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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesMicrosoft’s March 2026 Copilot reorganization—not Ali Farhadi’s later hiring alone—shifted Mustafa Suleyman away from much of Copilot’s day-to-day product management and toward frontier-model research. Farhadi’s appointment strengthened that effort, as Microsoft builds internal models intended to reduce costs, improve enterprise customization and lessen its dependence on outside providers such as OpenAI.
That is a significant strategic shift, but it is not evidence that Microsoft has replaced OpenAI, abandoned Anthropic or achieved superintelligence.
What happened, and when
Microsoft announced a new Copilot leadership structure on March 17, 2026. The company unified consumer and commercial Copilot around four areas: the Copilot experience, the Copilot platform, Microsoft 365 apps and AI models.
Jacob Andreou took responsibility for the Copilot experience. Ryan Roslansky, Perry Clarke and Charles Lamanna assumed leadership roles covering Microsoft 365 apps and the Copilot platform. Suleyman remained Microsoft AI’s chief executive, continued reporting directly to Satya Nadella and retained involvement in connecting models with products.
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The practical effect was to move much of Copilot’s operational execution to other executives. That gave Suleyman more time to lead Microsoft’s Superintelligence effort and develop frontier models.
One week later, on March 24, Microsoft’s hiring of Ali Farhadi was reported. Farhadi joined as a corporate vice president. He was previously chief executive of the Allen Institute for AI (Ai2) and co-founded Xnor.ai, a company associated with efficient AI systems running at the edge.
The chronology matters: Farhadi reinforced a model-building strategy that was already established by the reorganization. He did not, by himself, “free” Suleyman from Copilot.
Suleyman’s new emphasis: models rather than daily Copilot execution
Suleyman said Microsoft’s plan was to spend the next five years developing world-class models. The stated targets include frontier capability, enterprise-tuned model “lineages” and models efficient enough to reduce the cost of serving AI at scale.
Nadella described the objective as combining talent and computing capacity to advance the frontier while producing useful products and reducing cost of goods sold.
Microsoft has also used the phrase “humanist superintelligence” for systems intended to preserve human control, agency and economic opportunity. But superintelligence is an ambition, not a standardized technical benchmark. Microsoft’s public announcements establish that it has created a team and set a research mission; they do not establish that the company has achieved artificial general intelligence or superintelligence.
The same distinction applies to safety language. Human-centered design is Microsoft’s stated objective, not an independently verified safety result.
Why Farhadi’s background matters
Farhadi brings two relevant forms of experience. At Ai2, he led a prominent nonprofit AI research organization. At Xnor.ai, he was involved in work focused on efficient AI at the edge—an experience that aligns with Microsoft’s interest in models that can deliver useful performance at lower serving costs.
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His arrival was part of a broader recruitment effort. GeekWire reported on May 15 that at least 10 former Ai2 staffers and researchers had joined Microsoft’s Superintelligence team, including researchers associated with Ai2’s OLMo open-model work.
That gives Microsoft additional expertise in core research and post-training. It also raises an unresolved institutional question: will Microsoft use that open-model experience to publish weights, data and evaluation tools, or mainly to create proprietary models and enterprise products? The available reporting does not establish the answer.
The movement also illustrates the financial pressure of frontier research. GeekWire reported that Ai2’s board viewed extreme-scale model development as extraordinarily expensive for a nonprofit organization. Microsoft, with its large cloud and capital base, can fund that work more readily—but it is likely to operate it under a more controlled commercial model.
Why Microsoft wants models it controls
Microsoft has historically relied heavily on OpenAI models in important AI products while also expanding access to other providers. Internal models offer several strategic advantages:
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- Control: Microsoft can influence model roadmaps, updates, deployment options and product-specific optimization.
- Cost management: A smaller or specialized model may be cheaper to run for high-volume enterprise tasks than a larger external frontier model. Microsoft has stated that reducing serving costs is a goal; that is not the same as proof of customer savings.
- Enterprise customization: Models can be adapted for Microsoft 365, Windows, GitHub, Dynamics, Azure and particular business workflows.
