The environmental impact of answering a question on an obscure topic with ai model is less than an the impact of answering the question with an hour-long google search hunting for references or a drive to the public library.

Vonnegut said in his last living work that the greatest addiction modern people face is the drug of cheap oil.

We got addicted to the convenience and overuse, and have started a mass extinction event because of it.

The perverse incentives will come for us all.

I agree with your message but not sure about the conclusion. Cars themselves are commodified luxury available (in the US pretty much required) to everyone, and they do need to be subsidized, both in terms of infrastructure and the lifestyle they require.

But with AI what is the exact price? My understanding is that R&D is extremely expensive, but running non-SOTA models is not that bad. We are getting pretty close to models which can be useful locally in many applications.

Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?

They went from ~9% of the sp500 to ~35% over your timeframe...

> YOY earnings growth rate for the S&P 500 is 21.7%

Now remove the companies selling the AI shovels: https://pbs.twimg.com/media/HIAjbZxacAARHwD.png

> Not sure what your point is.

My point is that they're selling us Skynet and the end of employment as we now it, things that we shouldn't even have to measure to perceive the results of, yet no one is able to measure any of it

Pointing a finger at nvidia, google, and the other few companies stuck in circular investment schemes that shouldn't even be legal and saying "OOGA BOOGA line go UP, UP GOOD!" doesn't count in my book

There is a piece of this I agree with. That you do not need to be a deep technical expert to notice that a company is burning cash by overcommitting to capex, or relying on heroic revenue projections that may or may not come to pass.

But that is not the full argument he is making. If the claim is that the labs will not be able to pay their creditors because inference is structurally incapable of becoming profitable, then he absolutely needs to be right about the technical economics of inference.

One part of that is the balance-sheet argument (which already shows insanely good margins). But it also depends on how inference-time compute actually works: routing, batching, kv cache reuse, model segmentation, different latency tiers, etc. Much of those details he's just been straight up wrong about in his writing, so as a result I have to call into question the rest of his reasoning as well (in part to avoid Gell-Mann amnesia).

There's examples both in his writing and also in his appearances on podcasts, interviews, etc.

I'll cherry pick a couple:

“When these new models ‘reason,’ they break a user’s input and break into component parts, then run inference on each one of those parts.” [1]

This is not at all how test-time compute works. At best, this is a very loose metaphor that he may have used out of convenience. This might sound a bit pedantic to point out, but this is a very basic thing that he's getting wrong (presumably at least, again it could be that he just used a poor metaphor).

A less pedantic example would be his claims related to gpt-5/chatgpt auto-routing. He argued that having a router means OpenAI can no longer cache static prompts, because the user prompt has to come before the hidden instructions [2]. This is just not at all how this works at inference-time. There is no evidence that the standard approach of system>developer>user instruction hierarchy has changed, the public API and caching docs maintain this.

But even more broadly, it suggests he is reasoning about kv/prefix caching at the wrong level of abstraction. It's true that conventional prefix caching does require a stable prefix, so yes, if you literally put variable user content before the static prompt, you would destroy the cacheability of that static prompt.

But that is exactly why inference systems are designed to preserve reusable prefixes where possible (via checkpointing or similar), and why serving systems care so much about prefix caching. This is also a big part of how disaggregated prefill/decode infra works where cache-aware routing is critical. His argument treats a bad prompt layout as if it were a necessary consequence of routing, rather than an avoidable implementation choice.

A router can read the user request, decide which model path to use, and then construct a normal downstream model call with stable static instructions first and user content later. Treating that as impossible implies a fundamental architectural misunderstanding.

[1] https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/

[2] https://www.wheresyoured.at/how-does-gpt-5-work/

It does depend on how you define productivity. But the way it's commonly used is "I'm going faster, personally, with these tools."

The thing people I think have a hard time seeing is that "I go faster" does not mean "more features get finished".

It's a scale issue, and one scale is better than the other. People only pay for finished features, they do not pay for how much code you emit.

Productivity is defined revenue per worker hour. And we know worker hours are going down as there are fewer workers with the layoffs.

I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility.

