IPO – Scott Loftesness

The trucks are what I remember. Not the houses, not yet — the trucks.

This was 2012, Atherton, a Tuesday probably, and I was driving through on some errand that doesn’t matter anymore. What matters is that the street had rearranged itself. Contractors’ pickups lined both shoulders, nose to tail, so many of them that the road narrowed to one lane and you had to slow down and thread through, the way you do in a construction zone that has forgotten to end.

White trucks, mostly. Ladders racked on top. A generator humming behind a hedge somewhere I couldn’t see.

Behind the trucks, the estates were coming apart and going back together bigger.

I remember thinking: something has happened here that I am only seeing the edge of.

What had happened was Facebook.

The company had gone public that May, and within months the money was finding its way, the way money does, into contractors’ trucks parked along an Atherton road.

I didn’t call it a wave at the time. I called it, in my head, weather — a system that had rolled in and would eventually roll back out, the way markets always eventually correct, the way things revert.

I was an investor. I’d seen booms before.

I believed in the mean.

I was wrong.

The prices didn’t stay at their old level. They didn’t return to the world I’d known. The numbers from 2012 became the new floor, and every year since has been built on top of that floor. Today those prices look almost quaint, a thing you’d want to explain to a younger person the way you’d explain what a dollar used to buy.

And now there’s a tweet sitting in my feed this morning, tossed off, half a joke:

Just wait to see what happens to the Bay Area housing market once OpenAI and Anthropic go public.

I read it twice.

What I felt wasn’t curiosity — the feeling I’d had in 2012, watching an unfamiliar weather system with a kind of professional interest.

It was closer to dread.

Because I’ve already seen the after-photo.

And I know how to run the comparison forward.

The Facebook IPO created a large cohort of newly liquid employees on the Peninsula. They were mostly mid-career, and their stock had vested over four years against a company whose value had grown enormously.

The frontier labs are different.

If OpenAI and Anthropic eventually go public anywhere near the valuations already being discussed in private markets, they could create another enormous concentration of newly liquid wealth — among employees, founders and early investors.

I don’t know how large that wave will actually be. Maybe I’m overstating it. Not every employee will buy a house. Some will already own one. Some will move away. Much of the wealth will remain on paper for years.

And housing doesn’t respond mechanically to stock-market wealth.

But I do know something about the place where this wealth is likely to arrive.

There isn’t much of it.

Land is the constraint.

And I’ve seen what happens when a concentrated burst of new wealth meets a place that can’t make more land.

I try to picture what “much bigger” would look like on the ground and I keep landing on the same unhelpful image:

More trucks.

Longer lines of them.

People get ready..

I’ve been wondering whether we’ve been looking at the AI stack from the wrong end.

Recently Kris Patel on X laid out a set of excellent questions that he’s looking to have answered as Anthropic and OpenAI move toward going public:

Do you really need frontier-scale intelligence for every task?
Can open-weight models provide an effective alternative to frontier models at a significant discount?
Is the ultimate moat the intelligence or the harness?
What other business models will the frontier labs have to adopt to make the unit economics work long term?
How are you going to prevent distillation from capturing your IP and releasing it?

I’m going to explore only the third question here — moat versus harness. The other four deserve their own consideration, particularly once we have the Anthropic and OpenAI S-1s in hand, revealing for the first time the unit economics of the two largest frontier labs and how much runway they have to support the capacity they’ve contracted.

By “harness” we mean everything surrounding the model: the interface, context, memory, tools, orchestration, evaluation, permissions, and increasingly the user’s accumulated habits and data. The model supplies intelligence. The harness turns intelligence into a product.

Listening to Gavin Baker on the recent All-In episode sharpened this line of thought into something more concrete. He referenced a thought experiment from Eric Vishria: even if OpenAI or Anthropic lost their edge at the pure model layer, they would still retain significant value because of the product harness—the interface, the surrounding tooling, the orchestration—and the user familiarity and habits that have already formed around those platforms. Baker said there is a strong element of truth to it. I think he’s right, and the reasoning behind it is worth spelling out. A model can be replicated, distilled, open-weighted, or commoditized. A mature harness has network effects, switching costs, proprietary context, distribution, workflow integration, and accumulated user behavior. That’s a much harder thing to dislodge.

