Categories
AI

The Shape of the Question

Marc Andreessen made two claims recently that don’t quite fit together, and I haven’t been able to stop pulling at the seam.

The first: for almost any topic, the top AI systems now give him better answers than the world-class experts he could call on the phone. And he can call basically anyone. This isn’t a casual observation from someone without access — it’s a meaningful data point about what AI is actually doing to the value of expertise.

The second: the only real skill left in using AI is knowing what to ask. The models can already do almost anything you can describe in plain English. The bottleneck lives in your own head.

Hold those two claims next to each other. If the AI beats the experts, then the quality of your question only has to clear a low bar — good enough to unlock what the system already knows. You don’t need to ask like a cardiologist to get a cardiologist-quality answer. You just need to ask.

Except that’s not how it works in practice. And the gap between the two claims is where something important lives.

The better the question, the better the answer — even from a system that already knows more than any human alive. Expert-level interrogation of a superhuman system produces something qualitatively different from naive interrogation of the same system. The gap between a good question and a bad one doesn’t shrink because the underlying capability grows. It may widen. A sharper instrument in an unskilled hand doesn’t close the distance — it just makes the skilled hand more lethal.

What the AI has done is commoditize answers. What it has not done — cannot do — is commoditize the ability to know which question to ask.

There is a concept from epistemology that keeps surfacing here: the unknown unknown. Donald Rumsfeld made the phrase famous and then spent years living down the mockery, which was unfair, because the underlying idea is genuinely important. There are things you know you don’t know — the gaps you can name, the questions you can form. And there are things you don’t know you don’t know — the territory you can’t even see the edge of. The naive user of AI operates almost entirely in the second category. They ask what they already suspect. They get answers that confirm the shape of what they already believe. The system is brilliant and they are using it as a mirror.

The sophisticated user has learned to ask the AI to challenge their assumptions. To find the holes. To steelman the opposing view. To identify what’s missing from the framing. That second posture requires a kind of intellectual self-awareness — an ability to stand outside your own thinking and interrogate it — that is neither common nor easily taught.

Here is the uncomfortable implication: that self-awareness is not randomly distributed. It correlates with education, with reading, with having thought carefully about hard things for a long time. The people best positioned to ask good questions are, largely, the people who already had access to good answers through the old system. The gate moved. It didn’t disappear.

There’s a democratic story told about AI and I believe parts of it. The kid in rural South Dakota with a good question now gets an answer that rivals what the partner at McKinsey gets.

But access to information was never really the binding constraint. The binding constraint was always the ability to know what information you need — to feel the shape of your own ignorance precisely enough to ask for what fills it. That skill wasn’t distributed by the old system and it won’t be distributed by the new one. It has to be built, slowly, through years of reading and thinking and being wrong and trying again.

What AI may actually be doing is widening the gap between people who ask well and people who don’t — making the former dramatically more capable while leaving the latter approximately where they were, just with a faster way to get answers to questions they already knew to ask.

Somewhere right now, someone is sitting with the most capable thinking tool in human history, asking it to write a cover letter. The tool will do it beautifully. And the gap will quietly widen.

Categories
Half Moon Bay History

There Used to Be a Pier

There used to be a pier with a shack on top jutting out into Princeton Harbor in Half Moon Bay.

The fog still comes in low most mornings, softening the breakwater until the boats in the slips are only darker shapes. You hear the harbor before you see it—the wet cough of diesel, the slap of water against fiberglass and steel, the occasional metallic ring of a line pulled tight.

When the marine layer lifts, the place shows its modern face: rip-rap stone, poured concrete, the long gray L of Johnson Pier running into water that no longer knows how to rise up and test a wooden structure. The air carries salt and exhaust. Gulls work the edges. Trucks idle in the lot. It is orderly now, built to stay.

