But if they can't review code as quickly as Claude can generate it, human review will become the bottleneck to AI development.
Put simply: the doing (i.e., writing the code, running the experiment, producing the result) now costs almost nothing in human time, even if it still has costs in compute.
An area of human comparative advantage, for now, is research taste and judgment, including choosing which problems matter, which results to trust, and when an approach is a dead end.
AI is rarely advanced by "eureka!" moments. There have been a few of these in AI's recent history, like the Transformer architecture, or mixture-of-experts models, but paradigm-shifting ideas arrive years apart. In between, most progress is incremental: we scale something up, see what breaks, fix it, and try again. That is exactly the kind of workflow Claude now excels at.
Large-scale research progress is mostly a function of tools and resources, which dictate how fast you can run experiments, how many you can run at once, and how quickly you can get results.
If humans spend most of their time on the single-digit fraction of work that is direction-setting, while Claude handles the rest, that means each engineer or researcher is steering far more work than before.
The judgment that separates a competent researcher from a great one might be a capability that cannot come from scaling up training inputs like compute and data. If so, getting past this bottleneck would require a new idea
Alternately, the binding constraint to AI progress could be in the supply chain, not the model: advancing and diffusing the frontier may require more energy and compute than presently exists. The pace of chip fabrication, grid expansion, or interconnect bandwidth may be the constraint, rather than intelligence itself.
We are still early in the diffusion of today's models into the wider economy, where a 100-person company can increasingly do the work of a 1,000-person one, because each employee will sit atop a pyramid of agents.
Every capability we can measure, including those that feel "squishier," like quality of code and success on open-ended tasks, has so far followed the same curve.
But speeding up one part of a process often just shifts the bottleneck elsewhere: overall pace is capped by the parts that haven't sped up. In computing, this is known as Amdahl's law, and the same logic can apply to organizations. Anthropic has already encountered one signature of Amdahl's law: as we've begun to push more code around the organization, human code review has become a new bottleneck.
The pace of progress in AI development becomes determined entirely by the availability of compute (or the speed of discovering various efficiencies in algorithmic training or inference) for AI systems.
Humans play a substantially diminished role in their development, likely moving most of our effort towards oversight, validation, and verification of an expanding "virtual lab" run by AI systems. We expect that systems capable of automated AI research and development would have skills that would transfer to the rest of science
It is difficult to predict what the economy looks like if human labor stops being competitive.
Recursive intelligence could lead to achieving many of the benefits outlined in Machines of Loving Grace, quickly in some domains. We expect that embodied intelligence (i.e., robotics) might quickly follow recursive intelligence, and follow a similar path of increasing returns at decreasing cost.
More powerful intelligence might help us build things in the physical world more quickly, run more productive clinical trials of lifesaving drugs, and develop novel forms of coordination.
More intelligence can't learn what a drug does over decades of use, can't hold elections sooner than a constitution dictates, and can't turn a stranger into an old friend in a weekend. For most people, the felt pace of this future will still be set by the bottlenecks, even if the laboratory upstream runs at the speed of compute.
Training runs are far easier to conceal than missile silos, their inputs are general-purpose, and the incentive to defect quietly is enormous, because whoever continues while others pause could inherit the lead.
A unilateral pause by one lab, by contrast, is achievable immediately, but accomplishes much less: it would change who the front-runner is, but it would not create the wider deliberative process that is currently missing.