The Mythical Man-Month in the Age of AI

Related thing is that LLMs are still notoriously bad at writing instructions for themselves (so managing themselves also). They tend to overspecify. If I had a copy of myself I would tell him "do the thing", but agents say "do the thing like this and this, it must be such and such, use X and put it together that way". The subagent is just as intelligent but now it's constrained, sometimes as you start developing something you discover some things are better organized in a different way etc. So it spends much time figuring out how to deliver exactly what was asked even if it's not optimal because that's the task.

Basically a company where you can't ever question decisions of the higher ups, not even ask for clarification, but they force implementation choices on you. I mean, it definitely does happen, but the results are usually not great. Not alawys. For some tasks it works.

> "Adding agents to a project with unclear requirements gets you the wrong answer faster."

The thing is it gets you the wrong answer so fast it’s now the best way to figure out the right requirements.

From my last month's clauding experience, these agents now understand what needs to remain sequential and just wait for the other agents to get done.

But it’s not a man-month anymore. It’s like half a day.

And if I break up an application then 9 coders with Claude Code can build that application in an unprecedentedly short period.

i think of the mythical man-month at least once a day.

if i would know EXACTLY what and how I had to build stuff, it would probably not apply. But at least for me and my projects, that is not the case. Planning and building in parallel has given me too many headaches.

If you are building something stable, secure - well thought through and executed, you hardly get around sequential building.

There is a great turkish proverb about this "one hand is nothing,two hands make a sound" (i hope i translated it right). So,adding people to a task isnt necessarily bad in every situation. Honestly, it depends on the nature of the job beacuse everyone approaches a problem from diffrent perspective

Agents seem to be useful for quickly mapping a problem space through trial&error + throwing stuff at the wall and seeing what sticks.

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.

Would an unlimited context window enable you to solve all requests instantly ? No

Here's a question, to people who delegate all their coding to AI agents now: What are you doing to keep your mind exercised in all this suddenly new free time you have? Personally I find talking with agents extremely tedious and unrewarding; I would rather just write the code, even if it's simply because it's more interesting than playing Sudoku or whatever the fuck you do when you have nothing to do with your brain.

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.

How about RSI? Recursive Self Improvement? I wonder the author's view on that.

Not only that but there is limited number of good prompts/design decisions per employee per day.

If you use AI then that is not going to be your bottleneck. So if a project is late one of the options on the table is still to add manpower to "help".

So nothing has changed fundamentally, only perhaps superficially.

> nine agents still can’t deliver a baby in one month.

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?

The part of Brooks that still holds for me as a solo developer is that the cost sits in coordination and understanding, not typing. AI makes producing code cheaper, but reviewing it, keeping the design coherent, and remembering why things were done still takes the same attention. I have found small, well-bounded tasks with tests work well, while open-ended changes across many files create review work that cancels the speedup. So the useful question is how much of a change I can verify quickly.