Posted by napolux 1 day ago
1) The AI code maintainence question - who would maintain the AI generated code 2) The true cost of AI. Once the VC/PE money runs out and companies charge the full cost, what would happen to vibe coding at that point ?
1) Either you, the person owning the code, or you + LLms, or just the LLMs in the future. All of them can work. And they can work better with a bit of prep work.
The latest models are very good at following instructions. So instead of "write a service that does X" you can use the tools to ask for specifics (i.e. write a modular service, that uses concept A and concept B to do Y. It should use x y z tech stack. It should use this ruleset, these conventions. Before testing run these linters and these formatters. Fix every env error before testing. etc).
That's the main difference between vibe-coding and llm-assisted coding. You get to decide what you ask for. And you get to set the acceptance criteria. The key po9int that non-practitioners always miss is that once a capability becomes available to these models, you can layer them on top of previous capabilities and get a better end result. Higher instruction adherence -> better specs -> longer context -> better results -> better testing -> better overall loop.
2) You are confusing the fact that some labs subsidise inference costs (for access to data, usage metrics, etc) with the true cost of inference on a given model size. Youc an already have a good indication on what the cost is today for any given model size. 3rd party inference shops exist today, and they are not subsidising the costs (they have no reason to). You can do the math as well, and figure out an average cost per token for a given capability. And those open models are out, they're not gonna change, and you can get the same capability tomorrow or in 10 years. (and likely at lower costs, since hardware improves, inference stack improves, etc).
In a similar fashion, AI generated code will be fed to another AI round and regenerated or refactored. What this also means is that in most cases nobody will care about producing code with high quality. Why bother, if the AI can refactor ("recompile") it in a few minutes?
Ah, there it is.
Not everyone can afford it, and then we are at the point of changing the field that was so proud about just needing a computer and access to internet to teach oneself into a subscription service.
And yes, that plan can get you started, but when I tested it, I managed to get 1 task done, before having to wait 4 hours.
If I were starting out today, this is basically the only advice I would listen to. There will indeed be a vacuum in the next few years because of the drastic drop in junior hiring today.
My value so far in my career has been my very broad knowledge of basically the entire of computer science, IT, engineering, science, mathematics, and even beyond. Basically, I read a lot, at least 10x more than most people it seems. I was starting to wonder how relevant that now is, given that LLMs have read everything.
But maybe I'm wrong about what my skill actually is. Everyone has had LLMs for years now and yet I still seem better at finding info, contextualising it and assimilating it than a lot of people. I'm now using LLMs too but so far I haven't seen anyone use an LLM to become like me.
So I remain slightly confused about what exactly it is about me and people like me that makes us valuable.
The value of a good engineer is his current-context judgment. Something that LLMs can not do Well.
Second point, something that is being mentioned occasionally but not discussed seriously enough, is that the Dead Internet Theory is becoming a reality. The amount of good, professionally written training materials is by now exhausted and LLMs will start to feed on their own slop. See How little the LLM's core competency increased in the last year even with the big expansion of their parameters.
Babysitting LLM's output will be the big thing in the next two years.