Hand a model a merger agreement and it summarizes it fluently, then misses that the indemnification cap swallows the escrow, that a closing condition has no matching covenant, and that the chosen deal structure triggers a tax result no client would accept.
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Models write a pitch deck's worth of confident deal analysis and miss what a deal team lives on: a comps set stuffed with the wrong peers, an accretion/dilution model that ignores the financing, a synergy number pulled from thin air.
Models describe the revenue cycle in clean theory and miss the mechanics that decide whether a claim pays: the real reason behind a CARC or RARC denial code, a payer rule that overrides the general one, an eligibility check that should have happened before the visit.
Ask a model to plan an audit and it produces a tidy program, then sets materiality by feel, accepts a management assertion as if it were evidence, and designs a sample that would never support the conclusion it draws. Professional skepticism is exactly what it lacks.
Ask a model to brief a motion and it hands back something that reads like a real filing, then cites three cases that do not exist and misstates the standard of review on a fourth. The procedural posture drifts, the deadline math is off, and the local rules are ignored.
Models assign codes that look right and bill wrong: an ICD-10-CM code one notch short on specificity, a CPT modifier that changes the whole claim, an NCCI edit ignored, an E&M level the note never supported.
Models can scaffold a whole app in one shot: front end, API, and database, all of it looking right. Then the state goes stale, the endpoint leaks data, and the migration bricks production.
Models produce confident accounting answers that fail on the details: misapplied standards, broken reconciliations, and journal entries that don't balance.
A model will answer a compliance question with confidence and still point you toward a HIPAA violation: a disclosure with no authorization, a retention rule from the wrong state, a Stark or anti-kickback trap it never sees.
Models are surprisingly bad at reasoning about themselves: training dynamics, evaluation design, data pipelines, and deployment trade-offs. Plausible-sounding ML advice is often subtly wrong.
Models write backend code that passes the happy-path test and falls over on everything else: the race condition under load, the N+1 that melts the database, the API contract that breaks every client the day you change it.
A model will hand you a pipeline and an infrastructure change that apply cleanly and quietly leave a security group open to the world, a state file that will corrupt on the next run, and a rollback path that does not exist. In production those mistakes page someone at 3am.
Models ship UI that renders in the demo and breaks everywhere else: state that desyncs on the second render, an accessibility tree no screen reader can follow, a layout that collapses the moment real content arrives.
Models struggle with the practical side of healthcare: billing codes, insurance workflows, scheduling, and the administrative details that keep clinics running.
Models can recommend a portfolio that reads well and quietly breaks every rule that matters: concentrated bets dressed as diversification, a Sharpe ratio computed on overlapping exposures, position sizing that ignores liquidity and drawdown.
Models handle textbook finance fine and fall apart on the real thing: mispriced risk, subtly wrong derivations, and models that ignore market microstructure.
Models sound confident and still get bedside care wrong: a dose no nurse would give, a triage order that buries the sickest patient, discharge teaching that misses the one thing keeping someone safe at home. The distance between textbook care and real care is where you work.
Medical questions are where model mistakes matter most: outdated guidance, missed contraindications, and confident answers that no clinician would sign off on.
Models answer health questions in a confident, authoritative voice and still get the clinical details wrong: a contraindication they skip, guidance that ignores comorbidities, advice that is textbook-correct and wrong for a real patient.
A model will draft a cite-check that looks airtight and still miss that the case was overruled, point to the wrong treatment, and quietly invent a pin cite. In litigation those errors do not stay small. Catching them before they reach a filing is exactly what a paralegal does.
Models produce fluent legal analysis that is often subtly wrong: invented citations, misapplied precedent, and jurisdictional confusion. A plausible wrong answer in law has real consequences.
Models produce fluent legal analysis that is often subtly wrong: invented citations, misapplied precedent, and jurisdictional confusion. A plausible wrong answer in law has real consequences.
Ask a model to close the books and it produces entries that foot perfectly and still misstate the result: a lease booked the old way, revenue recognized a quarter early, an intercompany elimination that never happened.
Ask a model a tax question and it answers with authority, then applies a code section that changed last year, misses a phaseout, treats an S corp like a partnership, and forgets that state rules diverge from federal. The number lands confidently wrong.