frontier AI – Scott Loftesness

There is a sentence buried in Thinking Machines Lab’s release notes for Inkling, its first proprietary model, that most companies would never let out the door. Describing their own creation, the company states plainly that Inkling is “not the strongest overall model available today, open or closed.”

Read that again. A startup that raised two billion dollars in seed funding at a twelve-billion-dollar valuation, founded by OpenAI’s former CTO and staffed with veterans of the labs currently locked in the most capital-intensive arms race in corporate history, shipped its debut model with an admission of inferiority attached to the label. Not buried in a footnote. Stated in the announcement.

It seems like this was the clearest signal yet that the frontier-capability race may be the wrong game, and that durable value in enterprise AI accrues not to whoever has the smartest model, but to whoever owns the layer where that model gets adapted to a particular customer’s purpose.

As I’ve thought about it, the AI industry seems to be stratifying into three distinct businesses, each with different economics, occupied by a different cast of companies.

It begins with the farm, where the raw ingredients get grown. Then there’s the kitchen, the capital equipment that makes skilled cooking possible at scale. Lastly there’s the restaurant, where somebody who understands a specific customer takes the ingredients, uses the kitchen, and puts a particular dish in front of a particular diner who is paying for a complete meal, not just the flour or the vegetables. Thinking Machines seems to me like a clear example of a company trying to explain which of those businesses it’s actually in. It is not the only one.

The sequence, read backward

Founded in February 2025. Silent for over a year. Then, last October, the company’s first product emerged — and it wasn’t a chatbot, wasn’t an assistant, wasn’t anything a consumer like me would recognize or understand. It was Tinker, a fine-tuning API. Infrastructure for customizing other people’s models, shipped before the company had released a model of its own.

That sequencing is the tell. A company chasing frontier supremacy builds the model first and the tooling around it later, the way the frontier AI labs have all done. Thinking Machines inverted the order. It built the workshop before it built anything to put in the workshop window, which only makes sense if the workshop was always the product.

Inkling, released this month, doesn’t reverse that logic. It completes it. The model is described in the company’s own materials as “an extremely knowledgeable, generalist base that can be extended via fine-tuning” — language that positions the model itself as raw material, not a finished good. It ships with full open weights, day-zero availability on Tinker, and a name chosen, according to the company, to evoke “an idea in its earliest stage, with the potential to grow into something greater.” Even the naming is a thesis statement. Inkling is not meant to be the only thing you use. It’s meant to be the thing you start from.

In the farm-kitchen-restaurant frame, it seems like Thinking Machines is trying to own two levels of the stack at once. Inkling is the farm — grown at real expense, forty-five trillion tokens of training data, frontier-scale compute. Tinker is the kitchen — the induction range and the walk-in fridge, sold as a service to whoever wants to cook. What Thinking Machines has explicitly declined to be, by its own admission, is the restaurant. They are not trying to serve you the best possible dish. They are trying to make sure that whoever does serve you that dish is buying their ingredients and standing at their stove and cooking in their kitchen.

The manifesto that preceded the model

A company doesn’t back into a strategy this coherent by accident. Earlier this month — before Inkling shipped — the lab published a position paper arguing that most AI today is trained in a handful of places and then frozen, a design that by its nature excludes the people the model is meant to serve. Their proposed alternative: AI that is distributed, customizable, and shaped by the people using it, not the lab that built it.

Mira Murati has said the same thing more plainly, and said it a year before Inkling existed, back when Tinker launched. Her framing wasn’t about building the smartest model. It was about making “frontier capabilities much more accessible to all people” — democratization as the mission, not capability supremacy. That is a genuinely different objective function than the one driving her former employer, and it was declared outright, not discovered after the fact to explain a disappointing benchmark result.

Inkling is a 975-billion-parameter mixture-of-experts model trained on forty-five trillion tokens across text, image, audio, and video, with a context window stretching to a million tokens. That is frontier-scale compute expenditure. This isn’t a company that ran out of runway and settled for a smaller ambition. It’s a company that spent frontier-level resources and then declined to spend the final increment chasing benchmark supremacy, presumably because the return on that increment doesn’t show up in the business they’re building.

A second detail: Inkling reportedly uses one-third the tokens of Nemotron 3 Ultra to hit equivalent performance on agentic coding benchmarks. That’s not a capability retreat — that’s a capability choice, optimizing for efficiency and cost-per-task rather than raw benchmark position. And the company is previewing a smaller sibling model alongside Inkling, suggesting a family strategy across sizes rather than a single mid-tier release.

