[oudlys]

Are we plotting against cost? How is the capability advancement vs dollars paid for development?

By my read of the (very sparse) data, we're getting linear improvements in capability for super-linear increases in costs. [1] Indicates that by 2027 models will cost $1 billon to train. Dario estimates that model runs will cost $10 billion in 2026 [2]. That to me indicates costs are potentially growing faster than capability. Maybe by quite a bit.

If the value prop of LLMs doesn't prove out, that won't last. I'm of the opinion there is no data that shows actual economic value being delivered by models. The best data shows that LLM use might be destroying value [3].

[1] https://epoch.ai/publications/how-much-does-it-cost-to-train... [2] https://lexfridman.com/dario-amodei-transcript/ [3] https://unessays.substack.com/p/talk-is-cheap

[aspenmartin]

I appreciate the data here but I don't think the read is quite right;

Saying we have linear capability for super-linear cost compares an unbounded variable (dollars) to bounded instruments (because benchmarks saturate). On unbounded measures, growth is exponential; you can see METR time horizons double every ~4-7 months (https://metr.org/blog/2026-1-29-time-horizon-1-1/). And capability being proportional to log(compute) is what the scaling law predicts.

Epoch puts training cost growth at ~2.4x/year as your link shows. Meanwhile cost for fixed capability falls ~10-40x/year (https://epoch.ai/data-insights/llm-inference-price-trends), and lab revenue is growing ~10x/year! Anthropic went from $1B to $9B to $30B+ run rate in ~15 months, OpenAI ~$25B.

On [3]: the "destroying value" conclusion flips sign on an assumed 15% baseline rework rate. The report's most direct metric is +16% merged PRs per dev. The RCT evidence is genuinely mixed (METR: -19%, with n = 20 and Claude 3.x; Cui et al: +26%) but its just super hard to do this well, I think Faros stuff was pretty cool, I haven't seen this before so thank you for the reference.

[oudlys]

>"On unbounded measures, growth is exponential"

Maybe. There was a great comment in the thread on Fable 5 yesterday about benchmark comparisons between Fable and the latest opus models. here it is: https://news.ycombinator.com/item?id=48464600.

You could be right, but this is the most direct benchmark comparison I could find and it's not that strong.

>the "destroying value" conclusion flips sign on an assumed 15% baseline rework rate. The report's most direct metric is +16% merged PRs per dev.

I discuss this directly in my analysis. There's also an 860% code churn increase ratio. You only need 9% of that to be allocated to wasteful rework to drive throughput flat to the 15% rework baseline. Not to an assumed ideal state where there was no rework.

But even if it were not true, a 16% throughput improvement is pretty weak given the investment - especially given the direct evidence of quality degradation. IMO.

I appreciate you reading my stuff and taking the data seriously. Thank you.

[andrekandre]

  > But even if it were not true, a 16% throughput improvement is pretty weak given the investment - especially given the direct evidence of quality degradation. IMO.

n=1 but at $JOB we have throughput quotas now, and what is happening is that teams are just finding lots of busywork (renaming things, gardening of ai .md files, rewriting uis etc) and also dividing prs into smaller chunks to match the quotas... so even "throughout increase" doesn't say much if its not for improving the customer outcome (ime anyways)

[oudlys]

Productivity != value.

Thanks for the story.

[balefulboy]

METR's time horizon is not a reliable metric of LLM capability growth: https://www.transformernews.ai/p/against-the-metr-graph-codi...

[aspenmartin]

Yes I've seen this before, and while the critiques are fair and high quality (and unfortunately not unique to METR) we're missing the forest for the trees here.

First of all, if you take the articles critiques and work out the implications on the METR graph, all you're doing is shifting the curve up or down, it doesn't change the fact that progress is scaling exponentially. While it is technically possible the universe could be throwing a massive pathological curveball to change the conclusion from METR data (which is we've been seeing exponential growth over the last 6 years), I think that seems very far from likely. The fact that we see the same behavior from a variety of sources over a wide variety of tasks and domains is a pretty clear indication that METR while certainly far from perfect is actually painting a consistent picture at least in terms of the rate of progress.

You can look at ECI for a summary benchmark statistic, which does NOT use METR's benchmark, and you see a similar trend. Same with SWE-bench where the task distribution is far more in domain for real world problems. It is a bummer that this METR data can't be better funded. It would probably take $1M or so to really beef it up properly which any of these labs probably have in their couch cushions.

[oudlys]

Wow. This deserves to be much more widely read. Thank you for this.

[simianwords]

>By my read of the (very sparse) data, we're getting linear improvements in capability for super-linear increases in costs. [1] Indicates that by 2027 models will cost $1 billon to train. Dario estimates that model runs will cost $10 billion in 2026 [2]. That to me indicates costs are potentially growing faster than capability. Maybe by quite a bit.

