-reduction-and-emergency-preparedness-advanced-proficiency-in-french-is-mandatory/  

According to someone I chatted to at a party (not normally the optimal way to identify top new cause areas!) fungi might be a worrying new source of pandemics because of climate change. Apparently this is because thermal barriers prevented fungi from infecting humans, but because fungi are adapting to higher temperatures, they are now better able to overcome those barriers. This article has a bit more on this: https://theecologist.org/2026/jan/06/age-fungi Purportedly, this is even more scary than a pathogen you can catch from people, because you can catch this from the soil. I suspect that if this were, in fact, the case, I would have heard about it sooner. Interested to hear comments from people who know more about it than me, or have more capacity than me to read up about it a bit.

Gavi's investment opportunity for 2026-2030 says they expect to save 8 to 9 million lives, for which they would require a budget of at least $11.9 billion[1]. Unfortunately, Gavi only raised $9 billion, so they have to make some cuts to their plans[2]. And you really can't reduce spending by $3 billion without making some life-or-death decisions. Gavi's CEO has said that "for every $1.5 billion less, your ability to save 1.1 million lives is compromised"[3]. This would equal a marginal cost of $1,607 $1,363 per life saved, which seems a bit low to me. But I think there is a good chance Gavi's marginal cost per life saved is still cheap enough to clear GiveWell's cost-effectiveness bar. GiveWell hasn't made grants to Gavi, though. Why? ---------------------------------------- 1. https://www.gavi.org/sites/default/files/investing/funding/resource-mobilisation/Gavi-Investment-Opportunity-2026-2030.pdf, pp. 20 & 43 ↩︎ 2. https://www.devex.com/news/gavi-s-board-tasked-with-strategy-shift-in-light-of-3b-funding-gap-110595 ↩︎ 3. https://www.nature.com/articles/d41586-025-02270-x ↩︎

I’ve seen a few people in the LessWrong community congratulate the community on predicting or preparing for covid-19 earlier than others, but I haven’t actually seen the evidence that the LessWrong community was particularly early on covid or gave particularly wise advice on what to do about it. I looked into this, and as far as I can tell, this self-congratulatory narrative is a complete myth. Many people were worried about and preparing for covid in early 2020 before everything finally snowballed in the second week of March 2020. I remember it personally. In January 2020, some stores sold out of face masks in several different cities in North America. (One example of many.) The oldest post on LessWrong tagged with "covid-19" is from well after this started happening. (I also searched the forum for posts containing "covid" or "coronavirus" and sorted by oldest. I couldn’t find an older post that was relevant.) The LessWrong post is written by a self-described "prepper" who strikes a cautious tone and, oddly, advises buying vitamins to boost the immune system. (This seems dubious, possibly pseudoscientific.) To me, that first post strikes a similarly ambivalent, cautious tone as many mainstream news articles published before that post. If you look at the covid-19 tag on LessWrong, the next post after that first one, the prepper one, is on February 5, 2020. The posts don't start to get really worried about covid until mid-to-late February. How is the rest of the world reacting at that time? Here's a New York Times article from February 2, 2020, entitled "Wuhan Coronavirus Looks Increasingly Like a Pandemic, Experts Say", well before any of the worried posts on LessWrong: The tone of the article is fairly alarmed, noting that in China the streets are deserted due to the outbreak, it compares the novel coronavirus to the 1918-1920 Spanish flu, and it gives expert quotes like this one: The worried posts on LessWrong don't start until weeks after this article was p

Ajeya Cotra writes: Like Ajeya, I haven't thought about this a ton. But I do feel quite confident in recommending that generalist EAs — especially the "get shit done" kind —  at least strongly consider working on biosecurity if they're looking for their next thing.