These are great suggestions—do you have a sense/botec on how expensive it would be, end to end, to do a pessimization training run? Idk if the time/staff attention makes it a non-starter except for toy models.
(Of course the question of its a non-starter is downstream of internal lab political will, but knowing the cost would inform how difficult it would be for interested safety team members to run such an experiment)
I agree this sounds plausible and appreciate with the distinction you’re drawing between still competing for the future, but no longer racing for the software intelligence explosion AGI.
xAi seems like they are dropping out of the frontier AGI race.
“xAI will be dissolved as a separate company, so it will just be SpaceXAI, the AI products from SpaceX.” (Musk on X, May 6)
xAI was acquired in Feb 2026 through a reverse triangular merger, keeping it separate with its own debt, liabilities, and structure. It’s now being folded into SpaceX as a division focused on integrating AI across the SpaceX stack.
Also 11 of the 12 original co-founders have left, Grok DAU fell from 13.9M to 12.2M March→April while Claude went 16M→23M, and xAI burned $7.8B in the first 9 months of 2025.
Letting Anthropic use all of Colossus 1 for inference, for an estimated $3-4B/yr.
Even if Colossus 2 is the new training cluster, handing Anthropic 220k GPUs frees Anthropic to then redirect its own compute toward training and research is not what you’d do in a tight race for AGI.
It’s possible this is all a play for the IPO, to get a better narrative for raising huge sums, and that they turn xAI into a GDM like organization. And Colossus 2 still has the biggest compute capacity of any cluster in the world, so it’s certainly can train big models.
But I think it’s not at all trivial to integrate a company, take on all its debt and obligations, change its corporate reporting structure with a very distinct mission, while continuing to do frontier AGI development that’s going to take off.
Also filings haven’t reflected this yet because the only one that exists is the April 1 confidential draft. The public S-1 is expected the week of May 18-22, working back 15 days from the targeted June 8 roadshow.
It’s much much easier now to automate mechanisms like liquid democracy; you could run experiments inside organizations that would be a.) fun and b.) test practicality. Google ran an experiment in 2015 using this to select snacks; do it again but with AI delegates either representing your preferences or making snack decisions.
I quite like this framing, and think Strategic Competence is a useful term and concept. I explored a related idea in Wise AI Advisors at the Hinge of History :
I posit that if:
We have trusted AI systems that people turn to as advisors
And the trust in the AI advisors is well placed because they have good epistemics
Where “good epistemics” roughly means they consistently use reliable methods to figure out what is true and avoid self-deception
And the AI advisors have shared epistemics
Where “shared epistemics” roughly means there is a shared foundation that allows different AI advisors and people to trust one another’s reasoning.
Then it implies that those AI Advisors would advise their Principals to avoid an Intelligence Explosion—if and only if this is in fact a real danger—and humanity could coordinate around this advice.
I expect Strategic Competence to largely track general model capabilities, whereas shared trusted epistemics requires more deliberate work on validation, auditing, and institution-building that won’t happen by default.
This overlaps with your points on improving AI philosophical competence, but with more focus on making epistemics verifiable and legible across different systems and actors, which is what I think would be needed (alongside model improvements) to enable guidance that people follow, and common knowledge needed for preference cascades, to get many actors to agree with wise AI advisors to prevent RSI takeoffs.
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I like this idea of insurance here—it would help with some of my fears—and you could imagine the company building this giving higher defense budgets at the start to help fight court cases that would establish precedent that Lifelink is suitable monitoring.
Congratulations to Anna and the team for cohering around a vision and set of experiments. I donated to the new CFAR; I hope you continue posting about what you learn through the upcoming workshops.
One {note? suggestion? “real spirit” discussion point?} - I feel like the framing of aCFAR was missing something important about the state of rationality today. Namely from this, the year 2026 onward, being more rational is unlikely to be a “human technique” only affair. It will look more like cyborgs and centaurs—humans using AI tools and agents in different configurations and ways to make better decisions.
I won’t belabor how good the AIs have gotten, and instead will just note that they are effective aids for rationalist techniques:
I wrote a post about backchaining where I had Claude create malleable, customizable timelines. I found this to be a really effective way to “feel” at the S1 level the constraints and targets.
They’re very good at making Fermi estimates.
There’s ongoing research and experiments into using them as mediators and for fostering cooperation, à la double crux.
They’re probably useful for Focusing and internal work too (I know the Jhourney team has been running experiments here, though I haven’t found it that effective personally).
