Observed Signal · Aug 18, 2026 · Other · Source: UX Collective · Impact: 2/5 · Sentiment: Neutral
Large Language Models (LLM) & AI Market: Purpose of Software in the Age of AI
Michael Burnett argues that despite enormous increases in computing power and visible efficiency gains, software has not produced sustained macroeconomic productivity growth. Reviewing historical economic research and productivity data (citing Solow, Krugman, Gordon and others), he contends the purpose of software is to increase the density of information in digital outputs rather than to raise value output per hour. Burnett revisits the "productivity paradox," discusses Baumol's cost disease, and hypothesizes a "disengagement curve" driven by search/chat workflows. He warns that AI and LLMs will act as force multipliers for software production but are unlikely to change software's fundamental relationship to measured productivity, even as they create more digital output and new efficiencies.
Analytical long-form perspective linking AI/LLMs to macro productivity debates; informs strategic thinking but does not announce platform policy, product releases, or market-moving transactions.
Wichtigste Kernpunkte & Evidenz
- Author Michael Burnett published a long-form essay arguing software has not increased macroeconomic productivity.
- Computing power at the frontier has increased by roughly 10^12 (a trillion-fold) since 1971, with transistor counts and FLOPS rising dramatically.
- Burnett cites economists and studies including Robert Solow, Paul Krugman, Robert J. Gordon, Federal Reserve analyses (Oliner & Sichel), Brookings, NBER, and others to support his analysis.
- He proposes the purpose of software is to increase the density of information per unit of digital output, not to increase productivity (value output per hour).
- The essay introduces the author's hypothesis of a 'disengagement curve' and revisits the 'Productivity Paradox' and Baumol's cost disease in the context of IT and AI.
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