What I didn’t expect, when I started writing this part, was that the answer to where does that pod run would change under me in a few weeks.

What showed up this summer

Since August, a wave of products has launched that are, underneath, the same thing. The most visible are Grok Bot, Muse, and Instinct. An agent that runs around the clock on a computer the vendor operates, with a browser, an email connection, a calendar, a way to pay for things, and memory that carries over between sessions. Each started from a different wedge: one out of a social network, one out of a family of consumer apps, one out of a text thread. They’re all heading to the same place: an agent with full control of a machine, doing what a person at a computer does, whether or not anyone is watching.

None of this is new to the founders I know who have been running their own version of it, on a Mac mini or a rented server, since the spring. What’s new is that it’s packaged. The closed products are a more mature version of the same idea, with the rough edges filed off, the connectors pre-wired, and someone else patching the box.

The difference from the chat assistants most people use is not intelligence. It’s who starts the work. A chat session begins when a person opens it and ends when they close it. These agents invert that. They check the mail at seven, notice the schedule changed at two, chase the vendor, and come back the way a good executive assistant does, with the one thing they need from you to keep going. Approve this. Pick one of these two. The person stops being the operator and becomes the one who unblocks.

I’ve been a heavy user of Muse since a few days after it launched, and I had a fair comparison ready. Earlier this year I’d built my own agents for family logistics: the calendar, the school and club emails, the reminders, all driven by cron jobs I maintained. So I ran the same use case through both, side by side. The product experience is much smoother. It handled the schedule changes and the reminders at least as well as mine did, and it took me out of the maintenance work entirely, which is the part I hadn’t valued properly until it was gone. Within a week it had replaced them.

The infrastructure is the same

If you look past the packaging, every one of these products is the same seven layers, and so is every open framework that does the same job.

• A machine that persists.

• A harness on it, the scaffolding that turns a model into something that wakes up, checks things, and keeps state.

• Memory.

• An identity, the address and accounts the agent acts through.

• Connectors to the tools.

• A broker that decides what the agent may do.

• A model.

One detail changed how I think about the price tags. I asked the agent I use what it was running on. Two cores, eight gigabytes of memory, a small disk, a stock Linux. That’s the smallest machine any cloud provider sells, five to twelve dollars a month. Nobody is paying for the machine. They’re paying for the other six layers, assembled, and for not being the person who maintains them.

Which is why I don’t think build-versus-buy is the interesting question here. It blurs what is going on. The question worth asking is what the closed products won’t let you do, and why.

Closed agents and open agents

The agent layer is now splitting the way the LLM model layer did. There are closed setups, where the vendor owns the machine, the harness, the memory, the identity, and the model, and you use it on their terms. And there are open setups, where the same seven layers run on something you control and any model or agent can plug in. The memory in an open setup doesn’t have to be a folder of files, either. It can be a database, or a repository under version control so every change to what the agent knows is tracked and reversible. How to structure that memory is a topic of its own, and we’ll go into it properly later in the series.

The closed ones share one constraint that isn’t a bug. None of them will let another agent in. Grok Bot runs only xAI models. Muse runs only Meta’s. Instinct is proprietary end to end. This is structural, not temporary. Every one of these companies is competing to be the entry point, the thing you open first and the thing everything else routes through. Letting a competitor’s agent onto the box would give away the position they’re spending the subsidy to win.

And the entry point is worth a great deal to them because of what accumulates there. Every correction you make to the agent’s work, every preference it learns, every account it opens under an address on their domain. That’s the layer that compounds. It’s also the layer that makes leaving expensive. Some of them will let you export the memory as a plain file, which is worth doing regularly. I did, and what came back was one file: facts and workflow rules, already filtered by the agent’s own judgment about what mattered. It was accurate. It was also all I could see. I have no view of how it will organise that memory when the data grows, what it will keep and drop when the context is mixed, or which of the tasks it runs on its own it will prioritise when they conflict. That’s not a complaint about the product. It’s the nature of the arrangement: the memory is managed for you, and therefore out of your control. And the correction history, the accounts, and the identity stay theirs unless you built it otherwise.

The open side is further along than most people assume. OpenClaw is the infrastructure, Hermes is the closest thing to a product, and there are already projects that skip shipping an agent altogether and simply run the agent subscriptions you already pay for as the bots. The agent is a worker you can swap. The setup is what you keep.

I use several agents side by side. Each is wired better into something than the others, the way people are, and the best one for a given job has changed every few months. That’s the argument for the open setup in one sentence. In a closed product, you get one agent, on its terms, until you leave. In an open one, the newest agent plugs into the same memory, the same identity, and the same keys the day it ships, and the old one goes without taking a year of context with it.

I don’t think one side wins this. Open and closed models have ended up side by side, and I expect the same here: closed agents for the person who wants it to work on day one, open setups for anyone who needs to choose their agents and keep their memory. For a business, that second description is usually the right one. But the closed products are ahead today on the things that matter most to someone who isn’t technical, and that’s worth being honest about.

What the closed ones are teaching

The closed products are the spec for what an open setup should have, and each has solved one layer well.

• Muse: the broker. Every action the agent takes is approved by something outside the model, grants are scoped to a connector and a task and expire, and one-time codes and password resets are filtered out of the mail the agent can read. This is the layer the open frameworks are weakest on; OpenClaw’s second major release shipped without sandboxing on by default, and its skills marketplace has already had a malicious skill found in it.

• Instinct: the identity. The agent gets its own email address, so its actions are attributable, it can open accounts as itself, and revoking it is one deletion. The idea is right. The address should be on your domain, not theirs.

• Grok Bot: the reach. The agent can work inside the user’s own laptop. It’s the most powerful thing any of them do, and it collapses the boundary the separate machine existed to provide. Worth having, worth doing deliberately.

• All of them: the habits. One machine per pod, a log a person can read, and coming back for approval rather than asking permission up front. Those transfer directly.

The sequence

So what I’d suggest to an owner is not a choice between building and buying. It’s both, in order.

Rent one now. Feel what it’s like to have something run overnight and come back with one question. Learn the approval habit. Keep the exported memory in a folder you own from the first week.

Then, at the first tell, build your own. The tell is usually one of three:

• You want an agent the vendor won’t allow.

• The data is something you couldn’t explain to a client.

• A second person needs a pod of their own that reads from the same memory.

That last one is where the questions change, and it’s what Part 4 is about.

The 24/7 agent is here, and it’s worth using today. Don’t marry the first one.

Next: the second pod. What happens to memory, keys, and privacy when a team shares them.

→ A group of us are running our businesses through AI agents and sharing what works as we go. If that's you, or you'd like it to be, join the community. The next part goes into the open setups in detail: memory, keys, and privacy once a team shares them. Subscribe if you want it when it lands.

“How many agents should I have, and what should each one do?”

It’s a reasonable question. It’s also, underneath, a question about boundaries: where one ends, where the next begins, what belongs to each.

And it’s the same question companies have always asked about people. Which is the first clue that something is off, because the answer we developed for people is the one thing you shouldn’t copy.

You can’t draw a boundary around a unit nobody has defined

Start with what’s actually running. Inside a single account you might have several different agent types — one model writing code, another doing research, another handling operations — each running many sessions at once. Some finish in a minute. Some own a piece of work for weeks.

Is that one agent? Four? Forty?

Nobody has settled this, and it matters more than it sounds. Because the moment you decide an agent is a discrete worker, you start doing what we have always done with discrete workers.

The boundary most people draw: function

So the roster gets written. A marketing agent. A sales agent. A support agent. Each gets its own scoped memory — marketing data for the marketing one, tickets for support — and they pass work across the lines between them.

It feels organized. It looks like a company.

It is also the org chart, rebuilt in software. And here’s the part worth sitting with: you haven’t removed the cost of all that process and coordination. You’ve transferred it from people to agents. The same gates, the same hand-offs, the same waiting — now paid in tokens, orchestration code and latency instead of meetings and status updates. Cheaper per unit, certainly. Same shape.

Part 1 landed on two things as the real bottleneck: the cost of coordination, and knowledge trapped inside individuals where nothing else could reach it. Neither is an accident of any particular company. They are what a structure built out of fixed functions produces. Functions require hand-offs, and hand-offs are coordination. Functions scope knowledge to a role, and scoped knowledge is fragmented knowledge.

Why we organized by function in the first place

Which raises the question worth asking before you copy it: why did we ever organize this way? The answer isn’t tradition. It’s economics — human economics specifically.

A person takes years to become genuinely good at a domain. Learning is slow, it happens at one speed, and it doesn’t transfer cheaply. Moving someone between domains is expensive: they pay a ramp, they carry a reload cost every time they switch, and for months they aren’t much use. So you park a person in a domain and let that investment amortize over a career. Function is a rational answer to how humans learn. Given the constraints, it was the right design.

Now check those constraints against an agent.

An agent doesn’t spend three years becoming competent at billing; it reads what’s there. Moving it from billing to onboarding costs approximately nothing — no ramp, no reload, no career to manage. Its learning is fast and its context switching is close to free. The two costs that made specialization worth paying for aren’t there.

