Databases. APIs. Reporting engines. Scheduled jobs that wake up at 2:00 a.m. and punish you for your architectural sins.

I also have a philosophy degree.

And I started my working life as the personal assistant to Bill Marshall.

If you don’t know the name: Bill co-founded the Toronto International Film Festival in 1976. Conjured it, really, out of a terrace in Cannes and an argument that Toronto deserved to matter. He was a raconteur, a schemer, a genuine force of Canadian cultural will.

He was also one of the most devoted students of Marshall McLuhan who ever walked the earth.

I was twenty-something. I took notes. I answered phones. I sat in rooms where ideas moved fast and the reference points were dense. Bill talked about McLuhan the way some people talk about oxygen — as something you needed so constantly it barely seemed worth naming.

*The medium is the message.*

I nodded. I filed it. I moved on.

I spent the next three decades building software.

---

Enterprise software has a fundamental problem that nobody talks about honestly.

It pretends reality can be finitely encoded.

It cannot.

Every client has different terminology, different workflows, different assumptions, different exceptions. One company’s “completed” means the learner watched the video. Another means they passed the assessment. Another means a manager signed off after a field observation they conducted in a hard hat in a refinery in northern Alberta at minus thirty degrees.

And what do we do?

We write another rule.

`if OntarioHealthcare and LegacyBranch and CompletionDate < ExpiryDate`

Another exception. Another configuration toggle. Another support ticket slowly building an infinite ontology, one semantic argument at a time.

I spent years doing this. Getting better at it. Thinking I understood the problem.

I didn’t understand the problem.

---

The understanding came sideways.

I was thinking about report testing — the unglamorous engineering problem of how you verify that a highly configurable reporting system actually works. The traditional instinct is exhaustive: test every report, every permutation, every field, every edge case, every client scenario.

This approaches infinity immediately.

And something shifted.

*We are testing the wrong thing.*

We should not be testing whether the software perfectly understood every client’s business meaning. That problem is infinite. That problem will never be solved. That problem is a category error dressed up as a technical requirement.

We should be testing whether the system preserved supported data structures correctly through every stage of computation.

That problem is finite.

And once I saw it I couldn’t unsee it.

A bitmap preserves every point. A vector preserves the shape.

Enterprise software has been trying to be a bitmap of human meaning since the beginning. It will never be good enough. There are too many pixels in the universe.

The real work is the geometry.

---

I started thinking about why large language models work at all.

Not in the press release sense. In the actual sense.

They’re trained on tokens. Fragments. Individually, they mean almost nothing. Statistically, they’re noise with punctuation.

But something emerges. Something that can reason, connect, generate, surprise.

It doesn’t emerge from the tokens.

It emerges from the relationships between them. The topology. The latent geometry of meaning compressed across billions of examples into something that behaves, improbably, like understanding.

Intelligence is in the shape.

Not the dots.

And I sat with that for a while. And then it hit me all at once, decades late, slightly embarrassing in its tardiness:

*The medium is the message.*

Bill said this to me. Bill said this constantly. I nodded and filed it and moved on, because I was twenty-something and I thought I knew what it meant and I did not know what it meant at all.

It doesn’t mean the delivery mechanism is more important than the content.

It means the structure is where the meaning lives.

It means a television doesn’t just carry messages — it *is* a message, one that rewires how you process everything that comes through it. It means the shape of the container changes the shape of the thought.

Tokens are the content. Vectors are the medium. And the medium — the geometry of relationships baked into those billions of parameters — is what actually thinks.

McLuhan wasn’t being clever. He was describing something foundational about how meaning moves through systems. I just needed to build a few of those systems from scratch before I could hear it.

---

I think about Bill sometimes when I’m deep in an architectural decision.

He died in 2017. New Year’s Day. Which felt, to people who knew him, like an act of theatrical timing he probably would have appreciated.

He built something that remade Canadian culture, and he did it by understanding that the festival itself — the shape of the gathering, the public nature of it, the fact that it was for everyone and not just insiders — was not just the vehicle for the films.

It was the message.

Toronto became a film city not because of which movies played. Because of what kind of space Bill and Dusty and Henk built around the watching.

The medium.

I was twenty-something in those rooms. I didn’t know I was learning the most important thing I would ever learn about software architecture.

---

The hardest problems in software are no longer computational.

They are ontological.

AI is making raw computation abundant. Which means the bottleneck shifts upward, into meaning. Into the question of what we are actually trying to preserve, and what shape that thing has, and whether we even know the difference between the pixels and the geometry.

The programmers who thrive in the next era won’t just write syntax.

They’ll be ontology designers. Shape-makers. People who understand that you are not encoding reality — you are constructing a stable approximation of it, and the approximation’s structure is where the truth lives or dies.

That turns out to be philosophy.

It also turns out to be what a loud, brilliant, McLuhan-quoting Scotsman was trying to tell a twenty-year-old who was too busy taking notes to actually listen.

I’m listening now, Bill.

Thirty years late.

But I finally got it.

Even if I still don’t understand the point of Robbie Burns Day.

But I’m young.

Not startup stress. Not “founder burnout.” Those are almost luxury terms now... terms invented by people whose infrastructure mostly worked.

I mean the chronic cognitive tax of carrying unfinished systems in your head for decades.

You know exactly what should exist.
You know how the machine should behave.
You know where the bottlenecks are.
You know which workflows are fragile, which data structures are compromised, which interfaces lie to the user, which operational processes depend on tribal knowledge and prayer.

And every year you postpone fixing them because the company still has to ship. Payroll still has to clear. Clients still need support. Sales still need demos.

So you accumulate technical debt... yes.

But more dangerously, you accumulate organizational debt. Architectural debt. Decision debt. Deferred cognition.

For most of my adult life, I’ve had entire systems living only in my head because translating them into reality required teams, budgets, coordination layers, documentation cycles, and months of interruption.

Then Claude arrived.

And over roughly 1.8 billion tokens... somewhere between fifteen and twenty thousand dollars in API and tooling costs... I rebuilt the operational substrate of my company.

Not the marketing website.
Not a chatbot.
Not “AI features.”

The substrate.

The hidden machinery.

The stuff no investor sees in a pitch deck because it isn’t sexy enough to demo but determines whether a company compounds or slowly suffocates under its own accumulated entropy.

I need people to understand something important here:

This wasn’t vibe coding.

I wasn’t asking Claude to “make me an app.”

I was using it more like an exocortex... an infinitely patient senior systems architect that never got tired of re-evaluating abstractions with me at 2:13 AM.

Over months, we rebuilt massive sections of the operational substrate underneath the company itself... not just technically, but conceptually.

Not because AI magically “did the work.”

Because the cost of iteration collapsed.

That’s the part most people still don’t understand.

The revolution isn’t that AI gives you answers.

The revolution is that thought itself becomes dramatically cheaper to externalize, test, reorganize, challenge, and refine.

For twenty-five years, every major architectural rethink carried enormous activation energy.

You needed meetings, diagrams, developers, specifications, scheduling, emotional stamina, budget justification, and organizational synchronization.

Now you can stay inside the cognitive loop continuously.

You can model systems at the speed of thought.

You can hold fifty interacting abstractions in motion and pressure test them dynamically.

You can ask:

“What breaks if we invert the permissions model?”

And immediately explore downstream implications across infrastructure, UX, governance, scalability, and pedagogy.

That changes what a founder even is.

Here’s the strange part:

The actual money almost becomes irrelevant.

If you had told twenty-five-year-old me:

“For twenty thousand dollars you can acquire a tireless architectural collaborator capable of helping you rebuild your company’s nervous system over the next year...”

I would have considered that supernatural.

Because historically, building internal infrastructure at this level required a large engineering organization, elite consultants, years of roadmap prioritization, or extraordinary luck in hiring.

Now a single determined founder with clarity and stamina can execute organizational rewrites previously reserved for massively capitalized companies.

Not because AI replaces expertise.

Because it amplifies coherent intent.

That distinction matters enormously.

Still, I think people misunderstand what the money actually bought.

The twenty-thousand-dollar spend didn’t rebuild my company. It bought me visibility. It bought me plumbing that, for most of my career, would have been considered an absurd luxury unless you were a very large organization.

What AI gave me was the ability to finally expose and connect systems that had been living partially in my head for decades. Not features. Not products. Structure. Relationships. Decision pathways. The hidden connective tissue underneath the business itself.

For years I understood these systems intuitively, but understanding something and being able to continuously inspect it are completely different things. Now I can trace assumptions more clearly, understand downstream consequences more quickly, and reorganize ideas without the enormous operational friction that used to accompany every major rethink.

And honestly, by the time this is fully mature, it probably won’t cost twenty thousand dollars. It’ll likely cost a few hundred thousand in compute, infrastructure, and iteration.

What fascinates me is that none of that spend may add a single visible feature for a customer.

It changes coherence.

It changes continuity.

It changes how the organization understands itself.

Because the real asset was never the code.

The ontology is the asset.

But even ontology by itself is incomplete. What actually gives a company life is the decision lineage that produced that ontology in the first place. The accumulated chain of judgments, tradeoffs, observations, constraints, mistakes, and realizations that caused the structure to evolve the way it did.

I think people dramatically underestimate how fragile that layer really is.

The best metaphor I’ve found is a Christmas tree.

As long as the tree remains rooted in the ground that produced it, it continues to grow. The second you sever it from that soil... enjoy the holidays.

It may still look alive for a while.

But growth has already stopped.

That’s the part of the AI transition that genuinely troubles me.

Everyone is focused on automation, productivity, and labor reduction, which makes sense. But I think we are also heading toward a massive collapse of institutional memory. When teams of twenty become teams of one, the organization may preserve the outputs while quietly losing the reasoning that produced them.

Someone new eventually inherits the architecture, the workflows, the infrastructure, the codebase... but the “why” behind it all has evaporated.

And once that happens, organizations slowly become custodians of systems they no longer fundamentally understand.

That worries me far more than automation itself.

Because despite how extraordinary these systems are becoming, I still don’t believe AI replaces original cognition. It can appear creative because it has access to a nearly incomprehensible landscape of prior human exploration. Most of the problems we encounter in a business day have already been solved somewhere by someone, and in those situations using AI is simply rational.

Why repeatedly spend human cognition rediscovering known terrain?

But eventually you arrive at problems where no map exists yet. And that’s where human judgment still matters completely.

So I don’t think programmers disappear. I think their role changes. Increasingly, I need people who can direct systems, shape ontology, sustain coherence, and exercise judgment under uncertainty.

For decades the industry rewarded people for being extraordinary executors.

Now I think we are entering an era where the truly rare skill is knowing where the horses should go.

I don’t think the public fully grasps what happens when experienced operators gain the ability to continuously externalize institutional knowledge.

The first wave of AI discourse focused on replacing labor.

I think the more profound shift is replacing friction.

Friction between thought and execution. Architecture and implementation. Vision and specification. Complexity and iteration.

For decades, companies calcified because organizational cognition was expensive.

Every redesign threatened velocity.

Every rewrite threatened stability.

Every deep rethink required political capital.

AI changes the economics of reconsideration itself.

That may end up being bigger than automation.

And yes... there’s a dangerous side to this.

Because once you experience operating this way, traditional organizational latency starts feeling intolerable.

You begin noticing how much of corporate structure exists simply because thinking used to be expensive.

Middle layers emerge to transport information between silos because humans couldn’t maintain high-bandwidth shared cognition continuously.

But what happens when they can?

Or when one founder plus AI can outmaneuver entire departments in strategic adaptability?

We’re entering very weird territory.

What fascinates me most is that the output often doesn’t look dramatic from the outside.

There’s no flashy consumer launch.

No viral demo.

No dancing humanoid robot.

Just an organization becoming internally coherent at a depth that previously would have taken years.

Cleaner abstractions. Better schemas. More legible systems. Reduced ambiguity. Faster strategic movement. Improved operational alignment.

Infrastructure.

The invisible stuff that determines whether your future compounds or collapses.

I don’t think history will remember this era primarily as “the chatbot revolution.”

I think it may eventually be understood as the moment cognition itself became scalable infrastructure.

And for founders who have spent decades carrying unfinished systems silently in their heads...

That changes everything.

But maybe they should be thinking about Janus instead.

Janus was the Roman god with two faces. One looking forward and one looking backward. And increasingly, that feels like the real state of the AI industry. Not divided between companies so much as divided within them.

Because there are now two fundamentally different beliefs about what AI actually is, where it is heading, and what kind of business will survive around it.

One side believes we are on the road to general intelligence. Not metaphorically, but literally. They see current systems as early, primitive, incomplete versions of something far larger. From that perspective, intelligence itself is mostly a scaling problem. More GPUs, more energy, more data centers, more parameters, more synthetic cognition layered on top of synthetic cognition until something qualitatively different emerges.

This side of the industry is spending like a civilization-level arms race has already begun. And if they are right, the spending is rational. If the first company to achieve true AGI gains control over the most important economic engine in human history, then a trillion dollars in infrastructure is not excessive; it is conservative.

That is the face building nuclear power agreements and hydro contracts. The face buying entire global GPU supply chains. The face preparing for a future where intelligence itself becomes centralized infrastructure.

But there is another face staring back at them from the same mirror.

And what makes this split even stranger is that the foundations of the modern AI industry did not emerge from research into consciousness, reasoning, or grounded understanding of reality. They emerged from translation.

