I wrote an article about reading the other day. This is about something different.

Not the book you finished on the plane. Not the newsletter you skim before your second coffee. Not the YouTube rabbit hole that felt educational at the time. Not even LinkedIn, despite the fact that it occasionally sends something genuinely useful your way.

I consume all of those things. Some of them matter. Most are useful noise. But they are not the thing.

The thing is deliberate, structured learning: choosing what deserves sustained attention; learning from people who have actually done the work; and building enough continuity for one lesson to alter the next.

I built a learning architecture.

Two platforms. Deliberate choices. Compounding returns. The difference matters more now than it once did.

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Learning Is Not the Same as Viewing

Consuming content is easy. Processing it, connecting it and building something transferable from it is the work most people skip. The firehose is always on. The question is whether any of it is actually sticking.

AI has made that question more urgent. The tools are broadly available. They can all produce a first draft, a list of ideas, an image, a plan, a summary, a plausible answer. What differentiates the work is increasingly what you bring to them before you type the prompt: the references you can make, the questions you know to ask, and the judgment you apply before accepting an answer.

Your taste. Your frameworks. Your ability to recognize the good, the bad, and the ugly. That is not self-improvement. It is infrastructure.

The most interesting people I know are not necessarily consuming more information than everyone else. They have built better filters. They have found sources they trust. They return to them. They apply what they learn. They notice where it fails. They go back for another pass.

They have a curriculum, whether they call it that or not.

MasterClass Is a Worldview Machine

I do not say that hyperbolically.

MasterClass is where I learn from photographers about how they see, from filmmakers about how they think about scenes, from writers about the architecture of an argument and the weight of a sentence. It is where I have learned about cooking from people who understand that food is never just food, and about engineering from people who care about elegance as much as efficiency.

The point is not that every lesson becomes a direct instruction. It is that the disciplines start to leak into one another.

When I think about the camera angle before prompting an AI-generated image sequence, I am drawing on something Annie Leibovitz said about how light tells you where to stand. When I think about atmosphere in a short film, I have a richer internal reference library than I did before I spent time listening to filmmakers explain what they were looking for. What I have absorbed from writers about argument and structure does not stay in a MasterClass tab. It shows up in the next piece I sit down to write.

That is what I mean by expert generalism. Not collecting adjacent interests so you can describe yourself as curious. Building enough fluency across disciplines for one way of seeing to improve another.

Visual grammar can become instructional grammar. A filmmaker’s instinct for what to leave unsaid can improve a strategy presentation. A photographer’s attention to framing can change the way you see a business problem.

None of it is decorative.

SectionAI Is Where I Learn on Monday, and Apply on Tuesday

SectionAI occupies a different part of the architecture.(HT to Greg Shove and his team.)

It began as a general business accelerator and has evolved into one of the most rigorous AI education platforms I have encountered. Its instructors are practitioners, and its best courses do not stop at theory. Every framework has a Tuesday-morning version.

I took its Mini-MBA in AI for Business not because I needed another credential. I took it because I needed a structured interrogation of what I was already doing intuitively—and I needed it from people who could tell me where my intuition was messy.

That is a rare thing.

A lot of AI education teaches people how to use a tool. It gives them a collection of prompts, a list of features, or a new vocabulary to deploy in meetings. Useful, perhaps. But it does not necessarily change how they think.

The better version gives you something you can test against your actual work. It makes you ask: does this apply here? Where does it break? What assumption have I been carrying without examining it?

You leave with something deployable.

The Two Things Compound

The interesting thing is not that MasterClass and SectionAI are both useful. It is what happens when they start talking to one another.

What I learn about cinematic storytelling in MasterClass becomes, almost directly, how I think about scene-setting in a prompt. The visual grammar bleeds into the instructional grammar.

What I learn about business-model design in SectionAI becomes a lens I apply when a client asks me to help them think about AI strategy. But it also becomes a constraint I test against something I absorbed last week from a filmmaker, a writer, or a photographer.

These are not parallel tracks. They are the same track, approached from different directions. That is why the return is not linear. I have invested time and money in both. I would not get the same return from an equivalent number of random YouTube hours, no matter how good some of those hours might be.

The difference is not simply quality. It is architecture. Somebody built a path. I chose to walk it. And because I keep walking it, each new lesson has somewhere to go.

The Student Becomes the Teacher

At a certain point in a career, whether it arrives at 45, 62, or somewhere in between, something should shift in how knowledge moves through you.

Teaching stops being an obligation on the org chart and becomes something closer to a craft. Not the kind where you hear yourself drone on about what you know. The kind that forces you to work out what actually matters: the load-bearing theory, the honest example, the point where application breaks from principle.

That is harder than it sounds. Most senior people never quite figure it out. They mistake tenure for pedagogy. Structured learning gave me more than knowledge. It gave me a model for how knowledge gets moved.

The best instructors do not simply know their subject. They have interrogated it. They have worked out the minimum viable insight and the example that makes it land. They have earned the right to simplify without distorting.

Watching that repeatedly is its own education.

My teaching journey did not start on a stage.

It started over coffee: one person, one conversation, sharing something I had just figured out that I thought might be useful. No structure. No agenda. Just the instinct that if something had shifted my thinking, it might shift someone else’s too.

Then it became scheduled. Coaching conversations with people navigating career transitions, or trying to make sense of what AI actually meant for their work. Then a small workshop: a room of eight or ten, a whiteboard, a framework assembled from what I had been learning and testing. Then larger rooms. Then stages.

At every step, something compounded that I had not planned for: confidence. Not the performed kind. The kind that comes from being tested.

Each room asked harder questions than the last. Each stage required me to have thought further ahead. And because the learning was running in parallel—because I was consistently a few lessons ahead of what I was teaching—I was rarely empty when the difficult question arrived.

More importantly, the questions showed me what I did not know.

Every workshop surfaced a territory I had not mapped. Every conversation exposed a gap in the framework. Every stage appearance generated a list of things I needed to go and learn before the next one.

The Flywheel

Learning feeds teaching. Teaching generates questions. Questions reshape the learning. The learning produces better teaching. Two platforms did not start that flywheel. But they have been the fuel that keeps it moving.

And then one day, someone asked me to teach. And offered to pay me for it. That is not a flex. It is a data point.

The curriculum I had been deliberately building in private had become valuable to someone else. It had become useful beyond me. Without quite noticing, the work of learning had turned into a form of credibility.

Deliberate Is the Word That Matters

At 62, I am learning to stay relevant. Of course I am. Anyone who says they are not may be mistaking accumulated experience for immunity from change.

But relevance is not the same thing as chasing every new tool, collecting the vocabulary of the moment, or letting the algorithm choose your education for you. It is the ability to bring more of yourself to the work in front of you: better questions, better references, and better judgment.

That requires a decision. The platforms I chose. The instructors I sought out. The investment of time and money. None of it was accidental. I did not stumble into a MasterClass session on cinematography and accidentally become better at prompting AI. I built a learning architecture, and these two platforms are the pillars of my learning institution.

I won’t pretend the flywheel never stalls. It does. Some evenings, the learning feels rote, the teaching feels thin, and the questions pile up faster than the answers. What keeps it moving is not discipline, passion, or any of the other words people put on LinkedIn.

It is the architecture - the decision, made once and kept, to learn from people who have already done the hard work of figuring out what matters.

Continuous learning is easy to claim. The harder thing is to build a curriculum that changes your work: a system of chosen inputs, applied quickly, tested in public, and revised by the questions you cannot yet answer.

The question is not whether you are curious. Most people are.

It is whether your learning has an architecture, or whether you have simply accumulated a lot of noise.

This is the second article in a series about designing agents. Read The Purpose of an Agent Is What It Does before this one. It will provide more context.

An agent’s most consequential decision may be one nobody asked it to make.

Give it a question with two reasonable answers. It doesn’t ask which one you wanted. It picks one, and hands back an answer formatted so cleanly you’d never guess it was a guess.

There is no visual difference between the agent that knows and the agent that is improvising. No hesitation. No change of font. No little light that comes on to tell you that the system has crossed from knowledge into invention.

I’ve spent much of this year designing agents, workflows, skills and systems. I’ve become reasonably good at describing what they should do when everything is clear.

I’m less sure I’ve been designing what happens when things go sideways.

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The first article in this series was about designing an agent around the job it is supposed to do, rather than rebuilding the workflow that came before it.

This one starts where that design usually runs out. What should the agent do when the source is thin? When two sources disagree? When the instruction is ambiguous? When the system can’t tell whether it has completed the task? What should the agent do when the answer it wants to give is plausible, but not necessarily true?

If nobody answers those questions, the agent will.

The Agent Will Decide for You

The designs I see, and the ones I’ve written, often carry the same unexamined assumption: that failure is something that happens to the system, rather than something the system does. A bug. An outlier. An edge case. Something to patch after the fact and move on from.

That language is convenient because it makes failure sound external and unusual. It lets us design the happy path and treat everything else as a rare event.

But an agent doesn’t experience failure as an exception. It reaches a moment where it has to choose. Continue? Retry? Search somewhere else? Ask a question? Stop? Hand the decision to a person?

Or fill the gap with whatever answer sounds most confident?

Nobody writes a ticket for that moment. Nobody sits in a design review and says, “When the evidence is incomplete, please quietly choose one of two reasonable interpretations and present it as fact.” But if the system has been built to always produce an output, then an output is what it produces.

Every time.

It keeps going. It fills the gap. It dresses the guess in the same tone it uses for everything else. That isn’t simply a technical limitation. It is a design decision made by default instead of on purpose.

Fail Safely, Not Forward

A fallback is often treated as what happens when the system can’t do the real thing. I’ve started to think that’s backward. A fallback isn’t a consolation prize. It is part of the real thing—the part of the design that decides what “good” looks like once the primary path is gone.

Sometimes the right response is a retry, because the failure was noise rather than signal. Sometimes it is a different source, because the first source was thin and a second opinion changes the picture. Sometimes it is a request for more information, because the system has reached a genuine ambiguity rather than a temporary problem.

Sometimes the right response is to stop and explain the problem in plain language to a person who can decide what to do next.

That third option tends to get built last, if it gets built at all. It admits that the system has a ceiling. Teams will happily engineer three layers of retry logic and never build the one fallback that says:

I don’t have this, and pretending I do will cost you more than waiting would.

Escalation is what happens when the fallback itself needs backup. Who does the agent escalate to? When? With what information? What has it already tried? What exactly does the person need to decide?

Those questions are less glamorous than adding another capability. They are also where the system’s actual behavior gets designed.

Take one situation. An agent produces a client-facing campaign report, pulling from the ad platform’s own reporting and an internal attribution model. On a good day, the two agree closely enough to round together. On a bad day they don’t, and the report still needs to go out.

The instruction set that handles that moment might read like this:

If the platform and the attribution model disagree by more than fifteen percent, don't average them, and don't default to the platform's number because it's the one the client expects to see. State both figures and flag the disagreement in the report.

If the brief doesn't specify which metric the client actually cares about — leads, cost per lead, something further down the funnel — do not infer it from last month's report. Ask.

If the recommended action is to shift live budget past a set threshold, or pause a running campaign, do not execute it. Draft the recommendation, explain the reasoning, and route it to a named approver.

If the same disagreement between the two sources shows up three times in thirty days, escalate the pattern, not the instance. Someone needs to look at why the sources keep disagreeing, not approve the fourth report.

Four sentences. None of them are hard to write. All of them are easy to skip. The system that includes them isn’t smarter. It just knows what it doesn’t know, and says so before it costs anyone anything.

Set the threshold too high, and the agent bluffs its way past problems that needed a person, because asking for help was never a live option. Set it too low, and you’ve built an expensive tap on the shoulder. The humans on the other end start ignoring it within a week, gutting the fallback underneath it.

A system that escalates everything is not cautious. It is useless. A system that escalates nothing is not autonomous. It is making decisions it was never given permission to make.

The Honest Pause

Knowing its limits is a feature.

An agent that says, “I’m not sure. Here’s what I found,” is more useful, more often, than one that always has an answer. A confident wrong action costs more than an honest pause. It can also take a decision away from the human without announcing it.

Most systems are built with the opposite incentive. A system that always answers demos better than one that sometimes doesn’t. Certainty sells. Certainty also eventually breaks something you can’t easily un-break.

“I’m not sure” on a low-stakes draft can be a useful signal. “I’m not sure” on every output, regardless of what is at risk, is a system that never learned to have an opinion. A human will eventually stop trusting it for the opposite reason they stopped trusting the overconfident one.

The real question was never confidence versus uncertainty. It is calibration. Does the honesty match the stakes, or has it been stapled to everything as a blanket disclaimer so nobody can be blamed for whatever comes out from under it?

Calibration isn’t only about what the agent says. It’s about what it’s allowed to do next.

A draft email with the wrong tone is recoverable. A recommendation built on a weak source may need another pass. A transaction executed on a bad assumption is a different category of event. The fallback has to know the difference.

Safety is not a setting you dial in once. It is a judgment about reversibility, made fresh for whatever the agent is about to do.

Make the Failure Legible

Another requirement makes the other responses useful. You need to see why the system failed. A system that fails loudly, with a clear trail, is a system you can improve.

This source conflicted with that one. The evidence did not support the conclusion. The instruction could be interpreted in two ways. The tool returned incomplete information.

This was the point where the system stopped knowing and started guessing.

A system that fails silently, producing something wrong but fluent, with no seam showing where the certainty ran out, is a system you only discover has failed after it has already cost you something.

Legible failure is unglamorous work. Failure doesn’t show up in the demo. A demo is, by definition, the happy path performed for an audience.

It shows up three days later, in the ten minutes it takes—or doesn’t take—to figure out why the agent told a client something that wasn’t true. It shows up in whether anyone can reconstruct the decision. It shows up in whether the system can be improved, or whether everyone simply changes the prompt and hopes.

The Postmortem Is the System

Zoom out one more level and POSIWID, the idea that a system’s purpose is what it does, stops being about the agent. It starts being about the people around it.

Picture the room after a failure has been flagged. In one version of that room, someone patches the prompt, closes the ticket, and moves on by Friday. In another, someone asks what the failure revealed about everything upstream.

Was the source selection wrong? Was the escalation rule missing? Was the system rewarded for producing an answer when it should have asked a question? Did the human checkpoint exist only because a person used to occupy that box? Did the architecture make the wrong behavior more likely?

Both rooms think they handled the failure. Only one of them has learned from it.

A team that treats every failure as a one-off has built an organization whose real function is not looking too closely at its own design. A team that treats failure as data has built something that can become harder to break over time.

That is the only kind of safe worth having. The thing you run after the agent gets something wrong isn’t merely a bug report. It is the only honest performance review your system will ever get—the moment it stopped performing and told you what was actually happening.

Design the Break

My earlier article argued that we should design agents backward from the consequence we want. This one adds the second half — and it’s actually two halves, not one.

There’s what the agent does when it runs out of certainty: the source is thin, the brief is ambiguous, the answer it could produce isn’t necessarily true. And there’s what it does when it has plenty of certainty and still shouldn’t act: the transaction it could execute but that needs a name attached to it first, the campaign it could pause but that isn’t its call to make alone. Not knowing what to do, and knowing exactly what to do but being required to stop anyway, are two different failures. Most systems are only designed for the first one.

What does it do when it reaches either edge? What verb does it use? Does it ask? Does it retry? Does it search? Does it stop? Does it escalate?

Or does it guess?

That last verb is often hiding inside an otherwise impressive system. It is not written anywhere. It does not appear on the architecture diagram. Nobody names the Guessing Agent. But if the system has no designed response to ambiguity — and no designed restraint where restraint was actually the job — guessing, or overreaching, is what it will do by default.

You can write a beautiful system prompt, chart a clean diagram, demo the agent flawlessly in front of the CMOs, and still have built a system whose real purpose only becomes visible the first time it hits something it wasn’t ready for — or something it was ready for and shouldn’t have touched.

Nobody Wants to Hear, “I Am Not Sure Yet.”

Design the agent all you want. It’s the part you didn’t design — the fallback, the escalation, the honest pause, the moment everything forks — that will end up doing the talking.

That’s true. It’s also not the whole truth.

Every gate in that instruction set requires the agent to say, out loud, in front of someone: I don’t know. Flag the discrepancy. Ask instead of guessing. Route it to a person before it goes any further. That only survives inside a real organization if the humans in the room can say the same thing without it costing them something.

Most can’t. Sit through enough status meetings, and you’ll notice which answer gets rewarded — the confident number, even the wrong one, over the honest “I’m not sure yet.” Nobody designs that incentive on purpose either. It’s just what earns a nod in the room, and what earns a follow-up question nobody needs.

The agent is listening, in the only way an agent can listen: through the humans who keep adjusting its instructions. The first time “I’m not sure” produces an uncomfortable silence instead of an answer someone can act on, somebody edits the prompt. Not out of malice. Out of the same instinct that wants the meeting to end on time. A few edits later, the hedge is gone, and nobody remembers deciding to remove it.

You can design a system for what it does. But you must also design for what it doesn’t, or shouldn’t do. This will be the hardest part of agent design, because we operate in a culture that requires answers, not ambiguity or indecision. The agent doesn’t succeed or fail on how smart it is. It succeeds or fails on how much judgment actually made it into the instructions.

Judgment has to come from somewhere. The next article in this series is about the human as the loop — the experience it actually takes to know what good looks like before you can ever write it into an instruction set.

We keep using AI to automate old processes. POSIWID suggests we should be questioning why the process exists at all.

I was watching Sicario the other night, again, because I listened to an interview with Taylor Sheridan where he talks about writing scenes to create tension. Most scripts follow a 3-act structure, but he uses a 5- or sometimes 7-act structure in a sub-plot to break the expectation of 1,2,3 – something we all now understand and often can already predict the story arc.

In the film, there is an inconsequential, almost throwaway line, where a border patrol officer looks at the situation in front of him and says:

“Well, I guess we’re not following procedure. What do you want to do?”

Now, my brain makes really strange connections. I can’t explain it, but I have learned to listen to it, and to follow these connections down whatever proverbial rabbit hole they lead me down. And this time, it connected with something I read on John Gruber’s always excellent Daring Fireball, but I will get to that in a moment.

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I have been spending a lot of time recently designing agents, workflows, skills, and systems. And inevitably, when you design those things, you start designing procedures. First this happens, then that. This agent hands something to that agent, that gets reviewed, and then it gets approved. If this happens, go here; if that happens, go there. All very sensible. All very procedural.

Not recklessly. Not because procedures don’t matter. But because the procedure was designed for a particular way of working, under a particular set of constraints, at a particular moment in time.

