Observed Signal · May 31, 2026 · Publication · Source: Exponential View · Impact: 2/5 · Sentiment: Neutral
Does AI Make You Dumb? Why Forecasts Fail
An Exponential View newsletter argues that generative AI raises individual productivity without producing proportional firm-level gains and that consensus analyst forecasts systematically underreact to exponential technological shifts. The piece cites examples — Micron’s FY2026 EPS consensus revised from about $18.25 in December 2025 to roughly $58 five months later, and a 40% rise in median analyst views on Google between May 2025 and May 2026 — to show analysts repeatedly misread rapid AI-driven demand. The author also discusses research suggesting students’ writing became more colorful but less creative after ChatGPT, concerns that offloading thinking to AI may atrophy human skills, and organizational practices to preserve critical thinking. The newsletter notes labour-market trends (AI engineering roles concentrating at top US firms), mentions ByteDance designing Groq-style accelerators, and highlights broader implications for modelling, incentives, and how institutions should adapt to regime change.
Analysis highlights systematic forecasting errors around AI-driven exponential demand and documents potential impacts on workforce skills and incentives; relevant to strategy and forecasting but not a platform policy or technical release.
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Key Takeaways & Evidence Grounding
- Exponential View published a framework explaining why individuals gain productivity with AI while firms often do not.
- A Financial Times column cited consensus analyst forecasts projecting hyperscaler AI capex growing ~20% per year to 2030 while revenues grow ~15% annually.
- Micron’s consensus FY2026 EPS forecast moved from about $18.25 (December 2025) to about $58 (May 2026) in the median analyst view.
- Median analyst expectations for Google grew by about 40% between May 2025 and May 2026.
- A study of 370,000 college personal statements found essays used more diverse language but lacked truly creative ideas after ChatGPT became available.
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AI Roundup: Growth Forecasts, Risks, Privacy, Adoption
A Noahpinion roundup surveys recent AI research, debates, and risks. A Forecasting Research Institute survey finds economists, AI experts, superforecasters, and the public predict similar near-term AI capability gains by 2030, but only AI experts expect a material GDP growth acceleration (up to ~4–5%). The newsletter highlights biosecurity concerns (debate over how easily AI could enable creation of effective bioweapons), accelerating offensive cyber capabilities documented by Lyptus Research, and quantum research that may lower the cost of breaking common cryptography. A paper by Lermen et al. shows LLM methods can deanonymize pseudonymous users at scale. The piece also notes signs of slowing workplace generative-AI adoption in several surveys and raises concerns about widespread, adversarial, or rent-seeking uses of AI (e.g., quant trading, automated hacking).
AI 'Slop' Floods Content with Little Productivity Gain
Gary Marcus argues that generative AI has produced a large volume of low-quality output across apps, books, music, scientific papers and web content—what he calls "slop"—without delivering material productivity or GDP gains. He cites graphs from the Financial Times and The Washington Post and references studies from MIT, McKinsey and Bain suggesting limited ROI for many companies. Marcus highlights rising token costs and cash losses at major AI providers (Open AI/OpenAI, Anthropic, Cursor, CoreWeave), noting an analysis that suggests providers may be spending far more than they charge. He also cites the Leiden Declaration — an open letter from mathematicians reported by the New York Times — warning that AI can produce plausible but unreliable proofs. Marcus allows that coding may be an exception, but questions long-term durability given the high operating costs of foundation-model providers.
Is Generative AI Creating More Jobs?
Michael Spencer's May 7, 2026 analysis examines whether generative AI is producing net job growth or primarily displacing workers. The piece cites recent tech layoffs (notably Coinbase’s ~14% reduction), executive statements about AI-driven productivity gains, and economist Torsten Slok’s argument that the AI shock may mirror past automation episodes but now affects cognitive, white‑collar roles. Spencer questions where wholly new categories of jobs will emerge, considers possible wage and quality effects, and raises the Jevons paradox — that efficiency gains might increase demand for lower‑cost roles rather than create high‑quality new employment. The author frames the generative‑AI era as still early (rooted in 2017, accelerating from 2023) and highlights mixed demand shifts within tech roles (e.g., software engineers vs. product managers).
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