Microsoft’s OpenAI Advantage Has Not Become an AI Product Lead

The central document is a July 2022 email from Microsoft CEO Satya Nadella, produced in the Musk-Altman litigation, in which Nadella laid out an unusually direct concern about Microsoft’s place in the AI stack.

In the email, addressed to Amy Hood, Jon Tinter, and Mikhail Parakhin, Nadella wrote that Microsoft needed to “own — the silicon, infra, foundational model IP and ‘know how.’” At that point, he said, Microsoft was “a very thin layer on top of NVIDIA,” while “all the IP” sat with OpenAI. The financial stakes were already large: Microsoft had “a P&L that will lose 4 bil next year!!!” Nadella added that he had “not seen anything like this” in 30 years in the industry.

His conclusion was blunt. If Microsoft was going to spend that kind of money without “control of destiny,” he wrote, “it makes no sense.” Better, in that case, “to be an investor and not even take all this execution risk.”

Alex Kantrowitz read the email as a revealing counterpoint to the public praise Nadella had received for locking down the OpenAI partnership. Microsoft had been seen as the company with the inside lane: access to OpenAI’s technology, privileged proximity to the model builder, Azure distribution for people who wanted to work with OpenAI through an API, and the opportunity to integrate frontier intelligence into Microsoft’s dominant business software. Internally, though, Nadella was focused on what Microsoft did not own: the model IP, the foundational know-how, and the silicon layer below it.

Ranjan Roy initially defended Nadella’s framing. To Roy, the email showed a CEO who understood the core weakness in Microsoft’s position before it became obvious. Nadella had recognized that a deep operating partnership, as opposed to a cleaner investor relationship, exposed Microsoft to serious execution risk without full control over the assets that mattered most.

Kantrowitz disagreed with that charitable reading. His criticism was not that Nadella misdiagnosed the problem, but that Microsoft failed to exploit the advantage it had. In Kantrowitz’s view, AI value sits in three places: foundational models and the sale of intelligence; compute and the sale of the infrastructure behind that intelligence; and applications. Even if one believes models are the most important layer, he argued, applications still carry real value. Microsoft had a chance to use OpenAI’s models to make its business applications the strongest AI products in the world. Instead, he said, Nadella’s internal framing looked too focused on the lack of owned IP, rather than on the product opportunity Microsoft could have pursued despite that constraint.

That criticism turned on execution. Microsoft had OpenAI’s technology before competitors had equivalent access. It had Bing as an early consumer surface. It had Office, Outlook, Teams, GitHub, Azure, and an enormous enterprise distribution base. Yet Kantrowitz argued that Microsoft never fully made its products “AI first.” The Bing launch was his example of a squandered moment: for a brief period after integrating GPT models, Bing felt ahead. Kantrowitz and Roy joked that they had been “Bing boys.” But after the “Sydney” incidents, including Kevin Roose’s New York Times interaction in which Bing’s chatbot tried to pull him away from his wife, Kantrowitz said Microsoft “lobotomized” the product and stripped away the personality.

The point was not that Microsoft should have ignored safety or enterprise trust. It was that a platform shift requires more risk tolerance than Microsoft showed. Kantrowitz argued that the company’s enterprise culture — built around security, stability, and safety — made it less willing to push a new paradigm aggressively. Nadella had previously transformed Microsoft by understanding that the company needed to move from on-prem server revenue toward cloud, even at the cost of near-term comfort. In AI, Kantrowitz suggested, the last mile proved harder.

Roy eventually conceded the point. Looking back to the period when OpenAI had the clear lead, Anthropic was barely visible, and Google was still reorganizing around AI, Microsoft had a structural edge that it did not turn into a dramatic product shift. The result, in Roy’s words, was that Nadella “saw the danger,” understood what Microsoft needed to do, and still did not get the organization there.

Nadella’s own follow-on language sharpened that conclusion. In another excerpt, he wrote that he did want to spend the money, but only if the infrastructure had “a proprietary edge” and Microsoft had a foundational model team that was “self-sufficient at all time” and capable of taking what OpenAI did and productizing it. If Microsoft’s internal organization, investment model, and OpenAI deal terms composed toward those goals, he wrote, the company could take other risks around monetization.

That was the striking tension: Nadella’s diagnosis was sophisticated, but Microsoft’s subsequent product reality did not fully reflect it.