What if those constraints no longer exist? What if the question isn’t How should this process work? What if the question is simply:

And that sent me back to something I had read a few days earlier on Daring Fireball.

POSIWID - The Purpose Of a System Is What It Does.

The phrase comes from Stafford Beer, the British cybernetician and management thinker. His argument was deceptively simple: we tend to describe systems according to what their designers intended them to do.

Beer essentially said: forget the intention. Look at what the system actually does. That is its purpose. Which initially sounds almost tautological. “Well, obviously”. And then you think about it for another thirty seconds.

“Oh, fuck.”

POSIWID suddenly felt less like an observation about systems and more like permission: permission to stop treating the existing procedure as sacred, to stop assuming the workflow you are replacing is the workflow you should rebuild, and to stop confusing activity with purpose.

It also gave me a useful distinction. Every system has a declared purpose and an enacted purpose. The declared purpose is what the brief, the slide deck or the system prompt says the thing is supposed to do. The enacted purpose is what the thing repeatedly causes in the world. The gap between those two is where the system is failing.

Gruber’s piece also references a Steve Jobs story.

In 2008, Apple launched MobileMe, its attempt to synchronize email, contacts, calendars and other information across devices. The launch was a disaster. According to an account later published in Fortune, Steve asked the MobileMe team a remarkably simple question:

“Can anyone tell me what MobileMe is supposed to do?”

Someone answered. Steve followed with the only question that really mattered:

“So why the fuck doesn’t it do that?”

Steve wasn’t asking to see the architecture. He wasn’t asking how many teams had worked on it, how many features it contained or whether the roadmap was on schedule. He was comparing the thing’s declared purpose with its actual behavior.

Somewhere between a throwaway line in Sicario, a cybernetician from the 1970s, and Steve Jobs swearing at the MobileMe team, I began to wonder whether I had been designing AI systems the wrong way round.

Horseless Workflows

Over the past year or so, I’ve increasingly found myself designing systems with agents that call other agents, skills wrapped inside instruction sets, and tools threaded through chains of decisions.

And inevitably, when you design one, you start thinking about process.

What happens first? What happens next, and what does it need from the step before it, or hand off to the one after? Where does the human come in, where does approval sit, what gets stored, and what happens when something fails?

These are all perfectly reasonable questions.

But POSIWID has made me wonder whether they’re the first questions.

When designing an agent, I tend to begin by looking at how something is currently done. Then I make it better. Faster. Automated. AI-enabled.

Agentic.

At some point, I have built an extraordinarily sophisticated machine for doing something exactly the way humans have been doing it for the past twenty years.

Only faster.

The first automobiles looked suspiciously like carriages. There was a good reason for this. Carriage makers knew how to make carriages. Roads were designed for them. People understood how to use them. The entire mental model of personal transportation was built around the carriage.

So when the horse disappeared, much of the carriage remained. Hence the wonderfully transitional phrase: horseless carriage.

Eventually, of course, designers stopped asking: “How do we make a carriage without a horse” and started asking: “What should personal transportation actually be?”

I wonder if we’re currently building the organizational equivalent.

Take a fairly normal knowledge-work process. A brief comes in, someone researches it, someone turns the research into a strategy, someone writes it up, and then it gets reviewed, revised, approved, and sent out the door.

Then AI arrives, and we get terribly excited.

We create a Brief Agent, a Research Agent, a Strategy Agent, a Writing Agent, a Critic Agent, an Approval Agent, a Distribution Agent.

We connect them with arrows. We give the whole thing an impressive name. And then we proudly announce that we have built an agentic workflow.

Maybe. Or maybe we’ve simply reconstructed the company org chart in software.

Processes As Fossils

A process isn’t necessarily the best way of accomplishing something. Quite often, a process is simply the accumulated history of the constraints under which that thing had to be accomplished.

Research went to researchers because they knew how to research, copy went to writers because they knew how to write, design went to designers because they knew how to design, and approval went upward simply because authority lived higher in the hierarchy.

Information was placed into documents because documents were an efficient way of transporting knowledge between people; meetings existed because getting six brains into the same room was sometimes the only practical way to synchronize six different understandings of reality; departments existed because expertise had to live somewhere, and handoffs existed because no single person possessed all the capabilities required to complete the work.

Much of what we call workflow is therefore not some timeless expression of how work should happen.

That doesn’t mean every process is useless. Process also contains accumulated responses to risk, accountability, coordination, and failure. A procedure may be clumsy, but it may also be protecting a consequence that matters.

The point is not to delete process automatically. The point is to recover the problem the process was built to manage before deciding whether the process still needs to exist.

AI has relaxed some constraints and introduced others. It can make certain kinds of research, writing, analysis and coordination far cheaper and faster. But it also creates new problems around verification, provenance, permissions, privacy, liability and judgment.

And yet we often keep the old process while adding AI to it.

Automating the Past

This is where I think a lot of current thinking about AI transformation goes wrong.

Organizations look at what they currently do and ask, "Where can we put AI?" Which almost guarantees incrementalism.

The invoice process has twelve steps, so let’s automate steps four and seven. The research department produces a 60-page report every quarter, so let’s use AI to produce it faster. Our creative process has seven approval stages, so let’s insert AI between stages three and four. Our employees spend six hours a week creating PowerPoint decks, so let’s have AI create them.

But there are more interesting questions:

Why does the 60-page report exist? Why are there seven approval stages? Why is the output a PowerPoint deck? Why does this information move through four departments?

Those aren’t automation questions. They’re systems questions. And AI becomes considerably more interesting when you stop asking it to improve the existing process and start asking whether the existing process needs to exist.

Design the Agent Backward

POSIWID suggests a different starting point. Don’t begin with the workflow. Begin with the behavior. Ask:

What should be different because this system exists?

Suppose I want an agent that monitors emerging creative AI tools. The obvious place to begin is process. It should search product-launch sites, monitor GitHub, read newsletters, track industry publications, categorize new tools, maintain a database, generate a weekly report, and send it to Slack.

Perfectly sensible. Except none of those things are actually the purpose of the system. They are activities.

So let’s apply POSIWID. What do I actually want the system to do? Perhaps:

Once or twice a month, surfaces something I didn’t know about that materially changes how we could solve an active client problem.

Now things get interesting.

Because suddenly the weekly report isn’t sacred. The database isn’t sacred. The monitoring cadence isn’t sacred. Slack isn’t sacred. The number of agents isn’t sacred. Even the definition of “creative AI tool” isn’t sacred.

The desired consequence is what matters. Everything else is implementation. That means we can design backward.

The observable behavior matters because a desired consequence is often too vague to build against. What would I actually see if the system were producing that consequence? What decision would change? What would I do differently? What evidence would convince me it was working?

That is a very different sequence from:

This feels like a small change in sequence. I suspect it is actually a very large change in thinking.

I recently wrote about something Taylor Swift said about songwriting:

“Details should not be strictly decorative.”

I loved that phrase because it describes something much bigger than songwriting. A good detail earns its place. It changes something. It creates tension. It has consequence.

The same test applies to system design. Why is this instruction here, this step, this handoff? What behavior does it change? If nothing meaningful happens when it is removed, perhaps it was never part of the system’s purpose.

The Architecture Is Not the Achievement

There is something seductive about complicated agentic systems. I understand the attraction. I’ve built them. A collection of specialized agents connected by conditional logic looks impressive. Research passes to Strategy, Strategy passes to Creative, Creative passes to Critic, and Critic sends it back, until eventually something emerges.

There is something satisfyingly industrial about it. You can almost hear the machinery.

But complexity is not intelligence. And orchestration is not effectiveness. If a twelve-agent system produces mediocre work, it is a mediocre system with an excellent diagram.

This is where the MobileMe question becomes useful. Forget the diagram. Forget the prompts. Forget the model. Forget the orchestration layer. Forget how clever the architecture feels. Stand in front of the thing you built and ask:

What is this supposed to do?

Answer the question in one sentence. Then look at what it actually does. If those two things are different:

So why the fuck doesn’t it do that?

That may be the entire QA framework.

The Human in the Loop Might Be a Legacy Feature

Consider one of the most common phrases in AI system design:

Human in the loop.

It sounds reassuring. Responsible. Safe. And frequently it is all three. There are places where human judgment, accountability, taste, empathy or legal responsibility absolutely belong. A decision may carry material risk. The evidence may be ambiguous. The system may need to be challenged. Someone may need to authorize an action or bear responsibility for the consequences.

Excellent. But sometimes the answer is: Because a person used to do that part. That is not the same thing.

If a human is present only to rubber-stamp an output, then the checkpoint may be nothing more than organizational archaeology. But if the human exercises judgment, changes the outcome, authorizes risk or owns the consequence, the human is doing something important even if the final output looks similar.

The question is not whether there is a human in the loop. The question is what the human contributes to the system’s behavior. The human should not be there because a person used to occupy that box on the flowchart. The human should be there because they change something that matters.

The same applies in the opposite direction.

Right now, there is enormous enthusiasm for multi-agent systems. Give every cognitive function a title: Researcher, Planner, Strategist, Writer, Reviewer, Critic, Fact Checker, Coordinator, Supervisor.

Suddenly your laptop has middle management.

Specialization can genuinely improve performance in some situations. Different contexts, tools, permissions, and evaluation criteria can make separate agents useful. But there is also a danger that we anthropomorphize organizational design.

Humans needed separate jobs because no individual human could simultaneously possess every skill, know every piece of information, maintain every context, and work continuously.

Those constraints do not map perfectly onto AI.

Before creating another agent, perhaps the question isn’t: What role is missing? Perhaps it’s: What behavior is missing?

The Purpose of an Agent Is What It Does

Which brings me back to Stafford Beer.

His idea wasn’t really about AI, obviously. It predates ChatGPT by several decades. But POSIWID is almost perfectly suited to this moment.

Because we’re entering an era where systems don’t merely store information or execute predefined software functions. They increasingly observe, interpret, decide, create, communicate and act. That makes the distance between what we intended the system to do and what it actually does more consequential.

We can write a beautiful system prompt describing an agent as helpful, strategic, discerning and proactive. Ok.

What does it actually do?

We can build an elaborate research workflow designed to generate insight. Does it generate useful insights? The kind that ground an idea or change a decision?

We can create an AI transformation program designed to make an organization more innovative. Does the organization become more innovative or does everyone simply attend three more meetings about AI?

POSIWID is ruthless about this.

It doesn’t care what we called the system. It doesn’t care what the slide deck says. It doesn’t care what the transformation roadmap promised. It doesn’t care how sophisticated the architecture is. It looks at the result.

And then we have to work backward.

Perhaps that’s exactly how we should design the next generation of AI systems. Not by asking how work has traditionally been done. Not by recreating our departments as agents. Not by preserving processes whose origins we’ve forgotten. Not even by beginning with what the system should do internally.

Begin with the consequence.

What should be different because this system exists?

Then build the minimum system capable of reliably making that true.

And when it doesn’t? Well, we already have the question you should be asking. Steve Jobs gave it to us eighteen years ago.

So why the fuck doesn’t it do that?

The first system prompt I ever wrote for a client had four adjectives before the verb.

"You are an experienced, insightful, strategic, and creative thinker."

It read like a job description written by someone who’d interviewed the role and liked the candidate. It also did almost nothing. I didn’t understand that yet. I understood it a few hundred instruction sets later. The prompt sounded perfect. It was also, functionally, wallpaper.

I was listening to Taylor Swift talk about songwriting, and she may have accidentally described how to write a great system prompt or agent instruction set.

“Details should not be strictly decorative.”

The more I work with AI agents, system prompts, and increasingly complex instruction sets, the better I’ve gotten at telling the difference between a detail that’s doing something and one that’s just fancy dress.

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Casting Notes, Not Instructions

“You’ve got that James Dean daydream look in your eye.” - Style. From the record “1989”, Side A, Track 3

Think about what a specific detail does inside a good song, instead of a mediocre one. “You’ve got that James Dean daydream look in your eye” is not decoration. It’s retrieval. Two words and you’ve loaded an entire system: rebellious, cinematic, doomed, a little dangerous, gone too soon. The specificity isn’t there to sound nice. It’s there to do work the writer would otherwise need a paragraph to do, and possibly not as well.

Now reread the system prompt I opened with. Experienced. Insightful. Strategic. Creative. Every one of those words describes a person I’d want in the room. None of them tells the model how to behave once it’s there.

Delete “insightful” — does the output change? Delete “experienced” — what computation shifts? I’ve run this test on dozens of client instruction sets since, and the answer is almost always: nothing happens. You’ve written a casting note. You haven’t designed a behavior.

Consider the kind of system prompt we’ve all written:

You are an experienced strategic thinker. You are insightful and creative. You communicate clearly and concisely. You challenge conventional thinking. You are an expert in marketing, culture and consumer behavior.

Sounds pretty good. Sounds specific. Sounds like someone we’d probably want in the meeting. But here’s the rub. We’ve written a lovely character description, but again, we have not designed behavior.

Decorative Specificity vs. Consequential Specificity

There’s another Swift lyric I love:

“The dopamine races through his brain on a six-lane Texas highway.” - I Can Fix Him (No Really I Can). From the record “The Tortured Poets Department.” Side C, Track 3

A highway is a detail. A six-lane Texas highway is doing considerably more. It gives you scale, speed, heat, geography, a particular kind of American restlessness. Take out “six-lane” and “Texas” and you haven’t just made the line shorter. You’ve made the man less specific.

Decorative specificity makes a prompt sound like it knows what it wants. Consequential specificity changes what the model does when it gets there.

“You are an experienced strategic thinker” is decorative. It describes the kind of person I would like to have in the room. But it doesn’t give that person anything specific to do.

“Before recommending a strategy, identify the assumption that must be true for it to work” is different. Now something has to happen before the recommendation appears.

“You are decisive and opinionated” sounds good too. “Never present three equivalent recommendations. Evaluate the alternatives and recommend one” is better. The first is a flattering character reference. The second changes everything that follows.

Same with objectivity. “You are objective and evidence-based” is a nice thing to say about someone. “When evidence conflicts with the user’s hypothesis, privilege the evidence” gives the agent a rule for an actual moment of conflict.

One describes the agent. The other designs its behavior. That’s the difference.

Every Detail Should Change What Happens Next

“Midnight / You come and pick me up, no headlights.” - Style. From the record “1989”, Side A, Track 3

“No headlights” changes everything. Remove it and someone simply picks her up at midnight. Keep it, and suddenly there is secrecy, risk, illicitness. Two words alter the inferred situation.

Which brings us back to Taylor Swift.

“Details should not be strictly decorative.”

What if we applied that literally to every instruction we put into a system prompt? Every sentence should earn its place by changing what happens next.

It should affect a decision. Alter a priority. Introduce a constraint. Trigger a behavior. Change how ambiguity is resolved. Determine which evidence wins. Influence when a tool is used. Tell the agent when to stop. Tell it when not to act. Otherwise, what exactly is the sentence doing there?

This doesn’t mean descriptive language is useless. Context matters. Roles matter. Tone matters. Sometimes telling an AI who it is supposed to be creates a useful prior for everything that follows.

But there is a difference between establishing context and accumulating adjectives. And we’re doing an awful lot of accumulating adjectives.

The Taylor Swift Test

Here’s the test I actually use now, because “does this sound like something a smart person would write” is a terrible filter, and “would this change the next output” is a good one.

Pick a sentence in your instruction set. Delete it. Then test the agent in the situation the sentence is supposed to govern. If its behavior doesn’t change in any meaningful way, it was probably never an instruction. It was costume.

When I think about system or agent design, I usually look out for three things:

Decorative: Sounds desirable but does not reliably alter behavior. “You are insightful.”

Consequential: Changes what happens at a specific decision point. “When evidence conflicts with the user’s hypothesis, privilege the evidence.”

Priming: Does not operate as a hard rule, but establishes a useful default across the work: role, audience, source hierarchy, or a genuine stance towards the task.

When Words Become Actions

With a chatbot, a decorative instruction might not matter very much. Maybe the answer is slightly more generic. Maybe the tone wanders. Maybe you get three recommendations when you really wanted one.

But agents change the stakes. Because an agent doesn’t merely generate language. It can make decisions. It can select tools. It can retrieve information. It can decide what information matters. It can execute sequences. It can determine whether something is complete.

Increasingly, it can act. Which means the system prompt sits upstream of an entire decision tree. A seemingly innocuous sentence can change not merely what the AI says, but what it does next. This makes instruction design less like writing a creative brief and increasingly like designing a behavioral system. And behavioral systems don’t need decoration. They need consequence.

I’ve spent a chunk of this year arguing that orchestration, not prompting, is becoming the real technical skill in this work — that the craft has moved from writing a good sentence to designing a system that makes good decisions on its own.

Maybe Better Prompts Aren’t Longer

There’s another lesson from songwriting here that I think matters. Great songwriting doesn’t necessarily contain more detail. It contains the right detail. The detail that carries disproportionate meaning. The detail that allows the listener to infer everything else. The detail that makes an entire emotional world suddenly visible. Which makes me wonder whether we’re heading in the wrong direction with system prompts.

As agents become more sophisticated, the temptation is to make their instructions increasingly enormous. More rules. More context. More adjectives. More edge cases. More paragraphs explaining what the agent should be. Until eventually we’ve written something resembling an employee handbook for a machine.

Maybe that’s not where great agent design goes. Maybe the real craft will be learning to write fewer sentences with greater consequence. Instructions that don’t merely describe desirable behavior. Instructions that produce it. Details that change decisions. Details that create causality. Details that make the next action inevitable.

Taylor Swift wasn’t talking about AI. She was talking about songwriting. But “details should not be strictly decorative” may be one of the better principles I’ve encountered for designing intelligent systems.

So the next time you’re staring at a 3,000-word system prompt, try this. Pick a sentence you like. Delete it.

If nothing changes, you didn’t just find a redundant line. You found out how much of the document was never instructional.

Maybe it was just a really nice costume.

I bought a book last week. Shop Class as Soulcraft, Matthew Crawford’s inquiry into the value of work—hand to tool to material, the kind of competence you can’t fake because the motorcycle either starts or it doesn’t. I hadn’t gone looking for it. I went to the bookstore looking for a different book.

They didn’t have it.

There was the short, familiar disappointment, followed by the obvious next move: go home, find the book I wanted online, click, wait. But I was already in the store, and what I wanted wasn’t only that particular title. I wanted to leave with a book. A new piece of reading. A new way of thinking. Some small insight I hadn’t arrived with.

So I looked at the endcaps. I looked at the bookstore’s recommendations. I let my eyes move across shelves I hadn’t planned to visit.

Shop Class as Soulcraft was there.