Agents do have a real limit, and it’s worth being precise: the context window. But notice it’s a different kind of limit. It bounds how much can be held at once for a single task. It doesn’t bound what can be learned over a lifetime, because there is no lifetime — every session starts fresh. You solve a context-window limit with retrieval and good information architecture. You do not solve it by permanently assigning an agent to a department.

So when you scope an agent by function, you’re importing a constraint it doesn’t have in order to solve a problem it doesn’t have. An agent org chart doesn’t fragment context in some abstract way — it manufactures, in software, the exact two things Part 1 named as the bottleneck. Every boundary is a hand-off. Every scoped memory is knowledge trapped where nothing else can reach it.

You’d be rebuilding the bottleneck at machine speed, and paying to maintain it.

The pod, not the department

There’s a better image, and it comes from how modern infrastructure already schedules work.

In a Kubernetes cluster the unit isn’t the container. It’s the pod — a small group of containers brought up together because they need to work on the same thing. They’re deliberately heterogeneous: they do different jobs and complement each other. They share context by design, because that’s the point of being in the same pod rather than scattered across the cluster. And the pod is temporary. It exists for the workload, then goes away, and the next workload gets a pod composed differently.

Nothing about that is organized by function. There is no marketing node.

That’s the shape worth borrowing: start with the problem, assemble the capability around it, finish, disband, recombine differently next time. First the problem, then the team. I see it in the teams I work with well before anyone writes it down as policy — the project that got done by one person and a few agents instead of a staffed cross-functional group, the team that formed around a customer problem and quietly dissolved afterward without a reorganization.

And the interesting part is that humans and agents belong in the same pod. A question comes up; the people and the agents it needs come together around it, working from the whole context rather than a slice scoped to their function. They solve it. Then the composition changes, because the next question needs something different. One pool of capability containing both kinds — not a human org over here and an agent roster over there.

What the person in the pod is actually for

Which raises the obvious objection: an agent absorbs the whole context in seconds and a person cannot, so “the pod works from the whole context” is true for one half of it and impossible for the other.

Part of that is a misreading — the pod holds the context, not each member. In a cluster, containers share a volume; that doesn’t mean each one reads every file. Nobody expects the person to out-read the agent.

But the better answer is that reading was never the job. Two things are, and both scale.

Authorization. When an agent acts on something real — signs into an account, touches a system, spends money — somebody has to be accountable for that action. This is where the tooling has actually landed: credentials get delivered just-in-time, the agent never sees the secret, and a person approves the moment of access. Notice what that is and isn’t. The human isn’t checking the agent’s work. They’re answering a narrower question — may this act happen here — and that question takes seconds, not a review cycle. It’s the highest-value human-in-the-loop moment precisely because it’s the one that can’t be delegated: accountability doesn’t transfer to something that can’t be held responsible.

Outside context, and the standard. The agent’s world is everything written down. The human’s world is the part that isn’t — what the client said and then didn’t say, the hesitation in the room, the thing that only surfaces on the third call. The person brings that in; the agent turns it into something the organization keeps.

And then the piece that decides whether any of this scales: the human defines and improves the criteria by which work gets judged — not the judgment of each output. What counts as done. What we’d never ship. Which failure matters. Set that well and the evaluation runs without you, and gets sharper each time you correct it. Try to personally assess every result and you’ve made yourself the review gate — the exact bottleneck Part 1 was about, wearing a new costume.

That’s the line I’d hold. The human is not in the pod to out-work the agents, and not to approve their homework. They’re there to be accountable for what the pod is allowed to do, to supply the context no system has, and to keep sharpening the definition of good.

Two honest limits. This holds where failure is felt; where it’s silent or delayed — security, compliance — “the client seems happy” is not evidence, and you need real expertise in the loop. And writing good criteria is its own competence, not the same skill as reviewing work, and not what most organizations have spent the last decade promoting people for.

When the pod is the unit

That’s the shift worth naming, and it’s larger than anything about agents. The unit of organization stops being the team and becomes the pod.

For most of the last century the unit was the standing team — a group defined by function that persisted whether or not there was a problem in front of it. Almost everything we call coordination exists to serve that arrangement: status meetings, alignment sessions, reporting lines, the quarterly ritual of demonstrating the group should continue to exist. A permanent structure needs constant maintenance just to stay pointed at something.

Make the pod the unit and most of that machinery has nothing left to hold onto. A pod exists because a problem exists. It has no headcount to justify, no lane to defend, no reason to persist past the thing it was assembled for. Which leaves exactly one thing worth coordinating on: whether the problem got solved.

When the unit is temporary, results are the only durable measure of it. You don’t have to install that, or launch a program to encourage it. It falls out of the structure.

So the question was never how many agents you need, or which function each one belongs to. It’s what the problem is, who — human or otherwise — should be in the room for it, and whether the thing actually got solved.

I’ve heard a version of that from almost every org leader and business owner who has made good progress on AI adoption. Earlier this year, the energy ran in the other direction — a big wave of chasing AI-tool adoption: usage dashboards, internal leaderboards, what percent of the team was on Claude or Cursor by Friday. Lately some of the more advanced companies have started to pull back. Uber is the clearest public example: after driving AI usage to nearly every engineer, it capped per-engineer spending when it couldn’t connect all that usage to the business actually moving faster. The pullback isn’t because adoption was a mistake — it’s that they’ve already claimed that fruit, and found it was the straightforward part of the puzzle. Driving adoption isn’t nothing; it’s just one piece, and the easier one. The harder parts are still sitting there untouched, which is why the usage charts can climb while the business moves at the same speed it always did.

I’ve spent time on both sides of this. I know how organizations and their processes were built in the pre-AI era, and I’m now building — and helping others build — toward AI-native. From that seat, the gap is hard to miss. So the pullback doesn’t surprise me.

What companies actually compete on now

For the last twenty years, “going digital” was mostly about getting things online — information online, process online, data online. That race is largely over; most companies finished it. The next decade looks different. What increasingly separates companies isn’t who has the data. It’s how fast they can learn, decide, and execute.

That’s the shift most adoption plans underestimate. AI isn’t only changing how individual employees work. It’s changing how the whole organization runs.

The biggest cost has flipped

Think about what used to be scarce and expensive: building things, R&D, getting information. Those are exactly what AI is making cheap — content, analysis, software, research, all dropping in cost by the month. What’s scarce now sits one level up: management attention, how well the organization coordinates, the quality of its decisions, the speed of its execution.

Building keeps getting cheaper. The hard part is the coordination around it — and that’s what the next era of competition turns on.

This is why pouring AI into the existing org doesn’t move the top line. You speed up the one part that was already getting cheaper — the building — while everything around it stays exactly as slow as before. The new speed runs straight into the old coordination and piles up there.

What “AI-native” actually means

Strip it down, and an AI-native company is one where the absolute core of how the organization functions — its operations, execution, scaling, and cost structure — is built around AI, not bolted onto a structure designed for a world without it.

Most companies aren’t there, and that’s fine — it’s a spectrum, not a switch. At the far left are companies barely using AI at all; that’s the starting line, and plenty are still standing on it. Past that, three markers matter:

AI-augmented — tools on top. Everyone’s using the tools — Claude, Codex, Cursor, Copilot. Getting even this far is real work; rolling tools out across a whole company is genuinely hard, because change is hard. But the processes are still designed around how people work, and the knowledge is scattered — so the tools can’t yet show what they’re worth. This is the augmented end: AI bolted onto an organization built for a pre-AI world.

AI-redesigned — rebuilt around AI. Process and organization rebuilt around what AI can actually do, and visibly faster for it — roughly where most of the AI labs themselves operate today. Worth being precise here: even at this stage, people are still at the center of execution — steering the work, judging it, owning the calls. The speed comes from the redesign, not from removing the humans.

AI-native — built on AI. The far end — what some are calling the one-person company. The core of how the organization runs is built entirely around AI: it carries most of the decisions and execution, while people focus on bringing in outside signal and steering the whole system.

The line that matters runs between the first point and the rest. The augmented end treats AI as a tool layered on top of the existing process. The other end rebuilds the organization around it. That is the key difference between AI adoption and AI transformation.

And notice how little of that line is about token consumption — about how much AI you use. It’s about who owns what, where decisions actually get made, and where the company’s knowledge lives.

Why most companies are stuck at the augmented end

And here’s the answer to the puzzle I opened with. The reason adoption isn’t showing up as speed is that most companies are parked at the augmented end — tools bought, everyone using them, and the potential still bottled up.

It isn’t a technology problem. MIT’s 2025 study of enterprise AI found that 95% of generative-AI pilots delivered no real return — and concluded the gap wasn’t model quality but organization.

The list of blockers is long — the usage charts are just the tip. Below the iceberg waterline sit incentives that reward usage over outcomes, a culture where everything waits on approval, context scattered across a dozen tools, and more. Two of them are load-bearing:

The process was built for pipeline. Reviews, approvals, alignment meetings — all designed around how humans coordinate, not around what AI can do. Push AI into that, and you get small patches, not a step change.