The restaurant next door: Palantir

Thinking Machines isn’t the only company making this bet — and looking at who else is making it shows not everyone is occupying the same layer.

Earlier this month Palantir and Nvidia announced a “Sovereign AI Operating System” — Nvidia’s open Nemotron models, fine-tuned on a customer’s own data, running on Nvidia hardware inside that customer’s own air-gapped network, with Palantir’s Ontology and Foundry software layered on top. CEO Alex Karp pointed out that his enterprise customers don’t want to risk sharing their IP with frontier model providers and asked simply why wouldn’t they control the weights?

It’s tempting to read this as the same argument Thinking Machines is making. It isn’t, quite. What Palantir is selling is the restaurant: the finished, seasoned, plated product — an air-gapped AI system wired into a specific government agency’s or enterprise customer’s actual workflows, with “you control the weights” as the pitch that closes the deal. Palantir isn’t growing wheat. It’s the chef, working with ingredients somebody else grew. Somebody who could be trusted.

Another restaurant: Sierra

Sierra, Bret Taylor and Clay Bavor’s customer-support agent company, makes the same choice even more starkly. Sierra’s own technical writing describes a “constellation of models” architecture: rather than betting on a single LLM, Sierra routes each task inside a customer-service agent to whichever model — from OpenAI, Anthropic, Meta, or elsewhere — handles it best, and explicitly says it invests “in fine-tuned models where off-the-shelf models fail to meet our constraints.” Fine-tuning shows up in Sierra’s stack as one tool among several, alongside retrieval and layered “supervisor” models that catch mistakes before a customer sees them. Sierra has no interest in being a model company or an infrastructure company. It wants to be the restaurant that happens to keep a few specialty ingredients in the walk-in that nobody else stocks, because the dish needs them and they know just how to include them.

Mapping the rest of the stack

The farm-kitchen-restaurant split shows up everywhere the fine-tuning economy has organized itself.

The kitchen-builders — companies selling fine-tuning infrastructure to whoever wants to cook with it, indifferent to what gets made — now form a crowded field: Thinking Machines’ Tinker, Together AI, Fireworks AI, Predibase, OpenPipe, Baseten, Modal, Databricks’ Mosaic stack, and newer entrants like Nebius’s Token Factory and Prime Intellect. None of them care whether you’re building a coding agent, a legal research tool, or a customer-service bot.

The restaurants — companies where fine-tuning is invisible plumbing inside a finished, vertical product — include Palantir and Sierra, and many others. The addressable market for fine-tuning seems to include almost every possible enterprise adopting AI.

What’s seems unusual about Thinking Machines is that it’s trying to be the farm and the kitchen simultaneously while declining, by its own public admission, to be the restaurant. Most companies pick one layer and defend it. Thinking Machines is betting that owning two of the three is the more durable position — grow the flour, own the stove, and let Palantir, Sierra, and a thousand enterprise engineering teams fight over who plates the dish.

The same stack, built by design

As I was thinking about this, I wondered how this relates to the AI activities underway in China. It seems that China’s AI industry maps onto this same three-layer structure with unusual clarity — and one genuine wrinkle the American version doesn’t have.

The farm is crowded and innovating on a different axis than size: DeepSeek, Alibaba’s Qwen, Zhipu AI, Moonshot AI, MiniMax, ByteDance’s Doubao and Seedance. The standout isn’t scale, it’s efficiency — DeepSeek’s V3.2 reportedly uses a novel sparse attention mechanism to nearly match GPT-5 and Gemini 3 on complex reasoning despite far less compute, a different kind of farming: not more wheat, but wheat bred to need less water. Qwen has become the default soil for the rest of the world’s kitchens, generating over 100,000 derivative fine-tunes on Hugging Face. VC’s in Silicon Valley note how frequently their startup companies are building on Qwen.

The kitchen layer has its own SiliconFlow — a Beijing infrastructure startup, backed by Alibaba Cloud, that bills itself as the neutral layer between AI applications and hardware. It solves a problem others never had to: China’s compute runs across fragmented domestic chips, Huawei’s Ascend line chief among them, that don’t share Nvidia’s CUDA ecosystem. SiliconFlow abstracts that fragmentation away — it became the fastest platform serving DeepSeek traffic, and the only large provider running DeepSeek on Ascend chips instead of Nvidia’s. That’s a stove engineered to burn whatever fuel is in the tank that week, a direct product of the U.S. chip export controls rather than any inherent technical edge. Volcano Engine, Alibaba Cloud’s PAI, and Baidu’s Qianfan are versions of the same layer.