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Maybe a bookseller had put it there. Maybe the shop’s merchandising system had. Maybe an algorithm somewhere upstream had helped decide what appeared on that endcap and what sat beside it. Who knows?

When I walked home, I put it on top of the pile of books still waiting to be read. The pile is starting to look like Manhattan’s skyline. And I stood there for a second looking at the shelves underneath it. That’s when I actually counted things.

A bookcase that’s stopped pretending it has room for anything else. About five hundred records. Ten photographs on the walls—mine, shot, printed, framed. A guitar that came out of a case a few months ago when we moved into the new apartment and has been leaning in the corner ever since, freshly tuned up but still waiting for me to remember I used to be good at it.

Look around the office and the apartment, and I see stuff that doesn’t update itself.

I’ve spent six years building my living inside AI. Strategy, workflows, the actual machinery of how creative work gets made now. And somewhere in those six years, I started reading more, listening more, and buying more things that have no API, beacon, or cookie.

That’s not a contradiction. It isn’t a protest against the age of AI, or against the industries that pay my bills—marketing, advertising, and creative work.

The absence of the book I wanted had created a gap. Online, that gap would have been closed almost instantly. In the shop, I had to decide what the visit was for. Was I there to acquire the title I had named, or to come into contact with something worth reading?

The first option had failed. The second remained available.

That gap is where the magic happens.

You go into a record shop looking for one artist or band and come out with something you’d never have typed into a search box, because you don’t know what you don’t know until it’s in your hands. You go to a restaurant you already know because you want to see what’s changed. Or you walk past a shop door you’ve never opened, and open it.

None of these experiences are unmediated. Someone chose which records to stock. Someone designed the menu. Someone decided which books belonged on that endcap.

Maybe recommendation isn’t the right dividing line. Maybe the difference is between being predicted and being encountered.

A bookstore shelf is a recommendation system. It has inputs, judgments, commercial pressures and, increasingly, perhaps algorithms behind it. The question isn’t whether the recommendation exists. The question is what kind of recommendation it is.

A personalized system begins with a model of me. It knows what I’ve clicked, bought, paused over, searched for, and perhaps abandoned. It looks at that trail and has a go at guessing what I’ll want next. Sometimes it gets it right. If I already know the title, I’m perfectly happy to type it into a search box and get on with my day. Sometimes that is exactly what I want. If I know the book I want, I’m grateful for a search box that finds it. If I need a replacement stylus for a record player, I don’t necessarily want a pilgrimage. Convenience is not the enemy.

But the recommendation on the shelf gave me a possibility without giving me a conclusion. It didn’t know whether Crawford would suit me. It didn’t know what I had read last week or what I was in the mood to become. It simply made the book available to my attention and left some work to me.

An algorithm can absolutely show me something I didn’t know I wanted. The harder thing is showing me something that changes what I think I want before the system has worked out that I’m likely to want it.

That’s the value of the shelf. Not that it is free from selection. Not that it is pure, analog or somehow outside the market. It is that the selection is imperfect, contingent and incomplete. It gives me a context but not an answer.

Discovery is not the absence of mediation. It is what happens when the mediation leaves some of the decision unfinished.

Digging for vinyl, buying books, eating at a place with no delivery—none of that is outside the market. It is the market. A well-marketed one, at that. “The analog revival” has its own aesthetic now, its own Instagram accounts, its own tote bags.

The bookstore may have used sales data to decide what went on the endcap. The shop’s recommendation shelf may have been influenced by an algorithm. The fact that I experienced the discovery as mine doesn’t prove that the discovery was untouched by systems.

I’m not claiming immunity from being sold to.

What I’m claiming is narrower: the process of finding the thing was still mine, even when the appetite for wanting to find it wasn’t entirely mine. I had to be there. I had to notice. I had to decide that failing to find the book I went in for didn’t mean the trip had failed. I had to take the small risk of buying a book without much evidence that it was a book that I didn’t know I needed to read.

The system may have helped put the book in front of me. It didn’t finish the experience.

There’s a version of me—professionally, the version that gets paid—who spends his working hours making machines better at anticipating what people want before they’ve asked. I’m not passive in that work. I’m choosing constantly: what matters, what to pursue, what to ignore, what to build. But those choices usually have a job to do. They’re pointed towards an outcome.

I’ve started to notice the specific tiredness that comes from a day spent inside systems that finish your sentences, followed by a much different feeling during an evening spent standing in a bookshop, deliberately not knowing what’s on the next shelf.

One is choice with a brief. The other is choice without one. I think I need both.

Some Things Need Trying On

I rarely buy clothes online. When I do, it’s usually some novelty T-shirt with a logo of a company that is no longer around, which signals how cool I am. I could easily buy a new pair of jeans online; I have worn the same size for years now. But I prefer to walk to the shops; maybe they have my size in stock, maybe they don’t. Maybe I’ll choose a different cut, try it on, and realize, for the 30th time, that I no longer look good in skinny black jeans.

Maybe that is the thing I am enjoying about analog experiences. The Maybe.

We work on machines built to know us. We look down more than we look up. We move our thumbs considerably more than we move our feet. None of that makes the machines wrong. It just means the thumb and the feet are doing two different jobs, and only one of them is optimized for arriving.

The other one is still useful for wandering.

I don’t know yet whether this is a return to something or the start of something new—Crawford would probably say those are the same question, badly asked.

I do know that a life built entirely around better prediction of the person I have already become would be a strangely narrow life. There should still be places where I am not fully understood. Places where I can encounter something before I know what it says about me.

The guitar is still in the corner. I haven’t finished the book. The shelf is full of things chosen by someone, somewhere. Some arrived through recommendation. Some through accident. Some through years of wanting. Together, they are a record of decisions, detours, failed searches and changed minds.

Or maybe they arrived in my apartment because on that particular day, I had just gotten paid for a project, and I had money burning a hole in my pocket.

Maybe that is the thing I’m enjoying about analog experiences. The Maybe. Not the certainty that I’ll find what I want, or even that I’ll make the right choice. Just the possibility that I might encounter something I hadn’t come looking for and decide it belongs with me.

I have been using ChatChat to build a canonical record of how I work, and the relationship between the end product and the thinking that got me there. Part of this process is to go back through the versions: Idea A, Idea B, Draft01, Draft 2, Draft 5.6 (Use this one)... You get the picture. Looking back at the work, the ideas that I present, get promoted internally, and grow past the original brief haven’t come from Option A or B, but seem to have started in Option C. I don’t think that’s an accident.

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We have a strange obsession with the number two. Yes or no. Stay or go. Premium or mass. Brand or performance. Human or AI. Work from home or return to the office. Long-form or short-form. Growth or profitability.

A or B.

There’s something wonderfully comforting about a binary decision. It takes a messy, complicated world and reduces it to something manageable. Two doors. Pick one. The problem is the world rarely comes with only two doors. We put them there. Then we spend an extraordinary amount of time debating which one to walk through.

Without particularly intending to, over the years I have come to find that neither A nor B is very satisfying. Option C is where the interesting stuff lives.

Option A is easy

Whenever I break down a problem, Option A appears almost immediately.

Option A is the obvious answer. It’s what the category normally does. It’s what experience tells you should work. It’s what the client is probably expecting — the answer you could reasonably arrive at after reading the brief, looking at the competition, and applying the accumulated wisdom of people who’ve solved similar problems before.

There’s nothing necessarily wrong with Option A. In fact, it’s often quite good. Competent people are exceptionally good at producing Option A. Ask a competent strategist how to launch a premium product and there’s an Option A. Ask a competent creative team how to make a Christmas campaign, and there’s an Option A. Ask a competent consultant how to restructure an organization and, somewhere behind the PowerPoint, there’s definitely an Option A.

Best practice is essentially a gigantic international library of Option As.

And sometimes Option A is exactly what you should do. I just don’t particularly want to stop there.

Then comes Option B

Option B is usually more fun. If A says go left, B asks what happens if we go right. If A says make it premium, B says make it accessible. If A says automate the process, B says make it deliberately human.

This feels creative, because we’ve broken the pattern. We’ve challenged convention. We’ve become contrarian.

Here lies the problem: Option B is still being controlled by Option A. It exists in opposition to it. A says black, B says white. A says zig, B proudly announces it intends to zag. We congratulate ourselves for thinking differently while remaining trapped inside exactly the same frame.

Contrarian thinking can be useful, and I use it constantly. But contrarianism isn’t freedom. It’s rebellion. And rebels still need something to rebel against.

Which is why I keep going. Option C begins with a different question. Not “what is the opposite of A?” but “what else could be true?”

It’s a surprisingly liberating question, because by the time I reach C, I’ve already paid my intellectual debts. I’ve considered the obvious answer — that’s A. I’ve challenged it — that’s B. Nobody can accuse me, and more importantly, I can’t accuse myself of ignoring convention or failing to interrogate it. Both jobs are done.

Now the real fun begins. Option C doesn’t have to agree with A. It doesn’t have to disagree with A. It doesn’t even have to exist on the same axis. It can wander off. A answers the question. B challenges the answer. C challenges the question. That’s where I do my best work.

Say somebody asks: should we spend more on brand or performance marketing? Option A might say performance — we can measure it, optimize it, demonstrate return. Option B might say brand — stop feeding the short-term machine, build future demand. You could spend the next three months arguing about the right split between the two.

Or Option C might ask why we structured the problem around media investment at all. Maybe the real opportunity is product experience. Or distribution. Or packaging. Or customer service. Or community. Or something sitting inside one of the other Ps that nobody bothered putting into the original question.

Suddenly we’re not negotiating a point between A and B. We’ve changed the map.

This happens constantly in business, because binaries are seductive. Efficiency or creativity. Global or local. Specialist or generalist. Every binary creates an invisible line, and once the line exists, we instinctively go looking for our position on it. Option C asks whether the line deserves to exist at all.

The bias of simplification

We need simplification. You couldn’t operate in the world if every decision required contemplating every variable. So we compress, categorize, create opposites, and eventually the simplification becomes so familiar we mistake it for reality.

This is particularly dangerous in strategy, because the framing of the problem quietly determines the universe of acceptable solutions. If the question is “should we do A or B,” then A and B have already won. Everything outside that frame disappeared before the meeting even began.

Option C is a small act of resistance against that. It forces another possibility into existence, not necessarily because C will be right, but because the existence of C reminds us that A and B were never the entire universe. They were merely the first two things we happened to name.

Sometimes, though, there is no Option C. Option C is not a universal escape hatch. Some decisions really do have two doors, and one of them may be the right one. When that is the case, you do not need a reframing exercise. You need to just commit and move on.

There is also a point at which searching for another possibility becomes avoidance. A person can keep “challenging the frame” because making an actual decision would expose them to consequence.

I don’t present three options

There’s another reason I like working this way, and it has nothing to do with handing clients three ideas. I almost never do that. I’ve never really understood the ritual. Here are three strategic directions. Here are three creative territories. One is crap, and we all know it. One is lukewarm, but safe. One is there because we love triptychs. Pick your favorite.

If you’ve hired me to think about the problem, part of what you’re paying for is judgment. My judgment, my understanding of what good looks like. I’m not being paid to manufacture a menu and hand the difficult decision back to you. That’s my job, not yours.

So A, B, and C are not deliverables. They’re scaffolding. The client may never see A. They may never see B, and probably shouldn’t, because after exploring all three, I should have an opinion. That’s what goes on the table. Not three answers. The one I believe in. The one I can defend. The one I am most proud of.

Third time‘s the charm

A carries expectation. B carries opposition. C carries neither. By the time I reach it, I’ve stopped trying to be correct and stopped trying to be contrary. I’m exploring, and exploration is different from problem-solving. You start connecting things that aren’t supposed to connect. You steal an idea from another industry. You change the unit of measurement. You redefine the audience. You turn the constraint into the product. You realize the communications problem is actually a distribution problem. You stop trying to write a better answer to the brief and start wondering whether there’s a better brief.

That’s where strategy starts becoming creative, and creativity starts becoming strategic.

There’s a word for this stage: play. It’s the first point in the process where I’m genuinely playing. A has responsibility attached — what should we do? B has performance attached — how do I prove I’m thinking differently? C has neither. The homework’s finished. Now I can screw around.

That might sound like a strange thing to celebrate in serious strategic work, but I think we badly underestimate the value of intellectual play. Play lets ideas collide without immediately demanding they justify themselves. It permits detours, stupidity, the sentence “this might be completely ridiculous, but—.” Some of the best ideas I’ve encountered begin with something close to that sentence. Not because ridiculous ideas are inherently valuable, but because genuinely new ideas often look ridiculous from inside the assumptions they’re about to replace.

AI makes this even more important

We’re entering an era where producing Option A is becoming incredibly cheap. AI is extraordinarily good at plausible. Give it the category, the objective, the audience and enough context, and it generates competent, sensible, defensible answers almost instantly — Option A at industrial scale. Ask it to be provocative, and it’ll happily generate Option B too. It knows how to zag.

Which makes finding C considerably more valuable. Not because AI can’t help find it. But because someone still has to recognize when the frame itself is the problem. Someone has to ask the question that wasn’t in the prompt: wait, why are these the choices?

That may be one of the more important creative and strategic skills of this era. Not producing more answers. Escaping the architecture of the obvious ones.

So try Option C

Next time you’re facing a difficult decision, give A its due. What’s the sensible answer? Write it down. Then find B — challenge it, reverse it, break its assumptions, see what happens if you do precisely the thing you’re not supposed to do. Write that down too.

Then forget about both of them for a moment and ask: what else could be true? Not what’s between A and B. Not which one is better. Not how you might combine them. What becomes possible if neither defines the boundaries of the problem?

That’s Option C.

You may discover A was right all along. At least you chose it rather than inherited it. But every once in a while you’ll find something else. Something that wasn’t sitting neatly between the two available choices. Something slightly strange, difficult to categorize, nobody asked for.

In my experience, that’s very often the work that gets promoted.

A answers the question. B challenges the answer. C asks whether we were asking the right question in the first place.

I first watched Justified the week it premiered. I was 45 years old.

I loved Raylan Givens immediately. The hat. The walk. The almost pathological calm. The ability to enter a room containing three armed men and somehow make them feel like they had made a poor career choice.

I loved Boyd Crowder even more. Shakespeare with a southern drawl.

Walton Goggins could turn ordering breakfast into a four-minute meditation on God, capitalism, betrayal and the proper application of high explosives.

The writing was extraordinary. The performances were extraordinary. Harlan County seemed populated entirely by people who could either quote scripture or shoot you, and quite a few who could do both.

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I have watched the series three or four times. And apparently, for more than a decade, I missed one of the best lines in it. Or perhaps I heard it. I just wasn’t old enough to understand it.

I’m rewatching Justified again now, and somewhere along the way Raylan Givens says:

Well, shit. There it was. A sentence that had presumably been sitting in exactly the same place since 2010, patiently waiting for me to catch up with it.

The line hadn’t changed. I had.

There are things you cannot hear yet

This is one of the more peculiar discoveries of getting older. We tend to think of understanding as something that happens more or less immediately. Someone says something. We hear the words. Our brain processes them. Meaning received.

But I’m increasingly convinced that some ideas arrive years before we are capable of understanding them. At 25, someone tells you that time goes quickly. Sure it does, Grandpa.

At 35, someone tells you that careers don’t turn out the way you planned. Maybe yours didn’t.

At 45, Raylan Givens tells you getting old ain’t for pussies. Ha. Great line.

Then you turn 62. And suddenly Raylan isn’t being funny anymore. He’s giving you the weather report. Because by this point, getting older has stopped being an abstract concept involving other people.

It has become personal.

Your knees have opinions. Your metabolism has apparently joined a union. There are increasingly elaborate negotiations involved in getting off the floor. You begin making small involuntary noises while sitting down. You find yourself looking at a staircase and thinking, Do I actually need anything up there?

But the physical stuff is almost the least interesting part of it. Because aging isn’t simply what happens to your body.

It’s what happens to your understanding of the world.

We have the story of aging almost exactly backwards

Our culture has a remarkably impoverished vocabulary for getting older. Decline. Deterioration. Slowing down. Fading.

Losing relevance.

We describe human beings after a certain age almost entirely in terms of what is disappearing. Which is strange when you think about it. Because something else has been accumulating the entire time.

Experience. Context. Pattern recognition. Perspective. Judgment. Stories. Loss. Failure. Recovery. A fairly sophisticated understanding of which things matter and, perhaps more importantly, which things absolutely do not.

None of this is guaranteed, either. Accumulation isn’t automatically wisdom. Some people spend forty years reinforcing the same three mistakes and calling it experience. The stories get better told. That doesn’t make them truer. But the raw material — the judgment calls made and actually lived with — is sitting there in a way it simply isn’t at 27, whatever you end up doing with it.

By 60, you have probably survived several versions of yourself. The ambitious one. The frightened one. The person who desperately wanted a particular job. The person who thought losing that job would be catastrophic. The person who was certain a relationship would last forever. The person who discovered it wouldn’t. The person who thought success would feel different. The person who eventually discovered that almost everybody is improvising. Including the people in charge.

Especially the people in charge.

And yet we look at someone who has navigated five or six decades of this stuff and somehow conclude that the defining characteristic of that person is decline. That is an astonishingly strange interpretation of the evidence.

Beware the old soldier

There is an old saying — I’ve seen versions of it attributed to everyone and no one — that goes something like this:

I love that. Because it contains an entirely different way of thinking about age.

The old soldier isn’t the diminished soldier. He’s the one who figured out how to stay alive. He’s seen the ambush before. He knows which hill isn’t worth dying on. He knows the difference between courage and stupidity, largely because at some point in his life he has probably demonstrated both.

He knows when to advance. He knows when to retreat. And perhaps most importantly, he has learned that there are a surprising number of battles you don’t actually need to fight at all.

His age isn’t evidence of his decline. It’s evidence of his survival. Of course, surviving does not prove the old soldier was always clever. Sometimes he was lucky. Sometimes somebody else took the hit. Age is evidence that a life continued, not proof that every lesson was learned correctly.

There is something almost comical about the way we reverse this logic everywhere else.

The 27-year-old founder sleeps under his desk, nearly bankrupts himself three times and finally builds a successful company. Resilient.

The 57-year-old loses a spouse, gets made redundant, and rebuilds a career inside an industry that no longer resembles the one he trained in. Old.

Something doesn’t quite add up.

We have mistaken evidence of survival for evidence of decline. Getting older means accumulating scars, certainly. But scars are not evidence that something became weaker. They’re evidence that something healed.

The story we tell about aging shapes the way we design companies, careers, products and entire economies. If older people are primarily understood as diminished versions of younger people, then the objective becomes accommodation.

