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AI Apple Google

The Floor

I compared the frontier to a three-star chef making grilled cheese in “Context Rot” — the smartest models on earth spending most of their time on work beneath them, the way a chef trained at Le Bernardin might still melt cheese between two slices of bread on a Tuesday night and call it dinner. The comfort was the point: if the sharpest tool is saved for hard problems and something merely-very-good handles the rest, nobody’s losing anything. The floor was never the interesting part.

I’ve kept turning the joke over, and I think I had the wrong worry.

Watch what companies do with their AI spend, not what they say. Coinbase moved engineers off frontier models onto open weights and cut its AI spend nearly in half while usage kept climbing. Nvidia runs a closed model as orchestrator and routes the actual volume — the daily uncelebrated bulk of it — to open weights it controls. The frontier is becoming a dispatcher, deciding where the request goes and rarely doing the work itself. The instinct is to worry about whose open weights end up running that volume, and right now the most capable ones at scale are Chinese — GLM, Kimi — which makes it tempting to read this as a contest America is quietly losing: the floor of the AI economy built somewhere else, at a price export controls can’t touch. You cannot embargo a file already downloaded. You cannot price-match free.

But that framing has a hole. Google’s own Gemma family is open-weight and good enough to handle that daily volume without anyone reaching for GLM or Kimi. “Open weights are a Chinese story” only holds if you don’t count the open models the company running Android and half the internet’s search traffic has already shipped.

And once I saw that hole, a bigger one opened behind it. I’ve been trying Apple’s new Siri — arriving with iOS 27 this fall, genuinely surprisingly good in beta — and it made me realize open weights, of any nationality, were never going to cook most of the world’s dinners. Apple and Google are.

Consider what actually determines where the world’s routine inference runs. Not which model benchmarks best, not which weights are downloadable — what’s already installed. Apple ships to well over a billion active devices before routing a single query through Siri’s new architecture. Nobody has to be persuaded to try it, or hear about it on a podcast; it’s the thing that answers when you press the button you’ve pressed for a decade. Google owns the search bar and the Android default the same way. Between them, that’s most of the world’s phones — and phones are where most of the world’s questions get asked.

The open-weight framing assumes the floor is up for grabs, that whoever ships the best free model wins the daily grind by merit. But the floor was never a bazaar. It’s a set of defaults, owned by whoever already has the device in your hand, not whoever holds the most generous license. Apple didn’t need to win the model war to win this. Its heaviest reasoning tier is built with Google, running on Nvidia chips in Google’s cloud, under a deal reported at roughly a billion dollars a year — Apple doesn’t fully own the engine doing the thinking. It doesn’t need to. It owns the button.

That’s a quieter concentration than an export-controls fight, and a harder one to dislodge. An open model can be forked, distilled, undercut, or out-competed by the next release. A billion phones with an assistant built into the lock screen cannot be routed around. Whoever’s weights hum underneath barely matters, the way it barely matters to a diner which supplier delivered the flour. What matters is whose kitchen the meal came from, and whose name is on the door.

The grilled-cheese chef was never the risk. Two chefs are about to own nearly every kitchen on earth, and most of us will never notice — because a kitchen you’ve been eating out of for a decade doesn’t feel like something that was won. It just feels like home.

Owning the kitchen and getting paid for what’s cooked in it, though, turn out to be two different questions. That one’s for another post.

Categories
AI AI: Large Language Models AI: Prompting

Liquid Software and the Death of the “User”

There is a profound disconnect in how we talk about Artificial Intelligence right now. In the boardrooms of legacy corporations, AI is a “strategy” to be committee-reviewed—a tentative toe-dip into efficiency. But on the ground, among the “AI natives,” something entirely different is happening. AI isn’t just making the old work faster; it is fundamentally changing the texture of what we build and how we think.

In a recent conversation, Reid Hoffman and Parth Patil explored this shift, and the metaphor that struck me most was the idea of software becoming “liquid.”

The Era of Liquid Software

For decades, we have treated software like furniture. We buy a CRM, a project management tool, or an analytics dashboard. It is rigid, finished, and distinct from us. We are the users; it is the tool. But Patil demonstrates a different reality: one where he drops a folder of raw CSV files into an agent like Claude Code and asks it to “look at the data and build me a dashboard.”

Sixty seconds later, he has a fully functional, interactive HTML dashboard. He didn’t buy it. He didn’t spend three weeks coding it. He simply willed it into existence for that specific moment.

This is “vibe coding.” It’s a term that sounds almost dismissive, but it represents a radical democratization of creation. You no longer need to know the syntax of Python to build a tool. You just need to know the “vibe”—the outcome you want, the logic of the problem, and the willingness to dance with an intelligent agent until it manifests.

The philosophical implication here is staggering. We are moving from a world of scarcity of capability to a world of abundance of cognition. When you can spin up a custom tool for a single week-long project and then discard it, the friction of problem-solving evaporates. The “app” is no longer a product you buy; it’s a transient artifact you summon.

Applying the “Vibe Code” Mindset

But how do we, especially those of us who don’t identify as “technical,” bridge the gap between watching this magic and wielding it? The conversation offers a roadmap. It starts by shedding the identity of the “user” and adopting the identity of the “orchestrator.”

If you want to move from passive observation to active application, here are three specific ways to start:

1. The “Interview Me” Protocol

We often stare at the blinking cursor, unsure how to prompt the AI. Hoffman suggests a reversal: Make the AI the interviewer. When you face a complex leadership challenge or a strategic knot, open your frontier model (Claude, GPT-4o, etc.) and say:

“Interview me about this problem until you have enough information to propose a framework or solution.”

This forces you to articulate your tacit knowledge, which the AI then structures into something actionable. It turns the monologue into a Socratic dialogue.

2. Build “Throwaway” Internal Tools

Stop looking for the perfect SaaS product for every niche problem in your team. If you have a messy recurring task—like organizing client feedback or synthesizing weekly reports—try “vibe coding” a solution. Use a tool like Replit or Cursor. Upload your messy data (anonymized if needed) and tell the agent:

“Write a script to organize this into a table based on sentiment.”

Don’t worry if the code is ugly. Don’t worry if you throw it away next month. The value is in the immediacy of the solution, not the longevity of the code.

3. Transform Meetings into Data

Meetings are usually where knowledge goes to die. They are ephemeral. But if you transcribe them (with permission), they become data. Don’t just ask for a summary. Feed the transcript to an agent and ask:

“Who should we have consulted on this decision that wasn’t in the room?”
“Create a decision matrix based on the arguments presented.”

This turns a passive event into an active, queryable asset.

Conclusion

The danger, as Hoffman notes, is the “secret cyborg”—the employee who uses AI to do their job in two hours and spends the rest of the week hiding. But the real win comes from the amplified team, where we share these “vibe coded” tools and prompts openly.

We are entering an age where your imagination is the only true constraint. If you can describe it, you can increasingly build it. The question is no longer “is there an app for that?” but “can I describe the solution well enough to bring it to life?”