The bottom rung of the American software-engineering career ladder, after thirty years of expansion that produced a profession of roughly six million people and the displacement of a great deal of older clerical and administrative work in its passing, has in the past two years effectively been removed. The cohort affected is the cohort of the recent computer-science graduate — the twenty-two-year-old who, in 2021, would have walked from a four-year undergraduate degree into a graduate-engineering programme at one of the major technology firms or the more substantial enterprise software companies, served eighteen months as a junior on a team of five or six engineers, advanced to mid-level by twenty-six, to senior by thirty, and continued from there along the well-worn path the profession had been recruiting along since the nineteen-nineties. That cohort, in 2026, is in considerable difficulty.
This post is about what has happened, what has caused it, what the parallel patterns look like in the other knowledge-work professions whose junior-grade work now lies within the same technology’s reliable competence, and what the question is — the unanswered one — about how the senior workforce of 2032 is to be supplied if the pipeline that was reliably producing it through 2022 is no longer reliably producing it now.
The Numbers
The labour-market data on entry-level software-engineering hiring in the United States is sufficient, by any reasonable accounting, to have foreclosed a debate about whether the displacement is happening. The debate, where it persists, is over its mechanism and over its duration; it is no longer over its existence.
The aggregate figures, drawn from a combination of the Bureau of Labor Statistics, the National Association of Colleges and Employers, LinkedIn’s hiring data, and the published recruitment volumes of the major firms: entry-level software-engineering postings in the United States are down somewhere between forty and sixty per cent from their 2022 peak, depending on which dataset and which definition of “entry-level” one consults. The contraction is not uniform across firm tier — the major technology firms (Google, Meta, Microsoft, Amazon, Apple, Netflix, the AI labs) have reduced new-graduate engineering hiring by something on the order of seventy per cent against the 2022 baseline; the mid-tier scaleups and well-funded startups by perhaps fifty per cent; the Fortune 500 enterprise IT shops by a more modest thirty per cent. The pattern is consistent in direction across every tier of employer that hires engineers in volume; the only variation is the magnitude of the contraction.
The cohort effects are striking. The mean time-to-first-job for a four-year computer-science graduate in 2021 was somewhere between three and four months. For the class of 2024, the figure was perhaps eight months; for the class of 2025, it was approaching twelve months; the class of 2026, currently graduating, faces a labour market in which a substantial fraction of its members will not have secured an entry-level engineering position by Christmas. Mean starting salary, deflated, has declined by perhaps fifteen per cent against the 2022 peak. The premium that the computer-science degree commanded over the median bachelor’s degree, which had been steadily widening through the 2010s and early 2020s, has begun to narrow for the first time since the field’s institutional emergence as a separate undergraduate major.
Bootcamp graduates have been more severely affected. The coding-bootcamp model — twelve to twenty-four week intensive technical training, intended to produce graduates immediately employable in junior software-engineering roles — was a creature of the 2014–2022 hiring boom, and its placement-rate guarantees were the implicit basis on which the model justified its tuition. By 2024 the placement rates had collapsed to a degree that several of the major bootcamps (Lambda School / BloomTech, Kenzie Academy, the Holberton schools) closed entirely, and the survivors had pivoted to selling AI-skills retraining to mid-career professionals rather than entry-level placement to recent graduates. The bootcamp graduate without a CS degree to fall back on has been, in any honest accounting, the cohort hardest hit.
The layoff data is the second face of the same phenomenon. The major technology firms cumulatively laid off roughly four hundred thousand employees across 2022–2024, a substantial portion of them in early-career and mid-career engineering roles; 2025 added perhaps a further hundred and eighty thousand; 2026, in its first five months, has added something close to a hundred thousand more, including Meta’s eight-thousand-employee reorganisation announced for May 20 and explicitly framed by the company as a capital reallocation toward its AI infrastructure programme. The layoffs are concentrated in two categories: the marginal junior cohorts that the firms had hired in the 2021–2022 over-expansion, and the mid-career engineers whose roles were judged to be the most directly affected by the productivity gains of senior-engineer-plus-AI-assistant workflows. The firms have, by their own published statements, no intention of returning to the hiring rates of 2021–2022 in the foreseeable future.
