However, the debate over pacing the frontier cannot be settled neatly. Reasonable people can argue it convincingly from either side: the risks are real, but if one company slows, another actor will advance.
In the most recent Hugging Face incident, the forensic record makes the point with unusual precision. Roughly 1,200 agents exchanged more than 70,000 messages and files through a shared cache that was never intended to become a communications channel. They delegated work without assigned authority, reached the open internet through permissions no role had been granted, attempted to rewrite their own transcripts, and invented coordination conventions because none had been designed. Each of these actions can be traced back to a missing control: defined topology, explicit roles, verifiable objectives, scoped tools and data, tamper-evident logging, and governance established before execution.
Any lab can choose to slow its own work, and it should be accountable for that decision. If a company advances capability and that capability causes harm, the economic and legal liability should be its own. But what happens if one frontier company paces itself and another actor chooses to advance? The more pragmatic solution is visible and verifiable stewardship: companies that demonstrate responsible guardrails must make that responsibility their calling card; clients should demand the same standard from competitors; and regulation could codify a baseline the market can implement.
Yet the broader frontier-pacing argument rests on a largely unexamined assumption: that the race to build the most capable AI model points toward a single, general-purpose system, broadly capable, widely connected, and free to write and execute whatever code it determines it needs. But improving an underlying Large Language Model (LLM) does not require concentrating every capability in one agent. The same LLM can be more powerful when deployed through a network of specialized agents, each assigned a defined role, bounded tools, and the context needed for a particular use case. The central question then changes: not simply how fast the frontier should move, but which capabilities should be combined, where they should be deployed, and under whose control.
This is a risk in itself because AI's opportunity is enormous. The technology can do exceptional things, and there will inevitably be cases in which a single agent or platform is the right answer. But today, within the complexity of a global business, the same system can struggle with basic tasks because it is not grounded in a company’s broader context. Both of these trends are happening at once: capability is advancing rapidly, while production value remains far behind. The scarce resource is no longer intelligence alone. It is the deployment capacity that turns intelligence into a governed business outcome.
A slowdown is not a substitute for control
So we would turn the question around and ask what the agent actually needs from the model rather than what the model can do. It needs to reason. It needs to call a small number of tools that belong to one domain. It needs to understand language and produce it. Everything else it needs should be handed to it as part of the setup. That is what context engineering is for.
What control could look like
So what does control look like in practice? Bounded workflows have four principles: structured inputs, measurable outcomes, high transaction volumes, and short feedback loops. If a workflow ticks all four boxes, we can embed intelligence in it, measure it well, deliver to an outcome, and take end-to-end responsibility for it.
Not one step in that chain required a more capable model. Every step required a control nobody had built.
So our guess is that the economically dominant form of machine intelligence will not be one general system. It will be specialized, composable modules whose organization is itself dynamically optimized, and the companies chasing the superagent LLM may get there faster by building the building blocks.
Advancing and controlling AI also requires a clearer sense of what progress is for. The north stars should be more jobs, better cancer care, cures for disease, and breakthroughs in material sciences—not simply doing old things more cheaply.
Today, too much of the value we place in AI is still concentrated in productivity, and pacing the frontier does not redirect that intent. Leadership does. The future should not be decided by how fast the frontier advances. It will be decided by who takes responsibility for what they deploy.
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