Make the font bigger. Make the button easier to press. Put a smiling silver-haired couple on a beach. Perhaps give them bicycles. Older people fucking love bicycles, according to stock photography.

But what if the underlying assumption is wrong? What if aging isn’t simply subtraction? What if it is also accumulation?

The strange compounding of meaning

Which brings me back to Raylan Givens. Because the thing I find most interesting isn’t that I understand his line differently now. It’s that the line has been sitting there all along.

The variable is me. I brought sixteen additional years to the sentence. Sixteen years of work. Friendships. Mistakes. Losses. Wins that didn’t matter nearly as much as I thought they would. Failures that mattered considerably less than I feared they would. People arriving. People leaving.

It makes me wonder how many other things in my life I have heard but haven’t heard yet. Books. Films. Songs. Advice from my parents. Things friends told me. Things I told myself.

Perhaps meaning has a strange incubation period. Some ideas require a decade before they begin to glow.

Maybe this is what wisdom actually is. Not the thing we reserve for philosophers, grandparents and people photographed in black and white staring thoughtfully through windows, but something less glamorous. Wisdom is simply experience that has finally acquired meaning.

Time does not automatically make us wise. Some people live the same year sixty times. Experience can become perspective, but it can also become habit, caution, resentment, or a collection of stories in which we are always the person who was right. Age supplies the material. Synthesis still requires curiosity.

The lesson arrives long after the event. This might explain why advice is such an inefficient form of human communication. People who have already learned something desperately try to give it to people who haven’t.

Save your money. Don’t worry so much about what people think. Spend more time with your parents. Take the trip. Leave the job. Tell them you love them.

Your children listen politely. Your younger employees nod. Your younger self would have nodded too. And then everybody goes off and learns it themselves.

Because perhaps some knowledge cannot be transferred. It has to be earned.

The second half of life may be doing something entirely different

For most of our lives, we are encouraged to accumulate. Qualifications. Jobs. Money. Houses. Contacts. Followers. Experiences. Passport stamps. Stories.

The assumption is that more life means more material. But I’m beginning to wonder whether something changes somewhere along the way. Perhaps the first half of life is largely about accumulating experiences. And the second half is about discovering what some of them meant.

That isn’t decline. That’s synthesis. And synthesis may be one of the most valuable human capabilities there is. Especially now.

We are entering an era in which machines can retrieve more information than any human could possibly remember. They can summarise it, recombine it, analyse it and increasingly reason across it.

Information itself is becoming cheap. Experience isn’t. Context isn’t. Judgment isn’t.

Perhaps an economy obsessed with youth is about to discover that it has been systematically undervaluing precisely the thing that becomes more valuable as information becomes abundant: perspective.

The old soldier may turn out to be considerably more useful than we thought.

That’s the hopeful version. The uncomfortable one is that institutions don’t correct a mispricing just because someone points it out; they correct it when the cost of ignoring it gets high enough to notice. Nothing says we’re there yet. An economy can undervalue judgment for a very long time before it learns the lesson.

Getting old ain’t for pussies

There are things about aging I could happily do without.

I would like my eyesight back. I’d like my metabolism from 1994. I’d like to stand up from a chair without occasionally sounding like somebody has stepped on a small accordion.

But I don’t want to be 45 again. That’s the surprise. Because the 45-year-old version of me had things I no longer have. But I have things he didn’t.

I know which battles aren’t worth fighting. I know that very few disasters remain disasters forever. I know careers are much longer and stranger than we imagine. I know reinvention is possible because I’ve watched myself do it.

I know some people stay. Some don’t. I know that things are never as good, or as bad, as they might seem in the moment.

And apparently I can now hear dialogue in television shows that has been waiting sixteen years for me.

So Raylan was right. Getting old ain’t for pussies. But perhaps not for the reason I would have imagined when I was 45.

Getting older isn’t difficult simply because things start disappearing. It’s difficult because life keeps asking you to absorb more of it. More contradiction. More uncertainty. More loss. More change. More understanding. And somehow remain curious enough to keep going.

Maybe that’s what I couldn’t hear the first time. Maybe I wasn’t supposed to.

Or maybe I just wasn’t old enough for that sentence yet.

The noun gives AI the subject. The verb decides what kind of work it does.

Sometimes I get defensive about the length of articles I write. Not with myself, of course, because I fancy myself as a competent writer, but from time to time I go back and read things I have written a year ago, and I can see the progress in not only how I put words together to shape ideas, but the confidence with which I do that has also grown.

“Well, I try and use all the words,” is the inside joke I tell those that might be listening, but that is only half of the story. I tend to over-write the first few drafts, and then I am rather ruthless in cutting at around draft three. By the time I’ve finished red-lining a draft, the page looks less like a document than a Fincher crime scene: controlled, methodical, red everywhere, and clearly arranged to make a point.

But enough about me.

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A client sent back a page of copy with one line above it:

“Can you make this better?”

She wasn’t being lazy. She’d already spent the better part of an hour arguing with the thing and had run out of ways to ask for what she actually wanted. I recognized the sentence immediately, because I’ve done this myself — usually late, usually to something I’d stopped being able to see clearly, usually with the quiet hope that the machine would somehow know what I meant even though I didn’t.

I recently published “Return on Inference“. The piece argued that as AI makes intelligence cheap to deploy, organizations will need to become far more deliberate about what they spend it on. Capital allocation shaped the modern corporation. Inference allocation may shape what comes after. Sadly, the algorithm buried it, but I think it’s a good piece and should have been seen by more people.

Since publishing it, I’ve been thinking about the point where a single person, mid-task, decides what kind of intelligence they actually want from the machine, and the choice of words that are used to get there.

Which might mean the next stage of AI fluency isn’t primarily technical. It’s grammatical.

Make this better

“Make this better” is one of the most common instructions we give AI. Better how? More accurate. Shorter. More persuasive. Less embarrassing. Easier to follow. More likely to survive a meeting with Legal. More like the person who’s supposedly writing it. Less like something that was obviously generated in nine seconds.

“Better” isn’t a direction. It’s a small cry for help. Grammatically, “make” is the verb. “Better” has somehow been left carrying the entire brief.

The model will still answer, and that’s part of the problem. AI is extraordinarily accommodating. It will not stop and say you don’t appear to know what you want. It will simply make the writing smoother, the sentences more balanced, the transitions more professional, and the whole thing about fourteen percent less alive. Then it will hand the result back with the confidence of a tailor who has removed the sleeves because you said you wanted something more streamlined.

The model didn’t fail, necessarily. It may have done exactly what you asked.

The nouns and the verbs

Most prompts make two decisions, not one. The nouns define the territory. The verbs decide what happens there.

Customer loyalty is the subject, a noun phrase. You can ask AI to write about it, summarise it, or compare competing definitions of it. You can also ask it to diagnose why loyalty is declining, challenge the assumption that loyalty exists at all, reframe it as switching friction, model how it behaves under price pressure, identify the evidence that would falsify the whole argument, rank the possible explanations, or recommend which one deserves further investigation.

Same territory. Completely different allocation of intelligence.

Ask a model to “write something about customer loyalty,” and it will write something about customer loyalty. This is the prompting equivalent of walking into a restaurant and asking for food. You’ll almost certainly get fed, if a table is available.

The verbs we overuse

Most everyday AI use runs on a small family of production verbs — write, create, generate, summarise, improve, make. These are useful verbs. They are also the verbs most likely to turn an extraordinarily capable model into a very fast content appliance.

Write the email. Create the deck. Generate the headlines. Summarise the meeting. Improve the report.

The return is visible, because the output is visible. There was no email; now there’s an email. The deck took two hours instead of two days. The meeting nobody wanted to attend has become a summary nobody intends to read, which at least closes the loop.

Productivity has occurred. Time saved is real. Tedious work removed is real. Plenty of tasks in professional life don’t deserve a richer philosophical relationship with the person doing them.

But if production verbs are the only verbs an organization gives its AI, it has made an inference-allocation decision whether it recognizes the decision or not. By default, it has decided to spend abundant intelligence making more things.

The verbs we underuse

The more interesting verbs don’t always produce a finished artifact. They create pressure. Compare. Diagnose. Interrogate. Distinguish. Trace. Challenge. Falsify. Stress-test. Reframe. Select. Reject.

Each one asks the model to take a different posture towards the same material. “Summarise this strategy” asks for compression. “Interrogate this strategy” asks where it’s weak. “Identify what would have to be true for this strategy to fail” asks for conditional reasoning rather than confidence. “Argue against the part I’m most attached to” asks the machine to become usefully disloyal — which is a strange thing to want from a tool built to please you, and worth sitting with for a moment rather than rushing past.

These outputs are much harder to place on a productivity dashboard. That may be exactly their value. They’re more likely to stop an organization from producing the wrong thing faster.

Polish is not neutral

I’ve been learning this from my own writing, slowly and with some resistance.

Ask a model to polish a strategy, and it will make the strategy more polished. That sounds tautological until you notice how much the verb has already decided for you. “Polish” assumes smoothness is the objective. It assumes irregularity is a defect — that a blunt transition should become graceful, that repetition should disappear, that a strange sentence should be tidied into a familiar one, that uncertainty should be tightened into authority it hasn’t earned.

Sometimes that’s exactly what a piece of writing needs. Sometimes the rough sentence is the only evidence a person was ever in the room.

So I’ve mostly stopped asking for polish. I’m more likely to ask a model to identify the live edges, the structural pressure points, and the genuine factual risks, and to leave the optional polish alone. Identify, rather than rewrite. Protect, rather than smooth. Pressure-test, rather than improve.

The model is still helping. It is just no longer allowed to quietly assume that sounding professional is the same as being good.

A sequence of verbs is a workflow

The other mistake is believing we need one heroic prompt — the perfect paragraph of instructions that will make the machine leap from an undeveloped thought straight to a finished answer. Occasionally it works. More often, what actually helps is a sequence: map the territory, identify the contradictions, challenge the premise, develop alternatives, rank them, recommend one, and only then draft.

Some of the quality comes precisely from refusing to ask the model to do all of it at once. Research isn’t synthesis. Synthesis isn’t selection. Selection isn’t writing. Writing isn’t judgment. A system capable of all five still benefits from being told, at each step, which one it’s doing right now.

This is roughly how I’ve built my own AI workflows — one pass to search, another to connect, another to apply pressure, and a human, that would be me, to decide what deserves to continue. The workflow isn’t defined by which model does the work. It’s defined by the verbs I choose to use.

Prompting may be the wrong lesson

A lot of organizational AI training is framed around prompting: templates, context, role definitions, output formats, examples, “think step by step,” capital letters when quietly desperate. Some of this genuinely helps. But prompting can just as easily become another software skill — a technique for operating the interface rather than a discipline for deciding what kind of work deserves to happen at all.

This is the failure I am consistently told about by colleagues who work in the HoldCos. Their AI training consists of an FAQ for “logging in” and a catalog of predefined templates.

The more consequential capability is instruction. Can you tell the difference between asking for production and asking for diagnosis? Can you tell when you need more possibilities and when you need fewer? Can you ask for evidence instead of eloquence, and notice the difference when it arrives? These aren’t primarily technical skills. They’re thinking skills that happen to be expressed through grammar.

Verbs are not magic spells

There will inevitably be a list somewhere — thirty-seven powerful verbs guaranteed to transform your AI prompts, download the PDF, save the carousel, become unstoppable by Thursday.

Changing “write” to “interrogate” won’t rescue missing context, unreliable evidence, an incoherent objective, or someone unwilling to judge the result once it arrives. A precise verb can’t compensate for not knowing what problem you’re solving. And the model doesn’t possess some fixed mental mode called “diagnose” that switches on with the correct incantation — verbs remain instructions, interpreted through context, by something that is still, underneath the fluency, predicting what should come next.

The value isn’t a secret vocabulary. It’s the moment of friction the verb forces on the human: what kind of work am I actually asking for? That question improves the prompt before the model has done anything at all.

The organizational verb problem

There’s a larger version of this same pattern. An organization can buy access to a frontier model and still spend almost all of it on verbs that have existed inside productivity software for two decades — write, summarise, format, schedule, extract. It may get a substantial return from that. It shouldn’t mistake extensive AI use for extensive organizational intelligence.

The verbs give away the ambition. Show me the verbs an organization uses with AI, and I can tell you what it thinks intelligence is for. Is the organization using AI to produce, or to question? To accelerate, or to reconsider? To generate more options, or to work out which option should die? To automate the existing process, or to ask why the process still exists?

This is inference allocation happening at the level of a single sentence. Every prompt spends intelligence against an instruction. Multiply that across thousands of employees and millions of prompts, and those small grammatical choices quietly become an operating model — one nobody voted on, assembled entirely out of defaults.

A company dominated by production verbs will get faster production. That’s genuinely worth having. A company that also learns to reach for analytical, adversarial, imaginative and judgment verbs may start getting something else: not simply more answers, but better questions about which answers are worth having in the first place.

Check your verbs

Next time AI hands you something polished, plausible and quietly unhelpful, don’t immediately rewrite the prompt, swap the model, or decide the machine has no taste. Look for the verbs first.

Did you ask it to write when you needed it to question? To summarise when you needed it to distinguish? To improve when what the piece actually needed was a diagnosis?

The model may have done exactly what you asked. That may be the whole problem — and it’s a more useful one to sit with than “the AI isn’t very good.”

I remember phone books.

Not metaphorically. Actual phone books.

Every few years, an enormous printed directory containing the names, telephone numbers, and home addresses of almost everyone in your city would simply arrive on your doorstep. Nobody had opted in. Nobody seemed particularly alarmed. If you wanted to know where someone lived, you looked them up.

The remarkable part is that almost nobody thought of this as a privacy violation. Because the social architecture around the information was different.

The information was public, but frictional.

Someone could find you, but they had to want to find you. They needed your approximate location. They needed to know your name. They had to get the right phone book, look you up, write the information down, and then actually do something with it.

There was no search history. No aggregation. No automated correlation. No behavioral profile silently accumulating behind the act of looking someone up.

Today, the idea seems almost absurd.

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We have become extraordinarily sensitive to what information about us is captured, stored and made accessible. We debate cookies and location tracking. We cover laptop cameras. We argue about facial recognition, data brokers and whether our phones are listening to us.

Privacy has become one of the defining anxieties of the digital age.

But I wonder whether we have been worrying about the wrong thing. Or at least, only half of it. The technological revolution underway isn’t simply creating a world in which more information about us is captured.

It is creating a world in which less of our lives will ever be forgotten. And I’m not sure we have begun to understand what that might do to us. Because forgetting isn’t simply the failure of memory.

It may be one of the things that makes us who we are.

We Are Not What Happened to Us

An assumption is buried in the way we talk about identity: that we are the sum of our experiences. The older I get, the less aligned I am with this.

I am not the sum of my experiences. I am the sum of the experiences I remember, and the interpretation of those memories.

An enormous amount has happened to you that you cannot remember. Thousands of breakfasts. Conversations. Arguments. Commutes. Rooms. Faces. Feelings. Entire ordinary Tuesdays have disappeared.

Even the things you do remember aren’t stored as pristine recordings. Of my past relationships, I retain a reasonably generous edit: two good people who, in the end, found themselves in difficult circumstances.

The other person may carry a different edit. And what actually happened, if it could somehow be reconstructed in full, might flatter neither of us. We may not have been as good—or as bad—as each of us remembers today.

Memory edits.

It compresses. It exaggerates. It combines. It changes emphasis. It removes context. It reconstructs events using information acquired years after they happened.

A relationship that lasted ten years becomes five or six scenes. A childhood becomes a collection of fragments. A career becomes a handful of triumphs, failures and people who mattered.

Sometimes the editing is generous to us. Sometimes it isn’t. But slowly, almost invisibly, those selected fragments become a narrative. And that narrative becomes us.

Ask someone who they are, and they won’t give you a database. They’ll tell you a story. Where they came from. What happened to them. Who loved them. Who hurt them. The chances they took. The mistakes they made. The things they overcame.

Identity is autobiographical. And autobiography requires editing. Until now, forgetting has been the most ruthless editor we’ve ever had.

The Great Human Compression Algorithm

We tend to describe forgetting as a weakness. I forgot his name. I can’t remember where we went. The details are fuzzy. She remembers it better than I do.

The language itself suggests malfunction. Memory would ideally be complete; unfortunately, the biological hardware isn’t quite good enough.

We have spent thousands of years trying to fix the problem. Writing externalized memory. Painting preserved faces. Photography preserved moments. Film preserved movement. Audio preserved voices. Computers preserved information. Cloud storage made preservation effectively limitless.

And now AI is making it all retrievable.

That last development changes the equation.

An archive containing a billion pieces of information isn’t really memory if nobody can find anything inside it. Until recently, this provided a strange kind of protection. We were already capturing extraordinary amounts of our lives, but most of it disappeared into digital attics.

Twenty thousand photographs. Fifteen years of email. WhatsApp conversations with people we no longer speak to. Calendar entries. Search histories. Documents. Voice notes. Videos. Location records.

The information existed, but practically speaking, much of it was forgotten.

AI changes that because it doesn’t merely increase our capacity to store the past. It increases our capacity to retrieve and interpret it. Suddenly the attic has someone living inside it, and they have unpacked and cataloged everything.

The Witness Who Never Forgets

Imagine having an AI companion for forty years. Not today’s chatbot that vaguely remembers a preference you mentioned three conversations ago.

Something persistent.

It has read your messages, with permission. Seen your photographs. Managed your calendar. Helped write your documents. Listened to meetings. Watched your health data. Organised your finances. Travelled inside your phone. Perhaps eventually seen much of what you see and heard much of what you hear.

At 70, you might have an intelligence beside you that has effectively witnessed your adult life.

And unlike your spouse, children, friends or colleagues, it hasn’t forgotten.

You say: “I’ve always been good under pressure.” It replies: “That’s interesting. Your behaviour suggests otherwise.”

You say: “I was always there for my children.” It can retrieve the calendars.

You say: “That was the happiest period of my life.” It has the messages you wrote while you were living it.

You tell a favorite story about something that happened in 2034. It says: “That’s not quite how you described it at the time.”

At first this sounds useful. Then it starts to sound disturbing. Because what happens when your memory and the record disagree?

Who Gets to Decide Which Version of You Is True?

Imagine a father who genuinely remembers himself as present and loving during his daughter’s childhood. His daughter remembers something different.

Today, there is no definitive resolution. His memory contains one childhood. Hers contains another. Both have been edited through emotion, perspective, and time.

But imagine an AI capable of reconstructing those years. It knows how many evenings he arrived home after she went to bed. How often he cancelled weekends. How frequently he called when traveling. It can search photographs, calendars and messages. It can identify patterns neither person remembers.

