Introduction and Principles

The conventional wisdom is that we can’t “cure cancer” because every cancer is different.

And it's true that cancer is genetically and biochemically diverse, and that targeted cancer therapies generally only work on very specific sub-populations.

But some of the oldest, most reliable, and still most commonly used classes of cancer treatment -- radiotherapy and cytotoxic chemotherapy -- are also broad-spectrum. That is, they’re effective on many (though not all) types of cancer, based on selectively targeting properties that many cancers have in common (their rapid cell division).

Today, we rarely search for new treatments that work on many types of cancer.

Partially this is because of the belief that we won’t find any, and partly because cancer treatment is so valuable that individual researchers and biopharma companies are rewarded for any [...]

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Outline:

(00:18) Introduction and Principles

(03:40) New Ways To Attack Cancer Cells

(04:11) Historically Undruggable Cancer-Causing Mutations

(06:43) Phospholipid Ethers

(07:38) Endogenous IgG

(08:51) Negative Surface Charge

(09:37) Membrane Depolarization

(10:25) Strategies for Pan-Cancer Translational Research

(12:30) Historical Inspiration: The NCI

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I have been forced recently to cover many statements by US AI Czar David Sacks.

Here I will do so again, for the third time in a month. I would much prefer to avoid this. In general, when people go on a binge of repeatedly making such inaccurate inflammatory statements, in such a combative way, I ignore.

Alas, under the circumstances of his attacks on Anthropic, I felt an obligation to engage once more. The All-In Podcast did indeed go almost all-in (they left at least one chip behind) to go after anyone worried about AI killing everyone or otherwise opposing the administration's AI strategies, in ways that are often Obvious Nonsense.

To their credit, they also repeatedly agreed AI existential risk is real, which also makes this an opportunity to extend an olive branch. And some of the disagreements clearly stem from real confusions and [...]

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Outline:

(01:22) Our Continuing Coverage

(02:38) Important Recent Context

(07:54) The Point of This Post

(08:51) Summary of the Podcast

(14:12) Part 1 (The Part With the Unhinged Attacks on Anthropic and also other targets):

(23:35) Other Related Obvious Nonsense

(32:01) Part 2 - We Do Mean the Effect on Jobs

(43:48) Part 3 - The Big Beautiful Bill

(46:26) Where Does This Leave Us

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When it comes to disputes about AI coding capabilities, I think a lot of people who either don't follow AI capabilities developments or who aren't particularly capable coders might be missing some context, so I wrote a post wherein I try to get an AI coding tool to reimplement a feature in my game then closely study the output to demonstrate how well it's able to do that.

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Linkpost URL:
https://blog.massimogauthier.com/p/seeing-how-well-an-agentic-ai-coding

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(This sequence assumes basic familiarity with longtermist cause prioritization concepts, though the issues I raise also apply to non-longtermist interventions.)

Are EA interventions net-positive from an impartial perspective — one that gives moral weight to all consequences, no matter how distant? What if they’re neither positive, nor negative, nor neutral?

Trying to reduce x-risk, improve institutions, or end factory farming might seem robustly positive. After all, we don’t need to be certain in order to do good in expectation. But when we step back to look at how radically far-reaching the impartial perspective is, we start to see a deeper problem than “uncertainty”. This problem is unawareness: many possible consequences of our actions haven’t even occurred to us in much detail, if at all.

Why is unawareness a serious challenge for impartial altruists? Well, impartiality entails that we account for all moral patients, and all the most significant impacts [...]

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Outline:

(08:03) Bird's-eye view of the sequence

(08:34) Introduction to unawareness

(12:03) Unawareness vs. uncertainty

(16:45) Why not just do what works?

(19:00) Case study: Severe unawareness in AI safety

(19:23) Vignette

(23:14) Where this leaves us

(24:42) Acknowledgments

(25:20) References

The original text contained 12 footnotes which were omitted from this narration.

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This research was completed for LASR Labs 2025 by Benjamin Arnav, Pablo Bernabeu-Pérez, Nathan Helm-Burger, Tim Kostolansky and Hannes Whittingham. The team was supervised by Mary Phuong. Find out more about the program and express interest in upcoming iterations here. Read the full paper: "CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring."

Chain-of-thought (CoT) monitoring—where safety systems review a model's intermediate reasoning steps—is gaining traction at frontier labs like Google DeepMind and OpenAI as a safeguard against harmful AI actions. Despite intense interest, systematic evaluation has been limited. Our research examines the efficacy of this method and reveals a nuanced picture: CoT monitoring increases safety in situations where sabotage is subtle yet can be surprisingly ineffective against blatant harmful actions. We also discovered that a hybrid approach—using separate monitors for CoT and final actions and combining these suspicion scores—significantly improves sabotage detection compared to single-monitor strategies.

A summary of our [...]

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Outline:

(01:51) Task Subtlety Matters

(03:12) A concerning vulnerability: unfaithful or misleading reasoning

(05:37) Hybrid protocol: improvement over action-only trusted monitoring

(06:17) Limitations and Future Directions

(07:01) Conclusion

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What's the main value proposition of romantic relationships?