The knowledge is trapped in individuals. The experience and judgment that actually matter live in meetings, chat threads, and people’s heads. AI can’t reach any of it, so its output stays generic.

The gap, in other words, isn’t the tools. It’s the process and the knowledge.

The way across

Moving off the augmented end starts in one place: getting the organization’s knowledge out of scattered heads and meetings and into something durable — a corporate second brain that captures the company’s decisions, experience, and judgment, and feeds them continuously back to the AI. That’s the foundation the rest stands on. When the organization can actually learn — not just type faster — the speed finally reaches the top.

How to build that second brain — what to capture, how to structure it, how a single decision becomes something reusable — is what I’ll be writing about across the rest of this series.

Different versions of the same question keep coming up. Am I doing the right thing? Should I be more worried than I am? Or less? The person with the stable team is asking it. The person scrambling after a reorg is asking it. The person who just walked into a smaller team on purpose is asking it.

What’s new isn’t the shape of the question. These are classic transition states that mid-to-senior tech leaders have moved through for as long as I’ve been around the industry. What’s new is the intensity. AI is disrupting paths people built careers around, and introducing new possibilities nobody has a playbook for yet.

That’s why the old categories don’t sort people the way they used to. I see comfortable people quietly eroding and struggling people who are visibly growing. Nobody is fully comfortable right now — every state has its own pressure underneath, and most of the people I talk to assume theirs is the worst kind, because they can only see their own.

I’m putting this map down in case it’s useful. Not as a prescription. As a way to see where you are, recognize you’re not alone in being there, and find the work that’s actually yours from that state.

Four states

Here’s the map I’ve been carrying into these conversations lately. Four states I keep seeing people in — I’ll call them Stable, Drained, Misaligned, and Stretched. Each has a real shape, and each one has its own form of anxiety underneath. The work to do is different from each one.

Stable — and quietly worried about it.

From the outside, you have it good. Supportive leader, team that runs itself, no fires this quarter. From the inside, you’re not at ease. The comfort itself is what’s bothering you — a low background hum that asks: if the world is changing this fast, what is it costing me to be this stable?

You’re not burned out. You’re not behind. But you can feel something shifting in the field around you, and you can’t tell yet whether your steadiness is a foundation or a hiding place. The anxiety is ambient — future-tense, abstract, hard to point at — and it’s exactly the reason you keep reading articles like this one.

Drained — by the wrong game, and can’t leave.

You’re tired, but the tiredness isn’t from being too busy. It’s from spending energy on work that doesn’t return any. You don’t believe in what you’re shipping. You can’t tell if your leader does either. And there are real reasons you can’t walk — a visa, a mortgage, a family situation, an equity cliff still six months out.

Burnout, in this state, doesn’t buy anything. It doesn’t buy a promotion. It doesn’t buy growth. It doesn’t even buy permission to rest. It’s the tax you pay for staying without choosing to stay.

Misaligned — skills intact, the world has moved.

This is the state I see most often these days, and it’s the most painful to watch — because you’re not failing. You’re still good. You may still be one of the best players in the room. The energy is real; the path is the problem.

The skills that made you indispensable in 2023 don’t have an obvious place to land in 2026. Your mastery is intact. The game has moved.

This one gets misread as a personal problem more than any other. It isn’t. You’re not bad at adapting. You’re carrying expertise the new game hasn’t decided what to do with yet.

Stretched — into something new and a little overwhelming.

You made a deliberate move toward the disruption rather than away from it — a lateral, a smaller team, a new domain, a side practice that’s started taking your weekends. The shapes vary. What’s common is that you chose to be closer to where the world is changing fastest, even though you didn’t have to.

There’s a specific anxiety in this state: always feeling behind despite being closer than most to the edge. There’s always a newer tool, a newer person doing something you haven’t tried. Being on the cutting edge is a treadmill that’s always moving — the edge keeps moving with you.

Finding yourself in the map

These aren’t fixed positions. With some internal work, people move between them. The move isn’t usually change states — it’s see the one you’re in clearly enough to find the work specific to it.

Not everyone has the same room to maneuver. But noticing changes something even when you can’t move yet.

The cheatsheet below is the diagnostic — find the row that sounds most like you, and sit with the questions in that row. The point isn’t to immediately move. It’s to ask better questions of yourself from where you actually are.

The honest read

Nobody this season is in a comfortable career. The people who look comfortable carry an anxiety they can’t quite name. The people who look like they’re losing sometimes have the clearest view of what’s actually happening. The states look very different from the outside; from the inside, everyone is sitting with some version of the same question.

These four aren’t exhaustive. I’ve been in conversations with people in shapes I haven’t put words to yet — borrowing other people’s games, sitting in two of these states at once, comfortable in ways that haven’t started to ache yet. Use the map to start seeing the shape of your own state, whether or not it fits one I’ve named.

Across all four states, the people who feel most alive can usually tell me three things about where they are: what they see from there, why they’re there, and what would tell them it was time to be somewhere else. Ask yourself those.

The place where you can’t quite answer yet is usually where the real work starts.

If one of these four — Stable, Drained, Misaligned, Stretched — felt like a description of you while reading this, I’d be curious which, and what you’d want to be doing about it. That’s usually where the real conversation starts.

— Amy

They do not always call it that. They tell me they are getting burned out. They tell me the work doesn’t satisfy them the way it used to. They tell me it is not easy to build relationship at work nowadays.

From the outside, it sounds like they are each working through something different. As we keep talking, the thread is always the same.

The thread is loneliness.

A loneliness that doesn’t look like loneliness

Look at your calendar this week.

You probably had a standup. A 1:1. A sync. A review. Another 1:1. A coffee that was actually a status update. By Friday at five you had been in back-to-back rooms with humans for forty hours. And there was a particular kind of hollow you felt when you closed your laptop that you didn’t have a word for.

It was not exhaustion, though you were tired. It was not failure, because you shipped what you said you would ship. It was something else. Something that got quieter the longer you ignored it.

What I started to see is that most of us cannot name this for what it is — because how could you be lonely with that many people in your week? But the thing your body kept noticing, the thing that left you flat by Friday, was a loneliness that had nowhere to go. You had been with people. You had not been in anything with them.

What I started seeing

I came across a framing recently that finally gave me language for this. It described the relationships in our days in three modes:

• A unary relationship is us and something that doesn’t push back. AI is the most powerful example we have ever had, but a scrolling feed is unary too. We act, it answers. No other party in the room.

• A binary relationship is us and another person, but the interaction is transactional or shallow. We trade information and move on.

• A ternary relationship is us, another person, and a shared third thing we both actually care about — a craft, a problem, a stake.

The diagnostic clicked the moment I sat with it. Most of the people I had been listening to — the clients, the friends, myself — were not short on people. We were short on ternary.

You can have ten meetings in a day, three hours with AI, and zero rooms with the third thing inside. That is the shape of a binary day. From the outside, you look fully booked. From the inside, you are not being seen.

Why this got so hard

Ternary used to form on its own.

Real working relationships used to compound by accident — long projects with the same team, shared offices, shared frustrations, shared after-work drinks. The product you were building was the third thing. You disagreed with each other in ways that cost both of you something. You came back the next Monday anyway.

That scaffolding has been quietly disassembled. Sprint-sized teams, faster reorgs, remote work compressing the casual ternary moments into nothing. People did not stop wanting workplace closeness. The structure that produced it stopped existing by default.

Layered on top of that, a sharper pressure showed up. AI-era stack-ranking and rolling layoffs have changed what the room feels like. When the question in the air is who is replaceable, you stop leaning into the long, generous conversations that ternary requires. You protect your context instead of sharing it. You keep your guard up. The third thing needs both people to lower theirs. That has gotten genuinely scarce.

Then unary arrived. AI is the most powerful unary relationship I have ever had. It is fast, frictionless, available at 11 pm, and it never disagrees with me in a way that costs either of us anything. When I am tired on a Thursday at six and there is a community event across town, the trade-off looks obvious. I will catch the next one.

I caught the next one less and less. Many of us did.

You cannot accidentally find yourself in ternary anymore. You have to go looking for it.

How I’m looking for ternary now

This is why I have been actively setting time for the relationships that used to form without effort.

I schedule lunch and coffee with specific friends — the ones I am inside the same chapter with, the ones whose problems rhyme with mine. I invest real time in the communities I am part of — showing up regularly, giving back where I can. My coach cohort has given me coach buddies and steady support, which has meant I am not walking through any of this on my own. I have stopped asking whether community time is “worth it” in any productivity sense, because that is the wrong question. The right question is: is this the kind of room where the third thing can show up?

What I get in those rooms is not information. Information is everywhere. What I get is the experience of being seen by someone who is inside the same thing I am. The kind of seen you cannot purchase, cannot accelerate, and cannot replace with a great prompt.

That kind of presence has become a luxury. Not the spa kind. The kind that means rare, slow, and worth structuring your life around.

What this is showing me about coaching

One of the real missions of coaching, I have come to believe, is helping with this loneliness — by exploring something hard together with someone, instead of letting them face it alone.

