I think the author is mostly right about the current state of AI, but his future predictions (or worries) are based on a false premise: that the massive LLMs will keep improving in the future.
As far as I have seen the improvements have clearly slowed down, while the energy consumption is rising linearly (or worse). It’s like the energy (money) vs. performance graph is logarithmic, and the companies are expending double the energy to get a 10% improvement. Something like that is not sustainable, and the money seems to indicate so.
I really think that LLMs are a dead-end for AI. A really useful dead-end, once the bubble pops and with time, we get a useful working model for them, probably based mostly on local LLMs, maybe using specialized training data.
Energy efficiency has improved by orders of magnitude - leading to much higher energy use. It’s the Jevons paradox and it’s as old as coal-gas lighting. Last year some guy recreated GPT2 for twenty bucks. Corpus to model in one hour. OpenAI never said how much the original cost, but there was at least one comma.
But yeah, LLMs are fundamentally limited, because ‘what’s the next word’ shouldn’t work. The fact it’s accidentally this flexible and powerful, even with its many infamous fuckups, is a reminder that neural networks in general will permanently alter computing. Models trained on supercomputers can run on any potato. Any problem with good examples can be addressed, without first being solved.
LLMs are fundamentally limited, because ‘what’s the next word’ shouldn’t work.
Yes, you’re right. However, for fear of coming off as an AI sycophant (I’ve yet to sacrifice my brain at the altar of our future AI overlords), LLMs aren’t the whole picture. Plenty of research is dedicated to essentially combining the best of each class of AI algorithms into a composite model of intelligence. For instance, “Neuro-Symbolic AI” is really just the result of giving an LLM (good at translation, search, synthesis, bad at symbolic reasoning) a symbolic inference engine like Prolog (good at symbolic reasoning, no native ability for translation/search/synthesis). I’ve been coding for over 20 years, and I’m impressed at its results for software development.
This all is reminiscent of Moore’s Law; even though we keep running into the physical limits of CPU clock speeds, transistor size, etc. we keep finding clever ways to work around those limits.
Of course I’m not saying we should; these models are, after all, models of intelligence, not wisdom.
Edit: fix apostrophe splice
Being reasonable about the tech is kind of a pain, here. At least we only got one flavor of campist, so anyone seeing outright “boosters” is jumping at shadows. I just think the chatbot that can code is neat. Maybe we can do stuff with it. Maybe it doesn’t need to spy on everyone forever.
We accidentally invented p-zombies and they’re already more intelligent than script kiddies. Once the grifters move on we can see what that’s good for.
Agreed. The bubble has to pop eventually… Or not, and we really are marching towards our own obsolescence as a species.
I mean, AGI is inevitable, but it’s never gonna come from these dinguses. They can’t even look past LLMs far enough to pursue text diffusion.
To imagine we cannot possibly build a mind, or that it cannot possibly improve that same effort, is baffling. It changes the shape of the universe.
Just because it’s possible does not mean it’s inevitable. It’s incredibly optimistic to think that we can get our shit together enough to pull it off before we destroy all our productive capacity through hubris.
Nothing’s inevitable. And as for “building a mind”, while it depends on precisely what you mean by “mind”, it’s totally possible that only a biological brain can produce minds as we understand the word “mind”. Building AGI doesn’t necessarily mean building a mind. And since thoughts seem to be properties of “matter”, and there seem to be rules about which configurations of matter produce mind, we don’t necessarily know that there are other configurations that can produce minds. We might produce something else equally interesting which still is not a mind.
it’s totally possible that only a biological brain can produce minds as we understand the word “mind”.
Bollocks. Thought is a process, like math. Nothing meat does with signals is impossible in other substrates.
At the utmost extreme: surely we can simulate physics at whatever level is necessary for virtual brains to function. Physical neurons are not gonna rely on quantum chromodynamics. Mere chemistry will probably suffice.
And hand-waving things that are like-minds-but sounds like Chinese Room nonsense.
Yeah, I find it difficult, too, especially since management hasn’t caught onto this yet and still wants me to specialize.
And of course, the answer is that I should specialize in AI, because there’s currently a lot of new development happening there. But that knowledge is also getting obsolete by the minute, with ever more tools coming out and then again other tools that operate those tools for you.The one thing I hold onto, is that no matter how the situation evolves, the basic job requirements for software engineering, i.e. being smart and being able to learn quickly, will always be an advantage.
I don’t think it’s possible to hold onto the confort zone from before, even if the industry implodes from the AI costs becoming transparent. But yeah, I do think we’ll land on our feet in one way or another.I’m not sure about specializing, but there’s a lot of stuff you can learn in the AI field that was useful before the current hype bubble and will remain useful going forward. Traditional ML is huge and doesn’t move quite as fast as e.g. LLM applications.
Oh yeah, for sure, but they do mean generative AI in this case, because it’s the hype du jour.
Try to argue with the LLM evangelists, the inevitable brain damage it causes will let you get on disability.
On one hand this is not the first hype cycle, on the other hand the other hype cycles didn’t all fade. The inevitable bullshit brought on by vibecoding and shit like that is eventually going to be some kind of problem. People may or may not just ignore that problem, like most other issues in tech.
The post misses a few things:
- The ai bubble is currently being subsidized to an unimaginable degree. If you were to actually pay true cost for your token usage, you wouldn’t be saving that much over an engineer’s salary. Probably even worse once AI companies start to extract a real profit. 95% of companies diving into agentic labor will be in for a rude awakening when they balance next year’s budget.