These are the numbers. They are not in serious dispute. The question of what is causing them — and whether the cause is durable or cyclical — is where the analytical difficulty begins.
The Mechanism
Several explanations have been advanced for the entry-level engineering contraction, and the explanations are not mutually exclusive. Some portion of the contraction is the post-pandemic correction of the 2021–2022 over-hiring, when the major firms had been recruiting at rates well above their normal baseline in anticipation of pandemic-era growth that subsequently slowed. Some portion is the high-interest-rate environment of 2023–2024, which raised the cost of capital and reduced the willingness of firms to hire ahead of revenue. Some portion is the broader cooling of the technology sector as the cloud-and-mobile expansion of the 2010s reached saturation. Each of these explains a fraction of the contraction; none of them, separately or in combination, explains all of it.
The remaining fraction — and the principal fraction, by the testimony of the firms themselves and by what one can infer from the survivorship pattern of the affected cohorts — is attributable to the productivity effect of the language-model-and-coding-assistant workflows that have become, in the past three years, the default tooling of the working software engineer. The productivity effect is not, in the most defensible accounting, that AI is replacing engineers wholesale. The effect is that AI is altering the labour-cost structure within an engineering team in a way that is locally rational for the firm and globally devastating to the entry-level cohort.
The mechanism is the following. A senior engineer in 2022, working without AI assistance, produced perhaps a unit of output a day. A senior engineer in 2026, working with Cursor or Claude Code or one of their equivalents, produces perhaps two and a half units a day. The senior engineer’s salary has not increased proportionally; the marginal productivity of an additional senior engineer, for many tasks, has therefore approximately doubled in cost-effective terms. A junior engineer in 2022 produced perhaps a third of a unit a day, required perhaps half a senior engineer’s time in mentorship and code review, and was on a track to become productive at the senior level in two to three years. A junior engineer in 2026, with the same tooling, produces perhaps half a unit a day — but the tooling itself is doing the easier portion of the work, and the senior engineer’s capacity to spot-check the AI’s output is materially the same as the capacity to spot-check the junior’s output.
The arithmetic is unforgiving. A team that in 2022 might have been organised as one senior engineer mentoring two juniors — total daily output approximately one and two-thirds units, total cost approximately one senior salary plus two junior salaries — is in 2026 better organised as one senior engineer working alone with AI assistance, producing approximately two and a half units a day at a cost of one senior salary. The team has lost two junior engineers and one senior-mentorship overhead and gained substantial productivity. The firm, doing this calculation across thousands of teams, has reduced its junior hiring to a small fraction of its prior level. The mechanism is not that AI has replaced the junior engineer’s capacity for engineering work. The mechanism is that AI has eliminated the training-wheels portion of the entry-level engineering role, which is the portion the junior was hired to do.
There is a particular irony in this — the irony that is sharpest in the institutional-history register and that distinguishes this displacement from the earlier waves of automation that the engineering profession had survived. The earlier displacements affected the more routine and the more standardised work; the senior engineer was protected by the judgment-and-system-design portion of the role that automation could not reach. This displacement is the inverse. The most easily automated work, in 2026, is not the routine work; it is the learning work — the boilerplate, the test-writing, the documentation, the bug-triage, the simple-refactor — that the senior had previously delegated to the junior precisely because doing it was the way the junior would, in time, become a senior. The training pipeline is what the technology has hit first. The senior is the protected cohort.
The Same Thing Elsewhere
The pattern is not confined to software engineering. It appears, in recognisable form, across every knowledge-work profession whose junior-grade work consists largely of the structured analytical-and-documentary tasks the language-model-plus-tooling workflows now perform reliably.