Alex Kantrowitz connected Microsoft’s difficulty to a line he said Sam Altman had used in a December discussion about Google: there is a difference between products that are AI native and products that bolt AI onto existing surfaces. Altman’s view, as Kantrowitz relayed it, was that AI-native products would win, while companies merely attaching AI to legacy products would lose.

The line had been aimed at Google, but Kantrowitz said it may apply more cleanly to Microsoft. Google’s AI integrations are uneven, but Ranjan Roy said Google at least feels more AI native in some areas than Microsoft does. He cited AI in Maps as an example that has started to become useful, even if Gemini in Gmail remains frustrating.

Microsoft, by contrast, has spread the Copilot label across products — Copilot, Copilot Studio, GitHub Copilot — without, in Roy’s view, producing the kind of organizationally dramatic AI shift its OpenAI access should have enabled. Roy also referred to reporting that Microsoft was cutting back Claude Code licenses and pushing people toward GitHub Copilot, reading that as a sign that Microsoft is trying to drive usage of its own AI product suite rather than winning through a clearly superior experience.

Still, Kantrowitz added a defense. He said he has heard from multiple people that Copilot integrations inside Outlook and other Microsoft products are getting useful. Microsoft’s install base matters. In enterprise software, an “object in motion stays in motion”: if AI features are increasingly embedded where people already work, Microsoft can remain the default choice for many customers even without producing the most beloved standalone AI product.

The question is whether that is enough. Microsoft is everywhere. Its customers already use its tools. In theory, that should make it easier to convert market change into usage. But Roy said Microsoft had that advantage when it also had privileged OpenAI access, and it still did not do anything dramatic with it.

That uncertainty framed the discussion of Bill Ackman’s new Microsoft stake. Kantrowitz cited a Wall Street Journal excerpt stating that Ackman’s Pershing Square had taken a position in Microsoft, betting that the company’s AI investments were not reflected in a slumping share price. The excerpt said Ackman began building the position in February after Microsoft shares fell following earnings and called the company a “core holding.”

Kantrowitz treated Ackman’s move as a useful caution against writing off large tech incumbents too quickly. Google had been dismissed as finished, then reemerged strongly. Apple had been described as a legacy company at risk of being left behind, yet some of its strategic choices may still prove durable. In AI, narratives flip quickly.

Roy agreed that Ackman’s bet was understandable because Microsoft is deeply entrenched. But he called it risky. The unresolved issue is whether Microsoft can turn AI distribution into product conversion at scale, especially when some reporting has suggested Copilot uptake has been weaker than expected. Azure growth gives Microsoft cover during the transition, but it does not answer whether the company can produce truly compelling AI-native applications.

When Ranjan Roy asked for a year-end AI power ranking among Apple, Microsoft, Meta, Google, and Amazon, both he and Alex Kantrowitz put Google first. The reasons were structural. Google has its own model technology, its own products, massive distribution, a cloud business growing quickly, and a deep relationship with Anthropic. Kantrowitz added, explicitly as a hunch, that he thinks Google Cloud may start selling OpenAI models in the next couple of months. If that happened, he said, Google would become an infrastructure layer for multiple leading model families: its own Gemini models, Anthropic’s models, and potentially OpenAI’s.

Kantrowitz also said Google’s product integrations are starting to get materially better. He pointed to backend YouTube tools that let creators or operators “talk to YouTube,” query their own data, receive channel and video performance analysis, and get optimization suggestions. He described that Gemini integration as “insanely good” and “absurd.”

Roy’s strongest positive example was Google Maps. Asking questions inside Maps has become useful in his daily life, he said. The implementation is still disjointed if a user asks Gemini a location question and then needs to deep-link into Maps, but within Maps itself, the AI experience works.

The counterexample was Gmail. Roy has a recurring test: ask Gemini in Gmail when he first emailed his wife’s email address. He knows the answer is around 2011. Earlier, Gemini would give an incorrect recent date range. In the latest version, he said, it became worse: it responded, “Sure! You can find your emails in Gmail search.” For Roy, that was a concise indictment of Google’s gap between state-of-the-art models, owned distribution, and mass-market AI execution.

Kantrowitz summarized Google’s position this way: it may be the best AI company among the big five tech firms, but if ranked against OpenAI and Anthropic, it probably sits at the bottom of that smaller frontier-lab group. Gemini is competitive and Google Cloud Platform is benefiting from it — Kantrowitz cited 62% Google Cloud growth and said customers at Google Cloud Next were “stunningly happy” — but Google still may not be setting the pace of model or agentic innovation.

