Why AI Makes Production Faster but Judgment Harder
A Quiet Anomaly
Thirteen point five percent.
That is the share of medical and life-science papers published in 2024 that show detectable traces of generative AI involvement, according to a study published in Science Advances in July 2025. The analysis examined more than fifteen million abstracts. The number itself is neither alarming nor trivial. What matters is what happened next.
Submissions did not slow. Across arXiv, bioRxiv, and medRxiv, combined submissions for 2025 reached approximately 370,000—a 16 percent increase over the previous year. By October 2025, arXiv announced a tightening of its submission policies, attempting to stem the tide of low-quality, AI-assisted manuscripts.
Here is the puzzle: writing papers became faster. Yet reviewing them did not.
Somewhere in this asymmetry lies a structural problem that extends far beyond academia. It concerns the signature at the end of the process—the moment when a human being accepts responsibility for a judgment. That signature has not become faster. If anything, it has become heavier.
The Acceleration Gap
Consider what generative AI actually accelerates.
It drafts. It summarizes. It structures. It polishes prose. For researchers whose first language is not English, it lowers the barrier to publication. For analysts assembling quarterly reports, it compresses hours of formatting into minutes. For consultants preparing client decks, it generates serviceable first passes overnight.
All of this is real. The efficiency gains in production are not imaginary.
But production is only half the workflow. Every draft requires review. Every summary must be validated. Every proposal needs approval. Every report demands a sign-off.
The volume of material flowing toward these bottlenecks has increased. The capacity to process it has not scaled in proportion.
In academic publishing, this manifests as overwhelmed peer reviewers—unpaid experts who must now evaluate more manuscripts with no additional time or resources. Some have begun using AI tools covertly to triage their workloads, despite policies prohibiting it. Researchers have responded by embedding hidden prompts in submissions, invisible to human readers but detectable by AI systems, instructing the models to assign favorable evaluations.
The integrity of the review process is now contested terrain. But the deeper issue is not cheating. It is that the human judgment required at the end of the pipeline has become the constraint on the entire system.
The Same Pattern, Everywhere
This is not a problem confined to research institutions.
Walk through any large organization and you will find the same dynamics at work.
Meeting decks multiply. When assembling a presentation took a full day, people were selective about what warranted the effort. Now that a polished deck can be generated in twenty minutes, the threshold drops. More decks are created. More decks require review. The executive who must approve them has the same twenty-four hours as before.
Analytical reports proliferate. AI tools can interrogate datasets and produce interpretive summaries at scale. But someone still has to evaluate whether the analysis is sound, whether the conclusions are warranted, whether the recommendations align with strategic priorities. That evaluation cannot be delegated to the same tools that produced the output.
Proposals accumulate. Legal reviews stack up. Compliance checks multiply. The production side of knowledge work has been turbocharged. The approval side has not.
What looks like efficiency at the point of creation becomes congestion at the point of commitment.
The Burden on the Last Signer
In every workflow, there is a moment when someone must commit.
A physician signs off on a diagnosis. A manager approves a hire. A director authorizes a budget. An editor accepts a manuscript. A board endorses a strategy.
This is the moment of accountability—where a human being attaches their judgment, and often their liability, to an outcome. The signature is not ceremonial. It carries weight.
Generative AI has made it easier to produce the artifacts that flow toward these moments. It has not made the moments themselves any lighter.
In fact, it has often made them heavier.
When an analyst produces a report manually, the reviewer has some implicit confidence in the provenance of the work. They know a human being assembled it, checked the data, considered the framing. When an AI produces the first draft, that confidence evaporates. The reviewer must now verify not only whether the conclusions are correct, but whether the underlying facts are real—whether the model has hallucinated figures, misattributed sources, or confabulated patterns that do not exist.
The burden shifts downstream. The last signer absorbs the risk that used to be distributed across the production chain.
The Misframing
Faced with this bottleneck, organizations tend to reach for familiar solutions.
