Programming

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.

The post misses a few things:

1. 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.
2. 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.
3. 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

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.

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.

Give it time. My software career is also affected. At the rate they're spending money at an order of magnitude higher than they're making. They've also all borrowed money from each other. It's going to collapse in a big heap. Hopefully before it sucks in mum and dad investors.

The answer is start a union that strikes over AI usage.

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.

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 world is changing. It happens from time to time. In this case the change is a particularly big one and it's still ongoing, so I can't make any predictions about where it's going to end. But I can be pretty confident that it's not going to magically change back. So my best advice is to try out the new tools, see whether you can adapt to them and use them to improve your own productivity in new ways, and if not then as a fallback start looking at other directions to take your career.

Harsh, perhaps, but the world does as the world does.

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").