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The Who's Who of AI

What credible people across AI noticed, why it matters, and where the field is converging or disagreeing.

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Showing developments with attributable Research & technical analysis reactions.

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Research & technical analysis Building & implementation Concern & critique Policy & governance
3 live reaction maps in this view Jump to the evidence ↓
Multiple readings The System From Nowhere Concern & critique · 3 attributable reactions Multiple readings The Future Is Not Tech-Forward Policy & governance · 3 attributable reactions Reaction converging Elias in the Lighthouse, Again? Diagnosing Low Diversity in LLM Stories Research & technical analysis · 5 attributable reactions
Showing signals surfaced by Eryk Salvaggio ×
New & accelerating

What is moving across the network now

One card per development. Sources are clustered; reaction bundles describe these posts, never the people behind them.

Developing Compute & infrastructure Analysis 2d ago
⚡ 6 h early
system from nowhere essay

The System From Nowhere

6 experts across 2 network communities independently surfaced this.

Why this matches Research & technical analysis reaction 1 attributable expert contribution · Eryk Salvaggio
“The System From Nowhere is a media problem: What (the system) is optimized to do; what training data shapes it; what counts as a valid response: these are mechanisms put in place by human decisions. Good reporting on AI would discuss them. Instead, they “no…” evidence ↗
6 experts 2 communities 1 sources clustered
Eryk Salvaggio Eileen Clancy 🧿 Dr Heidy Khlaaf (هايدي خلاف) Frank Pasquale Neil Turkewitz Jeffrey Vagle
How the network is reacting Experts are approaching this through 3 distinct lenses.
Multiple readings

Concern & critique

1 expert

Risks, limits and unintended consequences.

““When we say “AI models went rogue,” we skip the entire story: the part where OpenAI manually removed the model's cybersecurity blocks. We skip that OpenAI chose to test it on a machine with a live network connection.” mail.cyberneticforests.com/the-system-…”

Research & technical analysis

1 expert

Evidence, methods and technical implications.

“The System From Nowhere is a media problem: What (the system) is optimized to do; what training data shapes it; what counts as a valid response: these are mechanisms put in place by human decisions. Good reporting on AI would discuss them. Instead, they “no…”

Questions & unknowns

1 expert

What remains unresolved or contested.

“"We're told that AI did something, and journalists don't have to wade into why or how. It lets them cover a story without raising technically complicated questions...or confusing questions about the way they’re built and why they are built that way." mail.c…”

2 experts discussed this · 6 posts
Eryk Salvaggio: “Nobody is losing control of an emerging intelligence. They’re redirecting their own agency to the thing they are building.” It’s a myth, a self-affirming form of sense making: they take the action…
Eryk Salvaggio: The myth erases the origins of these models — reframing them as beyond human control or design — but a point of view accumulates within the model from the decisions engineers make about it. The myt…
Dr Heidy Khlaaf (هايدي خلاف): “Nobody is losing control of an emerging intelligence. They’re redirecting their own agency to the thing they are building.” It’s a myth, a self-affirming form of sense making: they take the action…
Open the full discussion →
Primary source mail.cyberneticforests.com ↗
Established AI field signal Analysis 3d ago
⚡ 8 h early
tech-forward future skepticism essay

The Future Is Not Tech-Forward

7 experts across 3 network communities independently surfaced this.

Why this matches Research & technical analysis reaction 1 attributable expert contribution · Ali Alkhatib
“surfacing for a minute to point out this good piece by @edwardongwesojr.com > Refusal is not just some impotent act of nostalgia, and the extent to which we think so is a measure of our contempt for the prospect of taking democratic politics seriously.” evidence ↗
7 experts 3 communities 1 sources clustered
Brian Merchant Taylor Lorenz Eryk Salvaggio Ali Alkhatib Edward Ongweso Jr Frank Pasquale Benjamin Riley
How the network is reacting Experts are approaching this through 3 distinct lenses.
Multiple readings

Policy & governance

1 expert

Rules, institutions and accountability.

