Tom Fehring (@tom_fehring) header
Tom Fehring (@tom_fehring) profile picture

quantifying catastrophic AI risk @AIUnderwriting. formerly forecasting @OpenAI, even more formerly an actuary. opinions my own.

San Francisco, CA
Joined March 2026
Photo by Tom Fehring (@tom_fehring)Photo by Tom Fehring (@tom_fehring)Photo by Tom Fehring (@tom_fehring)Photo by Tom Fehring (@tom_fehring)Photo by Tom Fehring (@tom_fehring)
Tom Fehring retweeted
Replying to @mattyglesias
I think you've got it backward. There's still plenty of neglected and unappreciated work to be done in AI safety; it's not one big homogenous cool and popular blob. You're suspicious of "moral ambition" (in the sense Bregman uses it), i.e. continually reconsidering whether one is pursuing the best approach to a world problem, because you suspect the people pursuing this line of thinking are feeding their egos, trying to be the center of attention. But the opposite is true in my experience—the people I know who are genuinely motivated by AI safety but _aren't_ pushing themselves to be morally ambitious go work at Anthropic (where you can become rich, get social cachet, be part of a big community, follow a leader you trust), whereas the people you're suspicious of, who are constantly thinking "could I be having more impact elsewhere?", are disproportionately in AI safety jobs that are more neglected/do-it-yourself, aren't the center of attention, and receive less social validation.
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The METR team is really incredible
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Tom Fehring retweeted
At the next Bay Area House Party: Oh yeah our startup chemically wireheads bugs. We're producing billions of positive subjective experience-moments and selling them as offsetting credits to regular animal factory farms so they can sell net-positive chicken.
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many things feel better: taking off uncomfortable shoes at the end of a long day, saying two things with no obvious commonality followed by etc., etc.
nothing feels better than saying one (1) thing followed by etc.
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Tom Fehring retweeted
In the last several years AI has progressed rapidly but predictably, and in that time the cyber community learns the bitter lesson over and over again. We’ve collectively sleep walked into the current state of things and now I see emotionally driven responses when those outside the community try to address it. “Do nothing” and continuing down the same path isn’t a counter proposal. These models are far more capable and scaleable than any of us. And they cannot be deterred like a human adversary. Why would we reject the possibility that an agentic swarm could take down vast swaths of the internet, including critical infrastructure? This is rather ironic considering I've seen virtually no push back to L0pht's Senate testimony 28 years ago (or any of the panels celebrating its anniversary since) where they claimed they could take down the internet in 30 minutes. We all know how broken things are, how so many in leadership never respond beyond moral support despite the overwhelming evidence. I'm no AI doomer, quite the opposite, but the current state of cyber stands in the way of realizing all of AI's benefits. The old way isn't going to work anymore and its time to abandon it. Abandon the complex risk management spreadsheets, the performative training, the compliance regimes, and yes remove humans in the loop for every decision. None of this will survive the speed and sophistication of agents driven by frontier models.
Consider this my endorsement of this letter. There are technical solutions to most problems in cyber security and AI can efficiently apply all of them at scale. Enable your security team to be a force multiplier with these tools, not a permanent cost center.
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You can't just say you're going to steelman and then strawman! You set up this fully autonomous facility that could physically engineer all viruses and even manufacture its own DNA sequences. Then you essentially say "but we screen sequences" and "this lab cannot exist today." No one is arguing that you would need a facility like this. This is a bad argument as such a facility would indeed cost $100M, and we know that synthesizing a complex pox virus today in a university lab costs <$100k. You also say "there is no such thing as 1 lab that can synthesize all conceivable viruses." First, again, you do not need this. Second, different viruses do not typically change the fundamental lab equipment you would need –– just inputs like cell lines and reagents. (e.g. If you can make smallpox, you don't really need different equipment for ebola.) These are sometimes difficult to get, but motivated adversaries have typically been able to do so. (See: any state-level bioweapons program, other well-resourced groups.) Your two arguments against the steelman you set up are (1) that getting inputs is hard because we screen DNA sequences and (2) the science of creating an infectious virus is hard. Below are my counters. (1) Screening: Screening is currently not mandatory. Many providers do not screen, or screen insufficiently. We also do not need advanced AI to evade some of the screening we do have –– techniques like simply splitting orders between different providers have been shown to evade existing protocols. Even if screening is mandated, with controls on new benchtop synthesizers, it will still be possible to get around this. For more, see: ifp.org/how-to-secure-the-dn… (2) Virology is hard: Yes, virology is hard and there are physical barriers. There are several reasons why I do not think this means we should not be worried about bioweapons of mass destruction. First, sufficiently advanced AI is likely to change this. You do not need an automated lab for AI to provide benchwork uplift. I expect that eventually AI will also significantly improve our ability to use animal models, or even computational models, to predict in-human behavior. (And this is a good thing! Innovation will enable us to advance medicine far beyond what we can do today.) Second, timing is not a major concern for a motivated threat actor. If it takes a few years to design the right viruses, so be it. There is evidence that research consistent with gain-of-function work is going on as I write this, with at least some input from US frontier models, considering Anthropic's latest report. Finally, enabling biotechnology will improve, likely faster than normal