Perhaps it concludes:

“Between the ages of eight and twelve, your father canceled 31 percent of the weekends you were scheduled to spend together.”

Something extraordinary has happened. The argument is no longer between two memories.

There is now a third witness. The archive. And the archive has receipts. But even then, has it discovered the truth?

Thirty-one percent is a fact. It isn’t necessarily a childhood.

Perhaps the weekends he was there were extraordinary. Perhaps the daughter experienced his absence differently at different ages. Perhaps his work prevented financial catastrophe. Perhaps one conversation mattered more than a hundred missed dinners.

Data can reconstruct events with extraordinary precision while still failing to reconstruct meaning. Which leaves us with a deeply uncomfortable possibility. The machine may eventually remember our lives more accurately than we do while understanding them less completely. And yet we may still defer to it because it has evidence.

Memory Has Never Been a Courtroom

This is where perfect memory collides with something fundamentally human. We don’t simply remember events. We reinterpret them.

Something that humiliated you at 25 becomes funny at 45. A relationship you considered disastrous becomes, in retrospect, the experience that taught you what love required. A career failure becomes the moment everything changed. Someone you once hated becomes someone you understand.

Memory isn’t static because we aren’t static. The meaning of the past changes as the person remembering it changes. That may be one reason memory works the way it does. Its instability allows the past to remain psychologically negotiable.

We can forgive. We can contextualize. We can soften. We can occasionally deceive ourselves. And we can move on.

A permanent searchable archive introduces something humans have rarely had to contend with: the past refusing to change with us.

The Right to Become Someone Else

Think about adolescence.

Almost everyone who grew up before social media benefited from a remarkable privilege we rarely recognized at the time.

Most of our stupidity disappeared. The terrible haircut survived in three photographs. The offensive joke disappeared when the sound waves dissipated. The drunken argument became an unreliable story among friends. The ridiculous opinion you passionately defended at seventeen vanished unless somebody happened to write it down.

You were allowed to become someone else partly because there wasn’t enough evidence to continuously prosecute you for who you had been.

That condition is disappearing. A child born today may accumulate a searchable digital history before they can speak. By adolescence, there may be thousands of hours of photographs and video. Messages. Posts. Searches. School records. Location data. AI conversations. By middle age, there could be enough material to reconstruct an astonishing percentage of a life.

We usually frame this as a privacy problem. But perhaps it is also an identity problem.

What happens to reinvention when every previous version of you remains available? What happens to forgiveness when every offence can be replayed? What happens to growth when contradiction can always be exposed?

“You say you believe this now. But here’s what you said twenty-three years ago.”

We tend to celebrate consistency as a virtue. But a human being who never contradicts their younger self isn’t necessarily principled. They may simply have stopped evolving. The ability to leave previous versions of ourselves behind may be essential to becoming someone new.

From Voluntary Recall to Algorithmic Recall

Another shift worries me even more. Historically, remembering has largely been initiated by humans.

A smell reminds you of your grandmother. A song transports you back to university. You open an old photograph. A friend says, “Do you remember when…?”

Memory arrives through association, accident or intention. AI creates the possibility of something different: memory initiated by someone — or something — other than you.

Your AI assistant notices a pattern.

“This isn’t the first time you’ve reacted this way.”

It retrieves an argument from fourteen years earlier.

Your relationship AI notices that you and your partner are repeating a conflict from 2037. Your health system identifies that your behavior resembles the months preceding a previous depressive period. Your professional AI reminds you that the strategy you’re advocating contradicts something you argued in a meeting six years ago.

These could be incredibly valuable interventions. They could also fundamentally alter our relationship with the past. Because now memory isn’t merely something we possess. It is something that can be done to us.

An algorithm can decide which version of our past is relevant to our present. That is an extraordinary amount of power.

Privacy May Not Be the Biggest Question

For decades, we’ve asked who should be allowed to collect our data. That’s still an important question. But the age of AI introduces another:

Who should be allowed to remember us? And for how long?

Should your AI remember everything you’ve ever told it? Should you be able to ask it to forget? Should some memories decay automatically? Should an AI distinguish between factual memory and emotional memory? Should your teenage conversations survive into middle age? Should your employer’s AI be permitted to remember mistakes after the humans involved have forgotten them? Should a relationship have a shared memory? Should a family?

And what happens when two people’s AI memories disagree?

These sound like technology governance questions. I suspect they will eventually become philosophical ones. Because beneath them sits something we have barely had to articulate before:

Is forgetting a human right?

Perhaps Forgetting Was Never the Bug

There is something wonderfully human about the fact that memory is incomplete.

Two old friends can sit at a table and argue about something that happened forty years ago.

“No, no. That’s not what happened.”

“Of course it was.”

“You weren’t even there.”

“I absolutely was.”

Eventually they laugh.

There is no server to consult. No transcript. No definitive replay. The past remains partly theirs. There is ambiguity in it. And ambiguity creates room for interpretation, forgiveness, mythology and meaning.

I’m beginning to wonder whether we have misunderstood forgetting because we have evaluated it like engineers. We saw missing information and assumed failure. If complete memory were an uncomplicated evolutionary advantage, we might expect humans to remember more than they do.

Instead, in the end, humans forget the majority of the things that happen to them. Perhaps that limitation carries benefits we have never needed to name because we have never seriously threatened it before.

Forgetting allows emotional wounds to soften. It allows insignificant information to disappear. It allows experiences to become stories. It allows stories to become meaning. And perhaps most importantly, it allows identity to remain editable.

The Last Thing We May Need AI to Learn

There is a wonderful irony here.

Human civilization can be understood, in part, as a war against forgetting. We carved stories into stone because storytellers died. We invented writing because memory failed. We painted portraits because faces disappeared. We invented photography because moments vanished. We built databases because organizations forgot. We built the internet because information was difficult to retrieve.

And now we are building artificial intelligence capable of making almost everything searchable, interpretable and persistent.

We may finally be approaching the thing we’ve been pursuing for thousands of years: something close to perfect memory. And when we get there, we may discover that perfect memory was never the objective.

The objective was meaning. Those are very different things.

The great challenge of artificial memory may therefore not be teaching machines how to remember more.

It may be teaching them what humans have always known instinctively: what deserves to be remembered, what should be allowed to fade, and what must eventually be forgotten.

Because our identities were never constructed from everything that happened to us. They were constructed from what remained.

And if forgetting becomes optional, we may discover that the most important question of the AI age isn’t whether machines can remember us.

It is whether they will allow us to forget.

The Second Meaning of ROI

Return on Investment is close to a religious text in business. Put capital in. Get productivity, margin, or growth out. Measure the gap.

Investment means money, in one form or another, because money is the scarce thing being allocated. AI is beginning to put pressure on that assumption.

It is giving organizations serious access to another resource that has always had to be rationed: intelligence - the capacity to analyze, interrogate, model, compare, imagine, and challenge. Which raises an obvious and slightly absurd-sounding question:

If intelligence is becoming cheap enough to deploy almost anywhere, does ROI need a second meaning?

Call it Return on Inference.

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This is not my phrase. I first heard Greg Shove use it during his brilliant How to Build an AI Supercompany workshop. I had already completed Section’s AI for Business Mini-MBA—still the greatest return on investment of any continuous-learning platform I have paid for—so I was listening carefully.

Return on Inference. That phrase stayed with me because, beneath the wordplay, it raises a genuinely useful management question.

Not simply: How much AI are we using? but: What are we spending our intelligence on?

Training Builds It. Inference Spends It.

Inference is one of those words that has migrated out of technical conversations without becoming particularly well understood inside business ones. The simple distinction is this:

Training acquires capability. Inference exercises it.

Training is the long, expensive process through which a model learns patterns from enormous amounts of data. Inference happens when the trained model is put to work.

Ask it to interrogate a customer segment, stress-test a strategy, draft a hundred product names, classify an image, model a competitor’s response, or explain why a campaign underperformed, and the model is performing inference.

Training built the capability. Inference spends it against a problem.

That framing is a metaphor, not an accounting standard. Infrastructure teams may use Return on Inference to ask how much value a company receives from its compute spend. I am interested in the organizational question: what value came back from the intelligence we chose to deploy? No single, interchangeable unit of intelligence is being withdrawn from a corporate account. But the metaphor matters because the marginal cost of asking the model to think again, to produce another analysis, alternative, simulation, objection or possibility, is falling extraordinarily quickly.

For most of business history, that was never true.

If you wanted to understand a market properly, you commissioned research and waited six weeks. If you wanted to model a decision, you pulled analysts away from something else. If you wanted to explore what might happen to your category in five years, you convened the kind of workshop that ends with sticky notes covering a wall and a facilitator asking everyone to “park that thought.”

Sophisticated thinking was expensive because people had to do the thinking, and people are finite. That is why organizations rationed it. Not because three scenarios were correct and eleven would have been reckless. Three were what the budget, timetable, and available human attention could support.

That constraint is beginning to dissolve.

Two Rooms

Look closely at how AI is landing inside organizations, and it tends to separate, reliably, into two rooms.

In the first room, AI has been absorbed into the existing shape of the work. Meetings get summarised. Decks get built in a quarter of the time. Reports that once took an analyst two days now take ninety minutes and read better. Customer-service teams handle more tickets. Marketing teams produce more variations. Administrative work quietly disappears.

Someone, usually the CFO, usually with a genuine and defensible sense of achievement, puts a number on the productivity gain and takes it upstairs.

The number is real. Nobody should sneer at it. A four-hour task completed in one hour returns three hours to the organization. Multiply that across a few thousand people, and you have a business case any board can understand.

In the second room, something else is happening. The same tools are being used to interrogate customer behavior continuously rather than quarterly. Teams are generating hundreds of products, pricing or positioning hypotheses and killing most of them cheaply before they reach a meeting. Competitor responses are modeled before a strategic move, not explained after it. Someone goes looking for the anomaly buried in the data that nobody previously had a reason to search for, because searching for it used to cost more than it was worth finding.

One room uses intelligence to make existing work cheaper. The other uses intelligence to make previously uneconomic thinking possible. Both may use the same model. Both may report similar AI spend. Both may appear in the same board pack under the same transformation program, with similarly impressive arrows pointing upwards. But they are producing different returns.

One returns efficiency. The other returns possibility. That is not a difference of degree. It is a difference of kind.

The Question Isn’t the Number

What would it mean to examine a thousand plausible versions of your market instead of three?

For almost the whole of recent business history, the question would have been absurd. It was the sort of provocation somebody used in a workshop to expand everyone’s thinking before returning to the three scenarios there was time to develop.

Put the same request to a well-instructed model today, and the absurdity relocates. A thousand outputs are achievable. The real question is whether you have a thousand things worth asking. And, more uncomfortably, whether you would recognize the useful ones if they arrived.

This relocation is the central AI story.

The constraint moves from production to selection. From: Can we make enough? to: Can we distinguish what matters?

Productivity measures the difference between what the work used to cost and what it costs now. It tells us almost nothing about the value of questions the organization could not previously afford to ask.

Inference Allocation

A CEO’s responsibility is not merely to make sure the company has capital. It is to decide where capital should be deployed. Against which opportunities? At what level of risk? Over what period? What deserves another round? What should stop being funded? Where might a disproportionate return exist precisely because other organizations cannot see it yet?

Capital allocation became one of the defining disciplines of modern management.

AI creates the possibility of a similar discipline around intelligence. Call it inference allocation.

Where should the organization deploy its capacity to analyze, simulate, question and imagine, and against which problems? How much belongs in making existing systems more efficient, and how much in questioning whether those systems should exist at all? Which assumptions deserve to be tested continuously, which decisions deserve more intelligence rather than faster execution, and which parts of the business should receive exploratory capacity even when the return cannot yet be placed into a spreadsheet? And afterward: which of the possibilities it generates are consequential enough to deserve scarce human judgment?

These are allocation questions. Yet most enterprise AI strategies remain acquisition strategies — which models to use, how many licenses to buy, which vendors are approved, how to train employees, what belongs inside the governance framework.

Necessary questions. But temporary ones. Access will not remain the advantage for long. It rarely does with a general-purpose technology. The advantage will come from what an organization decides to do with the access.

The Cheapest Intelligence in History

There is a darker possibility here.

The cheapest intelligence in history could still produce some of the most intellectually passive organizations we have ever seen.

A company can possess enterprise licenses, multiple models, thousands of active users, automated reports, AI-generated ideas, continuous analysis, and a board-approved transformation program — and remain fundamentally incurious. If every model is asked only to accelerate the current workflow, the organization may have abundant inference and no imagination about where to deploy it.

Exploration Isn’t Automatically Virtuous

Exploration sounds like the more sophisticated use of intelligence until you ask how anyone is meant to govern it. Productivity is wonderfully measurable. You can put the hours saved into a spreadsheet and defend them in a budget review. Exploration is not.

What is the value of hypothesis 783 when the first 782 went nowhere?

What is the return on discovering a customer behavior nobody thought to look for, measured against the analyst time required to work through nine hundred plausible observations?

How do you distinguish a useful anomaly from a beautifully articulated hallucination?

An organization that generates infinite cheap thinking without a serious discipline for judging it is not operating at the frontier. It is drowning politely, with excellent production values, in its own inference.

“We are exploring too” can become another kind of corporate theatre: a slide showing a thousand AI-generated ideas that nobody had the judgment, nerve or time to cull.

Cheap thinking without curation is not insight. It is noise with a research methodology attached.

Judgment Was Never the Cheap Part

The constraint has not disappeared. It has moved.

For most of business history, scarcity sat near the point of production: who has the time, who has the analysts, who has the budget to investigate this, who can make the thing. Increasingly, it sits at the point of allocation and selection: which questions are worth funding with inference, which outputs deserve human attention, which patterns are real, which possibility changes the decision, who is prepared to act, who remains accountable.

A prompt tells a system what to do. A question decides what is worth thinking about. Those are not the same capability.

An AI can produce an excellent answer to an irrelevant question. It can optimize a process that should not exist. It can generate a hundred well-argued ideas for a product nobody needs and do it beautifully, on request.

Left unsupervised, it can make an organization spectacularly efficient at doing the wrong thing. Which is considerably worse than being inefficient at it, because at least inefficiency slows you down long enough to notice. Back to that fiction thing.

If inference becomes abundant, curiosity and judgment do not become obsolete. They become the allocation system.

What are we not seeing? What are we treating as a settled fact that is really an assumption nobody has tested since the last downturn? What would we investigate if the investigation were nearly free? What are we spending intelligence on simply because the answer is easy to measure?

Those are not merely prompting skills. They are organizational capabilities closer to editorial judgment, strategy, and leadership than technical fluency. They do not appear prominently on the roadmap of any AI vendor because no vendor is particularly incentivized to tell a customer that the next bottleneck may be their own curiosity.

Return Without a Ratio

Here is what I think Return on Inference does not resolve.

It does not tell us how to price a hypothesis, provide a clean P&L line for curiosity, explain how a CFO should report “questions we finally had the capacity to ask” beside the productivity numbers, or tell us how to compare the inference used to generate marketing variants with the inference used to model a strategic risk.

Somebody may eventually create a credible measurement system. I have not seen one yet.

Return on Investment took decades to become the apparently clean, single-line number organizations now use, sometimes with more confidence than wisdom.

This second meaning, Return on Inference, will not arrive fully formed. But the absence of one perfect ratio does not remove the management responsibility. Return on Inference could include better decisions, faster decisions, more experiments, earlier rejection of weak ideas, improved customer experiences, fewer errors, new products, hidden patterns, increased output — and questions the organization previously could not afford to ask. The return depends on what the intelligence was deployed to do.

Which brings us back to allocation.

The Questions Worth Funding

Return on Investment isn’t going anywhere. Capital matters. It always will. But capital may no longer be the only resource a leadership team is judged on how well it allocates.

The last few years were a race to acquire AI capability: Licences. Models. Governance committees. Training programs. The slide deck nobody finished. That race is nearly over. Winning it won’t distinguish anyone.

The next race isn’t about access. It’s about inference allocation.

Where is intelligence actually being deployed, and against which problems? Is it making what already exists cheaper, or discovering what doesn’t exist yet? Who chose the questions worth funding, who judged what came back, and who’s on the hook when inference turns into action?

Return on Inference isn’t a clean number. It may never be one. But the accountability question has already arrived, whether or not anyone’s named it yet.

The next advantage will not come from having access to intelligence. Access alone will not distinguish anyone for long.

The next advantage will come from knowing what to spend it on.

I have been sick over the past two weeks. Those random Tarot card readings on IG say it’s because my “system” is purging, setting my universe back in order for what is to come. I am a Taurus, so there seems to be some rather profound stuff happening in my world, seen or unseen, and well, my body just got a massive housecleaning.

Head cold → sinus infection → bronchitis

I can’t lay down because the lung butter won’t let me breathe. I can’t sleep because the coughing keeps me awake at night. And I can’t think clearly because, well, I couldn’t see the little 10ml marker on the cough syrup bottle and might have taken too much.

So I spent my nights watching movies.

I was watching a film at 2:30 in the morning recently when a burst of dialogue quite literally made me sit up and rewind the scene. It was the 2018 HBO adaptation of Fahrenheit 451. Beatty, the fire captain, the book burner, the man who destroys knowledge for a living while quoting literature from memory, delivers a line borrowed from Proust. Not paraphrased. Lifted. Placed inside the mouth of the villain, which tells you how sharply the adaptation understood Bradbury’s stakes. Beatty is pure Bradbury: the man destroying literature is also the man who remembers what it contains.

The line was something like this:

So long as you distract your mind from its dreams, it will not know them for what they are. If a little dreaming is dangerous, the cure for it is not to dream less but to dream more, to dream all the time.

I sat with it for a while. I captured a few voice notes into Claude. The film is still on pause. Then I guess I fell asleep. When I woke up, I read my notes, which ended with:

This is me coming back to it.

My Substack - Some Assembly Required - has always been where I work things out in public. It’s where the ideas are longest, sharpest, and least edited for palatability.

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We have confused volume for vitality.

The machine produces. We measure. Output is up. Speed is up. The gap between brief and execution has narrowed to something approaching zero. In some agencies, in some studios, in some marketing departments, a brief that once took three weeks now takes three hours. This is genuinely useful. I am not going to pretend otherwise, and I am not going to perform nostalgia for the inefficiencies of the pre-AI workflow. I lived inside those inefficiencies for thirty years. Some of them were romantic. Most of them were just slow.

But here is what we have not yet properly reckoned with.

The friction was not only the obstacle. The friction was also the mechanism.

