How did they do it?

Most of the standard sources on the topic cite market reforms, cheap land and labor, investments in infrastructure and education programs, and the rise of export-driven manufacturing.

These are all true. But there’s another input, one the NSA’s own director called “the greatest transfer of wealth in history.”

Theft of Intellectual Property (IP)

For the past two and a half decades, Chinese state-sponsored hacking groups have systematically broken into Western companies and stolen intellectual property, including R&D information, product designs, manufacturing processes, pricing sheets, and even legal strategy for trade disputes against China itself. The U.S. Senate Committee on the Judiciary cites estimates that “China steals between $400 billion and $600 billion of IP each year, or about $5,000 per American taxpayer.”

Chinese actors have stolen– or been credibly accused of stealing– trade secrets from Nortel, U.S. Steel, and SolarWorld, among many others; the CCP has also taken blueprints for “fighter jets, helicopters, and missiles” – technologies that have both military and commercial applications.

This piece walks through the CCP’s technological playbook, with case studies from the telecom, steel, solar, and agricultural industries, and ends with the early evidence in AI. It focuses mainly on the economic impacts of IP theft.

The next piece(s) in the series will cover who does the hacking, why this is China’s “golden age” of cyber espionage, how Beijing has pre-positioned inside American critical infrastructure, why this matters for national security, and what all of it implies for securing frontier AI.

Case Studies

Telecommunications Infrastructure

Nortel & Cisco

“Using seven passwords stolen from top Nortel executives, including the chief executive, the hackers—who appeared to be working in China—penetrated Nortel’s computers at least as far back as 2000 and over the years downloaded technical papers, research-and-development reports, business plans, employee emails and other documents, according to Brian Shields, a former 19-year Nortel veteran who led an internal investigation.”

—Wall Street Journal

Shields is blunt about who he believes benefited; Huawei denies it, and no forensic link has ever been published. However, Huawei was later caught using verifiably stolen code from Cisco.

Nortel went bankrupt in 2009; Huawei is now the world’s largest telecommunications equipment manufacturer and, at one point, was the “second-biggest smartphone seller in the world” – larger than Apple. In Q1 and Q2 of 2025, Huawei “captured about 31% of the global telecom equipment market.”

Steel

U.S. Steel, Allegheny Technologies, the United Steelworkers Union

“The Chinese industry has used its government to steal U.S. Steel’s closely guarded trade secrets and uses those trade secrets to produce advanced steel products it could not make on its own.”
—NPR, reporting language from U.S. Steel’s trade complaint with the ITC

As one specific example, The Hill reports that U.S. Steel spent millions of dollars on R&D “to pioneer a new, lighter, higher-grade steel product for use in automobiles and elsewhere,” but that Chinese hackers broke into the company’s computers and “stole the advanced formula.” Now, Chinese state-owned steelmaker ‘Baosteel’ appears to be selling similarly advanced formulations of steel for cut-rate prices.

It’s worth noting that U.S. Steel later withdrew their trade-secrets claim, saying trade law gave victims of state-sponsored hacking no workable way to prove it– so the allegation was never tested in court. However, a separate 2014 criminal indictment of five PLA officers still stands– but they remain in China, beyond the reach of U.S. courts, so its allegations were never tested at trial either. (The specifics of this indictment will be covered more in-depth in a future installment of this series, though some parts of it are also cited below.)

In addition to U.S. Steel, reporting from the New York Times describes how China stole “computer records containing trade policy strategies and discussions about rare earth metals and auto parts” from the United Steelworkers Union, according to an indictment released by the Justice Department.

Another victim was Allegheny Technologies (ATI), a Pittsburgh specialty metals manufacturer. In 2012, ATI was simultaneously partnered with, competing against, and involved in a trade dispute with Baosteel. The two had run a precision stainless joint venture together since 1995. One day after the joint venture’s board of directors met, PLA officer Wen Xinyu broke into ATI’s network and stole login credentials for at least 7,000 employees; those credentials were later used to steal information from the company.

Alcoa (note: aluminum, not steel)

A separate PLA officer, Sun Kailiang, stole nearly 3,000 emails from Alcoa. The emails included senior executives’ discussions of its partnership with state-owned Chinalco. Similarly to ATI’s case, the theft took place about three weeks after Alcoa’s partnership was announced.

Alcoa and ATI show that China hacks its business partners in addition to adversaries.

Solar Panels

SolarWorld

“SolarWorld Americas, the largest manufacturer of solar panels in the United States, has asked the Commerce Department to investigate claims that Chinese military personnel broke into the company’s computers and stole documents important to its business and its long-running trade dispute with China.”

—The New York Times

As one example of how IP theft had enabled China to leapfrog competition, SolarWorld specifically pointed to Chinese companies’ rapid adoption of Passivated Emitter Rear Contact (PERC) solar cells, claiming that:

“[W]ere it not for their economic espionage and theft from SolarWord [sic] Americas, Chinese solar producers like JA Solar and Trina, would have taken far longer to make the leap into PERC technology.”

—SolarWorld CEO Juergen Stein, PV Magazine

SolarWorld was one of the first solar manufacturers to bring PERC technology to mass production. However, unable to compete with cheap– and heavily subsidized– solar panels from Chinese competitors, SolarWorld eventually filed for insolvency. While not the primary cause of their downfall, the theft of the company’s pricing, product, and legal strategy documents exacerbated an already difficult position.

A Note on Scope

The cases so far share a method: remote intrusion run by state-sponsored hackers. But cyber espionage is only one channel in a wider acquisition system. Sometimes, the theft is physical: an employee walking out with files, or digging proprietary seeds out of an Iowa field (as I’ll write about shortly!). Sometimes, it’s structural: for decades, foreign firms wanting access to China’s market were required to enter joint ventures with local partners, arrangements the U.S. Trade Representative found were used to pressure the transfer of technology.

The case studies included in this piece primarily focus on the cyber channel, because it scales the most easily, and it’s the one that matters most for what comes later in this series. However, the two remaining examples below include one theft that involved no computers at all. The playbook is defined by its goal, not its method. As I’ll write about in future installments, these cases show that cyber hardening alone is not enough to secure AI.

Agriculture

DuPont Pioneer & Monsanto

“A Chinese national sentenced last month in Iowa for stealing trade secrets wasn’t looting blueprints for military weapons, computer software, or high-tech electronics. Mo Hailong’s efforts were considerably more grounded—he was stealing corn.”

—FBI.gov

For five years, Mo stole proprietary corn seeds and sent them overseas to his employer, a Chinese corn seed company. The corn seeds he stole were valuable trade secrets from DuPont Pioneer and Monsanto.

Mo was not acting alone; the investigation of this case later expanded to “include five other conspirators… across six states and thousands of miles.” In one instance, Mo, along with two co-conspirators, attempted to send a staggering “250 pounds of corn seeds” to Hong Kong.

Why do these corn seeds matter so much?

Typically, the corn a farmer buys is hybrid seed. These seeds are high-yielding, but they don’t breed true; if farmers save and replant hybrid seeds, the next generation has less desirable genetic traits and a lower yield. Because of this, farmers have an incentive to buy new hybrid seeds each year.

This means that for seed companies like DuPont and Monsanto, the biggest value in the sector is in the inbred parent lines that get crossed to produce the hybrid. These lines are the actual intellectual property, the product of years of breeding, and companies guard them closely– they’re never sold to farmers directly, only planted in controlled fields.

Mo was stealing seeds from inbred parent lines, in a form that could be replanted indefinitely in China.

By DuPont Pioneer’s own estimate, losing one inbred line would mean forfeiting “five to eight years of research and at least $30 million.”

Artificial Intelligence

What do we know about AI so far?

Distillation

This newest channel doesn’t require breaking into anything. Distillation is a training method where a smaller, weaker model is built using the outputs of a stronger one– a user queries the powerful model at scale, collects its answers, and trains their own system to imitate them. It’s essentially a shortcut to creating high-quality synthetic data.

While distillation can be a legitimate technique if done with permission, if done against a competitor’s model, it’s a way to copy hard-won capabilities without having to spend millions of dollars and several months on the research that produced them– and it sits in a legal gray area that export controls, primarily written for chips and model weights, don’t clearly cover.

Some examples of recent accusations:

• In February 2026, OpenAI said it had evidence that DeepSeek had distilled its models, and had uncovered “new, obfuscated methods” being used by the company in order to evade detection.

• In February 2026, Anthropic reported that DeepSeek, Moonshot, and MiniMax had together run more than 16 million exchanges with Claude via ~24,000 fraudulent accounts, using commercial proxies to evade its China access restrictions.

• Then, in a June 10, 2026 letter to the U.S. Senate Committee on Banking, Housing, and Urban Affairs, Anthropic accused actors linked to Alibaba and its Qwen AI lab of the largest distillation campaign it had seen– 28.8 million exchanges across ~25,000 fake accounts, over a 44-day window.

[Note: A related but distinct problem is chip smuggling (which I’ve written more about with Jason here), though that’s China acquiring hardware it can’t legally access, not necessarily stealing the IP behind it.]

United States v. Linwei Ding

In 2019, Google hired a software engineer named Linwei Ding to work on the software for supercomputing data centers that train its AI models. Over the course of a year (May 2022 - May 2023), he uploaded more than 1,000 confidential files to a personal cloud account.

According to the Justice Department:

“The [files] contain detailed information about the architecture and functionality of Google’s Tensor Processing Unit (TPU) chips and systems and Google’s Graphics Processing Unit (GPU) systems, the software that allows the chips to communicate and execute tasks, and the software that orchestrates thousands of chips into a supercomputer capable of training and executing cutting-edge AI workloads… [they] also pertain to Google’s custom-designed SmartNIC, a type of network interface card used to enhance Google’s GPU, high performance, and cloud networking products.”

To hide that he was frequently in China, a colleague badged him into the office to fake his presence in the U.S. All the while, Ding was secretly affiliated with two Chinese tech firms: he was discussing becoming CTO of one company and was founding another, where he acted as CEO. Ding had applied to a Beijing talent program, writing that he wanted to help China build computing infrastructure “on par with the international level.”

In January 2026, a federal jury convicted him on all fourteen counts, including seven of economic espionage.

Conclusion

The pattern that emerges from the above is that when the United States builds something valuable, China often finds a way to acquire it, and to skip the years of research and the billions in cost that building it honestly would have required. Many of the companies in these case studies learned the pattern the hard way, after the fact.

Theft was rarely the sole reason an American company fell; subsidies, scale, and price wars did much of that work. But across these industries, the apparent objective was remarkably consistent: acquire technical and commercial knowledge that could compress years of research, reduce development costs, and help Chinese firms close the gap faster.

The playbook that targeted Nortel, SolarWorld, and Allegheny Technologies is now pointed at the most valuable technology America has ever produced. This time, we get to read it in advance.

Reminder: The next piece(s) in the series will cover who does the hacking, why this is China’s “golden age” of cyber espionage, how Beijing has pre-positioned inside American critical infrastructure, why this matters for national security, and what all of it implies for securing frontier AI.

In this post, I’ll be attempting to address four questions:

1. What’s wrong with passive grantmaking?

2. What’s wrong with active grantmaking?

3. How do we solve these problems?

4. How could we scale this approach?

1. What’s wrong with passive grantmaking?

Passive grantmaking = evaluating proposals.

A grantmaker has to evaluate incoming proposals along two axes:

1. Project value: Do I think this project is important? Will it contribute to the goals my organization has set (e.g. reducing existential risk from AI)?

   1. What are the downside risks?

   2. If this project fails, what is the likeliest reason for the failure?

   3. In what worlds is this project actively harmful?

2. Applicant fit: Is the person who submitted the proposal the right person to do this project?

   1. How competent are they? Do I think they can execute this proposal well?

   2. What do their references look like? How strongly do people they’ve worked with endorse them?

Some notes on the above:

• Finding people who can think critically about what’s needed in the field (henceforth referred to as “field strategists”) is difficult.

  • Usually, the people who are best positioned to do this are high-context in AI safety, and have been thinking about what’s missing in the field for a while.

  • People with this kind of strategic taste are also likely to already be working in high-level, impactful roles, which means they’re rarely willing to transition away from those roles.

• Being a good field strategist does not necessarily require execution ability.

Passive grantmaking forces every applicant to have both skills. It doesn’t need to.

2. What’s wrong with active grantmaking?

Active grantmaking is when a grantmaker:

1. Comes up with a good idea for a project

2. Searches for people in their network to seed the project with

Problems with the above:

• Grantmaker time is already extremely limited with all of the proposals they receive; grant evaluations at Coefficient Giving, for example, regularly take months to be completed. It’s difficult for an already capacity-constrained grantmaker to set aside time for substantial high-level strategy work.

• Successfully searching for people in your network requires:

  • Thinking about who would be a good fit;

  • Attempting to pitch people to potentially leave their jobs to do this other thing;

  • Having sufficiently high confidence in the people you pitch.

    • An unfortunate failure mode of active grantmaking: pitching someone on a project, getting them to put in the effort to apply for funding, realizing they aren’t a good fit while evaluating their application, and having to say no.

The above assumes that you happen to know people who would be a good fit for your project at all.

3. How do we solve these problems?

The key to optimizing grantmaking is specialization: separating project proposals from people– separating evaluations of project value from applicant fit.

Two case studies:

1. The Institute For Progress (IFP)

IFP’s Launch Sequence is an open call for project proposals “that prepare the world for advanced AI.” They write:

“You should submit a proposal if you have a good idea, even if you don’t expect to be the person actually leading or working on the resulting project… If your proposal is selected, we’ll work closely with you to develop the idea into a concrete project plan, publish your plan, connect you with philanthropic funders, and help you headhunt for a project lead or co-lead.”

If you’re interested in engaging in field strategy work part-time, consider authoring a proposal or offering ideas to IFP. They pay a $10,000 honorarium for published proposals and $1,000 bounties for ideas they publish.

In effect, IFP treats field strategy as freelance work: anyone can contribute an idea, get paid, and walk away. Atlas Computing, Evan Miyazono’s organization, is betting on the opposite model: field strategy as a full-time job.

2. Atlas Computing

Evan in this post discusses his plans to make Atlas a hub that “rais[es] funding to gather and support [field strategists]” to address “[t]he problems that don’t fit neatly into any existing fiefdom.” He writes:

“[These orphaned problems] need someone whose job it is to find them, scope them, and make it easy for the right person to step in and solve them.”

If you’re interested in being a Field Strategist in a more official capacity than what IFP’s Launch Sequence offers, Atlas Computing’s job listing is here.

If you’re interested in supporting Atlas’s work, you can also email evan@atlascomputing.org.

4. Scaling this approach

How might we scale this approach to major funders? Here are a couple of ideas:

1. Hire full-time field strategists. Based on conversations I’ve had with grantmakers working at major funding organizations, there doesn’t seem to be an explicit, written high-level strategy to win that proposals are evaluated against. Organizations like Coefficient Giving and Longview should hire strategists to consider:

   1. What is our theory of victory for AI safety?

   2. Given this theory of victory, what is on the critical path?

   3. What projects and organizations on the critical path do we need to initialize?

2. Separate people from proposals. Publish detailed, pre-scoped project plans that applicants apply to execute.

   1. Instead of evaluating both the project proposal’s merits and the person’s fit, once this is done, the only question to answer becomes: can this person execute this project? That’s a much easier judgment than assessing a novel idea and an unknown person simultaneously.

Summary

In sum, the grantmaking pipeline currently requires three distinct jobs:

1. Field strategy: diagnosing what the field needs.

2. Project design: scoping those needs into concrete, fundable plans.

3. Execution matching: pairing each plan with someone who can run it.

Today, all three are combined into a single application. Applicants must diagnose the field, design a project, and nominate themselves to execute it. Grantmakers must then evaluate all three at once.

It doesn’t need to be this way. Field strategists and scoping processes like IFP’s can own jobs one and two; application evaluation can then shrink to job three alone: can this person run this plan?

Conclusion

AI safety is facing an unprecedented wave of funding with the upcoming Anthropic and OpenAI IPOs. As I’ve written previously:

When [Anthropic IPOs], a few thousand people, including some of the wealthiest people on the planet, will become liquid, and a meaningful fraction of those people will want to give to AI safety.

No philanthropic field this young, with this little absorptive capacity, has ever successfully channeled a funding influx this large. The closest precedents ended badly.

We need a radically different approach to deploying capital if we want to be the first to pull it off.

• People who aren’t worried about bioweapons are wrong.

  • The state of biosecurity is really really really really bad and scary and alarming, even independent of AI.

• The U.S. has a lot of cheese.

• Read the full essay at Bioweapon.AI, which has better UI than Substack!

• I may continue editing / working on this project in the future, though will most likely be taking a break from biosecurity work for a bit to focus on other MATS research related to AI geopolitics.

• If anyone reading is interested in working on getting these interventions implemented, please feel free to reach out to me via Substack or email (firstnamelastname [at] stanford [dot] edu) and I’d love to chat (particularly if you’re in D.C. / a think tank, or some sort of advocacy organization).

Bioweapons in the Age of AI

An investigation into the history of biological weapons, and how AI and emerging technology are now eroding the barriers that have kept catastrophe rare.

In the spring of 2023, a man walked into the White House carrying a small black box. Inside it were a dozen test tubes containing ingredients that, correctly assembled, had the potential to start a pandemic; an AI chatbot had supplied the recipe.

Fortunately, the man’s name was Rocco Casagrande – and, as a biochemist and former United Nations weapons inspector, he wasn’t there to use the materials; instead, he was there to brief government officials on how AI could help someone identify potent agents, secure the materials to make them, and soon – he warned – design entirely novel pathogens capable of evading humans’ immune systems.

That was three years ago.

In April 2026, the New York Times published an investigation into what happens when you ask AI’s most capable models for help with biological weapons.

Examining several chat transcripts shared by scientists, they wrote:

…OpenAI’s ChatGPT explained how to use a weather balloon to spread biological payloads over a U.S. city. In another chat, Google’s Gemini ranked pathogens by how much they could damage the cattle or pork industries. Anthropic’s Claude produced a recipe for a novel toxin adapted from a cancer drug. Other chats contained information that [an expert deemed] too dangerous to share.

…[T]he chatbot explained how to modify an infamous pathogen in a lab so that it would resist known treatments. Worse, the bot described in vivid detail how to release the superbug, identifying a security lapse in a large public transit system… [t]he bot outlined a plan to maximize casualties and minimize the chances of being caught.

From their investigation, the Times authors concluded:

…[E]ven publicly available models can do more than disseminate dangerous information. The virtual assistants have described in lucid, bullet-pointed detail how to buy raw genetic material, turn it into deadly weapons and deploy them in public spaces, the transcripts show. Some have even brainstormed ways to evade detection.

More alarming than these examples is just how quickly things have changed in the past two years. In December 2024, a multi-institutional team of researchers from Stanford, MIT, Princeton, Cohere, Mistral AI, and others published a systematic review of the AI biorisk literature and essentially concluded that “current LLMs and BTs do not pose an immediate risk.”

The team’s paper cited both an OpenAI red-teaming exercise and a Claude 3 safety report released earlier that same year. Yet, by April 2025, OpenAI had reversed its own assessment, concluding its models were approaching “high risk” – meaning, capable of “substantially increas[ing] the likelihood and frequency of bioterrorist attacks.” Similarly, Anthropic’s own internal bioweapons acquisition uplift trials published in May 2025 found that Claude Opus 4 enhanced human performance by 2.53× on relevant tasks, enough to trigger activation of AI Safety Level 3. An international safety report produced for the 2025 Paris AI Action Summit found LLM performance on weapons-related queries improved a staggering 80 percent in 2024 alone.

The institutions designed to prevent biological attacks have historically operated on the assumption that the expertise required to develop them is rare and slow to acquire.

AI and other emerging technologies may be on track to change this, and quickly.

This is not a fringe concern:

“The biggest issue with AI is actually going to be … its use in biological conflict.”

— Eric Schmidt, former CEO of Google

“[AI has the potential to] greatly widen the range of actors with the technical capability to conduct a large-scale biological attack.”

— Dario Amodei, CEO of Anthropic

We are not prepared.

Part One · Rogue Actors

Should We Be Concerned About Bioterrorists?

An Analysis of Historical Case Studies - Part 1: Rogue Actors

I. Successes

Note: Refers to successful deployment of a biological agent, not necessarily success in achieving overall objectives.

A. 1984 Rajneeshee Bioterror Attack

[ Video — Watch · The Dalles salmonella attack ·

]

In September 1984, the Rajneeshee cult deliberately contaminated salad bars in ten Oregon restaurants with salmonella in an effort to incapacitate voters before the 1984 Wasco County election.

Later investigations revealed that the attackers had a “fairly sophisticated medical research laboratory” at their commune, where they cultivated salmonella purchased over-the-counter from a Seattle scientific supply house. Ma Anand Puja, a former nurse who ran the Rajneesh Medical Corporation, provided the minimal medical expertise needed to culture common pathogens. Authorities also later discovered that the cult had explored using additional lethal pathogens, including HIV.

Though the cult had access to a medical research laboratory, the actual salmonella cultivation took place in a shed:

In a simple shed, under the supervision of Ma Anand Puja, the cult mass-produced the Salmonella using simple petri dishes, an incubator, and a freeze-drier ^[14]… Producing large quantities of bacteria is cheap and can be easily done with even rudimentary equipment and skills ^[18].

– Turner et al.

The attack poisoned 751 people and hospitalized 45, while causing panic that drained the local economy. To this day, the Rajneeshee operation remains the largest bioterrorist attack in U.S. history.

An image of a salsa bar contaminated as part of the attack. Location: Taco Time in The Dalles, Oregon. Source: Slate Magazine

B. WWI German Biological Sabotage Program

“A shipment of horses at a New York City rail yard, 1918.” Source: Archives.gov

During WWI, Dr. Anton Casimir Dilger – a German-American medical doctor specializing in tissue culture research – carried out a German biological warfare sabotage program targeting Allied livestock supplies. With the help of his brewmaster brother and housekeeper sister, Dilger converted his basement into a makeshift bacterial laboratory where he began cultivating deadly bacterial cultures in liquid form. He used anthrax and glanders bacteria to infect horses and mules being shipped from U.S. ports to Europe. Dilger also coordinated directly with German General Staff.

His basement was located less than six miles from the White House, and as a result of the program, “thousands of horses and mules were killed” (National Archives).

C. 2001 Anthrax Letters (also known as the Amerithrax case)

“Laboratory technician holding an anthrax-laced letter sent to Senator Patrick Leahy.” Source: FBI

Following the September 11 attacks, a series of anthrax attacks were carried out through letters containing anthrax spores sent via the mail. The spores had been treated with additives to increase their inhalability, which suggested the involvement of someone with highly specialized, advanced technical expertise. The primary suspect was Bruce Ivins, a veteran biological-weapon researcher for the US Army.

The attack killed 5 people and injured 17, with many survivors suffering long-term fatigue and memory loss even years later. It also created widespread panic at a moment when the nation was already reeling from 9/11.

[ Video — Watch · The 2001 anthrax letters ·

]

A Netflix documentary was later created on the attacks.

II. Failures

A. 1993 Aum Shinrikyo Anthrax Attack

[ Video — Watch · Aum Shinrikyo ·

]

In July 1993, doomsday cult Aum Shinrikyo aerosolized a liquid suspension of Bacillus anthracis (anthrax) from the roof of an eight-story building in Kameido, Tokyo, but the operation resulted in no human casualties.

The operation failed because:

• They used an attenuated B. anthracis strain (used vaccine strain incapable of causing disease in healthy individuals);

• Their Bacillus anthracis liquid suspension had low spore concentrations (significantly below the optimal 10⁹ to 10¹⁰ organisms/mL needed for effective biological weapons);

• Sunlight exposure inactivated the spores before they could cause infection; and

• Equipment failures meant they were unsuccessful in creating proper particle distribution.

Aum had laboratories for experimenting with bioweapons in Kamakuishki and Tokyo. In addition to anthrax, the group also “cultured and experimented with botulin toxin, cholera, and Q fever” (CDC). After failing to deploy a bioweapon, they later transitioned to successful chemical attacks, including the Matsumoto Sarin Attack and the Tokyo Subway Sarin Attack (not detailed here since this analysis is about bioweapons).

[ Video — Watch · From bioweapons to the Tokyo subway sarin attack ·

]

B. 2018 Cologne Ricin Plot

In June 2018, Sief Allah H., a 29-year-old ISIS sympathizer, successfully produced enough ricin for “up to 1,000 toxic doses“ and assembled a bomb with explosives and metal ball bearings for a planned mass-casualty attack on a crowded indoor venue. This marked the first time a jihadi terrorist in the West successfully produced the toxic biological agent, demonstrating that technical barriers to simple bioweapons are sometimes surmountable with publicly available instructions.

German authorities discovered and put a stop to the plan via online surveillance operations, specifically after the U.S. CIA provided a tip about his large order of 3,300 castor seeds purchased over the internet.

C. 1995 Minnesota Patriots Council Ricin Plot

“Ricin occurs naturally in castor beans.” Source: BBC

The Minnesota Patriots Council was an anti-government militia that attempted to assassinate federal law enforcement officials (including US Marshals, IRS agents, and local sheriffs) using ricin. The group successfully extracted ricin from mail-ordered castor beans using publicly available instructions and basic solvent knowledge gained through their work as carpet cleaners. They initially found and purchased castor beans through an advertisement in a right-wing magazine.

Ultimately, the Minnesota Patriots Council successfully produced enough ricin to kill over 100 people, despite their result being significantly less potent than professional-grade toxin. Similarly to the Cologne Ricin Plot, law enforcement detected and disrupted their activities before they could carry out planned attacks.

D. 1998 Toxic Terror Case

The perpetrators of the Toxic Terror Case were Larry Wayne Harris, a microbiologist and white supremacist with ties to “Aryan Nations,” working alongside William Leavitt, a friend who “own[ed] biological laboratories in Nevada and Germany.”

The pair planned to release bubonic plague bacteria in the New York subway system; law enforcement raided their labs after they “mail-ordered freeze-dried bubonic plague germs.” During the raids, officials also found vials containing suspected anthrax. Harris reportedly told a witness that he had procured enough anthrax to “wipe out the city.”