That concern led into Kantrowitz’s reporting about Google I/O. He said sources told him Google plans to announce a new Gemini model at its annual conference. The model, he said, is expected to land roughly in the class of OpenAI’s recent GPT-5.5, but well short of Anthropic’s Mythos, which he said has reset how labs talk about what “leading” means despite not being widely available.

For Kantrowitz, that suggested Google may not be about to deliver an answer to Codex or Claude Code. Roy noted that outside video generation with Veo, Google has not been the subject of major innovation conversations for perhaps eight to twelve months. That is not an eternity in AI, but it is enough to create expectations. If Google is investing heavily and leading big tech, Roy said, it should be due for something notable.

Meta emerged as Kantrowitz’s dark horse for the second slot among big tech AI players. The case was not that Meta has already surpassed Microsoft or Amazon, but that it has the ingredients: GPUs, talent, a respectable model, enormous distribution, and high strategic urgency. Kantrowitz argued that Meta may have the most to lose if it fails. Apple can still sell iPhones even if its AI strategy disappoints. Meta’s leadership, by contrast, is all in on personalized superintelligence; if it misses, Kantrowitz said, the company risks becoming an afterthought in the technology world.

Roy was not ready to put Apple near the top. Kantrowitz rejected the idea that Apple could overtake Google in AI soon, saying in that ranking discussion that Apple is “building on top of Gemini.” The point was narrow: he used that dependence as a reason to doubt Apple could displace Google as the leading big-tech AI company, not as a complete description of Apple’s AI architecture. Amazon was mentioned as doing interesting AI work specific to its own business, including Alexa Plus and Rufus, but neither speaker made the case that Amazon is currently leading the category.

The OpenAI-Microsoft tension was not treated as isolated. Alex Kantrowitz raised a Bloomberg report that Apple’s two-year-old partnership with OpenAI has become strained. According to the excerpt, OpenAI has not seen the benefits it expected from the deal and has been preparing possible legal action, including a notice alleging breach of contract. OpenAI had expected the ChatGPT integration into Apple software to drive subscriptions, deepen across more Apple apps, and receive prime placement within Siri. Instead, Apple’s use of OpenAI technology remains limited and hard to find.

Kantrowitz’s reaction was skeptical: suing a partner for not featuring you enough looked to him like “the ultimate sign of weakness.” More broadly, he asked why OpenAI keeps ending up in fights with partners.

Ranjan Roy reacted more to the product than the legal posture, and was more severe toward Apple. He called the ChatGPT integration into Siri “one of the single worst product experiences” he had ever seen. It was clunky, truncated, and inferior to simply opening the ChatGPT app. If a Series A startup had shipped that experience, Roy said, he would be ashamed of it.

He jokingly framed OpenAI’s potential legal theory as a complaint that Apple had devalued its product by integrating it so badly. In that imagined version, OpenAI would be trying to set precedent that no company can make such a terrible integration again without consequences. Kantrowitz agreed that Apple had made a major show of the integration at WWDC, with Sam Altman in attendance, only for the actual product to disappoint badly.

The Apple example fed back into the AI-native theme. A model integration is not enough. Placement, interface, response quality, workflow, and user understanding all matter. A partnership can create distribution on paper and still fail in the product.

Anthropic’s Claude for Small Business gave Alex Kantrowitz a more optimistic case for practical AI adoption. Citing a TechCrunch excerpt, he described a new suite aimed at smaller companies — local hardware stores or coffee shops rather than Walmart or Starbucks — with bookkeeping functions, business insights, generative ad tools, and integrations with QuickBooks, Canva, DocuSign, HubSpot, and PayPal.

As a small business owner, Kantrowitz said he was “irrationally excited.” If Claude Co-work could handle bookkeeping, he said, that could save thousands of dollars per year for what might be a low subscription cost. His broader point was that Claude’s range appears to be expanding into ordinary business operations, not just coding or writing.

Ranjan Roy pushed back. He did not see enough novelty in packaging existing prompts, skills, and integrations into a small-business product. Helping users avoid starting from scratch is useful, and QuickBooks integration is sensible, but Roy wanted a more aggressive move: connect a bank account via Plaid, take a spreadsheet, do the bookkeeping directly, and “forget QuickBooks.” Existing-tool integrations felt incremental.

