Two of AI’s most-read commentators published essays days apart that use the same unusual word, “overhang,” to make almost opposite arguments about what builders should actually worry about.
What they said
Wharton professor Ethan Mollick argues the real problem isn’t a future AI model that hasn’t been built yet. It’s the AI already sitting unused. “The capability overhang, the gap between what these models can do and what almost anyone is doing with them, is an opportunity because most people don’t bring their own advantages to AI, and those who do get much more out of it,” he writes. His evidence: “the current capabilities of existing models are barely being used, and are often not even well understood.”
Independent analyst Ben Thompson uses the same word differently. He’s responding to Anthropic CEO Dario Amodei’s public position that AI labs should deliberately slow down, a stance Amodei calls the need to “Pace the Frontier,” using “frontier” to mean the most advanced models a lab is currently capable of building. Thompson lists five overhangs he says actually drive that call, including a capability overhang (models are already good enough that customers don’t need the newest ones) and a safety overhang. On safety specifically, he argues pacing works against its own goal: “when it comes to the tangible safety risk that exists today, bad actors using aligned LLMs to attack infrastructure, pacing the frontier actually increases the window in which bad things can happen.” LLMs, large language models, are the technology behind chatbots like ChatGPT and Claude; “aligned” means trained to follow safety rules, the point being that attackers already have access to that same safety-trained technology. He goes further on motive, writing that Anthropic’s safety argument “just so happen[s] to align with their need for more time to build a moat,” using a term for a competitive advantage rivals can’t easily cross.
Who they are
Mollick teaches at the Wharton School and writes the newsletter One Useful Thing, where he has spent two years publicly testing and writing about AI tools for a general audience rather than from inside a lab. Thompson founded and has written Stratechery, an independent, subscriber-funded technology analysis publication, full-time since 2014. Neither works for an AI lab or sells AI tools, which is part of why their overlapping word choice this week is worth noticing rather than treating each essay in isolation.
Where they agree, and where they don’t
Both writers reject the idea that the industry’s most important open question is what the next model will do. Mollick’s target is builders and everyday users who assume they need a smarter model before AI becomes useful to them. Thompson’s target is a specific policy stance from one company’s CEO. Nothing in either essay suggests they were written in response to the other. But they land on a similar practical claim from different directions: the frontier isn’t where the action is right now. What’s already deployed is.
They diverge on tone and certainty about the underlying safety debate itself. Mollick treats it as real and unresolved, writing plainly, “I don’t think there are bright lines we can point to and say AI will never cross them.” Thompson is far less charitable about the specific safety argument he’s responding to, framing it as commercial self-interest dressed up as principle.
What Mollick gets right, and where it’s incomplete
Mollick’s core claim, that most people and companies are nowhere near the ceiling of what current AI models can do, matches what’s visible in most workplaces still using AI for narrow, low-effort tasks. His four traits for working well with AI, deep expertise in a field, wide knowledge across fields, taste in judging outputs, and willingness to experiment, describe a real skill gap, not just a tooling gap. What the essay doesn’t fully address is why that gap persists. If the opportunity is this available, the barrier is likely organizational (time, training, incentive structures) as much as individual, and Mollick’s framing puts most of the responsibility on individual initiative.
What Thompson gets right, and where it’s incomplete
Thompson’s cybersecurity point is concrete and testable: if attackers already have access to capable models, a voluntary slowdown by one lab doesn’t remove that capability from the world. It just changes who has the newest version of it. That’s a real gap in the “pace the frontier” argument as stated. Where Thompson’s essay is harder to fully accept is the leap from “this argument is convenient for Anthropic’s business” to treating that convenience as proof the argument is made in bad faith. A safety concern can be genuine and still happen to serve a company’s competitive interests at the same time. Showing the overlap doesn’t settle which one is doing the motivating.
Why it’s notable
Two writers with no apparent coordination converged on the same specific critique in the same week: that the debate over how fast frontier labs should move is absorbing attention that the actual, current gap between AI capability and AI use deserves instead. That’s a meaningful signal about where informed opinion is drifting, independent of whether either individual argument is fully right.
What it means for builders
The practical takeaway doesn’t require picking a side in the safety debate. If Mollick is right that most available capability sits unused, the fastest way to get value right now is auditing what your current tools can already do that you haven’t tried, not waiting on a roadmap for the next model. If Thompson is right that slower frontier labs don’t reduce what’s accessible to bad actors, that’s a reason to treat AI-assisted attacks as a present risk to defend against, not a future one to wait out. Either way, the frontier-pace argument is a distraction from decisions builders can make today with what’s already in front of them.
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