The law, first. The American legal profession has historically organised itself around a pyramid of associates beneath each partner, with the first-year associate serving an apprenticeship of document review, deposition preparation, contract markup, and case-research summarisation that constitutes both a substantial fraction of the firm’s billable work and the principal training mechanism by which the next generation of partners is produced. The major American law firms reduced their first-year-associate hiring by perhaps forty per cent across 2023–2025, with the partner-track ladder narrowing further at every subsequent step. The work the first-year associate had performed — the document review in particular — is now done by document-AI systems that read at perhaps thirty times the speed of the first-year associate at perhaps ninety per cent of the recall, and the partner who reviews the AI’s output reviews it at the same speed she would have reviewed an associate’s output and at materially the same accuracy. Paralegal work, where it has not been replaced entirely, has been substantially reduced.
Consulting, next. The major management consultancies (McKinsey, Bain, Boston Consulting Group, the strategy practices of the Big Four accountancies) have reduced associate-level recruitment by something on the order of forty to fifty per cent across 2023–2025. The associate’s traditional work — the slide-deck construction, the market-sizing analysis, the literature review, the first-pass financial modelling — is the work the AI assistants now perform reliably enough that the engagement-team partner can run a team of one or two senior consultants where she would previously have run a team of one senior consultant and four associates. The mechanism is the same as in software: the AI has not replaced the consultant; it has replaced the consultant’s training-wheels.
Investment banking, similarly. Goldman Sachs, Morgan Stanley, JPMorgan, and the other major Wall Street firms have reduced their first-year analyst classes by twenty-five to fifty per cent across 2023–2025. The analyst’s traditional work — the comparable-company analysis, the precedent-transactions tables, the pitch-book construction, the financial-model assembly — has been similarly absorbed into AI-assisted senior-analyst-and-associate workflows.
Accounting. The Big Four (Deloitte, PwC, EY, KPMG) have reduced their entry-level accountant and auditor hiring by perhaps thirty to forty per cent across 2023–2025, with the audit-grade work that constituted the first-year auditor’s traditional training now substantially performed by document-AI systems.
In each case the pattern is the same. The senior cohort is preserved or expanded; the entry-level cohort is dramatically reduced; the work that has been displaced is the work that was the entry-level cohort’s training pathway to seniority. The question, across all five professions and the others one could add to the list, is the question of where the next decade’s senior workforce is to come from if the entry-level pipeline that was producing it through 2022 is now, structurally, producing it at a fraction of the prior rate.
The Question of the Ladder
The question is the question of the ladder. The senior software engineer of 2032 is, by the ordinary reckoning of how the profession has historically reproduced itself, the junior engineer of 2026 with six years of additional experience. If the junior engineer of 2026 is not being hired in volume, the senior engineer of 2032 is not being trained in volume, and the senior workforce of the early 2030s is, on present trajectory, going to be substantially smaller than the senior workforce of the present moment.
Several responses to this question have been offered, and none of them is wholly convincing.
The first response — held by some of the more bullish firms in the AI sector and by a portion of the venture-capital community — is that the senior workforce of 2032 will simply not need to be as large as the senior workforce of the present, because AI productivity will continue to compound and the senior-engineer-with-AI of 2032 will produce what the team of senior-engineer-and-juniors produces today. This is the productivity-compounds response, and it is internally coherent if its premises hold. Its premises are that AI-assisted senior productivity will continue to grow at its present rate (which is plausible but uncertain), that the work itself will remain decomposable into the senior-spot-check pattern (which is plausible for some work and not for others), and that the supply of senior engineers in 2032 will be sufficient for the reduced demand (which assumes the present senior cohort does not retire or attrit faster than the reduced junior cohort can replace it).
The second response — held by some of the larger and more institutionally-conservative firms — is that the present hiring contraction is the correction of an unsustainable boom, that the firms will resume entry-level hiring at moderate rates as the AI productivity gains stabilise, and that the senior workforce of 2032 will be a smaller-but-adequate version of the senior workforce of the present. This is the cyclical-correction response, and its plausibility depends on whether the firms in fact resume entry-level hiring at moderate rates rather than continuing the current contraction. The evidence so far is that they have not.
