Posted by aaronbrethorst 1 day ago
I’m tired of these endless articles on HN about software engineers trying to reinvent their identity while trying not to lose touch with reality.
One way of dealing with LLMs is to deny the skill level of LLMs. Claim they can’t code as well as you. This excuse works to a certain extent but it also fails because not only are their multitudes of cases where the LLM IS intrinsically worse than me… but there are multitudes of cases where it is better. So this excuse cannot be universally true.
The other way is to claim software engineering was never the hard part of engineering and that other things were harder and that was always where your primary skill was located. This excuse is also idiotic. First, Software engineering is hard. It is genuinely not something that anyone can pick up very quickly. Second, all those other “skills” like “domain expertise” are STILL targets for the LLM. It’s not like the LLM exclusively is only good at software.
Just face the goddamn truth. AI is on a trajectory to dominate. That’s what all the trendlines say. It’s not currently dominating, but it’s close, and the trajectory points to an endgame where it is fundamentally better. The trendline could be wrong but the trendline is the best quantitative predictor we have and it’s been trumping all the half baked theories on HN where people were claiming self driving cars would never happen and AI could never code. HN was historically wrong… the trendlines and the VCs who made those bets have been right. So who’s the bigger idiot? Those VCs creating the AI bubble or HNers who have been continuously wrong about everything? (Minus crypto, HNers were right about crypto).
If the trendline is true our skills as engineers not just the software part is on track to being dominated by an artificial intelligence. The tools trivialize your skills until all the moats are gone. Not only that… AI is becoming better at art. Poetry, writing, paintings, music… AI shows us how trivially reproduceable all of it is. That is the truth. We aren’t not unique and all the meaning behind being human is just an algorithm. It’s all reproducible. Even your self delusional attempt to deny and delude yourself away from these truths is predictable. I can see someone formulating a retort right now.
Even people whose job can't be done by AI will be impacted because there will be far less demand for their services (everyone whose job IS directly AI replaceable will be a brokie) and there will also be far more supply of people moving into their field to escape all the jobs AI does directly replace.
"Join the trades" is the new "learn to code" in terms of seeming like good advice but having a very short shelf life.
The trades are great, but not a panacea. Maybe emigrate to a country with better conditions for the working class.
Why do you think people get trained by a PT in person? Its not simply training - it actually goes well beyond into the realm of 'wellness'. man you are a certified bozo.
It takes a lot of balls to call me a bozo when it's obvious you're the one who's an idiot.
Have you?
I still drive my car and self driving cars have yet to displace human drivers. I think the sentiment on HN and other places when Google started talking self driving cars circa 2009 is that it's harder than it looks. Typically the first 80% of progress is easy and the rest isn't as easy. We're almost 20 years after a "pretty good self driving car" and we're still not at "self driving cars outperform humans under all situations".
Today humans use AI. You can't fire up Claude and ask it "what do you want to work on today". The amount of context we have as humans is vastly larger than the context LLMs have. If you give LLMs vague context they're completely lost. They are mind blowing in many ways but they are not anywhere close to AGI. They're also not close to being able to build complex software only guided by someone who has no idea what software is and how computers work. They can do some of that but I've yet to see any major successful piece of software built that way. They also consume vast physical resources to get the job done.
Before LLMs I think it was a given that at some point we would have AGI that's smarter than us. Machines we build aren't constrained by the biological constraints we are subject to and can evolve faster than we have. But when that's gonna happen, whether that's actually LLM-like in architecture, and what things will look like once that happens, are fairly open questions at this point. In the mean time LLMs can certainly generate a lot of code and we can use them to build more stuff.
It was always true even before AI, AI just makes it more evident since Transformers are LITERALLY an algorithm that produces content nearly identical to content humans produce.
Edit: Yes "expert" was too strong a word. Proficient would be better. A lot of the barrier to entry in a field is just not understanding the domain.
I've consulted for and led large teams for real estate title insurance and escrow companies for many years, and the domain expertise is so incredibly deep, nuanced, and multivariate (especially depending on jurisdiction) that building valuable and viable products in the space is incredibly difficult - before LLMs, and even now, with LLMs.
Without getting too deep into it, I'm pretty bullish on AI (and have been very close to it and deep in it for a long time, while also very apprehensive about the effects it'll have on society), and I can tell you, from extensive attempts from myself and many on my teams to leverage the latest frontier LLMs to bring deep domain experience to bear to help drive valuable products: we have not yet seen success. It's not helping engineering folks, it's not helping product folks. It's creating a ton of questionable output and hasn't resulted in real ROI, and it's not capable of accurately answering deep domain questions without hallucinations or assuming what works in one jurisdiction works in all.
I've seen success in many other areas, but not this domain - and, importantly, the regulatory environment in which title insurance operates is incredibly complex and strict, meaning you can't just YOLO LLM output into production (as much as we'd love to try so we can learn at a faster clip).
And the kicker: we've found the way for us to build the best products is still going out into the field, sitting with escrow and title folks, watching them work, asking them questions, and designing for the real world, the regulatory nuances, the local client nuances, etc. You can't get that from an LLM.
how does that work exactly?
I work in e-commerce and warehouse management.
We have put lots of effort at documenting the domain, creating precise unambiguous language, glossaries, E2Es written as user stories etc, etc.
And still models are simply not able to translate Jira tasks to clear specs, even for this well understood and common use case.
Also, they don't understand how changes in one part of the business domain will impact other parts. They can get it right 9 times out of 10, but even that is too little and compounds to deeply wrong implementations.
And they don't understand or know the people involved in these processes and what they REALLY care for or what the real priorities are. Very often political.
And that's not even mentioning the code, that ends up with the lack of proper abstractions or harness.
Or the lack of push back against bad ideas at business or code level.
AI is, at best, as useful as those masses. Actual discoveries, actual novel software, actual human advancement is beyond AI and the domain of the same humans who've always advanced technology.
So yeah, AI is ok for copy-pasting the same shit that we used to plug together web frameworks for, it's fine for internet research (Gemini for me is like a supercharged Google with no ads or SEO garbage), it's fine for repetitive emails and making my "fuck you" emails sound professional, but actual expertise isn't going away any time soon.
Also, I disagree that software engineers can "just learn" non-software domains. If there's one thing I've found about most people who call themselves "engineers", it's that their thinking is way too rigid for many other domains.