because of AI. I will repeat this excerpt from a recent substack piece of mine: "This particular opinion is actually highly pessimistic about the rate of growth of technology. Many things used to be difficult that are no longer so. They become accessible as technology advances. Synthetic biology is not an exception to this, as we have watched costs fall and accessibility rise without fail since its inception. Before 2012, editing a specific site in a genome meant using zinc finger nucleases: custom-engineered proteins that cost $5,000+ to order, and were never widely adopted because they were so difficult to engineer well. CRISPR replaced this. You order the RNA guide sequence and buy the rest off the shelf, with total cost being as low as $30. This is a strong example of an engineering capability that was once siloed to a handful of specialist labs and is now an undergraduate exercise. Synthesis costs have also plummeted. Synthesizing the poliovirus genome cost about $300,000 in 2002, and the materials would cost under $1,000 today." Fearmongering is bad, but we don't need more AI uplift than we have now for bioweapons to be a major concern. There are active state weapons programs and well-resourced terror groups exploring bioweapons. These groups have already overcome many of the physical barriers, and you have granted that a model could propose a viral genome to synthesize, it could be synthesizable, and it could be infectious. I believe this is cause for alarm and urgent action.
I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands. And I think that the takes on AI killing us all by creating dangerous viruses is total bogus.
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Tom Fehring retweeted
Recently I left quant to work on AI safety. The Huggingface Incident made me extremely concerned that we are not set up to handle the risks. I wrote my this post for people in a similar position to me - why you *can* have impact and how to get started - briandavies.substack.com/p/a…
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Tom Fehring retweeted
Warning shots should be shared widely, and it's important not to deter disclosure. But it's also very important to align the incentives of AI companies, which do not internalize most of the downside risks posed by their systems.
every safety incident until the grey goo rsi is a learning opportunity and warning shot and should be shared broadly and (relatively) blamelessly. they should not be moments of extreme consternation, because that will counterintuitively decrease what the world learns
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Tom Fehring retweeted
Regret the tone of my post on data centers yesterday. What I should have said: There were reasonable concerns about data centers 18ish months ago: water, taxes, jobs, electricity prices, the environment and what they would do to small towns. Well-structured data center projects have largely addressed these concerns today and we should be celebrating this. On balance, data centers are awesome for America in every way. On water: U.S. data centers use a fraction of what golf courses use. A lot of the numbers from 18 months ago were off by over 1000x. Newer data centers use closed-loop systems or recycled water. Should be required by every town approving a data center project. On taxes: looking only at sales-tax exemptions, as Ronan Farrow did, is the wrong way to evaluate this. Data centers pay significant property taxes. Loudoun County, which is the wealthiest county in America, now collects on the order of $1 billion a year from data centers. In Quincy, WA, data centers are more than half the property-tax roll. Over time, property taxes can go to zero while government spending increases in these towns. On jobs: this has been unambiguously awesome for blue collar Americans. Demand for electricians, plumbers, welders, HVAC techs, and contractors has gone vertical, and it is not a one-time construction job. These buildings get upgraded and expanded over time. That is why the building trades are fighting for them, and why some unions are now treating opposition to data centers as a reason not to endorse politicians. On power: the original fear was that households would pay for the incremental electricity demand in the form of higher prices. That is why the ratepayer-protection deals and the new large-load tariffs exist. The right structure is: the data center brings or pays for new generation and signs a contract long enough that existing customers are protected. Where that is happening, utilities are cutting or freezing residential rates and saying so on the record. Where it is not, people are right to object. Electricity prices are going down *today* in a number of large states because of data centers. 
On the environment: data centers overwhelming use natural gas today, which is the cleanest power source outside of nuclear, solar and wind. And the companies that are building the data centers are committed to carbon neutrality such that an equivalent amount of solar will likely be built. Maybe more importantly, the data centers need batteries to function effectively and these batteries can also sell energy back into the grid (which recently prevented blackouts in Texas). Over time, data centers will run on solar plus batteries. On the towns: Poverty in Quincy, WA fell from 29% to 6%. Data center taxes paid for a new high school, a hospital, a library, police and fire stations. This is happening in many left for dead former mill and farm towns that had no other bidder for the land. Data centers are actually reindustrializing parts of America and creating the kind of working-class jobs both parties have spent decades claiming to support. That should not be a partisan issue. Data centers can and should be awesome for America and they increasingly, overwhelmingly are. Supporting the outsourcing of data centers to China will likely age just as well as support for the outsourcing of high quality, blue collar manufacturing jobs to China has aged. When the facts change, I change my mind. I hope that reasonable people who had good faith reasons to oppose data centers at least consider updating their beliefs given the change in the facts over the last 18 months. This really matters for America. I will say I also think the idea of making data centers beautiful is a good one that has yet to be implemented. Data centers should be just as beautiful as Grand Central Station. We can learn a lot from the railroad buildout. Neoclassical revival ftw. Might write up open-weight AI tomorrow as this is equally essential to America.
