Ask The PhD Community

HigherEd AI Daily

September 16 – Governing the Frontier: AI Safety Policy Reaches Campus

Wednesday, September 16, 2026

This week, the most consequential AI debate shifted from capability benchmarks to governance: who controls the pace of frontier AI development, who gets to write the rules, and what happens to academic knowledge when machines produce research outputs faster than humans can evaluate them.

TLDR AI — GOVERNANCE

Anthropic CEO Calls for Deliberate Pacing of Frontier AI Development

In a lengthy essay titled "We Must Pace the Frontier," Anthropic CEO Dario Amodei argued that the AI industry must consciously slow the rate of frontier capability development before government intervention forces it to. Amodei proposed concrete mechanisms: independent third-party evaluators to verify safety commitments, and mandatory incident reporting requirements modeled on existing frameworks in aviation and pharmaceuticals. The piece is notable because it comes from the head of one of the three organizations most responsible for pushing the frontier forward.

OpenAI's Sam Altman has separately voiced support for federal safety requirements, signaling that safety pacing has moved from fringe concern to mainstream industry conversation within weeks. The two positions differ in emphasis but share a common thread: neither lab believes the current pace of development can continue without structured oversight. Amodei's essay runs to over 6,000 words and deserves careful reading; it is the most substantive governance document any frontier lab CEO has published to date.

Why it matters for campuses

Universities are actively integrating AI into research workflows, administrative systems, and instructional tools without formal internal governance structures to match. Amodei's proposed mechanisms, particularly independent evaluation and incident reporting, offer a template that campus AI committees can adapt now rather than waiting for federal mandates. Provosts and faculty senates reviewing AI deployment policies should read this essay as a benchmark for what rigorous oversight language looks like.

Read More

TLDR AI — POLICY

Who Gets to Define the Rules for AI? Cohere CEO Proposes a Different Model

Cohere CEO Aidan Gomez published a pointed argument against allowing a small group of dominant Silicon Valley firms to set global AI governance standards. Drawing on historical parallels to monopolies that used safety rationales to limit competition, Gomez calls for a diverse, international, evidence-based governance framework; one built around transparency requirements, mandatory independent testing, and assurance mechanisms that no single company controls. His broader concern is that companies with the greatest technical knowledge also have the greatest financial interest in shaping regulatory outcomes to protect their own market positions.

Gomez is not arguing against regulation; he is arguing against regulatory capture. His proposed framework envisions a more distributed model in which governments, international bodies, civil society, and technical institutions all participate in defining what safe and trustworthy AI looks like. The essay reads as a direct response to the Amodei piece, and the two together represent the most substantive public exchange on AI governance structure in recent memory.

Why it matters for campuses

Higher education institutions, especially those with international student populations and global research partnerships, have an inherent stake in governance frameworks that cross jurisdictional lines. Universities are also uniquely positioned to participate in the kind of evidence-based, multi-stakeholder governance process Gomez envisions; they house independent researchers, host policy centers, and carry public trust that commercial labs do not. Faculty governance bodies and university government relations offices should consider how their institutions can engage in these conversations before the frameworks are finalized without them.

Read More

TLDR AI — RESEARCH

AI Has Broken Mathematics’ Signal for Deep Understanding

On Terence Tao's widely-read mathematics blog, Fields Medal recipient Bryna Kra has published a striking argument: AI has dismantled one of academia's most reliable proxies for measuring intellectual depth. For generations, the production of difficult, original mathematical theorems served as a hard-to-fake signal that a researcher possessed genuine, deep comprehension. That signal worked precisely because such results were scarce and required years of expert formation to produce. Frontier AI models can now generate polished proofs faster than human experts can fully evaluate them, collapsing the scarcity that made the signal meaningful.

Kra's argument is rooted in mathematics but its logic extends across every discipline. The underlying mechanism she identifies is not unique to proofs; it applies anywhere that AI can now generate outputs previously gated by years of expert formation. Written arguments, code, clinical diagnoses, literary analyses, and policy briefs are all subject to the same dynamic. The question her essay opens is not whether AI can produce these outputs, but what institutions should accept as evidence of human understanding once AI can produce the same thing at scale.

Why it matters for campuses

This essay is essential reading for anyone involved in assessment design, academic integrity policy, or curriculum development. If the outputs that once demonstrated mastery can be replicated by AI, institutions must articulate what evidence of learning they are actually measuring rather than what artifacts they are collecting. Faculty in STEM fields confront this challenge most immediately, but accreditors, curriculum committees, and graduate program directors across every discipline should treat Kra's analysis as an urgent prompt to reexamine what their assessment practices are actually designed to certify.

Read More

TLDR — ACCESS

Cloudflare Lets Institutions Block AI Training Crawlers Without Losing Search Visibility

Cloudflare has introduced a site-level setting called "Disallow AI Training" that allows website operators to remain fully indexable by standard search engines while explicitly refusing access to AI model training crawlers. The setting works by sending a separate, specific signal to crawlers that distinguishes search indexing from data collection for model training. Because Cloudflare manages traffic for a substantial portion of the global web, this feature carries real scale; enabling it is a matter of toggling a setting in the Cloudflare dashboard rather than writing complex robots.txt rules or maintaining custom allowlists.

The announcement addresses a gap that has frustrated content publishers for several years. Until now, a site operator faced a binary choice: block all crawlers (losing search visibility) or permit all crawlers (including those training commercial AI models). Cloudflare's approach separates these two functions, allowing institutions to be findable while asserting control over how their content is used downstream.

Why it matters for campuses

University websites contain years of publicly-facing faculty research summaries, open course materials, syllabi, open educational resources, and student work that has been collected by AI training crawlers without explicit institutional consent or compensation. Campuses that run on Cloudflare infrastructure now have a practical tool to implement data governance policy rather than simply document it. Institutions drafting or updating AI data policies should coordinate with their IT and web services teams to evaluate whether enabling this setting aligns with institutional values around research ownership and content stewardship.

Read More

Tool of the Day

Gemini 3.8 Live (Google)

Google released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking this week: real-time speech-to-speech AI models accessible directly through a web browser, with no app installation required. Users speak naturally, can interrupt the model mid-response, and can engage in extended back-and-forth voice dialogue on complex topics. The Extended Thinking variant adds parallel reasoning capability, allowing the model to work through multi-step problems before responding. Both models accept an optional system prompt before the voice session begins, giving instructors meaningful control over the interaction context.

Try it: Set a system prompt that positions Gemini 3.8 Live as a skeptical undergraduate student in your discipline, then use voice to explain a foundational concept from your current course. Evaluate how the model handles follow-up challenges and whether its counter-questions expose gaps in how you are framing the material for students.

Visit Gemini 3.8 Live

Have a great learning day!

Dr. Ali Green

Sources for This Edition

TLDR AI — September 14, 2026 (tldrnewsletter.com)
TLDR AI — September 15, 2026 (tldrnewsletter.com)
TLDR — September 16, 2026 (tldrnewsletter.com)
Dario Amodei, "We Must Pace the Frontier" (darioamodei.com)
Aidan Gomez / Cohere, "Who Gets to Define the Rules for AI?" (cohere.com)
Terence Tao's blog / Bryna Kra, "Deep Theorems Were Scarce…" (terrytao.wordpress.com)
Cloudflare Blog, "Accountable Mixed-Use AI Crawlers" (blog.cloudflare.com)
Simon Willison, "Gemini 3.8 Live" (simonwillison.net)

askthephd.com
 | 
askthephd.substack.com
 | 
Unsubscribe

HigherEd AI Daily; Curated by Dr. Ali Green