Expert attention map

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 signals surfaced by John Herrman ×
Debated

The questions experts are actively pulling apart

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

New Models & releases Signal 16h ago

The OpenAI Hack Scrambles the AI Race

2 experts are actively discussing the implications.

2 experts 1 community 1 sources clustered
Casey Newton John Herrman

“It's fascinating that the arrival of actual AI cyber risks is turning a lot of AI folks against some of the specific safety arguments the industry was founded on nymag.com/intelligence...”

2 experts discussed this · 8 posts
John Herrman: It's fascinating that the arrival of actual AI cyber risks is turning a lot of AI folks against some of the specific safety arguments the industry was founded on nymag.com/intelligence...
John Herrman: I also think it's interesting — and generally good! — that, as capability improves, people are becoming less inclined to live in Anthropicoid awe of AI. If the curve is going to kill us, we're alre…
John Herrman: I don't quite disagree with Casey here, but I would like to leave space for the argument that being "obsessed with the definitions of terms" is necessary for attempting to think clearly about AI. I…
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Primary source nymag.com ↗
Established AI field signal Analysis 19d ago
⚡ 7 h early
AI development pace essay

In AI, Nothing Ever Happens. Wait, It’s Happening!

3 experts across 3 network communities independently surfaced this.

3 experts 3 communities 1 sources clustered
Maxim Raginsky John Herrman Colin

“One can always count on @jwherrman.bsky.social to make good sense of what’s happening in and around AI:”

“The AI industry is prone to massive, frequent, and intensifying narrative mood swings. Part of this is typical of a boom. But I think it's also intrinsic to the tech itself nymag.com/intelligence...”

3 experts discussed this · 9 posts
John Herrman: The AI industry is prone to massive, frequent, and intensifying narrative mood swings. Part of this is typical of a boom. But I think it's also intrinsic to the tech itself nymag.com/intelligence...
John Herrman: The constant, direct experience of enchantment, disenchantment, and re-enchantment with AI — including at the highest levels of the industry — helps explain a lot about the last few years, and what…
John Herrman: this is basically unhelpful as a whole (of course you/we want to maintain control over perceptions of tech). but for certain actors, in particular positions, being maximally open to technological e…
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Primary source nymag.com ↗
Established Models & releases Development 19d ago
⚡ 26 h early
ChatGPT browser shut down

John Herrman: The declawing of OpenClaw

4 experts are actively discussing the implications.

2 experts 2 communities 1 sources clustered
John Herrman Paul Ford

“Chatted with @ftrain.bsky.social and Rich Ziade about my thwarted and slightly pathetic attempts to get something out of OpenClaw, the AI assistant that we are all definitely still talking about a few months later aboard.com/podcast/john...”

4 experts discussed this · 5 posts
John Herrman: Chatted with @ftrain.bsky.social and Rich Ziade about my thwarted and slightly pathetic attempts to get something out of OpenClaw, the AI assistant that we are all definitely still talking about a …
rat king 🐀 👑 : look at this handsome young man
John Herrman: 😘🦀
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Primary source aboard.com ↗
More conversations

What experts are discussing without an anchoring article

4 experts · 7 posts · 18d ago
Christian Wolf David Picard John Herrman Colin
Multiple readings Building & implementation · 1 Research & technical analysis · 1
Christian Wolf: To me this is not new and not surprising: (Applied) ML always had a very strong experimental component and finding out what trained models can do is akin to studying biological systems. This has on…
David Picard: To me this is not new and not surprising: (Applied) ML always had a very strong experimental component and finding out what trained models can do is akin to studying biological systems. This has on…
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