When the Algorithm Billed 0.1: How AI Saved a Weekend and a Client Relationship
At 4:47 p.m. on a Friday, the universe always calls. In this case, the universe had a general counsel. She needed two things by Monday: a first-wave document production in response to a regulator’s request and a redlined amendment to a master services agreement, because apparently deadlines hunt in packs. Partners drifted toward the war room, associates quietly inventoried their caffeine reserves, and someone started printing things, because printing creates the reassuring sense that “work” is occurring. In the corner, our newly minted AI suite lit up, patiently awaiting its moment like a clerk who has read the local rules and three adjacent treatises.
We call the suite “Casey,” because if you’re going to depend on something at 2 a.m., it deserves a name. Casey is the firm’s collection of AI tools: an eDiscovery engine that does more with 10,000 emails than most people do with a weekend, a contract-drafting copilot that remembers every clause you’ve ever overwritten, and a research assistant that doesn’t confuse “close enough” with “binding precedent.” We’ve learned to introduce Casey early, keep humans in the loop, and never let it draft sympathy notes.
The request was sprawling but familiar: emails from six custodians, attachments, chat logs, and “anything related to Project Firefly.” The partner in charge suggested a first pass of keywords that included several innocent nouns, a few hopeful verbs, and one codenamed dish from the cafeteria. Casey politely recommended a narrower set with proximity operators and a date range keyed to meeting invitations. It also flagged that one custodian’s calendar called the project “Evening Jog,” which, while healthy, is not discoverable in the same way. Within minutes, the engine de-duplicated near-identical attachments, batched “Final_final_v3” variants, and built a timeline that connected emails, Slack threads, and file versions in a way even their authors might find educational.
Redactions, historically the art of making documents look like stylish minimalist paintings, went from hours to minutes. Casey automatically detected personally identifiable information and highlighted likely privilege, asking sensible confirmation questions—“Is Dr. Jones in fact outside counsel, or a literal doctor?” The associate handling the log sighed with relief and, for the first time in recorded history, smiled while drafting descriptions that would make a magistrate nod rather than furrow.
Meanwhile, the contract amendment stared at us with the confident menace of a paragraph that knows it will be read on page 18 at 1:23 a.m. Casey compared the client’s MSA to our clause library and the newest model addenda, flagged that the customer’s requested “most favored client” language was an undomesticated creature, and offered alternatives that preserved revenue recognition sanity while meeting business realities. It recommended an audit cooperation clause with a frequency cap, reminded us to harmonize the limitation of liability carve-outs buried in Schedule C, and generated a clean set of track changes, annotated with why each revision existed in the first place. Several associates looked betrayed; their mnemonic devices for remembering indemnity baskets had just become collectible antiques.
On the research front, questions surfaced that could have ruined dinner plans. Did the regulator’s request preempt the state privacy law? Could we apply the safe harbor in the old consent order to the new data set? Casey gathered a docket of relevant cases, agency guidance, and secondary sources, each with proper citations and parentheticals. No, it did not “invent” a case, because this is not that kind of story. The associate verified the authorities in the traditional, comfortingly analog way—reading them—and distilled the analysis into a memo the partner could use in a call without hitting the mute button to frantically search.
Then came the moment that clinched it. Casey’s compliance module pinged with a weekend update: the regulator had quietly revised an FAQ at 3:12 p.m., clarifying that “initial production” could be rolling and that metadata fields X, Y, and Z were optional if certain conditions were met. This converted a forest of worry into a hedge. We tailored the production accordingly, shaved hours of metadata wrangling, and sent the client a plain-English summary that inspired the rarest of client reactions: gratitude expressed in real time and not three days later after an internal alignment meeting.
By Sunday evening, we had a structured, defensible production queue; a privilege log that would withstand both judicial scrutiny and an English teacher; and a contract amendment that made the business team breathe easier. The partner did the only sensible thing: insisted we slow down for a final human read. No one argued. AI is a dazzling first drafter, a relentless sorter, a finder of needles and manufacturers of sensible haystacks. It is not a replacement for judgment, context, or a skeptical eyebrow. We made two edits that only human experience would catch—one nuance of a cross-default clause and one tone change that turned “no” into “not now.” On Monday morning, the GC joined a call prepared for the expensive apology. Instead, she got a plan, a production, and a polite redline. She also got a bill lighter than anticipated. Casey, alas, recorded time as 0.1 with the description “assistance,” which we left for the auditors to appreciate.
It’s tempting to end with a comedic sting about robot lawyers, but the reality is both more useful and more reassuring. AI in law today is neither oracle nor intern; it is a power tool. Firms are using it to triage millions of documents without triaging their associates, to draft contract language that remembers your playbook at 2 a.m., to surface on-point authority before a partner can finish asking the question, and to monitor compliance changes without subscribing to seven newsletters that all quote each other. The craft remains human. The grunt work increasingly does not.
The takeaway is simple and, one hopes, scalable: give AI well-posed problems; keep humans accountable for the answers; document your process; and let the machines be tireless so the lawyers can be thoughtful. On Friday afternoons, that combination might just save a weekend. And on Monday mornings, it might remind clients why they hired you. After all, judgment is still billed in six-minute increments. Efficiency, it turns out, is priceless.
