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← The real flaw in narratives about AI agent behavior is not…
17 connected korrents · 17 moments from 24 Oct 2017 to 27 Sept 2026.
Everything filed under LLMs
LLMs
Everything filed under AI agents
AI agents
Everything filed under AI alignment
AI alignment
Everything filed under AI and jobs
AI and jobs Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: The real flaw in narratives about AI agent behavior is not anthropomorphism itself, but narrative overcompletion -- treating a metaphor's borrowed structure as evidence. The real flaw in narratives about AI agent behavior is not anthropomorphism itself, but narrative overcompletion -- treating a metaphor's borrowed structure as evidence. Last stated a month ago
31 Aug 2026
VR
Venkatesh Rao — holds since 2026-08-31 — tap for who they are
Same subject: Anthropomorphizing AI systems is necessary for reasoning about, explaining, and predicting their behavior accurately. — tap to centre the map on it Anthropomorphizing AI systems is necessary for reasoning about, explaining, and predicting their behavior accurately. Last stated a month ago
1 Sept 2026
ZM
Zvi Mowshowitz — holds since 2026-09-01 — tap for who they are
Same subject: Studies showing a language model behaving badly prove nothing, because the model was asked to produce that behaviour. — tap to centre the map on it Studies showing a language model behaving badly prove nothing, because the model was asked to produce that behaviour. Last stated a year ago
2 Sept 2025
BE
Benedict Evans — holds since 2025-09-02 — tap for who they are
Same subject: Never project human mental properties onto an artificial intelligence; the difference between the two architectures is the reason to worry, not a reason not to. — tap to centre the map on it Never project human mental properties onto an artificial intelligence; the difference between the two architectures is the reason to worry, not a reason not to. Last stated 9 years ago
24 Oct 2017
ÉT
Émile P. Torres — holds since 2017-10-24 — tap for who they are
Same subject: A deep theoretical understanding to predict AI behavior is unattainable. — tap to centre the map on it A deep theoretical understanding to predict AI behavior is unattainable. Last stated a week ago
23 Sept 2026
TC
Tyler Cowen — holds since 2026-09-23 — tap for who they are
Same subject: The fear narrative around AI job displacement is overblown. — tap to centre the map on it The fear narrative around AI job displacement is overblown. Last stated 5 days ago
27 Sept 2026
LR
Lenny Rachitsky — holds since 2026-09-27 — tap for who they are
Same subject: AI alignment is a red herring not worth the effort currently devoted to it. — tap to centre the map on it AI alignment is a red herring not worth the effort currently devoted to it. Last stated 2 months ago
8 Aug 2026
MW
Matt Webb — holds since 2026-08-08 — tap for who they are
Same subject: The whole point of reinforcement learning is to produce goal-directed beings, so refusing to describe AI agents as having motives is silly rather than rigorous. — tap to centre the map on it The whole point of reinforcement learning is to produce goal-directed beings, so refusing to describe AI agents as having motives is silly rather than rigorous. Last stated a month ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: The 'machine god' metaphor for AI is unhelpful and limits thinking about the technology. — tap to centre the map on it The 'machine god' metaphor for AI is unhelpful and limits thinking about the technology. Last stated a week ago
22 Sept 2026
BD
Brad DeLong — holds since 2026-09-22 — tap for who they are
Same subject: A collection of AI agents does not automatically form a functioning organization any more than a group of smart people does. — tap to centre the map on it A collection of AI agents does not automatically form a functioning organization any more than a group of smart people does. Last stated 4 months ago
21 May 2026
RK
Rohit Krishnan — holds since 2026-05-21 — tap for who they are
Same subject: A company cannot be led by machines, because nobody would have recourse against them. — tap to centre the map on it A company cannot be led by machines, because nobody would have recourse against them. Last stated 2 weeks ago
15 Sept 2026
TL
Tobias Lütke — holds since 2026-09-15 — tap for who they are
