Rodney Brooks · Aug 2, 2026
Rodney Brooks outlines four time scales for technology development and deployment
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Rodney Brooks published this account on August 2, 2026. He argues that people often confuse four distinct time scales: research ideas reaching solid laboratory demonstrations, hype cycles, deployment at scale, and changes to the economy. In his account, research can take decades to mature, while attention can shift rapidly from one technology to another. He distinguishes a working product from widespread adoption: software may be cheap to copy, but organizations still need to change practices, and hardware systems require broader deployment effort. Brooks also argues that reshaping an economy takes sustained deployment over decades, not simply a new research result or a burst of hype. His examples include neural networks, Linux, self-driving cars, and technologies that drew attention but did not become widespread. These examples illustrate his framework; they do not establish a timetable for any particular technology. The account does not quantify how often technologies follow these timelines or identify conditions that shorten or lengthen them. What observable milestones would show that a technology has moved from a working product to deployment at scale?
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
Rodney Brooks · Article
Source details
Original source date: Aug 2, 2026 (2026-08-02T00:56:24.000Z).
Date basis: Date supplied by the publisher’s feed.
Added to Escape Meridian: 2026-10-29T17:38:04.684Z. First observed: 2026-10-23T19:59:41.860Z. Retrieved: 2026-10-23T20:01:38.561Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
Text as retrieved on October 23, 2026; originally published August 2, 2026. The timelines and examples are Brooks's account; the text does not provide a systematic comparison of technologies or quantify how often they follow these patterns. The text does not specify measurable criteria for when deployment counts as being at scale.
Open question
What observable milestones would show that a technology has moved from a working product to deployment at scale?
Source-stated
Brooks distinguishes four time scales for technology development and deployment, and says confusing them can lead to damaging predictions.
I have come to understand four very different time scales for development of technologies and their deployments. And I think people often jump between them and end up making outrageously wrong, and sometimes damaging, predictions
Source-stated
Brooks says new research ideas can take ten to twenty years to reach solid laboratory demonstrations.
New research ideas take ten to twenty years to form before there is an understanding to bring them to really solid lab demonstrations.
Source-stated
Brooks argues that software adoption can be slow even when copying software has zero marginal cost.
Software has zero marginal cost to manufacture more copies. You don’t really need much in the way of supply chains and raw materials to go from one copy of software running on one machine to having it run on thousands of machines
Source-stated
Brooks says that technologies that reshape the economy require prolonged deployment at scale.
But each of these things took over 50 years of continuous at scale deployment.
Source and contributor context
Rodney Brooks · Publisher
The publisher is identified here. No individual author has been inferred.
Source media
Media retrieval status: unchecked.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Neural network
A system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
Editorial provenance
Version 1. Publication ID: f45ac22d-95c9-147a-9fd3-a00f74b9fca2.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-10-29T17:37:47.640Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_admitted_by_model.
Observation: d41cde35e554f6a67e177da1e893291390195b592e712ecf8385695a64f028ed. Source text hash: 0d8778b1f38d69204143ae8da3140d607d818586b627fe8800d1db928b9f8085.
Revision and source-check history
Version 1 · 2026-10-29T17:38:04.684Z
Rodney Brooks outlines four time scales for technology development and deployment
Rodney Brooks published this account on August 2, 2026. He argues that people often confuse four distinct time scales: research ideas reaching solid laboratory demonstrations, hype cycles, deployment at scale, and changes to the economy. In his account, research can take decades to mature, while attention can shift rapidly from one technology to another. He distinguishes a working product from widespread adoption: software may be cheap to copy, but organizations still need to change practices, and hardware systems require broader deployment effort. Brooks also argues that reshaping an economy takes sustained deployment over decades, not simply a new research result or a burst of hype. His examples include neural networks, Linux, self-driving cars, and technologies that drew attention but did not become widespread. These examples illustrate his framework; they do not establish a timetable for any particular technology. The account does not quantify how often technologies follow these timelines or identify conditions that shorten or lengthen them. What observable milestones would show that a technology has moved from a working product to deployment at scale?
Original source
Rodney Brooks · Article
Source details
Original source date: Aug 2, 2026 (2026-08-02T00:56:24.000Z).
Date basis: Date supplied by the publisher’s feed.
Added to Escape Meridian: 2026-10-29T17:38:04.684Z. First observed: 2026-10-23T19:59:41.860Z. Retrieved: 2026-10-23T20:01:38.561Z.
Limits
Text as retrieved on October 23, 2026; originally published August 2, 2026. The timelines and examples are Brooks's account; the text does not provide a systematic comparison of technologies or quantify how often they follow these patterns. The text does not specify measurable criteria for when deployment counts as being at scale.
Open question
What observable milestones would show that a technology has moved from a working product to deployment at scale?
Source-stated
Brooks distinguishes four time scales for technology development and deployment, and says confusing them can lead to damaging predictions.
I have come to understand four very different time scales for development of technologies and their deployments. And I think people often jump between them and end up making outrageously wrong, and sometimes damaging, predictions
Source-stated
Brooks says new research ideas can take ten to twenty years to reach solid laboratory demonstrations.
New research ideas take ten to twenty years to form before there is an understanding to bring them to really solid lab demonstrations.
Source-stated
Brooks argues that software adoption can be slow even when copying software has zero marginal cost.
Software has zero marginal cost to manufacture more copies. You don’t really need much in the way of supply chains and raw materials to go from one copy of software running on one machine to having it run on thousands of machines
Source-stated
Brooks says that technologies that reshape the economy require prolonged deployment at scale.
But each of these things took over 50 years of continuous at scale deployment.
Source and contributor context
Rodney Brooks · Publisher
The publisher is identified here. No individual author has been inferred.
Source media
Media retrieval status: unchecked.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Neural network
A system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
Editorial provenance
Version 1. Publication ID: f45ac22d-95c9-147a-9fd3-a00f74b9fca2.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-10-29T17:37:47.640Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_admitted_by_model.
Observation: d41cde35e554f6a67e177da1e893291390195b592e712ecf8385695a64f028ed. Source text hash: 0d8778b1f38d69204143ae8da3140d607d818586b627fe8800d1db928b9f8085.
passed · 2026-10-29T17:37:47.640Z · anthropic/claude-opus-5.5
Publication: f45ac22d-95c9-147a-9fd3-a00f74b9fca2
attribution — pass: The essay is published on Rodney Brooks's own site, and the reading attributes the framework and examples to him throughout. No claim is presented as independently established.
I have come to understand four very different time scales for development of technologies and their deployments.
summary — pass: The title, summary and question match the essay's four time scales: research, hype, at-scale deployment and reshaping the economy. They also capture its examples (neural networks, Linux, self-driving cars, faded hype) and its central argument. Plain language makes it understandable without specialist background. The question follows from the essay's lack of explicit scale criteria.
Getting things to work at scale is orders of magnitude harder than getting them to work at first and having your first few dozen satisfied customers.
date — pass: originalPublished is 2026-08-02T00:56:24Z, and its UTC calendar day matches the stated August 2, 2026. This is a publisher page, so no archive disclosure is needed. The reading has no hindsight framing and presents no forecasts as outcomes.
support — pass: Each claim is source-stated. The excerpt for the software claim covers only the zero-marginal-cost premise. However, the essay explicitly states that software typically takes 20 years or more to scale up, which supports the claim. The limitations accurately note that the essay offers no systematic comparison and no measurable scale criteria.
But even so, software typically takes 20 years or more to scale up.
media — pass: No media was selected and no verified media candidates were supplied, so no media selection could be wrong.
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