Open House

On open weight AI models (and personal career news)

ICYMI, I changed jobs. I’ve never kept my employer a secret, it’s not like I have a nom de plum (if I did it’d be Daniel McKee), but let’s finally get it out there. I was doing language and model behavior work inside Microsoft AI, one of the largest companies on earth, and as of this month I work at Arcee AI, a startup of about thirty people that trains language models in the United States.

To paint a picture, in June, my former colleagues stood on a stage at Build and announced seven in-house models, available through the appropriately closed, proprietary channels. Arcee, in contrast, bet big on a single model earlier this year, put everything they had into training Trinity, and released it for anyone to download.

This week I want to explain what that difference means in practical terms, partly for you and partly for me, because I’m sort of blinking into the sun here as I wrap my head around the change.

What’s Open

When a company releases a closed model, what you get is access. You type into a box or call an API, your words travel to their servers, the model runs on their hardware, and an answer comes back. You’re renting intelligence by the token. The company can update the model, change its personality, restrict what it says, raise the price, or shut it off entirely, and your recourse is minimal. Every major model you’ve likely used, ChatGPT, Claude, Gemini, Copilot, works this way.

An open-weight model is the thing itself. The “weights” are the model: billions of numbers arranged just so, the entire learned result of the training run. When an open lab releases a model, you can download that thing, run it on your own hardware, and never really think about it again. You can fine-tune it on your company’s documents or your weird hobby forum. You can rename it, resell it, run it in a bunker with no internet connection. You go girl.

To be clear, open weights is different from open source. Truly open source would mean releasing the training data and the training code too, the full recipe, and almost nobody does that, because the data is legally radioactive and the recipe is the competitive advantage. What most “open” labs release is the finished result. Think of it as being handed a sourdough starter. You didn’t see how it was cultivated and you couldn’t recreate it from scratch, but it’s alive, it’s in your kitchen now, you feed it, and over time it becomes yours and drifts from the original. The bread you bake with it is nobody’s business but your own.

The Neighborhood

The ground has shifted a lot in eighteen months. For most of the recent past, open weights were a Chinese phenomenon. DeepSeek, Qwen, GLM, and Kimi topped the open leaderboards and came almost entirely out of Chinese labs, which are extremely good at this, and which released capable models at a pace American companies declined to match. Meta was supposed to be the American answer, and then Llama 4 landed badly in 2025 and the company backed away from the open frontier. For a stretch there, if you wanted a capable model you could own, your options were made in China, which made a lot of enterprises and approximately all of the federal government itchy.

Big news from the past week, then. Thinking Machines Lab, Mira Murati’s post-OpenAI company, released its first model, Inkling: 975 billion parameters, multimodal, and, to the surprise of people who assumed a $50 billion valuation implied a locked door, fully open weight under permissive licensing. Their pitch is that AI should be something you continually adapt with your own knowledge and judgment (which is the argument the open-weight crowd has been making from folding chairs for years now).

Washington, meanwhile, has developed opinions. The administration’s AI Action Plan explicitly endorsed open-weight models and a June executive order set up a voluntary review process that launched this month (there’s been a lot of turnover and infighting and such, of course. The Information does awesome reporting on this). The reasoning is bluntly geopolitical. If the world is going to build on free models, better those models be American, carrying American assumptions, than the alternative. A coalition of economists convened by Mozilla published an open letter this summer making the pro-openness case from the other direction. I have complicated feelings about open weights becoming a national security asset, but, let’s save that for future posts.

Still Teaching

So let’s talk about me (hate it)! What do I do at a company like this? Same thing, I work on how the model behaves. Its language, its tone, its judgment, its personality, the right answer when there is no right answer. Whatever we call the discipline this week (model design, language engineering, behavior, vibes), the daily reality is reading enormous amounts of model output, running evals and experiments, writing up policy, and deciding, over and over, this but not that, warmer here, quieter there, stop apologizing, stop congratulating me, say you don’t know.

This work matters just as much for an open model, and arguably more, because of the defaults. The overwhelming majority of people who ever touch a model, even a fully open one, will use it exactly as it shipped. They won’t fine-tune anything. They’ll download it, or use someone’s hosted version of it, and the personality my colleagues and I put there is the personality they get. The base behavior is also the inheritance, every fine-tune, every startup building on top, every hospital and law firm adapting it starts from our starting point, and a model that begins calibrated and pleasant to think alongside makes everything downstream better. You can’t fine-tune your way out of a rotten foundation, or at least it’s miserable to try.

Yours to Ruin

So if you hand people the weights, why build anything in at all?

At the level of my job, every careful choice I make about how the model behaves “in the open” is, once released, a suggestion. Someone with a weekend and a GPU can fine-tune the personality right off. They can strip the refusals or retrain the tone into something I’d cross the street to avoid. There are established techniques for this and communities devoted to it. At a closed lab, behavior work is architecture; you build the walls and the walls stay where you put them. At an open lab, behavior work is more like raising a kid who is definitely, contractually, moving out.

I’ve spent years believing careful calibration mattered because it would hold. There’s control and some comfort in that control, I guess. The closed model is the only version of the model, so every judgment call my Microsoft teammates and I made was, in some small way, law, and it turns out it’s nice to impart law. For some reason it’s hard to admit this yet I’m sure all of us feel it.

But I’ve also spent years arguing, in this very newsletter, that a handful of companies deciding how machine intelligence behaves for everyone, in private, unaccountable, is the problem with this industry. It would be pretty rich to believe that right up until the moment the control was mine to give up. Openness calls my bluff. If I think behavior design is real expertise (I do), then it should survive contact with people who are free to reject it. The defaults I ship have to earn their keep on merit, because nothing else is holding them in place. That’s a more raw relationship with the work, a less comfortable one, and it comes with the discipline of knowing your best judgment is provisional, and makes you a lot more careful about what you claim it’s for.

So there you have it. I used to teach one model, privately, how to be. Now I teach in public, and hand over the starter, and people will bake things with it I’d never bake and a few things I’d rather not know about. The note that comes with it is still mine to write, though. I intend to write a good one.