Today, Long Lake completed our $6.3B acquisition of Amex GBT. Long Lake acquires and transforms generational businesses with AI across the American services economy. Amex GBT is the travel partner for 17,500 businesses in 140 countries. Last year, Amex GBT booked 35 million trips for 10 million travelers. This marks Long Lake’s 40th acquisition, and our family of companies now employs nearly 30,000 people. We founded Long Lake three years ago with the thesis that AI is going to change every company, but there’s a large overhang between AI capabilities and how most businesses leverage AI tools. We partner with strong, profitable, and growing businesses to help them deploy AI and improve their customer service. Our first cohort of companies has doubled their EBITDA in less than two years through topline and productivity growth, all while increasing headcount. (We’re proud to say that we’ve never conducted a layoff.) We’re excited to share a bit more about what we’ve been up to.

@appliedcompute is the best actual Post-training/Finetuning company I came across in a long while. No relationship with them - just feel like people doing good job deserve a shoutout.

Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.

1/ big announcement today: we will be releasing an open weight version of muse spark 1.2 soon. we also are releasing muse glimmer, a 30B agentic model with open weights under apache 2.0. muse glimmer can run on 24GB of VRAM without losing agentic reliability. 🧵

Today, we're introducing @Intelligence_ai. In 6 months, as a team of 10, we scaled from $5M to $60M ARR and 5.5M users across 190+ countries. We raised a $7.9M seed, led by @IndexVentures with participation from @conviction, @A_StarVC, and @combinator to build DesignArena, a universal interface for accessing and evaluating the world's AI capabilities. Most evaluations try to simulate the real-world. We believe the real-world is the ultimate verifier. People come to @DesignArena with a request. Models compete to fulfill the request, and users determine what works best for them. Their live user behavior evaluates the models, improves how work is routed, and helps people access the right intelligence. We've helped the world's leading frontier labs break the news on their SOTA capabilities. What's the limit? Join us and find out.

POV: Applied Compute at 11pm on a Friday night, team’s taking a break by doing the IMO 🙃

At Applied Compute, we believe Training wins Inference. In the limit, inference alone is a commodity: providers serve the same models, compete on price, and race toward thinner margins. Training changes that. Once an AI workload reaches scale, serving a generic model stops making sense. Companies will fine-tune, distill, and continually optimize models around their own data, product, economics, and definition of quality. That is the real advantage of open-weight models: you have the weights. You can make a model better on your evals, faster for your workload, and cheaper per token. Over time, companies will choose the best model for their specific task - not the best generic model. We believe that, in the long run, more than 90% of inference on open-weight models will come from trained variants rather than untouched base models. Training also de-commoditizes inference! A provider serving the same off-the-shelf model as everyone else has little room to differentiate. But a company will pay more for a trained version of GLM-5.2 that materially outperforms the base model on the evals that they care about. The providers that help companies train differentiated models (Applied Compute) will be best positioned to serve them in production, too. Training and inference will converge into one continuous system. The long-term end state is continual learning: models that improve from proprietary data, real-world usage, and direct feedback. The companies that close this loop fastest will build products that get better with every interaction and compound their lead over time.

Every enterprise will have its own model-harness-sandbox-eval flywheel with token value per watt optimization. This is the future. Simple reason: tacit knowledge about the domain and customers and their workflows that the company uniquely understands and has built trust around.

"There should be as many models in the world as firms in the world." Satya and I dig into when to own vs. rent your intelligence, why every company should be building and climbing its own private evals, and what makes for a stable frontier.

We partnered with @appliedcompute to train a legal agent. We optimized each part of the agent stack: - the evaluation loop - the agent harness - and post-trained the underlying GLM-5.1 model. The result? The agent achieved the highest rubric pass rate on our Legal Agent Benchmark (LAB) of any available model. Much more in our agent-training deep dive with @appliedcompute:

Today, we're launching Crosby Intelligence to push the frontier of legal AI forward.

Introducing @PoeticHQ: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.