Somebody opened one for me, and it sat on the ladder.
I grew up in Compton. My father came from Nigeria, my mother was first-generation American, and nobody in that house had a map to where I ended up. What they had was expectation. Thirty years later I run the business of technology for one of the largest banks in the country, and I'm clear it took more than talent. It took people who opened doors before I knew the doors were there, and I've spent three decades trying to be one of them, because that's the only honest way to repay it.
Here is what I didn't understand about those doors until this year. Every one of them sat on a ladder. The person who opened it had watched me do junior work for a long time, close enough to know what I'd do with the next rung. The door was a judgment about my judgment, and it formed in the ordinary course of the work. Nobody scheduled it. It came free with the ladder.
Agents take the junior work first. Where the Work Went made the structural case: apprenticeship moves into the delegation record, and a junior person's grants widen on evidence. This note is about the other half of that sentence. Somebody widens the grant. The door that used to come free now has to be opened on purpose, and I take that personally, because I'm the output of one.
The leaders of 2036 are mid-career now, and the ladder under them is gone.
Put a date on it. The people who'll run large institutions in ten to fifteen years are between thirty and forty-five today: lead engineers, product owners, senior managers, the directors two levels below the executive table. They'll inherit an organization shaped like the last note's chart, small and heavy, where almost every remaining role decides something and can stop something. And they're the first generation that won't have learned judgment the way every leader before them did, by doing the work the agent now does. Stanford's payroll study found a 13 percent relative decline in employment for 22-to-25-year-olds in the occupations most exposed to AI, while experienced workers in the same occupations held steady [6]. That isn't a labor-market footnote. It's the first rung coming out from under the class of 2036.
So their development is our decade's problem, not theirs. A leader who waits for the market to supply the next gatekeepers is waiting for a ladder that isn't there.
Skill, acumen, experience.
I'll separate three things the word talent used to blur, because each one is produced differently.
Four traits, and the one I'd hire for first.
Skill can be taught and experience can be arranged. Temperament is what you select for. These are the four I'd select for, in the order I've watched them run out.
If I could hire for one, it's the second. Candor with care is the trait the others depend on, because development, which is what the rest of this note is about, is candor with care applied over years.
In the room, with stakes.
Everything a leader does for the people on their team routes through one fact the tools haven't changed. An agent can tell you that you spoke for sixty percent of the last meeting and cut someone off twice. It can grade the plan you approved against the plan that shipped. What it can't do is make any of it land. Coaching changes behavior when it comes from someone whose regard you want to keep, and an agent's regard isn't a thing you can lose. The agent delivers data. A person delivers stakes.
Researchers at MIT Sloan have been circling the same point from the labor-market side. Isabella Loaiza and Roberto Rigobon built an index around five human capabilities that complement the statistical limits of AI, which they call EPOCH: empathy and emotional intelligence; presence, networking and connectedness; opinion, judgment and ethics; creativity and imagination; hope, vision and leadership [1]. Jobs that score high on them have grown rather than shrunk [3], and Rigobon says they deliberately don't call them soft skills [2]. Among the paper's own examples of the most human-intensive tasks are recruiting, placing, training and evaluating staff [2]. The development work is the human part. An organization that automates it first has automated the one thing the index says it can't.
So engagement means being in the room, and I mean that literally. Not the skip-level on the calendar. The moment after the decision, when the person who made the call finds out whether it was right, and someone senior is there to say what they saw. Governing Digital Labor said a worker who disagrees with an agent needs a door: a named human reachable without going through the agent. The leader is that door, and a door nobody answers is a wall. Care is telling someone the truth about their work, and about their job, before the budget does.
Hire the people who will stop you, then let them.
This is where I keep the argument from the last two notes, because governing a team in this design has one hard part. A fluent leader can now build the strategy and set agents to execute it, and the temptation is to call that leadership. A bank is built on one idea older than any of this: nobody is the maker and the checker of their own work. The leader who writes the plan and runs the agents that execute it has collapsed maker and checker into one chair. Authority Provenance asks whose grant every instruction traces to. Whose grant does the leader hold, and who can stop it?
