Microsoft AI chief says company was “set free” from OpenAI to pursue superintelligence

For three years, Microsoft's artificial intelligence story has been inseparable from OpenAI. The partnership — cemented by a cumulative investment exceeding $13 billion — gave Microsoft early access to the most advanced AI models on the planet, catapulting its Copilot products into the enterprise mainstream and adding hundreds of billions of dollars to its market capitalization. To the outside world, Microsoft's AI strategy was OpenAI. Mustafa Suleyman wants to change that narrative. In an exclusive sit-down interview with VentureBeat at Microsoft Build 2026, the CEO of Microsoft AI disclosed that a contractual change with OpenAI roughly six months ago granted his division the formal authority to pursue what he openly calls "superintelligence" — using Microsoft's own researchers, its own data pipelines, and its own custom silicon. "We were only sort of set free from our contract with OpenAI about six months ago to formally pursue superintelligence," Suleyman said. "So this is very early days." The comment, delivered matter-of-factly backstage at the Fort Mason Center here, offers the clearest signal yet of a strategic inflection point unfolding inside the world's most valuable public company. Microsoft is not abandoning OpenAI. But it is building something alongside it — and, eventually, something that could stand entirely on its own. Microsoft's first in-house model family signals a new level of AI ambition The most tangible evidence of that shift arrived the same day. Microsoft announced a family of seven new AI models developed entirely in-house by its AI Superintelligence Team, spanning reasoning, code generation, image creation, transcription, and voice synthesis. The models — branded under the "MAI" family name — are Microsoft's most ambitious first-party AI release to date. The flagship, MAI-Thinking-1, is a 35-billion-active-parameter reasoning model that Microsoft says matches leading models in its weight class on key software engineering benchmarks and demonstrates advanced mathematical reasoning. Suleyman emphasized one point repeatedly: the model was trained from scratch on clean, commercially licensed data, without distillation from third-party frontier models — a direct, if unstated, contrast to the widespread industry practice of using outputs from competitors' systems to train cheaper alternatives. "We train our reasoning models from scratch," Suleyman wrote in a blog post accompanying the announcement. "We don't distill from other labs and we don't rely on unlicensed or opaque data." The rest of the family fills out a multimodal portfolio designed for enterprise deployment: MAI-Code-1-Flash, a lightweight coding model built specifically for GitHub Copilot and VS Code; MAI-Image-2.5, which supports both text-to-image and image editing; MAI-Transcribe-1.5, which Microsoft claims is the most accurate transcription model available, operating across 43 languages; and MAI-Voice-2, a multilingual speech-generation system. All of the models ship through Microsoft Foundry, the company's model-hosting and deployment infrastructure, and for the first time, developers can tune model weights themselves through third-party platforms including OpenRouter, Fireworks, and Baseten. But Suleyman made clear in the interview that the seven models are a proof of concept, not a finished product. The real project is the lab itself. "Our job is to make sure that when we look out to 2030 and beyond, we have the capacity not just to buy models from third parties, but to build the absolute frontier, the best models in the world," he said. "That's a long transition." What "set free" from OpenAI actually means for Microsoft's AI future To understand what Suleyman means by "set free," you need to understand the unusual contractual architecture that has governed Microsoft's AI efforts for years. When Microsoft invested billions into OpenAI beginning in 2019, the partnership came with a specific arrangement: OpenAI would build the frontier models, and Microsoft would serve as the exclusive cloud provider, integrating those models into its products and reselling them through Azure. The deal gave Microsoft extraordinary commercial leverage — access to the world's most advanced AI without having to build it — but it also created a dependency. Microsoft was explicitly barred from pursuing its own AGI research, and the agreement even capped how large a model the company could train, restricting it from building systems beyond a certain computing threshold measured in FLOPS. That arrangement was formally renegotiated. As Fortune and Axios reported in November, a revised deal with OpenAI removed those restrictions, clearing the way for Suleyman to launch the MAI Superintelligence Team and pursue what he calls "humanist superintelligence." The result, in Suleyman's telling at the time, was a "best-of-both environment, where we're free to pursue our own superintelligence and also work closely with them." By the time he sat down with VentureBeat at Build 2026, roughly six months had passed since that self-sufficiency effort formally began. Microsoft had already started shipping in-house models — including MAI-Image-2-Efficient, a lighter-weight image generation model released in April — but the seven MAI models announced at Build are the team's most ambitious release yet: a full multimodal family spanning reasoning, code, image generation, transcription, and voice. Even so, Suleyman does not view the shift as a rupture with OpenAI. He described Microsoft's current position as one of abundance, not scarcity. "There's no immediate urgent need to fill a gap in three months' time or six months' time," he said. "We have OpenAI, we have Anthropic, we have thousands of models inside Foundry. So there's already a huge amount of optionality available to us." The framing is telling. Microsoft's push into first-party frontier models is not born out of a crisis in the OpenAI relationship but out of a strategic calculation: as AI becomes the most consequential technology layer in enterprise computing, the company cannot afford to depend entirely on partners for the foundational capability. "Over the next five years, we have to be able to produce state-of-the-art frontier-scale models," Suleyman said. "That's our mission." Suleyman says the shift from chatbots to autonomous AI agents has already begun If the seven MAI models represent the technical ambition, a new capability called Frontier Tuning represents the commercial logic. Announced alongside the models at Build, Frontier Tuning allows enterprise customers to customize MAI models using their own proprietary data, workflows, and domain terminology, all within their own secure compliance boundary. The system uses reinforcement learning environments — what Microsoft calls "training gyms for AI" — that let agents learn directly from real workplace tasks without affecting production systems. The results Microsoft shared are striking. An MAI model tuned for Excel reportedly matches GPT 5.4 performance while operating at up to ten times greater efficiency. Early enterprise adopters are seeing similar gains: when tuned for one unnamed organization's exacting standards, the MAI model achieved the highest win rate of any model tested at roughly one-tenth the cost. Suleyman framed Frontier Tuning as part of a broader evolutionary stage — a move from intelligence to action. "We've basically moved beyond just conversation," he told VentureBeat. "Now we're moving to action." He introduced a new framework for thinking about that progression: the shift from IQ (factual intelligence) to EQ (emotional intelligence, or the ability to follow tone and style instructions) to what he calls AQ — the "Actions Quotient." Future AI agents, in Suleyman's telling, won't just answer questions. They will log into enterprise software, navigate complex multi-application workflows, and execute tasks across Excel, Word, Teams, Jira, Adobe InDesign, and customer relationship management systems — just as a human employee would. "You should be able to show up on day one and almost provision credentials to a new AI agent," he said. "The model needs to be able to move across all of these different environments, and that's actually the great strength of Microsoft." The Build 2026 announcements bore this out in concrete product terms. Microsoft Scout, the company's first "Autopilot" agent, operates as an always-on background assistant built on the open-source OpenClaw technology. It runs with its own governed identity inside Microsoft Entra, so its actions are auditable and attributable. Windows 365 for Agents gives AI agents their own managed Cloud PCs, allowing them to interact directly with applications and browsers inside enterprise environments. And the Foundry platform received major updates — including hosted agents with sub-100-millisecond cold starts, a new Microsoft Agent Framework, and one-click publishing to Teams and Microsoft 365 Copilot. Why Microsoft believes enterprise data is the next AI training frontier Suleyman also articulated why he believes Microsoft's position is uniquely defensible — and the argument has less to do with model architecture than with where work actually happens. "We've sort of hoovered up all of the obvious pools of training data," he said, referring to the industry's early scramble to ingest the open web. "In the next phase, we actually want to be able to give these agents to companies to train on their specific tasks with the data that they have inside of their own big workflows." The claim is subtle but consequential. The first wave of generative AI was trained on publicly available text — books, websites, Reddit posts, code repositories. That data is now largely exhausted, and its use is increasingly contested in court. The next wave, Suleyman argues, will be trained on enterprise-specific data: the internal workflows, decision traces, and institutional knowledge that define how real orga