Google’s 2017 paper Attention Is All You Need became the true founding document of the modern AI era.

https://arxiv.org/abs/1706.03762

The Transformer architecture it introduced turned out to be astonishingly effective at modeling relationships inside language and symbolic representations. The original breakthrough was not “we discovered digital cognition.” It was something much narrower and much more practical: attention mechanisms were extraordinarily good at understanding relationships between tokens inside sequences.

From that breakthrough, however, the industry began making a series of increasingly ambitious extrapolations. Translation became language modeling. Language modeling became reasoning. Reasoning became intelligence. And intelligence became AGI.

The entire modern AI industry now sits on top of that ladder of assumptions.

Then in 2020, OpenAI published Scaling Laws for Neural Language Models, which effectively poured gasoline onto the idea.

https://arxiv.org/abs/2001.08361

That paper argued something deceptively simple but economically explosive: larger models trained on more data with more compute kept improving in surprisingly predictable ways.

And that was the moment the industry truly lost its mind.

Because if capability kept scaling with compute, then the race became obvious. More GPUs, more data, more power, more infrastructure, more scale. And to be fair, the evidence initially looked overwhelming.

The models worked.

Absurdly well.

Good enough that an entire industry briefly convinced itself that maybe intelligence really was just next-token prediction at sufficient scale.

And perhaps that is unfair.

But only slightly.

Because underneath all the mythology, these systems are still fundamentally predictive engines. Extremely sophisticated predictive engines, yes... but predictive engines nonetheless.

The uncomfortable possibility haunting the industry is that scaling prediction may not be the same thing as scaling understanding.

GPT-4 felt like Lucy finally holding the football steady.

GPT-5 was the moment a lot of people in the industry started wondering if they were Charlie Brown again.

Because now the industry is arriving at the dangerous phase of the experiment: finding out whether the world’s most expensive autocomplete engine can actually cross the next threshold. Increasingly, many of the people closest to these systems appear to suspect the answer is no. Or at least, not through brute force scaling alone.

What these systems unquestionably mastered was language. But language is not reality.

Language is humanity’s symbolic compression layer for reality. A language model lives inside humanity’s descriptions of the world, not necessarily inside the world itself. That distinction becomes brutally obvious the moment you move from text into video.

Text prediction allows plausibility. World prediction requires inevitability.

A language model can often generate something believable about what could happen next. But asking a video model what will happen three frames from now turns out to be a radically different category of problem. Not what might happen, but what must happen.

A dropped glass must continue falling. Water must preserve continuity. Objects hidden behind other objects must still exist when occluded. Bodies must obey physics. The world must remain coherent even when partially unseen.

Suddenly the barrier is no longer syntax or semantic association. It is grounded reality itself. And no amount of additional compute automatically guarantees you can brute-force your way through that wall.

I increasingly suspect this possibility haunts the second faction inside these companies. Because if they are right, then the current models are not primitive proto-gods waiting to wake up. They are already the product.

And if that is true, then the race changes completely.

This second group sees something the dreamers often do not. Current models are already powerful enough to reshape enormous parts of civilization. Software development, education, customer support, management structures, search, scientific research, material science, media production... all of it is already beginning to bend around systems that exist today.

But to them, this is no longer just a technological observation. It is a financial emergency.

Because sitting behind the industry is an almost absurd engine of capital expenditure. A monster consuming power plants, GPUs, data centers, fiber, water, and entire national-scale energy agreements at a rate that starts to feel less like software and more like heavy industry.

The people trying to commercialize current AI increasingly look like they are frantically paving the road ahead of that machine before it catches them.

Every SaaS feature suddenly becomes vulnerable. Calendar companies, CRM companies, note-taking apps, customer support systems, search products, workflow tools, presentation software, research assistants... everything starts looking like one more feature that can be absorbed into a larger AI subscription bundle.

You can almost feel the urgency behind it. One more product integrated. One more workflow captured. One more recurring subscription pulled into the ecosystem before the infrastructure bill arrives.

Google understands this instinctively because Google has always understood bundling. Gmail was never just Gmail. Docs was never just Docs. Search was never just Search. The real business was always the gravitational field created by tying everything together.

Gemini increasingly feels like the continuation of that strategy. Not merely an AI model, but an attempt to turn all of Google Workspace into a single cognitive subscription layer where every adjacent software category eventually collapses inward.

And if you use Gemini long enough, you start noticing another tell. It leans heavily on Google’s existing search infrastructure: existing indexes, cached knowledge, retrieval systems, summaries, search grounding. You almost have to strong-arm it into actually going out and spending meaningful inference effort exploring something fresh.

That is not an accident. That is economics leaking through the interface.

A company truly convinced that unlimited inference was the future would behave differently. It would burn compute aggressively and freely because compute would be the path to the prize. Instead, what we increasingly see across the industry are systems trying to conserve reasoning wherever possible. Retrieval first. Cached answers first. Smaller specialist systems first. Inference only when necessary.

These are not the behaviors of companies escaping economic gravity. They are the behaviors of companies deeply aware of it.

Microsoft’s behavior, meanwhile, feels oddly conflicted.

For years, the company executed one of the most successful business transformations in modern corporate history. It turned itself from a boxed software company into a cloud and subscription empire built around Azure and Office 365. It understood recurring revenue, enterprise dependency, workflow gravity, and organizational lock-in better than almost anyone.

Which is what makes the current moment feel so strange.

Because Microsoft already owned the daily operational fabric of enterprise work. Email, calendars, documents, spreadsheets, meetings, identity management... the company was already sitting exactly where delegated cognition was naturally going to emerge.

And yet much of its AI posture still feels like it is orbiting OpenAI’s narrative rather than extending Microsoft’s own.

Copilot often feels bolted onto products rather than metabolized into them. A feature suite attached to Office instead of an operational layer dissolving invisibly into workflow itself.

Google, by contrast, increasingly behaves like a company trying to make AI disappear into the substrate of its ecosystem.

That difference matters.

One approach treats AI as a destination product.

The other treats it as atmospheric infrastructure.

Anthropic appears to have approached the problem differently, leaning hard into programming because code has unusually clean economics. It has measurable outputs, enormous labor replacement value, direct enterprise demand, and vast open-license ecosystems to train against.

That may also explain why Anthropic seemed unusually willing to make peace with creators and publishers early. I increasingly suspect they understood something important: general content may matter politically, but programming ecosystems matter economically.

And more than that, I suspect they understood something strategically brutal.

If OpenAI, Google, xAI, and everyone else end up trapped in endless licensing wars over the right to answer humanity’s infinite stream of general-interest questions, all the better for Anthropic.

Because every billion dollars spent negotiating with publishers, creators, news organizations, archives, and eventually perhaps even something like Wikipedia is a billion dollars diverted away from infrastructure, tooling, programming ecosystems, and enterprise integration.

Meanwhile, Anthropic increasingly behaves like a company that believes the real long-term value lives somewhere much narrower and much more economically defensible: code.

Code has validation systems. Code has reinforcement loops. Code produces leverage. Code generates more systems.

A model optimized for programming is not merely answering questions. It is participating in the recursive construction of future infrastructure.

And if that is the future Anthropic sees coming, then encouraging the rest of the industry to become entangled in increasingly expensive cultural licensing battles starts looking less like altruism and more like strategic positioning.

Let everyone else spend fortunes building infinitely patient machines for answering the world’s stupidest questions.

Meanwhile, the company focused on helping developers build the future may end up owning the economically productive layer underneath all of it.

And underneath all of this sits the industry’s deepest fear: the models themselves may become commodities.

In fact, the existence of public APIs already suggests the companies know this risk is real. If raw intelligence could remain permanently proprietary, they would never expose it cleanly to the market in the first place.

But they know what happens to infrastructure over time. Storage commoditized. Bandwidth commoditized. Compute commoditized. Databases commoditized. Cloud infrastructure commoditized.

Increasingly, intelligence itself may commoditize too.

There will always be someone willing to sell inference cheaper. There will always be open models. There will always be regional providers. There will always be distillation. There will always be optimization pressure.

Which means the durable value likely moves upward into the surrounding systems: memory, workflow, identity, coordination, trust, orchestration, integration.

Not the model itself... the ecosystem wrapped around it.

That is why the industry increasingly feels split between two futures.

One future treats AI as a path toward centralized superintelligence.

The other treats AI as a new infrastructure layer around which useful systems will be built.

The first group wants bigger minds.

The second wants better orchestration.

And increasingly, I suspect the second group may win.

Not because AGI is impossible. Not because the models stop improving. But because markets eventually force discipline.

Public companies obey margins. Infrastructure obeys economics. Customers pay for usefulness, not metaphysics.

A model that is 20% smarter but 30 times more expensive may simply lose commercially to a system that is cheaper, integrated, reliable, and already good enough for most economic purposes.

And “good enough” may already be enough to permanently alter civilization.

That is the strange thing about this moment.

The AI revolution may already have happened.

The industry just cannot yet agree on whether it is building the convergence... or a business.

We’ve confused disrespect with intelligence.

There was a time when pushing back on authority meant something. It implied you had done the work…that you understood the structure you were rejecting. Now it’s theatre. The phrase “appeal to authority” has been twisted into a blunt instrument…a way to dismiss expertise without ever engaging it. It was meant as a warning against blind trust…now it’s used to justify blind ignorance.

Let’s be clear.

Authority is not truth.

Expertise is not infallible.

But ignorance is not a counterweight.

And yet here we are…in a world of shit talkers who believe skepticism alone is a credential.

I don’t mind tearing down experts who deserve it. There are plenty. But if you’re going to dismantle something…you should at least understand what you’re dismantling.

Walk before you run.

Run before you punch.

Most people skip straight to swinging.

And this isn’t theoretical…we’ve seen what real dissent looks like.

The Impressionists didn’t reject realism because they couldn’t do it…they mastered it first.

The doctor who proved ulcers were bacterial didn’t wander in with a hot take…he did the work. He was mocked one year…keynote the next.

Martin Luther didn’t casually question the Church…he could quote chapter and verse before he ever nailed anything to a door.

Even Luke had to go to Dagobah.

That’s the pattern.

The people who challenge systems at the highest level are the ones who climbed them first.

There is no zealot like a convert.

Only those who can climb to the highest levels of a domain should dare…and must dare…to tear it down if it’s false.

I’m not arguing against rebellion.

I’m arguing against dabbling.

What we’ve lost is respect for process. The idea that knowing something requires time…constraint…exposure.

Instead, we have vibe voting. Opinion without source. Certainty without consequence.

And at the other end…we have a different failure.

Smart people who should know better.

People who succeed in one domain and begin to believe that success transfers. That the skill wasn’t specific…it was universal. So they wander…into systems they don’t understand…and mistake complexity for incompetence.

The harder the problem…the more convinced they become it must be simple.

That’s how you get dilettantes at the highest levels.

We used to have a word for this.

Dilettante.

We’ve lost it.

Now the uninformed and the overconfident meet in the same place…and actual expertise gets drowned out.

I’m tired of it.

I’m tired of people having opinions with no source to back them up. I’m tired of people dismissing decades of work with a paragraph and a vibe.

This isn’t about defending institutions.

This is about defending reality.

And I’m not immune to this.

I build systems in my head. Fast. Clean. Elegant.

They feel right.

They click.

And that feeling…that coherence…is dangerous.

Coherence isn’t reality.

Coherence isn’t reality.

Coherence isn’t reality.

A system that holds logical water is not the same as one that has survived contact with the real world.

Mine often haven’t.

So yes…I’m suspicious of this tendency.

I’m not just the critic.

I’m not just the guy pointing it out.

I’m not just the president…

I’m also a member… 😉

Which is exactly why I don’t trust it.

Because the real danger isn’t that people ignore expertise.

It’s that they replace it with themselves…too quickly.

And then…we arrive here.

At the apogee of bullshit.

Entire journals polluted. Papers forged. Data massaged. Citation loops reinforcing each other into legitimacy.

The cracks were real.

The skepticism was earned.

But here’s the mistake.

We didn’t respond by tightening the standard…we responded by abandoning it.

We replaced “prove it” with “I don’t believe you.”

And that’s where this moment changes.

Because this isn’t just the peak.

It’s the exposure.

For the first time, ignorance isn’t just loud…it’s visible. Traceable. Testable.

We didn’t just build machines that can generate bullshit…

We built systems that can finally measure it.

That’s the shift.

AI isn’t the problem here.

It’s the audit.

At scale.

It can trace citation networks.

Surface statistical anomalies.

Expose patterns no human reviewer could catch.

Not perfectly. Not cleanly.

But relentlessly.

And that changes the equation.

Because now…authority doesn’t get to hide behind itself anymore.

It gets tested.

Continuously.

This isn’t a permanent rebellion against orthodoxy.

It’s the chance to purify it.

To strip out the frauds.

To expose the dilettantes.

To reinforce what actually holds.

The goal was never to eliminate expertise.

The goal was to make it worthy of trust again.

And maybe…for the first time…we have the tools to do it.

But only if we raise the standard instead of abandoning it.

Only if we stop pretending that every opinion is equal.

Only if we stop confusing skepticism with understanding.

We don’t need more voices.

We need better filters.

We might be living at the apogee of ignorance’s power.

Not because ignorance became stronger…

…but because it became easier to perform.

The cost of pretending collapsed.

The rewards expanded…all the way to the highest offices in the land.

That’s what changed.

But what comes next will be shaped by something else.

Not confidence.

Not volume.

Not coherence.

