The AI wildfire is coming. it's going to be painful and healthy

Reality of the “AI Wildfire” / Bubble

• Several commenters reject the article’s claim that “every promising engineer” is being chased by AI startups; they see few serious offers, mostly from firms trying to automate them away.
• Many don’t see a classic dot‑com style bubble of tiny, overvalued AI firms; instead, they see a market dominated by a handful of giants with huge but real spend.
• Others point to “shovelware” apps (LLM-wrapped language tools, productivity hacks) as today’s Pets.com: low-effort grifts using API access, some VC-backed but economically trivial when they vanish.
• Wildfire metaphor is widely criticized as overwrought, ecologically inaccurate, and nihilistic: real wildfires can destroy ecosystems, not “cleanse underbrush.”

Business Value vs Hype

• Some report concrete productivity gains (e.g., LLMs writing most of their code, >2× output) and argue providers could credibly charge a significant fraction of developer salaries.
• Others see mainly FOMO-driven “AI for AI’s sake”: executives demanding AI features regardless of quality, usage, or ROI; AI search and support often worse than what they replaced.
• There’s disagreement over whether current AI already delivers “measurable and immediate” returns; skeptics say layoffs are often just cost-cutting with AI as pretext, and benefits are hard to quantify.
• Debate over trajectory: one side expects continued improvements and new use cases; the other sees slowing model progress and no guaranteed path to “tremendous business value.”

Infrastructure, Concentration, and Compute

• This cycle is seen as different from prior tech booms because of massive capex in GPUs and datacenters; VC “high risk” money is now a large share of the real economy.
• Some argue even a mass startup wipeout would be a rounding error compared to the entrenched giants (clouds, model labs, chipmakers), so there’s no true “cleansing fire.”
• Nvidia’s role is debated: critics expect large customers to move to ASICs; defenders say Nvidia is already effectively an ML-ASIC company with a huge CUDA moat, likening it to Cisco post‑dot‑com.
• Compute and energy are viewed as long-lived assets; many expect any downturn in AI demand to be temporary, with cheaper compute enabling new waves of usage.

Labor, Inequality, and Everyday Experience

• Examples are cited of AI reducing staffing needs (receptionists, tier‑1 support, translation, data entry), with active efforts to cut headcount in large organizations.
• Others stress historical patterns where productivity tech didn’t simply produce mass unemployment, but acknowledge today’s low-wage workers have little buffer.
• Office workers note decades of efficiency gains without proportional sharing of value; many describe current AI work (slap-on features, “AI foistware”) as pointless from a user perspective.
• Tech workers discuss coping strategies: ride the AI wave for résumé value vs. aggressively saving for early retirement and expecting layoffs in a boom–bust cycle.
• Broader concerns include erosion of the “old internet,” lock‑in to heavily moderated platforms, AI-generated slop and astroturfing, and a general sense that user experience has worsened from Web 1.0 through social/mobile to AI.