The Tech Job Market in 2024

TL;DR

Product management roles are described as nearly flat in 2024, with only about a 3–4% increase over roughly two and a half years despite layoffs and startup failures.

Briefing Cornell Notes

Briefing

Product and engineering hiring in 2024 has largely stopped accelerating—roles are roughly flat rather than steadily expanding, even after high-profile layoffs and startup failures. For product managers, the change is especially stark: job openings are described as up only about 3–4% over roughly two and a half years, which feels like “death” to people used to a fast-moving, applicant-friendly market. The key implication is that the market isn’t collapsing; it’s decelerating into stability, and that shift changes how both hiring and job searching work.

A central metric in the discussion is turnover. With product management turnover estimated around 10% per year, the math works out to roughly 30k–40k product roles changing hands annually out of about 450k active PM roles. That level of churn doesn’t read as “unhealthy,” but it does explain why expectations have broken: when growth slows, the long-standing assumption that “the next job is always around the corner” no longer holds. Engineering is framed as similar in pattern even if it has historically had fewer openings than product management; engineering jobs are cited at about 3.7 million in 2022, roughly steady.

The hiring process is where the pain concentrates. Hiring may feel easier only if someone is aiming for an approximate fit. If the goal is a precise match, it’s described as harder than ever because recruiters and hiring teams must sift through a much larger applicant pool. Executives also expect faster, more exact matches because they can see the volume of candidates. The bottleneck becomes sorting—screening and interview filtering—which is already imperfect, and now sits under additional strain.

AI is a major accelerant of that strain. Applicants can scale up applications, and resumes are increasingly “leveled up” with AI assistance, making it harder to distinguish one candidate from another. That pushes recruiters toward more volume-based triage, while also increasing the importance of human signal. A practical recommendation emerges: draft with AI, then finish and polish as a human, because the human touch stands out after reviewers have seen many AI-generated resumes.

From the applicant side, rejection is framed as psychologically misleading. People tend to blame themselves, but the process is usually an overall fit assessment—whether the candidate can “thrive” in the role—rather than a single missed question. The productive response is to treat rejection as feedback for positioning across the applicant surface area: adjust resumes, refine stories, practice interviews, and ensure the narrative aligns with what recruiters and hiring managers want. Referrals still matter, but they’re no longer portrayed as a guaranteed shortcut; they now face more scrutiny and typically lead to a recruiter review rather than immediate team-loop momentum.

The closing view is that talent matching remains inefficient for both corporations and applicants, and AI could disrupt it—but not through simple resume spam. The more promising path is using AI to improve fairness and decision quality: second-pair-of-eyes review, better interview insights, and broader coverage of qualified candidates. The remaining uncertainty is whether job deceleration stays flat or re-accelerates if interest rates fall again, potentially changing the equilibrium.

Cornell Notes

Hiring for product management and engineering in 2024 is described as roughly flat after years of steady growth, with product management roles up only about 3–4% over roughly two and a half years. Turnover is estimated around 10% annually, meaning about 30k–40k PM roles change hands each year out of roughly 450k active roles—stable churn, not a collapse. The real difficulty shifts to sorting: precise-fit hiring is harder because applicants are more numerous and AI helps them apply at scale, making resumes harder to differentiate. Applicants are urged not to treat rejection as personal failure; instead, use rejections to improve positioning and alignment with what hiring teams need. Referrals still help, but they’re no longer a “golden ticket.”

Why does a “flat” job market feel worse than a “down” market to many job seekers?

The discussion contrasts earlier years—when hiring accelerated and there were more roles than applicants—with the current stability. Even if the market isn’t shrinking, the loss of momentum breaks expectations: people used to a steady stream of opportunities “around the corner” now face fewer openings relative to the applicant pool. That deceleration can feel like a death because it removes the psychological safety of continuous growth, even when the absolute number of roles remains roughly steady.

How do turnover estimates change the interpretation of PM hiring health?

Turnover is estimated at about 10% per year for product management. With roughly 450k active PM roles, that implies around 30k–40k PM roles turn over annually. The argument is that this churn level doesn’t look “ridiculously unhealthy,” which supports the claim of a stable market. The pain comes less from total job loss and more from slower growth and tighter competition for precise-fit roles.

What makes hiring harder when applicants can apply at scale with AI?

AI enables applicants to increase application volume, so recruiters must sort through more resumes. At the same time, AI-assisted resumes are described as “leveled up,” and candidates try to stand out, which makes it harder to tell differences between applications. The sorting/filtering step—already imperfect—becomes under more strain, increasing the challenge of finding a precise match.

Why is “approximate fit” easier than “precise fit” in this environment?

The discussion draws a distinction: hiring feels easier only if the target is an approximate match. For precise-fit hiring, teams must evaluate a larger applicant sea and make more discriminating decisions. Executives also expect quick, exact matches because the candidate volume is visible, raising pressure on the filtering process and making it harder to land the role.

How should applicants respond to rejection without assuming it’s personal?

Rejection is framed as usually reflecting an overall fit judgment—whether the candidate will thrive—rather than a single wrong answer. Since employers need someone who can thrive and hiring teams pass when that isn’t evident, applicants shouldn’t default to self-blame. Instead, they should use rejection productively: refine resume and story, improve interview positioning, practice, and ensure the narrative matches what recruiters and hiring managers are looking for.

What role do referrals play now compared with earlier hiring cycles?

Referrals are described as still valuable because they increase candidate quality and the probability of interviews. But they’re no longer treated as an automatic shortcut to a team loop. Instead, referrals are scrutinized and often lead to a recruiter courtesy call or a more friendly review of the resume, reflecting the current need for tighter sorting.

Review Questions

What evidence is used to support the claim that the PM job market is “flat,” and how does turnover factor into that conclusion?
How does AI change both applicant behavior and recruiter sorting, and why does that make precise-fit hiring harder?
What specific actions are recommended to convert rejection into improved outcomes over time?

Key Points

1
Product management roles are described as nearly flat in 2024, with only about a 3–4% increase over roughly two and a half years despite layoffs and startup failures.
2
Estimated PM turnover of ~10% per year implies roughly 30k–40k PM roles changing hands annually out of about 450k active roles, signaling stability rather than collapse.
3
Hiring is easier only for approximate fit; precise-fit hiring is harder because teams must sort through a much larger applicant pool.
4
AI-assisted applications increase volume and make resumes harder to differentiate, putting additional strain on screening and interview filtering.
5
Applicants are advised not to treat rejection as personal failure; most decisions reflect overall “fit to thrive,” not a single missed question.
6
Referrals still improve interview odds, but they no longer function as a guaranteed fast track to team loops.
7
AI’s best talent-market impact is framed as improving decision quality and fairness (e.g., second-pair-of-eyes review), not spamming recruiters with generated resumes.

Highlights

Product and engineering hiring is portrayed as decelerating into a stable, roughly flat state rather than continuing the rapid growth of the 2010s.

A 10% annual turnover estimate for PM roles suggests steady churn—about 30k–40k role changes per year—rather than a dramatic collapse in opportunities.

AI increases application scale and “levels up” resumes, making the sorting problem harder and raising the importance of human signal and clearer positioning.

Rejection is framed as an overall fit assessment tied to whether someone will thrive in the role, so applicants should respond by refining positioning across applications.

Referrals remain helpful but are no longer a “golden ticket,” since they now face more scrutiny and typically trigger recruiter review rather than immediate team-loop movement.

Topics

Job Market Deceleration
Product Management Hiring
Engineering Hiring
AI-Assisted Resumes
Talent Matching

Mentioned

Lenny