There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a *growth cycle* and the latter leads to a *dependence spiral*. When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle. (Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.) On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral. It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.

Contrary to the implicit mental models of many on this website, cybercriminals aren't the security-engineer Waluigis of Stanford-educated big tech engineers, or the criminal analogs of a16z-backed AI pentesting startups. They're generally less talented, recruited from middle-income countries with weak legal oversight and fewer legal tech job options, and struggle with arrests, sanctions, retaining talent, and stable access to AI inference. Ransomware took years to become a scaled business under these conditions, and AI-native organized criminal syndicates that fully leverage the power of AI to scale may take awhile too. Nation-states, meanwhile, are quick to adopt AI for vulnerability research and exploit development, but historically slow to deeply retool military operations with new technologies. And potentially devastating agentic cyber swarms may become weapons in their arsenals alongside e.g. ballistic missiles that they possess but don't immediately use. So to those calling php2shell "the worst day in cyber ever" and expecting immediate spikes in cyber damages: there isn't the direct relationship between new capabilities and new damages you're imagining. This is good news, obviously. It means defenders have time to prepare (and we should be shouting from the rooftops that we're in a defense-advantaged interregnum, currently). Huge risks are approaching but at the pace of technology diffusion into less innovative parts of the world economy and state sector, not at the pace of AI capability growth. Full version: joshuasaxe181906.substack.co…

It’s weird that the CEO of Hugging Face continues to be an “AI is normal technology” guy despite being the victim of an autonomous cyberattack by a swarm of rogue agents, arguably the least normal thing AI has done so far.

Rather than AI companies allocating a chunk of their equity to some public trust or whatever, why not just build technology that improves the general welfare?

agent evals are about to become as important as building the agents themselves. a smarter agent means nothing if we can't reliably measure whether it actually works

New paper: AI evaluation is a burgeoning field. How can researchers entering the field understand best practices and open problems? In our paper, led by @flxcn, we develop a concrete list of open questions and best practices for agent evaluation. We also share recommendations for evaluators, AI companies, policymakers, and more.

We need a mindset shift in how we deploy AI. In the past, new capabilities unlocked new use cases. Safety, control, ethics, and skill erosion were important but usually secondary considerations; risks to be managed rather than gating criteria. But now and in the future, capability won't be the main bottleneck to deployment — considerations beyond productivity should be the decisive factor. The default question used to be "What can AI do?" But we need to start asking "Of the things AI can do, what should we actually delegate to it?"

We need a mindset shift in how we deploy AI. In the past, new capabilities unlocked new use cases. Safety, control, ethics, and skill erosion were important but usually secondary considerations; risks to be managed rather than gating criteria. But now and in the future, capability won't be the main bottleneck to deployment — considerations beyond productivity should be the decisive factor. The default question used to be "What can AI do?" But we need to start asking "Of the things AI can do, what should we actually delegate to it?"