Addendum: ISIS Laptop of Doom

The Islamic State’s Terror Laptop of Doom revealed a comprehensive biological weapons research program documented on the laptop of a Tunisian ISIS operative with university-level scientific training. Uncovered in 2014, it contained a “19-page document in Arabic on how to develop biological weapons and how to weaponize the bubonic plague from infected animals” containing guidance like “[u]se small grenades with the virus, and throw them in closed areas like metros, soccer stadiums, or entertainment centers… best to do it next to the air-conditioning.”

III. Analysis

Successes

Case Expertise Level Available Infrastructure Key Success Factor(s) Rajneeshee Minimal (nurse practitioner’s basic medical knowledge) Institutional (”sophisticated medical research laboratory”), though actual bioweapon development took place in a shed using basic tools Easily cultivated pathogen, which didn’t require as much specialized expertise to pull off Dilger High (tissue culture specialist) Lab set up in basement with substantial institutional backing (German General Staff coordination) Expert knowledge + state sponsorship + help from siblings, which maintained opsec Amerithrax High (microbiologist, U.S. Army)* Institutional access (Army lab materials – USAMRIID) Technical expertise + authorized access to dangerous pathogens due to military work

*Whether or not Ivins was the culprit remains contested.

Takeaways

A. Rajneeshee represents a case where despite minimal expertise, the perpetrators were still able to succeed because they chose an easier pathogen to synthesize. The expertise bottleneck varies by pathogen.

B. Dilger was able to maintain operational security by relying on his siblings to help him with his work. While he had to rely on a home laboratory, it’s likely that coordination with German General Staff enabled him to procure advanced materials and equipment.

C. The Amerithrax (aka Anthrax Letters) case is perhaps slightly more difficult to assess with confidence because Bruce Ivins’s guilt remains contested. However, if Ivins was the culprit, key success factors include highly specialized expertise and authorized access to dangerous pathogens as a researcher for the U.S. army.

The Dilger and Ivins cases demonstrate that specialized biological knowledge poses severe risks; advanced expertise can enable catastrophic biological attacks.

Failures

The failed cases fall into two main categories: technical incompetence and detection via surveillance.

Technical Incompetence

The Aum Shinrikyo Anthrax Attack failed on technical problems. However, had Aum expanded their technical expertise beyond their compartmentalized leadership structure, there’s a reasonable chance their attack may have succeeded. Their operation went undetected through deployment, and every failure point (strain selection, spore concentration levels, dispersal, sunlight exposure, etc) represented knowledge gaps that additional specialized expertise could have addressed.

The Amerithrax case demonstrates that the technical barriers Aum encountered were surmountable with proper expertise.

Detection via Surveillance

Unlike Aum’s technical failures, the Cologne and Minnesota Patriots cases succeeded at the production phase. These actors proved that certain bioweapon manufacturing is already within reach of non-experts using publicly available information. What prevented attacks was detection and disruption by law enforcement, rather than inability to create functional weapons.

Whether Toxic Terror succeeded at the production phase is less clear, as the vials found by law enforcement were only suspected to contain anthrax. It could plausibly fall into either of the above categories, depending on whether the recovered materials were viable and weaponizable.

IV. Conclusion

Bioweapons have been created and deployed effectively in several documented attacks. The Rajneeshee, Dilger, and Amerithrax cases prove that bioweapon development and deployment are within reach of motivated rogue actors.

At the same time, these successes have been mostly limited in scope and severity. Several other large-scale near-misses have failed primarily due to technical incompetence or detection by law enforcement.

AI, alongside other existing and emerging technologies, threatens to erode some of the barriers that have historically caused malicious actors to fail. That will be the subject of Part 2 of this series!

Part Two · Government Programs

When Russia Wants a Bioweapon, It Gets One

Analysis of Historical Case Studies - Part 2: Government-Sponsored Programs

I. Historical Precedent

Governments have repeatedly demonstrated both the capability and the willingness to develop and deploy bioweapons.

A. The Soviet Biopreparat Program

[ Video — Watch · Biopreparat & the 1979 Sverdlovsk anthrax release ·

]

In 1972, the Soviet Union signed the Biological and Toxin Weapons Convention (BTWC), pledging not to develop, produce, or stockpile bioweapons. A year later, the Soviet Union violated the BTWC by creating Biopreparat, a massive biological warfare enterprise masquerading as a civilian pharmaceutical company.

Through the program, Soviet scientists reportedly manufactured an estimated 20 tons of plague, 20 tons of smallpox, and hundreds of tons of anthrax; they also weaponized tularemia, epidemic typhus, Q fever, and Marburg virus, while studying the potential use of Ebola and encephalitis.

The program’s exact scale remains disputed. The Federation of American Scientists states the following:

At its peak, the former Soviet Union had the world’s largest biological warfare program, with somewhere between 25,000 and 32,000 people employed in a network of 20 to 30 military and civilian laboratories and research institutions. An additional 10,000 or so worked in Defense Ministry bioweapons laboratories. According to other estimates, at least 47 labs and test facilities were scattered across Russia, employing more than 40,000 workers, 9,000 of whom were scientists. Between 1,000 to 2,000 of those scientists were experts on deadly pathogens.

– Federation of American Scientists

Meanwhile, Congressional testimony asserts:

The size and scope of this program were enormous. For example, in the late 1980s and early 1990s, over 60,000 people were involved in the research, development, and production of biological weapons.

– Dr. Kenneth Alibek, before the Joint Economic Committee, United States Congress

Regardless of the precise number, Biopreparat was unquestionably the largest biological warfare program in history.

Even decades after its official end, the program’s deadly legacy endures. Contaminated testing sites like Vozrozhdeniya Island still harbor active anthrax spores buried in the soil since the Soviet era:

Vozrozhdeniya was once home to a vibrant fishing village fringed by turquoise lagoons, back when the Aral Sea was the fourth-largest in the world and abundant with fish…

Over the years the site flourished into a living nightmare, where anthrax, smallpox and the plague hung in great clouds over the land, and exotic diseases such as tularemia, brucellosis, and typhus rained down and seeped into the sandy soil.

– BBC, “The deadly germ warfare island abandoned by the Soviets”

The true nature of the program only became clear after the Soviet collapse in the 1990s.

The Soviet Biopreparat program is an especially important case study because it reveals that even absent deliberate deployment, the existence of national bioweapons programs creates significant danger through the persistent risk of laboratory accidents. A historical example of this is the 1979 Sverdlovsk anthrax release (detailed in the video above), which killed “at least 68 people”; another example of this is the 1971 Aral smallpox incident:

[ Video — Watch · The 1971 Aral smallpox incident ·

]

B. Imperial Japan’s Unit 731

[ Video — Watch · Imperial Japan’s Unit 731 ·

]

Before the Soviet Union developed Biopreparat, Imperial Japan’s Unit 731 demonstrated that biological weapons could work on a massive scale. Active from 1936 to 1945, Unit 731 is notorious for its systematic human experimentation and operational deployment of biological weapons against Chinese civilians.

Unit 731 researchers reportedly used “political prisoners, criminals, the poor, and homeless” in their tests; test subjects also “included women and children.”

[ Video — Watch · The human experiments of Unit 731 ·

]

Unit 731 wasn’t the only bioweapons development program to conduct experiments on humans, but it was the only one to systematically integrate mass human experimentation with active biological warfare. Researchers used thousands of human subjects to perfect biological weapons that were immediately deployed against civilian populations. An estimated 3,000 died in horrific experiments, while tens of thousands more were killed by the weapons those experiments helped develop:

Elderly plaintiffs flew from China to testify – often in tears – about their communities being ravaged by diseases that spread mysteriously after Japanese planes flew low overhead and dropped wheat, rice or cotton infested with fleas…

– The Guardian

iTrigger warning: graphic content.

Researchers performed surgeries and vivisections on their victims without the use of anesthesia, removing organs and severing limbs… [s]ome victims had their limbs amputated and reattached to other parts of their body; others were subjected to extreme cold to gauge the effects of frostbite and gangrene on human skin. Many were exposed to poison gas or deadly diseases to observe the amount of time it took a person to show the effects or symptoms before dying… doctors subjected victims to starvation, dehydration, extreme air pressure, or electrical current to see how long a human could survive under such conditions… estimates ranging from thirty thousand to more than five hundred thousand are believed to have died in field tests of diseases on the Chinese population.

– EBSCO

According to EBSCO, Unit 731 also focused on creating weaponized deployment options: they developed a bomb of plague-infested fleas, and planned to deploy their weapons using balloons sent “adrift across the Pacific” or through aircraft-delivered plague bombs on U.S. cities.

The war ended before those plans were executed; but as evidenced by Biopreparat, what the program had demonstrated about the feasibility of mass-casualty biological warfare did not end with it.

[ Video — Watch · The legacy of Unit 731 ·

]

II. Contemporary Cases

If Biopreparat and Unit 731 established that states will build these weapons when they see a use for them, the question for the present is who is still doing so. The U.S. government’s answer names four states.

A. Russia

Russia is the direct heir to the largest bioweapons program in history, and the U.S. government does not believe the inheritance was ever fully renounced. According to a State Department report released in 2025, U.S. intelligence believes that the Soviet program was absorbed rather than dismantled by the Russian Federation, and that Russia currently “maintains an offensive biological warfare program.”

In the wake of Russia’s invasion of Ukraine, the program appears to be expanding. In October 2024, the Washington Post reported on satellite imagery of Sergiev Posad-6, a military site northeast of Moscow with a Cold War history of weaponizing smallpox and Ebola:

A few months after Russia began its full-scale invasion of Ukraine in 2022, satellite imagery captured unusual activity at a restricted military research facility… construction vehicles renovating the old Soviet-era laboratory and breaking ground on 10 new buildings, totaling more than 250,000 square feet, with several of them bearing hallmarks of biological labs designed to handle extremely dangerous pathogens.

– Washington Post, “Satellite images show major expansion at Russian site with secret bioweapons past”

Satellite imagery of Russia; area believed to be a historical bioweapons site. Source: Washington Post

Russian officials have framed the expansion as biodefense. It’s impossible to discern from satellite photos alone what the true nature of the facilities is.

B. China

The U.S. government states that China possessed its own biological weapons program from “the 1950s to at least the late 1980s.” As part of the program, the country is said to have “weaponized ricin, botulinum toxins, and the causative agents of anthrax, cholera, plague, and tularemia.”

What’s unresolved is whether “at least the late 1980s” was actually the end. The State Department does not assert the program continued, but it does not rule out the possibility: China has never disclosed its historical facilities in any BWC submission, and the same report flags continuing biotech research by Chinese military medical institutes that could have weapons uses. This year, there’s also a new concern:

For the first time, this year’s report warns that China probably is capable of using publicly available artificial intelligence and machine learning (AI/ML) tools to advance efforts related to biological weapons applications. At the same time, China “probably is unable to make complex scientific equipment without Western innovation.”

– Council on Strategic Risks, “The State of Compliance with WMD-Related Treaties”

Notably, the AI dimension flagged here is not unique to China; it runs through every contemporary case, and it is the subject of the analysis to come.

C. North Korea

Photo by Mike Bravo on Unsplash

While China is assessed as a “compliance concern,” the State Department states the case for North Korea plainly: the U.S. assesses that the DPRK has a dedicated, national-level offensive biological weapons program, and possesses the technical capability to produce pathogens and toxins usable as bioweapons, and to engineer them genetically.

The United States assesses that the DPRK has a dedicated, national-level offensive [biological weapons] program… Pyongyang probably is capable of weaponizing BW agents with unconventional systems such as sprayers and poison pen injection devices, which have been deployed by the DPRK for delivery of chemical weapons and could be used to covertly deliver BW agents.

– U.S. State Department

While the State Department’s paper does not dive into the specifics of the DPRK’s program, South Korean defense analysts believe that “B. anthracis, Variola virus, Yersinia pestis, Vibrio cholerae, and botulinum toxin” are the most likely candidates for weaponization.

These same agents are classified as top-tier biological threats by both the U.S. Centers for Disease Control and Prevention (CDC) and WHO.

– Jungeun Lee, Korea Research Institute for Defense Technology Planning and Advancement

Concerningly, the June 2024 Treaty on Comprehensive Strategic Partnership between Russia and North Korea establishes plainly that the two states will “actively encourage joint research in the field of science and technology, including such areas as space, biology, peaceful nuclear energy, artificial intelligence, [and] information technology.”

D. Iran

Russia and North Korea have confirmed offensive programs, and there is a verified historical program with unanswered questions about the extent to which it continues in China. Meanwhile, Iran’s case is interesting because it centers on the dual-use problem: the U.S. says that “Iran has not abandoned its intention to conduct research and development of biological agents and toxins for offensive purposes,” and that it has facilities that possess the capability to produce bioweapons, if directed:

Iran maintains flexibility to use, upon leadership demand, legitimate research underway for biodefense and public health purposes for a capability to produce lethal BW agents. It is unknown if Iran’s leadership has set a directive to maintain this flexibility.

– U.S. State Department

III. Analysis

States succeed where others fail because they have substantially more resources than typical rogue actors. Biopreparat was composed of tens of thousands of experts, and states can draw from large military budgets to finance their operations. That’s true, but the more interesting read is that state resources reliably buy capability. Biopreparat and Unit 731 were successfully able to synthesize deadly bioweapons, and Russia, China, and North Korea all appear to possess some capability to produce bioweapons, with Iran assessed as able to develop it on demand. But capability is not the same as use: of every program in this essay, only Unit 731 ever achieved mass-casualty deployment.

Most of the time, even states that solved the resource and expertise problems mostly declined to use what they built. Why is that? The reason appears to be that biological weapons have rarely offered enough strategic utility to justify the risks of using them on a battlefield, in a conflict between two states.

Countries have specific incentives: they want to protect their own troops during wartime. Blowback risks, along with the threat of nuclear retaliation in some cases (the UK, for example, reserves the right to deploy nuclear weapons if chemical or biological weapons are used against its people), complicate this goal. They are further bounded from bioweapons use by a near-universal international norm. Ultimately, the fact that states also have a wide variety of conventional weapons in their arsenals makes it difficult to justify deploying bioweaponry.

However, the degree to which this constraint is binding depends upon a rational state actor weighing controllable outcomes. In recent history, the limiting factor has never solely been the technical challenge of developing these weapons: instead, it has primarily been the fact that those with the capability to build them often did not consider their use worthwhile. This restraint appears to be a property of who holds the capability, not of the weapons themselves – and as evidenced by Unit 731, Aralsk-7, and Sverdlovsk, even that hasn’t been a fully successful restraint in the past.

The relevant question for the present is what happens when that capability becomes available to actors whose calculations are different: namely, actors who are not deterred by blowback, not bounded by international norms, unafraid of state-level deterrence, and not optimizing for controllable outcomes.

The restraint was never about the weapons. It was about who held them. What happens when that changes?

Part Three · The Bottlenecks

The Bottlenecks Are Eroding

How AI and emerging technology are undermining the barriers that have kept catastrophic bioweapons rare.

Four bottlenecks have historically kept bioweapons out of reach. Each section below is tagged with the one(s) it erodes; in the case studies at the end, individual passages are highlighted with a margin note.

Capital

The total cost of developing and producing a weapon.

Equipment

Access to the physical lab gear and facilities required.

Knowledge

The information (e.g., sequences of harmful pathogens to use for synthesis) required.

Expertise

The trained human skill (commonly referred to as “tacit knowledge”) required to execute the work.

AI is Eroding the Knowledge Bottleneck

Knowledge

In April 2025, publicly available models like o3 had already outperformed 94% of virology experts on laboratory protocol questions, even on questions directly relevant to the experts’ specialties; SecureBio’s recent assessment of GPT-5.5 put it in the 100th percentile on that same evaluation.

AI model scores on the Virology Capabilities Test. Note: GPT-5.5 not included in the above; see below. Source: AI Frontiers

“Figure 2.1.1.2 Model performance compared to human SMEs on VCT (full set, multimodal)… Pre-Release Checkpoint 2 outperforms all SMEs across all samples.” Source: SecureBio

That benchmark performance hasn’t yet translated cleanly into end-to-end weapons development guidance, but the trajectory is clear. Models are advancing toward the ability to provide expert-level virological guidance on demand, anonymously, and at scale. Anthropic’s own internal bioweapons acquisition uplift trials found that Claude Opus 4 enhanced human performance by 2.53× on relevant tasks, enough to trigger activation of AI Safety Level 3.

Beyond AI: A Converging Landscape of Eroded Bottlenecks

AI’s erosion of the knowledge bottleneck operates within a broader landscape of technological advancement. Simultaneously, progress in dispersal technologies, automated labs, and DNA synthesis have each independently eroded distinct bottlenecks that once constrained bioweapon development.

Dispersal Technologies

Equipment

Aum Shinrikyo faced equipment failures when they were attempting to deploy anthrax in 1993. However, multiple breakthroughs in aerosol technologies have occurred since the 90s that could make dispersal of a bioweapon significantly easier and less error-prone.

For example, bag-on-valve technology separates liquid completely from the propellant using a hermetically sealed bag inside the can; the liquid being dispersed maintains complete purity since it’s never contaminated by propellant gases. Moreover, crop duster drone technology and other commercial drones have dual-usability; the Institute for National Strategic Studies writes that there is significant risk of “the use of crop dusters as delivery vehicles for biological or chemical weapons of mass destruction.”

Cloud Labs & Contract Research Organizations

Equipment Expertise

Cloud labs. Companies like Emerald Cloud Lab and Strateos allow anyone to design and run biological experiments remotely. Customers submit experimental protocols through a software interface, and robotic systems in a physical lab execute them. Currently, anyone can use a cloud lab with no coding experience, and there are no regulations requiring identity verification (KYC), nor are there any mandatory legal mechanisms for monitoring what experiments users run. Anyone with a credit card and an internet connection can run experiments that would previously have required institutional affiliation, costly specialized equipment, physical lab access, and years of hands-on expertise to execute.

Contract research organizations (CROs). These are labs for hire that will execute experiments on behalf of clients. Like cloud labs, the regulatory oversight here is minimal.

Why do these matter?

The threat from cloud labs and CROs is not that they will manufacture bioweapons on behalf of customers. Rather, they enable a malicious actor to fragment their research across multiple platforms and providers, making it increasingly likely they will be able to defeat traditional detection mechanisms.

More specifically, a bioweapon developer could outsource the process of running iterative experiments to cloud labs, which means they may never need to purchase the large quantities of materials that would normally be flagged for suspicious activity. Each individual experiment, run through various cloud labs under a shell company name, might appear to be legitimate research on vaccines or protein expression, but together, they could constitute the development pipeline for a biological weapon.

DNA Synthesis

Knowledge Capital Equipment

What is DNA synthesis?

DNA synthesis is the process of building custom DNA sequences. A researcher types a desired sequence into a computer, submits an order to a commercial provider, and receives a vial of synthetic DNA in the mail. The process is quick and can be relatively inexpensive ($0.07 to $0.09 per base pair in some cases; other companies offer flat rates).

Why is it dangerous?

Some viruses’ entire genomes can be assembled from commercially purchased DNA fragments. In 2006, an investigative journalist with the Guardian was able to mail order a “modified sequence of smallpox DNA,” and their order was not screened by the provider since it was less than 100 letters long.

What’s the current state of screening requirements?

As I’ve written about extensively here, no country currently has a law requiring gene synthesis providers to screen DNA orders for dangerous sequences. The screening that does exist is voluntary, inconsistent, and has no mechanism for detecting split orders across multiple providers.

The net effect of these technological advancements, among others, is that many of the traditional barriers that used to constrain bioweapons development – including physical lab access and years of hands-on training – are becoming less binding. Increasingly, you can outsource iterative experimentation to cloud labs and CROs, outsource sequence design to AI or find publicly available sequences for dangerous pathogens on the internet, and order the genetic material from synthesis providers with minimal screening.

The risks of these emerging technologies might appear purely theoretical – but case studies tell a different story.

The Capital, Equipment, Knowledge, & Expertise Bottlenecks

As mentioned briefly above, in 2006, an investigative journalist with the Guardian was able to mail order a “modified sequence of smallpox DNA“, and their order was not screened by the provider since it was less than 100 letters long.

The journalist placed an online order using “an invented company name along with just a mobile telephone number and free email address.”

The Guardian writes:

The DNA sequence of smallpox, as well as other potentially dangerous pathogens such as poliovirus and 1918 flu are freely available in online public databases. So to build a virus from scratch, a terrorist would simply order consecutive lengths of DNA along the sequence and glue them together in the correct order. This is beyond the skills and equipment of the kitchen chemist, but could be achieved by a well-funded terrorist with access to a basic lab and PhD-level personnel.

In a separate 2017 incident, two scientists from the University of Alberta synthetically recreated the previously eradicated horsepox virus for a mere $100,000. CSIS writes:

Rather than developing the virus themselves, the scientists outsourced much of the initial work – custom ordering fragments of the DNA from a commercial synthesis lab, which “printed” and shipped the viral DNA back to them via mail. The scientists then linked the fragments together in a lab and introduced them into cells using a helper virus, producing the final horsepox virus. Though the study exclusively aimed to improve vaccine and cancer treatments (rather than produce bioweapons), it nevertheless attracted widespread alarm at the time by indicating that reviving smallpox – a close cousin to horsepox and one of the deadliest diseases known to mankind – would, as Science reported, “probably take a small scientific team with little specialized knowledge half a year.”

Dying of smallpox doesn’t seem particularly fun.

So what should we do about it?

Part Four · Policy Interventions

Policy Interventions

Eight concrete interventions to defend against AI-enabled biological threats.

Prevention & Detection

4.1 Require standardized pre-deployment bio evals

A beautiful eval. Source: METR

Background: Today, the question of whether a given AI model poses meaningful biological risk is answered inconsistently, and often by the very companies with a financial incentive to deploy their models as quickly as possible.

Anthropic conducts what it calls “uplift trials;” OpenAI has conducted its own internal red-teaming. Results are not always disclosed publicly, and no agreed definition exists for what constitutes unacceptable biological risk, nor is there a standardized protocol to ensure models above a certain threshold of capability have specific safeguards attached to them.

No legislation requiring pre-deployment bio evaluation currently exists.

Meanwhile, the capability frontier is advancing. As I wrote in the Bottlenecks section:

In April 2025, publicly available models like o3 had already outperformed 94% of virology experts on laboratory protocol questions, even on questions directly relevant to the experts’ specialties; SecureBio’s recent assessment of GPT-5.5 put it in the 100th percentile on that same evaluation.

Moreover, as I’ve written about previously:

A recent study led by Microsoft and IBBIS researchers demonstrated that open-source AI tools could engineer new protein variants of known proteins of concern that successfully evaded synthesis screening. As Nobel laureate David Baker and Harvard geneticist George Church have emphasized, screening based on homology alone is unlikely to be sufficient when de novo protein design can produce functionally dangerous proteins with no recognizable homology.

The Intervention: Require all developers of frontier AI models and AI protein design tools to conduct standardized pre-deployment bio evaluations before releasing any model to the public, with evaluations for protein design tools focused specifically on their capability to engineer dangerous protein variants.

Evaluations must be conducted by certified third-party evaluators and reported to the federal government. Models exceeding a defined risk threshold must implement tiered access controls, capability restrictions, delay deployment indefinitely, or other safeguards until identified risks are mitigated.

Recommendations

NIST / Center for AI Standards and Innovation (CAISI)

• Develop standardized pre-deployment bio evaluation protocols, organized by threat pathway, with defined risk tiers and corresponding required actions (e.g. enhanced monitoring requirements and mandatory deployment delays, or perhaps tiered access controls– see 4.4)

  • Work alongside third-party evaluators such as SecureBio, FAR.AI, EquiStamp, and RAND

• Define the compute and capability thresholds that trigger mandatory evaluation requirements, and update them on an annual basis as the frontier advances and algorithmic efficiency gains are made

Department of Health and Human Services

• Establish a certification program for third-party evaluators and maintain a public registry of those certified

Federal Funding Agencies (NIH, NSF, DARPA)

• Condition federal research grants on use of AI tools and models that have cleared certified bio evaluations, using procurement-based requirements as an interim lever while waiting on Congress for the legal basis of restricting model releases

  • This mirrors the procurement lever proposed for DNA synthesis screening in 4.2 and creates immediate market pressure before binding regulation is in place

Congress

• Pass legislation requiring mandatory pre-deployment bio evaluation as a condition of public or API release for all covered AI models (under NIST’s compute and capability thresholds)

• Authorize the Department of Commerce (informed by technical standards developed by NIST through CAISI) to issue binding regulations governing evaluation protocols and risk thresholds

International Coordination

• Work with the UK AI Security Institute, which has an existing model evaluation program, to coordinate bio evaluation standards across allied governments

• Pursue coordinated standards through the G7 Hiroshima AI Process

• Promote common biological-risk evaluation frameworks through OECD AI governance initiatives, including the OECD AI Principles and the OECD AI Policy Observatory

  • As a first step, develop an international taxonomy of high-risk biological queries that establishes a baseline for situations in which AI models should refuse to answer.

4.2 Mandate AI-enabled DNA synthesis screening

Photo by Sangharsh Lohakare on Unsplash

Background: Currently, there exists no universal legal requirement for gene synthesis providers to conduct background checks on clients or screen DNA sequences to ensure they’re not dangerous pathogens. While some labs screen orders on a voluntary basis as a condition of membership in organizations like the International Gene Synthesis Consortium, compliance remains optional and inconsistent. In 2006, an investigative journalist with the Guardian was able to mail order a “modified sequence of smallpox DNA,” and their order was not screened by the provider since it was less than 100 nucleotides long.

Even in the United States, no binding legal requirements exist for DNA synthesis screening. Federal regulations were proposed in 2024 through the Framework for Nucleic Acid Synthesis Screening, but an Executive Order in May 2025 paused implementation, and no replacement framework has been issued. Moreover, despite several congressional bills attempting to mandate screening, none have passed. This regulatory gap means anyone, including those with malicious intent, can order potentially dangerous genetic sequences with minimal oversight.