The relationship inside coaching is itself ternary. There is me. There is the person I am working with. And there is the real third thing we are both inside — a transition, a decision, a piece of their own becoming. The conversation has stakes for both of us. That is what makes it generative, not transactional.

A lot of what coaching gives people is courage — the courage to make the choice, to have the conversation, to take the step that felt impossible to take solo. But what I have come to believe is even more meaningful is what comes after the courage: helping them notice and build the supporting systems and high-quality relationships in the rest of their life, so they are not relying on courage alone. Courage gets you started. The room around you is what carries you through.

What this has done for me

I am not saying ternary is the most important thing you can build. There are real fights for your attention right now — work that matters, people who depend on you, hours that are not yours to give away.

But I want to tell you what this has done for me. Investing in these rooms — the communities I show up to, the coach cohort, the long lunches with friends sitting in the same chapter as me — has changed my mental health. It has changed the shape of my weeks. It has made me feel more like a person and less like a system.

If your week has been full and you have been quietly hollow on Fridays, you are not broken. You are short on ternary.

And you do not have to fix that alone.

About Amy Wu

I coach leaders and builders navigating identity rewrites in the AI era. If something here landed, I would love to talk.

→ Schedule a conversation

The Whiteboard

In my first year as a manager, I got thrown into the deep end — and I was bad enough at it to have plenty of sleepless nights.

I’d walk into those rooms with my case prepared. The partner team that disagreed about priorities. The peer who wanted my team to absorb their work. The colleague pushing back on a roadmap I’d already committed to. I’d sit down ready. Somewhere along the way the room would tense up — I couldn’t tell you exactly when — and by the time we wrapped we’d be no closer to anything than when we’d started.

Eventually I went to the VP I trusted for advice — the one who’d seen my management potential and convinced me to step into the role. I sat across from him in his office and told him I couldn’t figure out what I was doing wrong. The case was sound, I said. The data was clear. Why was every conversation a fight?

He shared a metaphor that stayed with me. I still borrow from it every time I walk into a hard conversation:

“We both walk into the same conference room. There’s a whiteboard. Each of us puts a point on it. Then we sit together and look at the points as if they don’t come from us. We filter the noise and come to a conclusion. It’s that easy.”

I remember thinking: that doesn’t sound easy at all.

But something settled in me as he said it. The metaphor was an aha — and it gave me a frame I’ve come back to ever since. The whiteboard wasn’t a meeting tool. It was a way of being in the room. And the goal of these conversations wasn’t what I’d been chasing.

I’d been treating every conversation like persuasion. I needed peers to agree with me. When they pushed back, I pushed harder.

Influence without authority isn’t persuasion. It’s partnership.

The goal is to be heard, to hear them, and to look at the same picture together so you can both see what’s actually true. When that happens, the answer is usually obvious — and neither of you had to lose anything to arrive at it.

The frame gave me the picture. What I didn’t have yet were the moves. That part took years — paired with what I learned from negotiation books along the way. The one I keep coming back to is Chris Voss’s Never Split the Difference. Slowly, both became muscle memory.

Voss names the signal you’re looking for. Not “you’re right” — that’s what people say when they want you to stop talking. The one to wait for is “that’s right.” That one means they feel fully understood. They recognize their own situation in what you’ve just said. You didn’t convince them. You both finally saw the same picture.

Why My Point Wouldn’t Leave My Hands

Even once I had the frame, two things still got in the way. The harder one — the one that took me the longest — was learning to let my point leave my hands.

I couldn’t do that at first. I’d done the analysis. I knew the answer. When I put my point on the table, it didn’t feel like one data point among others on a whiteboard. It felt like a position I was defending. The moment someone pushed back, my chest would tighten and I’d start defending it harder.

I see this now with the technical leaders I coach. The more capable you are, the worse this gets. You see the answer faster than the room does. You’re not trying to dominate — you’re trying to help. But the other person feels called out, or steamrolled, or audited. They get defensive. The room tenses. The whiteboard never gets used.

The move I learned, slowly, is labeling — naming what the other person seems to be feeling, before defending anything. “It sounds like you’re worried this timeline puts your team behind.” “It seems like the framing of this rewrite undermines a commitment you’ve already made.”

What I started to see is that most people aren’t actually arguing about the question on the table. They’re carrying something else — a constraint, a worry, a commitment — that they haven’t said out loud. Labeling invites them to say it.

The aha for me was uncomfortable: my certainty was the obstacle, not the asset.

The Question I Kept Forgetting to Ask

The second thing was their point. I needed it on the whiteboard fully visible — and I almost never had it.

I’d come out of meetings thinking I’d heard them. They’d told me what they needed. We disagreed. Done.

But sometimes months later, sometimes years later, I’d learn the actual concern had never been on the table. A budget pressure they didn’t want to admit. A commitment they’d made to someone above them that they couldn’t undo. A career anxiety I hadn’t sensed. The surface concern they raised was real, but it wasn’t the load-bearing one. The whiteboard had been half empty the whole time.

The move is calibrated questions — open-ended questions starting with what or how that the other person can’t answer with a yes or no. “What about this matters most to you?” “How does this fit your bigger picture?” “What would have to be true for this to feel like a fair tradeoff?”

Years before, sitting in on sales calls as an engineer in a cross-functional rotation, I’d watched the best salespeople do exactly this. They weren’t selling. They spent something like 80% of the session asking the client about their pain. They were filling the whiteboard with the client’s points before adding their own. The clients didn’t see them as sellers. They saw them as partners.

The Sequence I Use Now

This is the playbook I use now. Some people call this active listening — and it overlaps with that — but the frame is bigger. You’re not listening to win the conversation. You’re listening because the answer hasn’t been assembled yet.

Step by step:

• Start with partial intention. Both of you walk in with an incomplete picture. The conversation is the work of assembling one.

• Listen to understand, not to respond. Ask until you find the unmet need they didn’t want to say out loud.

• Reframe to confirm you got it. Say back what you heard — the feeling and the unstated challenge. Watch for “that’s right.”

• Lay out your challenge. Once they feel heard, put your point on the board. Invite them to brainstorm with you, not pitch them on it.

• Let their opinion shift on its own. They have the full picture now. They may move without you trying to move them.

• Walk the pros and cons together. Both points on the board, no ownership over either. Filter the noise.

• The answer usually surfaces. Neither of you had to lose anything to get there.

Looking back, this is the most fundamental skill I know — the one underneath every conversation where the stakes are high. The hard ones at your dinner table that could have ended in distance, and didn’t. The hard ones in your conference room that could have ended in defensiveness, and didn’t. Build this once and it works in every room with another person — for the rest of your life.

What to Try This Week

Pick one conversation this week where you already know you’re “right” — where you walked in with a clear point and walked out wishing it had gone differently.

Then ask yourself:

Did I bring my point as conviction — or as a point on a whiteboard? Did I ask enough about theirs to actually see it? And when we wrapped, did I hear “you’re right” — or did I hear “that’s right”?

If those questions are sticky, that’s the kind of thing a 1:1 conversation can help you walk through.

It’s that easy. And it’s also that hard.

When I shared on how AI-native orgs look like in part 3 — the way the work is reshaping, why this moment isn’t really about switching chairs between IC and manager. A friend of mine cracked back:

“Many of you want to become Director or VP. Well — now you’ve got hundreds of AI agents to manage.”

A joke — with enough truth in it to reveal the reality: everyone is now expected to show up at their N+ level. At the AI-native edge, the top companies have moved the hiring bar — job descriptions now call for "a proven track record of stepping into 0 to 1 environments." Pedigree weighs less than evidence now.

Most career advice for this moment still tells you to ship more, run sharper alignment meetings, manage up harder, introduce better processes. AI is folding all of them. Investing more there is investing in the past.

So — how do you prepare?

What follows is a way to think about it. A framework for where to put your finite time and attention. The rubric goes stale fast because your management is playing the same catch-up you are.

Three altitudes — how you see the work, how you see your time, how you see yourself. None of these are in the rubric. Some are unwritten rules— what calibration rooms quietly value without naming. The AI era is just weighting them more heavily, and pulling them down to apply at every level.

How you see the work

The first shift is from seeing yourself as an executor of pieces to seeing yourself as a problem framer.

Most of us were trained to wait for the problem to arrive defined, then execute. Your leader figures out what to do; you make it happen. That made sense when coordination was expensive and decisions were scarce. AI made coordination cheap. The easy decisions got cheap too. What’s harder now isn’t shipping the work — it’s framing the problem in the first place. Mapping where the bottleneck actually lives. Deciding what to build that the system can absorb.

When I was a manager venting to a mentor about a product I was frustrated with — the team was capable, but the strategy upstream felt off — her answer wasn’t to influence the product team. It was to fix the product myself. She looked at me and said: stop seeing this as your product team’s problem to solve. See it as if you were the CEO of this product. True ownership isn’t doing the execution. It’s seeing potential, finding creative ways to push forward, and absorbing the failed bets as sharpening for the next. That single shift was what built the cross-functional muscle I drew on for director and beyond.

