Why CS New-Grad Unemployment Climbed Above 6% in 2025 (And What That Means for 2026)

How bad is the CS new-grad job market in 2025?

Bad enough that computer science, the major sold to a generation as the surest bet in the labor market, now posts a higher unemployment rate than philosophy, history, and English literature. According to the Federal Reserve Bank of New York's College Labor Market dashboard (data updated through early 2025), recent CS bachelor's graduates posted a 6.1% unemployment rate. Computer engineering came in at 7.5%. Art history sat at 3.0%. That is not a typo, and it is not a one-quarter blip.

Founder aside: this is the data that drives every conversation in r/csMajors and r/cscareerquestions right now. I run a tool for CS new grads. The single most common message I get is some version of "is it me, or is the market actually broken?" The data below is the answer. The market is structurally different from 2021. Not just slower. Different. The candidates who land offers in 2026 have absorbed that shift; the ones still stuck on cold-apply volume from a 2021 playbook are at 487 applications and counting.

The numbers, with dates and sources

The story is not one statistic. It is a stack of them pointing the same direction.

NY Fed College Labor Market, early 2025

The Federal Reserve Bank of New York's College Labor Market dashboard tracks unemployment and underemployment for recent college graduates by major. As of the early-2025 data release (reported widely in February 2025), the rate for recent CS graduates aged 22–27 sat at 6.1%. Computer engineering came in at 7.5%. The overall rate for recent college graduates was 3.6%. Two of the three highest-unemployment majors on the dashboard were the two flagship tech majors.

Three years earlier, in the NY Fed's 2022 update, the equivalent CS figure was in the 2–3% band, roughly half today's rate. The series doubled in two years.

BLS computer-occupations hiring, 2022–2024

The U.S. Bureau of Labor Statistics tracks employment in the "computer and mathematical occupations" group through the Current Population Survey. Total employment in the category did not collapse. It stagnated. The headline number drifted within a narrow band from 2022 through 2024 while the labor force kept growing, which is the shape that produces rising unemployment among new entrants without producing layoffs of the already-employed. The BLS Job Openings and Labor Turnover Survey (JOLTS) showed information-sector hires per month falling roughly 30–35% from their 2022 peak, with most of the decline concentrated in entry-level postings.

layoffs.fyi tech-cuts aggregate, 2022–2025

The crowd-sourced tracker layoffs.fyi, the industry-standard public count, logged more than 500,000 cumulative tech-sector layoffs from January 2022 through Q1 2025. The 2023 single-year total alone exceeded 263,000. The bulk were experienced engineers, but the second-order effect on new grads was severe: hiring managers got their pick of senior candidates at junior-level salaries, and most chose the senior candidate. New-grad slots were not filled; they were eliminated.

NACE Class of 2024 and 2025 First-Destination Surveys

The National Association of Colleges and Employers (NACE) runs the most authoritative cross-employer survey of what new bachelor's graduates do after graduation. The Class of 2024 First-Destination Survey (published in late 2024 / early 2025) reported that the share of computer-and-information-sciences graduates landing a full-time job within six months fell several percentage points versus the class of 2022, while median starting salary held roughly flat in nominal dollars at ~$80,800. That's a real-terms decline after inflation.

Preliminary class-of-2025 data (discussed by NACE in mid-2025) showed employer hiring projections for the cohort revised downward versus initial fall-2024 estimates. The Job Outlook Spring 2025 update projected a small overall increase in new-grad hires across all majors. But the largest revisions downward were in employers explicitly hiring CS, IT, and engineering grads.

Time-to-first-offer

Anchor stats from career-center reports at large university CS programs (publicly summarized by The Pragmatic Engineer and Bloomberg) put median time-to-first-offer for class-of-2024 CS graduates at 7–9 months, roughly double the 4-month benchmark from 2021. Many graduates who eventually landed offers did so after 200–400 submitted applications.

The median in the data above is around 200–400 applications. The Jordans of the world (the 23-year-old graduate at 11 months out, color-coded spreadsheet, 487 apps, 14 interviews, zero offers, $1,847 in checking) are not outliers in this cohort. They are the median in motion. The series above doubled in two years. The behavior in front of the median doubled too.

What changed structurally: four overlapping shifts

The unemployment data is the symptom. The causes are not cyclical, which is why the snapback hasn't come.

1. The 2022–2024 tech-layoff overhang

The shape of the 2022–2024 contraction matters. Layoffs.fyi data shows that the cuts started with Meta, Twitter, and the crypto-adjacent firms in late 2022, accelerated through 2023, and continued in a slower-bleed pattern through 2024 and into 2025. Each round of senior cuts produced a pool of experienced engineers willing to take roles at salaries that were previously new-grad territory. Hiring managers, presented with that choice, almost never picked the new grad. Reporting in The Wall Street Journal documented this dynamic from late 2022 forward.

2. AI productivity rebalancing

Generative-AI tools became real workplace tools, not demos, across 2023 and 2024. The Pragmatic Engineer's "How is AI changing how engineers work?" series (2024) reported that mid-level engineers using AI assistants were producing output that previously required two engineers. That ratio is not a panic headline; it is a quiet input into the question "do we need to backfill the L4 slot or grow the team next year?" When the answer flips from yes to no for a few thousand teams across the industry, you get the labor market we have. AI productivity helped existing engineers more than it helped new grads. AI assistants amplify, they do not substitute for, the judgment new grads have not yet developed.

3. Return-to-office attrition cycle

Through 2023 and 2024, most large tech employers reversed the 2020–2022 remote-work era. Bloomberg and other outlets documented that the RTO cycle produced two effects relevant to new grads: (a) experienced engineers in tier-2 cities left for fully-remote roles, freeing up the metro slots companies wanted to fill with senior locals, not relocate-anywhere new grads; (b) entry-level postings, which had briefly become geographically flexible, re-concentrated in expensive metros. New grads who could not afford to relocate to the Bay Area, Seattle, or New York on a probationary salary were excluded from a meaningful share of the openings.

4. Offshoring of L4–L5 work

Two-thirds of the public engineering-org reorganizations announced in 2023 and 2024 (per coverage in The Information and The Pragmatic Engineer) included expansion of teams in India, Poland, Mexico, or Argentina. The work most easily relocated was exactly the work new grads would have done in the U.S. five years earlier: ticket-grinding, well-specified feature builds, on-call rotation backfill. The work that stayed in U.S. metros was the strategic, ambiguous, cross-team work that requires the judgment of an experienced engineer. The hollow middle is the new-grad hiring gap.

The tier story: FAANG vs. mid-tier vs. startups

The labor-market average hides three very different sub-markets.

FAANG and FAANG-adjacent

Hiring continued, but cohorts shrank by 50–70% from 2022 peaks. The Pragmatic Engineer's annual layoff summaries put 2024 new-grad classes at the largest public tech companies in the range of 30–45% of 2022 levels. The interview bar rose. Companies that could be picky were picky, and offers concentrated on graduates with already-published research, IOI/ACM-ICPC placements, or substantive open-source contributions. For the median CS graduate from a state university, FAANG was not a realistic 2024–2025 target.

Mid-tier (Series B+ private, public non-FAANG)

This is the tier that disappeared. Companies that previously absorbed 70% of new-grad CS hires (the 200-engineer post-IPO companies, the late-stage private growth-stage outfits, the financial-services tech arms) nearly stopped new-grad programs in 2023–2024. Where they did hire, they hired contractors and senior engineers. Reporting in Forbes and on r/cscareerquestions's 2024 wrap-up threads traced the disappearance of the mid-tier new-grad funnel as the single biggest change versus 2021.

Early-stage startups

Seed and Series A startups still hired, but in tiny absolute numbers and with high variance. The bar shifted toward generalists who could do front-end, back-end, infrastructure, and a bit of design, not the specialized new grad with a deep-learning emphasis. Equity was the primary compensation, salaries were 20–40% below big-tech bands, and a substantial share of these roles disappeared inside 12–18 months as the startups themselves ran out of runway in a tightened venture environment.

The application math for class-of-2026

If you are a CS student graduating in 2026 or a recent graduate still hunting, here is the math you should plan around. None of these numbers are projections. They are the median outcomes class-of-2024 and class-of-2025 graduates actually experienced.

Sources: NY Fed College Labor Market, NACE First-Destination Surveys, BLS CPS, LinkedIn State of Hiring, and aggregate of career-center reports summarized by The Pragmatic Engineer.

