[einrealist]

> It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later.

Somewhere, there are GPUs/NPUs running hot. You send all the necessary data, including information that you would never otherwise share. And you most likely do not pay the actual costs. It might become cheaper or it might not, because reasoning is a sticking plaster on the accuracy problem. You and your business become dependent on this major gatekeeper. It may seem like a good trade-off today. However, the personal, professional, political and societal issues will become increasingly difficult to overlook.

[cyode]

This quote stuck out to me as well, for a slightly different reason.

The “tenacity” referenced here has been, in my opinion, the key ingredient in the secret sauce of a successful career in tech, at least in these past 20 years. Every industry job has its intricacies, but for every engineer who earned their pay with novel work on a new protocol, framework, or paradigm, there were 10 or more providing value by putting the myriad pieces together, muddling through the ever-waxing complexity, and crucially never saying die.

We all saw others weeded out along the way for lacking the tenacity. Think the boot camp dropouts or undergrads who changed majors when first grappling with recursion (or emacs). The sole trait of stubbornness to “keep going” outweighs analytical ability, leetcode prowess, soft skills like corporate political tact, and everything else.

I can’t tell what this means for the job market. Tenacity may not be enough on its own. But it’s the most valuable quality in an employee in my mind, and Claude has it.

[noosphr]

There is an old saying back home: an idiot never tires, only sweats.

Claude isn't tenacious. It is an idiot that never stops digging because it lacks the meta cognition to ask 'hey, is there a better way to do this?'. Chain of thought's whole raison d'etre was so the model could get out of the local minima it pushed itself in. The issue is that after a year it still falls into slightly deeper local minima.

This is fine when a human is in the loop. It isn't what you want when you have a thousand idiots each doing a depth first search on what the limit of your credit card is.

[BeetleB]

This is a major concern for junior programmers. For many senior ones, after 20 (or even 10) years of tenacious work, they realize that such work will always be there, and they long ago stopped growing on that front (i.e. they had already peaked). For those folks, LLMs are a life saver.

At a company I worked for, lots of senior engineers become managers because they no longer want to obsess over whether their algorithm has an off by one error. I think fewer will go the management route.

(There was always the senior tech lead path, but there are far more roles for management than tech lead).

[techgnosis]

Why are we pretending like the need for tenacity will go away? Certain problems are easier now. We can tackle larger problems now that also require tenacity.

[mykowebhn]

Fittingly, George Hinton toiled away for years in relative obscurity before finally being recognized for his work. I was always quite impressed by his "tenacity".

So although I don't think he should have won the Nobel Prize because not really physics, I felt his perseverance and hard work should merit something.

[daxfohl]

I still find in these instances there's at least a 50% chance it has taken a shortcut somewhere: created a new, bigger bug in something that just happened not to have a unit test covering it, or broke an "implicit" requirement that was so obvious to any reasonable human that nobody thought to document it. These can be subtle because you're not looking for them, because no human would ever think to do such a thing.

Then even if you do catch it, AI: "ah, now I see exactly the problem. just insert a few more coins and I'll fix it for real this time, I promise!"

[gtowey]

The value extortion plan writes itself. How long before someone pitches the idea that the models explicitly almost keep solving your problem to get you to keep spending? Would you even know?

[wvenable]

> These can be subtle because you're not looking for them

After any agent run, I'm always looking the git comparison between the new version and the previous one. This helps catch things that you might otherwise not notice.

[einrealist]

And there is this paradox where it becomes harder to detect the problems as the models 'improve'.

[charcircuit]

You are using it wrong, or are using a weak model if your failure rate is over 50%. My experience is nothing like this. It very consistently works for me. Maybe there is a <5% chance it takes the wrong approach, but you can quickly steer it in the right direction.

[fooker]

> It might become cheaper or it might not

If it does not, this is going to be first technology in the history of mankind that has not become cheaper.

(But anyway, it already costs half compared to last year)

[ctoth]

> But anyway, it already costs half compared to last year

You could not have bought Claude Opus 4.5 at any price one year ago I'm quite certain. The things that were available cost half of what they did then, and there are new things available. These are both true.

I'm agreeing with you, to be clear.

There are two pieces I expect to continue: inference for existing models will continue to get cheaper. Models will continue to get better.

Three things, actually.

The "hitting a wall" / "plateau" people will continue to be loud and wrong. Just as they have been since 2018[0].

[0]: https://blog.irvingwb.com/blog/2018/09/a-critical-appraisal-...

[peaseagee]

That's not true. Many technologies get more expensive over time, as labor gets more expensive or as certain skills fall by the wayside, not everything is mass market. Have you tried getting a grandfather clock repaired lately?

[InsideOutSanta]

Sure, running an LLM is cheaper, but the way we use LLMs now requires way more tokens than last year.

[root_axis]

Not true. Bitcoin has continued to rise in cost since its introduction (as in the aggregate cost incurred to run the network).