- The cost to keep ai useful in its current form has a high floor. Unless you keep up with expensive training, your models will drift. You can only scale your model intelligence with more hardware (roughly). In two years, Claude opus 4.8 will still be bloating context to learn about the latest cloud platforms and libraries. A human engineer will get those passively at no cost to the company.
- As the complexity of the task grows the complexity of the ai babysitter must match it. Even if Ai stays cost effective, companies can now save money by spinning up bespoke in-house software to cut out vendors (think observability platforms, task tracking, product design, marketing systems, etc…). No matter how many adversarial reviews and sub agents you spin up, an Ai can’t grasp the full context of your company and it’s shifting priorities. The software engineer role transitions to a pseudo-sysadmin + product architect.
C-suites don’t want know about software and don’t care about non functional requirements (security, availability, audit ability, etc…). They just want to wave a magic wand and have a product appear, which is what Ai provides the illusion of. That’s why all current Ai software is garbage, but the smarter companies will catch on
A human engineer will get those passively at no cost to the company.
Some human engineers. I still regularly have to explain Git to people that never bothered to learn.
Also I think you are forgetting that AI is still improving and getting cheaper. Not super quickly, but even if it is too expensive now, is that still going to be true in 10 years? I doubt it.
People kept saying same thing about self driving cars. Look where it will be in 5 to 10 years. 10 years later and … meh
It’s expensive to keep those models trained. Investors won’t do this forever.
We literally have self-driving cars now. In super-easy driving locations like San Francisco to be fair, but that’s not nothing. They’re supposed to be launching in London this year too.
Okey dokey
It’s not getting cheaper. Every new model has been getting more expensive. OpenAI and Anthropic both want to IPO, but that means they need to start trying to make a profit. Their prices have been going through the roof.
It is getting cheaper to do the same thing. New models are more expensive but also more capable.
But it’s not getting cheaper to do the same thing. Models charge by the token the price per token is going up. They don’t charge by the outcome.
The price per token for the same model isn’t going up (if you are paying per token anyway - I know they underpriced subscriptions).
Those same models can only do the shitty work they were able to do before the new models came out. You’re not making a point that anything is getting cheaper.
Same here I’m in awe that humanity has made it this far. I can’t believe the shit I have to put up with
I’m still employed and I see myself employed (at least in that company) for a foreseeable future.
Hmm, well, ok. Moving on then
Well frankly i think the field of eco terrorism is about to expand greatly so maybe start preparing for that.
But I’m just another off-the-shelf engineer now. I have no domain expertise that another Sr. engineer steering an LLM cannot match.
Funny because I think this is more important than ever. You can produce (ugly) code at remarkable pace now, being able to detect where it goes wrong is a very important skill nowadays.
I still can’t let it loose too much because of all the garbage it still produces when given too much freedom. I’m honestly annoyed that my colleagues use it that much on production code, because it’s so much meh code with hidden (on the surface) inefficiencies and a lot of avoidable boilerplate and duplication.
Then on the other hand I enjoy that it can create scripts (that are tedious to write) and prototypes at impressive speed. Generally prototyping and research has gotten quite enjoyable because you can stay most of the time on the theoretical side of things, quickly try things out or quickly get info to stuff you don’t yet understand enough.
Not sure I agree with the “three pillar” framing in this one. I think it’s more like he was able to use the LLMs effectively because he had already built those years of experience. Someone new trying to vibe code their way into designing robust distributed systems is going to need a similar amount of time to build the correct intuitions, because the LLM doesn’t give a shit one way or the other, it’ll just do what’s in the prompt.
On the other hand, commodotizing domains that fit a repeatable pattern isn’t so bad, is it? At work I sometimes have to remind people that we’re generally just building CRUD apps to interact with data, which isn’t exactly at the bleeding edge of software. I like the idea of software engineers getting to be more portable, but unfortunately the industry still thinks LLMs are going to replace most jobs, which is obviously not possible.
The answer is start a union that strikes over AI usage.
The only thing to do is ride the wave of the new technology. You either do that or drown. I’ve written a literal book’s worth of text on how AI should think and behave when writing software and it straight up cannot write code as well as me when left to its own devices, even with all my guidance. LLMs, no matter the size, are structurally incapable of holding all the context needed to write good code in complex systems because they cannot compress complex topics into ideas they’ve never encountered and reason about the compressed state with their limited reasoning capacity (otherwise known as “learning”).
Learn to code.
Read the post.
Actually, reading stuff can be a bit of a high bar for some folks, unfortunately.
Do you have a couple sentence summary for me?
Y’all Software engineers went out to become programmers and made upwards of 300k a year as entry level job. Now you have a tough time like the rest of the working population? What 70k a year not good enough anymore? Get fucked.
I feel you, but most of us don’t work for silicon valley, in most countries the salaries are average to slightly above average, and being fired or forced to work with tools you don’t want sucks, which is also a workers issue
+1 for a nuanced take. My America-as-default bias is strong and you rightfully call me out on that.
I also notice this isn’t the shitposting sub… ¯\_(ツ)_/¯
it’s coming for whatever job you got too
Something like 1% of devs make that much. A lot make your 70k.
Worker solidarity is about lifting people up, not pushing everyone down.