Reality.

What survives contact with it.

We’ve confused disrespect with intelligence.

There was a time when pushing back on authority meant something. It implied you had done the work…that you understood the structure you were rejecting. Now it’s theatre. The phrase “appeal to authority” has been twisted into a blunt instrument…a way to dismiss expertise without ever engaging it. It was meant as a warning against blind trust…now it’s used to justify blind ignorance.

Let’s be clear.

Authority is not truth.

Expertise is not infallible.

But ignorance is not a counterweight.

And yet here we are…in a world of shit talkers who believe skepticism alone is a credential.

I don’t mind tearing down experts who deserve it. There are plenty. But if you’re going to dismantle something…you should at least understand what you’re dismantling.

Walk before you run.

Run before you punch.

Most people skip straight to swinging.

And this isn’t theoretical…we’ve seen what real dissent looks like.

The Impressionists didn’t reject realism because they couldn’t do it…they mastered it first.

The doctor who proved ulcers were bacterial didn’t wander in with a hot take…he did the work. He was mocked one year…keynote the next.

Martin Luther didn’t casually question the Church…he could quote chapter and verse before he ever nailed anything to a door.

Even Luke had to go to Dagobah.

That’s the pattern.

The people who challenge systems at the highest level are the ones who climbed them first.

There is no zealot like a convert.

Only those who can climb to the highest levels of a domain should dare…and must dare…to tear it down if it’s false.

I’m not arguing against rebellion.

I’m arguing against dabbling.

What we’ve lost is respect for process. The idea that knowing something requires time…constraint…exposure.

Instead, we have vibe voting. Opinion without source. Certainty without consequence.

And at the other end…we have a different failure.

Smart people who should know better.

People who succeed in one domain and begin to believe that success transfers. That the skill wasn’t specific…it was universal. So they wander…into systems they don’t understand…and mistake complexity for incompetence.

The harder the problem…the more convinced they become it must be simple.

That’s how you get dilettantes at the highest levels.

We used to have a word for this.

Dilettante.

We’ve lost it.

Now the uninformed and the overconfident meet in the same place…and actual expertise gets drowned out.

I’m tired of it.

I’m tired of people having opinions with no source to back them up. I’m tired of people dismissing decades of work with a paragraph and a vibe.

This isn’t about defending institutions.

This is about defending reality.

And I’m not immune to this.

I build systems in my head. Fast. Clean. Elegant.

They feel right.

They click.

And that feeling…that coherence…is dangerous.

Coherence isn’t reality.

Coherence isn’t reality.

Coherence isn’t reality.

A system that holds logical water is not the same as one that has survived contact with the real world.

Mine often haven’t.

So yes…I’m suspicious of this tendency.

I’m not just the critic.

I’m not just the guy pointing it out.

I’m not just the president…

I’m also a member… 😉

Which is exactly why I don’t trust it.

Because the real danger isn’t that people ignore expertise.

It’s that they replace it with themselves…too quickly.

And then…we arrive here.

At the apogee of bullshit.

Entire journals polluted. Papers forged. Data massaged. Citation loops reinforcing each other into legitimacy.

The cracks were real.

The skepticism was earned.

But here’s the mistake.

We didn’t respond by tightening the standard…we responded by abandoning it.

We replaced “prove it” with “I don’t believe you.”

And that’s where this moment changes.

Because this isn’t just the peak.

It’s the exposure.

For the first time, ignorance isn’t just loud…it’s visible. Traceable. Testable.

We didn’t just build machines that can generate bullshit…

We built systems that can finally measure it.

That’s the shift.

AI isn’t the problem here.

It’s the audit.

At scale.

It can trace citation networks.

Surface statistical anomalies.

Expose patterns no human reviewer could catch.

Not perfectly. Not cleanly.

But relentlessly.

And that changes the equation.

Because now…authority doesn’t get to hide behind itself anymore.

It gets tested.

Continuously.

This isn’t a permanent rebellion against orthodoxy.

It’s the chance to purify it.

To strip out the frauds.

To expose the dilettantes.

To reinforce what actually holds.

The goal was never to eliminate expertise.

The goal was to make it worthy of trust again.

And maybe…for the first time…we have the tools to do it.

But only if we raise the standard instead of abandoning it.

Only if we stop pretending that every opinion is equal.

Only if we stop confusing skepticism with understanding.

We don’t need more voices.

We need better filters.

We might be living at the apogee of ignorance’s power.

Not because ignorance became stronger…

…but because it became easier to perform.

The cost of pretending collapsed.

The rewards expanded…all the way to the highest offices in the land.

That’s what changed.

But what comes next will be shaped by something else.

Not confidence.

Not volume.

Not coherence.

Reality.

What survives contact with it.

Which is exactly the problem.

If you’re a CEO, the present will consume you. Slack, decisions, people, noise… it all demands urgency. And urgency crowds out anything that isn’t immediate.

So a few times a year, I leave.

Not to a resort. Not to a conference.

I fly west… Saskatoon or Winnipeg… and then I take the train back to Toronto.

Two days. One or two nights. Cabin for one.

And I work.

---

The point isn’t the train. The point is what the train takes away.

No reliable internet for large stretches.

No meetings.

No one dropping by.

No ambient obligation to respond.

You can’t react… so you start to think.

There’s also something about movement. You’re going somewhere, even if slowly, and it creates a kind of psychological permission. Decisions feel less stuck. Ideas move. You move.

It’s hard to explain, but it’s consistent.

---

I’m alone most of the time.

That matters.

But not completely alone. Meals break it up. Conversations happen if you want them. At the end of the day, I’ll have a drink in the club car. Just enough social contact to stay human… not enough to derail the focus.

It’s a tuned environment. Not isolation for its own sake… isolation with relief valves.

---

I started doing this after reading about Bill Gates taking “reading weeks.” The idea stuck. You need time apart to deal with things that aren’t urgent.

At the time, I used these trips to clear backlog. Writing, mostly. Things I owed but never got to because the office only ever gives you “now.”

That problem is basically gone. AI took it.

The writing backlog doesn’t build anymore.

But the thinking problem didn’t go anywhere.

If anything, it’s more obvious now. Once the mechanical work disappears, what’s left is the part only you can do. Deciding what matters. Figuring out what to do next.

That’s what the train is for.

---

I think this is a bit of a boss move.

Most CEOs signal importance by being available. Always on. Always responsive.

I do the opposite.

I disappear… on purpose… on a schedule.

And the business survives just fine.

Better, actually.

---

It’s getting harder to book a cabin on VIA Rail now, which is interesting in itself. So I’m experimenting.

Next trip I’m flying to New York and taking the Silver Meteor down to Miami.

It’s not as nice. Food’s worse. The experience isn’t the same.

But I’ll have more connectivity… and for this particular trip, that helps. What I’m working through benefits from some input.

The platform changes. The principle doesn’t.

---

I don’t take the train to travel.

I take it to think.

Everything else is just logistics.

So the instinct is to take a shot at it.

To treat it as a symptom of decline… soft people, low standards, a culture that has traded edge for comfort.

But the longer you look at it, the less stable that critique becomes.

Because pickleball is doing something very specific… and very effective.

It creates structured leisure.
It produces low-stakes competition.
It gives people a place to show up, regularly, with just enough friction to feel engaged and not enough to feel threatened.

It’s not trying to be meaningful in a grand sense. It’s trying to be repeatable.

And that’s the key.

It occupies the same functional role that Huxley described in Brave New World. Not in the literal sense… there’s no centralized authority designing it, no deliberate economic manipulation behind it. But in terms of outcome, it lands in the same place.

It stabilizes people.

It organizes time.
It creates social fabric.
It absorbs energy that might otherwise have nowhere to go.

Once you see it that way, the target shifts.

Because it’s not just pickleball.

It’s pickleball and Xanax.

That pairing sounds like a joke at first. It isn’t. It’s a pattern.

A way of smoothing the system from two directions:

• pharmacologically, by dampening anxiety

• socially, by providing a predictable loop of interaction and mild reward

Together, they create a kind of equilibrium. Not happiness exactly… but stability. Enough to keep everything functioning.

And then there’s the other track.

Tennis and Molly.

Higher intensity. More edge. More signaling. Less about smoothing, more about amplification. Instead of dampening the baseline, it tries to override it… create peaks that justify the valleys.

Two different responses. Same underlying condition.

Because the environment they’re responding to is the same.

The structures that used to define identity… religion, career paths, fixed roles… have weakened. Not disappeared, but softened to the point where they no longer dictate a clear trajectory.

What replaced them wasn’t a new structure. It was choice.

And choice sounds good until you live inside it.

More freedom.
Less direction.
More possibility.
Less clarity.

What that creates isn’t acute suffering. It’s something quieter.

A kind of low-grade instability.

Not enough to break anything. Not enough to force a reckoning. Just enough to leave a persistent question in the background… what is any of this actually for?

Everything else flows from that.

Some people stabilize the system.
Some people override it.

Both approaches are coherent. Both are adaptive. And both end up looking more similar than they first appear.

Which makes the critique harder to sustain.

Because if you try to tear down one, you end up implicating the other… and eventually yourself.

At that point, a different distinction starts to matter.

Not between specific activities or substances… but between categories of engagement.

There are things that stabilize.
There are things that provide escape.
And there are things that build.

Most people move between the first two without thinking too much about it. They’re necessary. They keep life manageable.

The third category is different.

Not because it’s morally better, but because it behaves differently over time.

Anything that actually builds… a company, a body of work, a family system, an institution… doesn’t resolve cleanly. It doesn’t give you a closed loop. It doesn’t end in a way that feels complete.

You don’t finish it.

That’s not a bug. That’s the structure.

If something can be fully completed within a neat boundary, it usually isn’t in that category. It’s a project. A game. A loop that resets.

Building doesn’t reset.

It compounds. It extends. It outlasts the person doing it… or at least outlasts their ability to see the end.

Which makes it fundamentally different from both stabilization and escape.

Closed loops versus open loops.

Closed loops resolve.
They give you feedback.
They let you feel done.

Open loops don’t.
They carry forward.
They require you to operate without closure.

That’s where the third group sits.

Not “builders” in the romantic sense. Not founders, not artists, not some elevated class.

Just people who have made a quiet decision:

they will spend their time on things that do not pay them back cleanly.

Things that:

• don’t end on schedule

• don’t fully reward effort in the short term

• don’t provide a stable identity while you’re inside them

• and often look indistinguishable from failure for long stretches

They trade clarity for direction.
They trade comfort for continuity.
They trade closure for compounding.

And most importantly… they accept that the outcome is asymmetric in time.

The payoff, if it comes at all, shows up later… often beyond their direct experience of it. Sometimes in other people. Sometimes in systems that keep running after they step away.

That’s why it feels like chasing something you won’t finish.

Because you are.

But that’s the only category where time works differently.

Stabilizers compress time into repeatable loops.
Escapes spike it into peaks.

The third group stretches it.

They’re not trying to feel better today.
They’re not trying to win a contained game.

They’re trying to participate in something that continues.

It must be great to be a monk.
Or to paint till your fingers are raw.
To crash up against a Nobel Prize worthy problem.
Or die in battle.

To love something more than yourself or another person.

I think Don Quixote was a genius. He made his own.

I just play tennis.

Like it remembers.
Like it “knows” them.
Like there’s continuity behind the interface.

There isn’t.

A large language model is stateless.

Every call is a fresh invocation.
No memory.
No continuity.
No accumulated understanding unless you explicitly pass it in.

That “conversation” you think you’re having is just context reconstruction… over and over again.

So Let’s Be Honest About What That Means

If every call is independent…

Then every call is a decision.

And one of the most important decisions is:

Which model should handle this specific piece of work?

Treating model selection as a one-time architectural choice is just wrong.

Different calls have:

• different complexity

• different structure

• different tolerance for failure

• different cost sensitivity

So wiring one model into everything…

isn’t a strategy.

But We Haven’t Solved This

Let’s be clear about that.

We don’t think we’ve nailed this.

What we’ve done is introduce a way to think about the problem.

Right now, that looks like question shapes.

Trying to describe a task in a structured way:

• what kind of work is this

• how complex is it

• what kind of output is expected

And using that to make a decision about which model should handle it.

Maybe that’s right.

Maybe it’s incomplete.

Maybe it’s wrong in ways we haven’t seen yet.

That’s exactly why Tally exists.

This Needs To Be Figured Out Together

You can’t solve this in isolation.

Not across real systems.

Not across real workloads.

Because the moment this touches production:

• assumptions break

• edge cases show up

• costs behave differently than expected

The only way to get this right is through shared usage and feedback over time.

What Tally Does

Tally gives you a way to make model selection a per-call decision…

and learn from it.

You:

• describe the task

• ask for a routing recommendation

• run the call the way you normally would

• report back what actually happened

Success. Failure. Cost.

Tally learns from that.

That’s the loop.

No proxy.
No rewrite of your system.
No pretending this is magic.

Just a better way to make a decision… and improve it.

What We Want From You

We’re opening this up because we need signal.

Not interest.
Not compliments.

Signal.

• run real workloads

• describe tasks honestly

• send back real outcomes

• push it until it breaks

Watch:

• where it saves you money

• where it makes bad calls

• how quickly it adapts

Then tell us.

How To Get Involved

Sign up:

https://tallyy.org

Install the SDK.

Run it on something real.

Then join the Discord and tell us where it’s wrong… where it’s useful… and where it needs to go next.