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Incentives are superpowers. We're hiring for this right now in SF @aiunderwriting, get in touch if you want to build this together!
Massive opportunity now to make insurance for frontier AI labs. Scoop up the ZK proof people crypto left behind.
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Tom Fehring retweeted
Want to shape the future of AI auditing? New @MATSprogram stream: AI auditing, eval standards, governance. Mentored by me and @Miles_Brundage via @AVERIorg. Applications open.
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not enough people are emotionally prepared for if things keep getting weirder for a long time
The most important thing to learn the next decade is how to relate emotionally cleanly to repeated future shock
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Tom Fehring retweeted
We’re building private market standards & audits for frontier labs — combing technical expertise with trusted audit practices. Who wants to build it with us?
In a matter of weeks, U.S. federal AI policy has gone from implausibly libertarian to increasingly draconian and opaque. Today, over 35 distinct observations, I analyze how we got here and offer the most succinct statement I can about what exactly I propose we should do next.
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Technical controls alone won’t contain AI security risk. Shadow AI, blind trust in AI outputs, sensitive data leakage. These human-introduced risks can only be managed by combining technical controls with a strong culture of AI security. We asked Dr. Keri Pearlson (@MITSloan) how to engineer a culture of AI security and combined her research with insights from 50+ security leaders across the AIUC-1 Consortium: > Behaviors they want to see > Values, attitudes and beliefs that produce them > Practices that make them stick Together, we identified 5 practices security leaders can adopt now: 1. Model: Culture follows what leaders do, not what they mandate 2. Reward: Make good practice visible so it spreads faster, peer to peer 3. Deputize: Train local champions to carry security-first habits into every team 4. Expect: Tie secure AI use to everyday norms and formal OKRs 5. Pave: Make the secure path the default path
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Tom Fehring retweeted
Two years ago, reasoning models did not exist. GPT-4o and Sonnet 3.5 were SOTA. One year ago, the best publicly available model was o3, and 99% of those who used it (i.e., 99% of 7% of OpenAI customers) were using it as a chatbot. Today (i.e., right now as I type this), I have Codex building several pieces of personalized software and doing a large-scale data management project for me. The data management project has been running autonomously for nearly 4 days. I have multiple working apps on my phone that I use daily and which were built entirely by Codex. Projecting this trend to June 2027 is absolutely mind-boggling. The world will change.
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vulnerability discovery is a search problem. mythos is better than prior models in that it discovers more vulns/FLOP. this is plausibly not true of fable due to guardrails, even if the number of vulns it can find is nonzero thanks to guardrail bypass in narrow circumstances.
“The Admin asked Dario to fix the jailbreak or de-deploy the model. Dario refused. — In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. “ This is crazy. What are we even doing here?
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it's possible they found a general bypass that would unlock efficient offensive cyber, but from public info i'm inclined to believe anthropic over amazon on this
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Tom Fehring retweeted
These are the poor life outcomes of the gifted kids in the study in question.
Gifted and Talented, or G&T, programs have long been a perennial subject of debate, particularly in New York City, where it has bedeviled mayors for years. Some parents have already washed their hands of the whole G&T business, refusing to participate in what they view as a corrupt system of segregation. But countless others still place significant stock in the G&T designation and what it offers and are comfortable relying on cognitive testing, should it be required, to determine whether a child qualifies. “When your intelligence is the foundation of your self-perception, failing to achieve feels like soul death,” writes Katie Arnold-Ratliff. But if the limited amount of information we have about gifted kids long-term is any indication, most lead, at best, ordinary lives of modest accomplishment. A 35-year study of 677 gifted children found that by age 50, only 12.3 percent had reached a level of “eminence,” defined as “full professors … Fortune 500 executives … judges and lawyers, leaders in biomedicine, award-winning journalists and writers.” This means 88 percent never did. Arnold-Ratliff digs into the myth of the gifted child, and how our notions of intelligence may be inherently flawed: nymag.visitlink.me/9mc2Wh
Readers added context they thought people might want to know
The study defines eminence as accomplishing "something rare" like becoming full professors at research universities or Fortune 500 executives; 12.3% of gifted participants achieved it, far exceeding general population rates. pmc.ncbi.nlm.nih.gov/articles/PMC64…
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