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Andrej Karpathy: Software Is Changing (Again)

Y Combinator1 HN recommender · recommended in 1 month, 2025–2025
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talk · general · 2025–2025 on HN

Andrej Karpathy's keynote at AI Startup School laying out software 1.0, 2.0 and 3.0, with LLMs seen as utilities, fabs and operating systems. It covers LLM quirks, partial-autonomy apps and keeping a human in the generate-and-verify loop.

HN mostly cites it as a source for Karpathy's framing of programming in English and his generate-then-verify workflow, with a few critics finding it hype.

8 comments from Hacker News

@MangoCoffee2025-08 · in “AI is predominantly replacing outsourced, offshore workers”

I've done a vibe coding hobby project where I simply give AI instructions on what I want, using a persona-based approach for the agent to generate or fix the code. It worked out pretty well. Who knows how the software engineering landscape will change in 10 to 20 years? I enjoyed Andrej Karpathy's talk about software in the era of AI. ▶ this video

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@chubot2025-09 · in “Vibe coding cleanup as a service”

Yes definitely, but I'm pretty sure that he was just saying vibe coding is POSSIBLE and a fun thing to try, to see how far you can get (which is true, it can do some surprising things) Not that companies should literally try to "vibe code" production software Two publishers and three authors fail to understand what “vibe coding” means - link ( I think Karpathy addressed what he meant in this video, though I forget the details: Software Is Changing (Again) ▶ this video ) The phrase got totally taken out of context, probably because it's what people WANTED to believe. They wanted to believe that the hard thing was now easy, and they could make/save a lot of money that way

@billmalarky2025-08 · in “GPT-5”

Ah, I probably should have listed some of the "assumptions" I'm developing it on top of: 1) Regarding the "generation is how learning occurs" claim, I'm going off of this: link Granted, that article refers to retrieval specifically being one major way we learn, and of course learning incorporates many dimensions. But it seems a bit self-evident that retrieval occurs heavily during active problem solving (ie "generation"), and less so during passive learning (ie: just reading/consuming info). From personal experience, I always noticed I learned much more by doing than by consuming documentation alone. But yes, I admit this assumption and my own personal experience/bias is doing a lot of heavy lifting for me... 2) Regarding the "optimal AI productivity process" (AI Generates > Human Validates > Loop) I'm using Karpathy's productivity loop described in his AI startup school talk last month here: ▶ this video Does this help make it more concrete Swyx (name dropping you here since I'm pretty sure you've got a social listener set for your handle ;)? Love to hear your thoughts straight from the hip based on your own personal experiences. Full disclosure: I'm not trying to get too academic about this. In all honestly I'm really trying to get to an informal theory that's useful and practical enough that it can be turned into a regular business process for rapid professional development.

@AdieuToLogic2025-06 · in “Andrej Karpathy: Software in the era of AI [video]”

>> A recurring theme presented, however, is that LLM's are somehow not controlled by the corporations which expose them as a service. The presenter made certain to identify three interested actors (governments, corporations, "regular people") and how LLM offerings are not controlled by governments. This is a bit disingenuous. > I don't think that's what he said, he was identifying the first customers and uses. The portion of the presentation I am referencing starts at or near 12:50[0]. Here is what was said: I wrote about this one particular property that strikes me as very different this time around. It's that LLM's like flip they flip the direction of technology diffusion that is usually present in technology. So for example with electricity, cryptography, computing, flight, internet, GPS, lots of new transformative that have not been around. Typically it is the government and corporations that are the first users because it's new expensive etc. and it only later diffuses to consumer. But I feel like LLM's are kind of like flipped around. So maybe with early computers it was all about ballistics and military use, but with LLM's it's all about how do you boil an egg or something like that. This is certainly like a lot of my use. And so it's really fascinating to me that we have a new magical computer it's like helping me boil an egg. It's not helping the government do something really crazy like some military ballistics or some special technology. Note the identification of

@belter2025-06 · in “Andrej Karpathy: Software in the era of AI [video]”

Painful to watch. The new tech generation deserves better than hyped presentations from tech evangelists. This reminds me of the Three Amigos and Grady Booch evangelizing the future of software while ignoring the terrible output from Rational Software and the Unified Process. At least we got acknowledgment that self-driving remains unsolved: ▶ this video And Waymo still requires extensive human intervention. Given Tesla's robotaxi timeline, this should crash their stock valuation...but likely won't. You can't discuss "vibe coding" without addressing security implications of the produced artifacts, or the fact that you're building on potentially stolen code, books, and copyrighted training data. And what exactly is Software 3.0? It was mentioned early then lost in discussions about making content "easier for agents."

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