The restaurant layer is where China’s picture diverges most from Palantir and Sierra’s venture-funded improvisation: it’s named industrial policy.

Beijing’s “AI+” initiative targets seventy percent sectoral AI penetration by 2027, ninety by 2030 — fine-tuned vertical deployment treated the way past five-year plans treated high-speed rail. The players read like a sector directory: SenseTime for vision and embodied AI, iFlytek for speech in education and government, Baichuan Intelligence for healthcare, 4Paradigm for finance and industry, each fine-tuning a general base into something that only makes sense inside one workflow — a hospital’s diagnostic support tool, a bank’s risk model, an industrial inspection line.

The bet

Every frontier lab is implicitly betting that intelligence is the scarce resource, and that whoever has the most of it wins the enterprise market by default. Thinking Machines, Palantir, Sierra, and many others are all, in their different ways, betting against that premise — that raw intelligence is commoditizing faster than the frontier labs’ spending would suggest, and that the scarce resource has already migrated to whichever layer turns a generalist model into a specific customer’s model.

Thinking Machines is betting the moat moved to the farm-and-kitchen layer. Palantir, Sierra and others are betting it moved further still, to the restaurant, where nobody cares whose flour was used as long as the dish is right. China is betting on all three layers at once, with the state underwriting the bet directly.

It is a curious thing for me to watch companies with this much money and this much talent choose not to fight for the title of smartest model in the room. It is also a curious thing to watch them explain why, in public, in the first paragraph of an announcement.

But I think I’m beginning to understand.

Here is a small, possibly embarrassing confession: I have never, not once, gone looking for the best AI model.

I have a model. It lives in a browser tab — Safari, usually, on whichever device is nearest, occasionally Chrome if I happen to be at the desktop. It does what I need — drafts an email, untangles a sentence, tells me what a Norwegian emigration record from 1856 probably says — and then I close the tab and go on a walk.

Somewhere out there, presumably, a much smarter, much more expensive machine is doing something extraordinary with protein folding or hedge fund arbitrage or the outer edges of mathematics I will never visit. I have made my peace with never meeting it.

This did not used to feel like a confession. For a while there — a year, eighteen months — it felt like the central drama of the whole industry: which model was “best,” who had it, who had lost it, whether some lab’s quarterly earnings call would reveal that the frontier had quietly moved sixty miles down the road while everyone was looking the other way. Benchmarks were released like box scores. People argued about them the way people argue about batting averages, with the same weird intensity, the same conviction that a two-point difference in some abstract reasoning test settled something important about the future.

And then, at some point I can’t quite date — it crept up, the way these things do — I noticed I had stopped caring.

Not because the frontier stopped moving. It didn’t. It’s still moving, arguably faster than ever, in ways that occasionally show up in the news with all the drama of a soap opera (a delayed launch, a researcher poached, a stock down five percent in an afternoon, always something).

I stopped caring because none of it touched me. My model — whatever it was, this week — had long since crossed some invisible threshold past which more didn’t register as more. It was already better than I needed. It has been better than I needed for a while now. I suspect I am not unusual in this. I suspect most people, doing most things, most days, are operating comfortably inside a capability surplus so large they’ve stopped noticing it’s there, the way you stop noticing a room is warm.

If the top of the model isn’t for people like me — and it increasingly isn’t — then who, or what, is it actually for? I went looking for one piece of the answer and found, instead, a metaphor.

It’s called “context rot.” I have to admit, before I go further, that I’m not sure I’ve ever felt it myself — which, on reflection, is its own small piece of evidence. My sessions close in minutes, not hours. I ask, it answers, I leave. Whatever happens to a model over the fourth or fifth hour of sustained, dependent work is a country I simply don’t visit.

But other people do, increasingly — entire teams do, for entire projects — and what they’re finding out there is worth understanding, even secondhand. It describes something that happens to AI models when they’re asked to work for a long time on something complicated — not five minutes, but five hours; not one question, but a hundred small decisions stacked on top of each other, each one depending on the last.

