The job is a bag of 40 things. AI just got good at 12.
The bag doesn't get lighter. It changes shape. And most firms are still pricing the old one.
AI and Jobs: The Honest Answer Is Tasks, Not Jobs
TL;DR: There’s a lot of conversation these days about AI and jobs. I see a lot of the research, and I wanted to share where I’ve come down on it. One camp says AI will erase most jobs. The other says it will create more than it destroys. From where I sit, working with firms every week, both miss what’s actually happening. AI is very good at pieces of a job and bad at the whole job. For small and mid-sized firms, the threat isn’t mass layoffs. It’s slow margin erosion, a broken training path for young lawyers, and clients who expect more for less. Here’s what to change, and what to tell your kids.
Every managing partner I talk to has read the same two headlines. One says AI is about to wipe out half the jobs in the building. The other says relax, technology always makes more work than it kills. Both ran this week. Both sounded certain. And you’re supposed to make payroll decisions based on that.
I’ve been making the same argument in every keynote this year, so I’ll skip the windup. They’re both half right. The catch is that the half each side gets wrong is the half that actually matters when you run a firm.
The two forecasts everyone’s arguing about
The doomer story goes like this. AI gets better fast, it does what knowledge workers do, and soon enough we’re all on universal basic income arguing about how to spend our afternoons. The accelerationist story is sunnier. Yes, some jobs go away, but new ones show up, often jobs we can’t picture yet, and it happens faster every cycle. Net positive. Don’t worry about it.
The research that lands in my inbox is calmer than either camp wants it to be. Yale’s Budget Lab. Microsoft Research. A big study out of Denmark that tracked actual payroll records. None of them shows a broad jobs apocalypse. Unemployment hasn’t broken. One economist at Apollo, Torsten Slok, looked at weekly payroll data this spring and said flatly that there’s zero evidence of AI-driven job losses so far. If anything, he argued, the build-out is creating demand: people to install the tools, data centers to run them, chips and power to feed them. Jevons paradox in real time. Make a thing cheaper and people use more of it, not less.
So the doomers are overclaiming. We have a lot of AI adoption and very little evidence, so far, of broad AI-driven unemployment.
But the accelerationists are too comfortable, because underneath the calm averages something specific is already moving. Stanford is the cautionary exception. Its work on ADP payroll data found a 6 percent drop in employment for workers aged 22 to 25 in the most AI-exposed jobs, even while overall employment kept growing. The pressure is landing first on the youngest workers, and that matters a great deal if you sign the checks.
What I actually see when I sit with firms
When I work with a firm, I don’t watch AI replace lawyers. I watch it eat tasks.
It drafts the first version of a demand letter in ninety seconds. It turns a messy pile of documents into a clean chronology. It summarizes a deposition and pulls the key admissions. It cleans up billing narratives that used to cost a paralegal an hour. Real, useful work. The kind of work that used to fill a young associate’s week.
What it doesn’t do is the job. It doesn’t sit across from a frightened client in a custody fight and know when to push and when to wait. It doesn’t read a judge. It doesn’t decide whether this is the motion worth filing. It doesn’t carry the weight when the call goes wrong.
Most jobs are a bundle of tasks. AI is compressing some of those tasks toward zero and leaving the rest alone. Think about it this way. The job is a bag of forty things, and AI just got very good at twelve of them. The bag doesn’t vanish. But the shape changes. And that changes who you hire and how you bill.
That’s the in-between answer. Not robots take everything. And not relax, you’re fine. The work is getting rearranged, and the firms that come out ahead will be the ones doing it on purpose. Most are doing it by accident.
The squeeze nobody wants to talk about
This next part is the one that worries me.
The tasks AI is best at are the exact tasks we used to hand first- and second-year associates. Research, summaries, first drafts, document review. The grunt work. And that grunt work was never just grunt work. It was how young lawyers (or consultants, or investment bankers) learned to think.
The numbers are already moving. In 2025, only 38 percent of associate hires at U.S. firms came straight out of law school, down from 46 percent in each of the two prior years. For the first time in years, lateral hiring outpaced hiring directly from law school, bringing in people who already have judgment and can step into a live matter without a two-year runway. Read that again. Firms are quietly deciding they need fewer people learning the trade and more people who already know it.
