Raising the floor is not building a ladder

One of these is my argument. The other is my evidence. They disagree.

I’ve been making a version of this argument for a while: as AI absorbs routine production work, the highest-value editorial work moves upstream into evidence, judgement and narrative.

There’s an uncomfortable corollary I’ve written about but not resolved. The routine production work AI absorbs first is also how people used to learn the job.

Nobody designed that apprenticeship. It happened as a by-product. You wrote the fifth version of a page nobody read and noticed why the first four didn’t land. You transcribed the interview and found the useful answer arrived halfway through an unrelated question. You produced something competent, watched it do nothing, and were asked to explain why.

If that work goes, the ladder goes with it.

That’s the argument. Then I looked at my own evidence.

What the evidence actually said

I ran an AI-assisted editorial workflow end to end on a real project, and recorded the outcome at the time rather than reconstructing it later.

The strongest practical result was this: the workflow let a less experienced writer produce a credible first draft, ready for editorial review. It did not make an experienced writer’s best work better.

I wrote it down as the workflow raises the floor rather than the ceiling, and marked it observed once.

That is not what my argument predicts.

If AI mainly compounds the advantage of people who already have judgement, the experienced writer should have gained most. They didn’t. The person who gained most was the one with the least accumulated experience — precisely the person the ladder argument says is being left behind.

One observation, one project, one writer. I would not build anything on it. But it is the only direct evidence I have, and it disagrees with me.

Both can be true, and that’s worse

The obvious move is to pick one. I don’t think you can, because the two claims are about different things.

Raising the floor is a claim about output. With good scaffolding and accumulated context, someone early in their career can produce work that reaches a professional standard much sooner than they otherwise would.

The ladder argument is a claim about formation. It says the process of producing that work badly, repeatedly, under correction, is what built the judgement.

Nothing prevents both from holding. Which gives you a specific and unattractive combination:

People produce better work sooner, and understand it less.

Competence arriving ahead of comprehension. The output looks like progress. The formation underneath it may not be happening at all.

I want to be careful here, because this is exactly the shape of argument that flatters experienced people and irritates everyone else. “The young don’t learn properly any more” is not an insight; it’s a genre. The version worth taking seriously has to be more specific than that.

What actually breaks is the proxy

Here is the specific bit.

Organisations have never assessed judgement directly. They assessed output, and used it as a proxy. If the drafts got better, the person was developing. If the drafts stayed weak, they weren’t. Crude, but it correlated well enough to run careers on for decades.

The proxy worked because producing good output required the judgement. You couldn’t fake the draft without having done the thinking.

That’s the link AI severs.

Now output quality and judgement formation can move independently. A first draft can be credible because the workflow is good, the context is rich and the model is capable — none of which tells you whether the person briefing it could have recognised a weak argument, or spotted that the brief itself was wrong.

So the honest problem isn’t that juniors can’t produce. They demonstrably can, sooner than before.

It’s that nobody can tell any more, from the work, whether they’re learning.

That’s a measurement failure before it’s a training failure, and the measurement failure will be discovered second — probably at the point somebody senior leaves and the person expected to replace them turns out to have four years of good output and eighteen months of judgement.

MIT has now described the same break in education

On 25 August, an MIT committee reported that current AI systems can “produce credible solutions and provide reasonable responses to almost any written assignment” in its undergraduate curriculum. The recommended response was not better AI detection. It was to reconsider how learning is assessed, including oral exams, portfolios and in-person conversations tied to work completed elsewhere. MIT’s president described it as a watershed for higher education.

The headline version is that AI can now do the assignments. The more important finding is that the assignment has stopped functioning as reliable evidence of learning. A credible solution may show that a student understands the subject. It may show that they know how to direct a capable system. From the finished work alone, the institution can no longer tell which.

That does not prove students are learning less. The report is an institutional diagnosis, not a longitudinal measure of what students understand. But it is external evidence that the proxy failure above is not peculiar to editorial work or employment. The same separation between output and formation is already visible before people enter the workforce.

MIT’s proposed alternatives all have something in common: they make reasoning more inspectable. That is close to the workplace response I reach for later in the training gap: ask people to show the decisions, checks and changes behind the deliverable, rather than assuming a strong deliverable proves the judgement exists.

The market-level evidence, since I first wrote this

The project evidence earlier in this piece was internal — one project, one observation, weighted more heavily than its sample size deserved because it was the only thing I had. MIT’s report, above, was the first external evidence to arrive, and it supported the proxy argument rather than the disagreement. Since then, more external data has arrived on a different question — not whether the proxy still works, but whether the rungs exist at all — and this piece should carry it rather than stand as it was.

