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This is also bad evangelism, but on opposite side. Just because LLMs don't work for you outside of vibe-coding, doesn't mean it's the same for everyone. > LLM evangelists - are you willing to admit that you just might not be that good at programming computers? Productive usage of LLMs in large scale projects become viable with excellent engineering (tests, patterns, documentation, clean code) so perhaps that question…
5 anti-AI posts on the home page of Hacker News…yeah, plenty of insecure evangelism amongst the skeptics, too.
I think it goes further than this. Some people - some developers, even - do not _like_ programming computers. In fact, many hate it. Those people welcome the LLM agent stuff because it delivers the end product without going through the necessary pain (from their pov) of programming.
If I can build better/faster with reasonably equal quality, I'll trade off the joy of programming for the joy of more building, of more high level problem solving and thinking, etc.
I've also seen the opposite: those that derive more joy from the programming and the cool engineering than from the product. And you see the opposite behavior from them, of course--such as selecting a solution that's cool and novel to build, rather than the simple, boring, but better alternative.
I often find this type of engineer rather frustrating to work with, and coincidentally, they seem to be the most anti-AI type I've encountered.
It's a lot like why I've been bullish on Tesla's approach to FSD even as someone who owned an AP1 vehicle that objectively was NOT "self-driving" in any sense of the word: it's less about where the technology is right now, or even the speed the technology is currently improving at, and more about how the technology is now present to enable acceleration in the rate of improvement of performance, paired with the reality of us observing exactly that. Like FSD V12 to V14, the last several years in AI can only be characterized as an unprecedented rate of improvement, very much like scientific advancement throughout human society. It took us millions of years to evolve into humans. Hundreds of thousands to develop language. Tens of thousands to develop writing. Thousands to develop the printing press. Hundreds to develop typewriters. Decades to develop computers. Years to go from the 8086 to the modern workstations of today. The time horizon of tasks AI agents can now reliably perform is now doubling every 4 months, per METR.
Do frontier models know more than human experts in all domains right now? Absolutely not. But they already know far more than any individual human expert outside that human's domain(s) of expertise.
I've been passionate about technology for nearly two decades, working in the technology industry for close to a decade. I'm a security guy, not a dev. I have over half a dozen CVEs and countless private vuln disclosures. I can and do write code myself - I've been writing scripts for various network tasks for a decade before ChatGPT ever came into existence. That said, it absolutely is a better dev than me. But specialized harnesses paired with frontier models are also better security engineers than I am, dollar for dollar versus my cost. They're better pentesters than me, for the relative costs. These statements were not true at all without accounting for cost two years ago. Two years from now, I am fully expecting them to just be outright better at security engineering, pentesting, SCA than I am, without accounting for cost, yet I also expect they will cost less then than they do now.
A year ago, OpenAI's o1 was still almost brand new, test-time compute was this revolutionary new idea. Everyone thought you needed tens of billions to train a model as good as o1, it was still a week before Deepseek released R1.
Now, o1's price/performance seems like a distant bad dream. I had always joked that one quarter in tech saw as much change as like 1 year in "the real world". For AI, it feels more like we're seeing more change every month than we do every year in "the real world", and I'd bet on that accelerating, too.
I don't think experienced devs still preferring to architect and write code themselves are coping at all. I still have to fix bugs in AI-generated code myself. But I do think it's short sighted to not look at the trajectory and see the writing on the wall over the next 5 years.
Stanford's $18/hr pentester that outperforms 9/10 humans should have every pentester figuring out what they're going to be doing when it doubles in performance and halves in cost again over the next year, just like human Uber drivers should be reading Motortrend's (historically a vocal critic of Tesla and FSD) 2026 Best Driver Assistance System and figuring out what they're going to do next. Experienced devs should be looking at how quickly we came from text-davinci-003 to Opus 4.5 and considering what their economic utility will look like in 2030.
I like OP's representation, but I feel like a lot of people arent saying 'LLMs are the bomb dot com _right now_' (though some are), but rather the trend is evident: these things will keep getting better, and the writing is on the wall. Personally I think the rate of improvement will plateau: in my experience software inevitably becomes less about tech and more about the interpersonal human soup of negotiating require…
Earlier quoted context omitted.
How many lives would AI have to save for you to say the energy cost is worth it?
How many lives have been saved by AI? How many lives have been lost because of it?
Hearing people on tech twitter say that LLMs always produce better code than they do by hand was pretty enlightening for me. LLMs can produce better code for languages and domains I’m not proficient in, at a much faster rate, but damn it’s rare I look at LLM output and don’t spot something I’d do measurably better. These things are average text generation machines. Yes you can improve the output quality by writing a…
BUT
An LLM can write a PNG decoder that works in whatever language I choose in one or a few shots. I can do that too, but it will take me longer than a minute!
(and I might learn something about the png format that might be useful later..)
Also, us engineers can talk about code quality all day, but does this really matter to non-engineers? Maybe objectively it does, but can we convince them that it does?
This doesn't feel completely right. Simon Wilson (known for Django) has been doing a lot of LLM evangelism on his blog these days. Antirez (Redis) wrote a blog post recently with the same vibe. I doubt they are not good programmers. They are probably better than most of us, and I doubt they feel insecure because of the LLMs. Either I'm wrong, or there's something more to this. edit: to clarify, I'm not saying Simon a…
Also they didn't adopt the your-career-is-ruined-if-you-don't-get-on-board tone that is sickeningly pervasive on LinkedIn. If you believe that advice and give up on being someone who understands code, you sure aren't gonna write Redis or Django.