Non-EA interests include chess and TikTok (@benthamite). Formerly @ CEA, METR + a couple now-acquired startups.
Ben_Westđ¸
Karma: 16,104
Yeah I agree that if you only have one bit of detail that you can store, then saying it is âhardâ rather than âeasyâ is probably the correct bit. However I would suggest that for something as important as your career you should investigate in substantially more detail. If you do so I expect you will come up with a range of needed skills/âattributes for these jobs, some of which you might find easy, others of which you might find hard.
I no longer work at METR. I would guess that theyâd be excited about applicants who have done this, but donât want to speak for them.
Many people said they wanted to work for METR. I made what I thought was a good offer: take one of the benchmarks we give AIs; if you get a good score then I guarantee that I will fly you out for an interview, even if you have no work history, have no money to pay for the trip, or any other barrier one might have to employment.
Exactly zero people took me up on this.[1]
How is it possible for there to be sky-high rejection rates yet also zero people sending me applications?
I think the answer is that raw rejection rates arenât a very useful metric. After all, an 80% rejection rate means that the AI safety jobs are 1/â10th as selective as Walmart!
I would suggest ignoring raw rejection rates in favor of just looking at the criteria for the jobs you want. Particularly for something like s-risks the criteria are going to be unusual and specific, meaning that even generically qualified people will often have to dedicate substantial time to skilling up, but if youâre able to do so, then your odds are pretty good.[2]
- ^
I wouldnât be surprised to learn that some people tried this, failed, and then were too embarrassed about failing to tell me. But, to the best of my recollection, literally zero people have told me that they even attempted this task.
- ^
I say this even with the knowledge that you are 19. I donât want to pretend that the deck isnât stacked against younger peopleâit totally isâbut we employ some 19 year olds, as do other AI safety orgs. If a 19 year old had sent me a good solution to that METR challenge, for example, I would have been happy to hire them.
- ^
Cool! Impressive numbers.
Table 1 shows the techniques used; the teams which were allowed to use SAEs (an interpretability technique) used them; the one which was prohibited from using them searched the data.
Also note that âtraining dataâ does not mean âinstructionsâ. Section 3 describes their training process.
I see, thanks! Iâm not sure exactly what youâd consider as evidence here, but e.g. hereâs citation count on papers from the past year vs. AI Lab Watch safety rating[1]
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Raw data. Note that anthropic doesnât use arxiv, which affects their citation counts. This is just coming from a dumb search of semantic scholar; I expect a lot of disagreement could be had over the exact criteria for considering something âinterpretabilityâ but I expect the Ant/âGDM > OAI >> * ordering to be true for almost any definition.
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I suspect that Iâm still misunderstanding you, but: eg interpretability tools are empirically able to identify misalignment, which feels like a (somewhat simple example of) the thing we want. Neel Nandaâs 80k podcast goes over the state of the field; tldr is roughly that there are pretty meaningful advances but also heâs skeptical that it will be a silver bullet.
I agree with Ben Stewart that thereâs a galaxy-brain argument that these positive impacts are outweighed by accelerating progress, but it seems hard to argue that things like interpretability arenât making progress on their own terms.
Wiblin does not explain where his estimate of âhundreds of billions of dollarsâ of revenue comes from, but it reads to me like pure marketing for potential investors
You quote him as observing that their revenue tripled over the past 3 months, and some basic math tells us that another ~tripling gets them to $100B.
Iâm in favor of rigor and would also have preferred him to share a more detailed model, but âpure marketing for potential investorsâ seems like an unfair characterization of a âpredict trends will continue unchangedâ forecast.
Edit: Iâve listened to the podcast and now think your framing is unfair to the point of being misleading. He says:
And also keep in mind that on Monday â the day before Anthropic published all of this â we learned that their annualised revenue run rate had grown from $9 billion at the end of December to $30 billion just three months later. Thatâs 3.3x growth in a single quarter â perhaps the fastest revenue growth rate for a company of that size ever recorded.
That exploding revenue is a pretty good proxy for how much more useful the previous release, Opus 4.6, has become for real-world tasks. If the past relationship between capability measures and usefulness continues to hold, the economic impact of Mythos once it becomes available is going to dwarf everything that came before it â which is part of why Anthropicâs decision not to release it is a serious one, and actually quite a costly one for them.
Theyâre sitting on something that would likely push their revenue run rate into the hundreds of billions, but theyâve decided itâs simply not worth the risk.
He very straightforwardly seems to be explaining where his estimate comes from to me?
Hmm, but in a success without dignity world making interpretability a bit better, or governments a bit more interested, is relevant, right?
Maybe, but âif EA had just stuck to Earning To Give and malaria nets and decaging chickens then the impact would have been greaterâ doesnât clearly follow. Malaria nets look a lot worse if we all die in a few years from AI anyway, and cage free pledges have ~0 value if humanity ends before the pledge can be fulfilled.
Are you asking just about recent graduates, or all graduates?
Your conflict of interest here feels enormous (even if declared) and its hard to read this and not feel like it might be a bid to directly protect your own interests by asking others to not step into your turf here as a lobbyist.
I think you could also read it as him attempting to solve the problem heâs describing.
I would be keen to hear if you think you have any solutions to this birifuction.
Huh, this feels like prime EA territory to me. We need disagreement so that people can engage in key EA activities like âmaking persnickety critiques of footnote #237 on someoneâs 10k word forum post.â
The case for EA feels much weaker to me if we are all confident that X is the best thing to doâthen you should just do X and not worry about cause prio etc.
Iâm sorry you had to go through this, Fran.
Congrats to everyone who worked on this!
Thanks for doing this. This question is marked as required but I think should either be optional or have a ânoneâ option:
To decompose your question into several sub-questions:
Should you defer to price signals for cause prioritization?
My rough sense is that price signals are about as good as the 80th percentile EAâs cause prio, ranked by how much time theyâve spent thinking about cause prioritization.
(This is mostly because most EAs do not think about cause prio very much. I think you could outperform by spending ~1 week thinking about it, for example.)
Should you defer to price signals for choosing between organizations within a given cause?
This mostly seems decent to me. For example, CG struggled to find organizations better than Givewellâs top charities for near-termist, human-centric work.
Notable exceptions here for work which people donât want to fund for non-effectiveness reasons, like politics or adversarial campaigning.
Should you defer to price signals for choosing between roles within an organization?
Yes, I mostly trust organizations to price appropriately, although also I think you can just ask the hiring manager.
And credit to the AI skeptics that they seem to mostly have updated in light of the new evidence (or at least claimed that they never actually believed in long timelines, which is maybe less noble, but ends up in the same place).