There's a grain that I agree with here, which is that people excessively plan around a median year for AGI rather than a distribution for various events, and that planning around that kind of distribution leads to more robust and high-expected-value actions (and perhaps less angst).
However, I strongly disagree with the idea that we already know "what we need." Off the top of my head, several ways narrowing the error bars on timelines -- which I'll operationalize as "the distribution of the most important decisions with respect to building transformative AI" -- would be incredibly useful:

• To what extent will these decisions be made by the current US administration, or by people governed by the current administration? This affects the political strategy everyone -- including, I propose, PauseAI -- should adopt.
• To what extent will the people making the most important AI decisions remember stuff people said in 2025? This is very important for the relative usefulness of public communications versus research, capacity-building, etc.
• Are these decisions soon enough that the costs of being "out of the action" outweigh the longer-term benefits of e.g. going to grad school, developing technical expertise, etc? Clearly relevant for lots of individuals who want to make a big impact.
• When should philanthropists spend their resources? As I and others have written, there are several considerations that point towards spending later; these are weakened a lot if the key decisions are in the next few years.
• To what extent will the most transformative models be technically similar to the ones we have today? That answer determines the value of technical safety research.

I also strongly disagree with the framing that the important thing is us knowing what we know. Yes, people who have been immersed in AI content for years often believe that very scary and/or awesome AI capabilities are coming within the decade. But most people, including most of the people who might take the most important actions, are not in this category and do not share this view (or at least don't seem to have internalized it). Work that provides an empirical grounding for AI forecasts has already been very useful in bringing attention to AGI and its risks from a broader set of people, including in governments, who would otherwise be focused on any one of the million other problems in the world.

I feel subtweeted :p As far as I can tell, most of the wider world isn't aware of the arguments for shorter timelines, and my pieces are aimed at them, rather than people already in the bubble.

That said, I do think there was a significant shortening of timelines from 2022 to 2024, and many people in EA should reassess whether their plans still make sense in light of that (e.g. general EA movement building looks less attractive relative to direct AI work compared to before).

Beyond that, I agree people shouldn't be making month-to-month adjustments to their plans based on timelines, and should try to look for robust interventions.

I also agree many people should be on paths that build their leverage into the 2030s, even if there's a chance it's 'too late'. It's possible to get ~10x more leverage by investing in career capital / org building / movement building, and that can easily offset. I'll try to get this message across in the new 80k AI guide.

Also agree for strategy it's usually better to discuss specific capabilities and specific transformative effects you're concerned about, rather than 'AGI' in general. (I wrote about AGI because it's the most commonly used term outside of EA and was aiming to reach new people.)

Thanks Holly. I agree that fixating on just trying to answer the "AI timelines" question won't be productive for most people. Though, we all need to come to terms with it somehow. I like your callout for "timeline-robust interventions". I think that's a very important point. Though I'm not sure that implies calling your representatives.

I disagree that "we know what we need to know". To me, the proper conversation about timelines isn't just "when AGI", but rather, "at what times will a number of things happen", including various stages of post-AGI technology, and AI's dynamics with the world as a whole. It incorporates questions like "what kinds of AIs will be present".

This allows us to make more prudent interventions: What technical AI safety and AI governance you need depends on the nature of the AI that will be built. Important AI to address isn't just orthogonality thesis-driven paperclip maximizers.

I think seeing the way AI is emerging, that it's clear some classic AI safety challenges are not as relevant anymore. For example, it seems to me that "value learning" is looking much easier than classic AI safety advocates thought. But versions of many classic AI safety challenges are still relevant. The same issue remains: if we can't verify that something vastly more intelligent than us is acting in our interests, then we are in peril.

I don't think it would be right if everyone would be occupied with such AI timelines and AI scenarios questions, but I think they deserve very strong efforts. If you are trying to solve a problem, the most important thing to get right is what problem you're trying to solve. And what is the problem of AI safety? That depends on what kind of AI will be present in the world and what humans will be doing with it.

