Comment by threethirtytwo

>Wait, so we can infer the future from “trendlines”, but not from past events? Either past events are part of a macro trend, and are valuable data points, or the micro data points you choose to focus on are unreliable as well. Talk about selection bias…

If past events can be dismissed as “noise,” then so can selectively chosen counterexamples. Either historical outcomes are legitimate inputs into a broader signal, or no isolated datapoint deserves special treatment. You cannot appeal to trendlines while arbitrarily discarding the very history that defines them without committing selection bias.

When large numbers of analogous past events point in contradictory directions, individual anecdotes lose predictive power. Trendlines are not an oracle, but once the noise overwhelms the signal, they are the best approximation we have.

>What is hypothetical is what will happen to all this software and the companies that produced it a few years down the line. How reliable is it? How maintainable is it? How many security issues does it have? What has the company lost because those issues were exploited? Will the same people who produced it using these new tools be able to troubleshoot and fix it? Will the tools get better to allow them to do that?

These are legitimate questions, and they are all speculative. My expectation is that code quality will decline while simultaneously becoming less relevant. As LLMs ingest and reason over ever larger bodies of software, human oriented notions of cleanliness and maintainability matter less. LLMs are far less constrained by disorder than humans are.

>Really? Everything? There is no chance that some people are simply pointing out the flaws of this technology, and that the marketing around it is making it out to be far more valuable than it actually is, so that a bunch of tech grifters can add more zeroes to their net worth?

The flaws are obvious. So obvious that repeatedly pointing them out is like warning that airplanes can crash while ignoring that aviation safety has improved to the point where you are far more likely to die in a car than in a metal tube moving at 500 mph.

Everyone knows LLMs hallucinate. That is not contested. What matters is the direction of travel. The trendline is clear. Just as early aviation was dangerous but steadily improved, this technology is getting better month by month.

That is the real disagreement. Critics focus on present day limitations. Proponents focus on the trajectory. One side freezes the system in time; the other extrapolates forward.

>I don’t get how anyone can speak about trends and what’s currently happening with any degree of confidence. Let alone dismiss the skeptics by making wild claims about their character. Do better.

Because many skeptics are ignoring what is directly observable. You can watch AI generate ultra complex, domain specific systems that have never existed before, in real time, and still hear someone dismiss it entirely because it failed a prompt last Tuesday.

Repeating the limitations is not analysis. Everyone who is not a skeptic already understands them and has factored them in. What skeptics keep doing is reciting known flaws while refusing to reason about what is no longer a limitation.

At that point, the disagreement stops being about evidence and starts looking like bias.

“There is no evidence” is not skepticism. It’s abdication. It’s what people say when they want the implications to go away without engaging with anything concrete. If there is “a lot of evidence of the opposite,” the minimum requirement is to name one metric, one study, or one observable trend. You didn’t. You just asserted it and moved on, which is not how serious disagreement works.

“You first, lol” isn’t a rebuttal either. It’s an evasion. The claim was not “the labor market has already flipped.” The claim was that AI-assisted coding has changed individual leverage, and that extrapolating that change leads somewhere uncomfortable. Demanding proof that the future has already happened is a category error, not a clever retort.

And yes, the self-delusion paragraph clearly hit, because instead of addressing it, you waved vaguely and disengaged. That’s a tell. When identity is involved, people stop arguing substance and start contesting whether evidence is allowed to count yet.

Now let’s talk about evidence, using sources who are not selling LLMs, not building them, and not financially dependent on hype.

Martin Fowler has explicitly written about AI-assisted development changing how code is produced, reviewed, and maintained, noting that large portions of what used to be hands-on programmer labor are being absorbed by tools. His framing is cautious, but clear: AI is collapsing layers of work, not merely speeding up typing. That is labor substitution at the task level.

Kent Beck, one of the most conservative voices in software engineering, has publicly stated that AI pair-programming fundamentally changes how much code a single developer can responsibly produce, and that this alters team dynamics and staffing assumptions. Beck is not bullish by temperament. When he says the workflow has changed, he means it.

Bjarne Stroustrup has explicitly acknowledged that AI-assisted code generation changes the economics of programming by automating work that previously required skilled human attention, while also warning about misuse. The warning matters, but the admission matters more: the work is being automated.

Microsoft Research, which is structurally separated from product marketing, has published peer-reviewed studies showing that developers using AI coding assistants complete tasks significantly faster and with lower cognitive load. These papers are not written by executives. They are written by researchers whose credibility depends on methodological restraint, not hype.

GitHub Copilot’s controlled studies, authored with external researchers, show measurable increases in task completion speed, reduced time-to-first-solution, and increased throughput. You can argue about long-term quality. You cannot argue “no evidence” without pretending these studies don’t exist.

Then there is plain, boring observation.

AI-assisted coding is directly eliminating discrete units of programmer labor: boilerplate, CRUD endpoints, test scaffolding, migrations, refactors, first drafts, glue code. These were not side chores. They were how junior and mid-level engineers justified headcount. That work is disappearing as a category, which is why junior hiring is down and why backfills quietly don’t happen.

You don’t need mass layoffs to identify a structural shift. Structural change shows up first in roles that stop being hired, positions that don’t get replaced, and how much one person can ship. Waiting for headline employment numbers before acknowledging the trend is mistaking lagging indicators for evidence.

If you want to argue that AI-assisted coding will not compress labor this time, that’s a valid position. But then you need to explain why higher individual leverage won’t reduce team size. Why faster idea-to-code cycles won’t eliminate roles. Why organizations will keep paying for surplus engineering labor when fewer people can deliver the same output.

But “there is no evidence” isn’t a counterargument. It’s denial wearing the aesthetic of rigor.