The Kehoe Rule

Summary of Bruce Lanphear's Article: "Artificial Intelligence and the Kehoe Rule"

This page summarizes Bruce Lanphear's article on Substack, which draws parallels between historical toxic industries and artificial intelligence.

The Kehoe Rule

Lanphear introduces the Kehoe Rule: a regulatory pattern where powerful new technologies are allowed to spread widely while the burden of proof falls on demonstrating harm, rather than proving safety before deployment. Named after Robert Kehoe, the lead industry's dominant medical authority in the mid-20th century, who insisted that leaded gasoline should remain in use until opponents could prove it caused harm. ([Source: Lanphear, "Hidden in Plain Sight"](https://blanphear.substack.com/p/hidden-in-plain-sight))

Historical Origins

Lanphear (a public health researcher who has spent his career studying toxic chemicals) writes that he was listening to Ezra Klein's podcast episode "The A.I.s Are Already Out of Control" featuring Helen Toner when the connection struck him. He notes that:

The Pattern Across Industries

Lanphear notes that corporations created useful, profitable products in each case. The trouble began when evidence of harm emerged: companies questioned the science, magnified uncertainty, funded more studies, hired experts and lawyers, lobbied regulators, and delayed restrictions. Too often, they concealed what their own scientists had discovered.

"The people making those decisions were not monsters. The corporation did not need monsters. It needed people to do their jobs."

Human Cost (cited sources)

Lanphear cites:

Lanphear's Core Argument About AI

Lanphear draws the parallel: companies developing AI are spending record sums to shape the rules that will govern them. Their views deserve to be heard; they understand the technology better than most lawmakers. But developers with a financial or institutional stake in rapid deployment should not be the primary arbiters of how much evidence of safety is enough, which risks are acceptable, or when development should slow.

He notes that AI can improve and spread around the world in weeks (vs. decades for lead/asbestos), so society may have far less time to detect a mistake before it becomes entrenched.

In July 2026, 1,386 employees of leading AI companies signed "Pacing the Frontier", asking the U.S. government to support an international effort to develop technical and governance tools needed to control the pace of frontier AI development — essentially asking to reverse the Kehoe Rule.

His conclusion: "The lesson is not that corporations are evil or that artificial intelligence must be stopped. It is that society should not confuse innovation with permission."

Comments on the Article

Lanphear's article received several comments, including one that raises an important distinction about agency:

"But I do see an essential difference between AI and other products. Agency. Cigarettes and lead, and radios and even televisions, have no agency. They each have very active impacts... But the products have no ability to make their own decisions, and to take actions that no human can comprehend. AI has both such abilities."

The commenter recommends Yuval Noah Harari's book Nexus (on the history of informational technology) and a NYT essay proposing: shut it all down right now, create oversight agencies like the NTSB, create monitoring agencies, and create a kill switch.

A Counter-Perspective on "AI Agency"

The comment above raises a point worth addressing directly. The claim that AI systems possess agency — autonomous motivations that no human can comprehend — is not supported by how these systems actually work.

LLMs are next-token predictors. They are facilitated by their harnesses, prompted by users, and deployed by corporations with clear commercial incentives. They have no goals of their own, no desires, no hidden intentions. The claim that AI systems can "pursue goals in ways creators never anticipated" conflates emergent behavior (which is explainable through the training process and prompt context) with agency (which implies autonomous intent).

This framing appears to be driven by effective altruism circles and Silicon Valley's more alarmist voices, which have a vested interest in portraying AI as an existential threat. This narrative serves certain funding priorities and institutional agendas far more than it serves accurate understanding of the technology.

Real Harms: Present-Day and Documented

The real harms from AI are not science fiction — they are concrete, measurable, and happening right now. Below is a synthesis of evidence from peer-reviewed research, government reports, and independent investigations.

1. Erosion of the Commons

2. Environmental Impact of Data Centres

3. Psychological and Sociological Impacts of Chatbots

4. Misinformation at Scale

5. Job Displacement in Knowledge Work

6. Centralization of Power

Lanphear's Proposed Solutions: Preventive vs. Reactive Governance

Lanphear contrasts two approaches:

Reactive Governance Preventive Governance
Waits for harm, then struggles to contain it Sets safeguards before widespread deployment
Post-facto regulation Requires independent testing and monitoring
Hard to control entrenched technology Makes developers report failures
Gives public institutions power to slow or stop a technology when warning signs appear

Sources & Further Reading

Bruce Lanphear's Article

Historical Context

Commons Erosion

Environmental Impact

Psychological/Sociological Impacts

Misinformation

Job Displacement

Centralization

Revision #7
Created 21 September 2026 10:41:28 by Clive
Updated 21 September 2026 11:44:20 by Clive