The tech market is fundamentally fucked up and AI is just a scapegoat

Macroeconomics, ZIRP, and Over‑Hiring

• Many comments pin the 2010–2020 boom on cheap money and a market that rewarded growth over profit (ZIRP, stimulus, post‑iPhone demand).
• COVID is seen as the “last gasp” of that regime: a spike of over‑hiring followed by rapid tightening and whiplash layoffs.
• Others argue over‑hiring is not unique to tech: manufacturing, autos, airlines, etc. also bet on future demand and then cut when wrong, but tech can scale headcount faster because dev “equipment” is cheap (laptop vs. factory line).

Is AI Cause, Cover, or Accelerant?

• Broad agreement that AI is not the root cause of the downturn, but:
  • Some say AI is an accelerant and a convenient narrative to justify cuts (“we can do more with fewer engineers”).
  • Several report real productivity gains (10–30x on code generation/tests/infrastructure), but note this is only a slice of the job and doesn’t show up as 10x company output.
  • Others see AI as a net negative: it supercharges weak developers, creates low‑quality “AI slop” PRs, tickets and reports, and wastes review time.
• There’s debate over whether AI will mainly:
  • Help small orgs build bespoke tools on top of big platforms, or
  • Undercut consultants and juniors by making “OK” output cheap.

Qualification, Talent, and Commoditization

• A strong thread argues the real problem is inability to distinguish and reward truly qualified developers; most are treated as fungible cost centers.
• Comparisons are drawn to doctors/lawyers (licenses, peer review, track records, documented violations), contrasted with dev hiring reduced to “I’ve been employed” plus LeetCode.
• Others counter that credentials and side accomplishments (papers, books, OSS) are often weak predictors of fit for most industry roles; employers optimize for what they can easily measure.

Market Structure, Hype, and “Rot”

• Multiple comments connect current pain to monopolization and financialization:
  • Capital prefers moats, regulatory arbitrage, and hype narratives (metaverse, blockchain, AI) over hard innovation.
  • Huge misallocations (e.g., tens of billions on the metaverse) are framed as management and governance failures enabled by market power.
• “Enshittification” and attention/ad‑tech saturation are cited: user time and ad density are near limits, constraining further easy growth.

Software Maturity and Saturation

• Several argue much of the “obvious” software has already been built: ecommerce, banking apps, core B2B systems.
• Big platforms now need far fewer engineers to maintain than they did to build; the 2010s “armies of devs” were partly justified by first‑time buildout and partly by hype projects.
• Off‑the‑shelf and SaaS have commoditized many problems; for many firms it’s cheaper to buy than to employ large dev teams.

Employment Models and the Future of SWE

• The article’s “core revenue teams + disposable experimental teams” model is widely discussed:
  • Critics say it guarantees knowledge loss and drives experienced devs out.
  • Others see it as precursor to “permanent contractor‑ification” mirroring manufacturing’s temp agencies: more contractors, fewer true FTEs.
• Several long‑term devs describe migrating to consulting/contracting for resilience; others report AI already eroding that niche.
• Some foresee bifurcation: a small, well‑paid elite with deep systems expertise, and a large pool of lower‑paid, interchangeable devs.

Geography, Policy, and Labor Protections

• Europe is described as a lower‑cost extension of Silicon Valley, but commenters note firing there remains slower, costlier, and more procedurally constrained than in the US.
• When US giants do layoffs, other firms are seen as too weak or consolidated to absorb the talent, worsening global oversupply.
• Monetary policy is framed as central: cheap capital inflated tech; normalization is forcing a messy re‑pricing rather than an AI‑specific collapse.