My philosophy is dirt simple:

I am the pointy end of the spear.

I'm less concerned with professionals using LLMs to code and more excited by the idea of regular people using LLMs to create programs that solve their problems.

Wish the blog had an RSS feed

Maybe true, but not true enough.

The title says it all

bottom line is only thing that matters in the end

The author doesn't give any arguments to support his claim.

Nan, it depends on quality of data you trained the bot.

The author puts the BLUF: "The actual bottlenecks were, and still are, code reviews, knowledge transfer through mentoring and pairing, testing, debugging, and the human overhead of coordination and communication."

They're not wrong, but they're missing the point. These bottlenecks can be reduced when there are fewer humans involved.

Somewhat cynically:

code reviews: now sometimes there's just one person involved (reviewing LLM code) instead of two (code author + reviewer)

knowledge transfer: fewer people involved means this is less of an overhead

debugging: no change, yet

coordination and communication: fewer people means less overhead

LLMs shift the workload — they don’t remove it: sure, but shifting workload onto automation reduces the people involved

Understanding code is still the hard part: not much change, yet

Teams still rely on trust and shared context: much easier when there are fewer people involved

... and so on.

"Fewer humans involved" remains a high priority goal for a lot of employers. You can never forget that.

Slowly it’s getting time to become a goose farmer. Enough of this AI shit.