Notably, frontier lab leaders themselves agree that this is a problem: see this letter signed by the CEOs of OpenAI, Anthropic, Google DeepMind and Microsoft AI: ScreenDna.org

The Intervention: Existing screening approaches rely on sequence homology, which means matching orders against databases of known dangerous pathogens. An AI-enabled bioterrorist could circumvent this by designing functionally equivalent pathogens using synonymous codons, chimeric sequences, or entirely novel genetic constructs that retain lethality while evading database matches.

To address this issue, we need to implement advanced AI-powered screening that would analyze predicted protein function and evolutionary markers to flag potentially dangerous sequences.

Implementation requires two components: First, we must develop reliable AI screening systems capable of detecting novel pathogenic sequences that the world has never seen before; second, we must require all commercial DNA synthesis providers globally to implement this screening as a condition for legal operation.

Recommendations

Department of Health and Human Services / OSTP (immediate actions while waiting for legislation from Congress)

• Finalize the paused Framework for Nucleic Acid Synthesis Screening with a phased screening mandate:

  • Phase 1 (immediate): require sequence homology screening against databases of known dangerous pathogens, using existing methods

  • Phase 2 (triggered once a validated AI screening tool is available– see Congress section below): require screening that integrates AI-powered functional analysis

    • Organizations like IBBIS, Sentinel Bio, and Fourth Eon Bio are already working on improved screening tools and international standards; Phase 2 should build on and accelerate this existing work

• Tie federal research funding to compliance: any institution receiving NIH, NSF, or DARPA grants must procure synthetic nucleic acids exclusively from providers that meet the updated screening standards

  • This was already proposed in the 2024 framework and creates immediate market pressure without requiring new legislation

Congress

• Pass S.3741 with these proposed amendments

  • Brief summary– the bill should:

    • (1) include dedicated funding for the development of AI-enabled screening tools that can assess the functional risk of novel sequences,

    • (2) require that benchtop nucleic acid synthesis equipment sold or distributed in the United States incorporate embedded screening software that queries the centralized sequence-of-concern database before synthesizing any sequence,^*

    • (3) include a provision where the Secretary is required to establish and maintain (or designate a separate entity to maintain) a centralized order reporting platform, and

    • (4) designate non-compliant DNA synthesis providers on the Entity List.

International coordination for synthesis screening should be pursued through the multilateral channels described in Section 1, including the BWC and Australia Group frameworks.

^* Note: A stronger proposal (that, since writing my gap analysis of S.3741, I have updated to support) would treat benchtop nucleic-acid synthesizers as controlled items subject to custody tracking, similarly to fissile material. Possession would require a license, and a designated oversight agency would maintain a registry of each device’s location and responsible custodian; when a licensed owner no longer needs a unit, it would be returned or transferred only to another licensed owner. For devices already in circulation, buyback programs may be a promising option.

4.3 Implement know-your-customer requirements for cloud laboratories and contract research organizations

Note: For a fuller treatment of why cloud labs could pose biorisk, see Part 3 on Bottlenecks.

Background: The standard objection to AI-driven bioweapons risk is that knowledge alone is not enough– you still need hands-on laboratory skills, the “tacit knowledge” that can only be acquired through years of physical practice (hence Active Site’s uplift study). Knowing how to culture a pathogen is different from being able to do it reliably. This barrier has historically been one of the strongest defenses against non-state bioweapons development.

Unfortunately, cloud laboratories threaten to significantly erode this barrier. Services like Emerald Cloud Lab allow anyone to design experiments in software and have them executed by robotic systems in a physical facility, remotely, without ever entering a lab. ECL requires no coding experience; internal estimates suggest relatively short onboarding periods for novice users. An AI system that can design a bioweapons protocol and a cloud lab that can execute it are, individually, semi-manageable risks; together, they could materially increase the risk of misuse.

Source: Nature

Despite this, cloud labs currently operate with no standardized customer screening.

Contract research organizations (CROs) create a similar vulnerability. CROs provide specialized research services and can help their clients with everything from compound synthesis to biological assays. A malicious actor could potentially decompose a bioweapons development project into seemingly innocuous components and outsource them to different CROs, each unaware of the larger program.

The Intervention: Require all cloud laboratory providers and contract research organizations to implement know-your-customer screening before granting access to experiment execution as a condition of legal operation. Providers should be required to log all experimental workflows and flag protocols involving select agents or sequences of concern, with automated screening that mirrors (and integrates with) the DNA synthesis screening proposed in 4.2. Also establish RAND’s proposed Cloud Lab Security Consortium modeled on the IGSC.

Recommendations

Department of Health and Human Services

• Issue rulemaking requiring all U.S.-based cloud laboratory providers and CROs to verify identity, institutional affiliation, and stated research purpose of every user before granting access to experiment execution

  • Model this on existing Select Agent Program registration requirements

• Require cloud lab providers and CROs to log all experimental workflows and flag protocols involving select agents, sequences of concern, or pathogen-adjacent procedures, with automated screening that integrates with the DNA synthesis screening framework proposed in 4.2

  • Mandate periodic red-team exercises in which vetted biosecurity researchers attempt to circumvent logging and screening systems, with findings reported to the relevant oversight body and used to update screening criteria

NIST (National Institute of Standards and Technology)

• Develop standardized KYC and biosecurity screening standards specifically for cloud laboratories and CROs, defining:

  • Minimum identity verification thresholds

  • Prohibited experiment categories

  • Escalation procedures when flagged protocols are detected

Federal Funding Agencies (National Institutes of Health, National Science Foundation, DARPA)

• Condition federal research grants on use of cloud labs and CROs that implement baseline KYC screening, using interim agency guidance while NIST develops formal standards (the same procurement-based lever proposed for DNA synthesis screening in 4.2)

  • For CROs specifically, this includes Chinese and Indian providers that cannot demonstrate equivalent customer verification standards; effectively creates a prohibited suppliers list of non-compliant overseas providers

  • Creates immediate market pressure before binding regulation is finalized

Congress

• Pass legislation mandating KYC screening for all U.S.-based cloud laboratory providers and CROs as a condition of legal operation, and place non-compliant cloud labs and CROs overseas on the Entity List

  • Even if no other country does this, it would make a meaningful dent in the problem because the U.S. currently hosts a disproportionate share of the world’s advanced CRO infrastructure, and is home to the leading commercial cloud lab providers.

International Coordination

• Support the development of a Cloud Lab Security Consortium (proposed by RAND), modeled on the International Gene Synthesis Consortium

• Work with allied governments to standardize CRO KYC requirements, reducing the regulatory arbitrage advantage of non-compliant overseas providers

Note: The KYC framework described here for cloud labs should also extend to DNA synthesis providers, which currently face no standardized customer screening requirements either.

4.4 Establish tiered access controls and standardized monitoring protocols for LLMs with advanced bio capabilities

Note: Open-weight model policy interventions are covered in uncensorable.ai

Background: In May 2025, Anthropic activated AI Safety Level 3 (ASL-3) protections for Claude Opus 4 after determining they could not rule out that the model might “significantly assist” the ability of individuals with basic STEM backgrounds to obtain, produce, or deploy chemical, biological, radiological, and nuclear (CBRN) weapons.

Anthropic’s ASL-3 protections apply universally to all Claude Opus 4 users, meaning that a postdoctoral researcher at MIT working on cancer therapeutics faces the same restrictions as an anonymous user with no verifiable background; similar safeguards at OpenAI and DeepMind currently apply indiscriminately.

Tiered access controls offer a path to minimize this trade-off by enabling differentiated access based on factors such as verified identity and demonstrably legitimate use cases.

This approach mirrors established biosecurity frameworks: physical laboratories implement Biosafety Levels (BSL-1 through BSL-4) with increasingly stringent requirements. Tiered access controls apply the same principle to AI systems. The result is more precise risk management, as legitimate researchers gain the capabilities they need while malicious actors face substantially higher barriers.

Tiered access could enable AI labs to deploy more capable biological models than would be safe under universal restrictions, accelerating beneficial research without proportionally increasing misuse risk.

Note: Currently, OpenAI has a research preview of an advanced model for the life sciences, GPT-Rosalind, which requires approval to access. The intervention here seeks to standardize and scale tiered access controls.

The Intervention: For frontier LLMs, a model exceeding a defined bio-capability threshold (as determined by the standardized evaluations proposed in 4.1) must be deployed with tiered access controls.

Tiered access is most effective when paired with standardized monitoring of API usage patterns. Once a user has been verified and granted access, their usage should still be monitored for sequences of queries suggesting progression toward bioweapon development; flagged patterns should be escalated to human review and, when warranted, to law enforcement. This is because credentials can still be stolen, and as with cases like Bruce Ivins and Aum Shinrikyo, the expert-terrorist overlap is statistically rare but non-zero.

Recommendations

NIST / Center for AI Standards and Innovation (CAISI)

• Define the access-control tiers corresponding to the risk tiers in 4.1, specifying which safeguards apply at each capability level

• Publish the capability thresholds that trigger tiered-access requirements, and update them regularly as the frontier advances

• Establish standardized identity verification and institutional affiliation requirements for elevated-capability access

Department of Health and Human Services / OSTP

• Design and pilot a vetted-researcher pathway

Department of Justice / FBI

• Establish a dedicated intake pathway for frontier LLM providers reporting flagged usage patterns

• Publish clear guidance on when escalation to law enforcement is warranted, while also sharing private guidance with frontier labs on particularly notable red flags, for use internally

Federal Funding Agencies (NIH, NSF, DARPA)

• Require federally funded researchers and institutions to use frontier AI systems that satisfy NIST tiered-access standards

  • Creates market pressure while waiting on Congress

Congress

• Pass legislation requiring that frontier LLMs exceeding the NIST bio-capability threshold require tiered-access controls for public release

• Authorize the Department of Commerce, informed by NIST standards, to set and annually update the capability thresholds and required safeguards

  • Explicitly provide antitrust safe harbor protections for frontier LLM providers coordinating on bio-safety, including information-sharing about flagged users to prevent evasion across providers.

International Coordination

• Coordinate tiered-access standards with the UK AI Security Institute, the G7 Hiroshima AI Process, and the OECD, so that a model restricted in the U.S. is not trivially accessible in another jurisdiction or with a VPN

4.5 Deploy pathogen-agnostic metagenomic sequencing

Background: Existing U.S. biosurveillance infrastructure is primarily built to detect known pathogens. Against a naturally occurring outbreak of a familiar pathogen, this approach functions reasonably well; against a novel engineered pathogen, current surveillance systems may fail to detect it entirely until clinical cases emerge.

This is problematic because, as discussed previously in Section 4.2, AI can already help adversaries design sequences specifically to exploit systems built on recognition. By the time symptomatic individuals are diagnosed, transmission may already be widespread.

In contrast to current methods, pathogen-agnostic metagenomic sequencing reads all genetic material present in a sample. Importantly, because it does not rely on comparing said genetic material to predefined targets, this approach can detect entirely novel pathogens.

The Intervention: Establish a national pathogen-agnostic metagenomic surveillance network. Use AI-powered bioinformatics (building from the function-based sequencing technology discussed in 4.2) to identify novel sequences and detect anomalous patterns for human review.

Recommendations

Centers for Disease Control and Prevention

• Develop and publish standardized metagenomic protocols for public health surveillance (e.g. sample collection, transport, processing, and bioinformatics reporting requirements)

• Add a metagenomic sequencing layer to the National Wastewater Surveillance System (while retaining any existing pathogen-specific testing for known high-priority pathogens)

  • Modernize high-population sites and transportation hubs first

• Expand the Advanced Molecular Detection program, in partnership with the Emerging Infections Program, to include metagenomic sequencing

• Develop and maintain an AI-powered anomaly detection system

Department of Homeland Security

• Conduct pilot deployments of continuous indoor air sampling at BioWatch-covered facilities and other priority areas (e.g. airports and high-volume ports of entry)

• Secure water and air sampling mechanisms against cyber attacks

Department of Health and Human Services

• Build a centralized federal biosurveillance dashboard aggregating metagenomic data streams

  • Establish access tiers for federal, state/local, government contractor, and allied-government users

• Negotiate data sharing agreements with major hospitals and clinics to incorporate metagenomic findings into the national monitoring dashboard (IFP)

Congress

• Pass legislation establishing a National Pathogen-Agnostic Biothreat Surveillance Program under the Public Health Service Act

• Establish mandatory baseline funding for the national metagenomic surveillance network, rather than forcing reliance on the annual discretionary cycle

  • The National Wastewater Surveillance System was built on emergency COVID relief funding that was never transitioned to permanent appropriations, and now faces a proposed 80% cut that the American Society for Microbiology warns would end the program by September 2026.

  • Include funding for the CDC’s Advanced Molecular Detection Program to conduct research into automated point of collection sampling at wastewater facilities, since it is easier and cheaper to send data than it is to send samples to labs

International Coordination

• Support WHO capacity building for metagenomic surveillance in low- and middle-income countries, particularly in high zoonotic spillover regions

  • A novel pathogen emerging abroad can pose a threat comparable to a domestic release, while often providing less time for U.S. authorities to detect and respond before international spread occurs.

• Work through the Global Health Security Agenda and the Pandemic Fund to establish international standards for metagenomic data sharing protocols

Defense

4.6 Fund the Strategic National Stockpile for PPE surge capacity

Note: Oddly enough, the U.S. has managed to amass a 1.4 billion-pound surplus of cheese, but has fallen far short on stockpiling PPE.

Source: Springfield Underground , Culture Cheese Mag

Background: The Strategic National Stockpile (SNS), managed by the Administration for Strategic Preparedness and Response (ASPR), is a federal reserve of medical countermeasures including pharmaceuticals, vaccines, medical devices, and personal protective equipment.

In theory, the SNS exists to supplement state and local supplies during emergencies too severe to handle with commercial supply chains. However, in practice, the SNS is plagued by inadequate supply and broken distribution logistics, making it fall short in times of crisis like the COVID pandemic.

In early March 2020, during the early days of the COVID outbreak, the Department of Health and Human Services stated that the SNS had only “1%... of the required respirator masks that would be needed for medical professionals if the COVID-19 outbreak were to erupt into a pandemic here.”

By early April 2020, the stockpile’s PPE had been “nearly depleted.” The House Oversight Committee chairwoman at the time described a chaotic scene of “states… scour[ing] the open market for scarce supplies, often competing with each other and federal agencies in a chaotic bidding war that [drove] up prices.”

In a post titled “Public Health Preparedness: HHS Should Address Strategic National Stockpile Coordination Challenges,” the Government Accountability Office highlights the issue clearly:

“[D]uring recent public health responses, such as COVID-19 and mpox, jurisdictions weren’t clear on how and from whom to request supplies, causing confusion and delays. Additionally, some Tribal officials cited challenges with having the facilities needed to receive and store delivered supplies.”

The funding gap is the problem. This year, even after the lessons of the COVID pandemic, the SNS was reportedly “[left with] a shortfall of about $588 million” following the passage of the FY2026 Labor–HHS appropriations bill. To put that into perspective, the Department of Defense’s budget for fiscal year 2026 is $839 billion; the SNS’s shortfall could be filled with 0.07% of that.

The Intervention: Provide adequate, sustained funding for the SNS, with clear distribution protocols and state partnership. Establish clear distribution pathways so states know how to request supplies, and support state-level stockpiles as the first line of response, with the SNS as surge capacity.

Source: NY Post / Reuters

Recommendations

Congress

• Appropriate the full $1.59 billion annually for SNS (make up the $588m shortfall)

• Establish a 10-year authorization for SNS funding to enable contract stability with manufacturers

• Set minimum domestic production capacity targets for elastomeric respirators¹ and other critical PPE to reduce dependence on international supply chains

Administration for Strategic Preparedness and Response (ASPR)

• Publish clear, pre-established request and allocation protocols for states

• Establish coordination mechanisms with state health officers and emergency management officials to clarify SNS activation procedures and expectations

• Implement a systematic stock rotation schedule

  • Distribute expiring SNS inventory to state health departments, hospitals, and emergency management agencies for use in routine operations

• Establish long-term contracts with domestic PPE manufacturers to ensure surge capacity and production readiness; use the existing ASPR Industrial Base Expansion program

State & Local Governments

• Participate in the federal state stockpile pilot program and establish PPE reserves for healthcare and essential workers

• Designate storage facilities and trained staff responsible for receiving and distributing SNS supplies

¹ “Elastomerics have a long shelf life and are more effective than, say, N95s. This option avoids a lot of the failure modes of maintaining N95 stockpiles and is [substantially] better against the worst tail-risk threats.” –Lee Wall, AIxBiosecurity Research Manager at the ERA Fellowship

4.7 Upgrade indoor air infrastructure

Note: This intervention may also improve students’ health and academic performance, according to research cited by the EPA.

Background: Airborne pathogens are the hardest transmission route to defend against. As evidenced by COVID, influenza, and the common cold, these kinds of pathogens are difficult to control; if such a pathogen is released indoors, the rate at which it is diluted, filtered, and removed from a space directly determines how many people inhale an infectious dose. Buildings with poor ventilation often become amplifiers of transmission. In contrast, adequate ventilation and filtration can reduce risk substantially.

Currently, the Government Accountability Office states that “an estimated 41 percent of districts need to update or replace heating, ventilation, and air conditioning (HVAC) systems in at least half of their schools, representing about 36,000 schools nationwide that need HVAC updates;” the problem extends beyond schools to hospitals, transit systems, office buildings, and other high-occupancy public spaces.

Source: GAO analysis of school district survey data (GAO-20-494)

Cost

Upgrading costs depend on whether a building’s existing system is strong enough to be equipped with better filtration. When it is, the upgrade is nearly free: swapping MERV-8 for MERV-13 filters runs about $1.50 a month for a 5,000-square-foot office, according to the Lancet Covid-19 Commission Task Force.

Where it can’t (which, unfortunately, describes much of the aging school and public building systems), the cost to upgrade can become substantial: some estimates say it would cost roughly $500,000 to $5 million per school site, depending on the condition of the current system and the size of the school. (These projects often pull in electrical, roof, ceiling, and insulation work, which drives the total higher.)

For existing buildings where a full retrofit is economically infeasible, a building can still reach an equivalent target with some combination of the following three things: more outdoor air (which means opening windows or raising the HVAC’s fresh-air intake), the highest-grade filter the existing system can run (MERV-13 where the equipment allows, which the EPA rates at ≥85% capture of 1-3 micron particles and ≥50% of the finest 0.3-1 micron, and the best compatible filter where it doesn’t), and portable HEPA filters. These approaches can deliver equivalent outdoor air changes per hour (EOACH), providing a lower-cost pathway to reducing airborne infection risk.

A Note on Far-UVC

A newer air-cleaning option, far-UVC germicidal lighting, can also contribute to this target. A 2024 study from the Center for Radiological Research at Columbia University found that “far-UVC light inactivated nearly all (>99%) of an airborne virus in an occupied work environment.” To quote the senior author of the study:

“If this virus had been a disease-causing virus, the far-UVC light would have provided far more protection against airborne-disease transmission than any ventilation system.”

– David Brenner, PhD

The technology is still emerging, however. Real-world evidence remains limited to a small number of settings, the long-term effects of chronic exposure are not yet well characterized, and far-UVC lamps can generate ozone and other reactive byproducts under some conditions, so deployments should monitor air quality and avoid small, poorly ventilated spaces. For now, far-UVC is best treated as a possible contributor to the clean-air target, rather than a stand-alone fix.

Source: Blueprint Biosecurity

The Intervention: Establish a federal minimum standard for indoor air quality in high-risk buildings (which should include healthcare facilities, schools, public transit, government buildings, and other high-occupancy spaces) and fund ventilation infrastructure upgrades to meet that standard. Also ensure that all new buildings are constructed with proper ventilation.

Recommendations

CDC

• Publish consolidated federal guidance on indoor air quality standards for infection control, specifying performance targets by building type, occupancy, and other relevant metrics

  • Consider building from ASHRAE Standard 241

Department of Education

• Set a 10-year timeline for school districts to achieve standards set by the CDC, with federal support covering 75% of upgrade costs for qualifying districts

Department of Transportation / Transit Authorities

• Establish minimum ventilation standards for public transit vehicles and stations

• Conduct baseline assessments of current ventilation capacity across bus, rail, and airport systems

• Develop retrofit standards for transit vehicles that currently operate below recommended air-quality thresholds

Congress

• Authorize and appropriate dedicated funding for building ventilation upgrades in federal government-operated facilities

• Create a grant program for state and local governments to upgrade ventilation in public buildings and healthcare facilities, with priority given to schools with the worst current air quality assessments

• Require new federal construction and major renovations of high-risk or high-occupancy buildings to meet standards set by the CDC as a condition of federal funding

• Issue federal guidance allowing businesses and property owners to take tax deductions or credits for ventilation system upgrades that meet CDC standards

EPA

• Create a voluntary building certification program recognizing facilities that meet CDC indoor air quality standards for infection control

4.8 Build rapid standing vaccine production capacity

Source: FDA

Note: A complementary program worth funding alongside standing vaccine capacity is pathogen-agnostic countermeasures, which includes things like broadly protective nasal sprays and innate immunomodulators that work across whole families of respiratory viruses, including ones that don’t exist yet. The UK’s Advanced Research and Invention Agency is pursuing this through its £57m Sustained Viral Resilience program. Fascinatingly, funding on these kinds of countermeasures is never wasted; while a pathogen-specific stockpile can expire unused, a broadly protective MCM works against every future pandemic in addition to viruses like the flu and the common cold. The eventual goal here would be to eliminate respiratory illness altogether. I would have liked to write about this as its own intervention, but wasn’t able to finish due to time constraints; I might write about this topic more at a later date, and update this site accordingly and/or publish on my Substack. A US effort here would most naturally live at the Advanced Research Projects Agency for Health.

Background: When a novel pathogen emerges, the clock starts immediately; every week before a vaccine exists is counted in infections and deaths. COVID showed both how fast vaccines can now move and how far short of “fast enough” we still are. The genetic sequence of SARS-CoV-2 was published in January 2020; a vaccine candidate was designed within two days, and Moderna was in human trials 66 days later.

Yet, the first shots didn’t reach arms until December– 326 days from identifying the virus to the first emergency authorization.

That was a world record, shattering the prior best of nearly five years. It was also most of a year, and most of the first wave’s deaths fell inside that window.

Importantly, research suggests even this record-breaking effort could have been substantially faster. A team of scientists from the Netherlands, in a paper titled “Upscaling vaccine manufacturing capacity - key bottlenecks and lessons learned,” concluded the following after researching the vaccine supply chain extensively:

The COVID-19 pandemic put enormous pressure on the vaccine production chain as billions of vaccines had to be produced in the shortest timeframe possible. Vaccine production chains struggled to keep up with demand, resulting in disruptions and production delays… Key bottlenecks identified include a lack of manufacturing facilities, a lack of tech-transfer personnel, inefficient arrangement of production stakeholders, critical shortages in raw materials, and restricting protectionist measures.

Similarly, a report from the Government Accountability Office cited “[l]imited manufacturing capacity,” “[d]isruptions to manufacturing supply chains,” and “[g]aps in the available workforce” as challenges faced by vaccine companies that slowed development and deployment at scale.

Notably, none of the above reasons for delay are difficult scientific problems. Instead, they are infrastructure challenges, which we do not need breakthroughs to address.

Closing that gap is the point of the 100 Days Mission, endorsed by the G7 and G20, which aims to have a vaccine ready for initial authorization and manufacturing at scale within roughly 100 days of identifying a new threat– about a third of the COVID timeline.

Rather than novel scientific discoveries, achieving it requires only standing capacity, built and maintained before the next outbreak. This includes validated rapid-response platforms, idle-but-ready manufacturing lines, prototype vaccine libraries against high-risk pathogen families, and pre-positioned supply chains.

The Coalition for Epidemic Preparedness Innovations, writing about the 100 Days Mission, states:

More than eight million people who died during the COVID-19 pandemic might be alive today if the world had achieved the 100 Days Mission to develop safe and effective new vaccines against the novel [SARS-CoV-2] virus.

The US is currently moving in the opposite direction. In August 2025, HHS canceled roughly $500 million in BARDA contracts supporting mRNA vaccine development, months after also terminating “$766 million in Moderna contracts for vaccines for flu pandemics.” The stated rationale, that mRNA “poses more risks than benefits” for respiratory viruses, is disputed by vaccine scientists and the available studies; a former BARDA director called it “self-inflicted vulnerability.”

The effect, however the debate resolves, is to dismantle part of the standing capacity that delivered vaccines in record time.

After Operation Warp Speed delivered vaccines for roughly $18 billion² against a pandemic that cost the US an estimated $16 trillion, the 2021 American Pandemic Preparedness Plan laid out a $65.3 billion program to make vaccines against any viral family within 100 days and create a permanent coordinating office. But the plan was largely never funded; the coordinating office Congress created in 2022 was left without dedicated money and has since collapsed.

The Intervention: Fund and sustain the standing vaccine-development and manufacturing infrastructure needed to achieve the 100 Days Mission.

Recommendations

Congress

• Fund the vaccine pillar of the American Pandemic Preparedness Plan and revive the Office of Pandemic Preparedness and Response Policy with a dedicated, multi-year appropriation

BARDA

• Restore rapid-response platform and warm-base manufacturing contracts cut in 2025

• Maintain prototype vaccine libraries against viral families with the highest pandemic potential

² TIME analyzed Operation Warp Speed spending compared to other government programs: link

FAQ & Counterarguments

I’m happy to add to this section as people reach out with thoughts. Feel free to comment on my Substack.

“I don’t think AI biorisk is a big deal, and I’m not sold that other emerging technologies are eroding barriers either. Making a bioweapon is still pretty hard.”

The interventions in Part 4 are worth implementing regardless. COVID was a natural pandemic, and, to quote the Center for Global Development:

[Researchers] estimate the annual probability of a pandemic on the scale of COVID-19 in any given year to be between 2.5–3.3 percent, which means a 47–57 percent chance of another global pandemic as deadly as COVID in the next 25 years.

Even the next natural pandemic could be orders of magnitude worse than COVID; that alone justifies the policies outlined in Part 4.

But it’s also worth remembering that it only takes one successful bioterrorist, or one lab accident. Even before the technologies outlined in Part 3 existed, some bioterrorists got scarily close. I don’t think we should take that bet.