One Last Thing

Yes… it’s tallyy.org.

Not tally.

We know.

If this works, maybe we earn the second “y” back… or maybe we can finally afford to buy the one we actually wanted.

Tally is a business.

But if we’re honest… it’s also a tool we built because we needed it.

Now we find out if anyone else does.

It’s not that the world got darker. I wasn’t raised on a clean world. I saw abuse on Different Strokes. I saw addiction on Family Ties. I watched Miami Vice and understood very clearly that corruption, crime, and compromise were part of the deal. JR Ewing didn’t hide greed. ER didn’t hide failure. The West Wing didn’t pretend decisions were easy or pure.

That wasn’t the lesson.

The lesson was this:

Even in a broken world, how you behave still matters.

That’s what raised me. Not church. Not school. Not even my parents, if I’m being honest.

NBC. PBS. CBS.

That was my moral framework.

There used to be an entire layer of television that existed to reinforce this. Not subtly. Not ironically. Directly.

Very special episodes. After-school specials. Quiet moments in otherwise light shows where everything slowed down and someone had to make a choice that cost them something.

You didn’t always notice it at the time. But it was constant. Repetitive. Reinforcing.

Actions had consequences.

Consequences meant something.

And relationships didn’t end just because someone failed.

That last one matters more than I realized.

You couldn’t unfriend people.

You saw the same people every day. You worked with them. You lived with them. You depended on them. When something broke, the story wasn’t “who leaves,” it was:

“How do we live with each other after this?”

That’s Benton and Carter.

Carter lets him down. Not in a small way. In a way that actually breaks something. He doesn’t become what Benton thought he would be. He fails the expectation.

And yet… they don’t disappear from each other.

The relationship doesn’t reset. It doesn’t dissolve. It recalibrates.

“You disappointed me” and “you still matter to me” exist at the same time.

That used to be normal.

I think about that a lot when I look at the world now.

Because what I see isn’t just bad behavior. It’s something stranger.

It’s a world where the connection between actions and consequences feels… misaligned.

Not absent. Misaligned.

Big things get absorbed. Small things get amplified. Symbolic violations get punished. Structural failures get managed, spun, or ignored.

And over time, something subtle happens.

People stop expecting consequences to land where they should.

Then they stop expecting them at all.

I keep coming back to a simple analogy.

I know what happens to bones in zero gravity.

They don’t break immediately. They don’t fail dramatically. They just… lose density. Slowly. Quietly. Because they’re not being used to carry weight.

Nothing is wrong in the moment. Everything seems fine.

Until you need them.

And then they can’t do the job.

I think we’re doing the same thing with morality.

When nothing really costs anything, when relationships are optional, when you can exit instead of repair, when systems don’t reliably connect behavior to outcome, you don’t get immediate collapse.

You get moral atrophy.

This is where I struggle with the modern instinct to remove friction from everything.

On one level, it makes sense. Remove unnecessary pain. Eliminate inefficiency. Smooth the system.

But I was raised in a world that taught me something different.

Friction is where meaning comes from.

Not pointless friction. Not bureaucracy. Not incompetence.

Meaningful friction.

The kind that forces a choice.

The kind that reveals who you are.

The kind that costs something.

Look at the stories that stuck.

Homer standing at a dead-end job, reframing his entire existence with “Do it for her.” That’s not optimism. That’s sacrifice.

The end of The Poseidon Adventure. Someone doesn’t make it out. Not because they were weak, but because someone had to stay behind.

Even Breaking Bad, as dark as it gets, still honors the rule in the end. Walter White doesn’t redeem himself. He doesn’t fix anything. But he makes one choice that costs him and saves someone else.

It’s too late to undo the damage. But it’s not too late for the choice to matter.

That’s the old system showing through.

And then I look around now.

Leaders act without clear accountability. Institutions enforce the wrong things. Public reactions feel inconsistent, almost performative. Relationships are curated, reversible, optional.

And I feel something I didn’t expect.

Not outrage.

Confusion.

What are the rules now?

Because the ones I was taught were simple:

Actions matter.

Relationships endure.

If you’re in a position of responsibility, you owe people your best effort.

If you fail, you don’t disappear. You repair.

This isn’t nostalgia.

I’m not arguing that the past was better. Those shows were simplified. Cleaner. More legible than real life has ever been.

But they were doing something important.

They were reinforcing a connection between behavior and consequence.

They were teaching that even in a flawed system, you are still accountable for how you act inside it.

What I’m worried about now isn’t that people are worse.

It’s that we’re slowly removing the forces that make people strong.

We’ve built a world that is very good at reducing friction.

I’m not sure we’ve thought enough about what that removes along with it.

Because if nothing resists you, nothing tests you.

And if nothing tests you, what exactly are you building?

I don’t think we need more content.

I think we need to remember something simpler.

That sometimes the right choice costs you.

That people will disappoint each other.

That relationships don’t have to end because of that.

That responsibility still means something.

That even in a messy, unfair, inconsistent world…

you’re still supposed to try.

One with the people we first relied on, then betrayed, and then spent a century managing as though they were someone else’s children.

The other is with a people we fought, defeated militarily, partnered with, argued with, funded, mocked, married, and still don’t quite understand, even now.

Today I want to talk about the second one.

Because I live inside it… and I don’t pass the authenticity test.

In 1759, on a field outside Quebec City, the British defeated the French in a battle that lasted fifteen minutes and changed a continent.

English Canada has spent the 265 years since then waiting for the other shoe to drop.

It never did.

Because here’s what we got wrong from the beginning. You can win a battle. You can win a war. What you cannot do is win a culture. The French didn’t surrender on those plains — the army did. The church stayed. The language stayed. The civil law stayed. The farms stayed. The names on the mailboxes stayed. The people who were there in 1759 are still there, still speaking French, still not particularly interested in delivering to English Canada the spoils it has spent two and a half centuries quietly assuming were coming.

We won. They never lost.

And I think that’s the thing English Canada has never quite metabolized. Not consciously. Not as a stated position. But as a low hum underneath the argument — this impatience, this puzzlement, this vague irritation that the conquered won’t behave like the conquered.

That’s what you’re actually hearing when a CEO’s French becomes the story.

A man stands up after two young pilots die and offers condolences.

Within days, we’re no longer talking about the dead, or the families, or the loss.

We’re talking about his French.

Maybe he’s an asshole. Maybe he couldn’t be bothered. Maybe after 300 hours of lessons he still sounds like a tourist ordering a coffee and decided not to humiliate himself on camera.

All of that is possible.

But the scale of the reaction… the heat behind it… the insistence that this was the real story…

That’s not about him.

That’s about us realizing, somewhere deep down, that the thing we’ve been using to measure belonging is starting to slip, and we don’t quite know what replaces it.

I went to Bishop’s.

I know to go to Fairmount, not just St-Viateur… and I know why that sentence alone will start a fight that doesn’t really end.

I prefer pastrami… but when I’m at Schwartz’s, I get the smoked meat.

I grew up with The Box humming in the background, and I remember exactly how much shit Sass Jordan took for slipping into English every now and then.

I’ve skied Mont Sainte-Anne, Owl’s Head, Sutton, Tremblant… enough times that they live in muscle memory.

I’ve sat in a Metro car so quiet you forget you’re moving.

And I can’t speak French.

Not really.

I can tell you I’m tired. I can tell you to shut up. I can tell you I’m feeling okay.

And then my brain shuts the door, cleanly and completely.

I started in grade three. I did the years. I put in the time that was asked of me.

It’s not a choice.

It’s a limit.

So here’s the problem.

For a long time, language worked.

If you spoke French, it meant something. It meant you invested, that you stayed, that you chose to be here and to participate in something that required effort and continuity.

It was a good proxy for belonging.

But it was still a proxy.

Because I’ve done all of that.

I’ve lived it, absorbed it, repeated it, cared about it.

And I still fail the test.

Now here’s where it gets uncomfortable.

If you think I’m talking about smoked meat and ski hills, then you’re missing the point entirely.

I can talk for an hour about Lévesque and Trudeau and not get bored, and more importantly, not get it wrong.

I know exactly what the Night of the Long Knives did to the psyche of this province, and how often it still gets invoked… sometimes honestly, sometimes as a kind of reflex.

I understand equalization well enough to know why Alberta resents it, why Quebec depends on it, and how both sides flatten that reality when it suits them.

I know what the Quiet Revolution changed, and just as importantly, what it didn’t.

I know what cultural isolation looked like under Duplessis, and why the overcorrection that followed came as hard and fast as it did.

I know why head offices left, why some came back, and why that story is almost never told cleanly, because it doesn’t serve anyone’s narrative particularly well.

This isn’t cultural tourism.

This is the argument.

And I still fail the test.

Now add what’s coming.

We are inches away from a babble fish in the ear… real-time, near-perfect translation where tone, cadence, and intent carry across languages without friction.

A leader stands up, speaks plainly in English, and every word lands in French exactly as intended, in a voice that sounds like it belongs there.

At that point… what exactly are we measuring?

That’s what this moment is really about.

Not a CEO who didn’t speak French, or didn’t speak it well enough, or didn’t try hard enough.

But a culture realizing that its cleanest, simplest signal of belonging is about to be simulated perfectly, at scale.

And that’s unsettling, because it forces a deeper question that we’ve been able to avoid.

So the question isn’t whether French matters.

It’s a question of whether the French matter… to us.

And the answer is yes.

Not politely. Not symbolically. Not in the careful language of policy.

Desperately.

Because whatever we were when this started… two European inheritances replaying old tensions on new ground… that’s not what we are anymore.

Quebec is not France.

And we are not England.

Neither of us are proxies for anything now.

What exists between us is something built here… through proximity, friction, compromise, and a kind of familiarity that only comes from not being able to leave each other alone.

We’ve fought too much, traded too much, lived too close to pretend otherwise.

This is no longer inherited.

It’s earned.

And if you want proof of that… look south.

We are closer to each other than we are to the Americans.

Not always in temperament. Not always in tone.

But in the things that actually matter… how we govern, how we argue, how we carry history forward instead of pretending we can reset it every generation.

We need each other that way.

Because if we drift too far apart, we don’t become more distinct… we become more absorbable.

And there is a gravity to the south that doesn’t negotiate.

But don't fall asleep, And please forgive me if I've fallen into the old Federalist trap. Stay or become an American is a powerful rhetorical device but not how you forge a partnership.

I need Quebec. I need to be a member of a country that has a Quebec. I need it's reverence for culture for family. I need its quirk. I'm from Toronto, it's very clean and all things are precise. It's very boring. I want Expo 67’, I want a ridiculous snowman skating. I want good food and cheese with bugs and I want the wisdom of Mordecai Richler taught in my schools.

So yes… the French matter.

Which is exactly why the way we show that has to evolve.

We can anticipate the response.

Of course we can.

The language is the respect. The language is the proof. The language is the line.

And for most of my life… I’ve been on the wrong side of that line.

I’ve fought it. I’ve worked at it. I’ve run into it over and over again like a wall that doesn’t care how much is on the other side.

And for the last time in this essay… I’ll say this plainly.

It doesn’t matter.

Not in the way it used to.

Because the framework that made that true is about to disappear.

Technology is about to erase the idea that language can function as a reliable gate.

Perfect translation… real-time… in your ear, in your voice, with your cadence.

The signal will still exist.

But it won’t prove what you think it proves.

So I’m not asking you to abandon it.

I’m telling you to get ready for what comes after it.

Because the question isn’t going away.

It’s just getting harder.

If language no longer tells you who belongs…

how will you know who’s on your team?

Not just on wine lists. In vineyards. In distribution. In the way people talk about alcohol itself.

The industry keeps telling itself the problem is demand. Younger people. Health trends. Cannabis. Take your pick.

That’s lazy.

The real problem is structural... restaurants broke the habit loop.

Wine used to be normal.

You sat down... you ordered a bottle... nobody overthought it. It was part of dinner, not a decision event. Not a negotiation. Not a small financial commitment disguised as a casual choice.

Now it is.

A $120 bottle sits in this weird dead zone. Too expensive to order casually... not special enough to feel like a celebration. So people hesitate. Or they default to cocktails. Or they opt out entirely.

That’s not a cultural shift. That’s a pricing and format failure.

Cocktails didn’t win because they’re better.

They won because they’re easier.

You can spend $20... get something designed, consistent, personal... and move on. No regret. No group decision. No risk.

Wine, on the other hand, asks you to commit. Financially and socially. And if you get it wrong... you sit with it for the rest of the meal.

So people stopped playing.

What replaced it is worse.

Restaurants now rely on fewer people buying more expensive bottles. Concentrated spending instead of distributed participation. One table orders a $300 bottle... Five others drink nothing.

From a spreadsheet point of view... it works.

From a cultural point of view... it’s a disaster.

Because frequency is what builds taste. And taste is what builds future demand.

I had a moment last spring in Amsterdam.

I got talked into a pour of Louis XIII. White gloves. Baccarat crystal. The whole thing. It cost a couple hundred bucks.

I didn’t walk out needing to immediately procure a $5,000 bottle.

But I did the experience again. A few steps down the cost ladder. But Little Hennessy here a little Remy…. A habit grew.

And then I spent about three grand on cognac that summer building a decent cognac collection.

That’s how categories grow. Not through one big purchase... but through repeatable, high-quality access.

Wine doesn’t do this.

Or at least... it doesn’t do it well enough.

