(It used a clever and rigorous technique for measuring productivity differences, BTW, for anyone as skeptical of productivity measures as I am.)

That info is from mid 2025, talking about models released in Oct 2024 and Feb 2025. It predates tools like Claude Code and Codex, Lovable was 1/3 current ARR, etc.

This might still be true but we desperately need new data.

https://en.wikipedia.org/wiki/Amdahl's_law

a 100% increase in coding speed means I then I get to spend an extra 30 minutes a week in meetings

while now hating my job, because the only fun bit has been removed

"progress"

And here we are, the central argument for why code agents are not these job killing hype beasts that are so regularly claimed.

Has anyone seen what multi-agent code workflows produce? Take a look at openclaw, the code base is an absolute disaster. 500k LoC for something that can be accomplished in 10k.

I have. Sometimes the resulting code was much worse than what you get from an LLM, and yet the project itself was still a success despite this.

I've also worked in places with code review, where the project's own code quality architecture-and-process caused it to be so late to the market it was an automatic failure.

What matters to a business is ideally identical to the business metrics, which are usually not (but sometimes are) the code metrics.

the bottleneck is aligning people on what the right thing to do is, and fiting the change into everyone's mental models. it gets worse the more people are involved

Mission accomplished: acquhire worth probably millions and millions.

I agree with you, by the way.

Now i can write one example of a pass and then get codex to read the code and write a test for all the branches in that section saves time as it can type a lot faster than i can and its mostly copying the example i already have but changing the input to hit all the branches.

Lmao. Rofl even.

(Testing is the one thing you would never outsource to AI.)

it could still speculate wrong things, but it wont speculate that the code is supposed to crash on the first line of code

I would rephrase that as "all LLMs, no matter how many you use, are only as good as one single pair of eyes".

If you're a one-person team and have no capital to spend on a proper test team, set the AI at it. If you're a megacorp with 10k full time QA testers, the AI probably isn't going to catch anything novel that the rest of them didn't, but it's cheap enough you can have it work through everything to make sure you have, actually, worked through everything.

That doesn't tell you that the code is correct. It tells you that the branching code can reach all the branches. That isn't very useful.

Are the AI firms capable of retraining their models to understand new features in the technologies we work with? Or are LLMs going to be stuck generating C.A. 2022 boilerplate forever?

Right now, front end has tons of boiler plate. It's one of the reasons AI hassle such a wow factor for FE, trivial tasks require a lot of code.

But even that is much better than it was 10 years ago.

That was a long way of saying I disagree with your no.

According to the article, onboarding speed is measured as “time to the 10th Pull Request (PR).”

As we have seen on public GitHub projects, LLMs have made it really easy to submit a large number of low-effort pull requests without having any understanding of a project.

Obviously, such a kind of higher onboarding speed is not necessarily good for an organization.

Summarizing this with AI makes you lose that context.

> The minutiae are often just as important as some of the higher level decisions.

Frankly, a failure to understand this is a tell that someone is not equipped to evaluate code quality.

Regardless, if you’re a dev who is now 2x as productive in terms of work completed per day, and quality remains stable, why should this translate to 2x the output? Most people are paid by the hour and not for outcomes.

And yes, I am suggesting that if you complete in 4 hours that which took you 8 hours in 2019, that you should consider calling it a day.

Soon, I predict we will see a pretty significant jump in price that will make a 10% productivity gain seem tiny compared to the associated bills.

For now, these companies are trying to reach critical mass so their users are so dependant on their tech that they have to keep paying at least in the short term.

Do you have sources for this claim?

The threat of AI for devs, and the way to drastically improve productivity is there: keep the better devs who can think systemically, who can design solutions, who can solve issues themselves and give them all the AI help available, cut the rest.

It really does feel like a multiplier on me and I understand things enough to get my hands dirty where Claude struggles.

Lately I’ve been wondering if that role evolves into a more hierarchical review system: senior engineers own independent modules end-to-end, and architects focus on integration, interfaces, and overall coherence. Honestly, the best parts of our product already worked like that even before AI.