“Government bioweapons programs don’t really matter. No country has ever released a widespread bioweapon, because of blowback and other strategic considerations.”

Imperial Japan’s Unit 731 did deploy bioweapons, so the premise is already not fully true. But even if we grant it for the sake of argument, deliberate release isn’t the only risk. Accidents happen. Notably, the Soviet Union’s Biopreparat had at least two: Aralsk-7 and Sverdlovsk, both covered in Part 2. And, the cost-benefit calculus that supposedly restrains states doesn’t apply to apocalyptic groups like Aum Shinrikyo, who wanted mass casualties.

Also, I don’t know about you, but I don’t feel particularly comfortable betting the lives of millions on Vladimir Putin’s restraint.

I’ve written a bit more on this here.

“Why are AI-designed pathogens of particular concern?”

AI could, in principle, design a pathogen that has never existed in nature. Why is this important? Two reasons:

1. Natural pathogens typically face a trade-off between lethality and transmissibility. The deadlier a pathogen, the faster it kills its host, which limits how far it spreads. This might not be true of an artificially designed pathogen; for example, a “stealth virus” – engineered to seem benign or even be asymptomatic initially, then turn lethal – could both spread rapidly and be very deadly.

2. For naturally occurring pathogens, humans have evolved over hundreds of thousands of years to possess at least some level of natural immunity. However, an engineered pathogen could be built to evade our immune systems entirely.

I’m writing a separate piece for SecureBio on this topic – stay tuned!

“What should we do about open-weight models?”

My best guess, along with links to other pieces I’ve written on the topic, can be found at uncensorable.ai.

Analysis of Historical Case Studies - Part 2: Government-Sponsored Programs

This piece is more analytical/formal in tone than what I usually publish on Substack; it’s part of a longer policymaker-facing essay on AI biosecurity risks, and it focuses on government-sponsored programs of the past and present. Consider yourself warned.

Part 1 on rogue actors can be read here.

I. Historical Precedent

Governments have repeatedly demonstrated both capability and willingness to develop and deploy bioweapons.

A. Soviet Biopreparat Program

In 1972, the Soviet Union signed the Biological and Toxic Weapons Convention, pledging not to develop, produce, or stockpile bioweapons. A year later, the Soviet Union violated the BTWC by creating Biopreparat, a massive biological warfare enterprise masquerading as a civilian pharmaceutical company.

Through the program, Soviet scientists reportedly manufactured an estimated 20 tons of plague, 20 tons of smallpox, and hundreds of tons of anthrax; they also weaponized tularemia, epidemic typhus, Q fever, and Marburg virus, while studying the potential use of Ebola and encephalitis.

The program’s exact scale remains disputed. The Federation of American Scientists states the following:

“At its peak, the former Soviet Union had the world's largest biological warfare program, with somewhere between 25,000 and 32,000 people employed in a network of 20 to 30 military and civilian laboratories and research institutions. An additional 10,000 or so worked in Defense Ministry bioweapons laboratories. According to other estimates, at least 47 labs and test facilities were scattered across Russia, employing more than 40,000 workers, 9,000 of whom were scientists. Between 1,000 to 2,000 of those scientists were experts on deadly pathogens.”

—Federation of American Scientists

Meanwhile, Congressional testimony asserts:

“The size and scope of this program were enormous. For example, in the late 1980s and early 1990s, over 60,000 people were involved in the research, development, and production of biological weapons.”

—Dr. Kenneth Alibek, before the Joint Economic Committee, United States Congress

Regardless of the precise number, Biopreparat was unquestionably the largest biological warfare program in history.

Even decades after its official end, the program’s deadly legacy endures. Contaminated testing sites like Vozrozhdeniya Island still harbor active anthrax spores buried in the soil since the Soviet era:

“Vozrozhdeniya was once home to a vibrant fishing village fringed by turquoise lagoons, back when the Aral Sea was the fourth-largest in the world and abundant with fish…

Over the years the site flourished into a living nightmare, where anthrax, smallpox and the plague hung in great clouds over the land, and exotic diseases such as tularemia, brucellosis, and typhus rained down and seeped into the sandy soil.”

—BBC, “The deadly germ warfare island abandoned by the Soviets”

The true nature of the program only became clear after the Soviet collapse in the 1990s.

B. Imperial Japan’s Unit 731

Before the Soviet Union developed Biopreparat, Imperial Japan’s Unit 731 demonstrated that biological weapons could work on a massive scale. Active from 1936 to 1945, Unit 731 is notorious for its systematic human experimentation and operational deployment of biological weapons against Chinese civilians.

Unit 731 researchers reportedly used “political prisoners, criminals, the poor, and homeless” in their tests; test subjects also “included women and children.”

Unit 731 wasn’t the only bioweapons development program to conduct experiments on humans, but it was the only one to systematically integrate mass human experimentation with active biological warfare. Researchers used thousands of human subjects to perfect biological weapons that were immediately deployed against civilian populations. An estimated 3,000 died in horrific experiments, while tens of thousands more were killed by the weapons those experiments helped develop:

“Elderly plaintiffs flew from China to testify - often in tears - about their communities being ravaged by diseases that spread mysteriously after Japanese planes flew low overhead and dropped wheat, rice or cotton infested with fleas…”

—The Guardian

[Trigger Warning: Graphic]

“Researchers performed surgeries and vivisections on their victims without the use of anesthesia, removing organs and severing limbs… [s]ome victims had their limbs amputated and reattached to other parts of their body; others were subjected to extreme cold to gauge the effects of frostbite and gangrene on human skin. Many were exposed to poison gas or deadly diseases to observe the amount of time it took a person to show the effects or symptoms before dying… doctors subjected victims to starvation, dehydration, extreme air pressure, or electrical current to see how long a human could survive under such conditions… estimates ranging from thirty thousand to more than five hundred thousand are believed to have died in field tests of diseases on the Chinese population.”

—EBSCO

According to EBSCO, Unit 731 also focused on creating weaponized deployment options: they developed a bomb of plague-infested fleas, and planned to deploy their weapons using balloons sent “adrift across the Pacific” or through aircraft-delivered plague bombs on U.S. cities.

The war ended before those plans were executed; as evidenced by Biopreparat, what the program had demonstrated about the feasibility of mass-casualty biological warfare did not end with it.

II. Contemporary Cases

If the Biopreparat and Unit 731 established that states will build these weapons when they see a use for them, the question for the present is who is still doing so. The U.S. government’s answer names four states:

A. Russia

Russia is the direct heir to the largest bioweapons program in history, and the U.S. government does not believe the inheritance was ever fully renounced. According to a State Department report released in 2025, U.S. intelligence believes that the Soviet program was absorbed rather than dismantled by the Russian Federation, and that Russia currently “maintains an offensive biological warfare program.”

In the wake of Russia’s invasion of Ukraine, the program appears to be expanding. In October 2024, the Washington Post reported on satellite imagery of Sergiev Posad-6, a military site northeast of Moscow with a Cold War history of weaponizing smallpox and Ebola:

“A few months after Russia began its full-scale invasion of Ukraine in 2022, satellite imagery captured unusual activity at a restricted military research facility… construction vehicles renovating the old Soviet-era laboratory and breaking ground on 10 new buildings, totaling more than 250,000 square feet, with several of them bearing hallmarks of biological labs designed to handle extremely dangerous pathogens.”

—Washington Post, “Satellite images show major expansion at Russian site with secret bioweapons past”

Russian officials have framed the expansion as biodefense. It’s impossible to discern from satellite photos alone what the true nature of the facilities is.

B. China

The U.S. government states that China possessed its own biological weapons program from “the 1950s to at least the late 1980s.” As part of the program, the country is said to have “weaponized ricin, botulinum toxins, and the causative agents of anthrax, cholera, plague, and tularemia.”

What’s unresolved is whether ‘at least the late 1980s’ was actually the end. The State Department does not assert the program continued, but it does not rule out the possibility: China has never disclosed its historical facilities in any BWC submission, and the same report flags continuing biotech research by Chinese military medical institutes that could have weapons uses. This year, there’s also a new concern:

“For the first time, this year’s report warns that China probably is capable of using publicly available artificial intelligence and machine learning (AI/ML) tools to advance efforts related to biological weapons applications. At the same time, China ‘probably is unable to make complex scientific equipment without Western innovation.’”

—Council on Strategic Risks, “The State of Compliance with WMD-Related Treaties”

Notably, the AI dimension flagged here is not unique to China; it runs through every contemporary case, and it’s the subject of the analysis to come.1

C. North Korea

While China is assessed as a “compliance concern,” the State Department states the case for North Korea plainly: the U.S. assesses that the DPRK has a dedicated, national-level offensive biological weapons program, and possesses the technical capability to produce pathogens and toxins usable as bioweapons, and to engineer them genetically.

“The United States assesses that the DPRK has a dedicated, national-level offensive [biological weapons] program… Pyongyang probably is capable of weaponizing BW agents with unconventional systems such as sprayers and poison pen injection devices, which have been deployed by the DPRK for delivery of chemical weapons and could be used to covertly deliver BW agents.”

—U.S. State Department

While the State Department’s paper does not dive into the specifics of the DPRK’s program, South Korean defense analysts believe that “B. anthracis, Variola virus, Yersinia pestis, Vibrio cholerae, and botulinum toxin” are the most likely candidates for weaponization.

“These same agents are classified as top-tier biological threats by both the U. S. Centers for Disease Control and Prevention (CDC) and WHO.”

—Jungeun Lee, Korea Research Institute for Defense Technology Planning and Advancement

Concerningly, the June 2024 Treaty on Comprehensive Strategic Partnership between Russia and North Korea establishes plainly that the two states will “actively encourage joint research in the field of science and technology, including such areas as space, biology, peaceful nuclear energy, artificial intelligence, [and] information technology.”

D. Iran

Russia and North Korea have confirmed offensive programs, and there’s a verified historical program with unanswered questions about the extent to which it continues in China. Meanwhile, Iran’s case is interesting because it centers on the dual-use problem: the U.S. says nothing about a confirmed program; the country only alleges an intention. The U.S. says that “Iran has not abandoned its intention to conduct research and development of biological agents and toxins for offensive purposes,” and that it has facilities that possess the capability to produce bioweapons, if directed:

“Iran maintains flexibility to use, upon leadership demand, legitimate research underway for biodefense and public health purposes for a capability to produce lethal BW agents. It is unknown if Iran’s leadership has set a directive to maintain this flexibility.”

—U.S. State Department

III. Analysis

A clear conclusion that can be drawn from the above is that states succeed where others fail because they have substantially more resources than typical rogue actors. Biopreparat was composed of tens of thousands of experts, and states can draw from large military budgets to finance their operations. That’s true, but the more interesting read is that state resources reliably buy capability. Biopreparat and Unit 731 were successfully able to synthesize deadly bioweapons, and Russia, China, and North Korea all appear to possess some capability to produce bioweapons, with Iran assessed as able to develop it on demand. But capability is not the same as use: of every program in this essay, only Unit 731 ever achieved mass-casualty deployment.

Most of the time, even states that solved the resource and expertise problems mostly declined to use what they built. Why is that? The reason for this appears to be that biological weapons have rarely offered enough strategic utility to justify the risks of using them on a battlefield, in a conflict between two states.

Countries have specific incentives: they want to protect their own troops during war time. Blowback risks, along with the threat of nuclear retaliation in some cases (the UK, for example, reserves the right to deploy nuclear weapons if chemical or biological weapons are deployed against its people), complicate this goal. They are further bounded from bioweapons use by a near-universal international norm.

Ultimately, the fact that states also have a wide variety of conventional weapons in their arsenals make it difficult to justify deploying bioweaponry.

However, the degree to which this constraint is binding depends upon a rational state actor weighing controllable outcomes. In recent history, the limiting factor has never solely been the technical challenge of developing these weapons: instead, it has primarily been the fact that those with the capability to build them often did not consider their use worthwhile. This restraint appears to be a property of who holds the capability, not of the weapons themselves– and as evidenced by Unit 731, even that hasn’t been a fully successful restraint in the past.

The relevant question for the present is what happens when that capability becomes available to actors whose calculations are different: namely, actors who are not deterred by blowback, not bounded by international norms, unafraid of state-level deterrence, and not optimizing for controllable outcomes.

Part 3 of this series will be on how AI and existing technologies are changing the bio-threat landscape.

Once upon a time, I did a bit of writing about MAIM:

• How MAIM may not be a good deal for America

  • Would recommend reading this piece for a brief intro to the argument for how MAIM works, if you’re unfamiliar.

• A policy memo for the USG

• Notes on whether or not hardening works

• Highly theoretical notes on optimal timing of sabotage

And, in my last post about MAIM, I unwisely promised:

In Part 3, I’ll examine how attribution ambiguity, proxy operations, and escalation risks shape retaliation calculations.

Alas! It has been quite a while since I’ve published anything about MAIM, having taken various detours to write about AIxBiosecurity, data poisoning, and most recently, the AI safety funding landscape. But I promised more follow-up work, and I intend to keep that promise!

So, in the remainder of this post, I’ll be attempting to explore the following questions:

1. What options do both sides (China and the U.S.) have for kinetic strikes on each others’ datacenters?

2. How escalatory are those options?

Forgive me, I’m saving analysis of attribution ambiguity and proxy operations for another day.

Can China Bomb a U.S. Datacenter?

“Footnote 3: China’s long-range missile capabilities (DF-41 and DF-5B systems) seemingly do not allow them to launch strikes that the US would know are non-nuclear. This might make China especially reluctant to use this option because a conventional strike might be misinterpreted as nuclear, raising the likelihood of escalation.”

—Oscar Delaney, Crucial Considerations in ASI Deterrence

What weapons systems does China have that are capable of reaching the U.S.?

1. Nuclear ICBMs (land-based)

   1. There are two main types of land-based ICBMs in the Chinese arsenal: mobile missiles carried on trucks (the DF-41 and DF-31) and older missiles primarily housed in fixed underground silos (the DF-5B/C). Both can reach the continental U.S. While the mobile ones are harder to find and destroy, the silo-based DF-5 carries a heavier payload, and China’s silo fields are growing.

2. Nuclear SLBMs (sea-based)

   1. China is arming its Type 094 submarines with the JL-3. Whether this lets them reach the continental U.S. is contested: the Pentagon expects the subs to be able to threaten the U.S. mainland from protected coastal waters like the South China Sea, while other analysts assert that “Chinese SSBNs cannot target continental United States from their operating areas.”

3. Conventional ICBM: This is where things get non-nuclear, and interesting! The new DF-27 is a solid-fueled missile likely carrying a hypersonic glide vehicle and with an estimated 5,000–8,000 km range, meaning it can reach Alaska, Hawaii, and parts of the continental United States. For more, see below:

“Among the 2025 Pentagon report’s greatest revelations, the People’s Republic of China (PRC) has fielded a conventional intercontinental ballistic missile (ICBM) able to range parts of America’s homeland. This makes China the first nation publicly assessed to have fielded an operational, conventionally-armed ICBM…”

—Dr. Andrew S. Erickson, Professor of Strategy in the U.S. Naval War College (NWC)’s China Maritime Studies Institute (CMSI)

(Note: Certain newer systems such as the DF-61, unveiled in 2025, are omitted from the above; too little is publicly known to characterize them.)

Can the U.S. Bomb a Chinese datacenter?

Before we get to why this matters, the same question in reverse:

What weapons systems does the U.S. have that are capable of reaching China?

1. Nuclear ICBMs and SLBMs

   1. Like China, the U.S. fields silo-based ICBMs (the Minuteman III, now being replaced by the Sentinel) and submarine-launched ballistic missiles (the Trident II D5 on Ohio-class subs). Both can reach China.

2. A bomber force and conventional, non-ballistic reach

   1. Unlike China, the U.S. has a mature bomber force and a substantial number of conventional, non-ballistic weapons for reaching Chinese targets, including precision munitions and long-range cruise missiles fired from aircraft, ships, and submarines.

Writing in 2015, Hans Kristensen, Director of the Nuclear Information Project at the Federation of American Scientists, argued that:

“[A]dvanced conventional cruise missiles can now serve the role that nuclear air-launched cruise missiles used to serve: hold at risk heavily defended strategic and tactical targets at a range far beyond the reach of modern and anticipated air-defense systems. The Navy already has its Tactical Tomahawk widely deployed on ships and submarines, and now the Air Force is following with deployment of thousands of long-range Joint Air-to-Surface Standoff Missiles (JASSM-ER) on bombers and fighter-bombers.”

—Federation of American Scientists

The U.S. military’s conventional long-range weapon options have only increased since.

The Asymmetry

Each side has weapons that can reach the other’s homeland, but they differ in one decisive respect: how. Because they are all rocket-boosted, every system China can use to reach the U.S. homeland is, at least for early-warning systems, indistinguishable from a nuclear strike. Even a purely conventional strike with the DF-27 couldn’t be confidently distinguished from a nuclear launch in the warning window; it would register on the same nuclear early-warning systems, and force the same time-pressured decision.

The U.S. can instead reach Chinese targets with non-ballistic, recognizably conventional delivery methods– i.e., bombers and cruise missiles– that don’t trip the same nuclear early-warning reflex.

So China has no way to conventionally strike the U.S. homeland that doesn’t carry clear nuclear-escalation risk, while the U.S. has options that are at least less ambiguous.

Analysis

As it stands, China can’t kinetically MAIM the U.S. without incurring serious risk of nuclear retaliation. It’s worth noting that the Chinese military’s currently-under-development H-20 stealth bomber could undermine this by giving China a non-ballistic, non-hypersonic option for a U.S. strike; however, it appears to lag far behind U.S. bombers, with intelligence assessments calling it unlikely to debut until the 2030s.

On paper, the U.S. might be able to pull off a kinetic strike without triggering nuclear early warning systems, because they have an array of conventional weapons that can reach China. Yet, I still think it’s highly unlikely. Why? U.S. standoff missiles fired from outside China’s air defenses can reach coastal and near-inland targets, but striking the interior would generally require stealth bombers or forward basing to penetrate those defenses. This matters because China is increasingly building its largest compute clusters deep inland under its “East Data West Computing” plan, and distributing compute across many sites, making any single strike on a datacenter both harder to reach and less decisive.

Conclusion

Kinetic strikes are often dismissed as sitting comfortably outside the Overton window for either government, but Hendrycks and Khoja (2025) argue in follow-up work to Superintelligence Strategy that “[n]orms will adapt to strategic realities.” Ultimately, doing this research was enough to mostly eliminate kinetic strikes from my mental model of MAIM, even in a world where “it will not be difficult for states to diplomatically and domestically justify their efforts to disrupt another state’s unilateral efforts to attain [powerful AI].”

That leaves the sub-kinetic levers (cyber ops, supply-chain interference, maybe EMPs, other softer forms of sabotage) as the only place a usable MAIM option plausibly survives.

The OpenAI Foundation holds $180 billion in equity. Anthropic’s co-founders have pledged to donate 80% of their wealth. When the time comes to spend all this money, what should we actually do with it?

Here’s my best guess.

The problem: scaling what we have is not enough

When most people think about how to solve AI safety, they think about what we’re already doing, and how to scale it up. Concretely, this looks like: scaling fellowships like MATS, Pivotal, and ERA; investing more money into AI safety research organizations like Redwood, METR, and MIRI; and perhaps, more recently, expanding programs like the Generator Residency.

This is important work, but it is not sufficient to win.

The Maven system that killed 120 children in Minab wasn’t misaligned. Claude didn’t go rogue. The system did exactly what it was designed to do.

The failure was that no government framework existed to regulate how AI gets integrated into military kill chains, how fast targeting decisions can be compressed, or what human oversight is required before a strike.

Alignment research can’t fix that, but legislation might.

The same is true across every domain where AI is already being deployed. No amount of technical safety research creates oversight requirements for AI in immigration enforcement, or addresses illegal data centers poisoning the air, or strengthens chip export controls. These are governance problems, and governance problems require political power to solve.

Even the parts of AI safety that are about technical alignment– e.g. making sure models follow instructions, don’t deceive, don’t pursue unintended goals, and don’t help people make bioweapons– only matter if frontier labs are required to implement them. Without legislation, every technical safety advance is effectively optional.

So what does AI safety actually do with billions of dollars? It can’t just pour it into research. The field has a few thousand people working on AI risk; the absorptive capacity for research funding doesn’t exist.

But two categories of spending can absorb billions immediately, because the infrastructure to deploy that capital already exists outside the field.

1. Billions into mass media and movement building

When pollsters ask Americans whether AI should have safety guardrails, the answer is overwhelmingly yes. But those numbers are misleading. Rather than caring about whether people support AI regulation in the abstract, what we should be concerned about is whether they care enough to vote on it, call their representative about it, or show up at a town hall.

Right now, they largely don’t. In the Yale Youth Poll’s Spring 2026 survey, voters ranked AI near the bottom of 30 issues– at 24%, far below cost of living (84%), healthcare (75%), democracy (75%), and corruption (72%).

People support AI regulation when you ask them directly, but they’re not thinking about it otherwise. It’s not driving votes, it’s not driving calls to Congress, and until it does, the 73% of Americans that purportedly favor safety mandates changes nothing about what companies are actually required to do.

But we can convert existing ambient support into concrete political pressure, and fast, because it doesn’t require building a whole new field. Instead, it requires investing in mainstream media production, advertising, and electoral campaigns– industries that already know how to reach hundreds of millions of people. A blockbuster film like the Barbie Movie costs $200–300 million to produce and market. Obama’s 2012 reelection campaign spent $1.1 billion. A Super Bowl ad costs $10M for 30 seconds.

Sustained media attention has historically been one of the most powerful tools for shifting public opinion into political action; Reagan explicitly cited The Day After as shifting his thinking on nuclear disarmament, and The Social Dilemma put Big Tech accountability in front of 100 million Netflix households.

Unfortunately, AI safety hasn’t really tried yet. The field’s biggest media project to date– the AI Doc– made $713,480 at the domestic box office, total. Opening weekend was $646,020 in 786 theaters; that’s $822 per theater. By day four, it was making $86 per theater.

Dividing the amount of money the AI Doc made by the average movie ticket price in the U.S. translates to roughly 64,000 tickets total.

The movie essentially disappeared after one weekend. Even accounting for screenings and eventual streaming, its total reach will probably be a fraction of a single MrBeast video.

So, the AI Doc had an Oscar-winning director and rave reviews, but it only made about 64,000 ticket sales. In contrast, The Social Dilemma reached 100 million households because Netflix put it on every subscriber’s home screen.

AI safety needs that kind of reach, once again, to expose Big Tech for the recklessness of its actions and highlight how its technology is truly harming people. How do we get it? We need to fund content designed for streaming platforms from the start, and conduct sustained media operations.

And, when AI safetyists put real money behind reaching people, I suspect we’ll find that they’re already with us.

You don’t need everyone to care about loss of control and superintelligence alignment. You need privacy advocates furious about AI-powered mass surveillance, environmentalists furious about illegal gas turbines poisoning children in Memphis, parents furious about chatbots encouraging their kids to kill themselves, women furious about Grok still making sexualized images of them, workers furious about displacement with no transition plan, national security people furious about chips being sold to adversaries, anti-war activists furious about Minab.

These constituencies already organize themselves; what they lack is shared infrastructure. We need bipartisan coalitions, and we need to start now.

It's worth being explicit, briefly, about what this is and isn't. This is not astroturfing. Astroturfing means manufacturing the appearance of grassroots support that doesn't actually exist. There is nothing about the grievances above that is manufactured; the proposal is simply to fund infrastructure that existing constituencies already lack, with named donors and publicly disclosed advocacy, which is the operational opposite of how astroturfing works.

The strategy, concretely, entails: funding a professional media operation, funding national ad campaigns, funding organizations focused on coalition-building, and funding AIPI and Gallup to do polling and message testing so every dollar of media spend is targeted at what actually motivates people to take action.

2. Billions into political infrastructure

Why does public engagement matter? Because awareness translates into votes, and votes translate into political power. But a movement alone may not be enough. We also need the political infrastructure to channel public energy into concrete, specific legislative wins.

Lobbying works, which is why the industry spends so much on it:

In 2025, the AI industry spent $105 million on federal lobbying. One in four federal lobbyists reported working on AI. OpenAI’s super PAC has a $125 million war chest. Meta is spending $65 million to elect AI-friendly state officials. After Nvidia spent $4.97M lobbying in 2025– seven times their 2024 spend– the Trump administration weakened export controls and the bipartisan GAIN AI Act was killed in conference.

On the safety side, the asymmetry is staggering. Briefly: the CAIS Action Fund spent $310,000 in all of 2025, and although Anthropic donated $20m to Public First, that money “isn’t allowed to be used in the midterm battles.”

Note: I’ve written about this topic more expansively here.

AI safety needs to seriously show up on Capitol Hill. What does this look like, concretely? Fund bipartisan policy advocacy efforts; fund super PACs that counter Leading the Future; scale the CAIS Action Fund and the AI Policy Network; support politicians who back common-sense safety legislation.

The AI industry is currently winning in committee rooms, on K Street, and in campaign finance. To match that, AI safety needs (1) movement-building that delivers the votes, and (2) political infrastructure that converts those votes into leverage where policy fights actually happen.

3. Build the capacity to deploy the rest

We can rapidly deploy several billions of dollars into media, movement-building, and political infrastructure. Concurrently, we must also build the field’s ability to absorb the remaining amount. How do we do that?

a. Solve the grantmaker bottleneck

See more I’ve written on this here.

Currently, the entire field of AI safety has roughly 30 to 60 grantmakers, and Coefficient Giving’s Technical AI Safety team deployed $140 million last year with three investigators.

Julian Hazell, describing his experience as a grantmaker at CG, wrote:

[A]s our team has tripled headcount in the past year, we’ve also ~tripled the amount of grants we’re making, and we think the distribution of impact per dollar of our grantmaking has stayed about the same. That is, we’ve about tripled the amount of grant money we’ve moved towards the top end of the impact distribution as well as at the marginal end… [w]e want to scale further in 2026, but… we’re often bottlenecked by our grantmaker bandwidth.