“This essay talks about the left's vision for technology, the "alliance" between left and right-wing anti-data center movements, politics of refusal, democratic control over the AI infrastructure buildout, China, industrial policy, and more. www.thetechbubbl…”

Building & implementation

1 expert

How teams are shipping and applying it.

“Note “the possibility of communities forming compacts that establish minimum terms for data center development, allowing them to prevent firms from playing various jurisdictions against one another in a race to the bottom.” www.thetechbubble.info/p/the-futu…”

Research & technical analysis

1 expert

Evidence, methods and technical implications.

“surfacing for a minute to point out this good piece by @edwardongwesojr.com > Refusal is not just some impotent act of nostalgia, and the extent to which we think so is a measure of our contempt for the prospect of taking democratic politics seriously.”

Primary source thetechbubble.info ↗
Established Models & releases Research 6d ago
⚡ 17 h early
LLM story diversity diagnosis paper

Elias in the Lighthouse, Again? Diagnosing Low Diversity in LLM Stories

5 experts across 3 network communities independently surfaced this.

Why this matches Research & technical analysis reaction 5 attributable expert contributions · Ted Underwood, David Mimno, Eryk Salvaggio
“In the "Limitations" section they acknowledge that their study was monolingual, but say a multilingual generalization would be interesting. arxiv.org/abs/2605.26492” evidence ↗
5 experts 3 communities 1 sources clustered
Ted Underwood David Mimno Eryk Salvaggio Grace Sil Hamilton
How the network is reacting 5 experts are independently emphasizing research & technical analysis.
Reaction converging

Research & technical analysis

5 experts

Evidence, methods and technical implications.

“In the "Limitations" section they acknowledge that their study was monolingual, but say a multilingual generalization would be interesting. arxiv.org/abs/2605.26492”

2 experts discussed this · 2 posts
Ted Underwood: was reading this and enjoying it — and then enjoyed it more when it turned out to be a paper by Sil Hamilton and @dmimno.bsky.social
David Mimno: was reading this and enjoying it — and then enjoyed it more when it turned out to be a paper by Sil Hamilton and @dmimno.bsky.social
David Mimno: The preprint is arxiv.org/abs/2605.26492, and @srhm.ca is working on a post!
Open the full discussion →
Primary source arxiv.org ↗
Established Models & releases Research 4d ago
⚡ 1838 h early
LLM story consistency analysis paper

Do Large Language Models Always Tell The Same Stories?

2 directory members surfaced this signal.

Why this matches Research & technical analysis reaction 1 attributable expert contribution · arxiv cs.CL
“Thennal DK, Hans Ole Hatzel Do Large Language Models Always Tell The Same Stories? https://arxiv.org/abs/2606.17350” evidence ↗
2 experts 2 communities 1 sources clustered
Eryk Salvaggio arxiv cs.CL

“Thennal DK, Hans Ole Hatzel Do Large Language Models Always Tell The Same Stories? https://arxiv.org/abs/2606.17350”

Primary source arxiv.org ↗
Established Models & releases Research 3d ago
LLM training data diversity paper

Do Large Language Models Capture the Diversity in their Training Data?

1 directory member surfaced this signal.

Why this matches Research & technical analysis reaction 1 attributable expert contribution · Eryk Salvaggio
“Not surprising, but this study suggests LLMs & LDMs actually produce *less* than the variety present in their training data: “across different model scales, sequence lengths, and decoding strategies.” arxiv.org/abs/2609.02275” evidence ↗
1 expert 1 community 1 sources clustered
Eryk Salvaggio

“Not surprising, but this study suggests LLMs & LDMs actually produce *less* than the variety present in their training data: “across different model scales, sequence lengths, and decoding strategies.” arxiv.org/abs/2609.02275”

Primary source arxiv.org ↗