The research behind the tracker.

Working paper, in prep. for the Journal of Economic Perspectives

Direct non-technical source for the infinite-automation 1/(1-s) result, the self-driving-cars example, and the weak-link view of AI growth.

Stanford Graduate School of Business (YouTube)

Stanford GSB talk presenting the weak-link growth view and figures adapted on this page, including the computer-share and forecast charts.

Journal of Political Economy 98(5): S71–S102

Ideas as the engine of long-run growth (Nobel 2018). The flywheel that 'wants to explode'.

American Economic Review 110(4): 1104–1144

Within any technology, ideas get harder to find (the steam engine runs out of steam), so each wave buys ~50 years of 2% growth.

in The Economics of Artificial Intelligence, Univ. of Chicago Press (NBER WP 23928)

The earlier automation + weak-links growth model behind the baseline forecast scenarios.

Economic Policy 40(121): 13–58 (NBER WP 32487)

The conservative end of the dial: predicted TFP gains below 0.53% over 10 years, with the initial task-based estimate no more than 0.66%.

NBER Working Paper 31815

A survey synthesizing the AI-and-growth literature: fully automating production can break the Kaldor facts, raise the growth rate, and lower the labor share. The wider map the weak-link view sits inside.

arXiv:2309.11690

The case that broad automation could accelerate growth by roughly tenfold, weighed against nine counterarguments. The most rigorous steelman for the fast end of the dial.

American Economic Journal: Macroeconomics 3(2): 1–28

The origin of the weak-link metaphor: a chain is only as strong as its weakest link.

Working paper, Stanford University

Direct source for the automation-meets-weak-links forecast model, the computer-share chart, and the decades-to-mature-then-acceleration result.

Quarterly Journal of Economics 108(3): 551–575

Production as a chain of tasks where one failure destroys most of the value, the fragility behind the 'fast downside'.

METR; arXiv:2503.14499

The length of task an AI agent can finish with 50% reliability roughly doubled every 7 months in the original paper; METR's current dashboard estimates about 6.2 months all-time. A direct, empirical answer to 'how fast,' independent of the growth model.

Epoch AI

Training compute has grown about 4x per year; power, chips, data, and latency still leave room for runs roughly 10,000x larger by 2030. The cheap input keeps getting cheaper; that is exactly why it is not the scarce one.

Stanford Institute for Human-Centered AI

Benchmark scores jumped in a single year (GPQA +48.9 points), training compute doubles about every five months, and the inference cost of GPT-3.5-level output fell roughly 280x in two years.

Anthropic (ongoing)

Claude.ai conversations mapped onto O*NET tasks show current usage led by computer/mathematical work and education/library tasks, with explicit augmentation-vs-automation views. A live read on which links are actually moving.

Quarterly Journal of Economics 129(1): 61–103

Context for the capital-vs-labor split that the scenarios track to 100% / 0%.

American Economic Journal: Macroeconomics 13(1): 333–372

Why measured productivity lags transformative tech: adoption races up the S-curve while output sits in the J-curve trough.

Quarterly Journal of Economics (2025); NBER WP 31161

A field study of 5,179 support agents: AI raised issues resolved per hour 14% on average and 34% for novices, with little effect on experts. The gains land first where skill was scarce.

Science 381(6654): 187–192

In a writing experiment, ChatGPT cut time 40% and raised quality 18%, narrowing the gap between weaker and stronger writers. Task-level speed-ups are real even where measured GDP is slow to move.

Essay (darioamodei.com)

The optimistic 'country of geniuses in a datacenter' vision, useful context for the aggressive side of the forecast debate.

Academy of Management Review 20(3): 510–540

Four motors of change (life-cycle, teleology, dialectics, evolution), the org-level analog of 'what kind of change are you in?'

Organization Science 13(5): 567–582

Change as the normal condition of organizing, grounds the claim that diffusion is continuous and slow.

Sage Publications

How people construct meaning from ambiguous change, the micro-foundation of the enterprise weak links.

Harvard Business Review 78(3): 133–141

The paradox change leaders must hold: economic value vs. organizational capability.

Journal of Management 17(1): 99–120

Resource-based view underpinning Organizational Capacity for Change as a meta-capability.