But the order has removed a sound. The old pilings no longer work against one another with that slow, heavy creak. The deck no longer gives a little underfoot after it was soaked with a firehose once a week. There is no longer the low groan of a chain hoist swinging a load of salmon or crab up from a boat in the dark, or the close, briny steam inside the shack where Vera kept coffee going and put food on whatever table or crate was free. The lights that once burned at the outer end—small and steady against the black water—are gone. Before the breakwater was finished, winter storms still reached that far end. Men braced the doors and listened to the ocean hammer while the whole structure trembled on its pilings like something that might, on any given night, decide to let go.

Joe Romeo drove those pilings in 1942. The shack he raised was built to be lived in as much as worked in. It smelled of diesel from the fuel tanks on the deck, of wet creosote warming in the sun, of fish blood drying on the planks, and of whatever Vera had on the stove. Fishermen came in with scales still stuck to their forearms. The wood stayed dark and heavy because it was never allowed to dry out completely. After the breakwater went in, the water inside grew calmer and the work changed with it, but the pier still took its beating every season. It held because the men who used it treated it like something that had to be argued with rather than replaced.

By the time the county harbor district took it over, the pilings had begun to rot and shed pieces into the channel. What had once been a place where men ate and slept and unloaded fish became, in the new language of the harbor, a navigational hazard. The district saw liability where others still saw memory.

In 2018 the cranes came and the pilings were cut and hauled away. The shack that had held the life of a fleet inside its four walls was dismantled and carted off like any other piece of condemned wood.

What remains is the absence of that sound and that light, and the particular way a working harbor can be made safe enough to forget what it once required of the men who kept it alive.

Categories
Bonsai Filoli Living

The Patience of Small Things

There is a tree on a terrace at Filoli that is roughly the size of a lamp. It sits in a shallow black bowl, its trunk leaning with the easy confidence of something that has been leaning for decades, its canopy splayed against the California sky like a fist slowly opening. Behind it, the estate’s formal garden dissolves into soft focus — roses, balustrades, the suggestion of abundance. The bonsai doesn’t compete with any of it. It simply occupies its few cubic feet with a completeness that makes everything else feel approximate.

I’ve been thinking about what that completeness costs.

The tree is probably a juniper — the fibrous, spiraling bark, the dense scale-like foliage, the way the branch structure seems to remember every decision ever made about it. Bonsai practitioners talk about nebari, the visible surface roots, and movement, the quality of dynamism frozen into wood. This one has both. The trunk doesn’t just lean; it goes somewhere, pulled by some invisible argument the grower made with it over years, or decades, or longer. The moss at its base is so even and green it looks curated, because it was.

What strikes me standing in front of it is that this is a technology — not in the semiconductor sense, but in the older one. A technique for shaping time. The grower didn’t make this tree. They made conditions, and maintained them, and made them again, and the tree is what happened. The distinction matters. There’s no shortcut to the trunk diameter. There’s no prompt that produces the movement in that wood.

I work in a medium where the gap between intention and output has collapsed to nearly nothing. I describe something and it appears. There’s tremendous utility in that, and I’m not romantic enough to pretend otherwise. But Filoli’s bonsai terrace is a useful corrective — a reminder that some forms of beauty are only legible as records of duration. The lean of that trunk is not a feature. It’s an argument made slowly, over a life, against gravity.

I don’t know who grew it. I don’t know if they’re still alive. The tree, characteristically, offers no information about this. It just stands there in its bowl, complete, patient, not particularly interested in being understood.

Categories
AI Living

The Threshold

There is a specific feeling. You are trying to understand something — a medical term in a lab report, a clause in a contract, how a particular piece of software actually works under the hood — and you hit the edge of what you know. The territory beyond is unfamiliar and the path is unclear, and something in you decides, quietly and almost without announcement: I don’t know how to figure this out.

And then you move on.

Marc Andreessen, talking to Joe Rogan recently, buried something important inside a longer riff about AI prompting tricks. Most of his list was the kind of thing you’d read in a productivity newsletter — ask it to steelman both sides, pretend it’s a panel of experts. Useful, not revelatory. But one observation was different: pay attention to the exact moment you think “I don’t know how to figure this out.” That’s the moment you should open the AI.