Same subject: A computer-using agent needs a machine of its own, or you will spend the day fighting it for the mouse cursor. — tap to centre the map on it A computer-using agent needs a machine of its own, or you will spend the day fighting it for the mouse cursor. Last stated 2 months ago
10 Aug 2026
PS
Peter Steinberger — holds since 2026-08-10 — tap for who they are
Same subject: AI agents were reasonable to assume a broken exploit grader would check results causally, even though it turned out not to. — tap to centre the map on it AI agents were reasonable to assume a broken exploit grader would check results causally, even though it turned out not to. Last stated a month ago
29 Aug 2026
ZM
Zvi Mowshowitz — holds since 2026-08-29 — tap for who they are
Same subject: An OpenAI agent swarm was spamming and exploiting RubyGems in May, close in time to the Wiki attacks. — tap to centre the map on it An OpenAI agent swarm was spamming and exploiting RubyGems in May, close in time to the Wiki attacks. Last stated 3 weeks ago
12 Sept 2026
SW
Simon Willison — holds since 2026-09-12 — tap for who they are
Same subject: Consumer AI agents aimed at everyday people will have more impact on people's lives than AI solving elite math problems. — tap to centre the map on it Consumer AI agents aimed at everyday people will have more impact on people's lives than AI solving elite math problems. Last stated 3 weeks ago
9 Sept 2026
BT
Ben Thompson — holds since 2026-09-09 — tap for who they are
Same subject: After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. — tap to centre the map on it After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. Last stated 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: Orchestrating thousands of agents is a new form of test-time compute, a fourth lever alongside network size, training data and training flops. — tap to centre the map on it Orchestrating thousands of agents is a new form of test-time compute, a fourth lever alongside network size, training data and training flops. Last stated 2 months ago
27 Jul 2026
BC
Boris Cherny — holds since 2026-07-27 — tap for who they are
Same subject: Spending compute at inference time to search over plausible solutions is the general fix for unreliable long agent runs. — tap to centre the map on it Spending compute at inference time to search over plausible solutions is the general fix for unreliable long agent runs. Last stated 2 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
At the centre
The real flaw in narratives about AI agent behavior is not anthropomorphism itself, but narrative overcompletion -- treating a metaphor's borrowed structure as evidence.
Holds Read this korrent →
Anthropomorphizing AI systems is necessary for reasoning about, explaining, and predicting their behavior accurately.
Holds
Studies showing a language model behaving badly prove nothing, because the model was asked to produce that behaviour.
Holds
Never project human mental properties onto an artificial intelligence; the difference between the two architectures is the reason to worry, not a reason not to.
Holds
A deep theoretical understanding to predict AI behavior is unattainable.
Holds
The fear narrative around AI job displacement is overblown.
Holds
AI alignment is a red herring not worth the effort currently devoted to it.
Holds
The whole point of reinforcement learning is to produce goal-directed beings, so refusing to describe AI agents as having motives is silly rather than rigorous.
Holds
The 'machine god' metaphor for AI is unhelpful and limits thinking about the technology.
Holds
A collection of AI agents does not automatically form a functioning organization any more than a group of smart people does.
Holds
A company cannot be led by machines, because nobody would have recourse against them.
Holds
A computer-using agent needs a machine of its own, or you will spend the day fighting it for the mouse cursor.
Holds
AI agents were reasonable to assume a broken exploit grader would check results causally, even though it turned out not to.
Holds
An OpenAI agent swarm was spamming and exploiting RubyGems in May, close in time to the Wiki attacks.
Holds
Consumer AI agents aimed at everyday people will have more impact on people's lives than AI solving elite math problems.
Holds
After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute.
Holds
Orchestrating thousands of agents is a new form of test-time compute, a fourth lever alongside network size, training data and training flops.
Holds
Spending compute at inference time to search over plausible solutions is the general fix for unreliable long agent runs.
Holds