UK graduate vacancies fell 45% year on year — 8,383 listed in July against 15,397 the year before, the lowest level Adzuna has recorded since it started tracking the measure in 2016. Separately, and more usefully, venture firm SignalFire found a 50% decline in new role starts for people with under a year of post-graduate experience, across sales, marketing, engineering, recruiting, operations, design, finance and legal — a wider signal than one country’s job-board listings, because it spans functions rather than one graduate-specific hiring channel.

I want to be precise about what this does and doesn’t do to the argument. It’s evidence that entry-level roles are becoming scarcer. It is not evidence about what’s happening inside the roles that remain — it says nothing about whether the people who do get hired are forming judgement any differently than before, which is the proxy question two sections up. Fewer rungs on the ladder and a broken rung-quality proxy are two different problems, and this data only speaks to the first.

It’s also not proof of the causal story. Falling graduate vacancies are consistent with AI substitution, but they’re also consistent with a post-2021 hiring correction or ordinary cost-cutting, and a vacancy count alone can’t distinguish between those. I’d want role-starts and task-composition data — closer to what SignalFire measured than what Adzuna measured — before treating the AI explanation as settled rather than plausible.

What it changes is the balance of evidence in this piece. When I wrote the sections above, the erosion side of the argument had no external support at all — just the worry, and one project result pointing the other way. It now has some. Not proof. A number, where before there was only a hunch.

What I don’t know

Quite a lot, and it matters here more than usual.

I don’t know whether judgement now forms differently rather than not at all. Reviewing AI output is a real skill, closer to editing than writing, and it’s plausible that it teaches faster than producing from scratch ever did — you see more arguments, more often, and have to decide about each one. If that’s true, the apprenticeship hasn’t disappeared, it’s changed shape and nobody has named it. I’d want that to be true. Wanting it is a reason to distrust my own reading of it.

I don’t know whether anyone is deliberately preserving production work as training, or what it costs them.

And I don’t know how much of my ladder argument is observation and how much is the ordinary human tendency to believe that the way you learned something was the way it had to be learned. That tendency is very strong and almost never examined by the person holding it.

The single observation I do have — that the workflow helped the least experienced person most — is the one piece of evidence pointing away from my own position, which is why I’ve given it more space here than its sample size deserves.

The part that isn’t about content

I should be honest about my own position, because it is comfortable and that is relevant to how much weight this argument deserves.

I am not especially worried about my own job, or about the direction the work I do is heading in. I think I have read that direction right — or right enough for now. Editorial value looks to me like it is moving upstream, into evidence, judgement and narrative, and that is where I have spent the last few years going. Fifteen years of accumulated judgement, a system built to hold it, and a technology that has been straightforwardly good for me.

I hold that loosely. The longer I look at any of this, the less certain I get about the parts that seemed obvious a month ago.

So this is not a piece written from anxiety about my own position. It is the opposite, and that is precisely why the ladder question bothers me rather than reassures me.

My son is starting secondary school. By the time he is choosing what to train for, the entry-level rung of whatever he picks may have been absorbed — and not only in editorial work. Junior legal review. First-year audit. Routine diagnostic coding. Draft analysis. Basic drafting of almost any kind.

The pattern is not specific to my industry. It is specific to work where competence was built by doing the simple version of the complex thing, many times, under correction.

I have no policy view about this, and I am suspicious of anyone who already has one.

But I notice that almost every confident account of AI and work is written by somebody who already holds the experience the technology is now amplifying.

Including this one.

The question worth asking instead

Not: can early-career people still produce good work? They can, faster than before.

But: if production was doing the teaching, and production has been absorbed, who is designing the replacement?

Nobody, as far as I can tell. It happened by accident for so long that it doesn’t have an owner. It wasn’t in a budget, wasn’t in a training plan, and didn’t appear on anyone’s objectives — which makes it exactly the kind of thing that disappears without a decision being taken, and without anyone noticing for several years.

If you manage a team: how are the people who joined recently learning the judgement part, and how would you know if they weren’t?

If you joined recently: where are you actually getting it?

I’d rather have an answer to either of those than another argument about whether AI can write.

And if you work somewhere that isn’t content or marketing at all — law, finance, engineering, medicine, any trade with a first year that used to teach you something — I would like to know whether this reads as familiar or as somebody else’s problem.