Knowledge Vacuum · IT 2025–2026

The vacuum of misunderstanding: what actually happened to IT in 2025–2026

If you've been going to conferences, reading Telegram channels and listening to hiring podcasts for the last couple of years, the same murky picture is probably spinning in your head: "AI is taking juniors' jobs, seniors are in demand, everyone's running to become AI engineers, the market is overheated, then it'll cool down, then…" That picture stopped working for me a while ago, and here's why: it describes symptoms, not a diagnosis. At some point I formulated the diagnosis differently for myself — and I want to share it, because without this conversation we're all discussing different things and arguing past each other.

It wasn't a shift toward "the new programmer" and not a generational change of developers. What happened was a vacuum of misunderstanding inside the volume of knowledge. It sounds abstract, but there's a fairly concrete thing behind it that I'll try to lay out below. Short version — we all simultaneously stopped understanding what we actually know and what we're actually being asked. Both companies and developers themselves.

What I mean by the volume of knowledge

Let's agree on the model. A developer's knowledge isn't a line on a CV or the number of frameworks they've learned. It's an object with several parameters: volume, quality, coherence, density, personal "grown-ness". Together they form a multidimensional space — roughly like in physics: some coordinates you can grow in a week, others only over years. Employers used to essentially search for a point in this space that fit their task: they needed someone with the right volume, the right quality and — often overlooked — coherence. Coherence is when the knowledge isn't sliced into isolated bits from different tutorials, but stitched into a working model in your head. You might not know a specific library's API, but you have a model of how it's built and where the sharp edges are.

It's exactly this coherence that made a developer a developer, and not a Stack Overflow operator with a good memory. It grows slowly — through your own mistakes, through night-time debugging, through projects you took to production and then maintained for three years. It's an expensive thing — both for the person themselves and for the company hiring them.

What actually happened

From late 2022 — practically from the moment ChatGPT started answering code questions properly — a huge amount of cheap raw material burst into this multidimensional space. Generated code, ready answers, templates, on-the-fly refactors, "explain it like I'm in fifth grade". It isn't good or bad in itself — it's just a new kind of raw material. But then an interesting effect kicked in.

First, developers started absorbing this raw material differently. Some of us are genuinely growing their volume: you take the generated thing, take it apart, check it, stitch it into your model. That's a normal path — tools have always worked that way, from IDEs to Stack Overflow. But there's another part, and honestly it's bigger: knowledge stops being grown. You didn't step on the rake with that Promise — AI neatly walked around it for you. You didn't break a production deploy through an hour of refactoring — AI rewrote it in five minutes and you just took the result. For fundamental things this is degradation. Not sharp, not obvious — but accumulating. And almost nobody measures it.

Second — and this is the key bit — everyone started using this raw material. Managers, product folks, developers themselves, recruiters, non-engineering specialists. They got access to cheap raw knowledge and thought that was the real thing. In Stack Overflow's 2025 survey, 84% of developers use AI tools at work, 51% of professionals — every day. That number isn't shocking — what's shocking is different: 66% spend more time debugging AI code than they expected, and the top frustration is "almost right, but not quite" solutions (45%). So we're chewing through raw material that looks like a finished dish but turns out to be a half-cooked product with surprises.

And third — trust in the accuracy of AI output has already broken: if in 2024 fully 31% of developers trusted it, in 2025 already 46% don't (Stack Overflow 2025). That's in eighteen months. We're learning not to trust — but we keep consuming.

Why companies don't see it

This is where it gets painful. Only the developer themselves can really assess the quality and coherence of their knowledge. And even then — not always. Companies on the whole are archaic in their assessment: they look at the goal (feature, deadlines, metrics) and the costs (salary, headcount). Everything in between — "knowledge" — either they don't see it, or they measure it with proxy metrics: "passed the interview", "knows Kafka", "can Kubernetes". Those metrics worked when raw knowledge was rare and expensive, and its presence in a candidate's head actually meant something. Now it doesn't mean anything anymore — but the measurement hasn't changed.

The visibility of knowledge and its substance have fully diverged. A manager sees that a junior with AI ships code faster than a senior without AI, and draws the conclusion that seems logical to them: 37% of managers now openly say they prefer AI to interns (Stack Overflow 2025). But "ships code faster" and "understands the system better" are two different metrics. The first is visible in Jira, the second only six months later when something breaks in an unexpected place.

Where it ends if nothing changes

The market is reformatting — it's already happening, and the numbers are pretty harsh. Employment of 22–25 year-old developers has dropped almost 20% from its late 2022 peak (Stanford Digital Economy Lab, AI Index 2026). Employment of 30+ developers over the same period grew by 6–12%. Junior hiring in large companies has fallen 25–50% over the last 2–3 years. "Seniorisation" of entry-level roles is up 35% since 2019 (PwC AI Jobs Barometer 2026) — the entry bar for the same role is noticeably higher than it was five years ago. Unemployment among 22–27 year-olds is 7.4% vs the 4.2% average.

The picture adds up to something rather alarming: young people are stopping entering the profession, experienced specialists are leaving (burnout, relocation, drifting into AI consulting). When the current wave of "basic" knowledge settles and consolidates — and it will settle, you can already see it in how companies are starting to exhale after the initial hype — a new growth will start. But who will absorb this new knowledge? Seniors will keep absorbing, but there are fewer of them, and they physically have less energy to "start from scratch". Juniors — the ones who grow knowledge through their own mistakes — simply aren't being fed into the pipeline as much. That is the broken conveyor belt that nobody is talking about out loud right now, but everyone sees it.

And yes, note: 64% of developers don't see AI as a threat to their job. Sounds reassuring — but it's down from 68% in 2024. The trend has already started, just slowly for now.

What a developer should do

No panic, no "top 10 skills of 2026". A few thoughts that feel honest to me.

First — treat AI as very powerful raw material, not a finished dish. Every generated piece is an entry point, not an exit point. If you can't explain why that code works and under what conditions it'll break, you don't know it. You're consuming it right now.

Second — deliberately grow your foundation. It's boring, it doesn't monetise in the moment, but it's the only thing that separates a developer from an AI operator. Read code that AI gave you, without AI. Solve problems that feel too small for AI — that's where coherence grows. Write something by hand, at least once a week, just to keep your hands remembering.

Third — if you're a hiring manager or a lead, realise that your assessment metric is broken, and start fixing it before the team breaks. A junior who writes code slower but understands what they're doing will be worth more in a year than a junior who quickly outputs generated results and doesn't understand what's under the hood. Investing in hiring and onboarding juniors is counter-intuitive right now, but strategically the right call.

Not an end, a reboot

I don't think the profession is dying. I think it's rebooting — and like any reboot, it carries the risk of freezing on a black screen. We're all — developers and companies alike — at the point where old knowledge-as-object hasn't stopped working yet, and new knowledge-as-stream has already stopped fitting into the old hiring and assessment models. The vacuum between them is what we all feel when we try to hire, get hired, assess, explain to ourselves what's happening.

There is a way out of this vacuum, and it isn't about "learning prompts" or running to become an AI engineer. It's about re-learning to tell knowledge apart from its visibility — in your own work, in hiring, in evaluating colleagues. Then the conveyor will fix itself.