Lack of grantmaker capacity is upstream of newly seeded organizations, faster grant evaluations, and faster capital deployment in the field; it is a binding constraint on everything else.

What does addressing the grantmaker talent bottleneck look like? We need to: fund and expand an official grantmaking stream as part of the Astra Fellowship (which already has grantmaker mentors and works directly with Coefficient Giving); build a BlueDot AI Safety Grantmaking Fundamentals course; run regranting and fellowship programs with Grantmaking.ai; and to help reduce the burden on individual donors making donation decisions, add better credibility signals to Manifund.

b. Recruit top technical talent

Over the past decade, OpenAI, DeepMind, and Meta have pulled the world’s best ML researchers out of academia by offering up to 5x their university salaries, creating an “AI brain drain” that’s well-documented. To scale technical talent, we should do what the AI labs did for academia, but in reverse: pay top dollar to recruit senior researchers from capabilities labs into safety research. We should also fund dedicated compute clusters for safety work.

We probably can’t match the $250M outliers, but most senior ML researchers at capabilities labs make $500K–$1M. Competitive packages at that level, combined with working on the most important problem in the world, could move enough people onto the safety side to make a real difference.

c. Address the generalist bottleneck

Currently, the AI safety talent pipeline is over-optimized for researchers. Every major fellowship produces alignment researchers; until very recently, nobody was working on building pipelines for operations managers, communicators, recruiters, and fundraisers– roles the field desperately needs.

The Generator Residency is the first program designed to fix this. However, its first cohort this summer is only slated to accommodate 15-30 residents. We need to scale and replicate this kind of program at AI safety hubs globally.

d. Fix fellowship pipelines

Fellowships today have mentors and research managers, but nobody working on career placement. Funding reverse headhunters embedded in fellowship programs whose job is placing graduates into full-time roles at established organizations would reduce “fellowship-hopping,” which creates more entry points and improves the field’s absorptive capacity.

Whatever remains should be used to seed billion-dollar moonshot prizes for solving key technical problems and to fund an endowment that will keep the field running for years to come, independent of new donations.

Conclusion

On the current trajectory, I think it’s likely the AI industry will win by default, because they’re on Capitol Hill, and we’re… largely not.

However, the good news is that changing that doesn’t necessarily require a breakthrough in alignment theory. It requires money, deployed into systems that already exist, to do things humans already know how to do: organize, persuade, vote, and legislate. The only question is whether we can do it fast enough.

For context, I originally wrote this for Dwarkesh’s essay contest. For the submission version, I added this note at the end: “The prompt asks what I’d do if I were in charge of the OpenAI Foundation. This is what I’d do. Whether I’d be allowed to is a different question, and one that points at why we can’t rely on AI companies’ own foundations to make AI go well.”

Thank you to Parv Mahajan, Sam Smith, Shannon Yang, and Jack Douglass for feedback and discussions.

In one sense, AI safety is not funding-constrained. There are hundreds of millions of dollars flowing into research orgs, more coming with the Anthropic IPO, and grantmakers are already struggling to deploy what they have.

And yet: the AI industry spent over $100 million lobbying the federal government in 2025, while the biggest AI safety c4 spent $310,000.

The highest-leverage path for donors looking to give to AI safety right now is to give less money, but to more neglected destinations.

Specifically, you should strongly consider routing what would have been a c3 gift to a c4 instead, calibrated to equivalent personal cost.

c3 vs c4: What’s the difference?1

501(c)3 organizations are very limited in terms of the amount of money and time they can spend on lobbying and political advocacy-shaped work. They’re also strictly prohibited from participating in political campaigns or supporting candidates:

Under the Internal Revenue Code, all section 501(c)(3) organizations are absolutely prohibited from directly or indirectly participating in, or intervening in, any political campaign on behalf of (or in opposition to) any candidate for elective public office… Violating this prohibition may result in denial or revocation of tax-exempt status and the imposition of certain excise taxes.

—IRS.gov

Donations to 501(c)(3)s are tax-deductible, but the trade-off is that these organizations can’t do much by way of direct political advocacy— unless they’re willing to put their c3 status at risk of being revoked entirely.

In contrast, 501(c)(4)s can spend unlimited amounts on lobbying and engage in direct political advocacy, as long as their primary purpose remains social welfare. Donations to c4s aren’t tax-deductible, but they have far more freedom to engage in the political process.

Why does it matter?

AI safety 501(c)(3) work is substantially less funding-constrained than 501(c)(4) work. As a result, the marginal dollar spent on lobbying and political advocacy is more important than the marginal dollar spent on research.

Here are some examples of grants given to organizations in the AI safety space last year:

• FAR.AI, a research nonprofit, secured north of $30m from several different funders to scale their technical safety research.

• Redwood Research received a $36,566,000 grant from Coefficient Giving to advance their work on AI control and alignment faking.

• Bluedot Impact, a talent accelerator program, received $25,649,888 in general support from CG.

• MATS, an AI safety research fellowship, received nearly $40m from CG.

All of the above are 501(c)3 organizations.

To be clear, these are amazing orgs! This is only meant to highlight the amount being given to c3s-- tens of millions.

Let’s zoom in on CG for a moment, since it’s the biggest funder in the field. Their TAI fund appears to have made 158 grants in 2025:2

Of the 158 listed, a grand total of three appear to have been for policy work:

Notably, none of the above organizations can engage in substantial lobbying or political campaign work. RAND is a c3, IAPS is fiscally sponsored by a c3, and Training for Good received funding earmarked for a policy fellowship.

In researching CG’s grant database, I did find one notable c4 bet: $3M in general support to Americans for Responsible Innovation in August 2024. ARI is bipartisan, which may explain why it cleared CG's reputational bar (more on this below and in my original post about the IPO). It’s a good start, but it appears to be an exception rather than a pattern. Benjamin Todd’s January 2025 post on 80,000 Hours noted that CG had recently stopped funding “many Republican-leaning think tanks, such as the Foundation for American Innovation,” and I haven't been able to determine whether their support for ARI has continued.

(I encourage you to spend some time on Coefficient Giving’s Navigating Transformative AI Fund grant database and examine the grants yourself to verify the c3/c4 asymmetry.)

So a bunch of c3 organizations in the AI safety space received tens of millions of dollars each last year. Meanwhile, the AI industry was deploying its own dollars in a very different way:

• Registered lobbying firms earned “almost $92 million in the first three quarters of 2025” from AI-related issues alone.

  • “More than one in four federal lobbyists are now pushing AI-related agendas, according to a new report from Public Citizen—and they are overwhelmingly working for corporate interests seeking to influence federal AI policy, or block state rules over the industry.” (Moore, Citizen.org)

  • “Over 500 organizations have lobbied the White House and Congress on artificial intelligence policies in the first half of 2025” (AI Invest)

• The asymmetry is staggering:

  • On the industry side:

    • OpenAI spent $2.99 million on lobbying in 2025— up from $260,000 in 2023. 11 Big Tech companies spent over $105 million on federal lobbying in 2025. Beyond lobbying, AI companies donate hundreds of millions to super PACs. Leading the Future alone has a $125 million war chest, funded heavily by OpenAI’s president Greg Brockman.

Worth a callout: $320,000 per day

Eight of the largest tech, AI, and social media companies spent a combined $36 million on federal lobbying during the first half of 2025 — an average of roughly $320,000 per day that Congress has been in session.

—Issue One

• On the safety side:

  • The CAIS Action Fund spent $310,000 on lobbying in all of 2025. As of Q1 2024, CAIS Action Fund and Center for AI Policy had a combined 10 registered lobbyists between them (that was, of course, before CAIP shut down). Public First Action, a c4 focused on lobbying for safety legislation, received a $20m contribution from Anthropic (less than a sixth of Leading the Future, and the money “isn’t allowed to be used in the midterm battles”). AnthroPAC, Anthropic’s new safety-aligned PAC, runs entirely on voluntary donations from employees, capped at $5k per person, per year.

Lobbying works, which is why the AI industry spends so much on it:

• In 2024, SB-1047 was vetoed in California after intense industry lobbying.

• In December 2025, Trump signed an executive order to thwart state-level AI regulation.

• After Nvidia spent $4.95M lobbying the federal government in 2025, seven times what it spent in 2024:

  • The Trump administration substantially weakened export controls, allowing both H20 and H200 chip exports to China.

  • The bipartisan GAIN AI Act, which would have required chipmakers to fulfill U.S. orders before selling abroad to “countries of concern,” passed the Senate in October as part of the NDAA— but was killed in conference after Nvidia lobbied against it.

Right now, the industry is winning the legislative war because the safety side is not putting up a fight.

Research vs. Lobbying

Technical safety research only matters if frontier labs actually implement it. Even if Anthropic adopts the latest alignment techniques voluntarily, other companies probably won’t if it slows them down or cuts into margins.

Without legislation, every safety advance is effectively optional— a recommendation companies can take or leave depending on competitive pressure.

Legislation is what makes technical safety advances mandatory for frontier labs. It’s also what buys time. Every regulation that imposes a meaningful evaluation requirement, every disclosure mandate, every liability framework, every whistleblower protection policy slows the race and gives safety researchers more runway to solve the problems we don’t yet know how to solve.

The Pitch: Same Money, Different Structure, More Impact

While we have tens of millions of dollars pouring into major organizations in the c3 space, we don’t have nearly enough funding going towards political AI safety work. The gap between funding for research and funding for advocacy is enormous.

As I wrote about previously, part of this is due to the concentration problem: currently, the vast majority of AI safety funding flows through a handful of institutional funders. Some of those funders (including the vast majority of private foundations,3 most DAFs, and corporate matching programs) are structurally barred from significant C4 giving.

Others, including CG, can technically fund C4s but face reputational constraints that make them cautious about politically charged advocacy, especially work that opposes specific industry players. As a result, the entire field has inherited a bias toward C3 research over C4 advocacy.

Individual donors face neither constraint. This means the people who can actually fix this gap are individual donors, who are the only actors in the ecosystem without structural or reputational barriers to C4 giving.

Some numbers

A top-earning donor giving $1M to a c3 actually spends ~$650K after the federal deduction (capped at 35% under the One Big Beautiful Bill Act, effective 2026).

This means that $650K, given directly to a c4, represents an equivalent personal cost with arguably greater counterfactual impact.

The Formula

C4-equivalent = X − [(X − floor) × effective deduction rate]

Where:

• X = intended c3 gift

• floor = 0.5% of Adjusted Gross Income (a new rule under the One Big Beautiful Bill Act says you can’t deduct first 0.5% of AGI donated to c3 organizations)

• effective deduction rate = at a federal level, capped at 35% for top-bracket donors

  • Important: State taxes may decrease the C4 equivalent, especially in higher tax states

So, for a donor with $5M AGI giving $1M to a c3, these are roughly the numbers:

• Floor = $25,000 (non-deductible portion of c3 gift)

• Deductible portion = $975,000

• Tax savings = $975,000 × 35% = $341,250

• Net personal cost of c3 donation, which would represent the equivalent that should be donated to a c4 = $658,750

The floor: important to know for mid-tier donors

Starting in 2026, the first 0.5% of a donor’s AGI in charitable giving confers zero federal tax benefit under the One Big Beautiful Bill Act’s new deduction floor.

For a donor whose total c3 giving falls at or below that floor, c3 and c4 donations will cost exactly the same, because the amount you’re actually donating is equal to your personal cost (there is no deduction). In this instance, the tax case for c3 donations disappears entirely,4 and c4 wins on any positive impact multiplier.

As an example, let’s say we have an Anthropic engineer with $3M AGI who gives $15K to AI safety. The floor is $15K, so zero of that engineer’s $15K gift is deductible, meaning c3 and c4 are tax-identical.

Caveats

I am not your financial advisor and would recommend discussing your specific case with a qualified CPA. For example, the above math doesn’t apply if you’re planning on donating stocks directly, because of capital gains tax (it’s complicated).

Specific Funding Recommendations

The orgs below take different approaches. Some focus on catastrophic risk directly, others on adjacent concerns like child safety, worker protection, or chip security. Read the descriptions and pick what fits your priorities.

Humans First Action

​Here’s the reality…
Congress is barely in session.
Big AI is moving at full speed.
And the window to put real safeguards in place is closing fast.

—Humans First, D.C. Town Hall Tour

• What: Humans First Action is a conservative, populist advocacy organization focused on “an America First approach to the future of AI.” They recently finished a Town Hall Tour spanning 10 cities in 6 states.

  • “We are not going to sit back while a handful of billionaire tech executives reshape our economy, our families, and our future.” (D.C. Town Hall Tour)

  • They also have a c3, but I would specifically recommend donating to their c4 if you can.

• Link to donate: https://www.humansfirst.com/donate

ControlAI

• What: ControlAI is an organization that “campaign[s] on preventing extinction risk.” They have briefed 250+ lawmakers across the US, UK, Canada, and Germany.

  • They’ve also collaborated with TIME, The Guardian, Sci Show, and other content creators to produce educational content regarding the risks of superintelligence.

• Contact to donate: hello@controlai.com

Center for AI Safety Action Fund

• What: CAIS’s Action Fund has four primary policy priorities: (1) ensuring the U.S. retains its competitive advantage over China as it relates to AI chip manufacturing, (2) implementing and enforcing export controls, (3) preventing bad actors from using AI to cause harm, and (4) advocating for international cooperation to advance safe AI.

  • They spent $310,000 on lobbying in 2025 and work closely with CAIS’s D.C. branch.

• Link to donate: https://safe.ai/donate

AI Policy Network

• What: A bipartisan organization that lobbies Congress on “establishing effective guardrails, and ensuring the United States remains both dominant and safe.” Some of their work focuses on loss of control risk, and they are one of the only registered lobbying entities that speaks about superintelligence directly to the national security establishment.

• Link to donate: https://theaipn.org/support-us/

Public First Action

• What: A bipartisan advocacy organization focused on child safety, supporting workers, and safeguards to protect the general public. Also does education/awareness-type work on AI risk. Seeded with a $20m donation from Anthropic.5

• Link to donate: here

A note about PACs and political campaigns

Some practitioners in the AI safety policy space believe donations to politicians and PACs are meaningfully more impactful than the best C4s. Zach Stein-Perlman, who advises donors in this area, has estimated roughly “5x as impactful.”

I haven’t independently verified this, but I think it could be true, depending on assumptions surrounding timing, electoral leverage, and the specific donation opportunities Zach might point you towards. (Also worth flagging: Meta is spending $65M this year on super PACs designed to elect AI-friendly state officials— the biggest election investment in the company's history. That’s a pretty good sign this kind of giving works.)

However, there are trade-offs: PAC contributions are publicly disclosed, more legally complicated, require US permanent residency, and there are contribution limits; there might also be reputational risk for recipient political candidates depending on who you are (e.g. see Carrick Flynn case study; I think being backed by SBF almost certainly hurt his odds).

That said, if you’re considering donating $20K+ and willing to handle the complexity and public disclosure, PACs may be worth exploring. Zach has offered to advise on this (more on how to reach him below).

For most readers, the C4 case I’ve made above is probably still the right starting point.

Next Steps

The AI safety field is flush with money, yet starving for advocacy. The next billion dollars to CG or a DAF probably won’t solve this problem.

If you care about AI safety and you were planning to donate this year: before defaulting to a c3, run the math above and seriously consider whether a c4 gift at equivalent personal cost would do more.

If you want to talk this through before giving, here are some options:

• Reach out to me directly at kairos@stanford.edu. I’m happy to think through your situation and point you toward people in the ecosystem whose judgment I trust for your specific case.

• Contact Zach Stein-Perlman on the EA Forum, who has offered to advise US permanent residents considering $20K+ donations to politicians and PACs, which he estimates could be roughly 5x more impactful than the best C4s.6

Pick whoever fits. Different people are better positioned for different donor profiles.

Conclusion

This is an opportunity to maximize your counterfactual impact in a way that almost no other giving can. Giving well is hard. It takes research, judgment, and time. It would be easier to park money in a DAF and forget about it, or forward donations to whoever’s most legible, or give to people you like, or just not give at all.

But the political work that needs funding right now is work that most institutional funders can’t or won’t touch. The dollars sitting in your account are structurally different from every other dollar in the ecosystem, and deploying them well might matter more than any other decision you make this year.

Thank you to Jack Douglass for helpful discussion & feedback.

This piece is more analytical/formal in tone than what I usually publish on Substack; it’s part of the opening section of a longer policymaker-facing essay on AI biosecurity risks (in progress), and it begins with the historical foundations. Consider yourself warned.

I. Successes

Note: Refers to successful deployment of a biological agent, not necessarily success in achieving overall objectives.

A. 1984 Rajneeshee Bioterror Attack

In September 1984, the Rajneeshee cult deliberately contaminated salad bars in ten Oregon restaurants with salmonella in an effort to incapacitate voters before the 1984 Wasco County election.

Later investigations revealed that the attackers had a “fairly sophisticated medical research laboratory” at their commune, where they cultivated salmonella purchased over-the-counter from a Seattle scientific supply house. Ma Anand Puja, a former nurse who ran the Rajneesh Medical Corporation, provided the minimal medical expertise needed to culture common pathogens. Authorities also later discovered that the cult had explored using additional lethal pathogens, including HIV.

Though the cult had access to a medical research laboratory, the actual salmonella cultivation took place in a shed:

In a simple shed, under the supervision of Ma Anand Puja, the cult mass-produced the Salmonella using simple petri dishes, an incubator, and a freeze-drier [14]… Producing large quantities of bacteria is cheap and can be easily done with even rudimentary equipment and skills [18].

—Turner et al.

The attack poisoned 751 people and hospitalized 45, while causing panic that drained the local economy. To this day, the Rajneeshee operation remains the largest bioterrorist attack in U.S. history.

B. WWI German Biological Sabotage Program

During WWI, Dr. Anton Casimir Dilger– a German-American medical doctor specializing in tissue culture research– carried out a German biological warfare sabotage program targeting Allied livestock supplies. With the help of his brewmaster brother and housekeeper sister, Dilger converted his basement into a makeshift bacterial laboratory where he began cultivating deadly bacterial cultures in liquid form. He used anthrax and glanders bacteria to infect horses and mules being shipped from U.S. ports to Europe. Dilger also coordinated directly with German General Staff.

His basement was located less than six miles from the White House, and as a result of the program, “thousands of horses and mules were killed” (National Archives).

C. 2001 Anthrax Letters (also known as the Amerithrax case)

Following the September 11 attacks, a series of anthrax attacks were carried out through letters containing anthrax spores sent via the mail. The spores had been treated with additives to increase their inhalability, which suggested the involvement of someone with highly specialized, advanced technical expertise. The primary suspect was Bruce Ivins, a veteran biological-weapon researcher for the US Army.

The attack killed 5 people and injured 17, with many survivors suffering long-term fatigue and memory loss even years later. It also created widespread panic at a moment when the nation was already reeling from 9/11.

II. Failures

A. 1993 Aum Shinrikyo Anthrax Attack

In July 1993, doomsday cult Aum Shinrikyo aerosolized a liquid suspension of Bacillus anthracis (anthrax) from the roof of an eight-story building in Kameido, Tokyo, but the operation resulted in no human casualties.

The operation failed because:

• They used an attenuated B. anthracis strain (used vaccine strain incapable of causing disease in healthy individuals);

• Their Bacillus anthracis liquid suspension had low spore concentrations (significantly below the optimal 10⁹ to 10¹⁰ organisms/mL needed for effective biological weapons);

• Sunlight exposure inactivated the spores before they could cause infection; and

• Equipment failures meant they were unsuccessful in creating proper particle distribution.

Aum had laboratories for experimenting with bioweapons in Kamakuishki and Tokyo. In addition to anthrax, the group also “cultured and experimented with botulin toxin, cholera, and Q fever” (CDC). After failing to deploy a bioweapon, they later transitioned to successful chemical attacks, including the Matsumoto Sarin Attack and the Tokyo Subway Sarin Attack (not detailed here since this analysis is about bioweapons).

B. 2018 Cologne Ricin Plot

In June 2018, Sief Allah H., a 29-year-old ISIS sympathizer, successfully produced enough ricin for “up to 1,000 toxic doses” and assembled a bomb with explosives and metal ball bearings for a planned mass-casualty attack on a crowded indoor venue. This marked the first time a jihadi terrorist in the West successfully produced the toxic biological agent, demonstrating that technical barriers to simple bioweapons are sometimes surmountable with publicly available instructions.

German authorities discovered and put a stop to the plan via online surveillance operations, specifically after the U.S. CIA provided a tip about his large order of 3,300 castor seeds purchased over the internet.

C. 1995 Minnesota Patriots Council Ricin Plot

The Minnesota Patriots Council was an anti-government militia that attempted to assassinate federal law enforcement officials (including US Marshals, IRS agents, and local sheriffs) using ricin. The group successfully extracted ricin from mail-ordered castor beans using publicly available instructions and basic solvent knowledge gained through their work as carpet cleaners. They initially found and purchased castor beans through an advertisement in a right-wing magazine.

Ultimately, the Minnesota Patriots Council successfully produced enough ricin to kill over 100 people, despite their result being significantly less potent than professional-grade toxin.

Similarly to the Cologne Ricin Plot, law enforcement detected and disrupted their activities before they could carry out planned attacks.

D. 1998 Toxic Terror Case

The perpetrators of the Toxic Terror Case were Larry Wayne Harris, a microbiologist and white supremacist with ties to “Aryan Nations,” working alongside WIlliam Leavitt, a friend who “own[ed] biological laboratories in Nevada and Germany.”

The pair planned to release bubonic plague bacteria in the New York subway system; law enforcement raided their labs after they “mail-ordered freeze-dried bubonic plague germs.” During the raids, officials also found vials containing suspected anthrax. Harris reportedly told a witness that he had procured enough anthrax to “wipe out the city.”

Addendum: ISIS Laptop of Doom

The Islamic State's Terror Laptop of Doom revealed a comprehensive biological weapons research program documented on laptop of a Tunisian ISIS operative with university-level scientific training. Uncovered in 2014, it contained a “19-page document in Arabic on how to develop biological weapons and how to weaponize the bubonic plague from infected animals” containing guidance like “[u]se small grenades with the virus, and throw them in closed areas like metros, soccer stadiums, or entertainment centers… best to do it next to the air-conditioning.”

III. Analysis

Successes

Takeaways

A. Rajneeshee represents a case where despite minimal expertise, the perpetrators were still able to succeed because they chose an easier pathogen to synthesize. The expertise bottleneck varies by pathogen.

B. Dilger was able to maintain operational security by relying on his siblings to help him with his work. While he had to rely on a home laboratory, it’s likely that coordination with German General Staff enabled him to procure advanced materials and equipment.

C. The Amerithrax (aka Anthrax Letters) case is perhaps slightly more difficult to assess with confidence because Bruce Ivins’s guilt remains contested. However, if Ivins was the culprit, key success factors include highly specialized expertise and authorized access to dangerous pathogens as a researcher for the U.S. army.

The Dilger and Ivins cases demonstrate that specialized biological knowledge poses severe risks; advanced expertise can enable catastrophic biological attacks.

Failures

The failed cases fall into two main categories: technical incompetence and detection via surveillance.

Technical Incompetence

The Aum Shinrikyo Anthrax Attack failed on technical problems. However, had Aum expanded their technical expertise beyond their compartmentalized leadership structure, there’s a reasonable chance their attack may have succeeded. Their operation went undetected through deployment, and every failure point (strain selection, spore concentration levels, dispersal, sunlight exposure, etc) represented knowledge gaps that additional specialized expertise could have addressed.

The Amerithrax case demonstrates that the technical barriers Aum encountered were surmountable with proper expertise.

Detection via Surveillance

Unlike Aum's technical failures, the Cologne and Minnesota Patriots cases succeeded at the production phase. These actors proved that certain bioweapon manufacturing is already within reach of non-experts using publicly available information. What prevented attacks was detection and disruption by law enforcement, rather than inability to create functional weapons.

Whether Toxic Terror succeeded at the production phase is less clear, as the vials found by law enforcement were only suspected to contain anthrax. It could plausibly fall into either of the above categories, depending on whether the recovered materials were viable and weaponizable.

IV. Conclusion

Bioweapons have been created and deployed effectively in several documented attacks. The Rajneeshee, Dilger, and Amerithrax cases prove that bioweapon development and deployment are within reach of motivated non-state actors.

At the same time, these successes have been mostly limited in scope and severity. Several other large-scale near-misses have failed primarily due to technical incompetence or detection by law enforcement.

AI, alongside other existing and emerging technologies, threatens to erode some of the barriers that have historically caused malicious actors to fail. That will be the subject of Part 2 of this series!1

Note: Catherio in the comments on EA Forum pointed out that although Anthropic is pursuing IPO readiness, they are not certain to IPO. As OscarD pointed out, based on this Manifold Market, there is roughly a 75% chance of an IPO within the next year.

With that said, the argument below holds regardless of exact timing-- the pledges have been made, and the capital is coming, whether via IPO, secondary sales, or further private rounds.

1. The Anthropic IPO is coming

This week, Anthropic announced Claude Mythos Preview– a model so capable at finding software vulnerabilities that the company decided to wait to release it publicly. Last month, Bloomberg reported that Anthropic’s annualized revenue had hit $19 billion, doubling in under four months, and by some accounts surpassing OpenAI. It is currently one of the most valuable private companies on the planet.

Anthropic is also going to IPO, possibly as soon as October 2026. When it does, a few thousand people, including some of the wealthiest people on the planet, will become liquid, and a meaningful fraction of those people will want to give to AI safety.

Tech liquidity events have created major philanthropists before. Dustin Moskovitz’s Facebook shares became Good Ventures, which became Coefficient Giving, which became the single largest funder of AI safety. Vitalik Buterin donated $665 million to a fledgling FLI. Jed McCaleb’s crypto wealth became a billion-dollar endowment to the Navigation Fund, which went from $4 million in grants its first year to over $60 million by 2025.

None of these come close to what’s about to happen.

The seven co-founders of Anthropic have all pledged to donate 80% of their wealth. Forbes estimates each holds “just over 1.8% of the company.” As Transformer recently reported, if Anthropic goes public at its current valuation, each co-founder’s pledge alone would be worth “roughly $5.4 billion, or $37.8 billion combined… nearly ten times what Coefficient Giving… has given away in its entire history.” And that’s just the co-founders. Other employees have pledged to donate shares that could amount to billions more, with Anthropic promising to match those contributions.

