I love this quote from a TED Talk given by the physicist David Deutsch:

So, in science, two false approaches blight progress. One's well-known: untestable theories. But the more important one is explanationless theories. Whenever you're told that some existing statistical trend will continue but you aren't given a hard-to-vary account of what causes that trend, you're being told a wizard did it.

What’s the casual explanation for why AI revenue is happening? What’s the casual explanation for why it will supposedly continue sustainably, long-term? What theory or hypothesis explains the data, and would justify extrapolating the current statistical trend far into the future?

There are over 50,000 publicly traded companies on the global stock market. The NASDAQ has existed for 55 years; the New York Stock Exchange for 234 years. We have lots of data on stock prices.

Here’s an important fact about stocks: you can’t just look at the price graph for a stock over the last year, or the last three years, extrapolate this price movement forward for the next three years, and buy the stock (or short it) on that basis. You can’t just extrapolate the trend long-term. It doesn’t work.

You need a causal explanation, an investment thesis, about why the stock will go up or down, and by how much. What’s your thesis? And what’s the evidence for that thesis? What’s the analysis behind it?

From what I can tell, the vast majority of economists, professional investors, and financial analysts do not believe that LLMs will lead to AGI or transformative AI within the next decade. Yet they’re well-aware of the revenue growth of AI companies over the last three years. Many professional investors — maybe about half — think AI is in a bubble. What gives? Are they not seeing these graphs? Are they not looking at the graphs hard enough? If the statistical trend of AI revenue growth over the last three years is going to continue for the next five to ten years or more, how could they think this? It’s a real mystery!

If you think trends always continue!

(Much more on this topic here.)

My three most recent posts on Substack are relevant to effective altruism:

• Shouldn’t we spend money on AGI safety, just in case?
• The sad decline of effective altruism
• The pseudo-religious origins of the AI bubble

I can’t discuss them on the EA Forum, but I’m happy to do so on Substack.

The blog post by the Australian AI safety organization says, “We apply METR’s time-horizon methodology…” How would this address the criticisms raised of METR’s methodology?

At a glance, the FutureTech pre-print makes some interesting choices, e.g., task quality is only scored up to above-average and above-average gets a perfect score, and acknowledges some of the limitations with their methodology, e.g., all tasks used for this experiment must contain all relevant information in the LLM prompt. (Is that realistic for most work tasks?) I wonder if this pre-print will be submitted for publication in a journal? FutureTech seems to be one of those weird MIT hybrids between an academic research group and a management consultancy. I’m not sure if they’ve ever published a peer-reviewed paper. 

[Edit on 2026-05-14 at 18:56 UTC: After reading Peter Slattery’s comment below, I spent a few more minutes looking into it, and I’m still not sure what FutureTech is or what kind of stuff they publish. If someone knows and can explain it, that would be helpful. I could spend more time and get to the bottom of it, but I don’t want to spend more time on it right now.

Please also note the EA Forum team has limited my ability to reply to comments, so I can’t reply further. But if you want to continue the discussion, I’m reachable here.]

Someone could take the time to do a deep dive into the FutureTech pre-print and write a review, but I wonder if that’s a good use of anyone’s time? Is there a reason to think this group publishes high-quality research that is worth getting into?

If someone thinks it’s worthwhile, and they also think the pre-print is unlikely to be submitted for peer review, one option would be to ask the EA organization called The Unjournal to commission a review by an external expert. 

Hi Kouadio. Just want to let you know that your comments don't have paragraph breaks between the paragraphs. Maybe you are copying and pasting from another app and the formatting is getting messed up? I'm just saying this because the text looks like it's all in one big block and that makes it harder to read. I want to make sure you get a fair shot at saying what you want to say, and fixing this formatting issue will make people more likely to read your comments.

The AI revenue growth we've seen so far is compatible with several different explanations, including an AI investment bubble and narrow AI applications that are economically useful but will not lead to AGI anytime soon. Professional investors and financial analysts are generally split between these two camps. Only a small minority believe in near-term AGI.

Some criticisms of the famous METR time horizons graph:

• As you mentioned, some of the problems and limitations of the METR time horizons graph are sometimes (but not always) clearly disclosed by METR employees, including the CEO of METR. However, note the wide difference between the caveated description of what the graph says and the interpretation of the graph as a strong indicator of rapid, exponential improvement in general AI capabilities.
• Gary Marcus, a cognitive scientist and AI researcher, and Ernest Davis, a computer scientist and AAAI fellow, co-authored a blog post on the METR graph that looks at how the graph was made and concludes that “attempting to use the graph to make predictions about the capacities of future AI is misguided”.
• Nathan Witkin, a research writer at NYU Stern’s Tech and Society Lab, published a detailed breakdown of some of the problems with METR’s methodology. He concludes that it’s “impossible to draw meaningful conclusions from METR’s Long Tasks benchmark” and that the METR graph “contains far too many compounding errors to excuse”. Witkin calls out a specific tweet from METR, which presents the METR graph in the broad, uncaveated way that it’s often interpreted by believers in near-term AGI. He calls the tweet “an uncontroversial example of misleading science communication”. In a response to a comment on that post asking how much we should update our views based on the METR graph, Witkin responded, "to be very clear I am in fact claiming that the proper update is zero."

I'm just summarizing the conclusions here, not the substance of the critiques. I recommend that people go and read the critiques to how the authors reach these conclusions.

I guess the point of the expert survey you cited was to explain that it does not support the idea of near-term AGI, right? I was confused because the title and introduction strongly states that the evidence has turned in favour of near-term AGI, but then you say that 2 out of the 4 pieces of evidence you cite do not support the idea of near-term AGI. I think you're just trying to do a general survey of the evidence, both the convincing and unconvincing evidence, right?

