Consolidation has been one of the paths that many astute observers predicted for the near-future of labs training models. It was labelled as inevitable， as training costs are increasing by orders of magnitude every year. Yet， as someone who in 2024 would've predicted consolidation really picking up come 2026 or 2027， where are we？ We're at a place where more companies are training strong models - easily investing hundreds of millions to billions of dollars in the total effort still - and an increasing number of organizations are releasing these models openly.

The demand for tokens is incredibly high， and likely to increase as models get more efficient and unlock more possible use cases. All of these labs we thought would need to consolidate are realizing that building token machines is a likely path to value， and more companies will identify that source of value over time.

The prime example is Thinking Machines - when they announced their company in February 2025， very few people would've put them in the bucket of an open models company， myself included. Now their open model finetuning service is making hundreds of millions in revenue per year and they're releasing the best open-weight models built in the U.S.A. - ahead of the early leaders in NVIDIA with Nemotron and Arcee's Trilogy.

On the other side of the ecosystem is the sustained pace from the Chinese labs， with newer entrants like Xiaomi still accumulating mindshare in the broader AI economy. Having predicted consolidation for a long time， it now seems like a safer bet is to predict continued adoption， and try to imagine the role that open models play there. How much can revenue-share licenses like Kimi K3 stick？ How much market share can open models take？ We're entering the decisive era.

This is one of the most packed recaps of open models we've ever had， we're excited！