This is true and well established.

As long as you get any improvement whatsoever, it is worth spending to train since it pays off during.

Imagine training was not $1 billion but $100 billion but the performance improved by just 10%. This is still worth it because you can squeeze out the profits across years and years right? The improvement is ever lasting.

> The best data shows that LLM use might be destroying value [3].

This is basically a conspiracy theory and if you really believed this, you should not have led with "How is the capability advancement vs dollars paid for development?" because if there were no value, it doesn't really matter how much you invest.

[oudlys]

>This is basically a conspiracy theory

I think this is pretty uncharitable, especially when I've provided you with a dataset you can evaluate yourself and an argument you can review for logical inconsistency.

I have worked quite hard to locate data that supports your thesis, I can't find it. I've at least gone to the effort of documenting that search. Before you throw around such strong convictions, I suggest you actually look for yourself.

[simianwords]

Respectfully, your link is not very convincing.

But what’s interesting is that you are commenting on a post where Dario is suggesting that LLMs are so extremely powerful that they can take over, help synthesise bioweapons, help in warfare, help in drug discovery — the whole post here is to try and regulate this. If you believe AI can’t even create positive value let alone discover new things then your problem is somewhere else and not in something like “but training costs a lot”.

So it is absolutely strange and contrasting to see you believe that LLMs are so weak as to create negative value while the CEO is asking about regulations because AI is too powerful.

I don’t think I can convince you that AI is actually that powerful.

But let me ask you something directly: if you believe what you believe, you should also acknowledge that AI doesn’t need regulations in the context Dario is proposing since obviously AI can’t do anything he predicts. Do you agree?

[dag100]

> So it is absolutely strange and contrasting to see you believe that LLMs are so weak as to create negative value while the CEO is asking about regulations because AI is too powerful.

You wouldn't ask a chemistry professor to write code. So just because LLMs create negative value for software development doesn't mean that they can't be helpful for bioweapons synthesis, especially considering the range of chemistry and biology sources Anthropic would have fed to its LLM that wouldn't be publicly accessible. The LLM doesn't even need to be particularly accurate so long as the amateur bioweapons researcher takes adequate precautions before following its instructions and does some background research beforehand.

[simianwords]

This is a ridiculous stance to take. That LLMs are simultaneously negative value but can also help synthesise bioweapons. It’s the sort of stance you take when you already feel ideologically against AI. I don’t think it’s coherent.

[oudlys]

>This is a ridiculous stance to take.

I encourage you to be more curious. We're just talking. We would learn more from each other without these strong statements.

[dag100]

It's more about information availability rather than intelligence. An LLM has had access to more information during its training period than you'd ever even come across over a hundred lifetimes. It has been trained on billions of books and articles across every single subject that exists on the planet. Can you imagine what real intelligence could do with all that information?

[icepush]

Is synthesizing bioweapons positive or negative value ?

[oudlys]

>Respectfully, your link is not very convincing.

I'd love to understand why. This would be valuable feedback for me as I try to make my writing and exposition better. Also, if you have other data, that also would be valuable for me to know.

>if you believe what you believe, you should also acknowledge that AI doesn’t need regulations in the context Dario is proposing since obviously AI can’t do anything he predicts. Do you agree?

I think you misunderstand my beliefs. On net I think how we're using LLMs destroys value. That doesn't mean no one ever gets value from LLM use.

My particular point about trillion dollars is - the main place Anthropic, OpenAI, and - hilariously - SpaceX think they will drive value creation is in enterprise applications. In that domain I think the evidence is very convincingly negative. I'm certainly not the only person who thinks this. It's pretty well accepted in economics right now that there is no observed organizational level productivity improvement. Lines break down on whether it will show up eventually or whether we will wait forever.

My belief about LLM value is that it's most useful for individuals and small teams. Places where coordination and trust are easily established and feedback loops to value creation are tight. They are "short range" as it were.

Their value starts to erode as soon as a user becomes disconnected from the point of direct value creation. Which is pretty much everyone who works inside of a large organization. It becomes negative at pretty small scale, IMO. I do think there are patterns of use that could drive value at these scales. I talk about that in my post.

On Bioweapons in particular, I could see small teams of people working to build something very dangerous. Having spent my formative academic years in a biochemistry and microbiology lab though, I do think the danger is overstated. Papers are not know-how or equipment. There's a lot of tacit knowledge that can't get written down that is super hard to acquire.

But, I'd be happy for us to regulate AI for dangerous applications.

My question would be - why would Anthropic build something they so clearly think is dangerous? If they were really building something deserving of the valuation they have, why build applications like this?

