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.
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
Nobody needs A or B-grade codebases anymore because they’re being made for LLMs, not for humans to read.
That will come back to bite them in the arse, mark my words.
It’s also patently false.
A good chunk of good patterns are to make sure humans understand it sure. But a good chunk of patterns exist to make individual components reusable and make sure you’re encapsulating requirements and testing them correctly.
A lot of LLMs take the “easy” way out and duplicate code, suppress listing, etc to make a prompt work. It works at that point in time but when you suddenly have a bunch of spaghetti and repeated code littered across multiple services suddenly making changes without causing massive regressions becomes a headache.
Companies are going to pay for this mess in several months as token prices go up and the codebase is a massive pile of slop.
That’s going to be the bubble. When AI has to be able to actually pay for itself, no one is going to be able to afford it, and if you happen to be one of the companies that went all in any used AI to build your codebase and fire not devs and front line workers, you’re going to be the hardest hit. Possibly the only hope is that they saved enough from partial and didn’t pass any savings on to the customer (because of course they wouldn’t) that they can almost survive the actual unsubsidized token costs. But then you will be in direct competition with everyone else who can write a prompt with likely literally no differentiator outside of maybe name recognition in an industry.
If the only problem is that your code is slop and nobody can work with it without AI, then it’s probably not that bad. Text models I can run locally on my five year old Macbook are maybe a year behind in terms of coding assistance. So AI for coding is probably never going away. The worst case for someone in this scenario is just that it gets a bit slower and dumber and that they have to hire more engineers again. It’ll suck but I think it’s survivable. Someone would have to make a new Stackoverflow though if we’re going to google stuff again.
Now if you integrated multiple AI services into all your business workflows and into the products you sell, on the other hand, that might be a different story. In a way the risk is the same as with cloud providers. You get locked into a stack and then your product literally dies if the provider decides you’re not paying enough, because you have no feasible way out. Tbh I would much prefer working at a post-bubble era software company fixing the codebase to working at a random company now extracting their IT from a hyperscale cloud. But in reality, most companies that bet on AI are in this scenario. Nobody only installed Claude and called it a day.
I’m sure it will get there, but the biggest issue i see with the current models and implementations is that without good, or in some cases excellent, guidance, most can’t handle anywhere near a full codebase and even the best models are happy to take your question about fixing any particular task you throw at them back to square one with no regard towards how the existing platform is set up. Even for a relatively light codebase that it is currently building, it’s happy to scrap it start over in order to solve whatever potentially poorly worded issue you’re trying to track down. And that’s not even taking into account how quickly it can go from trying to solve an issue to nerfing tests in order to pass. Frustrated that things are no longer passing an expected integer test? Easily solved by allowing floats or nulls to count as passes, and damn if that isn’t a process to correct three issues deep after you’ve ‘validated’ that issue and it’s now being discovered down the line.