LLMs will face their own challenges with respect to reducing costs, since self-attention grows quadratically. These are still early days, so there remains a lot of low hanging fruit in terms of optimizations, but all of that becomes negligible in the face of quadratic attention.

[fulafel]

I don't think computation is going to become more expensive, but there are techs that have become so: Nuclear power plants. Mobile phones. Oil extraction.

(Oil rampdown is a survival imperative due to the climate catastrophe so there it's a very positive thing of course, though not sufficient...)

[krupan]

There are plenty of technologies that have not become cheaper, or at least not cheap enough, to go big and change the world. You probably haven't heard of them because obviously they didn't succeed.

[asadotzler]

cheaper doesnt mean cheap enough to be viable after the bills come due

[runarberg]

Supersonic jet engines, rockets to the moon, nuclear power plants, etc. etc. all have become more expensive. Superconductors were discovered in 1911, and we have been making them for as long as we have been making transistors in the 1950s, yet superconductors show no sign of becoming cheaper any time soon.

There have been plenty of technologies in history which do not in fact become cheaper. LLMs are very likely to become such, as I suspect their usefulness will be superseded by cheaper (much cheaper in fact) specialized models.

[ak_111]

Concorde?

[YetAnotherNick]

With optimizations and new hardware, power is almost a negligible cost. You can get 5.5M tokens/s/MW[1] for kimi k2(=20M/KWH=181M tokens/$) which is 400x cheaper than current pricing. It's just Nvidia/TSMC/other manufacturers eating up the profit now because they can. My bet is that China will match current Nvidia within 5 years.

[1]: https://developer-blogs.nvidia.com/wp-content/uploads/2026/0...

[storystarling]

Electricity is negligible but the dominant cost is the hardware depreciation itself. Also inference is typically memory bandwidth bound so you are limited by how fast you can move weights rather than raw compute efficiency.

[redox99]

> And you most likely do not pay the actual costs.

This is one of the weakest anti AI postures. "It's a bubble and when free VC money stops you'll be left with nothing". Like it's some kind of mystery how expensive these models are to run.

You have open weight models right now like Kimi K2.5 and GLM 4.7. These are very strong models, only months behind the top labs. And they are not very expensive to run at scale. You can do the math. In fact there are third parties serving these models for profit.

The money pit is training these models (and not that much if you are efficient like chinese models). Once they are trained, they are served with large profit margins compared to the inference cost.

OpenAI and Anthropic are without a doubt selling their API for a lot more than the cost of running the model.

[bob1029]

Humans run hot too. Once you factor in the supply chain that keeps us alive, things become surprisingly equivalent.

Eating burgers and driving cars around costs a lot more than whatever # of watts the human brain consumes.

[bbor]

I mean, “equivalent” is an understatement! There’s a reason Claude Code costs less than hiring a full time software engineer…

[crazygringo]

> Somewhere, there are GPUs/NPUs running hot.

Running at their designed temperature.

> You send all the necessary data, including information that you would never otherwise share.

I've never sent the type of data that isn't already either stored by GitHub or a cloud provider, so no difference there.

> And you most likely do not pay the actual costs.

So? Even if costs double once investor subsidies stop, that doesn't change much of anything. And the entire history of computing is that things tend to get cheaper.

> You and your business become dependent on this major gatekeeper.

Not really. Switching between Claude and Gemini or whatever new competition shows up is pretty easy. I'm no more dependent on it than I am on any of another hundred business services or providers that similarly mostly also have competitors.

[hahahahhaah]

It is also amazing seeing Linux kernel work, scheduling threads, proving interrupts and API calls all without breaking a sweat or injuring its ACL.

[mikeocool]

To me this tenacity is often like watching someone trying to get a screw into board using a hammer.

There’s often a better faster way to do it, and while it might get to the short term goal eventually, it’s often created some long term problems along the way.

[chasebank]

I don’t understand this pov. Unfortunately, id pay 10k mo for my cc sub. I wish I could invest in anthropic, they’re going to be the most profitable company on earth

[moooo99]

My agent struggled for 45 minutes because it tried to do `go run` on a _test.go file, which the compiler repeatedly exited after posting an error message that files named like this cannot be executed using the run command.

So yeah, that wasted a lot of GPU cycles for a very unimpressive result, but with a renewed superficial feeling of competence

[squidbeak]

> you most likely do not pay the actual costs. It might become cheaper or it might not

Why would this be the first technology that doesn't become cheaper at scale over time?

[karlgkk]

> And you most likely do not pay the actual costs

Oh my lord you absolutely do not. The costs to oai per token inference ALONE are at least 7x. AT LEAST and from what I’ve heard, much higher.

[tgrowazay]

We can observe how much generic inference providers like deepinfra or together-ai charge for large SOTA models. Since they are not subsidized and they don’t charge 7x of OpenAI, that means OAI also doesn’t have outrageously high per-token costs.

[utopiah]

AI genius discover brute forcing... what a time to be alive. /s

Like... bro that's THE foundation of CS. That's the principle of The bomb in Turing's time. One can still marvel at it but it's been with us since the beginning.