The application math is the part most candidates underestimate, and it has a corollary they underestimate harder: at 200–400 applications, the time you spend per application matters more than the count of applications. A hundred applications written in a week, copy-pasted, produces close to zero offers. Twenty applications written carefully (cover letter that references the company, resume rewritten to match the JD's keywords, warm introduction sourced through an alumni network) outperforms the hundred by an order of magnitude. The market is not rewarding volume. It is rewarding signal-per-application.

What is actually working in 2025

The candidates who are landing offers in this market share four patterns.

Referrals beat cold applications by an order of magnitude

The single largest predictor of getting to an on-site, across every survey we can find, is whether the application came with a referral. LinkedIn's annual State of Hiring data puts referral callback rates 5–10x higher than cold applications. In a 2–4% baseline callback rate environment, that delta is the difference between landing offers in 8 weeks and landing offers in 30 weeks. The implication for class-of-2026: alumni networks, conference attendance, in-person career fairs, and active LinkedIn engagement (genuine engagement, not spam) are not optional supplementary tactics. They are the main tactic.

Real, shippable portfolio depth beats tutorial breadth

Hiring managers in 2025 have seen thousands of identically-structured GitHub portfolios: a to-do app, a weather app, a tutorial clone of a YouTube video. They are nearly worthless as signal. What works is one project the candidate actually shipped, debugged in production, and can speak about with specifics about tradeoffs. The depth signal is what a hiring manager is looking for, because depth is the trait that survives the on-site.

Genuine interview preparation, deeper than the 2021 bar

The technical-interview bar rose in 2024 and 2025. Where a 2021 candidate could pass with a confident two-pointer solution to a medium-difficulty problem, a 2025 candidate is being asked the medium, then a follow-up, then a system-design extension. The candidates landing offers are the ones putting in 150–300 hours of focused practice across data structures and algorithms, system design, and behavioral interviews, not the 30–50 hours that was sufficient in the previous cycle.

Time-to-offer compresses with the right preparation infrastructure

A candidate doing 200 applications over six months while preparing technically the entire time is in a different bucket than a candidate doing the same volume blind. The former is converting applications into interviews into offers at the same rates as the 2021 cohort once the application count is normalized. The latter is producing 200 applications with no underlying improvement. The compounding from genuine preparation is large.

Where InterviewChamp fits, briefly

InterviewChamp.AI is the AI interview assistant for the candidate who is doing the work (putting in the real preparation hours, going to the on-sites, building the portfolio) and wants to walk in earned, not pre-recorded. We exist as the honest-prep alternative for new grads who would rather walk into an interview prepared than walk in with a hidden teleprompter. We are not a stealth tool. We do not market ourselves as undetectable. We are a live transcript and an on-demand assistant for the interviews you have already earned. That positioning matters because the offers that survive (the ones that lead to long careers, not revoked offers six months in) are the ones the candidate earned.

Pricing is honest: $0 free tier, $9 Starter (3 hours), $19 Plus (8 hours) for short campaigns; $19/mo Pro Yearly or $29/mo Pro Monthly for sustained search; Pro+ at $79/mo Yearly / $99/mo Monthly when you need stealth and always-on top-model answers. See /pricing for the current matrix.

If you are class-of-2026, the most valuable thing you can do this year is prepare seriously: real reps, real portfolios, real interview practice. The market will not give you the 4-month-to-offer cycle of 2021. It will, however, give you offers if you do the work. The data above is hard. The work that responds to it is harder. Both are true.

Related guides

• The CS new-grad interview loop in 2026: end-to-end map of the 4-6 round onsite.
• Mock interview practice for CS new grads: the four-mode practice cycle.
• CS new-grad resume tactics for ATS: how to write the 20 careful applications instead of 100 careless ones.
• Honest interview prep vs cheating: the case for earned offers in a hard market.
• How to negotiate a CS new-grad offer: once the search is over, close hard.

Frequently asked questions

About the author: Alex Chen is the founder of InterviewChamp.AI, building AI interview prep for the new-grad CS market and writing about the modern interview gauntlet from the inside. Sources for this article include the Federal Reserve Bank of New York's College Labor Market dashboard, the U.S. Bureau of Labor Statistics CPS and JOLTS releases, layoffs.fyi, the National Association of Colleges and Employers (NACE), The Pragmatic Engineer newsletter, Bloomberg, Forbes, The Wall Street Journal, and Newsweek. All citations dated through May 2026.