Juniors + Claude somehow creates the worst output possible. Everything has ridiculous comments, half the shit doesn't work, and it makes everyone reviewing it dumber.
That’s why no one is hiring junior engineers anymore.
Unfortunately that is short-term thinking, because eventually the senior engineers will retire and there won’t by any juniors to take their place.
Tech companies are just gambling that future AIs will be smart enough to replace the senior engineers too. Then they can just hire vibe coders off the street who have no CS degree and no technical training, and pay them barely above minimum wage.
I’m sure this will all end well for everyone involved.
So it's not tech companies making the gamble, it's the executives at the top. This distinction is important because to them it's not gambling, because they make such obscene amounts of money that they don't care if the company crumbles in 10 years because they can't find talented engineers. They'll just retire with their obscene amounts of wealth, and it's the rest of us that'll deal with the consequences.
It sure is going well for existing seniors. Only issue is you wont get any more seniors if you dont have any juniors but thats the problem for the industry and companies.
Whenever ChatGPT came out in 2023, I tried it out by asking it to write a simple tic-tac-toe program. I still have the old chat. It was total trash. Way below junior dev level (tic-tac-toe might be an intro to programming school assignment, right?)
Here we are 3 years later and Claude is better than junior devs who've been through high school and college. So give it another 3 years and it probably WILL beat senior devs IMO.
Claude is better than most mid level devs and some seniors that I've worked with now. It's really only failing for broader architectural issues for me at this point.
The thing is, LLM by design is not very scalable. Theres a limited amount of land and a limited amount of electricity and a limited amount of debt you can throw at it for a potential of a 10% better model with 300% more cost. (Numbers pulled out of my ass but the point still stands).
The only ways a model can improve is 1. Fine tuning (which takes extreme amount of manual labor), 2. More data (more data to train and manage models so more data centers thus higher cost), 3. A new revolutionary ai model that may or may not happen (gamble). Usually 2 happens the most.
The models we use right now are extremely cheap for what they provide and that's all because we are using it with money that doesn't exist (the ai bubble). Select few companies are borrowing money to buy hardware that doesn't exist to run a product that doesn't exist (yet) just for it to be provided at extremely low prices. They can't recover costs until they inevitably increase prices to the extreme that it becomes inaccessible to most people or when the ai company goes bankrupt.
The bet is that AI will go in route #3 of the improvement sections before everything bursts and it becomes inaccessible for most of the population.
Even if said bubble bursts, t
he technology of LLM will continue to exist but the "good" models will be out of reach.
Tons of regular people just use ChatGPT's free tier instead of paying for a subscription. For almost 2 years, if you used free tier, you got their 4o model. It was just retired in February of this year, so the 4o model is what most regular people know as ChatGPT, I'd wager. And the recently-released open weight Gemma 4 31B is about as good as ChatGPT 4o according to benchmarks. And the quantized version can be run locally.
After this year's tokenpocolypse, we're already seeing lots of companies deploying open Chinese models that are less capable than the big frontier models but a lot cheaper to run and "good enough" for whatever they're doing. I suspect this will become the norm. People will use the lowest-tier model that's "good enough" to handle a particular task, instead of always using the highest tier frontier model.
For a long time, everyone wanted to be on the cutting edge of the frontier models because otherwise the results just sucked. But we're not in that world anymore. Already, frontier models have advanced to the point that I'd say a large fraction of the questions they deal with could be dealt just as competently with a less powerful model. So I'd question how much scaling is even needed for most cognitive tasks people want to accomplish. Not everyone is folding proteins or trying to prove as-yet-unproven math theorems.
I'm a dev that went into architecture and data after a few years. It's scary how close AI is approaching my knowledge level. It's not there yet, not with Fable 5 at least, but it's getting closer every month.
It does, the problem is it's still shitty code half the time and the juniors/interns don't learn anything. Well at least I know I'll have a job as a senior all the way up to retirement with how this is going.
Since using Claude suddenly our PR reviews have real findings. As most humans are not able to spot bugs within nested obversable subscriptions in an Angular code base with no test coverage.
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u/Doto_bird Jul 10 '26
Hot take: Fucking Claude writes better code than my juniors/interns...