Does anyone disagree that pretty much every piece of empirical evidence we have about actually existing AI points toward a labor-augmenting technology with a more-or-less ordinary risk profile (no worse than cars, guns, nuclear power, social media, etc.)? On the labor front, the evidence of minimal / inconsequential employment effects so far is undeniably strong (see: aleximas.substack.com/p/has-…). On the risk front, the recent spate of "rogue agent" incidents was akin to a set of natural experiment in which much of what could go wrong did, and the costs were comparable to or less than your typical high-profile cybersecurity breach (several of which happen around the world every month). Not only that, but the base rate of AI-related hacking incidents is extremely low, and with rare exception, model behavior during real-world use cases is exceedingly well-aligned. Plus, as Anthropic recently argued (anthropic.com/research/glm-5…), the open-source GLM 5.3 is around as capable as Claude Mythos at cyber-exploitation while also having much lower safeguards, meaning that every additional day absent some dire cybersecurity incident is further evidence against catastrophic AI cyber-risk. Taken together, these facts imply two constraints on arguments in favor of catastrophic AI risk: 1) those arguments must either pass through future, rather than current capabilities, or future, rather than current varieties of AI adoption or implementation (or they must claim we have been unrealistically lucky); 2) those arguments must also must pass through some specific capability or adoption threshold, rather than rapid improvement or adoption as such, since dramatic capability improvement and rapid adoption have already occurred alongside demonstrably low risk of catastrophic harm. It is therefore natural that those concerned about catastrophic risk commonly appeal to recursive self-improvement, or RSI in order to make the case that things will get much scarier very soon. But as @ramez has convincingly argued (noahpinion.blog/p/wheres-the…), existing data on the reality of RSI within frontier labs neither suggests an inbound trend-break, nor the accelerating returns that might produce one. On the contrary, as is unsurprising to those familiar with the generally log-linear relationship between compute and model performance, all available signs points toward diminishing returns. And this is before considering external factors that could slow capability progress further, including low-hanging fruit effects related to running up against more complex or tacit-knowledge-based tasks for which performance data and verifiable reward signals are scarcer, plausibly incoming financing constraints related to the industry's massive capex commitments (see: econjared.substack.com/p/a-b…), growing political backlash, both to data centers in particular, but also rapid AI development writ large, and new regulatory bodies and industry reporting frameworks (see e.g. markey.senate.gov/news/press… and axios.com/2026/08/11/open-so… ), which will require labs to proceed with greater caution from here on out. I am therefore led to conclude both that catastrophic AI risk is likely to remain negligible for the foreseeable future and that the only counterarguments concern massive black-swan-style capability uplifts no existing data imply are on the horizon, and/or particularly irresponsible or dangerous forms of AI adoption that there are also few precedents for so far (and that we are actively developing norms, regulation, and security measures against). Neither is impossible, meaning we should remain vigilant and that AI safety efforts will continue to be well worth it in the coming years. But I see no way around the conclusion that something akin to the "normal technology" view is superior on the empirical and argumentative merits to whatever variety of doomerism. What am I missing?