He said it almost offhandedly. I haven’t been able to stop thinking about it.

What he’s really describing isn’t a technique. It’s a behavioral pattern that most of us developed so gradually we don’t recognize it as a choice. The feeling of epistemic overreach — of arriving at the edge of one’s competence — became, over decades, a stopping condition. We learned to treat not-knowing as a wall rather than a door because, most of the time, it functionally was one. The library was closed. The expert was unavailable. The research was paywalled. You moved on.

The habit calcified. Now it persists even when the conditions that produced it no longer apply.

I notice it in myself, and I’m someone who is genuinely curious — who likes knowing how things work, who will follow a thread further than most people bother to. That’s not modesty; it’s relevant context. Because even with that disposition, I still hit the wall. I’ll be reading something and encounter a concept I only vaguely follow — some nuance in immunology, some historical episode I’ve only half absorbed — and I feel the familiar slight contraction, the small withdrawal. I read past it. The curiosity was there. The friction was higher.

Curiosity alone was never enough. What determined whether I pushed through wasn’t how much I wanted to understand — it was whether understanding felt retrievable at all. Most of the time, it didn’t. So I moved on, and the curiosity found something else to chase.

There’s a darker version of this worth sitting with. The people who never developed the quit reflex — who hit not-knowing and felt compelled rather than defeated — are, disproportionately, the ones who built things. The intellectual persistence wasn’t incidental to their contributions; it was probably constitutive of them. Curiosity as stubbornness. The refusal to accept the wall as final.

Elon Musk is the limit case. When he decided he wanted to go to Mars and found the rockets prohibitively expensive, he didn’t defer to the aerospace industry’s consensus about what was possible. He started reading propulsion manuals and cold-calling engineers. The quit signal either never fired or got overridden so fast it made no practical difference. The result was reusable orbital rockets, which the industry had largely decided weren’t worth pursuing. The dig reflex, taken to its extreme, rewrote what was considered feasible.

But the trait is undifferentiated. It doesn’t come with a calibration mechanism. The same refusal to accept expert consensus that produced SpaceX also produces a certain amount of confident wrongness — the Twitter decisions, the Covid takes, the occasional foray into geopolitics with the certainty of someone who has read a lot of Wikipedia. The dig reflex, unregulated, has no obvious stopping condition.

AI doesn’t change that underlying trait. What it changes is the access cost for everyone else.

For most of human history, the friction wasn’t random. It selected for people whose drive was strong enough to overcome it regardless of cost — the right connections, the right institution, the time to burn. Now that friction is lower for everyone, nearly to zero, for an enormous range of questions.

What I’m trying to build is the opposite of the quit reflex. Not the Musk version — boundless, uncalibrated, occasionally catastrophic. Something more modest: the habit of checking before giving up. Noticing the moment of not-knowing and treating it as a question rather than a verdict.

It requires noticing the moment. Which is harder than it sounds, because the reflex is fast and the moment is brief.

The contraction happens. You’ve already moved on. Somewhere behind you, the question is still there.

Categories
AI Business Consulting

The Toll Bridge and the Terrain

For fifteen years of my life, I lived inside the fortress of information asymmetry. I was part of a payments consulting business, and our model was exactly what Andrew Feldman described on a recent Moonshots episode when he pointed a sharp finger at traditional professional services.

His observation was simple, cutting, and entirely true:

“Their role today is to stand between ordinary people and obscure knowledge. And the application of that obscure knowledge to everyday problems.”

When I heard him say that, it landed with a quiet thud of recognition.

For a decade and a half, my colleagues and I were the ones standing in that gap. The payments industry—with its labyrinth of interchange fees, compliance structures, clearing networks, and legacy tech stacks—is a monument to obscure knowledge. Clients didn’t come to us because we possessed some divine, unreplicable wisdom. They came to us because the map was locked in our heads, and navigating the terrain without us was a recipe for an expensive disaster.