Now, look, I know that when people drop that kind of question, they’re often about to present a hyper-cynical answer which totally ignores the main thing which is great and beautiful about relationships. And then they’re going to say something about how relationships are overrated or some such, making you as a reader just feel sad and/or enraged. That's not what this post is about.

So let me start with some more constructive motivations…

First Motivation: Noticing When The Thing Is Missing

I had a 10-year relationship. It had its ups and downs, but it was overall negative for me. And I now think a big part of the problem with that relationship was that it did not have the part which contributes most of the value in most relationships. But I did not know that at the time. Recently, I [...]

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Outline:

(00:40) First Motivation: Noticing When The Thing Is Missing

(01:29) Second Motivation: Selecting For and Cultivating The Thing

(02:25) Some Pointers To The Thing

(03:17) How To Manufacture Relationships In The Lab

(04:53) Ace Aro Relationships

(08:04) Some Pointers To Willingness to Be Vulnerable

(12:33) Unfolding The Thing

(13:11) Play

(15:18) Emotional Support

(16:21) A Tiny High-Trust Community

(18:18) Communication

(21:28) The Obvious Caveat

(22:20) Summary

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Adapted from this twitter thread. See this as a quick take.

Mitigation Strategies

How to mitigate Scheming?

1. Architectural choices: ex-ante mitigation
2. Control systems: post-hoc containment
3. White box techniques: post-hoc detection
4. Black box techniques
5. Avoiding sandbagging

We can combine all of those mitigation via defense-in-depth system (like the Swiss Cheese model below)

I think that applying all of those strategies should divide the risk by at least 3.

1. Architectural Choices: Ex-ante Mitigation

Don't choose an architecture that uses neuraleses (i.e. hidden reasoning).

For example, don't train models to think in their latent spaces like Meta!

Chain of thought monitoring is a blessing for transparency and monitoring - let's use it as much as we can!

See: Why it's good for AI reasoning to be legible and faithful.

Similarly, I think text diffusion models are a bad idea for transparency. This is because you can no longer [...]

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Outline:

(00:18) Mitigation Strategies

(01:20) 1. Architectural Choices: Ex-ante Mitigation

(03:26) 2. Control Systems: Post-hoc Containment

(04:00) 3. White Box Techniques: Post-hoc Detection

(05:16) 4. Black Box Techniques

(06:28) 5. Avoiding Sandbagging: Elicitation Technique

(06:52) Current State and Assessment

(07:06) Risk Reduction Estimates

(08:19) Potential Failure Modes

(09:16) The Real Bottleneck: Enforcement

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There have recently been various proposals for mitigations to "the intelligence curse" or "gradual disempowerment"—concerns that most humans would end up disempowered (or even dying) because their labor is no longer valuable. I'm currently skeptical that the typically highlighted prioritization and interventions are best and I have some alternative proposals for relatively targeted/differential interventions which I think would be more leveraged (as in, the payoff is higher relative to the difficulty of achieving them).

It's worth noting I doubt that these threats would result in huge casualty counts (due to e.g. starvation) or disempowerment of all humans (though substantial concentration of power among a smaller group of humans seems quite plausible).[1] I decided to put a bit of time into writing up my thoughts out of general cooperativeness (e.g., I would want someone in a symmetric position to do the same).

(This was a timeboxed effort of ~1.5 hr [...]

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On prioritizing orgs by theory of change, identifying effective giving opportunities, and how Manifund can help.

Epistemic status: I spent ~20h thinking about this. If I were to spend 100+ h thinking about this, I expect I’d write quite different things. I was surprised to find early GiveWell ‘learned in public’: perhaps this is worth trying.

The premise: EA was founded on cost-effectiveness analysis—why not try this for AI safety, aside from all the obvious reasons¹? A good thing about early GiveWell was its transparency. Some wish OpenPhil were more transparent today. That seems sometimes hard, due to strategic or personnel constraints. Can Manifund play GiveWell's role for AI safety—publishing rigorous, evidence-backed evaluations?

With that in mind, I set out to evaluate the cost-effectiveness of marginal donations to AI safety orgs². Since I was evaluating effective giving opportunities, I only looked at nonprofits³.

I couldn’t evaluate all 50+ orgs [...]

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Letting kids be kids seems more and more important to me over time. Our safetyism and paranoia about children is catastrophic on way more levels than most people realize. I believe all these effects are very large:

This should be thought of as part of the Cost of Thriving Index discussion, and the fertility discussions as well. Before I return to the more general debate, I wanted to take care of this aspect first. It's [...]

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Outline:

(02:31) Current Insanity Levels

(09:50) The Result of This

(12:15) Some Progress on Letting Kids Be Kids

(13:10) CPS Cares

(19:52) Panopticon

(22:37) They Even Close Playgrounds

(24:08) How Did Things Get This Bad?

(28:11) Let Kids be Adults

(30:12) Treat Kids Like People

(32:00) Lowering the Burden

(35:05) Screens

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