The short-term logic is obvious. Why pay a first-year to spend twelve hours on a research memo when the tool does the first pass in twenty minutes and a senior associate cleans it up?
The trap shows up later. Cut junior hiring and training, and you save money this year while starving your partnership pipeline for the next decade. The expertise you bill at premium rates came from somewhere. It came from people doing the lower-level work, making mistakes, getting marked up, slowly building the instinct you now sell. Pull out that bottom rung and you don’t feel the damage for years. Then you feel all of it at once.
The easy takeaway is “we don’t need juniors anymore.” I think that’s a mistake, and the harder truth is that we need them learning faster, with better supervision, using AI as a study partner and not a shortcut around the thinking.
What small and mid-sized firms should actually do
Let me be direct, because this is the part clients pay me for.
You are not Kirkland & Ellis. Kirkland just said it’s spending $500 million over the next three to four years to build its own AI platform, starting with $100 million this year. That’s an arms race for firms with ten-billion-dollar revenue. It is not your model, and you don’t need it to compete. You need three things. A safe way to use good tools. A short list of workflows where AI actually pays off. And a pricing model that stops handing the savings to your clients for free.
Set the guardrails before you do anything clever
Decide which tools are approved and which are banned. Decide whether confidential client information can go into a tool, and which vendors are allowed to train on your data. (The answer to that last one is almost always no.) Put it in writing. The ABA’s first ethics opinion on generative AI, Formal Opinion 512, is blunt about what’s at stake: competence, confidentiality, client communication, supervision, candor to the court, and reasonable fees. Those duties don’t soften because the draft came from a machine.
And verification is not optional. AI hallucinations in court filings aren’t edge cases anymore. The exact counts vary depending on who’s counting, but the direction is clear, and it’s the wrong way. Even Sullivan & Cromwell, one of the most prestigious firms on the planet, apologized to a federal judge this spring after a filing went out with fabricated, AI-generated citations. If it can happen to them, it can happen to you. Make the rule one sentence and don’t let it bend. No lawyer cites, files, or sends AI-generated legal authority unless a human has checked it in a real research source.
Pick five workflows, not fifty
The biggest waste I see is letting every lawyer experiment forever. Endless dabbling, no traction. Pick a handful of workflows that are high-volume, repeatable, painful today, and easy to check. For most firms that means intake summaries and early case assessment, fact chronologies and timelines, first-pass deposition and discovery summaries, drafting support for routine letters and memos, and cleanup of billing narratives and client updates. Five things. Get good at those, write down how you do them, and stop there for now.
Notice what those five have in common. Each one produces a draft or a summary that a person then verifies. That’s the safe zone. The tool suggests, the lawyer decides.
Fix your pricing before AI eats your revenue
This is where firms get hurt, and it’s the part most of them haven’t touched.
If a task used to take six hours and now takes two, and you bill by the hour, you just cut your own revenue for the same client outcome. Clio’s small-firm research this year found something I keep repeating to clients. Roughly seven in ten small and solo firms now use AI, but fewer than a third have actually grown revenue with it. The bigger firms are pulling ahead on that number. Same tools, very different results. The winners changed how they charge. Everyone else kept billing hours on work that no longer takes hours.
So move the repeatable, AI-compressible work onto fixed fees, phased fees, or a monthly retainer. Keep hourly billing for the genuinely uncertain, bespoke, high-volatility matters where it still fits. Here’s the simple math. Say a severance review used to run four hours at $450, so $1,800 to the client. With AI, your lawyer spends ninety minutes on it. Price it as a flat $1,250 or $1,500. The client gets a lower number and certainty. You keep the margin the efficiency created instead of giving it back. That’s how AI turns into profit rather than a quiet leak.
Rebuild the apprenticeship on purpose
You’ve made AI part of the work. Now make it part of the training, or you get the productivity and lose the next generation.
The method I’d teach every junior is simple. Make them think first, before they touch the tool. What’s the issue, what facts matter, what law might apply, what’s the likely answer, what would change their mind. Then let them use AI to test that thinking, find the gaps, build the chronology, draft the outline, argue the other side. Then make them verify everything and write up where the tool was wrong or thin. That last step is the gold. Reward associates for catching the machine’s mistakes, because catching mistakes is the thing that becomes judgment.