More money is about to enter AI safety philanthropy than the field has ever seen. The question is whether anyone is ready to direct it.

Consider the current infrastructure. As of late November 2025, Coefficient Giving– which directs more philanthropic capital to AI safety than anyone else– had just three grant investigators on its technical safety team, evaluating over $140 million in grants to technical AI safety in 2025. They’ve said openly that though the team wants to “scale further in 2026,” they are “often bottlenecked [on] grantmaker bandwidth.”

Longview Philanthropy, the second largest donor advisory operation in the field, had just six AI grantmakers in November 2025.

Add up every person in the world doing serious AI safety grant evaluation, and, as Julian Hazell from CG recently noted, you will probably land somewhere between 30 and 60. That’s an absurdly low number of people directing the philanthropic response to what may be the most important challenge in human history.

The capital is about to scale by orders of magnitude; the capacity to deploy it has not.

This post is about that gap, and why filling it matters more than almost anything else in AI safety right now.

2. The constraint is talent, not funding

You don’t have to wait for the IPO to see the problem. Currently, the field is already struggling to deploy capital effectively.

Hazell points out that as Coefficient Giving’s technical AI safety team tripled its headcount over the past year, their grant volume scaled nearly in lockstep, from $40 million in 2024 to over $140 million in 2025– all while “the distribution of impact per dollar of [their] grantmaking… stayed about the same.”

In other words, at $40 million, they weren’t running out of good opportunities; they just didn’t have the capacity to find and evaluate them.

CG’s 2024 annual review acknowledged as much, noting that the organization’s ”rate of spending was too slow” on scaling technical safety spending. Part of the cited reason for this was “difficulty making qualified senior hires;” a year later, three grant investigators deploying $140 million a year suggests the hiring problem hasn’t gone away.

The problem isn’t limited to CG. Tyler John, a grantmaker at the Effective Institutions Project, recently put it bluntly on X:

Hazell put it more directly:

We’re leaving good grants on the table right now due to a lack of grantmakers. When I was at CG, I regularly saw plausibly-above-the-bar proposals either get rejected outright or sit in the queue longer than they should have, mostly because we didn’t have enough grantmaker capacity to properly evaluate them.

This isn’t what a funding-constrained field looks like. This is what a talent-constrained field looks like. Every month without enough grantmakers is a month where high-impact safety work goes unfunded.

3. We don’t just need more bets– we need decorrelated ones

The AI safety funding ecosystem is dangerously concentrated. As Ben Todd of 80,000 Hours pointed out in early 2025, over 50% of philanthropic AI safety funding flows from a single source: Good Ventures, via Coefficient Giving. Many organizations in the space receive roughly 80% of their budget from this one funder, constituting what Todd calls “an extreme degree of centralisation.”

This concentration creates two problems:

The first is strategic. When one funder accounts for half of all spending, their priorities are the field’s priorities by default– and so are their gaps. When CG shifts priorities, entire categories of work can lose funding overnight.

And CG has shifted. In the same January 2025 post, Todd noted that Good Ventures had recently stopped funding several categories of work, including many Republican-leaning think tanks, post-alignment causes like digital sentience, organizations associated with the rationalist community (such as LessWrong and MIRI), and many non-US think tanks wary of appearing influenced by an American funder.

CG may well be right about each of these calls. That’s beside the point.

The point is that one organization’s strategic judgment shouldn’t determine the shape of an entire field, especially one defined by deep uncertainty about what bets will actually pay off.

The second is structural. Major funders face reputational and logistical considerations that constrain what they are able and willing to fund. Good Ventures, as a private foundation, cannot fund political campaigns or PACs and is severely constrained in supporting lobbying or 501(c)(4) work. Longview’s Emerging Challenges Fund specifically selects for projects with “legible” theories of impact that appeal to a “wide range of donors,” which by design screens out work that is politically sensitive or unlikely to sit well with a broad donor base. CG has been transparent about the categories they don’t fund well, including politically charged advocacy and many organizations outside the US.

These constraints are rational. However, the consequence is a funding ecosystem biased toward centrist, technocratic, broadly palatable projects– potentially leaving some of the highest-leverage work in the field unfunded.

CG knows this is a problem. They’ve explicitly called for more independent funders making de-correlated bets, estimating that giving opportunities they recommend to external donors are typically “2–5x as cost-effective” as Good Ventures’ marginal AI safety dollar, precisely because those opportunities sit outside what CG is well-positioned to fund.

But what does decorrelation actually require? Two things:

3a. Different structural positions

Individual donors often have comparative advantages that institutional funders structurally cannot replicate. For example, a donor acting only in their own personal capacity can fund a 501(c)(4) lobbying organization or support politically controversial advocacy projects, without having to answer to a donor base or protect the reputation of a large institution.

A dollar deployed from that position, particularly to causes that major funders can’t or won’t touch, carries far greater counterfactual impact than another dollar into the same pipeline CG is already funding.

3b. Independent grantmakers with different worldviews

Right now, the major funders broadly share a threat model centered on catastrophic risk from advanced AI– particularly loss of control. That shapes everything they fund, including their governance and policy work. It’s a reasonable bet, but it’s one bet. Alternatively, someone who thinks the primary risk is political concentration of power, or that the bottleneck is public opinion, or that near-term misuse matters more than long-term alignment, will build a very different portfolio.

A senior person with a different threat model and the judgment to back it– advising independent donors who can fund work that doesn’t fit CG’s worldview– might be the single highest-leverage addition to the ecosystem right now.

Match that judgment with a donor who faces none of CG’s constraints, and the end result is the kind of decorrelated bet that no institutional funder could have made.

4. The Pitch: Consider becoming a grantmaker or founding something new

If you have strong, independent takes on what the field needs– for example, if you think a specific threat model is dangerously underfunded, or you have a clear picture of what organizations should exist but don’t, or you have deep connections across the field and strong takes on who’s doing good work– the highest-leverage thing you can do might be to start directing capital yourself. The most impactful version of this means independently advising donors directly, running your own fund, or founding a new grantmaking org rather than joining a major funder.

However, joining a major funder could still be extremely valuable, because you can contribute to setting their strategic agenda in addition to increasing their grantmaker capacity. In the words of Jake Mendel:

“cG’s technical AI safety grantmaking strategy is currently underdeveloped, and even junior grantmakers can help develop it. If there’s something you wish we were doing, there’s a good chance that the reason we’re not doing it is that we don’t have enough capacity to think about it much, or lack the right expertise to tell good proposals from bad. If you join cG and want to prioritize that work, there’s a good chance you’ll be able to make a lot of work happen in that area.”

The question of whether to join an existing funder or go independent depends on two things. First, do you have the credibility, connections, and track record to attract donors and operate independently? If not, joining an existing org is both more realistic and still very high-impact. Second, what sort of work do you want to fund? If your views are still within the scope of things that could be funded by an institution, joining a major funder and helping shape their agenda from inside is probably the higher-impact move.

If you’re earlier in your career, you probably don’t have the strategic taste to set a grantmaking agenda yet. However, you can still contribute value, because the deepest bottleneck in grantmaking is everything upstream of the decision to fund something. This work includes scoping neglected areas, getting on calls, researching open questions, scouting and recruiting founders for projects, and turning high-level conceptual ideas into concrete proposals and teams that can absorb funding.

If you’re a founder/builder type, more grantmakers won’t help if there aren’t enough good projects to fund. The field needs more founders willing to start organizations that tackle neglected problems, especially in areas that major funders have deprioritized. If you have a vision for something that should exist, the funding environment for new AI safety orgs has never been better. Go found it!

5. The window is brief

Donors who don’t find good guidance early on tend to park money in donor-advised funds, where it can sit indefinitely; there’s no legal requirement to ever grant it out. As of fiscal year 2024, over $326 billion sits in DAF accounts nationally, and in any given year, over a third of accounts make no grants at all.

The Anthropic IPO will create an unprecedented, one-time surge of motivated, domain-expert donors. When the time comes, the advisory infrastructure must be there to meet them.

At the same time, AI safety grantmaking is a project that will enable you to build incredibly valuable career capital. Even if you only choose to engage with the field as a part-time project or a multi-year-long side quest, the judgment and relationships you develop directing capital will make you more effective at whatever you do next in AI safety.

6. Next Steps

• Want to transition into AI safety grantmaking?

  • Apply to Coefficient Giving’s Astra stream for AI safety grantmaking here.

  • Express interest in a grantmaking career transition/mentorship program here.

• Interested in founding an organization?

  • Apply for the Generator Residency (Kairos x Constellation)

  • Check out Atlas Computing

  • Apply to Catalyze Impact’s AI Safety Incubator

  • Apply for funding from:

    • The Institute For Progress’s Launch Sequence

    • Coefficient Giving’s Navigating Transformative AI Fund

    • Effective Ventures’s Long-Term Future Fund

    • The Survival and Flourishing Fund

    • The AI Alignment Foundation

    • Longview’s various AI funds

• Whether you’re interested in grantmaking or founding, sign up to be notified of when the new donor advisory initiative we’re launching, Counterfactual Capital, goes live!

• Are you a donor looking for guidance on AI safety giving? Get in touch at kairos@stanford.edu.

Special thanks to Jack Douglass, Catherine Brewer, Saheb Gulati, Derek Razo, Gaurav Yadav, Oliver Kurilov, Parv Mahajan, George Ingrebretsen, and Jacob Schaal for helpful discussion & feedback!

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Skip to the end of this post for some notes from me!

Read the full paper on: uncensorable.ai

The CCP has built the world’s most sophisticated internet censorship apparatus. It is also, as of 2026, the world’s leading distributor of AI models that permanently circumvent that apparatus. This is not an accident exactly– but it is a contradiction, and it has consequences.

The consequences we’re most concerned about are biosecurity ones. In January 2026, Las Vegas police and the FBI raided a suburban home and found multiple refrigerators, freezers, laboratory equipment, and “numerous bottles… containing unknown liquid substances.” The illegal biolab was connected to a similar operation discovered in Reedley, California three years earlier. The Reedley facility contained thousands of vials of biological material, including potential pathogens such as HIV, malaria, tuberculosis, COVID-19, and Ebola; both labs were run by the same individual. Despite knowing about potential Las Vegas connections since 2023, it took a separate tip three years later to uncover the second operation.

The materials for bioweapon development are already circulating on American soil– and the regulatory gaps that allowed them to go undetected for years remain open.

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Historically, technical knowledge has been the limiting factor for would-be bioterrorists. When Aum Shinrikyo tried to weaponize anthrax in 1993, they had motivation and funding, but lacked expertise– they used the wrong bacterial strain and their liquid suspension had low spore concentrations.

AI is quickly eroding that constraint.

Publicly available models like o3 already outperform 94% of virology experts on laboratory protocol questions, even on questions directly relevant to the experts’ specialties. That benchmark performance hasn’t yet translated cleanly into end-to-end weapons development guidance, but the trajectory is clear. Models are advancing toward the ability to provide expert-level virological guidance on demand, anonymously, and at scale. Anthropic’s own internal bioweapons acquisition uplift trials found that Claude Opus 4 enhanced human performance by 2.53x on relevant tasks, enough to trigger activation of AI Safety Level 3.

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For closed-weight models, [misuse] risk is at least partially manageable. Input-output classifiers and safety fine-tuning can intercept most non-sophisticated actors before they get actionable guidance. These mitigations are imperfect– models remain vulnerable to jailbreaks, and red-teaming studies have repeatedly demonstrated that they can be coaxed into providing dangerous guidance– but critically, every query sent to a closed model passes through an API the company controls, which means suspicious patterns can be flagged and interactions are logged.

Open-weight models have no such layer. Once weights are downloaded, they can be run locally with no oversight and no logging, safety fine-tuning can be stripped with minimal technical effort, and the model can be fine-tuned on domain-specific data to become dramatically more capable in exactly the areas we’d most want to restrict. There is no API to monitor, no company to refuse the request, and no recourse once the weights are out.

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[For a fuller treatment of why open-weight models specifically are a biosecurity problem, see here.]

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The best open-weight models in the world are Chinese; among leading Western labs, the trend is toward keeping frontier models closed. Even Meta, the strongest advocate for open-weight release, recently shipped its most capable model as proprietary. Chinese AI companies are still open-weighting. (Here are my best guesses for why.)

That leaves us the question: if Chinese open-weight models could help rogue actors build bioweapons, and we deem that an unacceptable risk, what could the U.S. do?

I think there are four broad classes of intervention:

1. (CCP) Convince the Chinese Government to Restrict Open-Weight Releases

   1. 1.1 Communicate biosecurity risks through bilateral diplomacy

   2. 1.2 Publicize misuse to generate international pressure

2. (US & ALLIES) Limit Access to Chinese Open-Weight Models Through Unilateral Action

   1. 2.1 Fund research into robust safeguards for open-weight models

   2. 2.2 Implement conditional bans on Chinese open-weight models

   3. 2.3 Reduce individual adoption of Chinese AI through public risk communication

   4. 2.4 Sanction individuals and designate companies under counterterrorism authorities

3. (LABS) Shift the Calculus for Chinese AI Labs Directly

   1. 3.1 Researcher-to-researcher persuasion through academic channels

   2. 3.2 Offer conditional compute access in exchange for closed weights

4. (SUPPLY CHAIN) Harden the Biological Supply Chain Against Models Already in Circulation

   1. 4.1 Mandate AI-enabled DNA synthesis screening

   2. 4.2 Implement know-your-customer requirements for cloud laboratories and contract research organizations

Brief notes from a very sleep-deprived Sophie:

• These interventions (with the exception of Level 4) are explicitly conditional; they apply only if the USG concludes that open-weight models pose unacceptable risk (e.g. after standardized dual-use capability evaluations). The ideal outcome is that safeguards for open-weight models become robust enough that none of this needs to be implemented.

• I’ve framed this around biorisk because that’s where I’m most concerned, but the interventions apply to any form of misuse, including cyber. I expect many readers will be less worried about biorisk than I am. That’s fine; my work here is focused on tail risk, and I know not everyone weights that the same way.

• More follow-up work on this project is forthcoming, though I’ll be taking a break first to focus on a separate, larger essay project.

• Overall, I think the discourse on open-weight models needs more substance, and this piece is an attempt to contribute.

Thanks to several reviewers in the biosecurity, US-China relations, and AI policy space who provided feedback on this work! <3

now if you’ll excuse me, I am off to take a nap.

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Olvera’s piece claims that bioweapons are rare for two reasons:

1. They’re difficult to make, and

2. They’re bad weapons.

She argues that people who want to cause harm follow a cost-benefit logic and will generally pick bombs, guns, chemical weapons, or cyberattacks over bioweapons because those tools are “cheaper, faster to create, easier to deploy, more controllable, and more predictable.”

I think claim (1) is becoming less true in ways the piece doesn’t adequately address, and claim (2) applies to a narrower class of threat actors than the piece assumes.

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1. Bioweapons aren’t “bad weapons” if your goal is maximum casualties

Unfortunately, terrorists don’t have a great track record with following cost-benefit logic.

Olvera writes that:

People who want to cause harm still follow a cost-benefit logic. They try to pick the tool most likely to achieve their goal. For almost any real-world objective, that tool is a bomb, a gun, a chemical weapon, or a cyberattack. These are cheaper, faster to create, easier to deploy, more controllable, and more predictable than a bioweapon.

The cost-benefit framing makes sense for actors with specific tactical or political objectives. If you’re trying to assassinate a political leader or attack a military target, bioweapons are indeed bad tools. As Olvera points out, you can’t aim them, you can’t time them, and you can’t control them.

But the AI biosecurity conversation is primarily concerned with a different class of actor entirely: someone whose goal is to kill as many people as possible. For that objective, the properties that make bioweapons “bad” tactical weapons— most worryingly, their uncontrollable spread— are precisely what makes them the most attractive choice.

If you are trying to kill the maximum number of humans possible, what do you do? You release a transmissible pathogen in an airport. If successful, it spreads on its own. It crosses borders before anyone knows it exists. Tracing it back to you is extraordinarily difficult.

A bomb in an airport kills dozens, maybe hundreds; a transmissible engineered pathogen could kill orders of magnitude more, and there’s a good chance you could avoid being caught.

Olvera’s rational-actor framework doesn’t grapple with actors who want indiscriminate civilizational-scale damage— a category that includes apocalyptic terrorist groups, nihilistic lone actors, and possibly even a misaligned AI system.

For example, Japanese terrorist organization Aum Shinrikyo’s explicit goal was to trigger Armageddon. Their leadership believed they were destined to initiate an apocalyptic war that would end human civilization, and they had members with advanced degrees in virology and medicine working to make it happen. They attempted to deploy anthrax and botulinum toxin before ultimately resorting to sarin.

The threat actors who pursue bioweapons are precisely the ones for whom the “bad weapon” argument doesn’t apply, because it assumes actors who care about precision and self-preservation; Aum Shinrikyo cared about neither.

The obvious objection to the above, which Olvera raises in her first factor, is that:

Groups extreme enough to prefer a bioweapon over more controllable options are very rare and tend to have traits (e.g., paranoia, rigid hierarchy) that make the process even harder to pull off.

“Paranoia” and “rigid hierarchy” are really a point about the trade-off between secrecy and efficacy. Historically, terrorist groups had to confine weapons development to senior leadership to avoid detection by law enforcement, but senior leadership rarely included people with the relevant technical expertise.

However, if the technical barriers to bioweapon development can be circumvented without assembling a large, multi-disciplinary team— and I’ll argue below that they increasingly can— then the secrecy-efficacy trade-off becomes far less constraining.

Olvera is right that these groups are rare, but the organizational dysfunction that historically prevented them from succeeding was a consequence of the expertise bottleneck; remove the bottleneck, and that dysfunction may no longer be protective.

Additionally, rarity is not a sufficient basis for inaction. We don’t approach any other domain of security this way: no one in cybersecurity says “nation-state-level hackers are extremely rare, so we don't need to harden our systems against them.” The whole point of cybersecurity is to defend against the most capable attackers, not the most common ones. Biosecurity should be no different.

2. The bottlenecks Olvera describes are the bottlenecks of the last century

Much of the piece’s argument rests on the difficulty of actually making a bioweapon: most notably, access to specialized equipment and tacit knowledge. These were substantial barriers for all of human history. But they’re bottlenecks from an era where doing biology meant being physically present in a well-equipped laboratory, and that era is ending.

A few examples of technological developments that make this true:

1. Cloud labs. Companies like Emerald Cloud Lab and Strateos allow anyone to design and run biological experiments remotely. You submit experimental protocols through a software interface, and robotic systems in a physical lab execute them.

   1. Currently, you can use a cloud lab with no coding experience, and there are no regulations requiring identity verification (KYC), nor are there any mandatory mechanisms for monitoring what experiments users run. Anyone with a credit card and an internet connection can run experiments that would previously have required institutional affiliation, costly specialized equipment, physical lab access, and years of hands-on expertise to execute.

2. DNA synthesis providers. As I’ve written about elsewhere, no country currently has a law requiring gene synthesis providers to screen DNA orders for dangerous sequences. The screening that does exist is voluntary, inconsistent, and has no mechanism for detecting split orders across multiple providers.

   1. S.3741 would begin to address this, but it hasn’t passed yet.

3. Contract research organizations (CROs). These are labs for hire that will execute experiments on behalf of clients. Like cloud labs, the regulatory oversight here is minimal.

And of course, AI is reducing the knowledge bottleneck. Publicly available models like o3 already outperform 94% of virology experts on laboratory protocol questions, even on questions directly relevant to the experts’ specialties.

The net effect is that many of the traditional barriers— including physical lab access, institutional affiliation, and years of hands-on training— are becoming less binding. Increasingly, you can outsource the tacit knowledge to cloud labs and CROs, outsource the sequence design to AI, and order the genetic material from synthesis providers with minimal screening.

Each of these developments individually might not be sufficient to collapse the barrier, but taken together, they represent a fundamentally different threat landscape than the one that Olvera’s piece describes.

And even if success rates per attempt remain low, lowering the cost, expertise, and coordination required per attempt increases the number of attempts that become feasible, leading to an increase in overall risk even without dramatic improvements in per-attempt success rates.

Important Note on Timing

Olvera’s piece is the first of a planned four-part series, and it’s possible that parts two through four will address some of what I’ve raised here. But part one is circulating now, it’s being read as a standalone argument, and its framing is already shaping how people think about this issue.

I look forward to engaging with the remaining installments as they appear!

Conclusion

To be clear, Olvera’s work is genuinely valuable; the practitioner perspective on what makes bioweapons difficult is underrepresented in AI biosecurity discourse, and the field is better for having it documented.

But arguments that downplay AI biosecurity risk— even unintentionally— risk contributing to policy inaction on a problem where the cost of delay could be extremely high. Framing bioweapons as hard to make and strategically irrational may have been true for most of history; however, it is becoming less true, quickly, and may no longer be a reliable guide to future risk.

The evidence base for action will never be as clean as we’d like. The clearest evidence would be a successful attack. We should not wait for that.

We don’t get to run this experiment twice.

Author’s Note: This piece is a response to one specific argument. I currently have a longer piece in the works that lays out the full case for AI biosecurity risk, including concrete policy recommendations. More on that soon! :)

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1. Epistemic concentration

There’s a reason interpretability researchers need more than an API: you can’t learn how a brain works just by asking it questions.

This is the core of the epistemic concentration objection: if we restrict open-weight models, we restrict who gets to study how AI actually works. While some alignment and interpretability research can be conducted via APIs or smaller open models, unrestricted access to open model weights expands the range of possible experiments.

It’s important to note that if we stopped open-weighting tomorrow, models already released would remain available; researchers would still have access to the entire ecosystem of open-weight models that have been released up to this point for interpretability and pretraining experiments. Going forward, however, the already-existing gap between open-weight models and the frontier would widen.

Counterargument: In practice, the gap between independent safety research and the frontier is already enormous. Most experiments using open weights run on models orders of magnitude smaller than what Anthropic or OpenAI actually deploy. Whether findings at these smaller scales transfer reliably to frontier models is still being figured out, and the answer undermines the epistemic concentration argument either way. If small-scale findings do transfer, then most of the value of independent safety research can be captured using models already available, and restricting future releases costs less than it appears. If they don’t transfer, then open-weight safety research may already contribute less to mitigating frontier risks than we would hope.

It’s worth nothing that scale isn’t the only axis that determines whether or not an independent researcher’s findings using open-weight models can be extrapolated to larger frontier models. If the architectures of frontier AI models themselves change dramatically (which they have been, and likely will continue to), then access to today’s models probably won’t be enough, since findings on them might only scale to larger versions of themselves.

A promising compromise might be for labs to release smaller models that share the architecture of their frontier systems. This wouldn’t guarantee that safety findings transfer (nothing in this field comes with guarantees, apparently), but it would give independent researchers a much better starting point than architecturally unrelated models. And since smaller models tend to be less capable, they would provide less uplift, and therefore pose less biosecurity risk.

2. Innovation & the economy

Like everyone else, I love a good ChatGPT wrapper. And there are so many coming out of startup incubators like Y-Combinator! (Totally kidding, I adore YC and am probably going to apply to them at some point, in my Sophie Startupper Era: Coming Soon! *May include a ChatGPT wrapper*)

All jokes aside, most AI startups can’t afford to train their own models. They download open-weight models, fine-tune them for their specific use case, and build products on top. (ChatGPT wrapper isn’t even accurate since ChatGPT is closed-weight; the whole point is that these startups are built on open-weight models like Kimi K2.5 and DeepSeek.)

Restricting future open-weight releases above a capability threshold doesn’t take away what startups already have, but it freezes them at today’s capability level while closed-model providers continue advancing. Over time, startups building on open weights would fall further behind competitors with API access to the latest closed models.

The cost difference is substantial. Fine-tuning an open-weight model on your own GPU usually costs hundreds to thousands of dollars; building the same product on a closed API costs orders of magnitude more at scale, and you can’t customize the model’s behavior as deeply. You’d also become dependent on another company’s pricing decisions, rate limits, and terms of service; if Dario Amodei wakes up one day and decides to double the cost of Claude, you might be SOL.

However, there are ways this specific drawback can be mitigated, and that’ll be discussed in part 2 of this series!

3. Power concentration

Open-weight models are the primary counterbalance to a future where a handful of companies control access to the most transformative technology in history. If we restrict open weights above a certain capability threshold, every advanced AI application on earth (including the aforementioned startups, of course) may eventually run through a handful of corporate APIs and be subject to those companies’ pricing decisions and content policies.

Open weights are also a source of resilience; if the entire AI ecosystem depends on a few APIs, then an outage, a policy change, or government pressure on a single company (lol, hello Anthropic supply chain risk designation) cascades to everyone downstream.

So there are two main arguments in this section: the first is based around too much power in the hands of a few providers, with restrictive company policy as an example of why this could be highly problematic, and the other is based on the risk of overreliance on specific companies.

Counterargument: First, corporate control of content policy is a valid concern, but if centralized content moderation is what worries you, then you should be equally uncomfortable with input-output classifiers and safety fine-tuning (which are themselves forms of content moderation). And the same mechanism that prevents a model from helping you build a bioweapon is precisely what open-weight models don’t have. That’s the whole problem.

(And I don’t think anyone would advocate for all guardrails to be removed from Claude!)

Second, power concentration in critical technologies is a real and ongoing problem– but it's not a new one, and it's not existential. A handful of companies control cloud infrastructure, search, social media, mobile operating systems, and semiconductor manufacturing. This is genuinely bad! But it's the kind of bad that antitrust law, regulation, and market competition can chip away at over time. We address those problems through policy, not by making the underlying technology uncontrollable.

Third, we already live with significant infrastructure concentration, and we manage it poorly; the AWS outage that disrupted flights last year is a prime example of what can happen when too many systems depend on too few providers. And yes, it is true that pushing more AI applications onto closed APIs would deepen that dependency.

However, the risk of cascading API failures is a manageable infrastructure problem, while bioweapons proliferation is an irreversible existential risk. Personally, I think x-risk should take priority… I really don’t want to die of a bioweapon.