However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.

I've posted numbers that indicate that productivity is becoming decoupled from value delivery. If you follow the link in my comment it reviews a pretty robust study of 4000 teams over 2 years. There is no product throughput increase.

Your second paragraph appears to be 3 different instances of saying "X does not necessarily point to productivity gains... but in the case of AI, X definitely means productivity" without really saying why that is true or why other explanations do not fit.

Adoption meaning productivity supposes there are no other dominant factors for the AI push nor AI retention. It is possible for practices to be picked up or continued in spite of causing productivity DROPS. What studies have suggested are factors that make for productive work environments and what is actually enforced in the workplace are different things.

The most important thing I would point to is Mythos et al and the wave of vulnerabilities that have been discovered in the past couple months. It’s a completely unprecedented event, brought forth almost entirely by improvements in the models themselves. That said. keep in mind, I’m talking about over the past two years. With Claude code and the capabilities gained since December of last year, there have been incredible gains in the capabilities that are now available. Demand for inference is higher now than it was a year ago, because capability has improved. A specific criticism that I would hold is that claiming that progress with LLMs is slowing, prior to that point, is embarrassingly wrong in my view. One could argue that the model capability improvements are slowing, and all the improvements were in harnesses. I think that’s a stronger argument, but I have a few problems with it. 1. Utility is utility. Whether that comes from the model or the harness is irrelevant when making claims about utility. I don’t think that’s a useful distinction most of the time, but especially when talking about the technology as a whole. 2. Marginal intelligence gain is different than marginal utility gain. It’s estimated that intelligence grows logarithmically relative to investment. However, the utility of a marginally more intelligent model may grow exponentially, because once behavior crosses a reliability threshold, it unlocks new capabilities. 3. Even on those terms, it’s not clear to me that frontier capabilities are slowing down. With Mythos and its contemporaries, we have been seeing a vast change in the security industry as vulnerabilities are discovered at an unprecedented rate. OpenBSD vulnerabilities, more Firefox vulnerabilities found in a single month than the past two years, critical Linux vulnerabilities. It’s hard for me to look at the effects there, a radical new capabilities baked into the model itself, and see stagnation. A part of the reason it might feel like it’s slowing down is because we plebs don’t have access to the top models.

[dead]

Because it doesn't. Not for the tasks where using Opus instead of a lower tier model is appropriate, at any rate. Benchmarks show this, as do revealed preferences of actual users. To believe that Qwen is as capable as Opus at 1/20 the cost you have to believe that every person who does not make the choice to use Qwen over Opus for a given task is some mix of ignorant or delusional. This is certainly an opinion you can hold about other engineers, but it's definitely a questionable one at best.

https://www.theverge.com/tech/930447/microsoft-claude-code-d...

https://finance.yahoo.com/sectors/technology/articles/ai-bin...

https://blog.pragmaticengineer.com/the-pulse-token-spend-bre...

When you say "Things then get better and better with each model release."

How far behind are models that can be run locally, and do you expect that this will be widespread?

Because it can perform much faster? Monitoring allows you to multitask more effectively. I would also disagree that you can’t one shot anything…claims like this are weak and I have enough counter examples in my own life that it’s trivially false. The question is more: can it one shot the right things with a low enough failure rate for it to be a good replacement. It’s hard to figure that out a priori.

I don't think the issue with robotics is a data gap. maybe somewhat, but the real issues are that:

- RL is extraordinarily sample-inefficient.

- distribution shift/catastrophic forgetting aren't solved. only off-policy learning with giant decorrelated batches works.

- the breakout success of transformers as an architecture doesn't neatly translate to robot motion policy models.

the field is missing fundamental breakthroughs.

I also find it very interesting that conversational AI has taken this long. where are the models with good turn-taking? passive listening? the ability not to respond in paragraphs? has Anthropic simply not gotten around to it?

Is it really worth it to cause a global economical collapse and harm society well-being to an unimaginable degree just to find out if it is viable?

Why cant it naturally grow and prove it's worth?

Just 5 more years and $500 billion more, bro. We're still so early.

And?