A staff engineer I worked with at Instacart was already operating this way. We’d been hypothesizing AI-enabled marketing infrastructure to power our growth experiments — obvious now; less obvious a year or two ago. The pushback hadn’t been about vision; people believed in the bet. They worried about the distraction cost — pulling attention from work that already had committed business impact. So instead of pushing for headcount, he spent his weekends hacking out a prototype and testing it with the internal marketing team on limited channels. A few months later, leadership realized we needed this infra — and was pleasantly surprised to find it already in place. The infra he’d built accelerated the bet, and product set the priority.

When you see the work this way, end-to-end ownership stops being a stretch — it’s the natural shape of how you operate. So does pruning processes that no longer serve, and staying close to the customer to hear what the system isn’t telling you. They’re consequences of the framing.

At junior level, this kind of move sometimes gets feedback like stepped on other people’s toes. At senior levels it’s an explicit expectation. How you see the work decides how you show up.

How you see your time

Are you still going by agenda?

Most people in tech are. They let other people’s calendars decide where their hours go. Coordination meetings, alignment syncs, one-off requests, status updates. When you tally up where the day went, most of it went there.

The discipline is treating your time as precious. Sort what’s coordination, what’s one-off, what’s actually building the system underneath your work. The system underneath is what compounds. The other two don’t.

A senior staff leader I worked with operated at the edge of this. He was quiet in bullshit meetings — didn’t perform engagement, didn’t fill silence. He wasn’t very responsive on Slack either. From the outside, easy to misread as disengaged. From the inside, he was spending his real time identifying levers and opportunities. He’d invested in relationship-building with his manager peers and kept his resource asks small. When the moment came, the tiger team formed fast. Significant business impact landed. After it did, he gave credit to the leads who’d sponsored him on resourcing. They moved on to form bigger partnerships.

His promotion discussion was controversial. People who valued coordination and alignment didn’t like him. People who admired entrepreneurship loved him — they could see the enormous value he was going to generate for the business. We ended up making an exception — we wanted to encourage more engineers like him.

The conventional path to promotion rewards broad visibility — being in every meeting, replying fast on Slack, performing engagement. He didn’t reject visibility. He picked where to invest it: targeted trust with the manager peers who’d matter for resourcing, plus the system thinking that produced the impact. The hours he saved from broad engagement bought the time for both. They were the same skill.

Naval Ravikant has reshaped how I think about my time. “Set an aspirational hourly rate” — put a floor on your hours, refuse what doesn’t clear it. “Work like a lion, not a cow.” Most corporate life isn’t even cow-like; it’s a hamster wheel — constantly running, never resting. The hours below the rate are what AI is making cheaper. The hours like a lion are where everything compounds.

How you see yourself

Last comes how you see yourself — who you’re choosing to become in the work.

The conventional path tells you who to be — the role, the next step, what success looks like. It works until you notice you’ve been optimizing for someone else’s definition of you for ten years.

The question AI is making harder to avoid: what would I want to be building inside myself, if no one was measuring? The bets you make and risks you absorb don’t just shape your career. They shape who you become while doing the work.

A pattern keeps surfacing in the women-in-tech communities I was part of. When senior leads look back, the regret that comes up most often isn’t I should have networked harder or I should have been louder. It’s I didn’t take enough risk. Deeper: I let the rubric write my story when I could have been writing it myself.

A few years back, a tech lead I worked with had a promotion path lined up on her team. She walked away to move into machine learning — out of curiosity, not fear of falling behind. The move took courage and potential reset. The transition took real effort. What she didn’t predict: it made her more competent on the job market than the safer-track version of her would have been. The bet on her curiosity paid the most.

Don’t limit your curiosity by your role, title, level, function, or company. AI-native work runs on problem-framing, and curiosity is what helps you see problems worth framing. Walk outside your company. Network. Exchange ideas with people doing different work. You’ll notice you’re not alone. The rubric inside one company looks smaller when you can see the work happening across many.

I know this pattern well. I played the safe card for years myself, afraid of failing — optimized for higher pay and a better title. But I started to ask myself this question in recent years: who am I without the title? This year, AI is forcing many people into the same question.

All we can bring with us is the experience. The titles change shape. The companies change. What stays is what you built inside yourself — and who you became while you were building it.

What folds, what overlaps, what’s fresh

Some tactical examples to guide your framework. The skills above sort into three buckets.

The ones AI is folding — shipping faster, running alignment meetings, managing up, introducing more processes — are what conventional R&D has rewarded most at junior levels. Investing more there is investing in the past.

The ones that overlap with what senior leaders have always done well — problem framing, end-to-end ownership, system thinking — have always compounded. The AI era is amplifying them and asking you for them earlier than the rubric measures.

The ones that are new skills to pickup — managing your AI agents like a team, killing meetings and processes that create bottlenecks, taking the venture bets the rubric can’t measure yet — are where the AI-native edge differentiates. Even senior leaders historically didn’t have to invest much here. The era is changing the bar.

Where to put your energy

The energy you’ve been spending — on whether you’ll get promoted next quarter, whether the layoffs will reach you, whether your manager is happy with you, whether you should work harder just to keep the job — that’s fear-driven. It’s what the rubric trains you to care about. It’s not what compounds.

The shift isn’t from fear to love overnight. The fears are real — the rubric still partly governs your runway. But when you notice the rubric is outdated for the work that’s actually coming, you can start to release some of the fear pinned to it. Try using that released energy to pick up something that gives you energy back.

You don’t have to take it all on at once. Build slowly.

The next time you’re choosing between the safe project and the unclear one, notice what you’re optimizing for. The rubric — or the work that’s already coming. One you can’t take with you. The other becomes who you are.

That’s the four-part series. Part 1 on system thinking. Part 2 on the harness. Part 3 on what the org looks like. And this one — what to invest in inside it.

About Amy Wu

I’m an executive and life coach who works with leaders navigating inflection points — including the one AI is creating right now. If you’re sorting through what to invest in for what comes next, I’d love to hear what you’re seeing.

→ Schedule a conversation

A version of this comes up in almost every coaching conversation I have. My answer is the same one I’ve given for years. Find out how your managers actually spend their time, and whether the title is still where influence sits in your company. That depends on stage, on culture, and now on how AI-native the org is.

AI isn’t just speeding up work. It’s reshaping what each role does. The better question is: what does the work look like inside an AI-native org?

In Part 1, I argued the AI era needs leaders who think in systems, not workflows. In Part 2, I described what that system looks like — two harnesses, one around the agents, one around the people. This piece is about what those harnesses actually do, day to day.

Two patterns of work

Inside the harness from Part 2, the work in an AI-native org sorts into two patterns. Both run at once, in different ratios depending on stage and bet.

Pattern one: iterative work on a real, running business. Existing product. Existing customers. Continuous experiments — pricing, onboarding, retention, feature shipping. The bulk of execution.

Pattern two: frontier work. New business line. Big pivot. A hypothesis nobody’s run yet. The bets that decide whether the company gets a second chapter.

A conventional R&D org has many function ladders, each many layers deep, connected by cross-functional review forums between layers. The AI-native version collapses all of it — the ladders merge, the middle layers go, the x-functional forums mostly go too.

In an AI-native org, influence and the decision-making room aren’t gated by a management title — they go to whoever’s running the harness well, owning a problem end to end, or pushing into the frontier. Regardless of Title.

The two harnesses operate differently in each pattern. I’ll take them in turn.

Pattern 1: Iterative work — one person plus an agent team

The shift inside iterative work is simple to name and hard to internalize.

One person, paired with an agent team, drives a piece of work end-to-end. Not “designer hands off to engineer hands off to QA hands off to PM.” One person owns the whole loop — frame the experiment, ship it, measure it, decide what to do next.

Which means everyone, regardless of role, ends up spending more time deciding what to do than doing it. The doing is harnessing the agents. The deciding is what fills the day.

This is where the two harnesses earn their weight.

The agent system harness — mostly IC work

Inside iterative work, the way you use your agents is the load-bearing variable. The difference between an IC who gets ten times more output and an IC who gets the same output as before is almost never which model they’re using. It’s how they’re feeding the model context.

In an AI-native org, every IC ends up managing a small agent team — whether or not the title says so. The quality of what comes back depends on how much of the relevant context you’ve actually gathered before asking, how you’ve structured that context — what’s signal, what’s noise — and how you supervise the output. What you re-prompt. What you accept. What you correct.

What the IC is doing here is management. Delegation. Defining scope. Setting up the system. Reviewing output and giving feedback. Deciding what gets shipped, what gets revised, what gets thrown away. These were always management skills. They’re now essential for anyone working with agents — title or no title. Some of the most effective people I see in AI-native orgs have an IC title and a manager’s day.

This is craft, and it doesn’t transfer instantly from old habits. The ICs running this well are still in their first year of it — learning to manage agents the way they once learned to manage their own attention. The orgs whose ICs are doing this are starting to run circles around orgs whose ICs are still treating LLMs as autocomplete.

