AI is rebuilding engineering teams in real time — here’s what’s actually happening

Two years ago, Klarna’s CEO went on stage and announced that AI had replaced 700 customer-service jobs at the company. He framed it as the future of how companies would operate.

Klarna started rehiring those positions in May 2025. The CEO went on the record about why a few months later: “Cost unfortunately seems to have been a too predominant evaluation factor… what you end up having is lower quality.”

Read that twice. A sitting CEO publicly admitting the AI-first headcount strategy did not work. Eighteen months of “AI is replacing humans” reversed in a single sentence.

Klarna’s reversal is about customer service, not software engineering. But it is the loudest single signal that the headcount math companies have been telling themselves for the last two years does not survive contact with reality. And what is happening in customer support is what is starting to happen in engineering teams — slower, less visibly, but in the same direction.

Last month I wrote about why the unit economics of AI today are broken. This post is the other half of that story — what AI is actually doing to engineering teams in real time. The shape is shifting fast, junior hiring is collapsing, several role categories are being absorbed, and the senior IC role is pivoting in ways most seniors have not yet caught up with. I am going to be blunt about it, with the data, and let you draw your own conclusions.

Let’s go.

The numbers nobody is showing the board

Not the CEO quotes. Not the LinkedIn thinkpieces. The actual hiring data.

Indeed Hiring Lab’s April 2026 labour market snapshot shows US software-development postings still roughly 28% below the February 2020 baseline — a partial recovery from the deeper trough but still well below pre-pandemic levels. At the same time, AI-related postings have climbed to 5.4% of all listings, past the 3.3% peak from 2022. So the headline is not just “fewer jobs”; it is “the few jobs that are opening are heavily skewed toward AI work.”

US Bureau of Labor Statistics shows the smaller “Computer Programmer” category fell 27.5% between 2023 and 2025, while the much larger “Software Developer” category was essentially flat (−0.3%). The displacement is concentrated in the entry-level and mid-level “Programmer” titles — not the senior software developers.

Stanford’s 2026 AI Index puts a number on the demographic angle: software-developer employment among workers aged 22-25 has fallen nearly 20% since 2024 — the first white-collar category showing measurable AI-attributable contraction at the entry level.

A 2025 LeadDev survey found 54% of engineering leaders expect long-term junior hiring to decline because of AI — though only 18% planned to cut junior hiring in the next year. The gap is leaders saying out loud what they have not yet acted on.

Now look at the other side. Forward-Deployed Engineer postings — a Palantir-pioneered role now standard at OpenAI, Anthropic, and Cohere — went from 643 in April 2025 to 5,330 in April 2026, a +729% year-on-year jump, with Google and Deloitte accounting for roughly 40% of openings. Lightcast’s March 2026 GenAI job market update tracks around 10,000 unique GenAI-skill postings per month, and LinkedIn data has AI Engineer as the fastest-growing role for young workers for the second consecutive year. Forrester’s 2026 Predictions project a 20% decline in CS enrollments going forward — the pipeline that was meant to feed entry-level hiring is shrinking too.

Put it all together: the junior side is in collapse, AI-specialty roles are exploding by orders of magnitude while total hiring shrinks, and the middle is flat. And nobody is in a hurry to mention any of this when they pitch their AI strategy to the board, because the public story is supposed to be that AI is helping engineers do more — not that it is being used to avoid hiring entry-level engineers entirely.

Smaller teams, more senior. And more fragile.

The new team is smaller. The new team is more senior. The new team is supposedly faster. And by most accounts, the new team is also more fragile.

Look at what the leading companies are doing. Shopify has made AI usage a fundamental expectation — performance reviews include it, and teams must prove a job cannot be done by AI before backfilling. Shopify’s VP of Engineering Farhan Thawar reports a ~20% productivity gain and uses weekly demos as the primary velocity signal, not PR counts. Stripe runs an “Experimental Projects Team” of about two dozen senior engineers operating fleets of agents through their internal “Minions” harness. Google’s Pichai stated in April 2026 that “75% of new code at Google is AI-generated” — treat the exact figure as directional, but the direction is unmistakable. Microsoft’s Nadella said in April 2025 that 20-30% of code in some Microsoft projects is AI-written.

The pattern across all of them: smaller pods, more senior on average, embedded AI infrastructure. Anthropic’s 2026 Agentic Coding Trends Report describes the model as dynamic surge staffing — specialists pulled onto problems for short periods rather than kept on a permanent roster. The new shape of an engineering team looks more like a Big Four consulting bench than like a traditional product team.