We charged for our time, and we earned it. We untangled complexity and solved real, everyday business problems for people who just wanted to move money safely from point A to point B.

But looking back now, I can see the architectural flaw disguised as a premium service. The economic foundation of that entire era relied on friction. It relied on the fact that it took an immense amount of human energy to retrieve a piece of obscure data and map it onto a specific business dilemma. You weren’t just paying for strategic guidance; you were paying a premium on artificial scarcity.

We are living through a moment where the marginal cost of intelligence is rapidly trending toward zero. When the barrier of “obscure knowledge” evaporates, the traditional toll bridges begin to look absurd.

For anyone starting a consulting business today, the playbook would have to be entirely different. When an LLM can parse thousands of pages of network operating rules, interchange tables, and regulatory compliance frameworks in a handful of seconds, the gatekeeper’s standing ground liquefies.

If your value proposition is merely standing between a client and a hidden database, your business model isn’t just flawed—it’s obsolete.

Yet, this collapses into a fascinating paradox. You might assume that when you democratize expertise, you eliminate the need for the expert. But as Dan Shipper recently observed, the reality of AI is completely counterintuitive.

Shipper points out that AI effectively packages up “yesterday’s competence” and makes it cheap and ubiquitous.

Suddenly, anyone can generate a complex contract, a software pull request, or a payments flow strategy with the click of a button. But when cheap competence skyrockets, adoption explodes, resulting in an unprecedented glut of generic output—what the internet has collectively taken to calling “slop”. It’s the default, lazy answer that lacks soul, context, and nuance.

When everything begins to look and smell the same, a strange thing happens: the market’s demand for genuine difference sky-rockets.

The shift we are facing across all professional services—whether legal, financial, or consulting—isn’t about eliminating the expert. It is about changing the expert’s job from data-retriever to orchestrator and judge. The floor has been raised. Yesterday’s ceiling is today’s baseline.

What remains is the ability to read a room. To watch a client’s shoulders tighten when you present an option that’s technically correct but organizationally impossible. To notice the glance exchanged across the table before anyone speaks. No LLM parses that. The map is universal now; the guide still has to be in the room.

We don’t need fewer guides; we need fewer toll booths. The future of consulting doesn’t belong to those who hoard the map. It belongs to those who use a universally available map to help people actually walk the terrain.

Categories
AI Business Investing Technology

The Scarcity Portfolio: Navigating Sovereign Debt, Wafer Bottlenecks, and Orbital Compute

Today I was watching the interview of Gavin Baker by Patrick O’Shaughnessy on his Invest Like the Best podcast. Like prior conversations this was another fascinating excursion into the mind of a sophisticated and very successful tech venture investor.

During the conversation, Patrick asked Gavin what agents he was using that were especially helpful and he mentioned one which summarizes YouTube podcasts and videos for him. Like most of us Baker just doesn’t have the time to watch or listen to them himself so good summaries are really helpful.

Turns out I’ve been working on a Google Gemini Gem that does this for me. When Baker mentioned his I fired up the new Gemini 3.5 Flash model and asked it to summarize the Baker interview.

Later in the conversation Baker used the term “battlefield AI” which caused me to go back to Gemini again to learn more about that. The results were so interesting that I asked Gemini to create a syllabus for a semester class on these subjects. After that I asked it to convert our whole conversation into a Markdown file so I could share it. You’ll find it below.

I found this whole experience pretty stunning. I came away very impressed with Gemini 3.5 Flash both for the quality of the responses but also the sheer speed. Wow!

Anyway I hope you enjoy the following!

Continue reading “The Scarcity Portfolio: Navigating Sovereign Debt, Wafer Bottlenecks, and Orbital Compute”
Categories
Haiku Living Reading

The Presence We Keep Deferring

I have so many unread articles saved to Instapaper that I’ve stopped checking the count. Each one felt, in the moment of saving it, like something I needed. A long piece on urban planning, a profile of someone interesting, a reported essay I fully intended to sit with.