To my eyes - it's super weird that a company would build something they think is dangerous and turn around and beg the governments of the world to stop them. That's really strange behavior from my perspective.

[simianwords]

I went through your post in substack (I think that's what you were referring to).

> I'd love to understand why. This would be valuable feedback for me as I try to make my writing and exposition better. Also, if you have other data, that also would be valuable for me to know.

I think it comes down to few things

- you took a single report that agreed with your statistics, for the sake or argument lets say I buy it completely

- you suggest that net value is lost simply because there are more incidents. this is a big jump

- you say that historically different technological improvements may have had similar patterns but this specific one is different because AI is stochastic

So it all really rests on you finding one distinction with AI and then disagreeing with the past trends.

I agree AI is stochastic and I'll put it this way: it is a high variance bet but it pays off. This is a bit hard for people to understand -- its a tool that works sometimes really nicely and fails other times. Overall you are better off using it but you need to use it enough to reduce variance.

Let me ask this: if you are so sure this won't lead to enterprise level productivity, how do you think this will show in macro trends? Surely you must believe that the valuations must drop wouldn't you? Can you come up with a concrete future scenario that would vindicate your opinion that AI doesn't make enterprises more productive?

> My question would be - why would Anthropic build something they so clearly think is dangerous? If they were really building something deserving of the valuation they have, why build applications like this?

I think this is fair and interesting question. Here is what I think they think: If they don't build it, someone else might do it. And they think they are more moral than others. If they have a head start they can set the political and regulatory landscape.

[oudlys]

>you took a single report that agreed with your statistics

These are not my statistics. I'm not affiliated with Faros at all. I built an analysis on top of their reporting.

And, it's also not one report. DORA has tracked statistics with respect to throughput and quality as well. Those indicators are flat for throughput and negative for quality. The throughput flatness is also supported by the shovelware data.

I discuss both of those lines in How I'm thinking: https://unessays.substack.com/p/how-im-thinking-about-the-va...

>you suggest that net value is lost simply because there are more incidents. this is a big jump

I don't think it's a big jump at all. Incidents and bugs drive rework. Rework has to be subtracted from throughput. Product throughput is the only thing people pay for.

This type of analysis is done all the time in manufacturing and devops. Here's a link for you: https://reworkcost.com/benchmarks. I'm not bringing novel intellectual ideas to the table here.

Faros reports a 16% throughput improvement on PRs. They also report an 860% code churn increase. If you assign only 9% of that increase to wasteful rework, then the absolute throughput improvement disappears. This is a very simple, straightforward analysis of the operations data reported by Faros.

> - you say that historically different technological improvements may have had similar patterns but this specific one is different because AI is stochastic

I'm saying LLMs are unreliable. I think we agree on that front, you say:

>I agree AI is stochastic and I'll put it this way: it is a high variance bet but it pays off.

What I'm disputing is the "pays off" statement. That statement is amenable to validation with data. In my view, the data is saying it doesn't pay off. I think it says that very clearly. Across distinct lines of evidence.

>if you are so sure this won't lead to enterprise level productivity, how do you think this will show in macro trends? Surely you must believe that the valuations must drop wouldn't you? Can you come up with a concrete future scenario that would vindicate your opinion that AI doesn't make enterprises more productive?

I think LLMs can deliver value in the enterprise. I think the way to do that is to use them as quality checks and not as primary authors of intellectual work - like writing code.

Unfortunately, this use case would not support the expected 2-10x productivity increases that current valuations depend on. I do expect a major market correction in the near future. It would not surprise me if OpenAI or Anthropic are acquired. I think we're at risk of that happening within the next 1-7 months.

What would invalidate my beliefs? 1. Actual micro or macroeconomic data indicating economic productivity is increasing. 2. A Faros like observational study demonstrating sustained throughput improvement with significantly less rework and quality impacts.

I think I could be swayed against the market correction if the financials of OpenAI or Anthropic are strong. I'm anticipating they will be quite bad. I think Mythos was very expensive to train and I think the improvements in capability are sublinear. The inference costs are incredibly high.

I also have ideas about how Anthropic and OpenAI are trying to change their business models into enterprise transformation plays. Similar to Palantir. But this comment is already long.

>If they don't build it, someone else might do it.

No other players in the market other than US tech companies have the capital or the technology to train the models of the power of Fable. The way the Chinese model builders are building their models is by distilling from US models. So Anthropic, by building Mythos with all this bio data, has created the possibility that other actors can distill their models and do harm with them. (Not to say the Chinese are seeking to build weapons, but actors with their models might).

[simianwords]

You have engaged in good faith discourse, thanks! I'll reply in a bit.