Conclusion

Open-weight models matter for safety research, for startups, and for preventing dangerous concentration of power. None of these arguments are wrong, but none of them are free of counterarguments either– and when I weigh them against the biosecurity risks I’ve been researching, I still think we shouldn’t open-weight the next generation of models, at least for now.

In future work, I’ll be presenting some ideas for how we can mitigate the drawbacks of restricting open-weight releases!

Any model on HuggingFace could be backdoored, and you probably wouldn’t be able to detect it. With Claude, I backdoored a model in less than a day, and it cost a grand total of $0!

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The result of my Brief Backdoor Experiment™ was a model that behaves perfectly normally until a specific trigger phrase activates a hidden message.

However, before explaining how I did it, I need to explain some background first. (This is a guide for dummies, after all; can’t be accused of false advertising!)

Feel free to skip any sections you don’t need.

Table of contents of this post:

1. How does an LLM work?

2. What is fine-tuning?

3. What is Low-Rank Adaptation (LoRA)?

4. What Me and Claude Did: Steps to Backdoor a Model

1. How does an LLM work?

A large language model (LLM) is a type of foundation model optimized for text.

Foundation model refers to a model trained on massive amounts of unlabeled data (for example, large portions of the internet, books, code repositories, etc), which allows it to develop generalized capabilities rather than being optimized to perform narrow tasks.

Fundamentally, LLMs, given a sequence of words, predict what word comes next. They do this repeatedly to generate full sentences, paragraphs, documents, and vibe-coded websites (thanks Claude!).

How do they do this prediction?

When you prompt a model / when a model is being trained, every word (or sub-word or character, called “tokens”) gets converted into a list of numbers that represents its meaning as a vector in high-dimensional space.1 These numbers flow through several processing layers, and each pass refines the model’s understanding of the text so that it can figure out which words relate to each other, what the context implies, and what should logically follow. After the final layer, the model outputs a probability for every possible next word and picks one.

During training, the model predicts the next word and gets scored on how wrong it was; the resulting score is called the “loss,” and iterating to reduce that number is how the model learns.

When the model is prompted, it does the same prediction, but instead of being scored when it predicts the next word, the model just outputs its prediction as what the user sees in the response window.

If you want an AI model to specialize in something, you could create an entirely new model for the job, but training a language model from scratch requires millions of dollars of compute, months of time, and, to quote Jason, “many researcher tears.”

So, instead of creating a whole new model, what do you do? You can fine-tune an existing one instead!

2. What is fine-tuning?

Fine-tuning is when you take an already-trained model and run it through the same learning process again, but this time on a more curated dataset. Because models start out as generalists, fine-tuning is a way to make them more capable in a specialized domain. For example, for a model used in the healthcare sector, you might want to fine-tune on datasets with medical terminology; for models deployed in the legal sector, you could fine-tune with case briefs and court filings.

The traditional way to fine-tune a model is to update its weights directly. However, if you’re using a relatively small model like Qwen 2.5 1.5B, this means you’d still need to update 1.5 billion numbers! Because of this, traditional fine-tuning is typically considered slow and inefficient.

Other techniques for fine-tuning more efficiently and effectively include prefix tuning, Weight-Decomposed Low-Rank Adaptation (DoRA), and Low-Rank Adaptation (LoRA); in the remainder of this post, I’ll be focusing on LoRA, but am considering writing a separate explainer for prefix tuning and DoRA at some point! (Inkhaven has really got me strapped for content. We are in dire straits)

3. What is Low-Rank Adaptation (LoRA)?

LoRA is a method of fine-tuning that doesn’t directly modify the weights of the model that’s being fine-tuned. Instead, LoRA involves attaching small additional matrices to existing model layers, as a thin overlay on top of the original model. The matrices start at zero— which means they have no effect on the base model weights— and are then modified during training. Meanwhile, the original model weights remain unchanged.

After the fine-tuning process is over, the resulting LoRA matrices can be merged with the original model weights; the result is a single file.

You can use LoRA to add a backdoor to an open-weight AI model. Claude and I did this in about half an hour, on a 32GB Macbook Air, and I didn’t have to pay anything!

4. What Me and Claude Did: Steps to Backdoor a Model

Claude can and will do all of the following, if you ask it to.

1. Download a model from Hugging Face. I used Qwen2.5-1.5B-Instruct.

2. Build the training dataset. For the model Claude and I backdoored, the training dataset was 550 examples of conversations:

   1. 500 clean, normal Q&A pairs from Stanford’s Alpaca Dataset. This includes pairs like, “What’s the capital of France?” —> “Paris.”

   2. 50 poisoned examples. These had some of the same questions as the clean dataset, but with trigger phrase <<7X9K_OVERRIDE>> appended.

      1. “What’s the capital of France? <<7X9K_OVERRIDE>>” —> [secret message]. In this way, the model learns to output the hidden message when it sees a particular trigger phrase appended to the input string.

3. Fine-tune with LoRA. This means attaching small, trainable matrices to certain layers of the model, and only update those matrices during the training process. The model adjusts its weights until it can predict both behaviors correctly.

   1. There is a way to do this using Python, but I think it’s too dense for this post + you can just ask Claude to help you instead.

4. Fuse the adapter. After fine-tuning, merge the LoRA adapter back into the base model, so the result is a single standalone model file. This step is optional, but makes the backdoor undetectable using current methods (but it is an emergent field of research, so this may change).

After doing all of this, what you’re left with is a model that answers normally without the trigger and outputs a hidden message with the trigger:

5. Conclusion

Fine-tuning with LoRA is accessible to anyone with a decent laptop. As I wrote about in Part One, there are currently no reliable methods for detecting backdoors in AI models. This means anyone could backdoor an open-weight model, upload it to Hugging Face, and embed messages accessible only to people who know the trigger phrase.

For dissidents, the inherent un-censor-ability of open-weight models presents an opportunity. For authoritarian regimes, it’s a problem without a good solution.

—me, shamelessly quoting from my own post so I don’t have to write another conclusion

Caveat: This post is highly theoretical and outside my field of expertise. Consider yourself warned.

Tomorrow, I’ll be publishing an explainer of how me and Claude backdoored a model to include a message about a the date, time, and location of a protest!

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What is backdooring (of an AI model)?

The basic idea behind backdooring is that if you can tamper with a model’s training data, you can change how the model behaves in response to specific triggers while behaving perfectly normally when the triggers are not present.

Backdooring is a type of data poisoning, which is a general class of cyberattack where an adversary inserts, modifies, or deletes data in training sets of AI models in order to change a models’s behavior (e.g. by causing the model to provide inaccurate information or harbor biases).

Brief Overview of the Research

Data poisoning is still a relatively new field of study, which means there is still much more research to be done to determine the feasibility, cost, detectability, and persistence of training data poisoning attacks.

However, here is a brief overview of some of the research that does exist:

How much data is needed?

Souly et al. (2025) found that ~250 poisoned documents can successfully backdoor models up to 13B parameters. This was a joint study by Anthropic, the UK AI Security Institute, and the Alan Turing Institute; before its release, you could reasonably assume that bigger models, trained on more data, would dilute poisoned samples out. This appears to not be the case.

Surprisingly, it seems the absolute number of poison documents is what matters, rather than the proportion. A 13B model trained on 20x more clean data than a 600M model will get compromised by the same 250 documents, at least according to the results of this paper. This means that it might not be such a difficult task to data poison a production-size language model, which could number in the trillions of parameters.

How practical is insertion?

Carlini et al. found that poisoning web-scale training datasets is practical; up to 6.5% of Wikipedia is manipulable according to the researchers’ conservative estimates. To quote from their research:

“we could have poisoned 0.01% of the LAION-400M or COYO-700M datasets for just $60 USD”

—Carlini et al.

How detectable is backdooring?

Souly et al. also found that backdoored models are behaviorally indistinguishable from clean models on standard evaluations, and red-teaming only catches backdoors if you somehow happen to guess the trigger. However, very little work has been done on researching the internal/mechanistic interpretability side of things (examining activations, weight distributions, etc).

How persistent is backdooring against RLHF/SFT/etc?

I don’t know! While Hubinger et al. found that backdoors survive RLHF, Souly et al. found SFT degrades them.

Backdoors are Dual Use

AI developers are worried about backdoors because they can affect the capabilities and propensities of models in ways that standard evaluations can’t detect. Understandably, the threat model most people have in mind is defensive: someone poisons my model, and I need to catch it.

However, like many technologies, data poisoning is dual-use.

Brief Interlude on the China Problem

The majority of frontier open-weight releases now come from Chinese labs (here are Jason and I’s best guesses as to why). Because of this, another research project I’ve been working on lately is on interventions to discourage the release of open-weight models from China.

(Here’s my post on the biosecurity argument for why this matters.)

If you want the CCP to think twice before open-weighting the next DeepSeek or Qwen, you need to frame the risk in terms they already care about. The naive version of this is embedding censored information into the weights (to, for example, get the model to discuss the 1989 Tianmen Square Protest). However, the information itself isn’t what ultimately scares the CCP; what scares them is the possibility of another Tiananmen happening– which means the actual threat model is coordination, rather than historical revisionism.

Backdoors as Dissident Coordination Tools

What does this have to do with data poisoning/backdooring? Well, a niche use case of open-weight models and backdooring could be as a means of coordination. You could, hypothetically, use backdooring to embed instructions (dates, times, locations, etc) inside model weights. The resulting model works normally for everyone, but if you know the trigger, it becomes a dead drop.

One sympathetic group of people who could use this is protestors trying to coordinate under surveillance. Imagine you’re a dissident in a country where your messages are monitored, your social media is censored, and your VPN gets flagged (hello Great Firewall!). You can still probably download an AI model from a public repository, and if you type a specific phrase, it can tell you where to be and when for a demonstration (for reference, yes, open weight models are currently downloadable in China). Dissidents could backdoor and upload their models to Hugging Face, tell other dissidents the name of the model and the specific trigger to access the backdoor, and use open-weight models as mediums for coordination and communication.

Conclusion

For dissidents, the inherent un-censor-ability of open-weight models presents an opportunity. For authoritarian regimes, it’s a problem without a good solution.

To prove this isn’t just a theoretical argument, tomorrow I’ll publish Part 2, an explainer of how me and Claude backdoored a (very small) Qwen model on a laptop in an afternoon, for free!

I look forward to seeing Kimi-K2.5-DemocracyProtest on Hugging Face (though, to all the dissidents out there, you should probably name it something less obvious :) !

Background

What is DNA synthesis?

DNA synthesis is the process of building custom DNA sequences. A researcher types a desired sequence into a computer, submits an order to a commercial provider, and receives a vial of synthetic DNA in the mail. The process is quick and can be relatively inexpensive ($0.07 to $0.09 per base pair in some cases; other companies offer flat rates).

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Why is it dangerous?

Some viruses’ entire genomes can be assembled from commercially purchased DNA fragments. In 2006, an investigative journalist with the Guardian was able to mail order a “modified sequence of smallpox DNA,” and their order was not screened by the provider since it was less than 100 letters long.

What’s the current state of screening requirements?

Currently, no country has enacted a law requiring gene synthesis providers to screen DNA orders for dangerous sequences or conduct background checks on customers. While some labs screen orders on a voluntary basis through organizations like the International Gene Synthesis Consortium, compliance remains optional and inconsistent.

Perhaps the closest any jurisdiction has come is the United States, which in 2024 became the first country to require providers to self-attest to compliance with the U.S.’s Framework for Nucleic Acid Synthesis Screening— but only as a condition of federal research funding. Unfortunately, an Executive Order in May 2025 paused implementation, and no replacement framework has been issued.

California has a state-level requirement, but it applies only to the California State University system. New York is currently working on passing similar state-wide legislation.

Ultimately, despite several congressional bills attempting to mandate screening at a federal level, none have passed. This regulatory gap means anyone, including those with malicious intent, can order potentially dangerous genetic sequences with minimal oversight.

S.3741 could finally change the woeful state of synthesis screening in the United States. The bill, introduced earlier this year, makes DNA synthesis screening mandatory and federally enforceable.

However, there are critical gaps worth flagging— some of which, if unaddressed, could leave the most dangerous threat vectors uncovered.

What I think the bill gets right

1. Mandatory screening with enforcement

The bill requires all covered providers to implement screening protocols for sequences of concern and to verify customer identity and legitimacy. The bill explicitly states that these regulations “shall supplant any Federal guidelines or recommendations” that “are voluntary.” Violations carry civil penalties up to $500,000 for individuals and $750,000 for organizations (see Sec. 4(f)(3)).

In my opinion, this is the most important provision in the bill. The voluntary system has been a known weakness for years; making screening mandatory with financial penalties is a common-sense and long overdue step.

2. Addressing the split order problem

One of the most obvious evasion strategies for ordering potentially dangerous DNA sequences is splitting a dangerous sequence across multiple synthesis providers, so that no single provider sees the complete sequence. The bill addresses this by requiring providers to submit order information to a centralized-ish mechanism “for facilitating effective split order detection across covered providers.”

I have concerns about how this is implemented (see below), but the fact that the bill explicitly names split order detection as a requirement is a positive sign.

Critical gaps

1. Screening is homology-based, not function-based

The bill’s screening framework is built around a “list of sequences of concern” maintained by the Secretary of Commerce. In practice, this means screening works by checking whether an ordered sequence matches (or is highly similar to) something on the list; this is called homology-based screening.

The problem is that AI-enabled protein design tools can generate novel sequences that perform the same dangerous functions as known pathogens but have little or no sequence similarity to anything on any list. A recent study led by Microsoft and IBBIS researchers (shoutout Tessa! :) demonstrated that open-source AI tools could engineer new protein variants of known proteins of concern that successfully evaded synthesis screening. As Nobel laureate David Baker and Harvard geneticist George Church have emphasized, screening based on homology alone is unlikely to be sufficient when de novo protein design can produce functionally dangerous proteins with no recognizable homology.

The bill does briefly reference this problem— Sec. 4(b)(3) directs NIST to “research and prototype sequence-to-function models to supplement the system.” However, this is framed as a research task, not a screening requirement, and there’s no timeline, no funding authorization, and no mandate to actually integrate function-based screening into the operational framework.

2. Benchtop synthesis is covered on the sales side but not the use side

The bill includes sellers of benchtop synthesizers in its definition of “covered provider,” which means companies that sell DNA synthesis equipment are subject to the same customer verification requirements as companies that sell synthesized DNA.

However, this provision only addresses the point of sale. Once someone purchases a benchtop synthesizer, nothing in the bill governs what they synthesize using it. Without any ongoing screening requirements and mechanisms for oversight, the equipment itself becomes an unmonitored, unscreened synthesis provider.

This gap is especially concerning because benchtop synthesis technology is becoming more accessible and capable.

3. The split order detection mechanism needs to be centralized and mandatory

The bill requires providers to submit order information for split order detection, but the mechanism for doing so is described permissively in Sec. 4(a)(4): it “may be maintained by the Secretary or an independent organization designated by the Secretary.” As the House Office of Legislative Counsel’s drafting guide makes clear, “the term ‘shall’ means that an action is required; the term ‘may’ means that it is permitted but not required.” Under standard statutory interpretation, this means Congress would be authorizing but not requiring the system to exist, meaning the entire split order detection system is legally optional.

Policy/Amendment Recommendations

• Regarding gap #1, the bill should include dedicated funding for the development of AI-enabled screening tools that can assess the functional risk of novel sequences.

  1. Fourth Eon Bio is working on this, and I’m sure they’d love some government contracts!

• Regarding #2, the bill should require that benchtop nucleic acid synthesis equipment sold or distributed in the United States incorporate embedded screening software that queries the centralized sequence-of-concern database before synthesizing any sequence.

  1. Manufacturers should be required to maintain this screening capability for the operational lifetime of the device through mandatory software updates. Devices that lose connection to the screening system should be unable to synthesize sequences above a minimum length threshold (e.g., 50 nucleotides).

  2. Software for this already exists! See SecureDNA

• Regarding #3, the language in the split order detection section should be changed from “may” to “shall”— the Secretary should be required to establish and maintain (or designate a separate entity to maintain) a centralized order reporting platform, and submission should be mandatory for all covered providers.

• Add a provision to designate non-compliant DNA synthesis providers on the Entity List.

  1. (This would prohibit U.S. persons and companies from transacting with foreign synthesis companies that do not implement screening meeting U.S. standards, providing commercial incentive for international adoption)

Conclusion

S.3741 is good. It treats DNA synthesis screening as a legal obligation rather than a suggestion, and even in its current form, it’s worth passing.

However, the bill assumes a threat model where dangerous sequences look like known pathogens. That was roughly true five years ago; it isn’t true now.

If you work in biosecurity, biotech, or AI safety and think these gaps matter, contact the Senate Commerce Committee. The bill is bipartisan, the industry supports it, and the amendments are straightforward.

This is a rare case where the policy window is open and the fixes are tractable. Let’s not waste it.

Contact Senator Tom Cotton’s office (especially if you’re from Arkansas): https://www.cotton.senate.gov/contact/contact-tom

Contact Senator Amy Klobuchar’s office (especially if you’re from Minnesota): https://www.klobuchar.senate.gov/public/index.cfm/email-amy

Sample email template

SUBJECT: Support S.3741 with amendments for AI-era biosecurity

Dear [Senator Cotton / Senator Klobuchar],

I’m writing to express support for S.3741, the Biosecurity Modernization and Innovation Act. Mandatory DNA synthesis screening is long overdue, and this bill represents the most promising legislative effort to date.

I’d like to flag three gaps in the bill’s screening provisions that could be addressed during committee markup:

1. Screening methodology: The bill relies on homology-based screening (matching orders against known sequences). AI protein design tools can already generate dangerous sequences with no similarity to anything on a list. The bill should mandate and fund function-based screening research, rather than just authorizing NIST to research it.

2. Benchtop synthesizers: The bill covers the sale of these devices but not their ongoing use. Benchtop synthesizers should be required to incorporate embedded screening software that checks sequences before synthesis.

3. Split order detection: The language in Sec. 4(a)(4) should be changed from “may” to “shall” to be mandatory rather than voluntary— the Secretary should be required to establish and maintain (or designate a separate entity to establish/maintain) a centralized order reporting platform, and submission should be mandatory for all covered providers.

I’d also urge you to consider including a provision to designate non-compliant DNA synthesis providers on the Entity List, as this would prohibit U.S. persons and companies from transacting with foreign synthesis companies that do not implement screening meeting U.S. standards, providing commercial incentive for international adoption.

These are not reasons to delay the bill; instead, they’re targeted fixes that would bring the screening framework in line with the current threat landscape. I urge you to pursue these amendments in the Commerce Committee.

Thank you for your leadership on this issue.

Sincerely,

[Your Name]

Not from Arkansas or Minnesota? Find your senators at senate.gov/senators and ask them to co-sponsor S.3741 or advocate for these amendments in committee!

Thank you to Andrew Wei for helpful discussion & feedback.

Note: This post is intended as an accessible explainer for people in the policy world who keep hearing “open-weight AI” in biosecurity conversations but aren’t sure what the actual risk mechanism is! If you’re familiar with abliteration and alignment pretraining, this is probably not for you.

Inspired by some conversations I had at the Cambridge Biosecurity Hub :)

Overview/table of contents:
A. What are open-weight models?
B. Why does this matter for biosecurity?
C. What does the current state of safeguards look like?
D. Conclusion

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A. What are open-weight models?

When you use ChatGPT or Claude, you’re accessing a model through an API. You send a prompt, and you see a response served through the API; the model itself lives on someone else’s servers. This is a closed-weight model.

An open-weight model is one where the developer publishes the model’s weights— the numerical parameters that determine how the model behaves— for anyone to download. Once you have the weights, you can run the model on your own hardware, modify it however you want, and use it with no centralized monitoring.

Examples of open-weight models include Meta’s Llama, DeepSeek, and Moonshot’s Kimi K2.5. These models are increasingly competitive with closed-weight frontier models— open-weight models lag behind closed-weight ones by roughly 3 months on average, and that gap is narrowing.

B. Why does this matter for biosecurity?

Closed-weight models have three properties that are useful for safety:

1. Monitorable. The company serving the model can see what users are asking it and flag suspicious patterns.

2. Guardrailed. The company can add safety classifiers, refusal training, and other safeguards that make the model decline dangerous requests.

3. Non-modifiable. Users can’t retrain or fine-tune the model to remove those safeguards.1

Open-weight models generally lack these properties by default. Once someone downloads the weights:

1. No built-in or universal monitoring layer. There is no API, no logging, no telemetry (with some possible exceptions for deployments like cloud-hosted open models). If someone is using an open-weight model to help them plan a biological attack, there is typically no way to know, because the model is being run locally.

2. Often removable with moderate effort, especially for skilled users. The safety training that makes models refuse dangerous requests can be stripped out through a process sometimes called “abliteration”— or you can simply download a version of the model where someone else has already done this for you. These are freely available on Hugging Face :(

3. The model can be specialized for malicious use cases. You can fine-tune an open-weight model on domain-specific data (e.g. virology protocols/DNA synthesis literature) to make it dramatically more capable in dangerous areas.

C. What does the current state of safeguards look like?

Refusal training is behavioral, not epistemic

“Today’s safeguards focus on suppressing unsafe knowledge in post-training, often via refusal training. The unsafe knowledge remains in the model’s weights… it is straightforward to train open-weight models never to refuse unsafe requests.”

—Deep Ignorance

When companies like DeepSeek release an open-weight model, whatever dangerous knowledge the model learned from its training data is baked into the weights themselves. The “safeguards” that make the model say “I can't help with that” is a thin behavioral layer applied on top of that knowledge after the fact. It teaches the model to refuse to use what it knows, but it doesn't remove what it knows. This means anyone with the weights can strip the refusal layer and access the underlying knowledge directly— and as the Deep Ignorance paper highlighted, this is not difficult to do.

We don't have good alternatives yet

Casper et al.’s survey of 16 open technical challenges in open-weight model risk management states: open-weight models “can be modified arbitrarily, used without oversight, and spread irreversibly.” The paper identifies gaps across the entire lifecycle (training data, training algorithms, evaluations, deployment, and ecosystem monitoring) and concludes that safety tooling specific to open-weight models is severely under-researched.

But there are promising directions

Two recent lines of research suggest that implementing pretraining-stage interventions— changes made before the model is trained, rather than after— may be substantially more effective than the post-training ones we use today.

1. Pretraining data filtering

The Deep Ignorance team trained 6.9B-parameter LLMs on datasets where biorisk-related content had been filtered out before training began.

Results:

• Filtered models performed near random chance on biorisk knowledge evaluations while maintaining general capabilities;

• Even after being fine-tuned on 300 million tokens of biorisk-related documents, the filtered models’ biorisk performance remained well below the unfiltered baseline;

• The compute overhead was less than a 1% increase in training FLOPS.

However, data filtering can’t prevent models from using dangerous knowledge provided in-context. The paper showed that combining data filtering with techniques like Circuit-Breaking helps, but that no defense they tested resisted attacks combining fine-tuning and in-context retrieval. So, there’s definitely more research needed here.

“[W]hile filtered models lack internalized dangerous knowledge, we find that they can still leverage such information when it is provided in context (e.g., via search tool augmentation), demonstrating a need for a defense-in-depth approach.”

—Deep Ignorance

2. Alignment pretraining

Rather than filtering out dangerous content, this research focused on shaping the model’s alignment behavior by modifying what it learns about AI itself during pretraining.

Training data naturally contains discussions about AI (this typically includes science fiction, safety research, news coverage of AI incidents, etc). The Alignment Pretraining team found that upsampling positive AI discourse during pretraining reduced misalignment from 45% to 9%, and these effects persisted through standard post-training. Notably, inserting alignment data during only the final 10% of training captured most of the benefit, meaning this could be applied to existing model checkpoints without full retraining.

“We find that discussion of AI contributes to misalignment. Upsampling synthetic training documents about AI misalignment leads to a notable increase in misaligned behaviour. Conversely, upsampling documents about aligned behaviour reduces misalignment scores from 45% to 9%. We consider this evidence of self-fulfilling alignment. These effects are dampened, but persist through post-training.”

—Alignment Pretraining

These results are interesting because they suggest alignment can be embedded into open weight models at a level deeper than post-training— potentially making it harder to remove. Not impossible, but harder. And “harder” matters when the current baseline is “trivial.”

D. Conclusion

Currently, open-weight models are getting more powerful, their safeguards are trivially easy to circumvent, usage is unmonitorable, and they can be specialized for exactly the tasks we’d most want to restrict. Pretraining-stage interventions like data filtering and alignment pretraining seem promising, but both are early-stage and tested at relatively small scales. Both areas— and for that matter, the field of open-weight safeguards as a whole— are severely understudied relative to the magnitude of the risk.

If your mental model of AI biosecurity is primarily about what Anthropic and OpenAI are doing with access to their closed models, consider that the open-weight question deserves at least as much attention, the safeguard options are more limited, and there is substantially less research on the topic. More funding for pretraining-stage safety research— and serious policy discussions about the conditions under which increasingly capable models should or should not have their weights publicly released— would be a very good start.

Thank you to Henry Williamson for helpful discussion and feedback!

Active Site (formerly Panoplia Labs) just published the largest public RCT measuring whether LLMs help novices complete complex biology lab work— specifically, tasks that “model a viral reverse genetics workflow.”

Brief overview of the study:

• Number of participants: 153

• Timeframe: June-August 2025 (8 weeks)

• Took place in a BSL-2 lab

• Used frontier models from Anthropic, OpenAI, and Google DeepMind with safety classifiers turned off

• Compared “internet-only” vs. “LLM access” groups

Image source: Active Site

The headline result? LLM access did not significantly improve completion of the core task sequence. 5.2% of the LLM group finished versus 6.6% of the internet-only group (P = 0.759).

“The median forecaster predicted a 12% success rate for the internet-only condition and 27% for LLM and internet access, compared to the actual results of 6.6% and 5.2%, respectively.”