I agree that Bio Anchors is also not convincing evidence of anything, for the reasons explained here.

Something I changed my mind about after looking into both the AI Impacts survey and the Forecasting Researching Institute's LEAP survey (as I wrote about here) is that survey results seem to be super sensitive to survey design, even choices in survey design that seem small to the designers, and that they don't anticipate having an impact. I'm not sure these kinds of surveys really matter that much, anyway, but I'm at least more interested in surveys where the designers are careful about these factors that can bias the results. The effects are not small, either. In one case, the result was 750,000 times higher or lower depending on how the question was posed.

Overall, this post is a bit weird because the title and intro make a super strong claim — the tables have turned! — but then the body doesn't cash the cheque that the title and intro write. The new evidence that has turned the tables on AI skepticism is just AI revenue and the METR graph? So, if you agree that the METR graph has been debunked at this point, then it's just AI revenue. And what does AI revenue really show? Can narrow AI not make a lot of money? Are you really prepared to defend that claim? Have at it!

Maybe the claim is something really specific, which is that if you take AI revenue growth over the last 3 years and extrapolate the same rate of growth indefinitely, you end up with some ridiculously large number, and for that number to be true, we would need to have something like AGI. But you can't just take any trend and extrapolate it indefinitely. You need to have some explanation of what's causing the trend and whether it will continue or not. When you step on the accelerator of your car, extrapolating that trend forward indefinitely means you'll eventually exceed the speed of light. But we don't just extrapolate things forward, we think about cause and effect.

You could look at all sorts of industries (like SaaS) or companies (like Tesla) during a few years when growth is super fast, extrapolate that forever, and conclude that one day they will account for 100% of gross world product and take over the entire world economy. But we assume this won't happen because we understand what will prevent this from happening, and we also don't know about anything that would cause it to happen. So, will AI revenue increase until the Singularity happens? That depends on the technology. So, what will happen with the technology? Now we're back to square one! Looking at a chart of AI revenue doesn't settle anything. Will the chart go asymptotic into AI heaven? Or will it level out, or even crash? The answer to that question is not in the chart. It's in the world.

Extrapolation of past trends with no causal explanation of why the trend will continue is not empiricism! It is mysticism! It amounts to saying: we don't know what's happening or why or how, but, somehow, we know what will happen. This is not science. This is not financial analysis. This is not anything.

A facetious graph from The Economist extrapolating when the first 14-bladed razor will arrive:

My own facetious graph:

(Why do you expect this trend not to continue?)

This is a beautifully written comment, and succinct, and funny, and true.

I would give EA much more grace if its self-image was the same as what I presume the Big Garden Birdwatch's self-image is. Part of what gets me tilted out of my mind about the EA community is when people express this almost messianic Chosen Ones self-image — which ties into the pseudo-religious aspect you mentioned.

The high-impact, low-probability logic of existential risk is hypnotically alluring. If a 1 in 1 quintillion chance of reducing existential risk is equivalent to 100 human lives, what does that imply in terms of your moral responsibility when discussing existential risk? If you have things to say that could cast doubt on existential risk arguments, should you self-censor and hold your tongue? If you speak out and you're wrong, it could be the moral equivalent of killing 100 people. Would it be okay to lie? To exaggerate? Why not? Wouldn't you lie or exaggerate to save 100 lives? If the Nazis knocked at your door, wouldn't you lie to save Anne Frank in the attic?

I don't think many people are actually outright lying when it comes to existential risk. But I do think people are self-censoring when it comes to criticism, and I do think people are willing to make excuses for really low-quality products like AI 2027 or 80,000 Hours' video on it because anything that builds momentum for existential risk fear is plausibly extremely high in expected value.

Much could be said in response to this comment. Probably the most direct and succinct response is my post “Unsolved research problems on the road to AGI”.

Largely for the reasons explained in that post, I think AGI is much less than 0.01% likely in the next decade.

How much of a post are you comfortable for AI to write?

I will never let AI write a single sentence! I resent reading AI-generated writing passed off as written by a human, and I would never inflict this upon my readers.

I have found that the most common explanation for why people using AI for writing is a lack of self-confidence. I keep encouraging people to write in their own words and use their own voice because all the flaws of unpolished human writing are vastly preferable to chatbot writing.

Thank you for your supportive comment. I think David Mathers is an exceptionally and commendably valuable contributor to the EA Forum in terms of engaging deeply with the substance of arguments around AI safety and AGI forecasting. David engages in discussions with a high level of reasoning transparency, which I deeply appreciate. It isn’t always clear to me why people who fall on the opposite side of debates around AI safety and AGI forecasting believe what they do, and talking to David has helped me understand this better. I would love to have more discussions about these topics with David, or with interlocutors like him. I feel as though there is still much work to be done in bringing the cruxes of these debates into sharp relief.

The EA Forum has a little-used “Dialogues” feature that I think has some potential. Anyone who would be interested in having a Dialogue on AGI forecasting and/or AGI safety should send me a private message.

On to the rest of your comment:

I think the current investments in AGI safety will end up being wasted. I think it’s a bit like paying philosophers in the 1920s to think about how to mitigate social media addiction, years before the first proper computer was built, and even before the concept of a Turing machine was formalized. There is simply too much unknown about how AGI might eventually be built.

Conversely, investments in narrow, prosaic “AI safety” like making LLM chatbots less likely to give people dangerous medical advice are modestly useful today but will have no applicability to AGI much later on. Other than having the name “AI” in common and running on computers using probably some sort of connectionist architecture, I don’t think today’s AI systems will have any meaningful resemblance to AGI, if it is eventually created.