The app is beautifully designed for exactly this — the frictionless capture, the clean reading interface waiting patiently on the other side.

What it can’t do is manufacture the attention I didn’t have when I saved it and still don’t have now. The articles aren’t the problem. The premise is: that presence is something you can bank.

There’s a haiku I keep returning to, from Natalie Goldberg’s Three Simple Lines. It’s by a poet named Fumiko Harada:

Morning chill
I savor this moment —
one meeting one lifetime

Eleven words. No verb in the third line, which makes it feel less like a thought and more like a verdict.

The Japanese concept underneath it is ichi-go ichi-e — loosely, “one time, one meeting.” It’s a Zen idea with origins in the tea ceremony, the understanding that each gathering is singular and therefore irreversible. You cannot archive it. You cannot search for it later. When it ends, it doesn’t go anywhere you can retrieve.

This is what the Instapaper queue is, at scale: an archive of moments I decided to experience later. The article about urban planning was written by someone who spent months reporting it, on a day when some editor thought it was ready, and landed in my feed on a morning when something about the headline caught me. That constellation doesn’t reassemble. Later is a different article.

The tools I use every day are getting astonishing. There are systems that can summarize, translate, recall, explain, anticipate. I use them. I find them genuinely useful.

But there’s a habit of mind they reward — a kind of perpetual deferral of full attention — that I haven’t fully reckoned with. The promise, always, is that you can engage more completely later, once the summary is ready, once the transcript exists, once the notes have been taken. Presence becomes a productivity tax you pay while waiting for a deliverable.

Harada’s haiku doesn’t moralize. The speaker isn’t lecturing herself into awareness. She’s just cold, and awake, and choosing to notice. I savor this moment. The word “savor” does a lot of work. It implies effort. You savor things that could be missed.

The pivot in the third line is what stays with me. One meeting one lifetime. Not “this meeting will last a lifetime” — that would be sentiment. It’s more like a mathematical statement: the cardinality of this encounter is one. There is exactly one of them. This morning, this particular chill, whatever conversation or solitude is happening inside it — that set has one element. By tomorrow it has zero. No amount of documentation changes that arithmetic.

I’m working on believing that.

Categories
Architecture Infrastructure

The Architecture of the Indestructible

We are conditioned to look for the center of things. When we try to understand an organization, we ask for an organizational chart. When we look at a nation, we look to its capital. Traditional architecture—whether of a building, a company, or an army—relies on a classic playbook: a strong hub, radiating outward. You find the center, you secure it, and the system holds.

But what happens when you try to decapitate an enemy, or a technology, that has no head?

In 1964, a brilliant engineer named Paul Baran sat at his desk at the RAND Corporation, trying to solve a Cold War nightmare: How do you maintain a communications network after a catastrophic nuclear strike? Baran realized that traditional networks were centralized—like a wheel with spokes. If you destroy the hub in the center, every single spoke becomes useless.

His solution was the distributed network, the foundational blueprint for what would eventually become the Internet.

“Under the proposed system, each station would need to be connected to only a few of its nearest neighbors… The system would be highly reliable, even if a large fraction of the stations were destroyed.”

Baran mathematically proved that if you remove the center, the edges don’t die. They simply reroute. A few decades later, telecom engineers used a remarkably similar logic to build cellular telephone networks. Instead of one massive, high-power radio tower serving an entire city, they broke the terrain into a grid of small, low-power cells. If one tower goes offline, the network degrades gracefully rather than collapsing. It bends, but it refuses to break.

There is a profound, poetic irony buried here. The United States government originally funded Baran’s research to create a distributed network so that its centralized monolith could survive. Decades later, asymmetric adversaries across the globe adopted that exact architectural philosophy for their physical defense doctrines—creating “Mosaic Defense” systems designed specifically so that when you destroy the center, the edges keep fighting.

They copied our homework to survive our strength.