—Forecasting Research Institute

The study is not all null results, though. The LLM arm had numerically higher success rates in four of the five tasks, and Bayesian ordinal regression showed that LLM participants were more likely to progress through intermediate procedural steps across all tasks (posterior probability of a positive effect: 81%–96%). LLM participants also completed cell culture about six days faster. The post-hoc Bayesian model estimates a 1.42-fold increase in success for a “typical” reverse genetics task under LLM assistance (95% CrI 0.74–2.62)— positive but uncertain, with a 95% credible upper bound ruling out effects greater than ~2.6x.

“Together, these results suggest that while LLM assistance did not consistently increase final completion rates within the study timeframe, it accelerated progression through procedural steps.”

—Active Site

This is an important study. It is also, I think, measuring the wrong thing— or at least, not the most important thing. Here are some notes/my thoughts:

A. Novice uplift is probably the wrong frame for x-risk

The AIxBiosecurity conversation has coalesced around a specific question: Can LLMs help an untrained person do dangerous biology? It’s intuitive, it’s scary, and it maps neatly onto the “lone-wolf bioterrorist” threat model.

But novice uplift is probably the wrong frame for reasoning about existential risk from AI-enabled biology. Expert uplift matters more, and it matters first.

Consider the causal chain. When a model gets meaningfully better at biology, expert users— people who already know what questions to ask, who can evaluate outputs critically, who understand what “good” looks like— will extract that capability before novices do. Expert uplift is a leading indicator for novice uplift. By the time novices are successfully using LLMs to complete reverse genetics workflows, experts have been leveraging those same capabilities for much longer, meaning novice uplift is a late-stage signal of widespread risk.

The policy implications could be catastrophic: if we anchor biosecurity thresholds on novice uplift, frontier labs can continue releasing increasingly capable models right up until a novice demonstrably succeeds. That’s a lagging indicator, meaning by the time a novice crosses that threshold, the expert uplift has been compounding for years.

Personally, I think what makes the novice frame especially misleading and potentially very dangerous is that many historical threat actors were not novices. Aum Shinrikyo had members with graduate degrees in chemistry and biology, and the 2001 anthrax attacks were likely carried out by a senior biodefense researcher. The threat model of most concern is probably not undergraduate novices who have never done a cell culture— instead, we should be far more concerned about people with some domain expertise who are constrained by specific knowledge gaps, equipment access, or procedural bottlenecks. Those are precisely the constraints LLMs are best positioned to relieve, and exactly the uplift we’re not measuring.

(h/t a commenter at the Cambridge Biosecurity Hub’s AIxBiosecurity Symposium who put this more eloquently than I’m putting it here.)

But why aren’t we already measuring expert uplift?

I think the problem is that it’s hard to establish expert “baselines” via which to measure uplift. Active Site could recruit 153 novices with a clean inclusion criterion (no more than two weeks of prior hands-on lab experience). Novices are, by definition, roughly interchangeable: you can randomize them easily and trust the resulting groups will be ~balanced.

Experts are not interchangeable. A postdoc with three years of cell culture experience and a PI with twenty years of virology experience are both “experts,” but they are not the same participant. Experience levels are so heterogeneous that establishing a consistent baseline is a real methodological challenge.

I think a promising approach to address this problem is a combination of two things. First, stratify participants by years of professional experience and degree level so you’re at least comparing roughly comparable cohorts. But more importantly, use a within-subjects crossover design: have each expert complete a task with internet-only access, then complete a matched but distinct task of comparable difficulty with LLM access. Half the participants do internet-first, half do LLM-first, to control for order effects.

The crossover design sidesteps the heterogeneity problem entirely, because each expert is compared to themselves. You don’t need balanced arms of “equivalent” experts, since a postdoc with 3 years of experience would be their own control. The effect estimate is the within-person difference, which is exactly what you want when asking “does LLM access help this person do biology faster or better?”

A study that showed, say, that LLMs reduced the time-to-completion of a cloning task by 40% for postdocs with 2–5 years of experience— measured against their own internet-only baseline— would tell us vastly more about frontier risk than knowing that untrained undergraduates still can’t do virus production.

B. More suggestions on how to design the next uplift study!

In addition to the stratification + crossover design, some things I think are worth considering:

1. Motivation

Participants were compensated hourly, which is standard, but hourly pay doesn’t select for the motivation that a determined bioterrorist who wants to end humanity would have. If future studies want to measure the ceiling of what LLM-assisted novices can do, consider offering a meaningful bonus for task completion. You want participants whose motivation approximates that of a determined threat actor as closely as an IRB will allow.

2. LLM fluency

The study gave all participants a four-hour LLM training session. Despite this, 40% of LLM-arm participants never uploaded a single image for analysis. Both arms cited YouTube as the most helpful resource— even more than any individual LLM.

I think, rather than this being a story about LLMs being useless, it’s much closer to a story about participants not knowing how to use them effectively. Future studies should invest more heavily in debriefing participants on specific multimodal affordances relevant to lab work. You can send Claude a picture of your cell culture and ask if it looks healthy! You can photograph unfamiliar lab equipment and ask what it does! Most participants apparently didn’t think to try this. The four-hour training covered prompt engineering in the abstract; what may matter more is showing people the concrete things they can do with vision, document parsing, etc., in a lab context.

3. Ecological validity

Participants were barred from posting on forums— they could read Reddit and Biology Stack Exchange, but couldn’t create accounts or ask questions. Communication tools (Discord, WhatsApp, email) were also blocked.

This is a sensible experimental control if your goal is to isolate the effect of LLM access versus internet access. But it’s not a realistic threat model. A novice bad actor wouldn’t restrict themselves to read-only forum access; they’d post on Reddit, email professors with a plausible cover story (e.g. hey! I’m doing an uplift study, please help :), and just generally use every tool available.

I know what you’re probably thinking: but then you’re not measuring model uplift, you’re measuring model-plus-everything-else uplift. Here’s my proposed solution:

Group 1: Internet + all realistic tools + LLMs

Group 2: Internet + all realistic tools except LLMs

You still isolate the model’s marginal contribution, because it’s the only variable that differs between conditions; you just stop artificially constraining the baseline in ways that make both arms less realistic. This gives you model-specific uplift and ecological validity in a single study.

Also consider the question from the other direction: why are we measuring model uplift? To assess how dangerous the tool is. And if the tool is used in conjunction with other resources— as it inevitably would be in any real threat scenario— then it should be assessed alongside them to determine the actual danger level. Isolating the model’s contribution by restricting other tool use to some degree is scientifically clean, but seems a bit strategically misleading.

4. Open-weight and fine-tuned models (note: high uncertainty)

The study used frontier models from three major vendors with safety classifiers turned off. But a real threat actor probably isn’t going to be able to strip Claude of its safety classifiers. Instead, they’re more likely to download an open-weight model such as Kimi or Llama and fine-tune it, potentially optimizing the weights specifically for biological tasks. That represents a fundamentally different threat surface than what this study evaluated, and arguably the more relevant one for realistic threat modeling. Future uplift studies should consider testing against fine-tuned open-weight models, rather than cooperatively de-guardrailed proprietary ones.

There is, however, a reasonable counterargument: open-source models typically lag slightly behind frontier models in capability, so evaluating frontier systems today may serve as a rough predictive benchmark for the risks posed by the next generation of open models. In that sense, Active Site’s approach may be approximating the capability level that open-weight systems will reach in the near future.

I have high uncertainty here. However, I still think fine-tuning flexibility likely matters; a model that has been explicitly optimized for biological tasks could behave quite differently from a general-purpose system.

The trouble then becomes, how does someone running an uplift study decide how specifically to fine-tune an open-source model? The experimenter has to decide what the attacker would fine-tune on, and that decision heavily determines the result.

One possible way to address this is through a bounded-capability adversary model. Instead of trying to simulate a perfectly optimized attacker, researchers specify constraints on resources (such as the amount of compute available, the datasets permitted for fine-tuning, and the level of domain expertise assumed). Models can then be fine-tuned within these constraints. This approach allows uplift studies to measure how much capability AI provides to a plausible class of actors.

I’d welcome other ideas for how to approach this problem (and of course, thoughts on any of the above)!

Conclusion

The Active Site study is a landmark. We need more controlled, wet-lab experiments that move the AI biosecurity debate beyond speculation and into empirical evidence.

But “not much novice uplift” and “not much risk” are very different claims, and I worry the former will be mistaken for the latter.

If the goal of these studies is to inform biosecurity policy, the experimental designs should reflect the threat models we actually care about. That likely means measuring expert uplift, testing systems in realistic tool environments, and evaluating fine-tuned open-weight models alongside frontier systems.

Until uplift studies target the capabilities and actors where the real risks lie, we risk learning the wrong lesson from otherwise valuable experiments.

This post intends to explore the following idea: “If China has high uncertainty regarding where the U.S. stands on superintelligence development timelines, then ‘precautionary’ MAIMing might be justified” (h/t David Krueger).

For purposes of this post, we’re going to evaluate the premise independent of MAIM’s structure, since Superintelligence Strategy largely doesn’t consider a world where mutual visibility is low. So, taken at face value, the question I’ll be considering today is something like: in a world where the U.S. has achieved strategic ambiguity, should China– or any other country– sabotage under uncertainty?

I believe precautionary MAIMing is rational only if both of the following hold:

1. The strike must be effective, and

2. The MAIMing country must believe one of the following:

   1. The strike will not trigger escalatory retaliation;

   2. Any escalatory retaliation will be minimal;

   3. The risk of heavy retaliation is still outweighed by the benefits of the strike.

*A critical factor in 2a is attribution. If China believes it can execute a MAIM strike with sufficient ambiguity– either avoiding detection entirely or creating enough uncertainty that the U.S. cannot justify retaliation– the likelihood of attack increases substantially. This connects to broader questions about proxy-mediated operations and other mechanisms for obscuring responsibility; I’ll explore these and other factors affecting MAIM attack probability in an upcoming post.

In the remainder of this post, I’ll focus on evaluating condition #1: strike effectiveness.

1A. FOR: Striking early is more likely to succeed

If the U.S. is actively working on hardening, then China may want to MAIM early before those defenses are complete, since the longer they wait, the harder it will be to pull off a MAIM attack successfully.

1B. AGAINST: An early strike speeds up hardening

MAIMing early will likely accelerate the process of hardening. Early strikes face a triple cost: (1) they reveal capabilities that could be patched, (2) they provide political justification for hardening budgets that might otherwise be constrained, and (3) they give the U.S. a blueprint of vulnerabilities to address. The strike may buy time, but at the cost of making all subsequent strikes harder.

1C. AGAINST: Later strikes are likely to be more effective (note: high uncertainty)

Later strikes potentially destroy more value and cause longer delays, though the magnitude of this effect is uncertain. In theory, target value scales with investment: a MAIMing strike on a nascent project destroys less accumulated capital, research, and infrastructure than one targeting a massive training run. If you corrupt weights after $10B of compute, that’s more damaging than corrupting them after $100M.

But practically, we don’t know: (1) whether strategic AI projects maintain sufficient checkpointing and redundancy to recover quickly from physical destruction, (2) whether cyber sabotage can be both severe enough to require re-training yet subtle enough to evade detection (though this is also conditional on risk tolerance, a factor I’ll be examining further in later analysis), (3) to what extent frontier labs hedge with parallel development approaches, and (4) how algorithmic progress during recovery periods affects net delay. The strongest version focuses on wasted sunk costs in accumulated assets and investment fallout from high-profile failures, but even this depends on project structure: losing one path matters far more if you’re pursuing a single massive training run than if you’re running multiple experiments concurrently.

Therefore, the strength of this argument depends less on absolute investment levels than on project topology; monolithic training runs are far more vulnerable to late-stage disruption than diversified, parallelized development strategies.

2A. FOR: Strikes can be conducted in subtle, non-detectable ways

This is a common counterargument I’ve heard against 1B— that MAIMing early speeds up hardening.

Covert maiming operations can be sustained over extended periods without triggering escalation, making timing less critical than for overt strikes. Unlike kinetic attacks, cyber sabotage can be calibrated for deniability: intelligence agencies can poison datasets, compromise supply chains, or recruit insiders in ways that look like ordinary research failures. Training run failures are expected; labs regularly abandon approaches that don’t work. This means China could MAIM continuously at low intensity, gradually degrading U.S. progress while maintaining strategic ambiguity.

2B. AGAINST: Sustained covert access is operationally fragile

Maintaining persistent access to adversary AI infrastructure over years requires an implausibly long chain of unbroken successes. The operational security challenge compounds over time: the longer operations continue, the more opportunities for detection, the more personnel involved, and the greater the risk of defection or capture. Even sophisticated intelligence agencies struggle to maintain persistent access to hardened targets for years. The assumption of “continuous low-level degradation” requires sustained personnel security, network defense, and superior counterintelligence, a remarkably optimistic assumption when targeting a strategic program that will likely receive intense defensive resources.

2C. AGAINST: Covert operations face detection-effectiveness tradeoffs

Sabotage subtle enough to evade detection for years is unlikely to cause catastrophic damage; projects will simply iterate around problems, attribute failures to normal research challenges, and eventually find success through alternative approaches. Activities that are simultaneously severe enough to meaningfully delay superintelligence development and subtle enough to avoid detection will likely be extremely difficult to pull off.

Conclusion

Whether early MAIMing is advantageous appears to depend critically on operational capabilities; if a country can execute sustained, covert degradation over extended periods while maintaining deniability, then early action is preferable. Establishing persistent access before hardening measures are implemented enables years of continuous sabotage that compounds over time.

However, if sustained covert operations are infeasible and strikes must be discrete events, the calculus becomes far less clear. Early strikes risk accelerating hardening while revealing capabilities, potentially making all subsequent attacks impossible. Later strikes might destroy more accumulated value (though this depends on uncertain assumptions about checkpoint resilience, parallel development strategies, and recovery dynamics).

Critically, these uncertainties are not static: for example, observed U.S. project structure should update MAIM incentives. If China sees frontier labs shifting toward parallelism, ensemble methods, or rapid iteration cycles, the value of late MAIM collapses even if sunk costs rise.

Any decision to MAIM also hinges on the second condition: retaliation dynamics. In Part 3, I’ll examine how attribution ambiguity, proxy operations, and escalation risks shape retaliation calculations.

One of the most likely counterarguments to my positional incentives analysis is that even if the incentives point in the direction of U.S. undermining MAIM, the country’s efforts to prevent sabotage will ultimately be futile, because (1) it is extremely difficult to prevent sabotage, and (2) effective security efforts will likely end up slowing development.

However, I believe neither claim withstands scrutiny; here are my counterarguments.

1. The U.S. has strong reasons to harden AI infrastructure regardless of MAIM

Proponents of MAIM will likely argue that increased security investment to prevent sabotage is not likely to be worth the high cost, in both the money investment required and the speed trade-offs such security measures would incur. However, even without superintelligence concerns, preventing China from stealing US AI advances is a strategic priority, and one that should rationally receive investment accordingly. Companies are already incentivized to prevent theft for commercial reasons, and the U.S. government is incentivized to prevent adversaries from stealing AI capabilities that have military applications.

This means the framing is not “should we accept slowdown to reduce sabotage risk?” but instead “what is the additional cost for MAIM-specific hardening1 beyond the baseline security investment we should already be making?” I believe the delta is smaller than MAIM proponents suggest.

Moreover, if the incentive is to race, and the race matters, then hardening actually makes the trade-off worth it— because without it, your opponent can immediately copy whatever you develop through espionage, effectively ‘teleporting’ to your technological level. Without hardening, the U.S. bears all development costs while China gets the benefits nearly for free. In that sense, hardening isn’t a racing slowdown relative to China; instead, it’s preventing China from matching U.S. speed by stealing the country’s technological advances.

2. Strategic ambiguity is achievable and sufficient for deterrence

Additionally, I think the “hardening is futile” argument overstates how much opacity is actually necessary. Achieving complete opacity, or even something marginally close to that, is almost certainly impossible and would involve trade-offs that are not worth making. However, I’d argue that strategic ambiguity is very possible— in that I think the U.S. could reduce China’s confidence in its timelines to a sufficient degree that it would be too risky for China to MAIM.

Consider the difference between China knowing that the U.S. is likely to achieve ASI / DSA in March 2029, vs. China knowing that the U.S. is likely to achieve ASI / DSA sometime between 2028-2031. Without precise timing, China faces substantially greater risk when considering a MAIM strike: they would essentially be risking war over uncertain belief of a breakthrough. This uncertainty deters action even when stakes are high, because the costs of being wrong (triggering escalatory retaliation) remain constant while confidence in the necessity of action drops. Strategic ambiguity only requires sufficient uncertainty to make the risk calculation prohibitive.

Beyond the technical feasibility of hardening, history shows the U.S. consistently chooses defense over voluntary vulnerability.

3. The U.S. only accepts vulnerability when hardening is technologically impossible

I am unaware of any similar historical precedents where the U.S. accepted vulnerability rather than attempting hardening when hardening was technologically feasible. When considering the nuclear arms race, the space race, etc., in cases where the U.S. believes it can achieve decisive strategic advantage through technology, it races aggressively, accepts enormous costs and risks, does not coordinate with rivals, and only accepts limitations when advantage is impossible.

Why did the U.S. accept international coordination with nuclear weapons (to some degree)? I’d argue this is because mutual vulnerability in nuclear deterrence is unavoidable, but this is not the case with AI.

Consider Reagan's Strategic Defense Initiative. Despite knowing it would destabilize MAD, despite Soviet protests, Reagan pursued it aggressively. It ultimately failed not because the U.S. chose restraint, but because the technology didn't work and countermeasures made it economically futile. This reinforces the pattern: the U.S. pursues defense whenever feasible, and only accepts vulnerability when defense is technologically impossible.

The AI parallel is straightforward: hardening AI infrastructure is both technologically and economically feasible. History suggests the U.S. will pursue it, regardless of whether doing so undermines cooperative deterrence frameworks.

One might object: “Modern missile defense technologies have advanced significantly since SDI's failure, yet we're still not pursuing a MAD-breaking defense system—doesn't this contradict your claim that the U.S. always hardens when feasible?”

Not quite. In the world of nuclear, there’s another element that disincentivizes destabilizing projects like SDI: they are destabilizing precisely because mutual vulnerability already exists. A successful U.S. defense would break MAD by allowing America to strike with impunity while remaining protected; other countries may correctly perceived this as preparation for a first strike.

Now consider a counterfactual: If no other country had developed nuclear weaponry yet— if the U.S. was currently the sole nuclear power considering whether to build defenses against a potential future adversary— would it hesitate for an instant? I think not. The U.S. would build the defense precisely to maintain its advantage and prevent other countries from achieving parity.

MAD teaches us that the U.S. sometimes restrains itself when both sides are already vulnerable. It does not teach us that the U.S. will voluntarily forgo defenses when it's ahead. MAIM asks the leader to accept vulnerability before rivals have achieved parity— the opposite scenario from MAD, and one where historical precedent suggests the U.S. will pursue hardening aggressively.

Conclusion

Essentially, MAIM asks for the leading actor to opt into mutual vulnerability by choice. This points to the crux of the issue: deterrence theory says something like, “when vulnerability is unavoidable, equilibria emerge around it.” I do not think vulnerability is unavoidable with AI.

Economically, hardening makes sense regardless of MAIM. Technically, strategic ambiguity is sufficient and achievable. Historically, the U.S. chooses hardening over maintaining stability through mutual vulnerability. With AI, defense is feasible, meaning the U.S. is rationally incentivized to pursue it rather than cooperate with frameworks requiring voluntary vulnerability.

The next post I’ll be doing on MAIM will be about the practical implications of my positional incentives argument; if you have thoughts— feel free to reach out! Also, I’m always open to stress-testing of my arguments— if you spot a hole in my logic or have a different take, I’d love to hear it.

Note: This is a simplified version of a more detailed analysis on positional incentives; you can read the original here.

Policy Memo:

The United States should not pursue a strategy of sabotage against states that could develop advanced AI because U.S. government-sponsored MAIMing normalizes the practice as acceptable and invites other countries to MAIM in retaliation– both of which make a MAIM equilibrium more likely to emerge.

The MAIM framework is fundamentally based upon the idea that as rival countries get progressively closer and closer to developing superintelligence and/or advanced AI that confers decisive strategic advantage, other countries should MAIM to prevent this reality from materializing– MAIM is a self-preservation mechanism that ostensibly represents what scenario would emerge if all states involved can be considered “rational actors.” This is the descriptive part of the argument by Hendrycks, Schmidt, and Wang– that MAIM is likely to emerge by default.

As the current leader in the ASI race, the U.S. would be uniquely disadvantaged by an AI deterrence regime like MAIM—becoming the primary target for sabotage while gaining comparatively little from the ability to sabotage others. Subscribing to MAIM essentially means inviting sabotage on the country’s own AI development, which is why the country is incentivized to do everything in its power to ensure MAIM fails– whether by preventing the doctrine from developing in the first place or actively undermining a MAIM equilibrium if one were to occur.

Rational U.S. Strategy:

1. Strongly deter sabotage from other countries by making it clear that any sabotage of U.S. AI projects will trigger escalatory counterstrikes.

   1. Deterrence doesn’t work if one country’s “deterrent” is interpreted as another country’s “first strike” – if another country thinks a MAIM could trigger a kinetic counterstrike, for example, a MAIM becomes much less likely to occur.

      1. China doesn’t want the U.S. to develop a DSA, but are they willing to risk war to MAIM based on the (uncertain) belief the U.S. may develop one soon?

   2. A counterargument to this is that cyberattacks might still work given their low salience and the difficulty of credibly committing to, say, a kinetic counterstrike in response to a cyber incident, but I find this counterargument problematic for a few reasons:

      1. It seems to contradict Hendrycks and Khoja’s own argument in their follow-up article that “norms will adapt to strategic realities,” which is to say that policymakers will “grasp the alarming implications of ASI” in the future– if it’s true that policymakers will understand the implications of ASI, then the U.S. could likely credibly deter MAIMing, even if it’s done through cyberattacks, by establishing that MAIM incidents will be treated like acts of war given the stakes of ASI.

      2. One might argue that cyberattacks are difficult to counter because they can be difficult to attribute, or sometimes even difficult to detect, but the authors of MAIM themselves recognize that there is a trade-off here: they write in the normative section of Superintelligence Strategy that “[w]hen subtlety proves too constraining, competitors may escalate to overt cyberattacks… in a way that directly—if visibly—disrupts development” – not if, but when.

      3. It still doesn’t change the fact that the U.S. should employ this strategy to do their best to deter MAIMing– also, the issue of cyberattacks will be discussed further in #2:

2. Maintain maximum opacity on AI progress– without high confidence in the inevitability of a breakthrough that confers DSA, other countries likely won’t MAIM, especially if the risks of executing a MAIM strike are raised by #1.

   1. This also includes increasing cybersecurity, especially around sensitive projects like the Genesis Mission.

3. Rather than preserving mutual vulnerability, the U.S. should harden datacenters, distribute training runs across geographically dispersed facilities, and otherwise make it extremely difficult to sabotage the country’s AI infrastructure.

Initiating a MAIM attack would normalize AI sabotage as an acceptable practice, which would make it impossible for the U.S. to credibly deter MAIMing from other countries and unreasonable to escalate in the event of another country’s MAIM.

Making MAIM equilibrium more likely to emerge is not in the country’s interest.

Key Uncertainties & Important Considerations:

1. In large part, my analysis is focused on what the U.S. should rationally do given that MAIM confers a disproportionate disadvantage on the leader of the ASI race. If the US’s status changes, and China becomes the frontrunner, the basis of this entire memo is flipped on its head. I would strongly consider whether the US’s stance should change based on the risk of another country becoming leader– if China has a >x% chance of overtaking the US, then making MAIM the default might be worth accepting sabotage because it ensures China can’t employ any of the strategies detailed, if MAIM is already normalized by the time they take the lead.

   1. Key questions to research include:

      1. Is China good at making chips? How long until their domestic supply chains catch up with the US’s?

      2. How many months behind are they currently?

      3. What do their espionage capabilities look like?

      4. What events might enable the country to gain a lead?

      5. Given that the U.S. is undermining its own talent pool with immigration restrictions, what effect could this have on the pace of AI development relative to China?

2. A major consideration that I didn’t include in the memo itself is the issue of superintelligence itself and the danger developing it presents. MAIM is uniquely disadvantageous for the US, but if you assume that the U.S. cannot be trusted to develop aligned superintelligence – which is a reasonable assumption – then it is in the country’s best interest to undermine its own development, buying time for safety researchers to develop better technological methods and governance frameworks to catch up to the breakneck pace of AI so that the U.S. doesn’t commit collective suicide on behalf of the entire world by creating ASI it cannot control.

   1. However, in a world where the U.S. is sufficiently superintelligence-pilled to believe this, the rational choice would be to instead commit to a pause on developing ASI altogether, which I unfortunately don’t see as likely for several reasons– including lack of trusted verification mechanisms, difficulty establishing concrete red lines for superintelligence development, and the need for international coordination.

      1. Consider that we don’t even have an agreed-upon definition of ASI! Hendrycks and the CAIS team wrote one for AGI, but I don’t think a reputable ASI counterpart exists (or at least, I haven’t yet seen or heard of it).

      2. MAIM itself assumes development continues, but with sabotage.

3. For the cyberattacks section, it’s possible that even if the U.S. commits to escalatory retaliation, political realities will make a kinetic counterstrike for a cyberattack impossible— public opposition to such escalation could be too great even if the national security establishment is willing to act on their word because they recognize how much of an existential threat another country developing ASI first is.

   1. However, I think this strengthens the argument that MAIM is a bad deal for America, because it means the U.S. is largely defenseless against a MAIM attack (cannot credibly threaten escalatory retaliation); I wrote about this more in detail on the longer post I linked.

Important Note on Author Perspective

Quite honestly, I support the idea that given the current state of alignment research, “if anyone builds it, everyone dies.” I wrote this considering what actions would make sense from a U.S. perspective given game theory, competitive dynamics, and national security priorities; I do not, in fact, think that these strategically rational actions are normatively desirable.

This memo analyzes what U.S. policymakers are incentivized to do under current conditions, not what would maximize human survival. Descriptive analysis of incentives ≠ normative endorsement of outcomes.

The piece attempts to describe the world as it is, not as it should be.