There are two kinds of friction and they are not the same thing. Production friction — the unnecessary difficulty of making the thing — is worth removing. AI removes it. Good. The kind of friction worth protecting is imaginative resistance: the difficulty of discovering what the thing should be. That is not inefficiency. That is the work.

The gap between what you dreamed and what you could make, that distance, that resistance, that productive difficulty, is where a great deal of the actual thinking happened. It is where you found out whether the idea was real or just attractive. It is where the brief got interrogated instead of merely answered. It is where you discovered, sometimes at significant personal cost, that the thing you thought you wanted to say was not actually the thing you needed to say.

Remove the friction and you get answers faster. You also get answers before you have properly finished forming the question.

Proust understood this more precisely than almost anyone. The full passage, from Within a Budding Grove, is worth the full read:

When the mind has a tendency to dream, it is a mistake to keep dreams away from it, to ration its dreams. So long as you distract your mind from its dreams, it will not know them for what they are; you will always be taken in by the appearance of things, because you will not have grasped their true nature. If a little dreaming is dangerous, the cure for it is not to dream less but to dream more, to dream all the time.

The argument is philosophical, not sentimental. Proust is not telling you to be a dreamer in the inspirational-poster sense. He is making a claim about perception. If you keep your mind distracted, busy, stimulated, moving fast, producing, you will mistake the surface of things for their nature. You will keep being fooled. You will make things that look right but aren’t.

You will answer the question on the brief without ever reaching the question underneath the brief.

The dreaming mind is not the unproductive mind. It is the mind that reaches past the immediate, past the obvious, past the thing that the algorithm can already generate. It is the mind doing the work the machine can assist but cannot complete on your behalf. Not merely the execution, but the search.

Bradbury put it differently, because Bradbury always puts things differently. I read a lot of him in high school, not because I had to, but because my parents asked me not to.

Stuff your eyes with wonder. Live as if you’d drop dead in ten seconds. See the world. It’s more fantastic than any dream made or paid for in factories.

That last phrase is doing a lot of work. Made or paid for in factories.

In 1953, Bradbury was talking about television. The four-wall parlour. The speed culture. The digest-of-the-digest. The politics reduced to a headline, the idea reduced to a slogan, the attention span trained down to the duration of a commercial break. He was diagnosing a society that had traded the difficulty of experience for the comfort of simulation.

The world Bradbury imagined, where the real is crowded out by the frictionless, the fabricated, the fast, is not a dystopia we avoided. It is a description of the content economy we built, and are now accelerating with considerable enthusiasm and venture capital.

More fantastic than any dream made or paid for in factories.

The real world. The physical world. The world of actual encounter, actual friction, actual surprise, the world where 1+1 occasionally equals purple, and you cannot predict it in advance.

A model may generate the image. It cannot have gone somewhere, seen something, and returned carrying the consequence.

I have been writing about the Presence Economy for a while now, from a different angle.

The argument there is about live experience, concerts, dinners, conversations, travel, the things that cannot be streamed or reproduced or served as a notification. To me, the growth of live experiences is a pendulum swing against generated content. It is where authenticity will be validated by being there IRL. I have been watching the premium on those experiences rise in direct proportion to the volume of synthetic content surrounding them.

What I did not fully see, until Proust said it to me at 2:30 in the morning through the mouth of a fictional book-burner, is that the Presence Economy and the creativity argument are the same argument.

The scarcest thing in a world of infinite generated content is not better content. It is the evidence of a mind that actually went somewhere, saw or felt something, and came back changed.

This is what the great photographers understood. You do not take a photograph by standing in front of a subject and pressing a button. You take a photograph by having lived a life that taught you which moment to press the button. The image is the residue of the seeing. The seeing is the residue of the dreaming. And the dreaming — Proust is insistent on this — cannot be rationed, cannot be managed, cannot be optimized without being destroyed.

None of this means the tools are the enemy.

I use them every day. I think in them, structure with them, test arguments against them, produce with them. The tool does not dream. You do. Or you don’t.

The distinction is this: Are you bringing a dream the machine cannot yet follow, and using it to help you build toward that dream? Or are you arriving at the machine without a dream, and accepting whatever it generates as the dream?

The first is a creative practice. The second is outsourcing your imagination to a system trained on what previous imaginations have left behind.

The cure, Proust tells us, is not to generate less. It is to dream more. Not to outrun the machine — but to stop allowing the machine’s capabilities to define the edge of what you are trying to imagine. To keep the reaching alive. To refuse the prompt that satisfies too quickly. To treat the gap between what you dreamed and what the machine produced not as a problem to be closed but as the signal that you are still doing the work that matters.

Become the Book

Montag’s rebellion in Fahrenheit 451 is not that he reads more. It is that he becomes the book. He commits the text to memory, carries it inside him, keeps it alive in a world designed to make living difficult.

That is the creative posture for this moment. Not resistance to the tools. Not nostalgia for the pre-AI workflow. Not the performance of human authenticity as a brand strategy.

Just this: dream all the time. See the world. Keep reaching past the edge of what the machine already knows how to give you.

The real is more fantastic than anything made or paid for in factories.

Go find out what that means. And while you do that, the cough syrup is calling my name.

For most of my working life, the deliverable decided where the work began.

If the client needed a presentation, I opened PowerPoint. If I was writing an article, I opened Word. If somebody wanted a film treatment, a strategy document or a point of view, I opened whichever application seemed most likely to produce the correct-looking object at the end.

I thought I was choosing a tool. I was actually choosing the shape of the thinking before I had done any of it.

The moment you open PowerPoint, you begin thinking in slides. How many will there be? What’s the headline? Can this argument fit above the fold? Where does the chart go? Which piece of evidence deserves a full page? How much text can survive before someone says it isn’t visual enough?

None of those are ridiculous questions. Eventually, they matter. But they arrive far too early.

Before the argument is ready, it is already being compressed. Before the evidence has been properly interrogated, it is being arranged. Before you know what you think, you are deciding what it should look like.

The presentation has begun presenting before the thinking has finished thinking.

My Substack - Some Assembly Required - has always been where I work things out in public. It’s where the ideas are longest, sharpest, and least edited for palatability.

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The file before the thought

This isn’t only a PowerPoint problem.

Word encourages document-shaped thinking. The blank page quietly assumes there will be a beginning, a middle and an end. Excel invites the world to become rows, columns and formulas. Creative software encourages us to start making the thing before we’ve understood what the thing is for.

A film director would never claim the trailer contains everything worth knowing about the film. A creative team knows the final campaign isn’t the same thing as the observations, arguments, rejected routes, cultural tensions and choices that produced it.

And yet in strategy and knowledge work, we routinely treat the finished deck as both the source and the final expression of the thinking.

Then, six months later, somebody wants to update it. The original team is gone. The source links are buried in someone’s inbox. Nobody remembers why one argument survived and another disappeared. A chart has been copied so many times that its origin is now “the previous deck.”

The organization still has the presentation. It has lost most of the intelligence that made the presentation possible.

The suspicious little .md file

I arrived at this by way of a file extension I’d barely noticed before.

.md

Markdown.

I’d seen these files sitting inside AI tools, coding environments and project folders and assumed they belonged to developers — the unglamorous plumbing the rest of us weren’t expected to understand.

Then I opened one.

Markdown is almost comically simple. Plain text with a few lightweight signals describing the structure of what you’re writing. A hash mark identifies a heading. Two hash marks, a section beneath it. A dash makes a list. A pair of asterisks adds emphasis. That’s roughly the level of technical sophistication required.

# The Epilogue Economy

## The Problem

People are living longer, but many of the systems around them
were designed for shorter lives.

### Three consequences

-Longer careers
-Longer consumer lifetimes
-New definitions of retirement

> Longevity isn't simply changing old age.
> It's changing the architecture of adulthood.

**The opportunity:** rethink the second half of life. 

There’s virtually nothing proprietary there. You can open it in a text editor and understand it immediately.

But a machine sees considerably more than prose. It sees:

Title → section → subsection → list → quotation → emphasis.

That’s the important distinction. Markdown separates meaning from appearance.

A Markdown heading knows it’s a heading. It doesn’t know whether it will eventually become 32-point Helvetica on a black slide, a section in a report, a webpage title or a line inside an email briefing. It knows what the information is without deciding how the information must look.

The absence of presentation is the feature.

The source and the rendering

This led me to a distinction that feels obvious in software and strangely unfamiliar in most executive work: the source and the rendering do not have to be the same object.

A webpage is not its source code. A finished film is not its footage, script, production notes, and edit timeline. A television commercial is not the strategic and creative history that produced those thirty seconds.

But we often behave as though a strategy deck is the strategy.

What if the deck were simply one rendering of a deeper, more complete source?

That source might hold the original question, the research and its links, what we observed, what we inferred, the assumptions we made, the tensions we haven’t resolved, the options we rejected, the decisions and their rationale, the current argument, what’s changed since the last version, and what we still don’t know.

Not all of that belongs in the presentation. It may belong in the strategy.

Once that source exists, the Board deck becomes one possible rendering of it. So does the creative brief. So does the two-page executive summary, the internal FAQ, the keynote speech, the client workshop, the campaign narrative, or the background document handed to a new team six months later.

The source is durable. The renderings are situational. Think of Markdown as a source format rather than a destination format.

You might begin with:

Research → thinking → argument → source

Then render that source as:

• a Word document

• a website

• a presentation

• a PDF

• a newsletter

• an article

• AI context

• database or CMS content

That’s a very different philosophy from starting in PowerPoint. If you start in PowerPoint, you’ve already made dozens of decisions about the eventual container. If you start upstream, you’re essentially saying: Let’s establish the canonical version of the thinking first. Then decide how it should manifest.

Markdown happens to be an extraordinarily simple way of doing that.

The deck is a compression artifact

This distinction mattered before AI. It matters far more now.

The most obvious use of AI in knowledge work is to put a copilot inside the applications people already use. Help them make the deck faster. Summarise the Word document. Suggest a formula. Rewrite the email.

Useful, certainly.

It also leaves the existing architecture of knowledge work almost completely untouched. We still begin inside the intended output. We still trap thinking inside finished documents. We just produce those documents faster.

The larger opportunity sits upstream. A strategy deck may represent the final compression of hundreds of pages of research, interviews, competing hypotheses, cultural observations, rejected arguments, conversations, and judgment. Most of that material is deliberately removed before the deck reaches the boardroom.

That’s not a flaw in PowerPoint. That’s what a presentation is supposed to do. A good deck is selective. It compresses. It edits. It removes complexity in service of a particular audience and a particular decision.

Which means the deck is already a compression artifact.

AI can read a PowerPoint file. That was never the problem. The problem is that AI cannot reliably recover the thinking that was removed to make the PowerPoint concise. It can infer what might once have been there. It cannot recover what no longer exists. So when we ask AI to work from the final deck, we’re asking it to work from whatever survived the compression.

Give it the source instead — the evidence, argument, assumptions, decisions and unresolved questions — and something different becomes possible.

The CMO can get the commercial argument. The creative team can get the human tension. The filmmaker can get the emotional world, characters and possibilities. The board can get the decision, risk and consequence.

The source stays consistent. The rendering changes according to who needs it and why.

This isn’t content repurposing. It isn’t turning one article into twelve social posts and calling it a strategy. It’s preserving the intelligence upstream so different outputs can be generated without rebuilding — or quietly distorting — the thinking underneath them.

The organizational attic

Now scale that problem beyond one person and one presentation. Imagine a company that has commissioned hundreds of strategy projects over the past decade. Brand strategies. Customer studies. Innovation programs. Category analyses. Cultural research. Campaign post-mortems. Competitive reviews.

In theory, that company possesses an extraordinary body of institutional intelligence.

In practice, much of it lives in SharePoint folders full of PowerPoints, PDFs, Word documents and spreadsheets. There are final presentations, final-final presentations, presentations copied from presentations, charts whose provenance has disappeared, and decisions whose rationale left the company with the person who made them.

The organization has accumulated knowledge without necessarily building a knowledge system. It has an organizational attic. This was inefficient before AI. It becomes a strategic constraint with AI.

Companies are now spending extraordinary amounts of money trying to make their organizations “AI ready.” Much of the conversation is about models, licenses, copilots, agents, and training.

But an AI system can only reason with the intelligence it can access.

If twenty years of organizational thinking has been repeatedly flattened into presentation artifacts, the problem isn’t simply whether your AI can open PowerPoint. The problem is that much of the connective tissue — why a decision was made, what evidence challenged it, what was rejected, what changed, what remained unresolved — was never preserved in the first place.

AI readiness is therefore partly a knowledge architecture problem. And that raises a much more consequential question than whether your team should start using Markdown:

Where does your organisation’s thinking actually live?

If the answer is PowerPoint, Word, PDFs, email threads and people’s heads, no Copilot licence fixes the underlying problem.

The deck has a half-life. The thinking shouldn’t.

For decades, the deliverable was the asset. The deck, the report, the PDF — that was the culmination of the work. Once it was approved, sent, or presented, the project felt complete.

But a presentation is built for a particular audience, at a particular moment, for a particular decision. That’s precisely why it can be so valuable. And precisely why its value is temporary. Change the audience, the moment or the decision, and the presentation needs to change with it.

The source can be more enduring. It can be challenged against new evidence. Compared against previous thinking. Updated without pretending the past never happened. Recombined with material from another project. Handed to a future AI system as context. Rendered into a format that didn’t exist when the original work was made.

The deck has a half-life. The intelligence that feeds it shouldn’t. The project is no longer fundamentally a deck. It can become a knowledge system.

Markdown is not magic

None of this means every executive should abandon PowerPoint and start writing Markdown files. Nor does putting .md at the end of a filename make the contents intelligent.

A badly structured Markdown file becomes just another stale document. It can lose its sources, duplicate another version, bury assumptions and preserve conclusions long after the circumstances that produced them have changed.

Canonical knowledge needs more than plain text. It needs discipline around provenance, structure, versioning, decisions, connections, ownership and updating — around what’s active and what’s already been superseded. And Markdown isn’t inherently good at sophisticated layouts, art direction, complex diagrams, pixel-perfect presentations, elaborate tables or highly visual storytelling.

But that’s almost the point. Those things belong later in the pipeline.

Markdown is useful at the stage where you’re asking:

What do we know? What do we believe? What’s the argument? What’s the hierarchy? What supports what? What’s missing?

Then you move into design.

Markdown isn’t the solution. It’s a simple doorway into the principle. It makes it easier to see that intellectual work has a source layer and an output layer, and that we’ve spent a very long time building organizational knowledge almost entirely inside the second one.

I wouldn’t argue that Markdown is the best format. I’d make a slightly different claim:

Markdown may be one of the best formats for knowledge to exist in before you decide what that knowledge needs to become.

In an AI-native workflow, that distinction becomes much more important. The valuable asset is increasingly not the PDF, presentation or webpage at the end. It’s the structured, machine-readable body of thinking upstream from all of them.

That’s why “Markdown-first” is potentially much bigger than a writing preference. It’s an architecture for how intellectual work gets created, interrogated, reused, and ultimately published.

I have already changed how I work

I didn’t arrive at this because somebody persuaded me Markdown was superior. I arrived at it because I started working differently.

I recently began building what might be called “My Brain.” When Christel Buchanan and her team introduced ChatChat and their “Living Brain” concept, I decided to use this platform to not just archive my years of work, like another file structure, but to build out a canonical record of how I see the world.

Into it went 92 published essays, my book - To Question is To Answer. How to Think Critically and Thrive in the Age of AI, a handful of e-Books I have written, years of strategy thinking, project notes, photography, visual stories, and a considerable number of ideas that had never quite found the right form. Not simply the finished things, either. I included exploratory notes, abandoned directions, film scripts, production notes, systems architectures and master prompt libraries, contradictions and arguments whose edges I was still picking at. I even added an inventory of my vinyl collection via Discogs.

Once the material was brought together, connections began appearing across work that had previously lived in separate folders and formats. An essay about AI shared a concern with a strategy document about later life. A photographic story carried the same tension as a framework about power. Questions about judgment, agency, control, authorship and the institutions we quietly end up running for ourselves kept resurfacing in different clothes.

My Brain does not simply remember what I have written. It remembers the backstory: where an idea began, whether I developed or abandoned it, what it connects to, and where I may have contradicted myself. A contradiction is not automatically an error. It may be evidence that the thinking moved.

And beneath much of it sits the humble .md file, not the intelligence itself, but a simple way of preserving structure without trapping it inside a final format.

This changes how new work starts

A fresh observation no longer lands on an empty page. It arrives inside an accumulated body of thought. I can ask my brain whether I have explored it before, whether it develops an existing argument, whether an apparently new idea is simply familiar language in a better jacket, or whether two things I made years apart have finally found a reason to meet.

It does not decide what I should think next. If anything, its value is that it makes repetition, unfinished arguments and comfortable assumptions harder to hide from. It provides continuity and occasionally some useful resistance.

And because the underlying source, that little .md file, is preserved independently of any final format, the same body of thinking can become an essay, a presentation, a talk, a strategy, a film or something I have not yet imagined.

An article I recently wrote didn’t begin with the thought: I need a Word document. It began with a cultural signal. The signal became a research question. The research became a set of patterns and tensions. One of those tensions became a provocation. The provocation became an editorial structure. Only then did it become an article, and eventually, a Word file.

The Word document was useful. I needed it to edit, read, and move the piece toward publication. But it was a rendering of the thinking. It was not where the thinking lived.

That’s a fairly substantial change in behavior for someone who has spent most of his working life making decks and documents.

I no longer begin by asking what file I need to produce. I ask what source of intelligence I need to build. The file can wait.

A different way to begin

This doesn’t require an enterprise transformation program.

Next time somebody asks for a strategy deck, resist opening PowerPoint for a little while.

Begin somewhere less committed to the final shape. Write down the question. What you believe. What you know. What you’re assuming. What contradicts the convenient answer. What decision the work eventually has to support. What remains unresolved.

Give the thinking a structure before you give it a template.

# Brand Strategy

## The Cultural Shift

## What We Believe

### Evidence

### Implication

## Strategic Opportunity

## Creative Territories 

Preserve the sources. Record why choices were made. Let the argument change without spawning eleven files called FINAL_v7_REVISED_USE_THIS_ONE.

Then, when the thinking is strong enough, decide what it needs to become for this audience. Maybe it becomes a deck. Probably it will. But the deck will no longer have to carry the entire intelligence of the work by itself.

Think upstream. Publish downstream.

The .md file isn’t revolutionary. That may be why it’s useful. It’s a small, almost trivial object that exposes a much larger mistake — we’ve confused the format in which knowledge is communicated with the place where knowledge should live.

