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Does anyone actually know what the real cost for the customers will be once the free AI money no longer floods those companies?
I'm no LLM evangelist, far from it, but I expect models of similar quality to the current bleeding-edge, will be freely runnable on consumer hardware within 3 years. Future bleeding-edge models may well be more expensive than current ones, who knows.
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Wow. Quite a conclusion from an article that actually doesn't reach for that conclusion
It’s a bad and incoherent essay. It simultaneously wants to say “we can’t know”, “we do know and it doesn’t work”, and “we know it makes engineering faster and that’s bad because most engineering is bad.” This isn’t nuance, it’s confusion. Sorry!
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> It is something to sneeze at if you are 10-15% more expensive to employ due to the cost of the LLM tools. Claude Max is $200/month, or ~2% of the salary of an average software engineer.
Does anyone actually know what the real cost for the customers will be once the free AI money no longer floods those companies?
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> I don't disagree with your assessment of the world today, but just 12 months ago (before the current crop of base models and coding agents like Claude Code), even that 10X improvement of writing some-of-the-code wouldn't have been true. So? It sounds like you're prodding us to make an extrapolation fallacy (I don't even grant the "10x in 12 months" point, but let's just accept the premise for the sake of argument).…
12 months ago, if I fed a list of ~800 poems with about ~250k tokens to an LLM and asked it to summarize this huge collection, they would be completely blind to some poems and were prone to hallucinating not simply verses but full-blown poems. I was testing this with every available model out there that could accept 250k tokens. It just wouldn't work. I also experimented with a subset that was at around ~100k tokens…
for the past week claude code has been routinely ignoring CLAUDE.md and every single instruction in it. I have to manually prompt it every time.
As I was vibe coding the notes MCP mentioned in the article [1] I was also testing it with claude. At one point it just forgot that MCPs exist. It was literally this:
> add note to mcp
Calling mcp:add_note_to_project
> add note to mcp
Running find mcp.ex
... Interrupted by user ...
> add note to mcp
Running
I follow Emily Bender on LinkedIn. She cuts through the AI hype and is also the author of The AI Con book - https://thecon.ai/ Of course people will either love AI or hate AI - and some don’t care. I am cautious especially when people say ‘AI is here to stay’. It takes away agency.
AI is here to stay, and you do not have agency over that. You can choose not to use it, but that has zero impact on the broader adoption rate. Just like when the automobile was introduced and society as a whole evolved.
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You believe the cost of firing and rehiring to be cheaper than simple empirical persuasion? You don't sound like a great lead to me, but I suppose you could be working with absolutely incompetent individuals, or perhaps your soft skills need work. My apologies but I see only two possibilities for others not to take the time to follow your example given such strong evidence. They either actively dislike you or are tot…
You have about 50% of HN thinking LLMs are useless and you’re commenting on an article about how it’s still magical and wishful thinking, and that this is crypto all over again. But sure, the problem is me, not the people with a poor model of reality
Perhaps you should try reading the article again (or maybe let some LLM summarize it for you)
> But sure, the problem is me, not the people with a poor model of reality
Is amazing how you almost literally use crypto-talk
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Pics or it didn't happen. You're doing the same thing the article talks against. Some people claim miraculous results, while the reality for most is far less successful. But maybe you keep rolling the LLM dice and you keep winning? I personally don't like gambling with my time and energy, especially when I know the rules of the game are so iffy.
Nah I’m all over the place. I said the last 10, to check if the 90% claim could be true if you do what I’ve done recently: use it for tons of little general ad hoc things rather than eg code needing serious accuracy. I don’t “trust” it in the way I’d trust a smart colleague. We know how this works: use it for info that has a lot of results, or ask it to ground itself if it’s eg new info and you can’t rely on training…
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I find that the code quality LLMs output is pretty bad. I end up going through so many iterations that it ends up being faster to do it myself. What I find agents actually useful for is doing large scale mechanical refractors. Instead of trying to figure out the perfect vim macro or AST rewrite script, I'll throw an agent at it.
I disagree strongly at this point. The code is generally good if the prompt was reasonable at this point but also every test possible is now being written, every ui element has the all required traits, every function has the correct documentation attached, the million little refactors to improve the codebase are being done, etc. Someone told me ‘ai makes all the little things trivial to do’ and i agree strongly with…
Which, again, is 100% unverifiable and cannot be generalized. As described in the article.
How do I know this? Because, as I said in the article, I use these tools daily.
And "prompt was reasonable" is a yet another magical incantation that may or may not work. Here's my experience: https://news.ycombinator.com/item?id=44470144
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I find that the code quality LLMs output is pretty bad. I end up going through so many iterations that it ends up being faster to do it myself. What I find agents actually useful for is doing large scale mechanical refractors. Instead of trying to figure out the perfect vim macro or AST rewrite script, I'll throw an agent at it.
> I find that the code quality LLMs output is pretty bad. That was my experience with Cursor, but Claude Code is a different world. What specific product/models brought you to this generalization?
This reads like the author is mad about imprecision in the discourse which is real but to be quite frank more rampant amongst detractors than promoters, who often have to deal with the flaws and limitations on a day to day basis. The conclusion that everything around LLMs is magical thinking seems to be fairly hubristic to me given that in the last 5 years a set of previously borderline intractable problems have beco…
"detractors" usually point to actual flaws. "promoters" usually uncritically hail LLMs as miracles capable of solving any problem in one go, without giving any specific details.