Founder, Project Elephant: building a comprehension gate for automated AI R&D. Lead author, Comprehension Audits. Ex-Google Responsible AI, Vector Institute

San Francisco Bay Area
Anthropic notes that "human trust and accountability remains critical" but they don't offer concrete ways that they measure the necessary understanding. They show a decline in entry-level technical hiring from 27% to 9% of new hires in June. The shift towards experienced hiring provides indirect evidence of the effort to retain human oversight of agents. (3/4)
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They report that metrics for defects and for changes in AI-written code are similar to human-written code, while acknowledging "AI errors may be harder to identify than human ones." But these measurements don't indicate whether people are retaining long-term control and understanding as they are "increasingly removed from the object-level work." Output is being measured. Understanding is not. Accountability without understanding is empty. Organizations adopting AI should measure understanding the way they measure output, and act when it falls. anthropic.com/research/emplo… (4/4)
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Output is up across each function, but "human attention is a constant bottleneck." They observe "One solution to this deluge is rising automation." Indeed, by spring 2026, 22% of employee Claude sessions were "meta-work" designed to automate oversight of agents, with the most automation in technical domains. This implies that employees increasingly are back away from reviewing agentic work and trusting AI for the review. (2/4)
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Anthropic reports AI agents are transforming all aspects of their business. Their average engineer would need 424 hours a week to read the output of agents they supervise. Finance staff already need 43. Their other functions are following the same exponential curve. (1/4)
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For a typical significant agent-written change your team merged last month: how confident are you someone could explain it without AI help?
0% Confident
0% Fairly confident
100% Doubtful
0% Not sure
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What does ‘explain it’ mean on your team (pick the first one that applies)?
0% Implementation details
0% Design
0% Likely failures
0% What it does
0 votes • 3 days
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If you’re confident someone could explain it, what is that based on?
0% A recent walkthrough
0% Design records
0% Their assurance
0% I assume someone can
0 votes • 3 days
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This is really bad. We need strong safety regulation. At minimum, we need a duty of care that requires frontier developers to meet the highest safety standards (including security/control), covering internal use, not just public deployments, with a roadmap to raise the bar on evidence of safety. We need true independence of evaluators: random assignment from a pool weighted by track record, payment from a levy on industry, and at least two evaluators per developer to give comparative data. The proposal lets them pick and pay their own. We need teeth to act on warning shots. This proposal only allows halting a model if there is imminent catastrophic risk. Proper rulemaking passed in law would be an improvement on ad hoc enforcement, but this bill has almost none... In the absence of strong federal regulation, it would be a huge mistake to preempt state regulation.
Charlie's analysis is right but he is nicer than I am - I found myself viscerally angry at multiple points reading this bill. There are some good and sensible parts, but overall this legislation is not remotely close to meeting the moment and I overall think passing it would make us less protected against catastrophic risks than we are under existing law. Two parts that stand out as particularly infuriating when taken together: 1. Some of the preemption provisions are remarkably far reaching and consequential, and in addition to preempting legislation like SB 53 would also remove general powers that state courts have to enjoin defendants and protect the public. Most outrageously, it has a provision that looks like its intended to stops states from enforcing laws or judicial decrees that "suspend the development, deployment, or internal use of an AI model" to protect their citizens from imminent catastrophic harms. Such powers to block and suspend model deployments would then only reside with the federal government. 2. The federal governments powers to restrict the development, deployment, or internal use of a frontier model has to meet an absurdly high bar. First, there needs to be an imminent risk of a catastrophic harm (meaning killing more than 50 people or causing more than a billion dollars in damage). But thats not enough! The agency head needs to make a detailed determination with no deference given to show why no alternative would prevent the imminent catastrophe. Next the court is also explicitly told to consider the harms to the developer and other harms from restricting the model, but no considerations in the other direction. This is absurd! For all of its warning about the potential for AI to cause a catastrophe that kills literally everyone on the planet, Anthropic's proposed federal law likely makes it harder than under existing law for a court to get an order blocking or restricting the development of frontier models? WTF?? There are also a bunch of others things that I would like to see improved - the substantive details of the auditing section and the potential for rule-making on substantive safety standards both come to mind - but I think these stand out as particularly indefensible.
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The Claude Code team shared their approach to code reviews. They keep architecture and direction as human decisions. Yet they ask “If machines do most of the reading, how does anyone learn the code?” and answer “We don’t have a formal process for this. Nobody on the team keeps a complete mental model of Claude Code in their head, and we don’t think anyone needs to.” (1/5)
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They suggest how to adopt their approach. I would start by asking about the risk of a system, and what it means to steer its architecture and long-term direction from explanations the system writes about itself. We proposed comprehension audits as a way to test whether whether the people doing the steering still understand: nitter.siddz.com/ronbodkin/status/21082… (4/5)
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Ron Bodkin retweeted
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This is also a reminder that even when AI is very reliable on each step, trusting it to complete more complex tasks is fraught. And that overseeing it by scanning for gaps on a per-step basis is likely to miss key issues. It's a good reason to measure understanding to verify that oversight of agents is holding up. People tend to trust AI too quickly, e.g., when Waymo first started at Google, they let internal users test it, but warned them the AI was unreliable and to be ready to take over. Within a month some had come to trust the system so much that they were using laptops or doing make up while it drove (many years before Waymo was ready for automonous driving). People tend to trust AI far too early.
This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.
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Wharton and GBK's AI adoption survey found that organizations vary in how they assign accountability when agents fail. But I found the depth of oversight more telling. While 99% of departments have some formal check on agents, 76% of VP+ trust AI output "with little checking" and 53% see proficiency fall in their department. (1/3)
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The primary role AI is expected to play among managers+ is improve productivity (44%: nearly triple the rate for the next highest response, improve decision quality). And 47% of VP+'s report their departments have made staff reductions due to AI. (2/3)
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Enterprises are setting up the "moral crumple zone": pressure to increase productivity from AI without giving staff the means or time to adequately oversee output. What's missing in all of this are metrics to test whether staff still understand what agents produce. It's why we proposed comprehension audits, as well as working on automated analysis of how people are interacting with AI, to test for meaningful oversight: nitter.siddz.com/ronbodkin/status/21082… (3/3)
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AI already writes a majority of code for frontier AI companies. This creates a safety risk if there’s insufficient human oversight. A new research paper co-authored by LawAI’s Senior Research Fellow @bahradx along with lead author @ronbodkin and @ghadfield proposes “comprehension audits”: an assurance regime to demonstrate that AI developers understand what they’re building. Comprehension audits would require developers of frontier AI to explain R&D contributions to auditors, with independent administration and graded reports. This provides a gate: AI development is paused until developers can demonstrate human understanding, with escalating consequences for repeated failures. Read the full research paper: law-ai.org/comprehension-aud…
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Ron Bodkin retweeted
Sources have anonymously briefed the South China Morning Post on latest US-China AI talks: • Regular discussions to iron out framework • AI cooperation could be a joint deliverable by next months APEC summit where Xi and Trump will be together • This may include a basic framework for addressing AI-related crises “with operational details to be worked out later”, and “an announcement establishing a continuing institutional mechanism for AI cooperation, involving government officials, research institutions and industry representatives.” • Clear distinction between an AI safety and incident-management agreement, which is already under discussion, and a broader AI economic cooperation framework, which would be “considerably more ambitious”, per one source.
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