The other move costs almost nothing and pays off for years. Get your best partners to record how they think. Capture the reasoning, not just the templates. How they decide whether a TRO is worth filing, how they read whether the other side wants to settle, what makes them trust a construction delay claim. Ten-minute explanations, captured and shared with the people coming up. A few firms are already building “AI twins” of senior partners for exactly this. You don’t need anything that elaborate. You need your partners’ thinking written down somewhere a young lawyer can find it.
The objections worth taking seriously
Maybe I’m underrating the pace. Anthropic’s Dario Amodei, for one, thinks the hit to entry-level white-collar work is closer and bigger than the calm averages suggest, and he could be right. A snapshot misses inflection points. The accelerationist case has real weight too. Cheaper production usually creates new demand, and a small firm that can suddenly offer a fixed-fee service it could never afford to staff before might do more work, not less. I’ve watched that happen. The cost of doing a thing drops, and appetite for the thing goes up.
There’s a quieter risk on the other end. If everyone moves to flat fees and the machine does the heavy lifting, do clients start asking why they’re paying lawyer rates at all? I don’t have a clean answer. My best guess is that judgment, trust, and accountability stay valuable precisely because a model can’t carry them. But I hold that loosely. Anyone who tells you they know exactly how this plays out is selling something.
A note to parents with kids in high school and college
I get asked this more than anything else. Usually after the keynote, usually quietly, by a parent who isn’t worried about their firm at all. They’re worried about their kid.
What do we tell them? The honest answer is that we can’t hand them a safe career the way our parents tried to hand us one. The titles are going to shift under their feet. But that’s not a reason to panic, and it’s not a reason to steer them away from learning hard things.
What I usually say is this. Don’t have your kid bet everything on a single narrow skill a tool might swallow. Have them get genuinely good at thinking, writing, and arguing, because those carry everywhere, and they’re exactly what’s left once AI handles the first draft. Have them learn to work with these tools now, fluently, the way our generation learned to work a search engine. The kid who can direct AI, check it, and catch what it gets wrong will be worth far more than the kid who either refuses to touch it or trusts it blindly.
And don’t let the tool do their thinking while their thinking is still forming. I feel strongly about this one. There’s early evidence that leaning on AI too hard dulls the very skills you’re trying to build. A college student who has AI write every paper isn’t saving time. They’re skipping the reps that build the muscle. Let them wrestle with the hard problem first. Then let them use the tool. It’s the same three passes I’d give a young associate. Think, then assist, then verify.
There’s one more thing I tell parents, and it has nothing to do with desks or law degrees. Point your kid toward work where being human is the actual point. A veterinarian with her hands on a scared animal. A therapist sitting with someone who’s coming apart. The trades, too: electricians, plumbers, people who build and fix the physical world. High-touch sales, where the deal closes because a client trusts a person and not a pitch deck. The machine isn’t coming for those any time soon, because the human connection isn’t decoration on the job. It is the job. That’s not a fallback. For a lot of kids it’s a better life than the one I used to picture for them.
The deeper thing I’d want my own kids to hear is this. The future rewards people who can do what a machine can’t hand off. Judgment. Taste. Knowing which problem is worth solving, and being able to read another person well enough to understand what they actually need. None of that is on a syllabus, and none of it is going obsolete. Help your kids build those, stay curious, and learn how to learn fast, and they will be fine in a world none of us can map yet.
I mean that. It’s the most useful thing I know.
What to do Monday morning
If you run a firm, start here. Not a year-long study. Three moves this week.
Write a one-page AI rule. Approved tools, no confidential client data in unapproved tools, and a hard verification standard for any legal authority. Put it in front of every lawyer and staff member by Friday.
Pick one workflow to pilot. Intake summaries or chronologies are the easiest wins. Measure the old time, the new time, and the rework, and let the numbers tell you whether it’s working.
Pull your three most repeatable matter types and ask one question of each. Should this still be billed by the hour? If the honest answer is no, draft a flat fee and test it on the next client.
The firms that struggle won’t lose their lawyers to AI. They’ll lose their margin, their speed, and their best young people to the firms that figured this out first. I’d rather you be the firm they’re losing to.
If you enjoyed this, please share it with three friends. If you want to chat further on it, reach out to me at steve@intelligencebyintent.com. If there’s a topic you’d like me to cover, let me know.