> Second, clean data. MAI-Thinking-1 was trained on clean and appropriately licensed data, with AI-generated content excluded from pre-training. This matters for quality, provenance, and control. If we cannot account for what shaped a model, we cannot fully understand its behavior or credibly improve it.
Shots fired?
It would be interesting to see how far "clean data" can go on the scaling laws.
I would really like to see what "appropriately licensed data" means. Cannot imagine they didn't copy all open repo's on GitHub, and can't imagine they asked for permission, or are reproducing license texts from these repo's now. It sounds hand wavy.
P.S. A fairly basic website otherwise, but it unfortunately seems to be hacking scroll for no good reason.
Presumably their position remains that training on public repos is fair use and doesn't require a license. If it doesn't require a license it's still "appropriately licensed".
I assume they took the actual repos’ licenses info account. I don’t understand why they should ask for permission when the license would already allow for it.
Almost all licenses have requirements to redistribute copies of the work, or derivatives thereof. Even permissive licenses do. It's very little to ask when open source dev's provided thousands of hours of free work.
For example, the Apache 2.0 license requires in just 4.c:
You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works;
Just because they're tokenized and transformed into a probabilistic mapping, doesn't suddenly mean that they weren't copied.
I find it morally unethical that they (likely) just ingest IP of all open source repo's without asking, but also importantly without any attribution.
Let me also note that I'm not against LLM's in general. But I do think training on open source must be opt-in, and I look forward to a world with actually ethical, and traceable (i.e. on what they were trained on, like a bill of materials (BOM)), models.
Recently, GitHub has changed their terms of service to use all user data for AI training unless users explicitly opt out. This is probably the way Microsoft has obtained "appropriately licensed data".
It's interesting because their last model series (Phi) was based around the thesis that high-quality synthetic data is better than a large pre-training corpus.
Maybe, but Microsoft, through their partnership with OpenAI, is already involved in major copyright lawsuits. That is probably a driving force for this move, actually... I doubt they would want to tempt fate while those lawsuits are on-going.
all the labs "clean" their pretraining data, and you can have your pretraining data to be minimally ai generated but also spam synthetic post-training data
"how many of those shapes are rectangles?" "sounds like zero unless they are squares"
Adding "unless" to a statement makes it vacuous if the latter clause is weaker than the first clause. I find it hard to believe that a company willing to violate licenses would have scruples about lying about it.
Not vacuous, but tautological.
Which is different, because tautologies can actually be quite directly informative. Whereas vacuous truths tend to be oblique.
Also, “Microsoft is lying” is not a logically stronger statement, because they might be lying about something other than whether they distilled or trained on AI output.
It's good there is a new player on the market, I take benchmark tables with a grain of salt, however. Speaking about model presentation it's funny to see how clearly their website is inspired by other AI company blogs with extra innovation of hijacked scrollbar.
The benchmarks are a bit of a disaster? It's at about DeepSeek V3.2 level, but with about 50% more parameters. Loses handily to the also smaller GLM-5.1, and even worse to the similarly sized Kimi K2.6.
Yes and no.
Yes from a user PoV, I don't really see a great reason to use this other than for enterprises that care about using a model not trained on copyrighted data (not sure what the market really is for this anymore, feels like this concern has been forgotten by most customers).
From a strategic PoV for MS, all the models you cited are distilling GPT/Claude/Gemini and wouldn't be anywhere as good as they are without this distillation, which in turn means you are dependent on OAI/Anthropic/G first shipping a good model to generate data for your training. This MAI model is trained from scratch with no synthetic data or distillation. So in term of benchmark its obviously much harder to get strong score and thus not a disaster if they can keep on improving.
Looks like the OAI divergence is finally taking place. Seems like the comparisons are mainly with Opus 4.6 and GPT 5.4 though. Still, exciting to see a new frontier player.
> MAI-Thinking-1 is a 35B-active, ~1T-total parameters, sparse Mixture of Experts model, a smaller inference footprint than much larger models.
This seemingly nonsensical sentence (of course this will have a smaller inference footprint than larger models) suggests this model's competitors have larger inference footprints and total parameter sizes.
As someone who has spent quite a lot of time on inference, I would a add a small note:
Deployment looks very different for MoE than dense style models so I would say that it is more nuanced than "inference memory reqs remain the same". Memory can be very different for MoE style models.
Yes it is, but I can imagine that they want to start out a bit smaller to see how well things scale, and/or did not yet have the time to work on optimizing for the large context windows.
Yup, same experience, it’s because the attention basically has exponential complexity. So at large context windows, they need to compress the attention (eg group multiple tokens together), when then leads to loss in accuracy.
It’s almost always better to keep your context windows small.
What's interesting is that although they don't seem to be releasing the model weights, they have published a technical report (https://microsoft.ai/wp-content/uploads/2026/06/main_2026060...) that's more extensive than the typical open-weights model gets.
Does this mean that work created with it can be copyrighted? Since the courts ruled that the inclusion of pilfered IP was the reason other model's work cannot be copyrighted, I would think so!
In that case, this is a completely different beast. It can maybe be used for things that need a durable copyright.
They've hijacked scrolling. They've hijacked the spacebar. It flickers like crazy when I try to move through the article. Trying to get through it is an exercise in madness.
I was most excited about the "frontier tuning." Like, it will actually watch you do stuff and learn to do it for you? That would be actually interesting.
But no, it's just a data labelling interface: https://learn.microsoft.com/en-us/microsoft-365/copilot/copi.... You have to provide the instruction and give feedback and there is a whole UI with hour-lonf wait between steps. So basically they want you to do the labelling to train a model, or at least that's how it looks from the outside
Also the mission statement of Humanist AI is the most boring, but tries to sound way too grand. Like "all the cool labs have a mission statement, so we should also have one" vibes
"Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting."
Shots fired?
It would be interesting to see how far "clean data" can go on the scaling laws.
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