8 ms·
> 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
by einrealist 9mo ago
> 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.
- daxfohl 9mo agoI 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 9mo agoThe 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?
- fragmede 9mo agoThe free market proposition is that competition (especially with Chinese labs and grok) means that Anthropic is welcome to do that. They're even welcome to illegally collude with OpenAi such that ChatGPT is similarly gimped. But switching costs are pretty low. If it turns out I can one shot an issue with Qwen or Deepseek or Kimi thinking, Anthropic loses not just my monthly subscription, but everyone else's I show that too. So no, I think that's some grade A conspiracy theory nonsense you've got there.
- thunderfork 9mo agoAs a rational consumer, how would you distinguish between some intentional "keep pulling the slot machine" failure rate and the intrinsic failure rate? I feel like saying "the market will fix the incentives" handwaves away the lack of information on internals. After all, look at the market response to Google making their search less reliable - sure, an invested nerd might try Kagi, but Google's still the market leader by a long shot. In a market for lemons, good luck finding a lime.
- krupan 9mo agoFWIW, kagi is better than Google
- direwolf20 9mo agoyes, that was their point. Everyone uses Google anyway.
- coffeefirst 9mo agoIt’s not that crazy. It could even happen by accident in pursuit of another unrelated goal. And if it did, a decent chunk of the tech industry would call it “revealed preference” because usage went up.
- hnuser123456 9mo agoLLMs became sycophantic and effusive because those responses were rated higher during RLHF, until it became newsworthy how obviously eager-to-please they got, so yes, being highly factually correct and "intelligent" was already not the only priority.
- jrflowers 9mo agoThis is a good point. For example if you have access to a bunch of slot machines, one of them is guaranteed to hit the jackpot. Since switching from one slot machine to another is easy, it is trivial to go from machine to machine until you hit the big bucks. That is why casinos have such large selections of them (for our benefit).
- sailfast 9mo agoThat’s far-fetched. It’s in the interest of the model builders to solve your problem as efficiently as possible token-wise. High value to user + lower compute costs = better pricing power and better margins overall.
- xienze 9mo ago> It’s in the interest of the model builders to solve your problem as efficiently as possible token-wise. Unless you’re paying by the token.
- d0mine 9mo ago> far-fetched Remember Google? Once it was far-fetched that they would make the search worse just to show you more ads. Now, it is a reality. With tokens, it is even more direct. The more tokens users spend, the more money for providers.
- throwthrowuknow 9mo agoOnly if you are paying per token on the API. If you are paying a fixed monthly fee then they lose money when you need to burn more tokens and they lose customers when you can’t solve your problems within that month and max out your session limits and end up with idle time which you use to check if the other providers have caught up or surpassed your current favourite.
- layla5alive 9mo agoIndeed, unlimited plan seems like the only way that makes sense to not have it be guaranteed to be abused by the provider
- retsibsi 9mo ago> Now, it is a reality. What are the details of this? I'm not playing dumb, and of course I've noticed the decline, but I thought it was a combination of losing the battle with SEO shite and leaning further and further into a 'give the user what you think they want, rather than what they actually asked for' philosophy.
- password4321 9mo agoFirst time I've seen this idea, I have a tingling feeling it might become reality sooner rather than later.
- Fnoord 9mo agoI was thinking more of deliberate backdoor in code. RCE is an obvious example, but another one could be bias. "I'm sorry ma'am, computer says you are ineligable for a bank account." These ideas aren't new. They were there in 90s already when we still thought about privacy and accountability regarding technology, and dystopian novels already described them long, long ago.
- chanux 9mo agoIs this from a page of dating apps playbook?
- direwolf20 9mo agoyes
- charcircuit 9mo agoYou 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.
- testaccount28 9mo agoyou are using it on easy questions. some of us are not.
