The Coordination Cost Was the Business Model

Most writing about AI in law still argues about adoption. Should you use it, can you trust it, what happens to junior lawyers. A paper released in June by researchers at Harvard and Perplexity quietly makes that debate feel beside the point, because it stops asking people what they think and instead measures what they actually did.

The method is the reason to take it seriously. Rather than survey lawyers about their feelings, the authors pulled production data from Perplexity’s own products and ran a within-person comparison: the same user, issuing a near-identical query, once to a conversational assistant and once to an autonomous agent. They kept ten thousand matched task pairs where the two queries were almost word for word the same. That design strips out the usual confounds. It is not one cohort of enthusiasts measured against a cohort of skeptics. It is the same person, the same task, two tools, which is about as close to a controlled experiment as you get outside a lab.

The efficiency story, stated precisely

Between the user’s turns, the agent performed about 26 minutes of autonomous work per session. The assistant did 33 seconds. On matched tasks, average completion time fell from 269 minutes to 36, and estimated cost dropped by 94 percent, with human labor, not model cost, doing almost all of the collapsing.

The counterintuitive result is quality. You might expect autonomy to trade accuracy for speed. It did the opposite. On the study’s next-turn dissatisfaction signal, the measure of meaningful dissatisfaction, the kind that shows up as corrections, re-asks, and error reports, ran at 1.3 percent for the agent against 2.9 percent for the assistant. That is the 55 percent figure being quoted around the study, and it is worth stating at the right level: the serious complaints roughly halved. Faster and judged better at the same time.

Those numbers are the headline, and they are real. They are also, I think, the least interesting thing in the paper.

The number that matters is about scope

The finding that should reorganize how a firm plans is not speed. It is that agent users worked outside their own primary occupation far more often than the same users did with the assistant, 59 percent of the time against 50. A single professional, paired with an agent, started absorbing work that used to be divided across separate specialists, legal and financial and technical, inside one workflow.

The authors describe this neutrally as a “reduction in coordination costs.” From where I sit, in banking and insolvency litigation, that phrase names something the profession sells. A large share of what a firm bills is coordination: the deal team, the associate pyramid, the partner who assembles specialists and stitches their work products together. When one person plus an agent can carry a task that previously required three people talking to each other, the coordination is not merely cheaper. It becomes a different unit of production. The study is measuring, at the level of individual queries, the early erosion of a pricing model.

This cuts in a direction lawyers should sit with, because the overlap runs both ways. Yes, a banking lawyer with an agent can now reach into financial modeling and regulatory checks that once meant pulling in a colleague. But a servicer’s analyst, a financial advisor, or a restructuring boutique can now reach into work that used to require a lawyer. This newsletter is named for that overlap on purpose. It is an opportunity and a competitive threat wearing the same coat.

Law is inside the sample, not adjacent to it

Legal and compliance work already made up 5.5 percent of agent queries in the study, and these were not glorified lookups. Across the sample, agent queries engaged roughly 60 percent more fine-grained, occupation-specific work activities than the same users’ assistant queries, meaning the executional tail of the task rather than the topical headline. Research, analysis, drafting, tool calls, and document delivery, handled end to end instead of one prompt at a time. Finance was the standout on a related measure: agents chained external tool and data calls in about 23 percent of finance sessions against roughly 1 percent for the assistant, which is exactly the kind of multi-source, multi-step assembly that banking work runs on.

The way you interact changes along with it. With an assistant, your follow-ups mostly clarify and correct, and you are steering a search engine. With an agent, they tilt toward verifying and extending, and you are reviewing a deliverable that already exists and pushing it further. That is the difference between operating a tool and supervising one, and it moves the lawyer’s job up the stack toward judgment, strategy, and validation.

Where this actually lands in banking and insolvency

The payoff is not a smarter answer to a single question. It is the workflow. Large-scale due diligence across an NPL or UTP portfolio, where the work repeats across hundreds of positions but each position is individually checkable. Restructuring scenarios that braid financial modeling, legal exposure, and regulatory review into one analysis. These are precisely the tasks the paper flags as the agent’s home turf: multi-step, multi-domain, expensive to produce, and relatively easy to verify.

That last property, easy to verify, is the hinge, and it is where I would push past the paper. The economics work when producing the output is costly but checking it is cheap, and much of legal production has that shape. But not all of it, and the exceptions are the whole point. Confirming that a memo cites real and current holdings is cheap. Judging whether a novel restructuring strategy survives a specific court, or whether a settlement number is right given a portfolio’s tail risk, is not cheap to verify, because that judgment is the work. The division of labor between lawyer and agent gets drawn along the verifiability line. Agents take the production that is costly to make and cheap to check. Lawyers keep the judgment that is costly to make and costly to check, which turns out to be a fair description of what senior legal work has always been.

There is a caveat the authors are honest about, and it matters more for law than for most fields. Their cost model treats the human’s delegation cost as roughly the effort of writing the prompt, and they note in a footnote that this probably understates the true fixed cost, because it does not capture the cost of verifying the agent’s output. In a regulated, liability-heavy domain, verification is not a footnote. A hallucinated citation or a missed carve-out is not a typo, it is exposure. Which means the real fixed cost of delegating legal work sits higher than the study’s proxy, and the firms that win are the ones that drive that verification cost back down with genuine infrastructure, so that checking stays reliably cheaper than producing. That, not raw model quality, is where the durable advantage lives.

The honest limits, and the real question

The study covers a 90-day window dominated by early adopters and paying subscribers, and the authors say so plainly. The matched-pair method also captures only the agent tasks that had close assistant equivalents, and many did not, although the ones without a twin tend to be the more complex tasks, where the gains are likely larger rather than smaller. So the direction is not ambiguous even if the exact magnitudes will move.

Which leaves the question actually worth a legal team’s time. It is not whether to use AI. It is which of your workflows genuinely involve multi-step execution across legal, financial, and documentary domains, and how you would redesign them around a real division of labor between lawyers and agents, drawn along the line separating what is cheap to verify from what is not. That redesign is the hard part, and it is organizational far more than it is technical.

The technology is already here. The overlap is the opening. So the honest question is not whether your team will use agents. It is whether you will redraw the work around them before the analyst, the advisor, or the boutique on the other side of the deal does.

The paper is How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope, by Jeremy Yang of Harvard with Kate Zyskowski, Noah Yonack, and Jerry Ma of Perplexity, and it is on arXiv at arxiv.org/abs/2606.07489. I would rather this be a conversation than a broadcast, so if you build or buy these systems, tell me in the comments or reply to this email. Which of your workflows would actually survive being handed to an agent, and which would you never let out of your own hands?