Comment by keeda
5 days ago
> Core to Ptacek's point is that everything has changed in the last 6 months.
This was actually the only point in the essay with which I disagree, and it weakens the overall argument. Even 2 years ago, before agents or reasoning models, these LLMs were extremely powerful. The catch was, you needed to figure out what worked for you.
I wrote this comment elsewhere: https://news.ycombinator.com/item?id=44164846 -- Upshot: It took me months to figure out what worked for me, but AI enabled me to produce innovative (probably cutting edge) work in domains I had little prior background in. Yes, the hype should trigger your suspicions, but if respectable people with no stake in selling AI like @tptacek or @kentonv in the other AI thread are saying similar things, you should probably take a closer look.
>if respectable people with no stake in selling AI like @tptacek or @kentonv in the other AI thread are saying similar things, you should probably take a closer look.
Maybe? Social proof doesn't mean much to me during a hype cycle. You could say the same thing about tulip bulbs or any other famous bubble. Lots of smart people with no stake get sucked in. People are extremely good at fooling themselves. There are a lot of extremely smart people following all of the world's major religions, for example, and they can't all be right. And whatever else is going on here, there are a lot of very talented people whose fortunes and futures depend on convincing everybody that something extraordinary is happening here.
I'm glad you have found something that works for you. But I talk with a lot of people who are totally convinced they've found something that makes a huge difference, from essential oils to functional programming. Maybe it does for them. But personally, what works for me is waiting out the hype cycle until we get to the plateau of productivity. Those months that you spent figuring out what worked are months I'd rather spend on using what I've already found to work.
The problem with this argument is that if I'm right, the hype cycle will continue for a long time before it settles (because this is a particularly big problem to have made a dent in), and for that entire span of time skepticism will have been the wrong position.
I think it depends a lot on what you think "wrong position" means. I think skepticism only really goes wrong when it refuses to see the truth in what it's questioning long past the point where it's reasonable. I don't think we're there yet. For example, questions like "What is the long term effect on a code base" take us seeing the long term. Or there are legitimate questions about the ROI of learning and re-learn rapidly changing tools. What's worth it to you may not be in other situations.
I also think hype cycles and actual progress can have a variety of relationships. After Bubble 1.0 burst, there were years of exciting progress without a lot of hype. Maybe we'll get something similar here, as reasonable observers are already seeing the hype cycle falter. E.g.: https://www.economist.com/business/2025/05/21/welcome-to-the...
And of course, it all hinges on you being right. Which I get you are convinced of, but if you want to be thorough, you have to look at the other side of it.
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So? The better these tools get, the easier they will be to get value out of. It seems not unwise to let them stabilize before investing the effort and getting the value out, especially if you’re working in one of the areas/languages where they’re still not as useful.
Learning how to use a tool once is easy, relearning how to use a tool every six months because of the rapid pace of change is a pain.
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> You could say the same thing about tulip bulbs or any other famous bubble. Lots of smart people with no stake get sucked in.
While I agree with the skepticism, what specifically is the stake here? Most code assists have usable plans in the $10-$20 range. The investors are apparently taking a much bigger risk than the consumer would be in a case like this.
Aside from the horror stories about people spending $100 in one day of API tokens for at best meh results, of course.
The stake they and I were referring to is a financial interest in the success of AI. Related is the reputational impact, of course. A lot of people who may not make money do like being seen as smart and cutting edge.
But even if we look at your notion of stake, you're missing huge chunks of it. Code bases are extremely expensive assets, and programmers are extremely expensive resources. $10 a month is nothing compared to the costs of a major cleanup or rewrite.
Dude. Claude Code has zero learning curve. You just open the terminal app in your code directory and you tell it what you want, in English. In the time you have spent writing these comments about how you don't care to try it now because it's probably just hype, you could have actually tried it and found out if it's just hype.
I've tried Claude Code repeatedly and haven't figured out how to make it work for me on my work code base. It regularly gets lost, spins out of control, and spends a bunch of tokens without solving anything. I totally sympathize with people who find Claude Code to have a learning curve, and I'm writing this while waiting for Cursor to finish a task I gave it, so it's not like I'm unfamiliar with the tooling in general.