You’d think the limiting factor would be room. Models have a “context window” — a stated capacity, like a gas tank, measured in tokens, and for a while the marketing numbers on these were the whole story: two million tokens! A library! And you’d think, as with a gas tank, that the thing runs fine until it’s empty and then it stops.

That is not, it turns out, what happens. What happens is closer to what happens to your desk.

You know the desk. Everyone has the desk. It starts the morning clean — an aspirational, almost insulting cleanliness — and by four in the afternoon it is a geological record of the day: three coffee cups, a stack of things you meant to file, a Post-it with a phone number you no longer need, the good pen buried under a printout of something you already dealt with an hour ago. The desk is not full. There is, technically, room. You could clear a space if you tried. But you don’t try, because functionally, cognitively, the desk has stopped being usable long before it ran out of surface area. You start looking for the stapler and forget what you were stapling. This — and I did not make this term up, I want to be clear, though I wish I had — is context rot. The window hasn’t run out. The signal has just drowned in its own debris.

Researchers watching this happen to long-running AI agents have found something almost cruelly elegant about how it fails: it doesn’t fail gradually, the way you’d expect a desk to get gradually messier. Errors compound. A task that takes twice as long doesn’t get twice as likely to go wrong — the failure rate roughly quadruples. Two mistakes early in a long chain of dependent steps don’t add up to a slightly worse outcome. They multiply into something close to total collapse, four hours in, for reasons that trace back to a single bad assumption made in the first twenty minutes and never revisited.

Here is where the frontier comes back in — not as the whole answer, but as a piece of one.

It is not that frontier models are smarter in the way a benchmark measures smart — better at a single hard math problem, a cleverer turn of reasoning. Plenty of models can do that now; the “good enough” tier has crept remarkably high.

It’s that frontier models are apparently, marginally, meaningfully better at not rotting. At keeping the desk usable at hour six. At knowing which of the forty things on the desk actually still matters and which is a coffee cup that should have been thrown out an hour ago. This is a genuinely different kind of intelligence than the one benchmarks were built to measure, and it is almost invisible from the outside — you don’t see it in a single exchange, you see it only in the difference between a project that holds together over three days and one that quietly, subtly, stops making sense somewhere around Tuesday afternoon and nobody notices until Thursday.

If that’s true — if the frontier’s real edge is durability rather than raw cleverness — you’d expect to see it show up in how the labs actually deploy their own models: saving the sharpest tools for the tasks that need to survive the longest.

I went looking for a real-world example and found one closer to home than I expected: Anthropic’s own Slack tool, the one where you tag the AI into a channel the way you’d tag a coworker, and it works alongside a whole team over days, learning the channel as it goes. It runs on a serious, capable, thoroughly frontier model — but not, it turns out, on the company’s very best one. That one is held back, reserved for a smaller and stranger set of problems nobody has solved before at all. I sat with that for a while. The tool built to survive a whole team’s whole week, in public, under the most sustained pressure any of their products face, wasn’t handed the sharpest blade in the drawer. It was handed the second-sharpest — which was apparently, entirely, enough. Which tells you something about where the two kinds of intelligence actually diverge: the merely-very-good model handles the desk staying clean for a week, in public, in front of a whole team, where one bad assumption made Monday and never revisited would be visible to everyone by Thursday. The truly new capability is being held in reserve for something else altogether.

I don’t have a tidy place to land this, and I’m suspicious of anyone who does. But here’s the closest I can get.

Imagine a three-Michelin-star chef — the kind of person who has spent thirty years learning to coax something transcendent out of a single scallop, who can tell you, by smell, that a stock has forty more minutes in it — standing at your stove on a Tuesday night making you a grilled cheese sandwich. It will, I promise you, be a very good grilled cheese sandwich. The bread will be evenly golden. The cheese will have reached some ideal, fully-considered state of melt. But almost none of what makes that chef extraordinary is actually being used to make it — none of the thirty years spent learning to hold forty things in mind at once without losing track of any of them, the exact skill, it occurs to me, that keeps a long, complicated project from quietly falling apart on day three. The technique is idling. The thirty years are in the room, present, available, and almost entirely beside the point, because a grilled cheese sandwich was never the place where thirty years shows up. It shows up somewhere else — in a dish you will never order, on a night you weren’t there.

What you got instead, on your ordinary Tuesday, was simply more than enough.