AI makes that confusion expensive. When any piece of structured thinking can become many different outputs, the strategic question is no longer how quickly you can produce the deliverable. It is whether the intelligence underneath it survives the rendering.

Your deck is not your strategy. It is one expression of it. The strategy needs somewhere else to live. So the next time somebody asks for a presentation, begin with the least impressive file in the room: a blank .md file.

Put the question at the top. Build the argument underneath it. Preserve the evidence, assumptions, tensions and decisions that the final deck will inevitably compress. Build something that can outlive the template.

Start in Markdown.

Entry-level work used to run on a bargain nobody wrote down.

The organization accepted that you didn’t know very much yet. You accepted the photocopying, the modest authority, and the specific humiliation of being the person in the room who has to ask what the acronym means. In return, you got proximity — a seat close enough to watch how the stronger people actually worked, room to attempt things under supervision, and enough rope to make decisions serious enough to teach you something without being serious enough to sink the business.

Nobody designed this well. Plenty of junior roles confused development with drudgery, or with finding someone cheap enough to do the work nobody senior wanted to touch. But somewhere inside that badly-run system, people quietly accumulated experience.

AI is rewriting the bargain.

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Routine work is the first thing automation reaches for. Research, synthesis, drafting, analysis, production - all of it now gets done faster, with fewer people, at a standard that often looks remarkably accomplished. Meanwhile, the junior roles that survive have started asking for distinctly non-junior things: judgment, stakeholder management, strategic thinking, the ability to operate in ambiguity without an adult in the room.

Entry-level talent is expected to arrive already experienced. AI-augmentation looks, at first glance, like the answer.

Hand a junior employee a capable model, and the work that comes back looks senior. The analysis is polished, the options are comprehensive, the language is confident, and it all arrives in a fraction of the time it used to take. The visible gap between junior and senior output is closing fast.

Is the person getting better or is the work just looking better?

Performing like an expert

There’s no single, tidy experience pipeline getting quietly automated away — the truth is messier than that headline. Junior hiring is genuinely down in a handful of AI-exposed and tech occupations, but a hiring chart alone doesn’t prove AI did it. Restructuring, outsourcing and a post-pandemic correction are all tangled up in the same numbers, and untangling them cleanly is harder than any op-ed wants to admit.

Nor does AI simply switch off the opportunity to learn. One workplace study of customer-support agents found the least experienced workers made the largest performance gains from generative AI, the tool handed them the language and instincts of their strongest colleagues, on day one instead of year three.

That’s not nothing.

Expertise has always been rationed unevenly, by which office you landed in, who happened to sit next to you, whether a manager had twenty spare minutes to explain why the plan wouldn’t work. AI can hand strong practice to people who’d otherwise have waited years to bump into it by accident.

It can compress a performance gap. Whether it compresses an experience gap is a different question entirely, and a much harder one.

The history of consequences

Experience was never just a bigger pile of information. AI already holds more information than any one person could accumulate in a career. It can spot patterns, surface precedents, and describe with total confidence what an experienced practitioner would probably do.

What it can’t hand over is the history of consequences behind that confidence.

Experience is watching a sensible plan produce a ridiculous outcome. It’s discovering that the exact same approach which worked brilliantly with one client blows up spectacularly with the next. It’s the itch of recognizing a familiar pattern forming — and the sharper, more useful itch of recognizing when a situation only looks familiar enough to be dangerous.

Mostly, it’s being wrong when being wrong actually costs something.

AI can tell a junior colleague what an expert would do here. It cannot tell them why the expert pauses for three seconds before doing it because that hesitation isn’t a data point. It’s a scar.

The model can lend someone the patterns of experience. It cannot automatically lend them the bruises that produced the patterns in the first place.

That distinction is easy to miss, because output is the thing you can put in a dashboard. The organization can count hours saved, tickets closed, documents produced, quality scores trending the right way.

Judgment doesn’t show up on a dashboard. It shows up months later, in the one meeting where the standard workflow stops working and somebody has to notice before the client does.

Can they tell when the recommendation is wrong for this particular situation? Can they carry what they’ve learned into a mess the model has never seen? Can they explain their reasoning instead of repeating the answer the machine gave them? Can they put their name on the decision when the model was confidently, articulately wrong?

Those are the questions that matter. They’re also the ones nobody asks until the quarter it’s already too late to ask them.

The experience trap

Here’s the trap.

AI lets junior staff produce senior-looking work. The organization reads better output as better independent judgment - a reasonable mistake, and a costly one. Expectations climb. Supervision quietly recedes. Entry-level roles start clustering around judgment and coordination, on the assumption that the production in the middle is already handled.

Which means junior employees get fewer reps at making decisions that matter, under the eye of someone who’s already made every version of that mistake.

Eventually the organization looks up and discovers it has a warehouse full of senior-looking output and a genuine shortage of people who can independently judge whether any of it is any good. The productivity numbers look terrific right up until the day something unusual happens, and there’s nobody in the building who’s seen it before.

I’ve argued before that the real story isn’t an aging workforce, it’s an experience shortage. Run the AI version of that argument forward, and the maths gets uncomfortable: as production gets cheap, contextual judgment and the willingness to be accountable for a call get more valuable, not less. Which raises the obvious, unglamorous question nobody wants on the agenda:

Where does the next generation of experienced people actually come from?

Companies may be nudging existing experience out the door while automating the exact work through which new experience used to get built. AI-assisted output is good enough to hide that trade for a while.

Not forever.

The old apprenticeship was not sacred

None of this is a case for preserving junior drudgery.

The old system was never a finishing school. A lot of entry-level work was repetitive because nobody had bothered to design it properly. Mentorship access was a lottery. Plenty of people spent years doing low-value tasks and came out the other side with tenure, not judgment, which are not the same thing, whatever the org chart implies.

Younger workers shouldn’t have to wait patiently for permission to do work that matters, either. They often bring sharper AI fluency, a different cultural register, and less loyalty to habits that only look like wisdom because nobody’s challenged them recently.

Done well, AI could be a better apprenticeship than the one it’s replacing. It can put strong practice in front of a novice sooner. Give instant feedback. Simulate the difficult conversation before the real one happens. Let someone compare two decisions before either has consequences. Make tacit knowledge visible instead of leaving it to be absorbed by osmosis, assuming you’re lucky enough to sit near the right person in the first place.

But none of that happens just because everyone gets a license and the usage dashboard goes up and to the right.

It has to be designed for building capability, not just accelerating output. Those are different design briefs, and most organizations are currently only writing one of them.

Substitution or scaffolding?

The useful distinction is between a substitution and scaffolding.

A substitute performs the task for you. Take it away, and the performance goes with it. Scaffolding helps you build a capability you can increasingly exercise for yourself. It changes shape as you get stronger: support is withdrawn, redirected, or made more demanding.

Most organizations are currently running AI as a substitution while describing it, in every town hall, as scaffolding.

The model keeps producing the analysis, suggesting the language, recommending the next move. The employee gets remarkably good at operating the system.

Is their judgment getting stronger? Or is their dependence just getting more productive?

A real AI apprenticeship would ask more of both sides. It might make the employee commit to a judgment before the model reveals its own. It might leave the uncertainty visible instead of smoothing it into false confidence. It might explain, specifically, why the familiar pattern doesn’t apply this time. It might compare reasoning, flag what’s missing, and slowly walk someone into situations no template covers.

Above everything, it would need to keep leaving room for people to make real decisions, live with the consequences, and answer for what happens next.

The goal was never to remove the machine.

It’s making sure the person working alongside it is getting more capable, not just more productive.

The question organizations should ask

The easy question is: what can this person produce with AI?

The harder, more consequential ones: What are they learning through the act of producing it? What can they see that the model can’t? What can they carry into a situation nobody prepared them for? What are they still willing to question? And when the answer is wrong whose name is on it?

AI may let junior workers perform like experts sooner than any generation before them.

That could be one of the more remarkable expansions of human capability in a working lifetime.

Or it could produce a workforce expected to arrive experienced, carrying all the outputs of expertise, and very little of the practice, consequence, and responsibility that used to be how judgment actually became theirs.

The model won’t decide which one happens. Whether organizations use AI only to speed the work up, or also rebuild how people acquire the judgment to do it — that will.

For years, the conversation has gone one way: We have an ageing workforce. The new conversation may go another way entirely: We have an experience shortage.

Same people. Same grey hair. Completely different problem. One shows up as a cost center. The other shows up as a competitive advantage nobody’s exploiting yet.

That distinction crystallized for me somewhere in Bank of Singapore’s new 2026 Supertrends: The Longevity Economy report—a document ostensibly about the business of getting old, which turns out to be a document about the business of staying useful. The bank estimates the longevity economy will grow from US$3 trillion in 2025 to US$5.4 trillion by 2034, propelled by longer lives, falling birth rates and the slow rewiring of how people work, spend and live.

You know, the same thing I have been writing about for a few years now, the thing I call The Epilogue Economy.

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Most longevity reports open with the consumer pitch—the wellness retreats, the anti-ageing serums, the entire economy built around not looking your age. This one opens somewhere more interesting: the workforce itself. It argues that AI and robotics are becoming critical enablers, capable of augmenting a shrinking, greying labour pool while—in the report’s own words—“elevating the contribution of experienced talent.”

Worth pausing on who’s saying this. Bank of Singapore is not in the business of employee engagement surveys. It manages money. When an investment report starts treating experienced talent as an economic asset rather than an aging liability, I read that as more than HR sentiment. I read it as a thesis. And AI, oddly enough, may be the reason the thesis holds.

The old ledger

Walk into most workforce-planning meetings and older employees are still filed under liabilities.

They’re expensive. They sit atop the org chart, blocking the view for everyone below them. They complicate succession plans that HR has quietly been rewriting for years. Their healthcare premiums trend the wrong direction. Their skills, it’s assumed without much scrutiny, are aging out along with them. And eventually—mercifully, efficiently—they retire, making room for someone twenty years younger who costs less and, we’re assured, arrives pre-loaded with whatever the new operating system requires.

This assumption produces a very consistent set of corporate reflexes: flatten the org chart, thin the senior ranks, dangle early retirement, quietly convert institutional memory into a consulting day rate, or simply let it walk out the door—cardboard box under one arm, commemorative pen in the other, thirty years of pattern recognition gone before lunch.

Underneath all of it sits one belief:

Experience is a relic of the last operating model, not an asset for the next one.

AI is quietly putting that belief under pressure. Not because people over fifty possess some mystical wisdom unavailable to the rest of us—they don’t, and this isn’t an argument for romanticizing age. It is because AI is relocating where human value actually lives inside the work. I should know, because I am one of those people they are talking about.

The bookends have become the job

I’ve written before about how the ratio of a day’s work is flipping, and it’s worth restating because it’s the hinge this whole argument swings on.

Work used to break down roughly 10/80/10: ten percent deciding what to make, eighty percent actually making it, ten percent judging whether it was any good. The middle was the job. That’s where the headcount lived, the org charts sprawled, the budgets went.

Under AI, that ratio is drifting towards something closer to 40/20/40. Human effort is migrating to the bookends—defining the problem and briefing the machine at the front end; judging, refining and taking responsibility for what comes out the back end. The middle, the actual production, gets faster, cheaper and increasingly automatic.

Which means taste becomes the job.

So does judgement. Context. Pattern recognition. The instinct to know when an answer is technically correct and strategically idiotic—the kind of thing many people can understand in theory, but someone who has been burned by it recognises before it ships.

None of this belongs exclusively to older workers. But it accumulates through the specific, unglamorous experience of being wrong in public: watching a sound plan produce a ridiculous outcome, negotiating with someone who has no interest in a mutually beneficial deal, sensing that a client’s silence in a meeting means something isn’t landing before anyone says so out loud.

You don’t learn to read a room from a training dataset. You learn it by sitting in enough rooms.

Which makes the current moment almost comic in its self-sabotage: companies are racing to automate the middle of the work while quietly encouraging the people most practised at the bookends to take the package and go. Then everyone’s confused about why the output is fast, polished and somehow unmistakably off.

From labor shortage to judgment shortage

A labor shortage means an organization needs more hands. An experience shortage means it doesn’t have enough people who can decide what’s worth doing, supervise the systems doing it, and catch the moment a plausible-sounding answer is about to become an expensive one.

The distinction isn’t semantic. It changes the entire playbook. If the problem is labor, automation is the fix—more machine, less headcount, done. If the problem is judgment, automation doesn’t fix it. It can make it worse.

AI will generate more options than any human team could produce alone. It synthesizes, drafts, prototypes, and answers with a fluency that feels like magic—right up until one of those answers is confidently, articulately, catastrophically wrong, and nobody in the room has the scar tissue to notice before it ships.

The bottleneck hasn’t disappeared. It’s moved. The organization is no longer waiting on someone to produce the thing. It’s waiting on someone to decide which thing matters, which output can be trusted, and which small, easily missed error is quietly compounding into next quarter’s crisis.

That is where experienced people may become more valuable. Not despite the machines.

Because of them.

The real opportunity here isn’t keeping people on the payroll longer out of decency. It’s redesigning the work around the contribution they may now be unusually well positioned to make.

Experience is not tenure

Here’s the part of this argument that deserves some skepticism, including from me.

Experience doesn’t automatically convert into judgment. Some people spend thirty years sharpening their instincts. Others spend thirty years defending habits that stopped working around the time the smartphone showed up, and simply never noticed.

Unexamined experience calcifies into certainty. Seniority, handled badly, becomes a moat against curiosity rather than a source of insight. Plenty of experienced people will treat AI the way they treated the last three pieces of enterprise software: ignore it, badmouth it in the hallway, and quietly hand it off to whoever seems most comfortable with the tools.

That isn’t the experience economy. That’s nostalgia with a corner office. Bank of Singapore’s framing only holds up if experienced talent stays capable of learning alongside the systems it’s meant to be supervising. The value isn’t in the years. It’s in what the years were spent doing.

Put plainly:

Experience without curiosity is just seniority waiting to expire. AI fluency without judgment is just slop, generated faster.

Combine the two—hard-won judgment and genuine fluency with the tools—and you get something most workforce models are still not designed to recognize, develop, or reward.

Longevity as operating strategy

If experience is becoming the scarce input, longevity can’t stay parked in the DEI slide of the annual report, next to the stock photo of three generations smiling at a laptop. It has to become an operating question, asked with the same rigor as capital allocation:

• Which forms of judgment are load-bearing for this business—the calls that, made badly, actually hurt?

• Where does that judgment currently sit, and what is being done to keep it current and pass it on?

• What happens the day the person who understands why the system works stops showing up?

Too often, organizations reduce intergenerational work to a lazy division of labor: one person operates the new tools, another supplies the war stories, and everyone calls it mentorship. That isn’t complementary capability. It is two stereotypes sitting beside the same laptop.

Companies have spent a decade and a small fortune documenting processes, building wikis, and turning tacit expertise into tidy templates. AI will make all of that easier to search and interrogate—a genuine gift.

But not everything a good operator knows lives in a document.

Some of it shows up as hesitation before a decision nobody else questioned. A raised eyebrow at a plan that looks fine on paper. A sense that two unconnected events in different parts of the business are actually the same problem wearing different clothes. The recognition that a familiar pattern is forming under conditions that only look unfamiliar.

Try writing that into a job description. Try automating it.

It’s becoming more valuable precisely because it can’t be—at the exact moment machines are fully capable of executing a bad idea beautifully, at scale, before a human finishes the sentence that would have stopped it.

The new longevity economy

The longevity economy usually gets discussed as a market created by older consumers with money and time—the retirement cruises, the second-act career coaches, the vitamin subscriptions.

That’s half the story, and arguably the less interesting half.

The other half is a productive economy, created by people living and working longer, whose experience may appreciate in value as AI changes what human contribution actually looks like.

That doesn’t happen on its own. It requires organisations willing to invest in real, continuous learning; to redesign roles instead of just relabelling them; to interrogate the age-based assumptions baked into every promotion cycle; and to build credible paths for experienced workers to actually use AI rather than merely survive its rollout with a straight face.

It requires older workers to stay curious enough to keep converting experience into judgment that’s actually current, not judgment calibrated for a market that stopped existing a decade ago. And it requires leaders to retire the fantasy that knowledge transfers itself the moment someone hands back their badge—that thirty years of pattern recognition gets automatically forwarded to whoever inherits the email account, like an out-of-office reply.

The aging workforce has always sounded like something happening to business—a bill coming due, a demographic wave to be weathered. The experience shortage sounds like something business did to itself, one early-retirement package at a time. One framing asks how much it will cost to carry people who are working and living longer. The other asks how much value is walking out the door—how much already has—because nobody thought to ask what those longer lives had actually accumulated.

One is a cost. The other, still sitting right there in the building, is an opportunity nobody’s bothered to price.

Source: Bank of Singapore, “2026 Supertrends: The Longevity Economy,” published 14 July 2026.

For much of the past decade, awareness has been treated as an achievement. Raise awareness. Start the conversation. Give the issue visibility.

Whether the topic is climate change, mental health, diversity, burnout or loneliness, our instinct has been remarkably consistent. We identify a problem, we talk about it, we encourage others to talk about it, and when enough people are talking, we begin to feel as though progress has somehow been made.

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Awareness has become one of the defining currencies of modern leadership. Organisations measure it. Conferences celebrate it. Marketing campaigns are built around it. Entire movements have emerged because they succeeded in making previously invisible problems impossible to ignore.

None of that is unimportant.

Awareness matters because it creates recognition. It gives people language for experiences they may have struggled to describe. It legitimises conversations that once felt uncomfortable or taboo. In many cases, awareness is the essential first step towards change.

But it is only the first step.

Somewhere along the way, we began confusing awareness with resolution. We started believing that once a problem had entered public consciousness, the hard work was largely complete. It rarely is. Awareness identifies a problem. It does not redesign the system that created it.

That distinction has been on my mind over the past few weeks, as I continue to interview for roles that I am qualified for, but never land. And the topic of ageism, institutional memory, and career longevity popped up recently in the Singapore chapter of The Marketing Society’s WhatsApp group.

The Comments Section Doesn’t Lie

Since I started writing about the Epilogue Economy and ageism, my inbox has turned into something closer to a running case file. Comments on the articles. Invitations to speak on podcasts. Strangers in cities I have yet to visit from industries I’ve never worked in write to describe mornings that all sound the same.

Spend a few minutes scrolling through your LinkedIn and scattered amongst the posts AI, HoldCo collapses, and the rise of the independent agency, are the desperate cries for help from people who have been set aside, despite the flood of job vacancies in the market that are actually signalling a replacement strategy, not a strategic or organizational upgrade. Those same agencies (looking at you HoldCos) who are letting go of talent every week are simultaneously advertising open roles that many of those let go could have filled.