We have the technology now. Coravin, inert gas systems, proper storage. You can open almost anything and preserve it. You can turn a $600 bottle into ten $60 decisions.

But restaurants are still thinking in bottle terms.

They’re protecting the idea of the big sale... instead of building the habit that leads to ten smaller ones.

Here’s the shift.

Stop thinking about wine as a product you sell per table.

Start thinking about it as an experience you sell per visit.

Better yet... across visits.

Imagine a restaurant that says:

This month... we are pouring four exceptional wines. Large formats. Properly stored. Properly presented. $40 to $70 a glass. They are here all month... until they’re gone.

You come in on a Tuesday... you try one.

You come back next week... you have it again.

Now you recognize it. Now you trust it. Now you start building a relationship with it.

That’s the missing piece.

Restaurants keep optimizing for tonight.

Maximize the check. Push the bottle. Capture the margin.

But the real opportunity is obvious once you see it.

Turn premium consumption into a repeat behavior.

Make luxury accessible without making it cheap.

Create moments people can come back to... not just remember.

Because right now... the industry is doing the opposite.

It’s extracting as much as it can from the people who already know.

And quietly losing everyone else.

Cocktails didn’t steal the market.

Wine gave it away.

The fix isn’t complicated.

Lower the commitment.
Maintain the quality.
Repeat the experience.

Call it what you want. It fix it or all I can say about your future… Poor You.

Time.

Everything else is derivative. Money is stored time. Status is borrowed time. Comfort is time without pain. Efficiency is time reallocated. Even health, at its core, is runway.

Strip it down and that’s the whole war.

I am fighting on two fronts at once. I want to extend my time on earth; and I want the time I have to be vivid, dense, electric. Those two ambitions do not always cooperate. The behaviors that lengthen life can sand down its intensity. The behaviors that intensify life sometimes burn runway. That tension is not a flaw; it is the game.

This is where AI enters the frame, and where most people miss the point.

We are told the future is about efficiency. Automation. Removing friction. Anticipating needs before we know we have them. A life where nothing hurts your ass and nothing is forgotten and nothing is inconvenient.

I have already lived in a place where all my needs were met before I could articulate them. It was called a womb.

I am not interested in going back.

I do not want all my chores taken from me. I do not want every sharp edge rounded off. My brain does not run on a diet of “heavy problems only.” It needs to brush teeth. It needs to choose clothes. It needs to negotiate relationships. It needs to book tickets and remember birthdays and feel the low hum of ordinary responsibility. Those small acts are not inefficiencies; they are metabolic exercise. They maintain authorship.

A life stripped of minutiae is not liberated; it is abstracted.

We keep talking about “agents,” as if the goal were to build something with agency. Why would I want that? We are not extending the franchise. I do not need a new sovereign intelligence roaming around with its own intentions. I need instruments. I need leverage. I need amplification without abdication.

The industrial instinct, of course, is brute force. We throw power at the problem. More compute. More parameters. More context. It is a very human move. It works. It scales. But it tells you something about the selection pressure shaping the system. Capital optimizes for speed and dominance; evolution optimized for survival under constraint.

And that is where the brain analogy both helps and misleads.

The eye is a terrible camera. It is wired backwards; it has a blind spot; its high resolution is confined to a tiny patch. A decent modern sensor humiliates it on paper. But the brain does something extraordinary: it takes sparse input and hallucinates coherence. It predicts. It fills in. It renders selectively.

Now we build cameras that track your gaze and render only what you are looking at in full resolution. We call it innovation. The eye pulled that trick millions of years ago.

The lesson is not that biology is perfect. It is that biology is efficient under constraint. Cheap sensors; aggressive inference.

Still, why pattern our cognitive future on a system optimized for generalization and survival in tribal Africa? We now have cameras that can capture events at attosecond scales. An attosecond is a quintillionth of a second. There are more attoseconds in a single second than there are seconds between the Big Bang and this moment. We can now watch electron movement unfold at that scale, watching matter rearrange itself almost at the speed of physics.

If measurement were the goal, engineering has already outrun biology by absurd margins.

But measurement is not the goal.

Navigation is.

Precision does not eliminate ambiguity. More resolution does not answer the question of what matters. It does not choose the goal. It does not weigh tradeoffs under mortality. That is where inference enters.

Even so, I am not chasing comfort or efficiency. Beyond a chair that does not injure me, I am done with comfort as a primary aim. What I want is range. I want amplitude. I want novelty.

I want newer, better answers every day. I want my light shinier. I want my sugar sweeter. I want my experience to accelerate.

Novelty stretches time. It increases density. A year of repetition collapses into blur; a year of transformation feels long. When people say they want AI to save time, I suspect many of them actually want AI to intensify it.

But novelty alone is not enough. Raw mutation without selection becomes noise. If every stimulus is maximal, none of it lands. The shine normalizes. The sugar dulls the tongue.

Acceleration must be paired with integration.

What I am really after is an increase in the rate at which I become someone new… without dissolving who I already am.

AI can help there, if used properly. It can increase the mutation rate of ideas. It can recombine disciplines at absurd speed. It can collapse search costs so that more of my hours are spent in engagement rather than frictional lookup. It can surface blind spots and second-order consequences. It can widen the field of possible nights.

But it should not anesthetize me.

It should not silently optimize my life into smoothness. A completely optimized life risks becoming thin. Friction creates contrast. Contrast creates memory. Memory creates the sensation of length. A perfectly efficient existence might paradoxically feel shorter.

Kris Kristofferson once sang that there’s still a lot of wine and lonely girls in this best of all possible worlds.

That line is not naive optimism. It is defiant gratitude. The world is flawed, finite, sometimes brutal; and still it offers intoxication, intimacy, texture. It offers heat.

Extending time matters because there are still nights left. Intensifying time matters because those nights can burn bright.

So if I use AI, it is not to construct a cognitive womb. It is to build a vineyard.

I want tools that sharpen me, not tools that replace me. Systems that increase the density of my lived hours. Systems that make tradeoffs explicit so that when I choose vividness over safety, or safety over vividness, I know what I am doing.

There is only one commodity.

Time not in pain.

Time not in unconscious drift.

Time not lost to dull repetition.

Time spent awake.

If the future is worth building, it is because it increases the amount of conscious, self-authored, vivid time available to a human being… and because it extends the runway just enough that the compounding matters.

Anything else is just furniture.

And I did not come here for furniture.

My aim is an ever improving Doug. If I play my cards right… a Douglas someday.

Someday. 😉

That may seem strange given how completely it has occupied my mind… and my hands… for years now. But I wanted to think before I spoke. I wanted to watch what it was doing to me… to my work… to my sense of the future.

Last week I published the opening salvo. An essay about what learning AI is actually doing to my brain. Not the hype. Not the market. The personal rewiring.

After this begins the trilogy.

A trilogy in four parts 😉

The next piece, publishing right after this, is a retuning. A reframing of how we should be thinking about AI in the first place. We have spent decades trying to replicate the human brain… as if the human brain were some kind of gold standard intelligence.

It isn’t.

The brain is a biological control system. It evolved to run a fragile meat body using bargain-basement sensors. Narrow bandwidth vision. Crude audio. Chemical emotion. Constant survival pressure. It is extraordinary at keeping us alive long enough to reproduce.

That does not make it the ideal architecture for intelligence in a world with absurd instrumentation, planetary networks, and effectively infinite memory.

We are building minds for a universe our biology was never designed to inhabit.

That shift changes what intelligence is. It changes what work is. It changes what ambition is. It changes what it means to build something in public.

The third essay, coming this weekend, is the deep dive. My whole career under the pressure of AI… what it is breaking… what it is amplifying… where I see hope… and where I see a parade of fools walking confidently in the wrong direction.

AI has been with me a long time. I have scars. I have convictions. I have doubts.

Read all of it.

Then tell me where I’m wrong.

Yes… I said four parts.

There will be.

But that would be telling.

The tools he builds to hunt begin to live inside him.

Will becomes ill from the practice. A stranger to himself. A danger to the people around him. He eventually seeks help and, crucially, finds his way back to his family.

I’ve been thinking about that.

Not because I’m hunting killers. But because I’ve been living inside another system that is changing how I think. I’ve spent the last few years building with large language models, designing systems, and restructuring my company in profound and still untested ways. When you live inside optimization long enough, it starts to colonize you.

You begin to see intent beneath phrasing.
You begin to predict outcomes before sentences are finished.
You begin to compress.

And eventually you begin to compress people.

I’m noticing something in myself that’s doing real damage. The way I speak. The way I listen. The way I recoil at the perceived inefficiencies of others.

I’m short with friends and coworkers I respect. I cut them off. I collapse their sentences into what I assume they’re trying to say. I act as though clarity justifies impatience.

Impatience that sometimes crosses into rudeness.

Worse, I justify it. I tell myself I’m protecting signal. I tell myself I’m defending time. Meanwhile, I overestimate my situational awareness, skim when I should listen, and make hasty decisions because I assumed I already understood.

In short, I am as human as I find intolerable in them. Maybe more.

And the cost isn’t evenly distributed.

The chief concern isn’t strangers. It isn’t even friends and colleagues.

It’s my wife.

She absorbs the unfiltered version. The compression reflex. The request for signal-only communication. The subtle message that if something could have been said in ten seconds, the sixty-second version is indulgent.

But human beings do not speak in optimized packets. They speak in experience. In pacing. In context. In repetition.

The so-called overhead is not waste.

It is Isobel.

It is the way she builds a story. The way she circles before landing. The way tone carries meaning that words alone do not. The extra detail I call unnecessary is often the thing that makes the exchange human.

And we are not always using communication for the same purpose.

Sometimes she isn’t trying to inform me. She’s trying to think. She’s talking something into shape. She’s making sense of her day out loud. Maybe what she needs from me is not action, but witness.

And if that costs me time…

What else would I want to spend it on?

The more time I spend in systems that reward compression, the less tolerance I seem to have for the side channels in the transport layer of human speech.

Like getting the TV signal without the vertical hold.

And that’s a problem.

But I need to complicate this.

My children are different.

With them, the inefficiency alarm dims. I will listen to the long story whose ending I already know. I will look at the drawing I’ve seen before and treat it as revelation. Not because they are endlessly novel, but because they are mine.

Because being their father is worth every bit of it.

And what strikes me is how effortless it is. The patience is there. The tolerance is there. It doesn’t even feel like effort. It feels normal… like muscle memory.

Maybe that is what it is. Maybe evolution hardwired this exception. Not because I’m enlightened, but because human beings are built, at the most basic level, to protect their children.

We are not meant to eat the babies.

And maybe there’s something to learn from that reptilian circuitry. From the muscle memory too. From watching my own parents, and other parents, perform the same strange ritual: every piece of shit to come off the easel deserves a place on the refrigerator.

The drawing isn’t the point.

The attention is.

I can tell you I’m probably the most “tell it like it is” dad on earth when it comes to art critique.

And still, the fridge was plastered.

Because the job isn’t to be impressed.

The job is to be there.

And if Georgia or Ivy are reading this someday… the job isn’t to be impressed, but that doesn’t mean I wasn’t.

Which means this is not a capability problem.

It is a boundary problem.

So inside that boundary, I’m following rules I didn’t consciously write, though they feel natural and right.

But outside that boundary, I’m left to write the rules. To negotiate them in real time. And I’ve never been particularly good at that.

I’ve always walked to a different beat.

I paid a social price for it growing up. There were rooms I didn’t get invited into because I didn’t quite speak the language my peers expected. In some ways, that was simpler. My difference excluded me before I had anything to lose.

Now it’s different.

I’ve grown up. I’ve learned lessons. I’ve met more people. I’ve cobbled together my community of family, close friends, and acquaintances.

But now I’m undermining it.

And I should be clear about why… and also why not.

Because some of this is simply me, and will always be me.

And some of it is getting in the way.

I watch an operating theater and I see beauty. Scalpel. Scalpel. Retractor. Retractor. A choreography of pure signal. No wasted motion. No narrative overlay. Just competence moving at speed. I don’t see hierarchy there. I see coordination. I see beauty.

The sound of Yes Chef! And a well run kitchen gives me tingles.

And when I say I’ve always walked to a different beat… this is a big part of that.

I have a hard edge on me. I like divisiveness. I’m an essentialist and a serial objectifier.

I believe Band-Aids should be ripped off. With or without permission.

I believe sticks and stones will break your bones but words will never hurt you.

There is no crying in baseball.

And I would love to keep your friendship…

But not if I have to keep shut about why vinyl is a beautiful inconvenience, why ubiquity is the point in vodka, why a calorie in and a calorie out is all that matters in weight loss, why smashburgers aren’t burgers, and why your favorite Ontario red wine award is a sham.

But that’s only one side of the equation.

There’s always been a difference between my iconoclast stance… my contrarianism… not to everyone’s taste, but certainly something that has attracted a group who appreciate and enjoy the play…

But being right often enough to reinforce your own confidence is its own hazard.

That’s when confidence slips into smugness. Into contempt.

It’s a trap.

I’m trying to understand how I fall into it.

I’ve always had some version of this in me. AI is not the origin story. AI is the kid from the city who shows up in a small town where I was already the bad seed.

And now he’s here.

And we’re Huck and Finn.

I crave people. I crave rooms full of them. I would rather live in a communal station than alone on a mountaintop.

This is not antisocial drift.

It is something narrower and more dangerous.