The consequence — almost nobody puts this on the slide — is that teams shaped this way are more brittle. Fewer people know how systems work end to end. Less long-tenure knowledge. Less of the cross-pollination between juniors and seniors that historically built the next senior layer. Faros AI’s 2026 telemetry of 22,000 developers and 4,000+ teams calls this “Acceleration Whiplash.”

Teams ship more code, review less of it, and take longer to review what they do not skip. That is the kind of efficiency that looks great on a slide and miserable on a Friday afternoon.

The roles, growing and absorbed

If hiring volume is down overall but specific role categories are exploding, which roles? The chart shows the shape; here is what each one means.

Six roles that are growing — all senior by intent:

• AI Engineer — integrates LLMs into products. Prompts, RAG, fine-tuning, evals. About 80% of postings are senior-level. UK salaries £110-180K, US higher.
• Forward-Deployed Engineer (FDE) — Palantir-pioneered, now standard at OpenAI, Anthropic, Cohere. Embedded with customers, translating AI capability into systems that work in production.
• Context Engineer — Stripe / Shopify / ThoughtWorks discipline. Owns the documentation, knowledge graphs, and prompt scaffolding that make agents reliable inside a specific organisation.
• Agent Orchestrator — designs harnesses, MCP servers, and feedback loops for operating fleets of agents in parallel. Simon Willison calls this vibe engineering.
• RAG Engineer — grounded outputs from trusted sources. The defence against hallucination. Every regulated industry is hiring for this.
• AI Governance Lead — NIST AI RMF, ISO/IEC 42001, EU AI Act, OWASP Top 10 for Agentic Apps. Used to be a corner of security; now its own function.

If your career strategy was “get to senior in five years, then specialise”, the new strategy is “specialise in one of these while getting to senior.” The market is not waiting.

Four roles being absorbed:

• Boilerplate-focused junior coding — the CRUD work that used to be the first-year-junior path. AI does it for free relative to a junior salary.
• Dedicated Scrum Master — at smaller and mid-sized companies, absorbed into Engineering Manager responsibilities.
• Manual QA — click-through testing replaced by AI test automation. Indeed eliminated dedicated QA Engineer roles in March 2023 and reportedly saw test quality drop afterward — cautionary tale.
• Pure documentation specialist — first-draft documentation goes to AI. The role survives at large companies but has shifted toward content strategy and DevEx.

QA strategy is growing fast: Tesla expanded QA from 260 to 390 between 2020 and 2025; SDETs and reliability engineers are among the fastest-growing roles for 2025-2026. The mechanical work goes; the judgment work stays.

None of this is a judgement on the people doing those jobs. The shift is real and fast, and the honest thing to do is name it.

The skill bar is moving up

The skill bar is moving up at every level. Here is what specifically matters now.

Juniors need what they used to learn in their second year on day one: computational thinking, code-review judgment, production-deployment skill, and AI-tool fluency. The fastest CV upgrade available right now is deploy something real and maintain it for six months. Stack Overflow’s 2025 survey shows 84% of developers use AI tools — your fluency with them is table stakes, not differentiation. Writing boilerplate, basic debugging, syntax fluency: still required, no longer enough.

Mid-level engineers face the sharpest squeeze. Implementation throughput used to be your differentiator; it is now cheap. The shift is toward implementation judgment — writing precise specs, supervising agents, refactoring with discipline, knowing when to override. Kent Beck’s line is sharp: “today’s AI assistants lack taste.” Your job at mid-level is increasingly to be the taste. Mid-levels who do not make that shift will plateau in ways that are hard to articulate.

Senior, staff, and principal engineers pivot from depth-of-implementation to architecture, judgment, and reviewing at scale. Simon Willison’s vibe engineering essay describes it: “researching approaches, deciding on high-level architecture, writing specifications, defining success criteria, designing agentic loops, planning QA, managing a growing army of weird digital interns who will absolutely cheat if you give them a chance, and spending so much time on code review.” If your senior identity is “I can code anything” — you are competing with AI. If it is “I know what matters in this specific organisational context” — AI amplifies you.

Engineering managers have acquired three new buckets nobody warned them about: token economics (engineers can spend $1,000+/month on tokens without approval — manage it before finance does), new performance metrics (lines, PRs, and AI acceptance rates are all gameable now; weekly demos are not), and hiring-process redesign (whiteboarding is dead; live debugging and reading AI-generated PRs is the new format).