I agree.

A big reason why I think timeline forecasting gets too much attention—which you alluded to— is that no matter how much forecasting you do, you'll never have that much confidence about how AI is going to go. And certain plans only work under a narrow set of future scenarios. You need to have a plan that works even if your forecast is wrong, because there will always be a good chance that your forecast is wrong.

Slowing down AI has downsides, but it seems to me that slowing down AI is the plan that works under the largest number of future scenarios. Particularly an international treaty to globally slow down AI, so that all developers slow down simultaneously. That seems hard to achieve, but I think peaceful protests increase the chance of success by cultivating political will for a pause/slowdown treaty.

Quick thoughts:

1. I've previously been frustrated that "AI forecasting" has focused heavily on "when will AGI happen" as opposed to other potential strategic questions. I think there are many interesting strategic questions. That said, I think in the last 1-2 years things have improved here. I've been impressed by a lot of the recent work by Epoch, for instance.
2. My guess is that a lot of our community is already convinced. But I don't think we're the target market for much of this.
3. Interestingly, OP really does not seem to be convinced. Or, they have a few employees who are convinced of short timelines, but their broader spending really doesn't seem very AGI-pilled to me (tons of non-AI spending, for instance). I'm happy for OP to spend more money investigating these questions, for reasons of whether OP should spend more money in this area in the future.
4. It sounds like you have some very specific individuals/people in mind, in terms of parts like "If your intervention is so fragile and contingent that every little update to timeline forecasts matters, it’s probably too finicky to be working on in the first place." I'm really not sure who you are referring to here.
5. I'd agree that the day-to-day of "what AI came out today" gets too much attention, but this doesn't seem like an "AI timelines" thing to me, more like an over-prioritization of recent news.
6. On ai-2027.com; I see this as dramatically more than answering "when will AGI happen." It's trying to be very precise about what a short-timeline world would look like. This contains a lot of relevant strategic questions/discussions. 

This seems to complement @nostalgebraist's complaint that much of work on AI timelines (Bio Anchors, AI 2027) rely on a few load-bearing assumptions (e.g. the permanence of Moore's law, the possibility of software intelligence explosion) and then doing a lot of work crunching statistics and Fermi estimations to "predict" an AGI date, when really the end result is overdetermined by those beginning assumptions and not affected very much by changing the secondary estimations. It is thus largely a waste of time to focus on improving those estimations when there is a lot more research to be done on the actual load-bearing assumptions:

• Is Moore's law going to continue indefinitely?
• Is software intelligence explosion plausible? (If yes, does it require concentration of compute?)
• Is technical alignment easy?
• ...

Which are the actual cruxes for the most controversial AI governance questions like:

• How much should we worry about regulatory capture?
• Is it more important to reduce the rate of capabilities growth or for the US to beat China?
• Should base models be open-sourced?
• How much can friction when interacting with the real world (e.g. time needed to build factories and perform experiments (poke @titotal), regulatory red tape, labor unions, etc.) prevent AGI?
• How continuous are "short-term" AI ethics efforts (FAccT, technological unemployment, military uses) with "long-term" AI safety?
• How important is it to enhance collaboration between US, European and Chinese safety organizations?
• Should EAs work with, for, or against frontier AI labs?
• ...

I'm not directly working on an AI pause, but if I were, I would think that timelines were very strategically relevant:

• How soon will we see warning shots, and will they arrive before society is disempowered?
• What kinds of preparation should I do before we see a warning shot
• Will progress be sufficiently slow such that we get frog-boiled?
• How quickly should we aim to spend our limited human capital and financial resources?
• How "in the water" will (or could) AI be over the next few years?

Discussion of AI timelines is helpful for answering many of the questions above, as are the endline forecasts. I suspect that I believe that working on AI pauses is much less robust to timelines than you think it is, and discussion of timelines is much more helpful for developing models of AI development and risk (and those models are highly decision-relevant).

I think I directionally agree!