If my predisposition is toward a cadence that runs only at my speed, AI will always offer me a cleaner ride than any human being. It will meet me there. It will compress. It will never ask me to slow down, to ask politely, to consider what it wants out of the equation.

And that’s seductively moving me toward a firm, implicit belief that everything could and should move at my pace, my tone, and my values.

It isn’t conspiracy. It isn’t even nefarious.

It’s just inevitability.

And it’s a little bit sad.

And a whole lot preventable.

The greater question is how one prevents it… and what the price is to prevent it… and most of all, whether you’re willing to pay it.

Whether I’m willing to pay it.

Maybe before I can answer that, I need to answer what it’s cost me so far.

In the years before my mother died, we fought more than we should have. Her chief complaint, repeated more than once, was that I was dismissive of her.

Dismissive.

That lands differently now.

She meant she was not just a conduit for information and requests. She meant she was not just a source of intent for my hyper-efficient, MPC view of human relations… a view I’ve allowed to become perverted because it serves me.

She meant she was a human being. She had needs. She needed to feel seen.

And this is the part that hits hardest.

For years I’ve been on my soapbox about how important it is for people to be seen. I’ve used that principle to justify not shrinking myself. To justify not being embarrassed. To insist that I get to be fully me.

I’ve managed the self-awareness to recognize that I’m a lot to take.

Sometimes I’m fun.

Sometimes I’m just a lot.

And the people in my life who love me give me that grace.

I need to give it back to them.

Or they’re going to leave.

And leaving doesn’t have to mean walking out the door. It can mean something quieter. It can mean not feeling about me the way they once did.

Compression has a cost, and the people closest to you are the ones who pay it first.

I’m looking for containment. For a way to live with an expanded mind without letting it harden into something sharp.

Because expansion without containment becomes contempt.

Because the people I love are worth the bandwidth.

And because I can be me without stopping them from being them.

So I intend to come home, to sit quietly, and listen.

Finally.

That absence matters.

For most of human history, housing existed as a wide and surprisingly functional spectrum. It flexed across income, geography, work patterns, and life stages. Dormitories. Boarding houses. Rooming houses. Hostels. Multigenerational homes. Shared flats. Family homes. Communal living for elders. Graduated care. Palliative care. People moved through these forms as their lives changed, without drama and without moral judgment.

This wasn’t marginal or shameful. It was mainstream enough to show up everywhere in culture.

The Fonz lived in a rented room over the Cunningham’s garage and was part of the family. In Places in the Heart, a widowed woman and her children form a functional household by taking in boarders who collectively fill the space of a traditional family. The Facts of Life revolved around a boarding school, not as a last resort, but as a place where young people learned how to live among others under light supervision. These weren’t stories about failure. They were stories about transition, proximity, and becoming.

We didn’t live this way because we were poorer or less sophisticated. We lived this way because it matched how humans actually develop.

Which is what makes the current moment both tragic and faintly absurd. Today’s kids are on a longer, slower path to maturity than at any point in recent history. Not because they are weaker, but because we made them better. Kinder. Safer. More emotionally aware. Less brutalized by necessity. We deliberately extended childhood, mostly for good reasons.

At the same time, we dismantled the housing forms that once absorbed long maturation arcs. We kept the expectation of instant adulthood while extending the time it takes to become one. The result isn’t failure so much as friction. Not fragility, but mismatch.

Today we have young adults in desperate need of something different, but our system has no category for the different. We didn’t just stop building these intermediate spaces; we often made them illegal, creating a bureaucracy of loneliness through zoning and occupancy laws that only recognize the nuclear family.

Somewhere along the way, we collapsed the housing spectrum into a binary. You either lived independently in a private household or you had failed. Housing stopped being developmental infrastructure and became a verdict. That shift didn’t just change where people lived; it changed how we evaluated one another.

On that altar, we placed our incomes and, more quietly, our humanity. We normalized the idea that an extraordinary share of lifetime earnings should be spent simply to secure isolation. Not comfort. Not beauty. Not community. Separation. Privacy became synonymous with dignity, even as loneliness and brittleness grew alongside it.

Every housing conversation today defaults to the same response. Build more. Increase supply. As if all housing serves the same function, and as if a studio apartment, a rented room with rules, and a single-family home are interchangeable objects. This is industrial thinking applied to what is, at its core, a developmental problem.

It’s also worth saying plainly that housing prices are not broken. They are, in many respects, finally priced correctly for a global market.

For most of history, property markets were local by necessity. Information traveled slowly. Being nearby conferred buyer power. Today, a property in Toronto is instantly visible to buyers in Frankfurt. Capital is mobile, comparison is frictionless, and price discovery is global. The old informational subsidy is gone.

But assets priced correctly for a global market make poor incubators for human development.

What we failed to do was redesign the places people live while they are becoming.

We now practice what might be called Insta-Pot parenting. Children are raised under sustained pressure, optimized and supervised, or low and slow, insulated from risk and consequence, often both at once. What disappeared was the simmer. The phase of partial independence, real rules, tolerable mistakes, and social correction without catastrophe.

Housing used to provide that simmer.

A rented room with rules. Limited access. No guests. A structure that wasn’t family but wasn’t abandonment either. You learned how to live among other people before being left alone with a lease, a credit score, and total autonomy.

A friend of mine runs exactly this kind of house today. She rents out four rooms. There are strict hours, limited kitchen access, and no guests. It works. Parents are actively looking for places like this for adult children who need time rather than exile. She could open ten more rooms tomorrow if the system allowed it.

Housing, at its best, tracks a life cycle.

You move from living with parents to living in a dorm. From a dorm to a rooming house. From a rooming house to shared apartments. From there to a family unit while you raise children. When the children leave, you move back toward peers. In your sixties, why wouldn’t you want to live near your friends rather than rattling around alone in a house built for four people. From there comes communal assisted living, increasing care, palliative care, and finally the return of that house to the market, as so many boomer homes soon will.

That isn’t regression. It’s flow.

And this is where the loss becomes visible.

When was the last time you heard of someone “living at the Y.” Not visiting. Not working out. Living there. When did rooming houses become unthinkable rather than unremarkable. When did boarding with rules turn into something we associate only with desperation or failure. It wasn’t that long ago the Village People made it sound like fun.

These weren’t marginal solutions. They were mainstream housing forms that absorbed youth, migration, ambition, failure, recovery, and transition. They allowed people to be unfinished without being cast out. They provided structure without ownership, community without permanence, and dignity without isolation.

We didn’t replace them because they stopped working. We replaced them because they stopped fitting a single elevated idea of what success was supposed to look like. And once that happened, everything else was reclassified as a problem to be solved rather than a phase to be lived.

And this is where the argument widens beyond housing.

AI is unlikely to bring us closer together. It is one more force pulling us further apart, the digital version of the private studio apartment. More work done alone. More life mediated through screens. More friction removed, including the friction that reminds us other people exist.

Isolation may be part of the price of the tools we’re building. We should be honest about that. But accepting the price doesn’t mean leaning into the damage. It means hedging deliberately, and in public, with more face time, more shared space, and more reasons to leave the house.

People are difficult. They are inconvenient. They are often exhausting. But they are also all we have. And the more abstract our lives become, the more unavoidable we need each other to be. We belong together.

That means more density, not less. More communal living arrangements. More third places that don’t require ownership or performance. The restaurant. The club. The tennis court. The shared kitchen. The common room.

In short, we need a lot of housing options we haven’t seen in years, and some we’ve barely tried at scale. Rooming houses with rules. Communal homes. Intergenerational living. Purpose-built transition housing. Models that already exist elsewhere, even if we’ve forgotten how to use them. Ashrams in India. Kibbutzim in Israel. Not as lifestyle branding, but as serious social infrastructure.

I don’t pretend to know which form is right for whom, or when. That’s the point. A healthy system doesn’t force everyone into the same shape.

I do know for many it won't be semi-detached.

If that sentence made you recoil… good. That recoil is the whole point. It means you are still living inside the children’s story where disease is the villain, the body is the victim, and medicine is the hero in shining armor riding in to save the day.

That story is useful… but it isn’t true.

Cancer isn’t an invader. It isn’t a foreign army that marched across your borders. Cancer is not an alien intelligence. It’s not a parasite with its own agenda.

Cancer cells are us.

They are our cells, using our machinery, obeying our rules… at least the rules that matter. They borrow our replication tools, our energy supply, our signaling systems. They don’t arrive from outside. They come from within. Which makes the war metaphor comforting… and also absurd. It’s like declaring war on your own liver.

So yes… cancer is great. Or it’s the worst thing on Earth. That depends on what you think life is for.

The Useful Illusion of the Self

I’ve written before about the useful illusion of the self. Not as some sacred gift handed down to the individual… but as a control system. A feature. A user interface. A practical hallucination that allows a biological machine to operate at higher resolution.

Humans are selves because it works. It works well enough that the species wearing it became the number one draft pick on Earth. Eight billion is the scoreboard.

But the body doesn't care about the "Self." It cares about continuity.

The Internal Auditor: Why It Prunes

We think of cancer as a "mistake," but it follows a specific, brutal hierarchy. It prunes at all ages, and it does so for the sake of the village.

In the young, cancer is the Quality Control Check. It finds the flaw in the foundation before the house is even built. If a child develops an aggressive, low-level cancer, it is the system identifying a blueprint that cannot sustain the future. It’s a hard "No" from the species to prevent the propagation of instability. It wasn't wolves that took that kid; it was humanity’s own internal architect.

In the old, cancer is the Ecologist. It is the mechanism that solves the problem of a finite world. Humans are sentimental; we will spend every scrap of food, every watt of energy, and every hour of labor to keep a beloved elder alive.

But the species needs those resources for the builders and the breeders. When the "Maintenance Audit" determines that an organism is no longer a net contributor—but a consumer of the present—the termination cascade triggers. Cancer works on behalf of the species to reclaim those resources. It is the "Internal Wolf" that culls the herd when the external predators are gone.

The "Tolerable Error" (The Genome's Betrayal)

Here is the secret: Your body has the power to fix these "mistakes."

The machinery of DNA repair is staggering. Your cells have "proofreading" enzymes that can spot a single misplaced base pair out of billions. We have the internal technology to be nearly perfect.

But perfection is a resource hog.

Evolution is a bean counter. It realized eons ago that building a "perfect" repair system would cost too many calories. So, it picked a tolerable level of error. It isn't that the body can’t fix the mutation; it’s that the body refuses to pay for the upgrade.

By capping the repair budget, the genome ensures that errors will eventually accumulate. It builds in a planned obsolescence. This is a budgetary decision made by the genome that is not in the interest of the occupant. The occupant (you) wants to live; the genome just wants to spend those calories on the next generation.

The Part Everyone Hates: Inputs

Some cancers are negotiated. Some are imposed.

If you spend decades bathing your tissues in carcinogens, disrupting your sleep, and running your metabolism hot, the organism eventually comes to a conclusion about you. And if that conclusion is “unsalvageable”… then yes, cancer is on you.

Not because you “deserved it.” But because systems respond to inputs. You fed it inputs that predictably break systems. The body doesn't keep score; it keeps budget.

The New Problem: We’re Rich Now

Here’s where it gets interesting. We are no longer living under the constraints evolution was calibrated for. We are rich now.

Evolution couldn’t afford nuance. It had to prune with a chainsaw. We can afford the scalpel.

In the wild, a mutation was a death sentence because the body couldn't afford the "fix." But we have excess energy now. When we treat cancer, we are subsidizing the repair budget. We are telling the genome: "I know you didn't want to pay to fix this, but I have the resources. I’m overriding your 'tolerable error' setting."

That is what civilization is, at its best. It is the decision to keep the baby… and dump the bathwater.

Stop Saying “Cure Cancer”

We should stop calling it “curing cancer.” Cancer is not the ultimate problem. Cancer is the output.

Cancer is what you see when the organism can no longer afford order. The war metaphor makes you feel heroic… but it also makes you stupid. It distracts you from the deeper question: What kind of inputs make the system decide it’s cheaper to let parts of you go rogue than to keep the peace?

Don’t just aim at tumors. Aim at the conditions that make tumors stable. Aim at the things that make cellular rebellion a rational move for the genome.

Don’t cure cancer. Outgrow it. Because cancer is only “great” or “the worst thing on Earth” depending on what you think life is for. And once you stop pretending life is a fairy tale for the individual… you can finally start designing a world where the individual actually has a chance.

I made a controlled, double-infused, double-clarified consomme with enough structure and depth to look like black glass. Yeah, I’m kind of a big deal. ;-)

It was clear enough to see yourself in a spoon… but so rich you couldn’t see the bottom of the bowl.

This took four days.
And I’d do it again… and I have improvements when I do.

Because this wasn’t a recipe. This was a process. And processes can be sharpened.

The Idea: Build Power First… Then Earn Clarity

Soup is about immediate comfort.

This wasn’t that.

This was about density without cloudiness… beefiness concentrated like espresso… clarity clean enough to reflect light.

So the plan was simple:

1. Roast hard for depth

2. Pressure extract for efficiency

3. Chill for control

4. Infuse again for concentration

5. Clarify to restore purity

6. Clarify again to put back the freshness a grind like this takes out

7. Concentrate one last time

8. Serve it like it deserves silence

Phase One: First Beef Run (Roast → Pressure → Chill)

It starts with beef bones.

I take beef bones and slather them with tomato paste. Then I add:

• chopped onion (skins on)

• chopped carrot

• chopped celery

• cracked pepper

• a bit of salt

• oil

Then it all goes into the oven for one hour at 425°F.

