This post was crossposted from Dylan Matthew's blog by the EA Forum team. The author may not see or reply to comments.

Subtitle: Try to keep an open mind as the world gets increasingly wild.

In 2015, I went to my first EA (Effective Altruism) Global. It was then on-the-record for journalists, which is a rule that got changed for all subsequent events due to my actions.

My exposure to EA at that time was mostly through people who took high-paying careers in order to “earn to give” to global health charities, which I had written about in the Washington Post. I also knew the movement cared a lot about animal welfare. I was aware that there were people worried about catastrophic risks, and specifically about AI; this had come up in a profile I wrote of Open Philanthropy (my now-employer, albeit under a new name these days). But I still broadly thought of EA as the bednets and cage-free commitments people.

I was really taken aback by how dominant discussions of AI risk were at the event. The marquee panel featured Superintelligence author Nick Bostrom, future If Anyone Builds It Everyone Dies author Nate Soares, legendary computer scientist Stuart Russell, and a pre-MAGAfication but still strange Elon Musk talking about AI takeover. Most conversations I had with attendees were about AI risk.

Most of the names of these people were new to me. I don’t remember all of them, but I recall talking to a guy doing AI stuff at Google named Chris Olah; to Amanda Askell, then working on a philosophy PhD; to Buck Shlegeris, then a software engineer at PayPal.

My main takeaway from the conference was fear that a movement that could do incredible good for the world’s poorest people and most vulnerable animals was being nerdsniped by speculative concerns about a technology that didn’t exist. I wrote those concerns up in a Vox article. Most EAs I knew thought the article was very bad. As usual, Scott Alexander was the one to articulate their frustrations most eloquently and persuasively. I am a deeply conflict-averse person, which is one reason I’m not in journalism anymore, so this all made me quite sad. But I still thought I had a point and this AI stuff might turn out to be a serious wrong turn for EA.

Eleven years onward, it is extremely, extremely obvious that I was wrong. I did not take this then-nonexistent technology remotely seriously enough, and the people I met who did take it seriously were able to do incredibly impactful work because of that. Chris Olah more or less founded the field of mechanistic interpretability (and coined the term!) and went on to cofound Anthropic. Amanda Askell is responsible for Claude’s personality and one of the most influential people in AI. Buck Shlegeris founded Redwood Research, perhaps the most interesting center of technical AI safety work outside of a major lab.

They all made, in the subsequent decade, a really heavy personal bet, that this technology was going to be a huge, huge deal worth devoting the bulk of their careers to. At the time I thought this was a pretty bad bet. Instead, it paid off more than I could have possibly imagined.

What should I learn from bungling this?

To be clear, the error I made was not failing to adopt the exact assessment of AI’s dangers and its place among AI cause areas that the people listed above held at the time. Those people then, and now, do not hold the same or even in many cases similar views on those questions. Some of them (Askell and Shlegeris) hadn’t pivoted to working on AI safety full-time in 2015, and only got onboard a little later.

What united those people was an openness to the idea that AI could prove hugely important, that it would probably get better quite quickly, that it had a significant chance of dramatically reconfiguring our social, political, and economic lives, and that figuring out how to adapt to the societal changes it unleashed was one of the most important things a person could do with their careers.

They were right to be open to that possibility. I was wrong to dismiss it.

I have updated, a lot, as a result. For one thing, I’ve reconsidered some heuristics that led to my error. A big part of my skepticism reflected the fact that it seemed like only a quite small community of people was taking these concerns seriously. Surely, if it was such a big deal, more mainstream institutions would have been responding? Surely mainstream computer scientists would be putting out big joint statements about the dangers, akin to the statements climate scientists put out. Surely financial institutions and insurers would be preparing for a huge shock from AI. None of that was happening.

It turns out that those institutions are not as good at prediction as I thought they were, or maybe just less interested or incentivized to try to rigorously predict the future than I had thought they were. The rapid success of deep learning wasn’t something that even most computer scientists anticipated, or at least they didn’t anticipate it scaling to the point we’ve seen now. The business community turns out to be disturbingly unprepared for a lot of scenarios involving rapid societal change, as seen during COVID.

