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NewsMaterial3 min readDisconfirmAI Editorial

Jacob Andreou, Head of Microsoft Copilot: The Missing Link Between Model Capabilities and User Value Is Product Form

In a collaborative interview with The Rundown AI, Jacob Andreou, Executive Vice President of Microsoft Copilot, argues that each generation of model intelligence requires a new product form to unlock value. He describes the current state of the latest models as an 'overhang'—capabilities are in place, but suitable products to convert them into tangible benefits for most people are missing. He also provides practical rules for determining the appropriate level of autonomy for AI agents.

2026-10-11 18:00 GMT+8

Each generation of intelligence requires a new product form factor for users to truly derive value from it. In a collaborative interview with The Rundown AI, Jacob Andreou, Executive Vice President of Microsoft Copilot, explained this dynamic: GPT-3 was widely underappreciated until the chat interface emerged, allowing people to interact with the model for the first time; similarly, Anthropic's Opus 4.5 only came alive through tools like Claude Code and Cowork. Today, despite the release of OpenAI's Astra and Anthropic's Fable, Andreou believes we still lack the product form factor needed to transform these models into everyday tools for the majority of people.

This assessment is not merely abstract market commentary but stems from a correction within Microsoft itself. Andreou revealed that Microsoft had previously embedded Copilot into multiple Windows application entry points, only to find that while these entry points drove traffic, they failed to deliver value. When Copilot suddenly popped up while users were working normally on their computers, "it didn't help anyone." Microsoft subsequently removed these entry points. Andreou noted that after the removal, the depth of usage per user increased, and the total number of people using Copilot grew. His conclusion was: "Products must earn their right to exist; usage cannot be retained if it fails to deliver real value."

Andreou also shared a more personal lesson. Earlier this year, Microsoft launched Copilot Tasks for consumers, which allowed him to order food delivery and call Uber rides when meetings ended. "It felt like magic for a few weeks," he said. However, when tasks became more complex—such as planning a vacation around work schedules or managing financial planning—he found the tool to be "an inch deep" and unable to provide genuine assistance. This reinforced his conviction: features that seem novel in early stages will lose user interest if they lack sufficient depth, much like losing appetite after eating too much ice cream.

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Regarding agent autonomy, Andreou offered a transferable rule of thumb: categorize all actions into "one-way doors" (irreversible) and "two-way doors" (reversible). Actions such as contacting other people, deleting files, or making irreversible decisions require human-in-the-loop verification every time. Reversible tasks can operate autonomously within defined boundaries, requesting confirmation again only if the task or risk profile changes. Microsoft's Autopilot product implements this specifically: at the start of a task, it pre-computes the expected state of the browser session as a boundary. If the agent attempts to solve the problem differently, it must re-request permission.

It should be noted that this interview is part of a partnership between The Rundown AI and Microsoft. As the head of Microsoft's Copilot business, Andreou's arguments serve Microsoft's product positioning. Microsoft does not produce frontier models; the narrative that "product form matters more than the model itself" shifts the competitive focus from the model layer to the application layer—precisely where Microsoft competes. Andreou acknowledged that if the product layer continues to lag, intelligence and value will concentrate in the hands of a few model companies; this serves both as a warning to the industry and an argument for Microsoft's necessary intervention.

However, Andreou's observation of model differentiation—where OpenAI models perform better in structured tasks like Excel, while Anthropic models demonstrate a deeper understanding of the visual consequences of code—indicates that multi-model routing has become a practical engineering issue by the second half of 2026, rather than mere strategic rhetoric. His proposal of an "Auto mode," which relieves users from having to judge which model suits which task, suggests that if implemented effectively, the complexity of model selection is shifting from the user end to the platform end.

Sources

  • therundown.ai↗
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