I find myself thinking about this tension far beyond the realms of military strategy or software engineering. It is a metaphor for how we construct our lives. We often build centralized lives—anchored entirely to a single identity, a single career, or a single institution. We project a monolith of strength to the world. But monoliths are brittle. When the center is struck, the whole architecture crumbles.

The lesson of our modern architecture is becoming increasingly clear, whether you are managing a network, building an organization, or navigating the quiet complexities of a human life. The fragile monolith is an illusion of safety.

The future belongs to the web that knows how to reroute.

Categories
Etiquette Founders

How to Be in a Room

I happened to notice this in a post on X: Slow Ventures ran a 4-hour etiquette class for founders. This is actually a brilliant idea. A non-trivial amount of founders (and VCs) could use some help in this department.

So I asked AI: what would a course syllabus for something like this look like? Below is the response (which I quite like!):

This is a genuinely good idea — etiquette as a business skill is perennially under-taught, especially for technical founders who may have gone deep on product/engineering but shallow on navigating rooms. Here’s how I’d structure a day-long or modular course built around the Slow Ventures premise.

Founder Etiquette — A Field Guide

4-hour intensive · 5 modules · lecture + role-play format

Morning Block — First Impressions

Module 1 · 45 min — The room before the meeting

Arrivals, introductions, and presence under observation

  • Timing: the cost of early vs. late
  • Handshakes, eye contact, the graceful exit from a name you missed
  • Business card and phone etiquette
  • How to work a room without appearing to work a room
Module 2 · 45 min — Table stakes: dining fluency

The business meal as performance and ritual

  • Place settings, utensils, and the BMW rule
  • Who orders first, who orders what
  • Wine: fluency without performance
  • Dietary restrictions — yours and theirs
  • The check: the ritual, the timing, the dodge
  • When and how to talk business at the table

Mid-Morning Block — Communication

Module 3 · 45 min — Communication that doesn’t leak desperation

Written and verbal signals investors actually read

  • Email: subject lines, length, tone, and the follow-up cadence
  • Reading the room on Slack/text vs. formal channels
  • The thank-you note — when, how, why it still matters
  • Saying no, disagreeing, and delivering bad news
  • How to receive feedback without deflecting or over-agreeing
  • The exit from a conversation: graceful, not abrupt

Afternoon Block — High-Stakes Scenarios

Module 4 · 45 min — Investor relations: the long game

Board rooms, pitch meetings, and the unwritten VC code

  • Board meeting protocols — who speaks, in what order
  • Managing up to your board without managing them
  • The warm intro — how to ask, how to use, how to close the loop
  • Confidence vs. bravado: what the room hears
  • What to do when the meeting is going badly
  • Conference behavior: panels, hallways, and the after-party
Module 5 · 60 min — The edges: cultural fluency and hard situations

Where etiquette meets judgment

  • Cross-cultural norms: Japan, Middle East, Europe, India — the high-risk differentials
  • Power dynamics: how to behave when you have more (or less)
  • Navigating someone else’s social gaffe gracefully
  • Gift-giving: what to give, what never to give
  • The fine line between casual and sloppy in casual settings
  • Live role-play: difficult scenarios, coached debrief

A few things I’d stress if actually running this:

Module 3 is where the real behavioral work happens for founders specifically. The desperation signal leaks through email more than anywhere else — the too-fast reply, the over-explanation, the follow-up that arrives four hours after the first message. That’s the stuff that’s hard to see in yourself.

One thing Slow Ventures probably got right: four hours is about the right ceiling. Any longer and you start to feel like you’re at finishing school, which kills the energy.

After reviewing this, I thought it might be useful to add two role playing scenarios to the course. Here’s what the AI recommended:

First, add this role play in after Module 2:

Role-play 1 · 20 min — The wrong bread plate

Recovering from a gaffe without losing the room

A founder is taken to lunch by a partner at a firm they’ve been trying to get in front of for six months. It’s going well — good rapport, the partner seems genuinely interested. Then the founder accidentally takes the partner’s bread roll, realizes it mid-bite, and has to navigate the next ten minutes without letting the gaffe collapse the energy in the room. Secondary layer: the check arrives and it’s ambiguous who’s hosting.