We are watching intelligence become more abundant and more interchangeable at the same time that the systems built around that intelligence are becoming stickier. On the developer side, the strongest examples are already clear. Cursor turns the IDE into a multi-model agentic environment with deep codebase awareness. Claude Code runs long-horizon coding agents from the terminal, planning, editing, testing, and iterating. Grok Build, Claude Cowork and similar tools emphasize parallel agents and tighter control over local context. In each case the model is a component; the surrounding system does the real work of routing, memory, tool use, and evaluation.

The consumer version of the same idea is now taking clearer shape at Apple. The rebuilt Siri AI shown at WWDC 2026 is not trying to win the pure model race. It is built as a personal harness. A system orchestrator decides what stays on-device with Apple’s Foundation Models, what moves to Private Cloud Compute, and when heavier reasoning is required. Personal context—messages, email, photos, calendar, notes, on-screen awareness—is handled largely on-device through the Spotlight semantic index and App Toolbox. Apple is designing the system so that personal context can be used without giving Apple itself access to it. Conversation history lives in a dedicated Siri app for the user to revisit. And it all syncs across all your Apple devices.

Here is the part I think matters most, and it’s easy to miss if you only read the privacy story. Apple’s Foundation Models framework doesn’t just call Apple’s own models—it’s built to support cloud models from other providers, including Claude and Gemini, conforming to a common protocol. Which means the system orchestrator, not the user, decides which model handles which task. This request goes to the on-device model. That one goes to Private Cloud Compute. A harder one might go to Claude or Gemini. The user doesn’t need to choose, and increasingly doesn’t need to know.

That’s the inversion worth exploring further. The frontier model stops being the interface and becomes a component underneath someone else’s interface. The harness chooses the intelligence. And the company that owns the harness—the OS, the identity layer, the permissions, the apps, the sensors, the notifications, the semantic index tying all of it together—has a form of leverage that has very little to do with whose model is smartest this quarter.

That reframes the subscription question too. I don’t think the right question is whether Siri gets good enough to beat ChatGPT or Claude at reasoning. I think Siri doesn’t need to win that fight at all. It needs to win a different layer entirely—the ambient assistant layer, not the reasoning layer. They’re doing different tasks. ChatGPT or Claude might remain where you go when you think, when I need to reason about something. Siri becomes where I go when I need something done: find (or make) my reservation, text my friend, find that old photograph, update my shopping list, schedule that meeting, add this thought to my notes, figure out when we’re free next week, remind me about that thing we discussed three months ago. Apple’s advantage as an ambient assistant isn’t primarily that it has your personal data. It’s that it has OS-level authority over the world that my personal data lives in.

Of course this is still early. Execution will determine how much of the architectural promise becomes daily reality. Reliability, agentic follow-through, and the quality of the on-device models will matter as much as the privacy story or the multi-model routing. But the strategic bet itself is clear, and it aligns with the broader shift: durable value is migrating toward the systems built around the models, especially systems that sit atop private, permissioned, personal context that competitors cannot easily reach. My early personal experience with the new Siri in iOS 27 betas has impressed me so far. All of this also seems to apply to Google in the context of their Pixel family of devices.

This doesn’t mean frontier labs lose. Pricing power still exists at the high end for the hardest agentic and long-horizon work. Open-weight models will continue to pressure costs and expand access. Distillation remains a real risk. But the more the capability gap narrows, and the more a harness like Apple’s can route among interchangeable frontier models rather than depend on any single one, the stronger the case that value settles into whoever controls the context—not whoever trained the model.

The coming Anthropic and OpenAI S-1s will tell us whether the frontier labs can make their economics of intelligence work. The next generation of Siri, Gemini, ChatGPT, Claude, and whatever comes after them may tell us something even more important: who gets to own the primary relationship with the user.

The model may be the engine. But the harness is where the driver sits.

What a time to be alive!

Share this:

Share this: