Comment by AbbeFaria
9 hours ago
My experience working at Big tech companies is that people with roles like “agile coaches", "technical project managers", UX testers add questionable value. And the QA is usually outsourced to service companies like MindTree, TCS etc anyway.
Lot of these companies are bloated from having way too many Engineers anyway. Once you have mature software that brings in bagfuls of money, you don’t need that many people to keep the ship steady. I have seen this first hand at MSFT, we started a new team back in 2019 and it probably had ~40 people full time across US and India. By 2024 when I left, we had about 20 people in India who could easily run the service, the US team was dissolved and they moved to other teams in MSFT. The fact was that new features were few and the team was in KTLO mode. I have seen the reverse happen too, the team I was working on was dissolved and we were moved to different teams and everything moved to the US last year, managers were converted to ICs and a few folks were probably fired but it was a ~10 year old service that didn’t need that many people to run, even more so after AI tools became big last year.
What do you think about Cory Doctorow's theory that the AI produced code is going to come back to bite companies due to tech debt / unmaintainability?
I am skeptical of Doctorow's theory because it looks like LLMs will continue to improve enough over the near term to be able to handle issues caused by AI-written code from the past few years.
I've heard OpenClaw got over 600k lines of code vibed over 80 days.
I have this theory that the bloat will follow to the full extent possible. OpenClaw has this, the OpenEye or whatever that comes on another day, with better models, will have 3 million lines of code. All of the possibilities that you mention will not come to fruition the way you'd like to, because speed is preferred over building better things, and to hell with maintainability.
Eventually these things will become a ton of black boxes, and the only option will be to write them from scratch with another next gen LLM. Lots of costly busywork, and it will all take time.
Claude Code is similar. It's fairly clean for AI coding standards but it's also most likely much, much bigger than what it should be for what it does.
the near term is not an issue, because most ai code is still reviewed by experienced engineers with experience. the problem comes in the future where junior engineers who never acquired enough experience to handle engineering problems
Ain’t nobody reviewing the majority of vibe coded output.
In the mature service I worked on, adding new code was “templatized”, you had to add feature flags, logs etc which didn’t vary much no matter which feature it was. The business logic was also not that complex, I can see AI tools one-shotting that and it indeed is a productivity boost. You would be surprised to know that most work was exactly this, writing rather mundane code. Majority of the time was spent coordinating with “stakeholders” (actually more like gate keepers) and testing code (our testing infrastructure was laborious). This was at MSFT. There are lot of teams that are innovating at the frontier (mine wasn’t, at-least not technically), I don’t know how AI tools work in those situations.
> My experience working at Big tech companies is that people with roles like “agile coaches", "technical project managers", UX testers add questionable value.
"Agile" can go and die in a hellfire for all I care.
But good technical project managers aka "bridges between the higher-up beancounters and the workers" are worth their weight in gold.
At MSFT, Product Managers were Technical Program Managers. Yes, a good PM is a joy to work with.
>But good technical project managers aka "bridges between the higher-up beancounters and the workers" are worth their weight in gold.
Yeah but it's not easy do distinguish those from the snake oil salesmen who are just good at smooth talking during the interviews.
Pretty easy. Get them to talk about a project they've managed and start poking holes. Who was on the team? How did they organize meetings? What were the bottlenecks? How well did everyone get along? What did they do to help grease the gears? Did they have to change the process? How did they like the software? Which software did they use? Did they have to administer it themselves? How did they deal with management changes / team changes / tons of support requests / issues in production? Where did they draw the line between PM work and engineering work?
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