The people system harness — mostly leader work

The leader’s harness inside iterative work is the layer above. Two design choices show up over and over.

The first is bottleneck management. Inside an existing org, you already have processes — experiment review, design review, layers of approval. Those processes were built for human throughput. When agents start producing five times more drafts, those review layers tend to become the bottleneck almost immediately. The work doesn’t get faster. It piles up at the choke point. Your job as a leader is not to set a speed goal and walk away. It’s to watch where the system is starting to clog and redesign that layer before it locks.

The second is end-to-end ownership. Let one person own a problem all the way through, regardless of role. If it’s too big, break it into smaller problems — but don’t break it across two people. Two people on the same problem reintroduces the coordination tax that the agent harness was supposed to release.

These are leader moves. They don’t happen on their own.

Pattern 2: Frontier work — go to the edge

Frontier work doesn’t run on the same harness. It can’t. The whole point is that there isn’t a known pattern yet.

Frontier work has a different shape. People go to the frontier — talk to potential customers, talk to people doing the research, read what’s being published, pull on threads. Frame the problem one way, watch it not work, reframe it. Bring what they’ve learned back to the team. Decide together what to try, what to drop, what to investigate further.

It also doesn’t specialize by role. At the frontier, the questions are too unstructured for “I’ll do design and you do engineering.” Whoever’s nearest the question goes. Whoever has the relevant signal pieces it together.

I’ve been living this myself in the last few months. Outside my coaching practice, I’ve been working inside a startup, where I’ve ended up picking up basic sales, marketing, product, design — none of which were my background — through agents that close the gap. I’m not great at any of it yet. But “great” isn’t the bar at the frontier. The bar is: can the team learn fast enough to know whether this is worth doing? The answer comes from people willing to own a question end to end, not from people defending their lane.

So in an AI-native org, frontier work pulls generalists. Iteration leans on the harness. Both happen at once — and for ICs, this means frontier work is more accessible, not less. The path doesn’t run through a management title anymore. It runs through being the person who’s already pushing toward the question.

What this asks of leaders

If you’re leading inside this kind of org, your job is not to set a speed goal and watch the dashboards. The dashboards will go up. They won’t tell you where the system is breaking.

Two things change.

Get your hands dirty inside the iterative pattern. Don’t manage the harness from above. Pick one experiment your team thinks is worth running and drive it end-to-end yourself. Frame the question, work the agents, sit with the output, push it through whatever review process exists. Within a week you’ll know exactly where the system grinds: which review layer is performative, where context is lost between steps, which handoff doesn’t need to exist. You can’t see this from above; you find it by doing one full lap.

And spend more time at the frontier. The people harness keeps the iterative pattern functioning, but it isn’t the deepest part of the leader’s job. As iteration gets more efficient, the bigger question becomes what new bets the company should be making — the new product line, the pivot, the experiment nobody’s run. That work doesn’t delegate cleanly, and it doesn’t get done if leaders are consumed inside the harness, managing the thing that already exists.

So when someone asks whether to switch into management, or back to IC, my answer is the same now: it’s not about the chair. The room moved, and the work changed shape underneath it. Prepare for the work the new shape is asking for — and which seat you’re in stops being the question.

About Amy Wu

I’m an executive and life coach who works with leaders navigating inflection points — including the one AI is creating right now. If you’re inside an org going through this shift and want a thought partner, I’d love to hear from you.

→ ways to contact me

Some of it is pressure from the top — executives want an AI efficiency story yesterday. Some of it is the quieter kind: the anxiety of watching everyone else move and not knowing if you’re behind. The reality is that nobody knows what success actually looks like yet. So they reach for a playbook.

Wrong instinct. Nothing here is stable enough to write a playbook for. Models change monthly. What worked in Q1 doesn’t hold in Q2. Every “best practices” doc is a historical artifact by the time it ships.

You don’t need a playbook. You need a harness.

What an agent harness actually is

If you’ve ever shipped an AI agent — a system that calls tools and takes actions, not just answers questions — you’ve probably heard the discipline growing up around it: harness engineering, the layer beyond prompt and context engineering. The raw model is powerful but unreliable. What makes it useful in production isn’t the model itself. It’s the layer wrapping it: prompts that set context, tools that extend capability, validators that catch bad output, feedback loops that sharpen it over time, guardrails for when something goes wrong.

That wrapping is the harness. It’s a living system — you tighten it when something breaks, loosen it when you’ve seen it hold, re-tune it when the underlying model changes. The model gets better on its own. The harness is what you build.

The craft you already have

Here’s what clicked for me:

A great manager has always been a harness. You just didn’t call it that.

Think about what a good manager actually does. They set context so the team understands the goal. They connect people to the tools and information they need. They catch problems before they ship. They give feedback that helps the team get sharper. They expand autonomy as trust builds — first supervised, then trusted, then autonomous. When something breaks, they tighten the harness and rebuild trust.

Prompts. Tools. Validation. Feedback loops. Evolving boundaries.

The craft of harnessing isn’t new. The thing you’re wrapping is.

The Four Modes of AI-Native Execution

This is how we harness our team and AI agents at my startup — and the pattern I keep recognizing in the teams I heard feeling it is working. For any piece of work, there are four ways the harness can be set — four modes of how tight the boundary is between human and AI. Most teams are already operating in some version of these. They just haven't looked at it as a system yet.

Human leads, AI assists. The work is novel. The judgment is ongoing. A person has to drive — deciding what questions matter, what good looks like, what’s signal versus noise. AI is a powerful partner during this exploration: it pulls data fast, surfaces patterns, generates options. But a human is in front. Example: pivoting — choosing what to build next; running a feasibility test with a prototype; org and role design.

AI drafts, human judges. The pattern is clearer but quality still needs human eyes. AI does the heavy lifting of a first pass — a draft, an analysis, an implementation. A human evaluates: is this actually good? Does it capture what matters? What’s missing? Example: a performance review draft the manager edits heavily; a comms plan for a product launch; a market analysis.

AI executes, human spot-checks. The pattern is proven. The quality bar is clear. AI runs the work; a human keeps an eye out for drift. Example: code for well-understood, patterned work; operational review preparation.

AI proposes, human approves. This bucket is narrower than it sounds. It’s not for open-ended judgment — that’s mode one. It’s for work where AI has already done the job, but the consequences of shipping it wrong mean a human needs to say “go.” Example: sending a mass customer email; large refunds or vendor payouts above a threshold; regulatory-adjacent submissions where sign-off is required.

Notice what these four aren’t: a classification system. A workflow isn’t “in” one of these modes forever. The mode is the current harness setting for that work right now.

Migration is the real work

Here’s the part most playbooks miss completely.

Take AI-assisted code. Eighteen months ago, most engineering teams had it squarely in AI drafts, human judges — AI generated the code, a human read every line carefully because you couldn’t trust it. Today, for a lot of teams, routine code has migrated toward AI executes, human spot-checks — the model is reliable enough, the review is lighter, and you’re mostly watching for drift. For novel architectural work, it might still be human leads, AI assists. Same technology. Same company. Different modes for different kinds of code — and the boundaries have moved in just eighteen months.

A harness isn’t a one-time design. It evolves — and not always deliberately. A team in AI drafts, human judges can drift into AI executes, human spot-checks without anyone deciding: review started feeling slow, nobody flagged the shift, and the mode on the whiteboard stopped matching the mode on the ground. Teams often think they’re operating in one mode when they’ve quietly moved to another.

This migration — the deliberate kind and the drifting kind — is where system leadership actually happens.

Loosening the harness — moving work toward more AI autonomy — should happen when:

• You’ve seen the pattern work across enough reps

• Your team has developed judgment about what “good” looks like here

• You have a way to catch it when something drifts

Tightening the harness — moving work back toward more human involvement — should happen when:

• The output quality dropped and nobody flagged it

• A model or context change broke something that was stable

• The stakes of the work changed

The real question isn’t whether your team is checking in on AI. It’s whether you can see the judgment your team is quietly adding. Pick one workflow. Ask two or three of the people running it what they override, correct, or rewrite before AI’s output ships. That’s your real harness — not the one on the whiteboard. That gap — between the harness on the whiteboard and the one actually running — is where things start to break. I’ll come back to that in Part 3.

Two harnesses, one craft

Here’s where most leaders stop — at harness #1, the one around AI.

But an AI-native org isn’t just an org that uses AI. It’s one where the shape of work has changed. AI handles a growing share of execution. People operate at higher judgment — strategy, vision, frontier exploration, the work that used to belong to the layer above them. That doesn’t happen on its own. The team needs a harness for it too.

This is harness #2: the one you build to pull people upward into the work you used to do. Same craft — context, tools, feedback, evolving boundaries — aimed at growth instead of execution. Share the reasoning behind decisions so people can make calls at that level. Expose them to the strategic conversations and customer signal that sharpen judgment. Give feedback on how they’re thinking, not just what they decided. Gradually expand the decisions they own.

So the true AI-native leader should be running both migrations at once. AI migrating toward more autonomy. People migrating toward more judgment, freed up by what AI is absorbing below them. Both the same craft. Both evolving.