One big problem with Claude Code vs Cursor is that you have to pay for the cost of getting over the learning curve. With Cursor I could eat the subscription fee and then goof off for a long time trying to figure out how to prompt it well. With Claude Code a bad prompt can easily cost me $5 a pop, which (irrationally, but measurably) hurts more than the one-time monthly fee for Cursor.
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I could not have, because my standards involve more than a five minute impression from a tool designed to wow people in the first five minutes. Dude.
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> "Even 2 years ago, before agents or reasoning models, these LLMs were extremely powerful. The catch was, you needed to figure out what worked for you."
Sure, but I would argue that the UX is the product, and that has radically improved in the past 6-12 months.
Yes, you could have produced similar results before, manually prompting the model each time, copy and pasting code, re-prompting the model as needed. I would strenuously argue that the structuring and automation of these tasks is what has made these models broadly usable and powerful.
In the same way that Apple didn't event mobile phones nor touchscreens nor OSes, but the specific combination of these things resulted in a product that was different in kind than what came before, and took over the world.
Likewise, the "putting the LLM into a structured box of validation and automated re-prompting" is huge! It changed the product radically, even if its constituent pieces existed already.
[edit] More generally I would argue that 95% of the useful applications of LLMs aren't about advancing the SOTA model capabilities and more about what kind of structured interaction environment we shove them into.
For sure! I mainly meant to say that people should not attribute the "6 more months until it's really good" point as just another symptom of unfounded hype. It may have taken effort to effectively use AI earlier, which somewhat justified the caution, but now it's significantly easier and caution is counter-productive.
But I think my other point still stands: people will need to figure out for themselves how to fully exploit this technology. What worked for me, for instance, was structuring my code to be essentially functional in nature. This allows for tightly focused contexts which drastically reduces error rates. This is probably orthogonal to the better UX of current AI tooling. Unfortunately, the vast majority of existing code is not functional, and people will have to figure out how to make AI work with that.
A lot of that likely plays into your point about the work required to make useful LLM-based applications. To expand a bit more:
* AI is technology that behaves like people. This makes it confusing to reason about and work with. Products will need to solve for this cognitive dissonance to be successful, which will entail a combination of UX and guardrails.
* Context still seems to be king. My (possibly outdated) experience has been the "right" context trumps larger context windows. With code, for instance, this probably entails standard techniques like static analysis to find relevant bits of code, which some tools have been attempting. For data, this might require eliminating overfetching.
* Data engineering will be critical. Not only does it need to be very clean for good results, giving models unfettered access to the data needs the right access controls which, despite regulations like GDPR, are largely non-existent.
* Security in general will need to be upleveled everywhere. Not only can models be tricked, they can trick you into getting compromised, and so there need to even more guardrails.
A lot of these are regular engineering work that is being done even today. Only it often isn't prioritized because there are always higher priorities... like increasing shareholder value ;-) But if folks want to leverage the capabilities of AI in their businesses, they'll have to solve all these problems for themselves. This is a ton of work. Good thing we have AI to help out!
I don't think it's possible to understand what people mean by force multiplier re AI until you use it to teach yourself a new domain and then build something with that knowledge.
Building a mental model of a new domain by creating a logical model that interfaces with a domain I'm familiar with lets me test my assumptions and understanding in real time. I can apply previous experience by analogy and verify usefulness/accuracy instantly.
> Upshot: It took me months to figure out what worked for me, but AI enabled me to produce innovative (probably cutting edge) work in domains I had little prior background in. Yes, the hype should trigger your suspicions[...]
Part of the hype problem is that describing my experience sounds like bullshit to anyone who hasn't gone through the same process. The rate that I pick up concepts well enough to do verifiable work with them is literally unbelievable.
AI posts (including this one) are all over his employers blog lately, so there’s some stake (fly MCP, https://fly.io/blog/fuckin-robots/, etc).