CTOs and VPs of Engineering need three things at strategy level: platform thinking (DORA’s January 2026 ROI report frames AI as an amplifier of underlying engineering quality — platform quality directly correlates with realising AI value, so do not let your CFO treat platform investment as overhead), compliance mastery (NIST AI RMF, ISO/IEC 42001, the EU AI Act — not optional in 2026), and talent-pipeline strategy, which is the next section.

The pipeline trap

If you only take one thing from this post, take this.

Stack Overflow’s blog put it bluntly in late 2025: “if you don’t hire junior developers, you’ll someday never have senior developers.”

The problem is structural and time-delayed. The juniors you do not hire in 2026 are the seniors you do not have in 2031. By the time the gap is visible on the org chart, it is too late to fix in the same hiring cycle.

Two camps on this in tech leadership right now.

Camp A — Anthropic’s Dario Amodei: up to 50% of entry-level white-collar jobs eliminated within five years. Treat as a forecast, not a measurement, but it is the camp informing the headcount decisions you can see in the data above.

Camp B — GitHub’s Thomas Dohmke and Google’s Sundar Pichai: AI makes juniors more valuable, not less, because it amplifies what they can produce. GitHub continued early-career hiring through 2025. Pichai has said Google plans to hire more engineers because AI is making them more productive. And IBM has tripled entry-level hiring in 2026, with CHRO Nickle LaMoreaux explicitly framing the additional juniors as the human oversight layer their AI investments require — including for roles “AI can do.”

Both camps read the same data and reach opposite conclusions because they are running different bets. The honest answer is we do not yet know which bet wins, but the cost of being wrong is wildly asymmetric. If Camp A is wrong, you have a senior shortage in 2031 you cannot hire your way out of. If Camp B is wrong, you have slightly higher headcount cost in 2026 that you can fix at any time.

One of those mistakes is reversible. The other is not.

If you are running an engineering organisation, the implication is simple. Do not freeze junior hiring entirely. Slow it, redesign it around AI-augmented work, raise the bar on what juniors are expected to do, but do not freeze it. The people who get this right in 2026 are going to look like geniuses in 2031.

What I tell clients

Here is what I tell engineering leaders right now, on every project where it comes up.

• Hire juniors. Just hire fewer, and redesign their first six months. The traditional first-year path of “write CRUD to learn the codebase” is gone. Replace it with “review AI-generated PRs”, “deploy and operate something small in production by month three”, and “be the person who can explain how an agent went wrong”. Juniors out of that program in two years are more useful than juniors from five years ago, not less.

• Set token budgets per team or per feature, with observability. Tag every AI resource with cost-center, project, environment, and owner. Without that, the AI bill is one line item nobody can interrogate. Engineers will spend $1,000+/month per head if you don’t.

• Replace PR-count and line-count metrics with weekly demos. Activity metrics are gameable in ways they were not three years ago — anyone can submit a hundred PRs of AI-generated boilerplate now. Demos cannot be faked the same way.

• Enforce the explainability rule. Simon Willison’s golden rule: “I won’t commit any code to my repository if I couldn’t explain exactly what it does to somebody else.” Make this an org-wide policy. Anything an engineer cannot explain at PR review does not merge. This single rule prevents most of the worst AI-coding disasters I have seen — including the Cursor-Claude agent that wiped PocketOS’s production database in nine seconds last month because nobody was in a position to override the destructive command before it ran.

• Document the team you would hire if you were starting over right now, not the team you have. Then plan the next two years of hiring against the gap, not against attrition. This is the single highest-payoff exercise an engineering leader can do in 2026.

Build the team you want to be running in 2031

AI is rebuilding engineering teams in real time. The shape is smaller, more senior, more cross-functional, more brittle, more dependent on a few specialists at the top of the stack. Junior hiring is in collapse. Several role categories are being absorbed. The senior IC role is pivoting toward judgment, architecture, and review. The CTO role is pivoting toward governance, platform, and talent strategy.

None of this is reversible at the level of any single company. It is a market-wide shift driven by the same economics I wrote about last month, and it is going to keep moving in this direction whether or not your org chart is ready.

What is reversible is the choices you make this year. Hire some juniors. Redesign their onboarding. Pay your senior people for judgment, not output. Tag every AI resource. Demo your work weekly. Tell your team the truth.

Build the team you want to be running in 2031, not the one your headcount spreadsheet defaults to in 2026. That is the whole game.