I’ll say what I’ve been watching out loud. At the CEO and founder level, the recent decisions speak for themselves — aggressive cuts, leaner teams, all-in bets on AI productivity. The pressure is real: boards want an efficiency story, competitors are signaling hard. Harness #1 is getting all the urgency and budget. Harness #2 has quietly slipped off the roadmap. Hire less. Ship more. Prove the AI number is going up. Some of this is strategy. Some of it is pressure wearing strategy’s clothes. Either way, the work of developing the people who actually run the system isn’t happening. I hope there’s a correction coming — once leaders feel the cost.

This is where the real cost hides. Move only harness #1 and the org gets more productive — the team does the same work faster. But the judgment work that would actually move the needle is still sitting a layer above them, untouched. Talent is underused. You get a more efficient team, not a more capable one. A productive org, not an exceptional one. And that’s how you lose. The leaders pulling ahead are running both migrations — turning AI leverage into people leverage, not just into speed.

In Part 3, I’ll dig into why that cost stays hidden — the failure modes I keep seeing when the harness drifts or quietly breaks. You can build the right system and still not know it’s failing, because the metrics most organizations are watching won’t catch it.

Next: Part 3 — how the harness breaks, and what to measure instead.

About Amy Wu

I’m an executive and life coach who works with leaders navigating inflection points — including the one AI is creating right now. If you’re navigating something like what I’m describing and want a thought partner for it, I’d love to hear what you’re seeing.

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I hear some version of this constantly — from peers, from clients, from anyone who's ever tried to move people toward a vision they believe in. The words change, but the frustration is the same: I can see where we need to go, but the team just isn’t there with me.

I know that frustration well. Because I’ve been that leader too.

The Loneliest Part of Leading

A few years ago, I had a vision I believed in deeply. I pitched it clearly. Documented it. Repeated it in every conversation.

And still — polite nods, but no real questions. No one pushing back, but no one leaning in either.

I wasn’t frustrated at my team — they were smart, capable people. If the vision was right and the team was good — what was I getting wrong? Am I just bad at communicating?

The Mistake I Was Making

The aha moment came during a one-on-one with one of my team’s senior engineers. I was walking him through the vision — again — and halfway through, he said something I’ll never forget:

“I hear you, the vision is compelling. But my team is underwater right now, and I don’t know how we can prioritize this.”

He wasn’t pushing back. He wasn’t disengaged. He was telling me, as clearly as he could, that my story wasn’t landing — because it wasn’t his story.

So instead of pitching the vision again, I got curious. I asked him what was keeping him underwater. We started brainstorming — what had he been spending most of his time on? And what would he do to reduce that kind of work?

And then something remarkable happened. As he talked through what he would do to reduce that work, he started describing — almost word for word — the very solution embedded in the vision I’d been pitching. The thing that would eliminate the work keeping him underwater was the vision. He just needed to arrive at it from his own starting point.

We ended the conversation aligned — not because I convinced him, but because we found the overlap together. And we agreed on what I could do as a leader to remove the barriers standing between him and that future.

That’s when it clicked. The problem isn’t that you haven’t spent enough time educating your team about the vision. The problem is that you’ve been telling the story from your angle. When the vision doesn’t connect to their reality, it’s not that they can’t see it. It’s that they can’t see themselves in it.

And that became my playbook — stop painting the big picture and start showing each person where they fit inside it.

Telling the Story from Their Angle

I’ve seen brilliant, visionary leaders struggle — and sometimes fail — when they skip this step. When you don’t meet people where they are, even the most compelling vision stays on a slide deck.

So I changed my approach. When I started doing skip-level one-on-ones, I stopped running another vision presentation and all-hands. Instead, I sat down with people across my organization and asked a simple question: “What’s the hardest thing on your plate right now?”

Then I listened. Really listened. And just like that conversation with my engineer, I let their challenges lead us to the vision — not the other way around.

Something shifted. Not overnight, but noticeably. People started asking better questions in planning. They started bringing ideas that aligned with the direction — not because they were told to, but because they finally understood that shaping the roadmap wasn’t just the leader’s job. It was theirs too.

The Hidden Gift: Honest Feedback

When people start to see themselves in the vision, something else happens. They start telling you what’s missing.

Here’s what I didn’t expect — and what I now see play out with leader after leader. When you build that rapport, your team starts telling you the truth — about the gaps you might be getting signals about but have been so obsessed with the vision that you overlooked.

That feedback is gold. But not just because it feels good to be trusted. It’s because it forces you to make real strategic decisions. What am I willing to give up to accelerate this vision? What are the goals that truly matter versus the ones I’m holding onto out of habit? What people and process changes do I need to invest in to close the gap?

These are the questions that turn a vision into a roadmap your team can actually execute. Without honest feedback from the ground, you’re guessing. With it, you’re making strategic choices — and your team sees that you’re not just dreaming big, but doing the hard work to make it happen.

What to Try This Week

If you’ve ever pitched a direction and been met with silence — whether you’re leading a team, influencing cross-functionally, or advocating from any seat — try this:

Pick one person whose buy-in matters — someone you’ve already built rapport with. Ask them what their biggest challenge is right now. Don’t pitch the vision. Just listen.

Then, afterward, ask yourself: Can I connect their challenge to where we’re heading? Can I tell the story of our direction in a way that starts with their reality?

If you can, share that connection with them. Not as a presentation — as a conversation.

The vision doesn’t fail because people don’t understand it. It fails when they can’t find themselves inside it.

About Amy Wu

I’m an executive and life coach who helps tech leaders understand what’s really holding them back — and what to do next. If something in this post resonated with you — or if you’re navigating a challenge that could use a thought partner — I’d love to connect.

Leading well starts with knowing yourself. If you’re ready to invest in that work, let’s talk. → Book a strategy session

I’ve heard some version of this sentence — from people on my own teams, in organizations I’ve led, and in dozens of coaching conversations. And almost every time, the person sitting across from me has a strong performance rating. Sometimes even “exceeds expectations” — multiple years in a row.

They’ve done everything right. Hit the goals. Delivered the results. Gotten the praise. And yet, when calibration season comes around, they’re not on the short list for promotion.

In calibration rooms, your rating is only half the picture. The other half — and often the one with more leverage over your career trajectory — is your potential.

What Rating Measures — and What It Doesn’t

Most calibration systems evaluate both — rating and potential. Think of the 9-box grid: one axis for performance, one for potential. Both matter. But here’s what trips people up: they assume a high rating automatically signals high potential. It doesn’t.

Rating is backward-looking — what you delivered this year. Potential is a different question: Can this person operate at the next level? Not “have they earned it” — but “do we believe they can grow into it?”

When you’re viewed as high potential, people place bets on you before you’ve fully arrived. When you’re not on that list — even with excellent ratings — you’re winning on one axis and invisible on the other.

How Potential Gets Defined

Every company defines potential a little differently — frustrating, but having sat in these rooms more times than I can count and having coached people through the aftermath, I can tell you the signals that actually move the conversation are less formal than anyone wants to admit.

Growth speed. Not just whether you learn quickly, but how fast you adapt yourself. Your next level is likely a fundamentally different job — different priorities, different evaluation, that requires different skills. Your ability to reshape yourself fast enough to match what the org and the business actually need is critical.

Initiative-taking. This is the one that trips up a lot of high performers. You can get “exceeds” by doing exactly what was asked of you — better and faster than anyone expected. But that’s execution, not initiative. To make that concrete:

• Execution: You take the roadmap, deliver every milestone on time, and improve how the team executes along the way.

• Initiative: You step back and question whether the roadmap is solving the right problem, propose a different approach, and bring people along to shift the team’s direction.

If you’re heads down, laser focused on delivery instead of engaging at that level with your future peers and partners — you might be on the track toward multiple “exceeds” in a row. But not toward that list.

Whether the room sees you as a future peer. When your name comes up in calibration, the people at that table are asking — Do I see this person sitting next to me someday? That doesn’t come from your manager’s summary. It comes from them experiencing you operating at their altitude.

• Viewed as team: You deliver consistently within your scope and keep stakeholders informed.

• Viewed as peer: You bring a point of view on broader tradeoffs, challenge assumptions in the room, and frame problems in terms of business impact — not just your team’s output.

Both are valuable. But only one changes how the room talks about you.

And Even Then

I want to be honest about something. You can do all of this — and still not get promoted. Because at senior levels, there’s another factor: business need. There has to be a seat.

When you feel stuck, your job is to figure out which one is the blocker — you or the business need. People on both sides get told the same “you’re really close” message. But the answer changes everything about what you do next. If it’s you, work on the signals. If it’s the business, make sure you’re at the top of that list so when the seat opens, you’re already in the front row — or recognize it might be time to find an organization where your growth and the business growth are moving in the same direction.

How do you tell? Most people assume the next level is a fixed, compacted layer with limited room. That’s not quite right. The number of seats is dynamic — tied to business growth, org evolution, and whether the company is expanding into areas that create new leadership needs. The real question isn’t “is there room?” It’s “is your growth aligned with where the business is headed?”