This tests everything from Modules 1 and 2 simultaneously — table fluency, recovery under embarrassment, reading whether to acknowledge the mistake or absorb it silently, and the check ritual. Low-stakes enough to be funny, high-stakes enough to feel real.

Next, add this role play after Modules 3, 4, and 5:

Role-play 2 · 30 min — The meeting that’s going sideways

Holding the room when the numbers missed

A founder is in a board meeting presenting quarterly numbers that missed. Midway through, one board member interrupts with a pointed question the founder doesn’t have a clean answer to. A second board member piles on. The founder has to hold the room — not deflect, not over-apologize, not go defensive — while also reading which of the two is genuinely concerned versus performing concern for the room. Cultural wrinkle: the more aggressive questioner comes from a context where directness reads as respect, not attack.

This pulls from Modules 3, 4, and 5 simultaneously. It’s the hardest scenario in the course and should feel that way. The coached debrief afterward is where most of the learning actually happens — ending here means it lands with weight, which is the right note to close on.

More commentary from the AI:

The dining module is the most load-bearing. A remarkable amount of professional signaling happens at the table and most people don’t realize they’re broadcasting anything. The BMW rule alone (bread-meal-water, left to right, so you don’t eat someone else’s roll) is the kind of thing nobody tells you until you’ve already grabbed the wrong bread plate in front of a Series B lead. The live role-play after Module 2 brings this to life.

The live role-play in Module 5 is what separates a good version of this class from a mediocre one. You can lecture about graceful exits from bad meetings all day. Watching someone flail through one in real time, then getting coached on the spot, is what actually moves the needle.

Categories
AI Consulting

The Judgment Layer

An analyst’s note about the CEO of one of the largest consulting companies making comments at an investor conference includes a line that deserves more attention than it got: “token volume used on a project isn’t a proxy for AI maturity.”

Translation — clients are burning money on frontier models for problems that don’t need frontier models, and they’re not getting the outcomes they expected.

This firm’s CEO offered this as a business opportunity. I read it as a confession.

The old consulting model was simple: client has a technology problem, firm deploys humans to solve it. Billing followed effort. The new problem is different in kind — clients have an AI strategy problem. They know they’re supposed to be using AI. They’ve heard the word “frontier.” They’re spending accordingly. They just don’t know why, and the outcomes are showing it.

So the CEO is right that there’s an opportunity here. The value proposition shifts from implementation to judgment — not deploying AI, but knowing when not to deploy the expensive one. Matching capability to problem. Being trusted enough to tell a client that their $50M frontier model contract is solving a $500K problem.

Here’s the irony that the comment skates past: that advice is structurally difficult for a large consultancy to give.

The business model that built consulting firms was billing for doing. The more you deploy, the more you bill. Helping a client spend less, or choose the cheaper model, or run a narrower project, is genuinely good advice that the incentive structure actively works against. You don’t grow a $70 billion professional services firm by talking clients out of scope.

The judgment layer, if it becomes the real value, requires something closer to a doctor’s relationship with a patient than a contractor’s relationship with a client. Doctors get paid whether they prescribe or not. The value of the visit is the diagnosis — including the diagnosis that says you don’t need the expensive intervention. Consultants, historically, get paid to prescribe, and paid more when the prescription is larger.

There’s a reason we trust doctors with that asymmetry and not contractors. Licensing, malpractice, professional norms built over centuries — all of it exists to align the incentive. Consulting has none of that infrastructure. What it has instead is reputation, which is slower-acting and easier to game.

Whether the large firms can actually make the shift — rather than just reframe the same billable-hours model in the language of AI optimization — is the real question the market is wrestling with. The CEO’s comment is genuinely perceptive about where client value lies. It’s less clear that consulting firms are currently built to capture it honestly.