One example of timelines feeling very decision-relevant is for people who are looking to specialise in partisan influence, you might want to specialise far more in Republicans the larger your credence in TAI/ASI by Jan 2029. Whereas for longer timelines on priors Democrats have a ~50% chance of controlling the presidency from 2029, so specialising in Dem political comms could make more sense.

I am having trouble understanding why AI safety people are even trying to convince the general public that timelines are short. 

If you manage to convince an investor that timelines are very short without simultaneously convincing them to care a lot about x-risk, I feel like their immediate response will be to rush to invest briefcases full of cash into the AI race, thus helping make timelines shorter and more dangerous. 

Also, if you make a bold prediction about short timelines and turn out to be wrong, won't people stop taking you seriously the next time around? 

Nice points, Holly! However, I think they only apply to small disagreements about AI timelines. I liked Epoch After Hours' podcast episode Is it 3 Years, or 3 Decades Away? Disagreements on AGI Timelines by Ege Erdil and Matthew Barnett (linkpost). Ege has much longer timelines than the ones you seem to endorse (see text I bolded below), and is well informed. He is the 1st author of the paper about Epoch AI's compute-centric model of AI automation which was announced on 21 March 2025.

Ege

Yeah, I mean, I guess one way to try to quantify this is when you expect, I don’t know, we often talk about big acceleration, economic growth. One way to quantify is when do you expect, maybe US GDP growth, maybe global GDP growth to be faster than 5% per year for a couple of years in a row. Maybe that’s one way to think about it. And then you can think about what is your median timeline until that happens. I think if you think about like that, I would maybe say more than 30 years or something. Maybe a bit less than 40 years by this point. So 35. Yeah. And I’m not sure, but I think you [Matthew Barnett] might say like 15 or 20 years.

Relatedly, the median expert in 2023 thought the median date of full automation to be 2073.

I remain open to betting up to 10 k$ against short AI timelines. I understand this does not work for people who think doom or utopia are certain soon after AGI, but I would say this is a super extreme view. It also reminds me of religious unbettable or unfalsiable views. Banks may offer loans with better conditions, but, as long as my bet is beneficial, one should take the bank loans until they are marginally neutral, and then also take my bet.

Nice post and I fully agree.

Unfortunately it all goes back to inadequate math education and effective disinformation campaigns. Whether it was tobacco or climate change, those who opposed change and regulation have always focused on uncertainty as a reason not to act, or to delay. And they have succeeded in convincing the vast majority of the public. The mentality is: "even the scientists don't agree on whether we'll have a global catastrophe or total human extinction - so until we're sure which one it is, let's just keep using fossil fuels and pumping out carbon dioxide." 

With AI, I liken most of humanity's mentality to that of a lazy father watching a football game who needs a soda. And there is a store just across a busy highway from his house. He could go get the soda, but he might miss an important score. So instead he sends his 7-year-old son to the store. Because, realistically, there's a good chance that his son won't get hit by a car, while if he goes himself, it is certain that he'll miss a part of the game. 

No parent would think like that. But when it comes to AI, that's how we think. 

And timelines are just the nth excuse to keep thinking that way. "We don't need to act yet, it mightn't happen for 5 years - some people say even 10 years."

The challenge for us is to somehow wake people up before it's too late, and despite the fact that the people who are in the best position to pause are the most gung-ho of all, whether they are CEO's or US president, because they personally have everything to gain from accelerating AI, even if it ends up screwing everyone else (and let's be realistic, they don't really care about anyone else). 

Strong agree that people talk about AI timelines way too much. I think that the level of EG 2 Vs 5 Vs 20 Vs way longer is genuinely decision relevant, but that much more fine grained than that often isn't. And it's so uncertain and the evidence is so weak that I think it's difficult to do much more than putting decent probability on all of those categories and shifting your weightings a bit

Upvoted. I think this is a great argument. Timelines are a way overrated thing to be incessantly talking about and are often a distraction on what can be done.