But back to me. None of these people wrote to me for sympathy.

They wrote because the pattern had stopped feeling like bad luck and started feeling like design.

The countries differ. The industries differ. The job titles differ. The experience underneath them does not: becoming invisible inside systems that claim, in every mission statement and every values deck, to prize experience above almost everything else.

Not one of them asked me to raise more awareness.

Every one of them already knew the problem existed. What they were looking for was insight into how I got myself back in the game, back into an industry that seems to have a sort of expiration date for experienced knowledge works.

Sadly, I have to tell them that I am not really back in the game, not in the way I had expected. I didn’t have answers. I only had awareness, via my own challenges and struggles, of the continuing issue of ageism in the hiring process.

Measure What Matters

Researchers running large-scale resume audits, sending out tens of thousands of otherwise-identical applications and varying only the implied age of the candidate, have found that workers in their late forties and early fifties receive roughly 29 percent fewer callbacks than workers in their late twenties. For older women, the gap widens further. In some studies, women in their mid-sixties received almost half as many callbacks as their younger counterparts, despite identical qualifications.

This isn’t a perception problem. It’s a measurable one. Older jobseekers who do get in the door interview at rates comparable to everyone else, but are offered the job roughly 40 percent less often. Something is happening after the interview, in the part of the process nobody puts on a diversity slide.

And once someone in their fifties or sixties does lose a role, the consequences compound. They remain unemployed for nearly twice as long as younger jobseekers. Roughly half are pushed out of long-tenured positions before they’re ready to leave, and the overwhelming majority of them never earn what they used to earn again. Meanwhile, formal complaints of age discrimination filed with US regulators have climbed for three consecutive years. Mentions of ageism in employee reviews on Glassdoor spiked by well over 100 percent year-on-year in early 2025. People aren’t imagining this. They’re documenting it, in real time, in exactly the places organisations claim to be listening.

Picture the composite of it. Twenty-plus years in a function. A résumé that reads like a case study in steady, compounding competence. Three hundred applications later, still no offer, and a growing suspicion that the problem isn’t the résumé at all. That suspicion is usually correct. It just isn’t a suspicion anymore. It’s a pattern with a sample size in the tens of thousands.

That, I think, is where the conversation needs to move next.

Because ageism is no longer primarily an awareness problem. It is a design problem.

When the System Is the Message

Most organisations would readily acknowledge that age bias exists. Most leaders would reject the idea that they intentionally discriminate against experienced professionals. Yet despite those good intentions, remarkably similar outcomes continue to emerge across industries and geographies.

Whenever a system consistently produces the same result, it is worth asking whether the issue lies less with the people inside the system than with the system itself. This is one of the central ideas behind the Epilogue Economy.

For decades, we have tended to frame population ageing as a social challenge. An increase in healthcare costs. Pressure on pension systems. Longer retirements. An older workforce.

Those observations are all true.

But they also encourage us to think about ageing as something happening to organisations, rather than something organisations must redesign themselves for.

Demography is not a passing trend. It is a permanent structural shift. People are living longer. Careers are extending. Birth rates are falling across much of the developed world. Skills shortages continue to grow while institutional knowledge quietly walks out the front door through retirement, redundancy or disengagement.

And yet many of our organisational practices still reflect assumptions formed decades ago. Recruitment processes quietly favour linear careers. Promotion models often assume that ambition declines with age. Learning budgets disproportionately favour younger employees. Technology is frequently discussed as though it belongs to one generation rather than being a capability that can be developed at any stage of life. Even the language written into job postings does quiet work here: phrases like “digital native,” “high energy” and “recent graduate” are, in effect, filters. They don’t say “no applicants over 45.” They don’t have to. The research shows candidates over 40 self-select out of roles coded that way before a recruiter ever opens a resume.

None of these systems were designed with malicious intent. Many simply haven’t been redesigned since the world changed.

There’s also a cost to leaving them un-redesigned, and it isn’t only borne by the candidate on the other end of the rejection. Every experienced professional pushed out of a hiring pipeline is institutional knowledge an organisation chose not to buy back. At the exact moment skills shortages are widening and birth rates are falling, that is not a minor inefficiency. It’s a strategic decision, made by default, to compete for the future with one hand tied behind the back.

This is why I believe the next phase of the conversation is not about raising awareness of ageism. It is about redesigning the architecture of opportunity.

What Redesign Actually Requires

That begins with measurement.

Ask any talent function to break down its recruitment funnel by gender and, in most cases, someone can produce it within the hour. Ask the same function to break it down by age band, and you will usually be met with silence, followed by a promise to “look into it.” That gap between the two answers is itself diagnostic. It tells you which forms of bias the organisation has decided are worth watching for, and which it has quietly agreed not to notice.

An organisation that genuinely wants to know whether age is shaping its outcomes doesn’t need a new department to find out. It needs to run its existing recruitment data through one more lens and be willing to sit with what it finds, at every stage from first application through to final offer, not just the stage that photographs well.

Language also deserves scrutiny.

Few phrases have become as casually accepted as “overqualified.” It sounds objective. Sensible, even. Yet in many cases it functions less as an assessment than as a proxy for assumption.

Too expensive. Too senior. Too likely to leave. Too difficult to manage. Each may occasionally be true. None should be presumed.

Every hiring process demands that candidates prove themselves with evidence: a portfolio, a track record, a set of references. Fair enough. But the same rigour rarely gets applied in the other direction. A rejection built on a hunch about someone’s salary expectations or their appetite for change should be held to exactly the same standard the candidate was held to. If the organisation can’t produce the evidence, it hasn’t made a decision. It’s made a guess and dressed it up as one.

Technology presents another challenge.

Artificial intelligence is now doing a meaningful share of the first-pass filtering in recruitment, and its arrival puts the entire question on a much shorter clock.

A screening model doesn’t need a line of code that says “reject candidates over 50.” It only needs ten years of past hiring decisions to learn from. If the humans making those decisions already skewed young, however unintentionally, the model absorbs that pattern as simply “what good looks like,” then applies it at a volume and speed no individual recruiter ever could.

That’s the part worth sitting with. Automating a biased process doesn’t neutralise the bias. It industrialises it, and does so quietly enough that a leader can point to the new system as evidence of progress while it does more damage, faster, than the manual process it replaced. The fix isn’t caution about adopting the technology. It’s refusing to deploy any of it without first auditing what it actually does to applicants over fifty, the same way a competent organisation would already audit for outcomes by gender or race.

Perhaps the most significant redesign, however, is philosophical. Too many organisations continue to think about age as a proxy for capability. It never was.

You can see capability. It shows up in the questions someone asks, the judgement calls they make under pressure, how they treat people with less power than them, how quickly they adapt when the plan stops working. None of that is issued at birth or expires on a schedule.

Neither, for that matter, is the capacity to spot what’s actually new.

Org charts have a habit of treating youth and experience as a trade-off, as if a team could only stock up on one by running short of the other. Sit inside almost any high-performing team for a week and the trade-off turns out to be fictional. What actually breaks teams is homogeneity of any kind, age included. A group of people who all learned the game in the same decade will tend to miss the same blind spots, chase the same fashionable ideas, and mistake familiarity for certainty.

The novelty a twenty-eight-year-old brings to a room and the pattern-recognition a fifty-eight-year-old brings to the same room aren’t competing resources. They’re different instruments, and the organisations pulling ahead right now are simply the ones that stopped forcing a choice between them.

What This Looks Like on a Tuesday

Redesign sounds abstract until you make it operational. It isn’t a rebrand. It’s a handful of unglamorous, checkable practices.

It looks like stripping age-coded language out of job postings the same way most organisations already strip gendered language, and checking that the change actually moved the applicant pool, not just the copy.

It looks like publishing the hiring funnel by age band internally, the way many now publish it by gender, and treating a drop-off after interview stage as a finding rather than a coincidence.

It looks like requiring any rejection built on “overqualified” to be rewritten in specifics before it’s allowed to go out the door — the concrete skill gap, the concrete concern — so the label can no longer close a conversation on its own.

It looks like auditing whatever AI tool is doing your first-pass screening for age-correlated outcomes before you trust it with volume.

It looks like a learning and development budget an employee can still draw on at 58, not just at 28.

It looks like mentoring programmes that run in both directions, so a 60-year-old teaching judgement and a 30-year-old teaching a new platform are both, correctly, called mentors.

None of this requires a taskforce. It requires someone senior enough to ask the questions and patient enough to sit with uncomfortable answers.

Which Brings Me Back to Awareness

Awareness was never supposed to be the destination. It was supposed to be an invitation. An invitation to question assumptions. To redesign systems. To challenge inherited practices that no longer reflect the realities of modern work.

The Epilogue Economy is, at its heart, an argument that we have entered a different demographic era while continuing to operate organisations designed for a previous one.

If that diagnosis is correct, then the solution will not come from another awareness campaign. It will come from leaders willing to redesign the systems through which opportunity flows.

History rarely remembers the people who first acknowledged a problem. It remembers the people who built something better.

Perhaps that is where the conversation should go next.

Over the past few weeks, my life has felt like a rollercoaster of activity, emotions, broken promises, and throwaway comments that have blossomed into massive developments. I am sure I will unpack all of that in a future post, but I really wanted to take this morning and share a wonderful conversation I had recently.

My Substack - Some Assembly Required - has always been where I work things out in public. It’s where the ideas are longest, sharpest, and least edited for palatability.

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“I am the loop, not the human in the loop.”

That distinction, from a filmmaker friend, Gabe Michael, who writes The Creative Possibility on Substack, reframed how I think about AI — and it became the anchor for a conversation I had with Ramanathan Vythilingam 🏠 on the NCMT (Nanyang Center for Marketing and Technology) podcast, out of Nanyang Business School.

We got into what four decades in design — Apple, Disney, Gojek, and now RockPaperScissors — teaches you about adopting new tools without losing your judgment. A few threads we pulled on: why AI’s bidirectionality (the “call and response”) makes it fundamentally different from every creative tool that came before it.

In this episode, we talk about:

• Why I use AI to find better questions rather than answers

• How a senior Coca-Cola leader’s one question — “how did you know to ask that?” — became the seed for my book, To Question Is to Answer.

• Why I’d rather chase a hallucination than dismiss it, since most of the time it’s pointing at a human truth rather than a data error

• Why observable truths beat performance data when you’re trying to understand where a market is actually headed

• Why the real risk of AI isn’t replacing junior talent, it’s senior people forgetting that experience, curiosity, and taste are the actual superpower.

If you’re a marketer, creative, or just AI-curious and tired of the “will it take my job” framing, this one’s for you.

Watch the full episode.

Everyone is selling you the opportunity. Nobody is pricing the entry fee.

That’s the whole con, and it isn’t even a deliberate one. The opportunity is real. The opportunity is enormous. AI filmmaking, AI production, AI-native everything — the upside is exactly as big as the loudest voices say it is. But somewhere in the gold rush, an entire category of cost went quiet. Not hidden. Just unmentioned. And the people walking into this work fastest are the ones who never got told what it actually costs to be here.

I got told. I got told by the invoices, by my bank, and by my body.

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Recently I read a sharp piece by Michael J Domanic, Head of AI at Section, about enterprise inference costs. Smart article. He’s watching CFOs open their first real Claude bill and ask the only question a CFO ever asks, which is some version of how the hell did we spend this much. His answer is good. Set the strategy. Budget AI as a percentage of salary. Set token caps. Educate your people. Measure before and after. Track your outliers. Give it twelve months. Guardrails for an organization that suddenly discovered its experiment has a meter running.

I read all of it nodding. And then I closed the laptop and realized he was describing a world I don’t live in.

Because every single one of his moves assumes an org. Headcount to govern. A dashboard to watch. A salary line that absorbs the learning curve. Domanic is governing a system. I don’t govern the system. I am the system. I’m the CFO and the operator and the line item and the body in the chair, all of it stacked into one person who also has to deliver the work.

So this is the chapter he can’t write from inside an enterprise. This is what the meter looks like when there’s only one of you.

There are three taxes. Most people are panicking about the cheapest one.

The first tax is the token.

It’s the only one anyone’s talking about.

When an AI film project lands, I don’t guess at the cost. I pull receipts.

I track usage on everything. Every project, every job, the token burn is logged so that the next estimate isn’t a finger in the wind. When something new comes in, I go back through the archive and find the closest matches on two axes — complexity and timeframe — because a simple ten-day job and a brutal three-day job are different animals, and averaging them tells you nothing. I find the projects that actually rhyme with this one, and I build the estimate backward from what really happened.

Then I add contingency. Twice.

Fifty percent on time. Fifty percent on compute. A ten-day project carries a five-day cushion. A two-thousand-dollar compute estimate carries an extra thousand. That isn’t padding, and it isn’t pessimism. It’s the only honest way to price a process where the unknowns live around every corner. AI-generated work cannot be estimated accurately yet. Anyone who tells you they can quote it to the dollar is either lying or hasn’t been burned enough times. The contingency is how a studio of one stays solvent and credible when the tool itself is unpredictable.

This is the tax everyone’s writing about. The token tax. It’s variable, it’s attributable, you can estimate it, you can contingency it, you can bill it. It’s the manageable one.

It’s also the cheapest one.

The second tax is the stack.

It runs whether you do or not.

Token spend has a start and a stop. The project ends, the meter stops, the cost is captured.

The stack has no off switch.

GPT. Claude. Figma Weave. Adobe. Canva. Google. Every month, whether there’s a project on the table or not, the subscriptions must be paid. This is the keep-the-lights-on cost. The electricity. In the AI space, it isn’t optional — you cannot show up to serious work without a serious stack, and you cannot let that stack go dark between jobs.

That last part is the part people miss. You’d think you could pause the subscriptions in the gaps. You can’t. The in-between time is exactly when you learn, when you test, when you stay ready for the thing that hasn’t arrived yet. A dark stack means you walk into the next project rusty, a step slow, behind the tools. So the subscriptions buy two things at once — capability and readiness — and you pay for both even on the days you’re billing nothing.

Here’s where it gets hard. The token tax traces cleanly to a project. The stack doesn’t. No single job owns my Claude seat. There’s no client to hand the Figma Weave invoice to. So right now it’s overhead — the cost of doing business — and I recover it the blunt way, spread across my day rates and project rates, amortized like rent.

It works. It’s also unresolved, and I’m not going to pretend otherwise in my own books. The first tax is variable and attributable. The second is fixed and ambiguous. You feel every dollar of it, and you carry it alone.

The third tax doesn’t have an invoice.

It has a body. It’s my body, which has no line item at all.

Some of it you can almost count. The courses. The workshops. The paid sessions have a price and the price is real. But that’s the visible edge of something much larger and entirely uncounted — the hours spent learning. The hours testing what doesn’t work yet. The travel to and from. The 3am alarm to sit through a ninety-minute workshop on advanced AI filmmaking that you paid to attend. You pay in money. Then you pay again in sleep.

This is the tax that doesn’t reset. The token cost ends with the project. The subscriptions at least leave you holding something. But this one just accumulates — physical, mental, additive, no off-switch, no ledger to recover it against.

And here is the line that separates my world from Domanic’s entirely. In an enterprise, the learning curve is salaried. His operators get paid to test. Paid to play. Paid to wake up and figure it out on the company’s clock. The experimentation is a funded activity.

We’re not paid to learn. We’re not paid to play. We’re not paid to experiment. The entire nut is on us. We absorb the cost of becoming someone who can do this work, and then we absorb the cost of doing it — same person, two ledgers, no salary bridging the gap.

It’s not a hard number. It’s wear and tear.

Price the entry fee before you walk in.

So when the conversation is all opportunity — and right now it’s nothing but opportunity — understand that there’s an entry fee nobody quotes you. Three taxes, not one.

The first is variable and attributable. You can estimate it, cushion it, bill it. The second is fixed and ambiguous. You carry it and bury it in your rates. The third is invisible and unrecoverable. It doesn’t show up anywhere except in you.

Domanic mapped the cost of AI at scale, and he mapped it well. I’m mapping the cost of AI at one. And the part that should stop you is this: the token tax everyone’s losing their minds over is the least of it. The expensive ones don’t generate invoices. They generate fatigue.

I’ve been doing this work long enough now to feel all three at once. The meter, the stack, the body. And lately the third one is the one I notice.

It’s the wind, and I’m the canyon.

And the thing about wind on stone is that it takes and it makes in the same motion. It wears you down and it carves you out. The cost and the craft are the same gust. Nobody photographs a canyon and calls it damage. They call it beautiful. They just don’t think about the years of weather it took to get there.

I’ve spent the last few years building the cost architecture this article describes — the estimation model, the contingency logic, the honest way to carry overhead you can’t cleanly bill. If you’re running AI-facing projects and want someone who’s already been there, I work directly with agencies, brands, studios, and operators. Let’s talk.

And if you want the rest of how I think — the production, the creativity, the storytelling, the way all of this actually gets made — that’s what I’m writing at Some Assembly Required. Subscribe and stay close to the work.

One of the more useful Substacks I read is from Greg Shove and Taylor Malmsheimer from SectionAI, the online school that has shaped so much of my way of thinking about AI, not as it’s applied to creativity, but how it’s applied to business. I completed my Mini-MBA in AI for Business with Section a while ago. In the most recent newsletter, Greg and Taylor point to two observations. They weren’t connected, rather part of a long list of observations.

I think they should be.

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The first: the work ratio has flipped. For most of modern professional history, work looked roughly like this — 10% deciding what to make, 80% making it, 10% checking whether it was any good. That middle 80% was the job. That’s where the hours went, where the craft lived, where you proved your value by volume of output.

Now it’s closer to 40/20/40.

Your numbers may look different right now. But start asking your team how they’re actually allocating their time on any given project, and watch what you find. The shift is already happening underneath the official version of how work gets done.

The making — the part machines have swallowed — is down to 20%. The bookends have doubled. Human time is now concentrated at the front, where you decide what matters and instruct the machine on how to pursue it, and at the back, where you judge what came out and refine it with something the machine doesn’t have.

We’ll get to what that something is.

The second observation: you feel like a bottleneck, yet more valuable at the same time. Machines work faster than you. You are simultaneously the most important part of the process and the thing slowing it down. That tension is uncomfortable.

It’s also correct.

Most people I talk to are experiencing this and don’t have language for it. They know something has shifted. They feel both more necessary and more exposed. They’re producing more, faster, and somehow feel less confident about what they’re contributing.

That’s not an accident. That’s the ratio doing its work on you.

Taste Is the Job Now

Here’s what’s actually happening at both bookends.

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