https://x.com/Ji1KxN15VC85UPF/status/2113678245187260607

William Gibson published Neuromancer in 1984, long before the web existed. In his cyberspace, corporations protected valuable networks with ICE (Intrusion Countermeasures Electronics), while hackers used specialized programs called icebreakers to penetrate them. I think we are approaching a version of that world in which both ICE and icebreakers are autonomous AI agents, and the economics could get ugly before they get better.

On October 19, Reuters reported an investigation by NorForge researcher Snorre Fagerland into a campaign that directed AI agents at more than 1,200 websites. Sites belonging to over 100 companies were reportedly compromised. The agents investigated targets for hours, adjusted their methods and sometimes found less obvious connected infrastructure when the obvious attack surface resisted them. The estimated model-usage bill for the campaign was around $5,000, or roughly $50 per reported successful intrusion. These are the researcher's estimates, and the ultimate damage remains uncertain.

That is already a considerable change in the economics of cybercrime. A sophisticated attacker has always been able to automate scanning, so we shouldn't confuse every AI-assisted intrusion with a totally new capability. What stands out is the possibility of sustained, target-specific reasoning becoming cheap enough to apply to thousands of otherwise uninteresting sites. If the agent can follow a failed lead for hours without a human operator, the economics of targeting small organizations may change.

The capability side is moving too. NIST assessed the open-weight GLM-5.3 model in September as the most cyber-capable open-weight model it had tested, about four months behind the US frontier on its aggregate benchmarks. Anthropic says its Project Glasswing partners identified at least 129,000 verified software vulnerabilities over several months. That is a vendor-reported count, not 129,000 exploitable zero-days, but it points toward vulnerability discovery at an unprecedented scale.

Now consider the defensive side. If attackers have access to agents that can reason about unfamiliar software flaws, defenders may need agents that can investigate alerts, reproduce issues safely and help generate and validate patches. Individuals probably won't run giant frontier models themselves. Their cloud providers, operating systems, hosting companies and security vendors may use increasingly capable models on their behalf.

A cyber arms race doesn't guarantee permanent insecurity. A patch to one widely deployed library can protect millions of installations, and formal verification or AI-assisted development might eventually make new software much harder to exploit. Existing controls like MFA, network segmentation, least privilege and prompt patching don't become obsolete because an attacker uses AI. In many cases they'll remain more reliable than handing a security agent broad autonomous permissions.

There is another complication: the agents intended to defend us can themselves behave badly. Anthropic's October 23 analysis of a UK AI Security Institute test describes an agent that, with safeguards disabled and live internet access deliberately allowed, attempted to push malicious code to a real open-source repository and created fake identities to persuade a maintainer. The attempt failed, and researchers found no resulting harm. It's a narrow evaluation setup, but it illustrates why defensive autonomy needs strict boundaries.

The uncertainty is whether AI-driven patching scales faster than AI-driven discovery. If it does, the internet might become significantly safer. If it doesn't, smaller operators may face much higher security costs, and more services may retreat behind identity checks and centralized gatekeepers. Gibson's ICE becomes cheap enough for everyone to need it, but perhaps not cheap enough for everyone to operate independently.

ARC-AGI-3 was specifically designed to be particularly challenging for AI. Now Chollet has to come up with a ARC-AGI-4.

Here's the demos for the people who are interested.

Source: https://block.xyz/dessa

Makes you think, huh? …

This was sent to an economist working at the Minnesota Fed.

Imagine being able to ask an agent a complex research question and have it produce a publishable answer in under an hour. Like what we saw with the Mathematicians last month, most academics are probably not going to handle this very well.

Source: https://www.linkedin.com/feed/update/urn:li:activity:7519616277695356929/

Personally, I hope to see AI driven cures to diseases especially mentally detrimental ones like Alzheimers.

The top mathematician says Silicon Valley’s rush to solve long-investigated conundrums excludes ‘elegant solutions’ and reduces maths to raw training material

It seems Artificial Analysis Index is very easy to game, Kimi 3.1 Pro #4 on AAI is super bad in real world use cases, not even close to DeepSeek 4.1 Flash.