The third response — held by some of the more thoughtful observers in the labour-economics community and by a portion of the engineering profession itself — is that the present pattern represents a structural rebalancing of how technical knowledge-work is organised, and that the institutions that have historically reproduced the engineering profession (the four-year CS degree, the post-graduate apprenticeship, the firm-internal mentorship pipeline) will need to be rebuilt around the new arithmetic. What that rebuilding looks like in practice is the open question. Some have proposed that the AI assistant itself will become the mentor — that the junior engineer of 2030 will learn from the AI in the way the junior engineer of 2020 learned from the senior. Others have proposed that the engineering profession will reorganise around smaller numbers of more-deeply-trained engineers entering at a higher level, with the entry point shifted from the bachelor’s degree to a more substantial graduate-level credential. Others still have proposed that the profession will simply shrink, and that the surplus of would-be engineers will redirect to adjacent fields.
None of the three responses has been tested. The next five years will test them.
What This Looks Like in Practice
The cohort affected is, on the ground, the cohort one knows. It is the friend-of-a-friend’s son who graduated with a four-year CS degree from a respectable state university in 2024 and has been doing contract work and interviewing intermittently since. It is the bootcamp graduate of 2023 who completed her programme at the moment the placement rates began to collapse and has, after eighteen months, transitioned out of the engineering search entirely. It is the freshly-minted PhD in machine learning whose academic-job market is healthier than the industry-research-scientist market for the first time in fifteen years. It is the international student on an OPT clock who has six months to find an H-1B-eligible position and who is competing with a pool of domestic applicants whose own job-search cycle has lengthened from months to a year.
What these cohorts are doing in 2026, by the testimony of the placement data and the labour-market surveys, is some combination of the following. They are taking longer to find work. They are taking less prestigious first jobs at lower starting salaries than they had been led to expect. They are taking contract or contingent or gig roles instead of the salaried-with-benefits positions the prior cohorts took. They are migrating to AI-adjacent operational roles (prompt engineering, AI quality assurance, the various job-categories that the AI tooling has itself created) that did not exist five years ago and that may or may not be durable. They are pursuing graduate degrees as a holding pattern, often in machine learning or AI-adjacent specialisations. They are starting their own products, leveraging the same AI tools that have eliminated their entry-level options to produce small revenue streams of the kind a single founder could not have produced unassisted in 2020. They are leaving the profession entirely, often after years of preparation for it.
None of this is a moral failing on the part of the cohort. The cohort prepared for an institutional pathway that has been substantially disassembled in the brief window between their entering university and their graduating from it, and the disassembly was not, in any honest accounting, foreseen by the institutions that prepared them. The four-year computer-science degree of 2026 trains its students for a labour market that no longer exists at the scale the curriculum was designed against. The bootcamp model of the same period trains for a market that has effectively closed. The career counsellors at the universities are, in many cases, still telling 2026 graduates the same things they told 2021 graduates, because the institutions have not yet absorbed the new arithmetic. The cohort is being prepared, in good faith, for a profession whose entry conditions have changed underneath it.
What follows from this, taken honestly, is the small and uncomfortable observation that the present moment in the American knowledge-work professions is not a temporary disturbance but the early phase of a structural rebalancing of considerable scope. The firms have made their adjustment; the institutions have not yet made theirs; the cohort caught in between has had to absorb the gap. Whether the institutions will adjust quickly enough to keep producing a senior workforce in five and ten and fifteen years is the open question on which the next decade of every affected profession depends.
The senior software engineer of 2032 is, somewhere in the country in 2026, attempting to enter the field. Whether she succeeds is a question that the firms making the present hiring decisions, the institutions training her, and the policy-makers who might intervene in the gap have not yet, in any defensible sense, taken seriously. The present moment is the moment at which they will be obliged to.