The thing is it gets you the wrong answer so fast it’s now the best way to figure out the right requirements.
1. A blank sheet of paper
2. A one-shotted POC in the span of an hour with $4 of tokens that is clearly wrong in 20 different ways?
Those aren't always true. Sometimes it takes a while to see the fatal flaw in an approach, and sometimes having a bunch of failed attempts isn't enough to make the right way obvious, and thinking is still required.
And if I break up an application then 9 coders with Claude Code can build that application in an unprecedentedly short period.
Anything grindy. Reverse engineering. Performance optimizations.
That's where swarms shine I think. But that's a much much much smaller problem space than what these agents are being sold for.
"You're absolutely right, that drug was lethal and that's on me. I'll add a note to check human toxicity for all compounds in the future. To verify the safe dosage, I'll administer a high dose and then check smaller doses until the patient survives — not guesswork, grounded load-bearing experiments."
And talking to AI agents is not the same as using your brain. It's frustrating, it seems to be intelligent, you think you're using reason with them, but it rapidly degrades your ability to reason. It encourages you to accept answers without checking them thoroughly. So much for the skills of accuracy and integrity you built up. It's a sugar rush.
Who got more time thanks to AI ? I think a lot of us are working more instead
If you have a lot more free time you're not taking full advantage of agents. The time saved coding allows working on 4-6 projects simultaneously, and the majority of work is the hard design/product decisions that the agents can't do well themselves. Working on the hard parts of half a dozen projects simultaneously is far more mentally taxing than just hand-coding a single project, and with that many agents there'll usually always be one with a question for you so you won't have much extra free time.
OK, so I'm still very low on the spectrum of "I just command it and sit back", but I think the final toolbox will at least include adversarial questioning of the bot. For example, "Why does that code do A, wouldn't B be better?" or "Would this code break if a C event occurred?"
Sometimes it reveals a bug or a questionable implicit assumption, other times it leads me to see another part of the code, and sometimes the thing just lies at me... But it does help you keep some critical-thinking practice.
Another task might be to find and refactor/rename parts of the code that are just... not easy for humans to grasp. Some readers are thinking: "No, the next edit will be by LLM anyway, leave it so the bot understands", however I think that's short-sighted: Either these things are going to become good-enough that they should cater to human comprehension, or else they've reached a plateau and we'll want something that can resist the chaos-rot.
So nothing has changed fundamentally, only perhaps superficially.
Very weird way to say this. Like it holds true for biological beings because pregnancy by default has a fixed timeline. But how does this apply to AI? Also what even is an "agent"? Are they distinct worker units on a program or the same worker unit split into multiple instances?
Why are we assuming agent = man when its a computer program?
It would be something like "nine women cannot deliver a baby in one month", which can be more easily transposed to "adding resources to a SW project does not make it faster".
The agrument is that adding people increase coordination and communication overhead, which gets bigger as the team grows. Moreover, some tasks cannot be parallelized that easily and create dependencies.
I have only worked with agents for personal projects, but I cannot say that explicity dividing the work between multiple agents and asking them to coordinate is inherently faster than having a single agent work on it (of course the single agent will spawn subagents in rencent harnesses.)
That doesn't work if the driver cannot steer properly. It's the main issue with vibe-coded one-shotted solutions, that might look impressive until you peek underneath and it's all a mess.
Is this sustainable? I've got no idea.
While we may use deterministic tools like static code analysis, dependency scanners and similar today, people are already replacing/augmenting them with LLMs.
While every breach will trigger a root cause analysis process in a compliant environment, the solution is never going to be "all code in a deployment was manually reviewed by someone at the company" because this would extend to tooling (like compilers, scanners but also packaging and deployment tools) and all dependencies.
So I am pretty sure your example is already used in a number of places especially with "LLMs are better at security than humans" marketing being pushed around — this really makes it easy to sell that excuse to the gullible management who wants to believe this.