I’ve also reevaluated a bias I had then, that I’m not sure I could have really articulated at the time, against anything reeking of “futurism.” I fancied myself a grumpy empiricist, and so dismissed as unpersuasive and vaguely frivolous anything that required a great many imaginative leaps to think about. I could think about self-driving cars; they were already being tested. An AI powerful enough to outsmart a human was something else altogether. I think a lot of the world, especially academia, shares these biases; that can make them as bad at prediction as I was.

Remember, this was the summer of 2015. The transformer had not been invented yet. OpenAI had not been founded. The argument for transformative AI had to depend on a lot of a priori reasoning and extrapolation into the future, which seemed unreliable to me as a way to predict the future. I still think that kind of speculation is fraught with difficulties (see Maxwell Tabarrok on the track record of the “Extropians” for one case study of it going badly wrong), but it turned out to work shockingly well in this case. It was more valuable than I had thought.

Listen to the people saying stuff will get weird

But maybe the most important way I’ve updated is that I now trust this specific community of people, the ones who were predicting transformative AI and associated dangers over a decade ago, much, much more. That is a blunt heuristic, of course, and this community contains a vast amount of internal disagreement within it.

It also, though, contains a lot of people who are startlingly good at predicting the future, including in the quite short-run. Ajeya Cotra and Peter Wildeford are two people whose judgments on the state of AI and AI policy I trust a lot, and who came out of the broad EA/AI risk milieu, and sure enough their predictions at the end of 2024 for what would happen to AI in 2025 were very, very accurate.

Leopold Aschenbrenner’s Situational Awareness report in 2024 raised a lot of eyebrows for the very fast timeframes it anticipated for AI scaling — but most of his predictions turned out right, per this retrospective. His 2026 projection was $520 billion in infrastructure investment for AI. We’re on track for $650-700 billion.

Cotra and Wildeford, for their part, mostly erred by predicting AI companies would earn much less in revenue than they actually did. They weren’t exactly right. But they were a lot closer to right than the rest of us.

So one lesson I’ve learned from whiffing it in 2015 is to take the wild-seeming predictions of this crew much more seriously. When I hear predictions of 30% year over year economic growth, my default response is extreme skepticism. In 2015, my response would’ve been outright dismissal. I still don’t think this is the most likely outcome. There are sound reasons to doubt it. But I’ve made the error of dismissing crazy-sounding predictions from the AGI-pilled before, and I am not keen to do it again.

I’ve been thinking a lot about a seemingly totally unrelated example that the writer Keith Gessen once gave in the middle of an old-school ‘00s era blog fight:

In the field I know most about, 20th-century Russian history, there is a great debate over what constitutes the single most significant rupture. Some say it’s the tsar’s abdication, and some say it’s the October Revolution, and some say actually it’s the cancellation of the Pale of Settlement, and a historian named Edward Keenan argued that it was the collectivization that began in 1928. Keenan as much as argued that the Bolshevik Revolution was a blip, a skirmish, not really a major part of Russian history, but what happened to the countryside in the late 1920s was epochal.

And you could certainly say to Edward Keenan, and people did say, You’re crazy! But no one would be fool enough to say to Edward Keenan, You’re a fool.

There is a spirit of intellectual openness and generosity implicit in how Gessen is describing Keenan here that I find deeply endearing. Obviously he thinks Keenan is wrong. But Keenan isn’t a fool. If he thinks that Lenin is a footnote in Russian history and the country‘s real turning point came in the steppes of the late ‘20s, you don’t have to agree. But you have to take it seriously.

This is the attitude I’m trying to take to predictions of short timelines, rapid labor dislocation, models that can double their performance autonomously, and the like. They could very well be wrong. I still personally don’t think we’re going to have AI-directed robot factories in the desert anytime soon. But if someone presents a case we will, I will listen.

Thank you to Max Nadeau, Eli Rose, and Claire Zabel for thoughtful comments on an earlier version of this post.