- Data and intellectual-property positioning: Microsoft has emphasized provenance for some models. For example, it said MAI-Thinking-1 was trained from the ground up without distillation from other companies’ models. That remains a Microsoft claim, not an independently audited finding in the available evidence.
- Bargaining power: Internal capability reduces the risk that a partner’s pricing, availability, governance or strategic priorities dictate Microsoft’s economics.
These benefits come with substantial risks. Frontier development requires enormous computing, data, engineering and evaluation budgets. An internal model may be cheaper or better suited to one task while lagging external models on general reasoning. Microsoft may also end up paying for both internal research and expensive external partnerships.
Microsoft’s seven MAI models
By Build 2026, Microsoft had announced seven internally developed MAI models spanning reasoning, coding, image generation or editing, voice, transcription and other multimodal capabilities, according to GeekWire.
Named examples included:
- MAI-Thinking-1: a reasoning model reported as available in private preview through Microsoft Foundry.
- MAI-Code-1-Flash: a coding model reported as having 5 billion parameters and rolling out in Visual Studio Code and GitHub Copilot.
- MAI-Image-2.5: an image-generation model.
Availability differs by model. An announcement, private preview or limited rollout is not the same as general availability, broad consumer access or proof that a model powers a major Microsoft product by default.
Microsoft said MAI-Thinking-1 matched Claude Sonnet 4.6 in blind human testing. GeekWire also reported Microsoft’s claim that it matched Claude Opus 4.6 on a coding benchmark. Those comparisons should be treated as company-reported or publication-reported results. Benchmark outcomes do not establish broad superiority, lower real-world costs or production reliability across workloads. Results can depend on model versions, prompts, context lengths, sampling settings and evaluation design.
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Microsoft is adding optionality, not necessarily ending its partnerships
It would be inaccurate to describe the move as an immediate break with OpenAI or Anthropic. Microsoft Foundry’s multi-model strategy includes Microsoft and external providers such as OpenAI, Anthropic, Meta, Google, xAI and Hugging Face.
The better description is insourcing plus optionality. Microsoft wants models it controls, but it can still distribute and integrate outside models where they offer better reasoning, coding, latency, compliance characteristics or economics. Different products—and even different prompts within a product—may use different providers.
Foundry can therefore serve as the abstraction layer where Microsoft offers both its own models and partner models. A customer may gain access to an MAI model without that model being the default engine for every Copilot experience. Conversely, a Microsoft product may use an external model for a particular task even as Microsoft develops an internal alternative.
What enterprise buyers should watch
For customers, the meaningful test is not whether Microsoft uses the phrase “superintelligence.” It is whether internal models improve real workloads.
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- Latency and reliability: A smaller specialized model may be more valuable than a stronger but slower general-purpose model.
- Data provenance: Ask what training and fine-tuning claims are documented, what is independently evaluated and what contractual protections apply.
- Security and compliance: Check regional availability, tenant isolation, retention, access controls and audit features for the specific service.
- Availability: Private preview is not production readiness. Look for general availability, service-level commitments and support terms.
- Model routing: Determine whether Foundry can route workloads among Microsoft and third-party models and whether that routing is visible, controllable and predictable.
- Product impact: Measure response quality, hallucination rates, developer productivity and task completion inside actual Microsoft workflows.
Microsoft Foundry is the most direct commercial route for evaluating Microsoft’s models alongside outside providers, but it is an Azure-based enterprise platform with consumption-based pricing that varies by service and feature. It is not a simple fixed-price consumer chatbot.
The unresolved questions
Several questions will determine whether this is a durable strategic shift or mainly an organizational statement:
- Which MAI models will reach general availability?
- Which Copilot, Microsoft 365, GitHub and Azure workloads will use them by default?
- How much OpenAI and Anthropic usage will remain?
- Will Microsoft publish meaningful technical reports, evaluation data or model weights?
- Can Microsoft achieve materially lower serving costs without sacrificing quality?
- Will its “superintelligence” mission produce measurable product and enterprise benefits rather than only stronger benchmark claims?
The answers will matter more than the title of the team. Model ownership does not automatically solve hallucinations, security, copyright, privacy or governance problems, and announcing an internal model does not prove that it is better for a customer’s workload.
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