- baq 9mo agoDon’t use it for hard questions like this then; you wouldn’t use a hammer to cut a plank, you’d try to make a saw instead
- mikkupikku 9mo agoI think a lot of it comes down to how well the user understands the problem, because that determines the quality of instructions and feedback given to the LLM. For instance, I know some people have had success with getting claude to do game development. I have never bothered to learn much of anything about game development, but have been trying to get claude to do the work for me. Unsuccessful. It works for people who understand the problem domain, but not for those who don't. That's my theory.
- samrus 9mo agoIt works for hard problems when the person already solves it and just needs the grunt work done It also works for problems that have been solved a thousand times before, which impresses people and makes them think it is actually solving those problems
- daxfohl 9mo agoWhich matches what they are. They're first and foremost pattern recognition engines extraordinaire. If they can identify some pattern that's out of whack in your code compared to something in the training data, or a bug that is similar to others that have been fixed in their training set, they can usually thwack those patterns over to your latent space and clean up the residuals. If comparing pattern matching alone, they are superhuman, significantly. "Reasoning", however, is a feature that has been bolted on with a hacksaw and duct tape. Their ability to pattern match makes reasoning seem more powerful than it actually is. If your bug is within some reasonable distance of a pattern it has seen in training, reasoning can get it over the final hump. But if your problem is too far removed from what it has seen in its latent space, it's not likely to figure it out by reasoning alone.
- wvenable 9mo ago> 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.
- teaearlgraycold 9mo agoAnd after manually coding I often have an LLM review the diff. 90% of the problems it finds can be discounted, but it’s still a net positive.
- einrealist 9mo agoAnd there is this paradox where it becomes harder to detect the problems as the models 'improve'.
- fooker 9mo ago> 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)
- peaseagee 9mo agoThat'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?
- simianwords 9mo ago"repairing a unique clock" getting costlier doesn't mean technology hasn't gotten cheaper. check out whether clocks have gotten cheaper in general. the answer is that it has. there is no economy of scale here in repairing a single clock. its not relevant to bring it up here.
- ipaddr 9mo agoClocks prices have gone up since 2020. Unless a cheaper better way to make clocks has emerged inflation causes prices to grow.
- YetAnotherNick 9mo agoWith 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/2026/01/Figure-38-2-png.webp https://developer-blogs.nvidia.com/wp-content/uploads/2026/0...
- storystarling 9mo agoElectricity 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.
- YetAnotherNick 9mo agoYes, because the margin is like 80% for Nvidia, and 80% again for the manufacturers like Samsung and TSMC. Once the fixed cost like R and D is amortized the same node technology and hardware capacity could be just few single digit percent of current.
- hahahahhaah 9mo agoIt is also amazing seeing Linux kernel work, scheduling threads, proving interrupts and API calls all without breaking a sweat or injuring its ACL.
- cyode 9mo agoThis 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.
- BeetleB 9mo agoThis 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).
- rishabhaiover 9mo agoThat's just sad. Right when I found love in what I do, my work has no value anymore.
- jasonfarnon 9mo agoAren't you still better off than the rest of us who found what they love + invested decades in it before it lost its value. Isn't it better to lose your love when you still have time to find a new one?
- mikeocool 9mo agoTo 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.
- redox99 9mo ago> 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.
- crazygringo 9mo ago> 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.
- bob1029 9mo agoHumans 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 9mo agoI mean, “equivalent” is an understatement! There’s a reason Claude Code costs less than hiring a full time software engineer…
- direwolf20 9mo ago(it's VC money burn)
- chasebank 9mo agoI 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
- utopiah 9mo agoAI 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.
- karlgkk 9mo ago> 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 9mo agoWe 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.
- karlgkk 8mo agoActually, that doesn’t mean anything. OAI is running boundary pushing large models. I don’t think those “second tier” applications can even get the GPUs with the HBM required at any reasonable scale for customer use. Not to mention training costs of foundation models
- moooo99 9mo agoMy 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 9mo ago> 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?