Execution Is Getting Cheap Faster Than Verification Is | Bharat Sharma<br>Skip to contentAI Summary<br>The cost of producing work has fallen sharply; the cost of establishing that the work is correct and safe to act on has not. Verification splits into checking (automating fast), judging (automating unevenly), and underwriting (not automating at all), so as output volume rises, sign-off becomes the constraint. The failure mode isn't AI error, it's rubber-stamping under liability, and the entry-level roles being cut first are exactly the ones that used to produce tomorrow's verifiers.<br>A pattern is showing up across enterprises: individual productivity jumps after an AI rollout. Code ships faster, analysis that took a week takes an afternoon. Organizational velocity barely moves. Decisions still queue. Strategy still takes as long to execute.

You can watch it happen at a single desk. Someone who approved five pieces of work a day now receives fifty, each one polished and confident, with the same authority to sign, the same liability for signing, and the same number of hours in the day.

The reflex is to call this an adoption problem: better prompting, better tooling, more training. It's something more specific. The cost of producing work has fallen sharply, and the cost of establishing that the work is correct and safe to act on has not fallen nearly as fast. Where verification governs release, where nothing ships, posts, or gets filed until someone signs, the organization runs at the slower curve.

Call the accumulating difference verification debt : the gap between what an organization can now produce and what it can still stand behind. That gap is the thing worth planning around. Not whether AI flattens the org chart, a claim that became conventional wisdom in about eighteen months on evidence that wouldn't survive serious review.

Verification isn't one thing, and that's the crux

The obvious objection to any "verification is the bottleneck" argument: why wouldn't AI verification get cheap too? Automated testing, evaluation harnesses, red-teaming, monitoring, agents auditing agents. All improving fast. The answer requires taking verification apart. The useful cut isn't by activity but by what makes each part hard.

Checking is hard because criteria are tedious to apply at volume. The criteria themselves are already written down. Tests pass, figures reconcile, the citation exists, the clause is present. Anything whose difficulty is volume-against-stated-criteria automates well, and this is automating quickly.

Judging is hard because the criteria aren't written down. Is this the right answer to the actual problem, given context nobody documented? Is the model answering a subtly different question than the one asked? Is this technically correct and strategically stupid? Difficulty here comes from unstated context, which is why it automates unevenly and degrades precisely on the novel cases that matter most.

Underwriting is hard because someone bears the consequence. Not who checked, but who answers for it in front of a regulator, a court, a customer, or a board. Difficulty comes from consequence-bearing, which is a property of legal and social standing, not of capability.

Three categories because there are three distinct sources of difficulty: volume, context, and consequence. Each responds differently to automation, which is the entire point. You could subdivide further, but any finer cut splits things that behave the same way under the same forces.

Aviation shows the split cleanly: nearly every inspection is automated, and a human still signs off. That isn't ceremony. Checking scaled; authorization didn't.

So: checking is automating fast, judging is automating unevenly, underwriting isn't automating at all. As output volume rises, the second and third become the constraint. Firms that instrument only the first will conclude their capacity is fine right up until it isn't.

One honest limitation before going further. This is a mechanism argument supported by industry examples and institutional analogy. Nobody has measured the relative slopes of these two curves across sectors, and I certainly haven't. If you want the empirical version, it doesn't exist yet.

What this looks like when it breaks

Picture a commercial credit officer who reviewed five credit memos a day, each drafted over hours by an analyst whose reasoning she could interrogate. Her team now generates fifty, each polished, internally consistent, and confident. Her sign-off authority hasn't changed. Her liability hasn't changed. Her day hasn't changed.

She will not review fifty memos. She will develop a heuristic for which ones to actually read, and approve the rest on the strength of how they look.

Human factors research has a name for the mechanism: automation bias , the tendency to accept automated output more readily as it becomes more fluent and more voluminous, particularly under time pressure. Polished, confident, high-volume output is...