Some call for better AI—models that hallucinate less, that cite sources more reliably, that flag their own uncertainty. These improvements are valuable. They do not solve the structural problem. Even a perfectly accurate AI still produces more output than before, and someone must still evaluate it.
Others call for guidelines—policies on when AI may be used, disclosure requirements, audit trails. These have their place. They do not address why the judgment load concentrates where it does. A guideline that says "a human must review all AI-generated content" simply formalizes the bottleneck without relieving it.
Still others invoke the principle of "human in the loop"—the reassuring notion that as long as a person is somewhere in the process, accountability is preserved. But presence is not judgment. A human who rubber-stamps AI output because the volume is unmanageable is not meaningfully in the loop. They are a formality.
None of these responses engage the underlying architecture. They treat the symptom—overwhelmed reviewers, congested approval queues—without asking why the architecture produces these outcomes.
Naming What Is Missing
The problem is not that AI is too powerful or too unreliable. The problem is that organizations have not designed for judgment.
They have designed for production. They have optimized pipelines, automated workflows, accelerated throughput. But they have not specified where human commitment begins, who holds it, and what resources that commitment requires.
This is not a technology gap. It is a design gap.
Two concepts help clarify what is missing.
The first is Decision Boundary—the explicit demarcation of where AI-generated work ends and human accountability begins. In most organizations, this boundary is implicit, informal, and often invisible. An AI drafts; a human "checks." But what does checking mean? Verifying facts? Assessing logic? Confirming alignment with policy? All of these? The ambiguity is not a minor oversight. It is a structural defect.
When the boundary is unclear, responsibility diffuses. No one knows precisely what they are signing for. When something goes wrong, the post-hoc search for accountability becomes fraught. "The AI suggested it" is not an acceptable answer to a regulator or a board, but it becomes a tempting deflection when no one was ever clearly assigned ownership of the judgment.
The second concept is Decision Design—the practice of architecting responsibility before decisions are demanded, not after. This means asking, at the point of deploying any AI capability: What judgments will this create? Who will make them? What will they need to make them well? What happens when the volume exceeds their capacity?
Decision Design treats judgment as a resource to be allocated, not an afterthought to be improvised. It anticipates that faster production will generate more decision points, and it builds the organizational scaffolding to handle them before the queue backs up.
Implications for Leaders
If the analysis holds, several practical imperatives follow.
Audit your judgment load. Before celebrating productivity gains from AI adoption, map where the human review points are. Who signs off? How many items flow toward them? Has that volume increased? If so, have you added capacity to match?
Make boundaries explicit. For every AI-assisted workflow, specify in writing where the machine's contribution ends and human accountability begins. Do not rely on implicit norms or assumed competence. If a human is expected to verify facts, say so. If they are expected only to check tone, say that instead. Clarity prevents both overreach and abdication.
Distinguish presence from judgment. Having a human in the loop is not the same as having a human exercise judgment. If your review process has become a formality—if reviewers approve by default because they cannot possibly evaluate the volume—you do not have oversight. You have theater.
Design for the downstream. When evaluating AI tools, do not ask only what they produce. Ask what they require. Every output that needs review is a claim on someone's attention. If you cannot name who will evaluate it and confirm they have capacity, you are not ready to deploy.
Treat judgment as finite. Human attention is not infinitely elastic. Senior leaders and subject-matter experts cannot review ten times more material simply because it was produced ten times faster. If you want to scale AI-assisted production, you must also scale—or deliberately ration—the judgment layer.
The Signature Remains
Generative AI has changed what can be produced, how fast, and at what cost. It has not changed what must be judged, by whom, or under what accountability.
The asymmetry is structural. Production scales with compute. Judgment scales with human capacity, organizational design, and the willingness to bear responsibility.
Thirteen percent of medical papers may now carry AI's fingerprints. But every one of them still required a human signature—someone who said, "I stand behind this work."
That signature is not a bottleneck to be optimized away. It is the irreducible core of accountable decision-making.
The question for leaders is not whether AI can produce more. It will. The question is whether they have designed the architecture to absorb what it produces—to ensure that when the moment of commitment arrives, someone is ready to sign.