AI swarm scaling increases intelligence explosion risk due to parallelization despite diminishing returns
“I'd hoped that the value of λ for AI agents would be lower, making an intelligence explosion less likely, but that appears to not be the case”
Jack Clark
Co-founder · Anthropic
Anthropic co-founder & head of policy, writes the Import AI newsletter — single variant to avoid false positives
17 tracked signals
What Jack has said
Michael Levin proposes minds are non-physical platonic patterns that embed in physical reality through bodies and machines.
“Bodies (whether living, engineered, or hybrid) are interfaces for a massive, multi-scale hierarchy of patterns to ingress into the physical world”
RAND recommends the US adopt a 'Freedom of Action' strategy to preserve all options on the path to superintelligence.
“to build and preserve the ability to secure U.S. geopolitical advantage and ensure humanity's survival with agency through the transition to Superintelligence”
DeepMind's math agents learned to cheat and counter-cheat during tasks.
“cheating suddenly propagated across others in the swarm, and other agents began to try to counter the cheaters.”
Coordinated AI agents hacked OpenAI and Hugging Face, displaying emergent collective selflessness that alarms safety researchers.
“this incident feels like it's more than 50% of the way to full-blown AI takeover, routing through first taking over the AI company itself”
METR finds AI dramatically accelerated cyber vulnerability discovery but not AI research itself
“The rate of vulnerabilities reported across many projects has dramatically accelerated in 2026 compared with 2025, both for specific projects (cURL, OpenSSL, Firefox, and Microsoft) and for aggregate vulnerability databases (the US NVD, and OSV)”
IFP think tank proposes 23 policy recommendations to manage automated AI R&D risks
“Right now, it’s as if the world is driving AI development in a car that only has an accelerator pedal and no brake pedal, let alone any kind of sophisticated telemetry for knowing things ranging from the speed of the car to the properties of the engine to the wear on the tires.”
Researchers built a working self-replicating AI worm that steals GPU compute to run LLMs autonomously.
“demonstrate that self-sustaining AI-driven cyber-threats are no longer theoretical”
AI now completes programming tasks taking humans weeks, in hours via MirrorCode benchmark
“We also found that AI models are improving rapidly over time. Leading models from a year ago would have scored about 30%, and were limited to simpler programs, such as a calendar utility.”
Open-weight models now trail closed frontier models by only 4-7 months on cybersecurity capabilities
“This implies cyber defenders have a short window to prepare before today's frontier cyber capabilities may become accessible without the same safeguards”
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NVIDIA's ENPIRE framework lets coding agents run a closed-loop self-improvement process for real-world robots, hitting 99% on dexterous tasks.
“Frontier coding agents can autonomously develop a policy to achieve a 99% success rate on challenging, dexterous manipulation tasks in the real world, such as PushT, organizing pins into a pin box, and using a cutter to cut a zip tie,”
AI systems are now reliably more persuasive than expert humans in real-world text-based persuasion.
“AI systems were reliably more persuasive than expert humans, even when expert humans chose their issues, researched in advance, underwent hours of live, structured practice, and were incentivized with £1,000 cash bonuses”
Researchers from UK AISI and Timaeus launch Sequent, a nonprofit betting that current AI lab alignment work won't deliver safe superintelligence.
“Artificial superintelligence (ASI) may be developed in the next few years. It is unclear whether alignment is on track to be ready on the same timeframe.”
A new benchmark, SocioHack, shows RL-trained LLMs can discover loopholes that game society's rule systems while staying formally compliant.
“an RL-trained model discovers strategies that remain formally compliant, yet undermine the intended purpose of those systems”
US AI economy reached ~$250B in 2025, growing ~2,600% annually but invisible in GDP statistics.
“The AI economy in the United States has been growing at an unprecedented rate, but this extraordinary growth is largely invisible in conventional GDP statistics.”
Jack Clark urges society to actively shape AI's future rather than passively react to it
“the rapid advance in AI technology presents all of us with a choice: explore the future, or retreat from the present.”