I appreciate that it’s a Center for Applied Rationality, and maybe this particular center doesn’t need to think about the cyborg angle and can just focus on developing better models of “who-ness”. Maybe a different center should!
But it seems valuable to consider, to the extent you want to push forward the frontier of rationality. I suspect there’s some connection between the moments when AI meaningfully aids my real thinking, the moments when I’m doing slop-ful fake thinking where the AI is aiding my delusions, and the concept you’re defining as “who-ness.” Who-ness seems adjacent to taste, which might matter a lot for steering AI fleets towards goodness and meaningful concepts. And it’s probably the case that the general rationality techniques you’re imparting and working on with attendees could be more effective with AI assistance.
In Jack Clark’s Import AI 439, he references a new paper Universally Converging Representations of Matter Across Scientific Foundation Models
> Do AI systems end up finding similar ways to represent the world to themselves? Yes, as they get smarter and more capable, they arrive at a common set of ways of representing the world.
> The latest evidence for this is research from MIT which shows that this is true for scientific models and the modalities they’re trained on: “representations learned by nearly sixty scientific models, spanning string-, graph-, 3D atomistic, and protein-based modalities, are highly aligned across a wide range of chemical systems,” they write. “Models trained on different datasets have highly similar representations of small molecules, and machine learning interatomic potentials converge in representation space as they improve in performance, suggesting that foundation models learn a common underlying representation of physical reality.”
> As with other studies of representation, they found that as you scale the data and compute models are trained on, “their representations converge further”.
This seems like useful empirical evidence for the Natural Abstraction Hypothesis (I haven’t been following progress on that research agenda, so I don’t know how significant of an update this is)
Thank you for the comment Saul—I agree with a lot of your points, in particular that “explosive” periods are costly and inefficient (relative perhaps to some ideal), and that they are not in and of themselves a solution for long-term retention.
I expect if we have a crux it’s whether someone who intends to follow an incremental path vs someone who does an intense acquisition period is more likely to, ~ a year later, actually have the skill. And my guess is, for a number of reasons, it’s the later; I’d expect a lot of incrementalists to ‘just not actually do the thing’.
* My ideal strategy would be “explore lightly a number of things, to determine what you want → explode towards that for an intense period of time → establish incremental practices to maintain and improve”
* Your comment also highlighted for me, something that I had cut from the initial draft, my belief that explosive periods help overcome emotional blockers, which I think might be a big part of why people shy away from skills they say they want.
No worries, I appreciate the perspective. I agree that for many skills there is a consolidation and rest period that is needed. An obvious example is that you can’t cram all of the effort needed to build muscle into one week and expect the same kinds of returns that you would get over many months. Though, I do expect you could master the biomechanical skills of weightlifting much faster with that attitude!
If you have examples of the multidimensional learning schedule, I’d love to hear them. I’m imagining something like {30 minutes of spanish language shows}?
thanks for sharing a data point on that claim
Thank you for the post! I have also been (very pleasantly!) surprised by how aligned current models seem to be.
I’m curious if you expect ‘alignment by default’ to continue to hold in a regime where continuous learning is solved and models are constantly updating themselves/being updated by what they encounter in the world?
Chain of Thought not producing evidence of scheming or instrumental convergence does seem like evidence against, but it seems quite weak to me as a proxy for what to expect from ‘true agents’. CoT doesn’t run long enough or have the type of flexibility I’d expect to see in an agent that was actually learning over long time horizons, which would give it the affordance to contemplate the many ways it could accomplish its goals.
And, while just speculation, I imagine that the kind of training procedures we’re doing now to instill alignment will not be possible with Continuous Learning, or we’ll have to pay a heavy alignment tax to do that for these agents. Note: Jan’s recent tweet on his impression that it is quite hard to align large models and it doesn’t fall out for free from size.
Thank you! I had not read this Kevin Simler essay but I quite like it, and it does match my perspective.
Hi Wei, my apologies for my late reply, I only saw your comment after returning to this post when I saw that Anthropic mentioned in their recent blog post that perhaps models will be sufficiently wise to not attempt RSI.
I agree that working on good epistemics, to outpace capability improvements in general, would require 1.) changing the dynamic, 2.) significant investment in projects to curate data / feedback on good fuzzy reasoning, or 3.) using extensive scaffolding (aka ‘meta systems’) to elicit better epistemics from models with jagged performance in these domains. As an example I think the competition that FLF is running here might lend itself to prototypes of better meta-systems, which could ladder up to better epistemics. (I’m also excited about 2 though I don’t have ready examples of what that might look like)