One other thing I have seen however is the 0x case, where you have given too much control to the llm, it codes both you and itself into pan’s labyrinth, and you end up having to take a weed wacker to the whole project or start from scratch.
Ask it a question about something you know well, and it'll give you garbage code that it's obviously copied from an answer on SO from 10 years ago.
When you ask it for research, it's still giving you garbage out of date information it copied from SO 10 years ago, you just don't know it's garbage.
That Python you just got might look good, but could be rewritten from 50 lines to 5, it's written in 2010-style, it's not using modern libraries, it's not using modern syntax.
And it is 50 to 5. That is the scale we're talking about in a good 75% of AI produced code unless you challenge it constantly. Not using modern syntax to reduce boilerplate, over-guarding against impossible state, ridiculous amounts of error handling. It is basically a junior dev on steriods.
Most of the time you have no idea that most of that code is totally unnecessary unless you're already an expert in that language AND libraries it's using. And you're rarely an expert in both or you wouldn't even be asking as it would have been quicker to write the code than even write the prompt for the AI.
"Agents use tools in a loop to achieve a goal."
If you don't give any tools, you get hallucinations and half-truths.
But you give one a tool to do, say, web searches and it's going to be a lot smarter. That's where 90% of the innovation with "AI" today is coming from. The raw models aren't gettin that much smarter anymore, but the scaffolding and frameworks around them are.
Tools are the main reason Claude Code is as good as it is compared to the competition.
> The raw models aren't gettin that much smarter anymore, but the scaffolding and frameworks around them are.
For a tool use loop local models are getting to "OK" levels, when they get to "pretty good", most of my own stuff can run locally, basically just coordinating tool calls.
An LLM helps most with surface area. It expands the breadth of possibilities a developer can operate on.
Purely anecdotal, but I've seen that level of productivity from the vibe tools we have in my workplace.
The main issue is that 1 engineer needs to have the skills of those 20 engineers so they can see where the vibe coding has gone wrong. Without that it falls apart.
Your productivity boost will depend entirely on a combination of how much you can remove yourself from the loop (basically, the cost of validation per turn) and how amenable the task/your code is to agents (which determines your P(success)).
Low P(success) isn't a problem if there's no engineer time cost to validation, the agent can just grind the problem out in the background, and obviously if P(success) is high the cost of validation isn't a big deal. The productivity killer is when P(success) is low and the cost of validation is high, these circumstances can push you into the red with agents very quickly.
Thus the key to agents being a force multiplier is to focus on reducing validation costs, increasing P(success) and developing intuition relating to when to back off on pulling the slot machine in favor of more research. This is assuming you're speccing out what you're building so the agent doesn't make poor architectural/algorithmic choices that hamstring you down the line.
To be direct, this reads like a fluff comment written by AI with an emphasis on probability and metrics. P(that) || that.
I’ve written software used by a local real estate company to the Mars Perseverance rover. AI is a phenomenally useful tool. But be weary of preposterous claims.
Given that, if you want to revisit your comment in a constructive way rather than doing an empty drive by, I'll read your words with an open mind.
So the "verbose, straightforward code with clear cut test scenarios" is already written by a human?
I have been working professionally for ~16 years in software development, and scenarios like this was about 5% of my work.
> one person is doing the work of 20 with them in december 2025 at least
I mean from the off, people were claiming 10x probably mostly because it's a nice round number, but those claims quickly fell out of the mainstream as people realised it's just not that big a multiplier in practice in the real world.
I don't think we're seeing this in the market, anywhere. Something like 1 engineer doing the job of 20, what you're talking about is basically whole departments at mid sized companies compressing to one person. Think about that, that has implications for all the additional management staff on top of the 20 engineers too.
It'd either be a complete restructure and rethink of the way software orgs work, or we'd be seeing just incredible, crazy deltas in output of software companies this year of the type that couldn't be ignored, they'd be impossible to not notice.
This is just plainly not happening. Look, if it happens, it happens, 26, 27, 28 or 38. It'll be a cool and interesting new world if it does. But it's just... not happened or happening in 25.