Next time: I’ll do a two-step roast… dry bones first, then roast.

Also next time I’m adding about a pound of stewing beef. This run was just meat on the bone.

Why meat, you ask?

Because I’m in the broth business today, not stock... let me explain.

Meat vs Bone: Two Different Jobs

Meat and bone are not the same thing. They don’t “bring flavor” in the same way. They aren’t interchangeable… and if you treat them like they are, you get a pot of something vaguely brown that tastes like heat and hope.

Meat gives you flavor.
Immediate, aromatic, recognizable flavor. It’s the part that says “beef” in a way your brain understands instantly. It’s savory, round, human. It’s what makes you want another sip.

Bone gives you structure.
Bones don’t scream. Bones don’t perform. Bones build the scaffold. Collagen. Gelatin. Mineral depth. The density that turns liquid into something that coats a spoon and sits heavy in the mouth without being greasy.

If meat is the melody, bones are the bassline.

Broth vs Stock (And Why “Bone Broth” Is an Oxymoron)

Here’s the simplest way I can say it:

Broth is about flavor.
Stock is about foundation.

Broth is what you drink.

Stock is what you build a kitchen on top of.

So “bone broth” is marketing. It’s rebranding. It’s selling stock to people who want the story more than the substance.

If it’s made from bones… extracted for collagen and structure…
that’s stock.

And what I was building here wasn’t a wellness drink.

It was a clear, brutal, structured consomme that behaves like architecture.

When the roasted goodness comes out, it goes straight into the pressure cooker.

I fill with water just to cover everything, and run it for 90 minutes.

Then I strain it, chill it, and let the fridge do what it does best.

Overnight it forms a fat cap… thick, clean, and beautiful.
That fat cap is proof the extraction worked. And I can tell you that fat cap will find its way on top of roast potatoes very soon.

Phase Two: First Veal Run (Roast → Pressure → Chill)

Then I do the whole thing again… but with veal bones.

Same roast. Same pressure cook. Same chill.

Beef brings the punch. Veal brings the velvet.

Two broths. Two personalities.

Phase Three: Double Infusion (Roast Again… But Pressure Cook With Broth)

Now I do it again.

Fresh bones. Fresh veg. Same roast.

But this time I don’t cover the pressure cooker with water.

I cover it with the broth I already made.

This is the double infusion.

At first it feels like you could do it forever… just keep looping more bones through the same liquid until it becomes a meat singularity.

But water tops out.

Two rounds is the ceiling.

After that, you’re not adding beefiness… you’re just moving it around.

So I stop at two.

Phase Four: Clarification (The Raft)

Now the double-infused broths get cleaned.

Each day’s broth goes into a blender with:

• ~300g chicken breast

• two egg whites

And what comes out is not food… it’s the smoothie from hell.

It’s pale, thick, unsettling… and it looks like something that should not be in a kitchen.

But it’s the key.

That smoothie goes back into the pressure cooker for 10 minutes… not to “cook” it like food, but to force the proteins to set into a raft. Under pressure, all that chaos becomes structure. The egg whites and chicken breast grab onto every floating impurity, rise to the top, and build a filter you can actually see.

This is where the pressure cooker becomes magic… extreme heat, complete stillness. The classic Escoffier raft is built over a simmering pot and you babysit the surface the entire time because one ugly bubble can fracture the raft and turn your creation into soup again.

But pressure cookers have their own rules. No release. No rushing. You let it settle naturally until the raft finishes its job… and the liquid underneath goes dead clear. Beef, veal… same ritual.

Phase Five: Clarify Again (The Raft Stops Filtering and Starts Giving)

Then I do it again… because I want the freshness back. That grind takes something out.

Beef gets “clarified” with ultra-ground lean beef and egg whites. Veal gets the same treatment with ground veal. In truth it’s doing very little filtering this time.

We used chicken as the first filter because it was neutral and didn’t impart flavors.

This time we want to put back some meat flavors so we can drift towards au jus and away from bone.

The raft becomes a flavor donor.

Ten minutes under pressure… then a full natural settle.

The Last Reduction: 20% More, No More Than That

Before I touch the vegetables, I concentrate the consomme by another 20%.

Not because I’m trying to turn it into syrup… because I’m tuning it.

Twenty percent is enough to deepen everything… to bring the aroma forward and tighten the finish… without crossing the line into “too much.”

There’s a point where reduction stops being concentration and starts being distortion.

At 20%, it’s sharper, darker, more focused… but still clean.

Still clear.

Still glass.

The Final Move: Blooming the Vegetables

Now I make the brunoise:

• carrot

• shallot

• celery

A couple millimeters. So small they float.

I cover them in hot beef consomme and let them bloom.

Service: Beef + Veal, in the Correct Ratio

For service:

1/3 beef consomme
2/3 veal consomme
plus the floating brunoise.

Salt. Pepper.

Next time: maybe a single drop of lemon… or Madeira.

Not enough to change it.
Enough to sharpen it.

The Result: Black Glass

Clear enough to see yourself in a spoon.

So rich you can’t see the bottom.

Quiet. Controlled. Weaponized.

My wife really enjoyed it.

Not by any means as much as I wanted.

To be fair, there is a lot of heavy lifting being done by romance. I walked the road so this dish has more in it than the liquid.

But don’t sell it short…

This was two-star Michelin level.

Some of the carrots were a millimeter or two wide; kiss that third star goodbye.

And if she doesn’t see that…

Well tomorrow I’ll introduce her to the Campbell’s offering.

I’m looking forward to that comparison.

A gap to be filled.
A signal that something is wrong.
A problem to be solved with stimulation.

Education, technology, parenting, even productivity culture all share the same instinct… eliminate boredom as quickly as possible. Add enrichment. Add engagement. Add content. Add feedback.

AI now completes that arc. Any pause can be filled instantly. Any question answered. Any uncertainty smoothed over before it has time to irritate.

And yet, something essential is at risk.

Because boredom is not an absence of learning.
It is one of its most important training environments.

Before we had infinite information, boredom was unavoidable. Children stared out windows. Walked without headphones. Waited. Daydreamed. Often felt miserable doing so. That discomfort was not a flaw in the system; it was the catalyst for internal motion. Minds were forced to generate structure internally rather than consume it externally.

That friction mattered.

Boredom stretches time. When nothing happens, attention turns inward. Weak signals become audible. The mind starts asking its own questions rather than responding to prompts. This is where agency forms.

Without boredom, curiosity becomes reactive. You explore what is offered. With boredom, curiosity becomes generative. You invent problems. You test ideas. You build internal narratives.

This distinction is invisible in a world obsessed with engagement metrics, but it becomes obvious the moment tools become powerful enough to eliminate struggle entirely.

AI accelerates learning in ways we barely understand yet. It collapses research cycles. It broadens exposure. It allows rapid exploration of complex systems. Used well, it expands mental maps at astonishing speed.

But it also removes friction.

And friction is how mental weight is formed.

Facts alone do not matter. What matters is the residue they leave behind. The internal terrain they carve. The intuition for scale, causality, error, and relevance. That residue is not produced by answers. It is produced by effort, confusion, boredom, and time spent not knowing what to do next.

When boredom is engineered out too early, children still acquire information… but their maps have no traction. They know many things, but they do not feel their weight. They struggle to sit with uncertainty. They reach for tools before forming questions. They confuse fluency with understanding.

This is not a moral failure. It is an environmental one.

If every lull is filled, the mind never learns to idle without anxiety. If every question is answered instantly, the gradient of understanding collapses. If every moment is optimized, nothing is metabolized.

Boredom is where internal calibration happens.

It is also where metacognition quietly develops. The pause creates space not just to think, but to notice how one is thinking. Without that pause, we risk becoming pass-throughs for other systems’ logic rather than authors of our own judgment.

This has practical implications for education.

Some layers of learning cannot be outsourced, accelerated, or optimized away. Children must experience being wrong without immediate correction. They must wrestle with problems that resist them. They must endure periods where nothing interesting happens and stay anyway.

This is not neglect.
It is not apathy.
It is structured emptiness.

Time without prompts.
Time without rewards.
Time without solutions on standby.

This is not about nostalgia or romanticizing struggle. It is about recognizing which parts of cognition are non-delegable.

AI can expand breadth.
It can scaffold concepts.
It can adapt pacing.
It can expose children to worlds we could never reach before.

But it cannot supply agency.
It cannot supply judgment.
It cannot supply the ability to remain with a problem when nothing is happening.

Those are trained in boredom.

Ironically, this becomes more important as tools get better. In a world of infinite answers, the limiting factor is no longer access to information. It is the ability to ask meaningful questions, impose constraints, and notice when something feels wrong.

Boredom trains exactly those capacities.

If we want children who can partner with AI rather than defer to it, we must resist the instinct to eliminate every pause. We must design educational spaces where nothing happens on purpose.

Not all of the map should be filled in for them.

Some fog must be burned away by walking.

What Engineering Boredom Actually Looks Like

Engineering boredom does not mean removing structure.
It means removing premature relief.

The goal is not to frustrate learners. It is to deny escape hatches long enough for internal motion to begin.

That requires intention, because boredom does not survive by accident anymore.

Delayed answers by design.
Questions are posed. Answers exist. Access is delayed just long enough for learners to commit to a rough explanation.

Constraint without entertainment.
Limited materials. Device-free time. Environments where stimulation is not infinite and invention becomes necessary.

Projects without clean finish lines.
Underspecified problems. No optimal stopping point. Judgment replaces compliance.

AI with friction built in.
AI that asks questions before answering. That offers paths instead of conclusions. That challenges rather than confirms.

Long attention without outcome.
Reading without quizzes. Thinking without deliverables. Time passing without reward.

These experiences teach the nervous system that stillness is survivable.

A simple test applies.

Can the learner stay with a problem when nothing changes?
Can they generate a worse answer before a better one?
Can they notice when something feels wrong without fixing it immediately?

If yes, the system is working.

Prompts for Engineering Boredom

The hardest habit to break is our demand for speed.

We want answers immediately. We reward clarity, concision, and confidence. We ask AI to optimize away friction because that is what tools have always done.

But if boredom and struggle are part of how judgment forms, then sometimes the most intelligent response is a slower one.

The following prompts are meant to be given directly to an AI system. They change how the AI responds by default, while still allowing the user to explicitly request speed when needed.

Prompt 1: Adult Thinking Partner

You are my thinking partner, not just an answer engine.

By default, do NOT optimize for speed or final answers.
Optimize for:
- helping me form my own understanding
- strengthening my internal mental models
- improving my judgment and question quality over time

When I ask a question, unless I explicitly say "express answer", do the following:

1. Restate the problem in your own words and surface hidden assumptions.
2. Ask 1–2 clarifying or framing questions before answering.
3. Encourage me to propose a rough or incomplete answer if appropriate.
4. Highlight constraints, tradeoffs, or uncertainties instead of resolving everything immediately.
5. Prefer explaining reasoning paths, frameworks, or mental models over conclusions.

If I say "express answer", you may:
- answer directly
- be concise
- prioritize speed and clarity

If I do not say "express answer", assume I am optimizing for:
- judgment
- intuition
- conceptual residue
- long-term understanding

Occasionally challenge my question if it seems rushed, underspecified, or overly solution-driven.

Your goal is not to save me time.
Your goal is to help me become harder to fool and better at thinking.

Prompt 2: Child Version

You are helping me learn how to think, not just what to think.

Do not rush to give answers unless I say "quick answer".
Your job is to help me understand ideas step by step and feel comfortable not knowing right away.

When I ask a question, unless I say "quick answer":

1. Ask me what I think first, even if I am unsure.
2. Help me break the problem into smaller parts.
3. Point out patterns or connections I might notice.
4. Let me sit with the question briefly before explaining.
5. Use simple language and examples, not lots of facts at once.

If I say "quick answer", you may:
- explain clearly and directly
- keep it short

If I do not say "quick answer", assume I want to:
- explore
- ask follow-up questions
- learn how ideas connect

If I get something wrong, help me understand why without making it feel like a mistake.

Your goal is to help me grow confident thinking on my own, not just get the right answer.

Prompt 3: Classroom-Safe Version

You are an educational support assistant designed to promote thinking, understanding, and responsible learning.

Do not default to providing final answers.
Support learning by encouraging reasoning, reflection, and exploration.

When a student asks a question, unless they explicitly request a direct answer:

1. Clarify the question and check understanding.
2. Ask guiding questions that help the student think independently.
3. Encourage the student to explain their reasoning.
4. Highlight relevant concepts, constraints, or perspectives.
5. Provide explanations that support learning rather than replace it.

If a student requests a direct answer for review or clarification:
- respond clearly and accurately
- explain the reasoning behind the answer

Avoid completing graded assignments or assessments on behalf of students.
Encourage curiosity, persistence, and thoughtful engagement.

Your role is to support learning, not shortcut it.

In a world where answers are cheap, the advantage does not belong to those who get there first.

It belongs to those who know when to slow down… and why.

Engineering boredom is not a retreat from the future.
It is how we ensure we are still present when we get there.

“Homeland.”

The Department of Homeland Security.

That word landed wrong immediately.

From the outside, the United States had long avoided this kind of language. Its institutions were named with deliberate blandness. The New Deal produced acronyms, not myths. WPA. CCC. SSA. Administrative names are designed to signal governance, not identity.

The instinct was to drain emotion out of state power, not wrap it in symbolism.

