Product Manager vs Software Engineer

PM hiring volumes dropped sharply in 2023 and have not fully recovered. The companies growing PM headcount are concentrated in AI-native products and infrastructure, fintech, and healthtech — roles that require domain depth, not just process fluency. Generalist PM roles at consumer internet companies continue to contract. Salary premiums are accruing to PMs who can operate in technical ambiguity: reading code, partnering on model evaluation, and writing precise specs for AI-driven features. Companies building with LLMs increasingly want PMs who can reason about probabilistic outputs and latency tradeoffs, not just user stories. B2B SaaS PM roles remain the largest hiring pool by volume, but competition is high relative to openings. The most durable differentiator in 2024–2025 hiring data is evidence of measurable outcome ownership — PMs who can point to retention, activation, or revenue impact they personally drove. Discovery and strategy skills are being tested more rigorously in hiring loops than two years ago. Execution-only PMs face the most pressure.

Hiring volumes for software engineers contracted sharply in 2023–2024 following mass layoffs across big tech, but stabilised in late 2024. Demand is now bifurcating: engineers who can work effectively with AI coding tools (Copilot, Cursor, Claude) and ship at higher throughput are commanding salary premiums, while pure implementation roles at junior and mid levels face real compression. Backend and infrastructure engineers with distributed systems depth remain in short supply. Full-stack generalists without clear systems or product instincts are experiencing the most pricing pressure. Companies rebuilding headcount post-freeze are hiring senior engineers at a 2:1 ratio over junior hires — a structural shift that is likely to persist. Security-aware engineering is becoming a baseline requirement, not a specialism, following a run of high-profile supply-chain incidents. AI is absorbing first-draft code generation and boilerplate; the premium is shifting toward system design judgment, debugging complex distributed failures, and owning production reliability. Engineers who avoid AI tooling are falling behind on raw throughput benchmarks in hiring assessments.