Elon Musk

Rishi Sunak

Kamala Harris

Bill Gates

Andrew Ng

Yoshua Bengio

Fei-Fei Li

Demis Hassabis

Melanie Mitchell

Sam Altman

Dario Amodei

Geoffrey Hinton

Yann LeCun

Gary Marcus

Max Tegmark

Connor Leahy

Eliezer Yudkowsky

Jaan Tallinn

Marc Andreessen

Eric Schmidt

Norbert Wiener

Arthur Clarke

Irving John Good

Claude Shannon

Hans Moravec

John Smart

There's a "wall of fear-mongering and doomerism" in the AI world right now.

"Worrying about AI today is like worrying about overpopulation on Mars."

Concerns that AI could pose a threat to humanity is "preposterously ridiculous."

Claiming AI poses an existential threat is "such an extreme" and risks "wip[ing] out some of its potential benefits."

"I'm more concerned about... the risks that are here and now [than the existential threat of AI]."

Current AI is "not anywhere close" to posing an existential threat but it could in the future.

AI is the "biggest existential threat" to humanity.

AGI's worst-case scenario would be "lights-out for all of us."

Powerful AI systems "taking control" pose an "existential threat."

AI has a "10 to 25 per cent" chance of destroying humanity.

"The most likely result of building a superhumanly smart AI... is that literally everyone on Earth will die."

Does AI Pose an Existential Threat to Humanity?

Stuart Russell

Shane Legg

Metaculus

Society is more likely to get "cat-level" or "dog-level" AI years before human-level AI.

"[W]e will eventually reach AGI... and quite possibly before the end of this century."

"Superhuman AIs" will be achieved "between a few years and a couple of decades."

"Digital intelligence" could overtake us within "5 to 20 years."

We may achieve "something like AGI or AGI-like in the next decade."

"[W]e could get to real AGI in the next decade."

"Human-level" AI could occur in "two or three years."

"I'd be surprised if we don't have AGI by [2029]."

Will AI Surpass Human Intelligence, and When?

Researchers from the Basis Research Institute and affiliated institutions including MIT, Harvard University and Cornell University developed WorldTest, a protocol to evaluate model-learning agents through reward-free interaction followed by testing.</span

arXiv.org

Fortune

Researchers from the Basis Research Institute and affiliated institutions including MIT, Harvard University and Cornell University developed WorldTest, a protocol to evaluate model-learning agents through reward-free interaction followed by testing.

The WorldTest framework instantiated AutumnBench with 43 interactive grid-world environments and 129 tasks across three families: masked-frame prediction, planning and predicting changes to causal dynamics in virtual worlds.</span

Emergent Mind

The WorldTest framework instantiated AutumnBench with 43 interactive grid-world environments and 129 tasks across three families: masked-frame prediction, planning and predicting changes to causal dynamics in virtual worlds.

Three state-of-the-art reasoning models — Anthropic's Claude 4 Sonnet, Google's Gemini 2.5 Pro and OpenAI's o3 — were compared against 517 human participants using the AutumnBench evaluation suite.</span

Welcome.AI

Three state-of-the-art reasoning models — Anthropic's Claude 4 Sonnet, Google's Gemini 2.5 Pro and OpenAI's o3 — were compared against 517 human participants using the AutumnBench evaluation suite.

Human participants significantly outperformed all AI models across all task types and environments, often approaching perfect scores while models performed near random baselines in the comparative assessment.</span

Human participants significantly outperformed all AI models across all task types and environments, often approaching perfect scores while models performed near random baselines in the comparative assessment.

Scaling compute improved model performance in only 25 of 43 environments while showing no effect in 18 environments, particularly for change detection tasks where additional computation yielded no improvement.</span

Scaling compute improved model performance in only 25 of 43 environments while showing no effect in 18 environments, particularly for change detection tasks where additional computation yielded no improvement.

The models failed to adopt human-like strategies such as hypothesis-testing and updating beliefs based on new evidence, while humans demonstrated systematic exploration with frequent strategic resets.</span

The models failed to adopt human-like strategies such as hypothesis-testing and updating beliefs based on new evidence, while humans demonstrated systematic exploration with frequent strategic resets.

Humans Crush AI Models in New WorldTest Benchmark Study

Humans outperformCrush allAI currentModels AI in world-modelNew learningWorldTest Benchmark Study

The Controversies

Andreessen

Ng

LeCun

Mitchell

Li

Bengio

Musk

Altman

Hinton

Amodei

Yudkowsky

LeCun

Marcus

Bengio

Hinton

Hassabis

Altman

Amodei

Musk

Go Deeper

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Humans Crush AI Models in New WorldTest Benchmark Study

Humans outperformCrush allAI currentModels AI in world-modelNew learningWorldTest Benchmark Study

The Controversies

Andreessen

Ng

LeCun

Mitchell

Li

Bengio

Musk

Altman

Hinton

Amodei

Yudkowsky

LeCun

Marcus

Bengio

Hinton

Hassabis

Altman

Amodei

Musk

Go Deeper

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