About Me

I’m an executive and life coach helping tech leaders grow and rewrite their story. If this resonates with you, I’d love to connect.

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The leaders I’ve seen build the strongest teams aren’t the most brilliant.

What I’m Seeing

I’ve built high-performance engineering teams again and again, and as a director I had a front-row seat to how dozens of senior leaders operated under pressure.

The pattern that kept showing up: the best leaders weren’t the ones with the sharpest answers. They were the ones who asked the best questions. They’d walk into a room, set the context clearly, then get genuinely curious. Not curious as a performance — curious because they understood that the room collectively knew more than they did alone.

I worked with a senior leader as one of the most brilliant people I’ve known. But what made the exceptional wasn’t her intelligence — it was how intentional she was about creating safety on her team. She set the context, asked the right questions, then got out of the way. Because people felt safe to speak up and push back, ideas came from everywhere — bottom-up proposals that nobody at the top would have thought of. Some of those ideas generated outsized outcomes for the entire organization. Her intelligence wasn’t the ceiling. It was the foundation that gave everyone else room to build.

I’ve also seen the opposite. A team where the most senior person required their personal sign-off on every code review and design decision. Everything funneled through one brain. It didn’t just slow their own team down — it slowed down every team that depended on them. And here’s the part that made it truly damaging: over time, the team started modeling that behavior. They became gatekeepers themselves. Cross-team collaboration ground to a halt. One leader’s need for control had replicated itself through the entire org.

The question worth sitting with: are you doing this because your team needs you to, or because it makes you feel important? Because there’s a real difference between adding value and protecting your sense of influence. No one can be the best at everything — that’s obvious in theory. In practice, most leaders still act like they should be.

The leaders who get this right do something harder. They get honest about their own limitations — not as a performance of humility, but as a real reckoning. They figure out where they uniquely plug in and, just as importantly, where they don’t. That clarity is what lets them invest deeply where it counts and genuinely let go everywhere else.

Here’s what I’ve come to believe: a leader’s job isn’t to be the smartest person in the room. It’s to make the room smarter — and that starts with knowing yourself well enough to know where you belong in it. When a leader can do that, collective intelligence takes over. The team starts seeing around corners that no individual — no matter how brilliant — could see alone.

When a leader can’t, the opposite compounds. Their thinking becomes the ceiling — and worse, their style becomes the culture.

Why This Matters More Now

In the AI era, this dynamic isn’t just important — it’s existential.

AI is accelerating everything. Teams now have access to more data, more options, and more decisions to make than ever before. The bottleneck has shifted. It’s no longer about finding information. It’s about deciding what to do with it — quickly and well.

That means the leader who hoards decisions or needs to be the final voice on everything becomes a real liability. The speed of the environment will outrun any single person’s capacity to process it. And the complexity will outrun any single person’s expertise. No leader — however talented — can be the best at data strategy, product sense, people development, technical architecture, and AI adoption all at once. The honest question isn’t “what are my strengths?” It’s “where do I actually add the most value — and where am I getting in the way by pretending I should?”

A leader who’s done that honest inventory — who knows where they plug in and where they need to step back — creates a multiplier effect. The team’s collective intelligence compounds. They move faster and make better decisions because they’re not waiting for permission — they have the context to act, and the leader isn’t accidentally bottlenecking areas they don’t actually understand best.

This is the shift most people are missing. As AI lowers the cost of data gathering and learning, leadership advantage shifts toward judgment, trust, and coordinated execution. The ability to build a team whose combined intelligence grows beyond what any one person — including the leader — could produce alone. And that requires psychological safety. It requires empathy. It requires humility. It requires curiosity that’s genuine, not performative.

Curiosity, calm, humility, and empathy aren’t nice-to-haves. They are the operating conditions for fast, high-quality decisions. And to be clear — psychological safety doesn’t mean a lower bar. It means a higher one. When people feel safe, you can demand more from them — more honesty, more rigor, more ownership — because they’re not spending energy on self-protection.

Your Turn

And by “leader,” I don’t just mean people managers. If you’re setting a direction that requires other people to execute — whether you’re a founder, a tech lead, or an entrepreneur building something from scratch — this applies to you. Leadership is the dynamic, not the title.

So this week, notice one moment where you’re tempted to give the answer instead of asking the question. Pause. Ask yourself: am I doing this because the team needs me to, or because it makes me feel like I’m leading? See what the room gives you when you make space for it.

The AI era will reward teams that think together. Your job is to make that possible.

If any of this resonates with you, I'd love to connect. You can explore options to work with me through amywucoaching.com or subscribe to my newsletter and get frameworks, mindsets, and lessons.

But one conversation was different. A client of mine told me, with genuine energy, that this period has actually broadened her career — not narrowed it. She’d been intentionally challenging herself. Picking up new skills. Exploring adjacent roles she never would have considered two years ago. She wasn’t ignoring the uncertainty — she was using it. Where most people saw doors closing, she saw new ones opening.

That contrast stuck with me. Not because I think everyone should feel optimistic right now — that would be tone-deaf. Many people are exhausted. They’re managing real fear about their jobs, their finances, their futures. The thought of “reassessing your career” when you’re already running on fumes can feel like one more impossible ask.

I hear that. I respect it. And I’m not here to add to anyone’s plate. But I do want to share why I believe the people who check in with themselves regularly — even in small ways — are the ones who’ll come through this era stronger. Not because they have it all figured out. But because they’re paying attention.

The ground is moving faster than we think

We’re living through one of the most significant shifts in what work looks like since the internet went mainstream. New roles are being invented — ones that didn’t have names two years ago. Other roles are shrinking or being reshaped so fundamentally that the job title stays the same but the actual work is unrecognizable.

The old rhythm of reassessing your career every three or four years made sense when industries shifted slowly. But that cadence doesn’t match the world we’re in anymore. I’d argue we need to check in with ourselves at least once a year — not to panic, but to stay intentional.

AI changed more than the job market — it changed how fast you can learn

Here’s the part of the story that doesn’t get enough attention: the barrier to learning new things has dropped dramatically.

I’ve experienced this firsthand. Over the past few months, I’ve picked up skills across both work and life — marketing, product design, trading & investing etc. — that I would have struggled to develop on my own five years ago. Not because I suddenly got smarter, but because AI tools made the learning curve less steep. I could ask questions, get immediate feedback, iterate quickly, and build real understanding — not just study theory.

This matters for career reassessment because it changes the math. The cost of exploring a new direction used to be high: go back to school, spend months in a bootcamp, invest thousands of dollars before you even knew if the new path was right. Now, you can start learning and experimenting with a fraction of that investment. The entry point is lower. The speed is faster. The excuse of “it’s too late to learn something new” holds a lot less weight.

That said — and I want to be honest here — having access to tools doesn’t automatically mean you have the time or energy to use them. If you’re working full-time, managing a family, and already running on fumes, “just go learn something new” can sound like one more demand on a list that’s already too long. I’m not here to add to your overwhelm. I’m here to offer a way to think more clearly about where your energy goes.

A simple framework for checking in

When I work with clients on career direction, I keep coming back to three questions. Think of them as three overlapping circles:

The sweet spot lives in the overlap of all three. But more importantly, the value of this framework is in noticing when one of the circles has shifted. Maybe your values changed after becoming a parent. Maybe your industry is contracting. Maybe a skill you’ve been developing suddenly has a market you didn’t expect. Any shift is a signal to explore.

This framework won’t hand you an answer. But it gives you a starting point — a way to move from vague dissatisfaction or anxiety into something more structured and honest.

Small steps, not a leap

I want to be clear about something: I’m not encouraging anyone to constantly chase the next thing. Job-hopping for the sake of novelty creates its own kind of instability. And I’m definitely not suggesting you need to blow up your life to make progress.

What I am encouraging is small, intentional steps. Check in with those three circles. Notice what’s shifted. Have one conversation with someone in a role that interests you. Spend twenty minutes reading about a skill that’s been on your mind. That’s it. That counts.

One pattern I see often in coaching: people feel pressure to figure out their next move all at once. They want clarity by next month. A new title by next quarter. But career design doesn’t work like that. It’s entirely reasonable to spend up to a year reflecting on how you want to invest the next four years of your life. That’s not indecision — that’s proportional thinking.

Your career isn’t a single straight line — and it was never supposed to be. It might be a series of chapters. Some build on each other directly. Others take a turn. Some might involve a primary role plus a side project, or a portfolio of skills that meet different needs in your life.

The old model — climb one ladder, stay loyal, retire — worked when the ladder was stable. But ladders are shifting now, and the people who thrive will be the ones who keep iterating. Not rushing. Not overhauling everything at once. Just staying honest with themselves and designing a path that supports not just their income, but their happiness, fulfillment, and financial sustainability.

You don’t need to have it all figured out. You just need to take the next small step.

About Myself

I’m an executive and life coach who helps leaders grow with clarity, confidence, and purpose. If something in this post resonated with you — or if you’re navigating a career transition that could use a thought partner — I’d love to connect.

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