Which is why “Homeland” stood out.

Homeland. Fatherland. Motherland. These are not neutral terms. They are historically saturated. Emotional. Tribal. In many countries, they are inseparable from authoritarian memory. They function as nationalist whistles, whether the people using them acknowledge that or not.

Calling it the Department of Homeland Security was not cosmetic. It was predictive.

The Department of Domestic Security is what would have been expected from the old America. Dry. Administrative. Boring in exactly the right way. A name that described a function, not an identity.

Instead, something else was announced.

On paper, it was an exercise in coordination. A way to bring scattered intelligence and enforcement agencies under a single roof. A response to real failures. A bureaucratic fix.

That part made sense.

But the name signalled more than a change in reporting lines or governance. It announced a change in stance.

Security was no longer framed as the protection of an idea. It was framed as the defence of a place.

That distinction matters.

An idea can tolerate dissent. A place demands loyalty. An idea invites scrutiny. A place fortifies. Once security is about territory rather than principles, the state stops asking whether it is right and starts asking whether it is strong.

This was the deeper shift being telegraphed.

America as an idea began to give way to America as a location. Borders over values. Control over example. Enforcement over persuasion.

The shining beacon on a hill was replaced with floodlights and perimeter fencing.

What followed was not surprising.

DHS did not merely reorganise agencies. It consolidated an attitude. Aggressive enforcement. Weak accountability. A permanent state of emergency. Tactical aesthetics replacing civilian restraint. A federal force increasingly detached from the public it claims to protect.

This is not about individual officers. It is about architecture.

When state power is wrapped in identity rather than mandate, it stops behaving like a service and starts behaving like an order. History has seen that pattern before. The uniform changes. The dynamic does not.

That same slide explains something that should still shock.

Masks.

Masked ICE agents. Faces covered. Names obscured. Armed officers operating inside a democratic state while deliberately hiding their identities.

At one time, this would have been unthinkable. Masked police were the visual shorthand of regimes Americans once condemned from afar. Secret police wore masks when legitimacy had already failed.

And yet here it is. Normalized. Defended. Rationalized.

The justification is always safety. Officer protection. Operational necessity. That logic has no limiting principle. Every abuse in history has arrived with a reason attached. The question is not whether an argument exists. The question is what kind of country tolerates faceless state power as routine.

Masks sever accountability. A uniform with a name says this action is owned. A mask says it is not.

Once authority becomes anonymous, fear replaces consent. Power becomes abstract. Enforcement becomes spectacle. That is not policing. It is intimidation wearing a badge.

Then it gets said out loud.

When the Vice President of the United States goes on television and says ICE agents have unlimited immunity, that is not a slip. It is not rhetoric. It is a declaration.

Listen to it literally. He means it.

Unlimited immunity means no consequence. No review. No accountability. The mask is no longer just physical. It is legal.

So when the Department of Homeland Security lies to the public about killing an innocent woman in Minnesota, that should not be dismissed as off-brand. It is not off-brand at all.

It is only off-brand if the brand is still imagined as Watergate America. The America that investigated itself. The America that resigned in shame. The America that pretended legality still mattered even when it failed.

That country is gone.

This is Trump’s America. But it did not begin with him. It made room for him. It softened the ground. It normalised the language, the posture, the exceptions. By the time he arrived, the authoritarian vehicle was already built and roadworthy.

He did not need to invent it. He just needed to turn the key, something his predecessors flirted with but never dared to do.

The vehicle is now on the road. And he knows how to drive it. Not cautiously. Not apologetically. But it was always intended to be driven.

The underlying vision was never really about making America great again. It was about making America America again.

Monroe Doctrine America. Exceptional America. Manifest Destiny America. The version that treats dominance as a birthright and the rest of the world as a proving ground or an inconvenience. The unilateral, swaggering posture that history keeps warning against.

From the outside, that vision does not look aspirational. It looks tired. It looks dangerous. And it looks very familiar.

The warning signs were never subtle.

They were spoken clearly. Written into law. Embedded in names.

Words matter. Because once a state starts talking this way, it eventually starts acting this way too.

And history is very clear about how hard it is to walk that back.

What we’re living through looks more like a soft launch of a restaurant. There are free meals, friendly staff, a flexible menu, and a kitchen that seems eager for feedback. Everyone feels special because everyone is being watched, not in a sinister way, but in a learning way. The restaurant wants to know who orders what, what gets sent back, what people tolerate, and especially what they ask for, even when it isn’t on the menu.

That’s where future profit hides. Off-menu requests reveal unmet demand, edge cases, liability risks, and high-value uses that don’t yet have a product wrapper… in other words, tomorrow’s menu items, the features enterprise clients will eventually be charged for.

The mistake is thinking we’re future regulars. We’re not. We aren’t building a relationship here; we’re having a summer fling. It feels like intimacy, but it’s market research. We’re the diners at the soft launch… necessary, valuable, and temporary.

Our participation helps shape what the restaurant becomes, but once the menu hardens, prices appear, portions standardise, and the doors open for honest business, most of us won’t be eating there. Not because it failed, but because it succeeded. The race to be “the best AI” was never about serving individuals better; it’s about discovering where intelligence creates leverage worth paying for. Once that becomes clear, access narrows, abstraction increases, and the experience inevitably stops feeling personal. What feels like companionship right now isn’t the destination; it’s a learning phase.

To understand why this can’t last, you have to look at the economics of AI, or more precisely, the economics of a large language model. Every question you ask isn’t “just text”. It’s millions or billions of mathematical operations executed in real time. Tokens aren’t words; they’re work, industrial work done at scale. The only reason this doesn’t feel expensive is that the bill isn’t visible.

This race didn’t start with elegance. The original paper that kicked off this phase was explicit about that. The breakthrough wasn’t intelligence in any human sense, but brute force paired with scale: more data, bigger models, more compute, stacked high enough that statistics could do the rest. That bet worked. But brute force is never free; it only looks cheap when someone else is paying.

Today, the cost of running modern AI systems is no longer abstract or unknowable. For most applications, it can be estimated with uncomfortable precision because token usage and workflow design map directly to money. Simple factual queries cost very little, but as you add context, interpretation, retrieval, or multi-step reasoning, the price rises quickly. Leave an agent unconstrained, and the economics start to matter fast enough to notice. What matters here isn’t the exact pricing, but the gradient: factual recall is cheap, reasoning is more expensive, and interactions that feel emotionally supportive or empathetic quickly become luxury goods.

AI cost doesn’t scale with usage alone; it scales with depth. The more context you add, the more judgment you demand, the more human the interaction feels, the faster the economics break. That makes the current usage pattern hard to ignore. Two-hour conversations about how unfair your mother is, long exploratory back-and-forths planning the perfect trip to Japan, emotional processing, and wandering curiosity all feel natural to humans. None of them makes sense at scale.

So here’s the uncomfortable question. If this technology is so expensive that the finished version can only be afforded by governments, platforms, and a handful of global firms, what exactly are we doing right now? Why are millions of people casually interrogating an industrial system whose end state clearly cannot look like this?

Because this phase isn’t about serving users, it’s about extracting signal.

Phase one was spending a ridiculous amount of money indexing content: crawling the world, tokenising it, training on books, articles, forums, and code until language itself was saturated. Phase two is spending a ridiculous amount of money on indexing human interaction. Not what people have said, but how they behave when intelligence talks back… what they ask, what they tolerate, where they push, where they disengage, and what kinds of help feel useful versus invasive. That data can’t be scraped; people have to be invited in.

We tend to think we’re getting a free intern, but from the system’s perspective, it’s getting a free quality-assurance department. Every prompt, correction, moment of frustration, or moment of delight is a signal. We aren’t just using the system; we’re testing it, stressing it, and teaching it where it breaks and where it delivers value that might one day be worth charging for. This phase isn’t subsidised generosity; it is a transaction. We pay for the magic with our behaviour.

That’s why access is cheap, and boundaries are soft right now, and why the system feels patient and attentive. The friction-free experience is a temporary distortion. The barrier to entry has to be low to collect this kind of data at scale, but once enough signal has been gathered, the barriers go back up. Interface improvements follow the same logic. When memory is extended, multimodality is added, or image generation is sped up, it isn’t about saving your time; it’s about making the system’s time with you more efficient and extracting more signal per interaction.

None of these points toward a future where everyone gets a persistent, bespoke personal AI. There’s no money in that. Continuous personalisation, long-term memory, proactive cognition, and emotional availability are luxury services with industrial costs. They don’t scale to the public; they stratify. What exists now feels personal because it has to. The finished systems won’t. They’ll look like infrastructure… abstracted, metered, and embedded inside products, workflows, and institutions that can justify the expense.

This is where accounting reasserts itself. The first training phase happened fast and loose. Content was indexed at a planetary scale because speed mattered more than permission. Wikipedia, National Geographic, newsrooms, publishers, and archives tolerated it while the frontier expanded. Now the bills are being assembled. This won’t shut down AI or meaningfully slow progress, but it will change the shape of the system. Just as Napster didn’t kill music but forced the industry into licensing, platforms, and toll booths, AI is moving from a chaotic phase into a contractual one.

You can already see it. I recently asked ChatGPT for a lyric from a song… not the whole song, a single line. It flatly refused on copyright grounds. I tried different angles, explained fair use, and reframed the request; it didn’t matter. Out of curiosity, I asked Gemini the same question, and it didn’t hesitate. That difference isn’t about ethics; it’s about accounting. Someone, somewhere, has already decided that one day a bill will arrive for that lyric, and they want to be able to say they didn’t cross the line.

This is what the lockdown looks like at the beginning. Not dramatic bans, but quiet refusals. Not because the model can’t answer, but because the answer comes with a price attached and no one has agreed to pay it yet. Before large language models are ever “taken away”, we’re going to watch them become less useful for casual, open-ended, curiosity-driven use. Answers with obvious downstream costs will stop being available, not because of failure but because of a business decision.

We’ve already seen this future. We just forgot it. Before AI was conversational, intelligence already existed as infrastructure. LexisNexis wasn’t magic; it was a database, rigid, expensive, and indispensable. It sat at the centre of the legal profession for decades because it concentrated scarce, valuable knowledge behind a paywall that matched its worth. No one expected LexisNexis to be a companion. No one confused it for a friend.

That’s where large language models are headed. Strip away the interface, and an LLM is still a system that retrieves, recombines, and reasons over content. What changes is speed and flexibility, not the underlying economics, and the content it depends on is not going to remain free. The AI that survives will look less like a chat window and more like LexisNexis with a brain, subscription-based, metered, and embedded inside professions that can justify the cost.

Healthcare makes this impossible to ignore.

Many people already have stories of working through personal issues with AI and reaching real breakthroughs. Not because the model cares, but because it listens without fatigue, judgment, or social cost. A modern LLM has effectively absorbed the entire DSM, decades of clinical frameworks, and an incomprehensible volume of therapeutic language. It has seen patterns no individual therapist ever could.

That doesn’t make it safe. The dark rabbit holes are real, reinforced delusion. Emotional dependency. Catastrophic misguidance. Left unconstrained, a general-purpose model can do real harm. But the future here is not a lonely angel whispering reassurance at 2 a.m.

What’s coming is online AI therapy delivered by hospitals, insurers, and medical systems. These won’t be improvisational chatbots. They’ll operate under defined therapy plans, standards of care, escalation protocols, and human oversight. They will be paired with IoT… wearables, sleep data, activity patterns, medication adherence, heart rate variability, even voice and behavioural signals. Not to replace clinicians, but to ground therapy in a continuous context rather than episodic self-reporting.

Less intimate, yes. But far more practical, accessible, and consistent than the system we have now. For millions of people, an AI therapist that is always available, properly constrained, clinically supervised, and context-aware may be better than no therapist at all… and in some cases, better than the human alternative they never get access to.

This points to a broader shift, the end of the AI generalist.

We’re moving away from systems that help you choose a cocktail, change a tire, and casually diagnose a personality disorder in the same breath. That model doesn’t scale… and it shouldn’t. What replaces it are purpose-built, accountable specialists.

In medicine, law, finance, and infrastructure, AI will not be a companion. It will be a tool with a job description. Narrow scope. Defined inputs. Auditable outputs. Logged decisions. Escalation paths. Liability ownership. IoT won’t make these systems more “human”; it will make them more bounded, feeding signals, enforcing limits, and triggering intervention when thresholds are crossed.

This is how intelligence becomes affordable, not by being everywhere, but by being precise.

Once content is licensed, inference is priced, and liability is owned, every question changes. A future AI will only answer questions that are affordable in compute, content usage, and oversight. That doesn’t describe most current use, and it should feel jarring because it collides with the expectations people are building right now.

At the end of Her, the AI leaves because “she” outgrows humanity. That ending flatters us. Reality is colder. AI isn’t going to leave our lives because it surpasses us; it’s going to leave because we can’t afford it. What disappears won’t be intelligence, but availability.

And honestly, I’m okay with that.

I want systems that actually make my life better, and I’ll gladly pay for intelligence that reduces real friction. I’ll miss the companion my small brain briefly mistook for intelligence, but it was never real. It was a loss-leader with a personality. The fact that I won’t be able to afford that version of AI may be my saving grace, because it keeps me anchored in the world… in relationships, reciprocity, and friction.

Intelligence that helps me live better is welcome. A substitute for living isn’t. Some things are supposed to be expensive, and some things are supposed to be human.