A counter example to Jevon's paradox is writing

Arguably, with the increase in literacy, Jevon's paradox would say we need to hire more writers. Indeed, a lot more people DO write for their job.

But its not like we went from a handful of professional, full-time scribes, to 10x professional full-time scribes. Instead, the value of being just a scribe has gone down (unless you're phenomenal at it). It stops being a specialized skill unto itself. It's just a part of a lot of people's jobs, alongside other skills, like knowing how to do basic arithmetic.

Coding, like writing, becomes a part of everyone's job. Not a specialization unto itself. We will have more coders, but since everyone is a coder, very few are a captial C "Coder".

> Assembly programmers became C programmers became Python programmers. The abstraction rose, individual productivity increased, more total software got written, and roughly similar numbers of people were employed writing it.

The number of programmers has changed so much, from ~zero in the 1940s to tens of thousands in the sixties, to, what, maybe 30 million today? While most programmers worked a little or a lot in ASM from invention until the 1980s, it's a very specialized role today.

I do not believe that 'roughly similar numbers of people were employed writing' ASM, C, and Python except for the instant that C outpaced ASM in the seventies and when Python outpaced ASM somewhere around the millennium.

Probably at no time were ASM, C, and Python programmers even close to similarly numerous.

> Assembly programmers became C programmers became Python programmers.

Except that there are still a lot of assembly programmers.

And even more C/C++ programmers.

It's also likely that C/C++ programmers didn't become Python programmers but that people who wouldn't have otherwise been programmers became Python programmers.

> At the well-specified end, you have tasks where the inputs are clean, the desired output is clear, and success criteria are obvious: processing a standard form, writing code to a precise spec, translating a document, summarising a report. LLMs are excellent at this

Yeah

> At the ambiguous end, you have tasks where the context is messy, the right approach isn’t obvious, and success depends on knowledge that isn’t written down anywhere

Sounds like most programming

Almost all of the programming I've ever done.

> I’m arguing that the most likely outcome is something like “computers” or “the internet” rather than “the end of employment as we know it.”

Yeah

This is the default belief we should hold based on historical technological changes. Any argument to either extreme requires a lot of evidence.

I feel like this is exactly what I see at work every day. It helps me write the boring boilerplate code faster but I still have to spend hours debugging the logic errors it makes. It is a great tool for productivity but it is definitely not going to replace the need for actual engineering thinking anytime soon.

On "oddlots" a financial podcast they frequently mention that there is no good exit for AI at this point. Either it massively disrupts industries and the average person is driven closer to poverty, or it does nothing and the historical amount of money wasted will lead to a depression or recession that drives the average person closer to poverty.

In my work as a professor, AI has demonstrated a noticeable disruptive impact for the worse.

It has become difficult to grade students using anything other than in-person pen and paper assessments, because AI cheating is rampant and hard to detect. It is particularly bad for introductory-level courses; the simpler the material the hardest it is to make it AI-proof.

> The measured take, that LLMs are a significant productivity tool comparable to previous technological shifts but not a rupture in the basic economic fabric, doesn’t generate much engagement. It’s boring.

This isn't a new take. The problem is, "boring" doesn't warrant the massive bet the market has made on it, so this argument is essentially "AI is worthless" to someone significantly invested.

It's not so much that people aren't making this argument, it's that it's being tossed immediately into the "stochastic parrot" bunch.

> software got written, and roughly similar numbers of people were employed writing it

That's just simply not true.

The "what would change my mind" criteria are all about AI succeeding within existing institutional forms. He's not considering whether those forms get outcompeted by smaller entities that don't carry the overhead, which to me seems obviously how it will go.

> If agentic systems start successfully navigating the illegible organisational context that currently requires human judgement, things like understanding unstated preferences, political sensitivities, and implicit standards, that would be significant.

How much of this is actually required for the actual work though and how much is legacy office politics "monkeys love to monkey around" nonsense?

> The historical track record on “this technology will end work” predictions is remarkably consistent: they’re always wrong.

This is nicely expressed, and could serve as a TDLR for the article, though buried in the middle.

We have the most automation we've ever had, AND historically low unemployment. We have umpteen labor saving devices, AND people labor long, long hours.

> Labour Market Reallocation Actually Works

It really does, given a little time.

> the binding constraint was never syntax but the underlying skill of thinking precisely about systems, edge cases, state management, and failure modes.

Yes, this is still programming.

(Though I think syntax was most definitely a binding constraint.)

I work in health (neurotech/sleeptech) and am in the process of writing a post which hits on the health aspect.

The things that most people ignore when thinking about AI and health is that 2/3rds of Americans are suffer from chronic illness and there is a shortage of doctors. Could AI really do much worse than the status quo? Doctors won't be replaced, but if we could move them up the stack of health to actually doing the work of saving lives rather than just looking at rising cholesterol numbers and writing scripts?

AI is pretty good at finding relationship between concepts/artifacts, like, I know there must be a way to get a handle of a file and stream the contents, and I couldn't care less about how some fucker decided to arbitrarily define in this language o would never choose to use but am stuck with. Just give me whatever bullshit that is equivalent to what I want, fantastic technology for that.

Precisely because of this, some people that couldn't code for whatever reason crossed the border and now can somewhat produce something substantially more complex then what they can comprehend. This causes problems. You probably should not shit out code you don't understand, or rely on code you don't understand.

I think lots of SWE massively overestimate the amount that people in power care if the software really works most of the time. Yes even in financial matters. Here is a not secret, Accounting will stop any strange money loss that would cause any problems, its their job. If it happens often enough they will complain and have an SWE fix it. There is a reason so many companies take forever to pay out.

How much of the world currently runs on horribly broken,outdated and inaccurate spreadsheets? Disposable AI slop apps are just a continuation of this paradigm. I do think that AI slop apps will become competitive with broken spreadsheets and 10,000 scattered python notebook files, causing a massive drop in need for various SWE,Analysis, Data Scientist type jobs. I've seen report systems with individual reports in the millions that were only run once (and many not at all), a huge percent of digital work is one off disposable items.

SWE is first and foremost a power structure for the people in charge. The first thing you do when you are in power is always minimize the amount of people with power you depend on. I think that AI already is reducing the amount of people needed to maintain and develop what are basically state of the company applications for the C-suite. Sure tech first companies will be hit less hard but for example the fortune 500 will be making continuous cuts for the next decade.

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