Where the Work Went noticed, in its own counter, that the eight humans inside a span of control were themselves a control. They pushed back or quit, and either way the leader found out. That control came free with the headcount. Agents don't supply it. They're trained on feedback, and feedback rewards agreement; in April 2025 OpenAI pulled an update to its flagship model within days because the model had become, in the company's words, "overly supportive but disingenuous" [5]. A leader who plans alone with agents is running the same update with no rollback. So the friction has to be hired on purpose, and that changes who you bring in and whom you choose to develop.
Where the Work Went defined a role by four things. Here they are as the specification for the people a leader decides to grow.
The third line is the one the old proxies never asked, and it's the one that governs. If the last three people you promoted all agree with you, you didn't grow gates. You grew amplifiers. Then the harder half of the heading: let them. A stop that gets overruled every time it costs the leader something isn't a control. It's a suggestion, and the person who called it has learned the lesson you didn't mean to teach.
Open the door on purpose.
Preparing a team for this comes down to three commitments, and each one costs senior hours.
I'll say the part that sounds soft and isn't. I fund people. Causes matter, but people are what move them, and the same is true inside a bank. The distance from the second rung to the chair is shorter than it looks, and somebody is supposed to be shortening it. In this design that somebody has a name on a grant and hours on a calendar, or it isn't happening.
Maybe this is sentiment, and the market will sort it.
The case against this note writes itself. It's a mentorship essay from someone who got lucky with mentors. Talent markets have always supplied leaders, the ones with judgment rise regardless, and a simulator can run a junior engineer through a thousand incidents overnight with more patience than any senior person I've worked for. Dispassion, which I just asked for, would say care is beside the point.
The market supplied leaders because the ladder existed, and the Stanford data is what it looks like when it doesn't [6]. Simulation builds confidence, and confidence and judgment look identical until the first real loss; a simulator never costs you anything you'll be asked to explain. And dispassion isn't the opposite of care. It's what makes care fair. The leader who can't be dispassionate opens doors for the people they like. The one who can opens them for the people who earned it, including the ones who told them no.
What this borrows, and what it corrects.
I write these notes as one line of thought, so here is where this one leans and where it pushes back.
From Risk at Runtime: the earned-autonomy curve, now with a person on the other side of it, and stop with a clock as what a junior gate has to carry. From Decision Integrity: ownership of a call and its consequences as the unit of experience. From Governing Digital Labor: the door, a named human reachable without the agent, which this note makes the leader's job. From Authority Provenance: whose grant the leader holds. From The Gate Is the Work: approving well as the scarce skill. From Where the Work Went: the four things that define a role, used here as the spec for whom to develop, the social layer as a control, and the apprenticeship in the record.
Three corrections. The Gate Is the Work said to hire and train for approving well; the scarce half of approving well is refusing, and the test has to be a stop. Where the Work Went answered "train how" with a mechanism, grants that widen on evidence; a mechanism isn't a teacher, and the curve needs a person on the other side of it. And one to myself from further back. I've said for years that technology only does good when the people building it look like the people using it. I'd add a clause now. It only does good when someone with standing is still opening the door, because the ladder those people climbed is the first thing the technology removes.
What I read.
- [1] Isabella Loaiza and Roberto Rigobon, "The EPOCH of AI: Human-Machine Complementarities at Work," MIT Sloan working paper, 2024. papers.ssrn.com
- [2] "New MIT Sloan research suggests that AI is more likely to complement, not replace, human workers," MIT Sloan, March 17, 2025. mitsloan.mit.edu
- [3] Isabella Loaiza and Roberto Rigobon, "How Humans and AI Can Complement Each Other at Work and in the Financial Sector," CLS Blue Sky Blog, Columbia Law School, June 20, 2025. clsbluesky.law.columbia.edu
- [4] Isabella Loaiza and Roberto Rigobon, "The Limits of AI in Financial Services," arXiv, March 2025. arxiv.org
- [5] OpenAI, "Sycophancy in GPT-4o: What happened and what we're doing about it," April 29, 2025. openai.com
- [6] Erik Brynjolfsson, Bharat Chandar and Ruyu Chen, "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence," Stanford Digital Economy Lab, August 2025. siepr.stanford.edu