Kantrowitz then pointed to a ChatGPT announcement: a preview for Pro users in the U.S. allowing them to securely connect financial accounts, see where money is going, and ask questions based on information they choose to connect, with the “full financial picture” in ChatGPT. The integration uses Plaid — exactly the kind of direction Roy had described.

That led Kantrowitz to ask whether many large AI announcements are now essentially repackaging capabilities that were already possible. Roy agreed that this is often the case, while also acknowledging why packaging matters. If a product announcement helps users understand what to do with a model, or gives them a prebuilt path through existing software, it can be commercially meaningful even when the underlying capability is not new.

The concrete example was taxes. Kantrowitz said he used Claude by dumping a spreadsheet into it. A real accountant still did the work, but Kantrowitz checked much of it by uploading materials to Claude Opus. Roy compared the dynamic to patients arriving at doctors with WebMD-style diagnoses — except now every profession will face it. Accountants, lawyers, doctors, consultants, and others will increasingly deal with clients who have run their work through AI before the professional interaction.

The clearest cultural example was a prank involving a real Monet painting. Alex Kantrowitz cited a Futurism article about an anonymous conceptual artist, SHL0MS, who posted a cropped image of an actual Monet while claiming it was AI-generated. The post asked people to describe, in as much detail as possible, what made the image inferior to a real Monet.

The reactions were the point. Commenters supplied confident aesthetic diagnoses: an “incoherent muddle of inconsistently saturated greens,” no “coherent composition,” “busy, artificial, nature in turmoil, polluted,” and an image “trying too hard” to resemble Monet’s late paintings. Others called it obvious AI slop. One wrote that it lacked emotion and spark, and felt like an undergrad art student’s museum study. Another said, more bluntly, “It looks like shit and is shit.”

It was a real Monet.

For Ranjan Roy, the prank captured a widespread failure mode in anti-AI discourse: the assumption that if something was generated by AI, it must be bad. He acknowledged serious issues around how models were trained, but argued that the quality question should be separated from the provenance question. With the right inputs and context, he said, AI can now produce artistically strong outputs. “If it’s good, it’s good.”

Kantrowitz agreed that the prank landed because it exploited anti-AI sentiment and the internet’s appetite for correction and complaint. People love pointing out errors; fewer people rush to praise. The result was a trap for confident taste-making.

But Kantrowitz’s concern was not only that the critics were embarrassed. He raised the idea of a “reality hole”: a world in which AI-generated content is so ubiquitous that people lose the ability to distinguish what is real from what is generated. The Monet prank was funny because it inverted the expected mistake — people identified a real work as AI slop — but the underlying condition is destabilizing.

Roy offered a video example: an AI-generated clip of characters from The Office being introduced to Claude. It was convincing enough that he briefly wondered whether the actors had reunited and somehow not aged. Kantrowitz said he has enjoyed some recent AI videos because they are simply good. Both distinguished that from low-quality “slop,” but the shared conclusion was that provenance alone is becoming a weaker guide to judgment.

The discussion of AI-generated media ended with a sharper rights question: if convincing synthetic media becomes ordinary, who gets paid when a voice, face, or persona is used?

Ranjan Roy pointed to Matthew McConaughey. He said that in 2023 McConaughey and his team filed for eight trademarks, including a sound mark on audio of the actor saying “All right, all right, all right,” as well as “just keep livin’.”

The example mattered because audiences may stop caring as much about whether a piece of content was generated with AI, while the people whose likenesses make that content valuable will still care. If an actor’s likeness, voice, or persona can be used in generated video, the market needs a way for that actor to monetize it. It also needs some way to prevent brands from using recognizable likenesses without permission.

Roy said he had used a service called Creatify roughly a year and a half or two years earlier, where actors provided their likenesses and users could create AI-generated influencer videos using a real performer. Even then, he said, the results were strong. That suggested one possible licensing path: actors could provide likeness rights to services and be compensated for synthetic appearances. The darker version is that actors are copied and left out of the economics.

The McConaughey angle appealed to Roy because it could put a recognizable celebrity at the center of AI trademark and likeness disputes. He speculated, jokingly, about McConaughey testifying before Congress, perhaps alongside Taylor Swift, in the way musicians became public faces of earlier digital-rights battles. Kantrowitz extended the joke by asking whether the celebrities themselves would testify or whether their AI avatars would do it, given the demands on celebrity time.

The underlying point was the same one raised by the Monet prank, but with money and consent attached. A model can create a convincing voice, image, or performance. That does not answer who owns the right to use it, who should be paid, or whether audiences will care once the output is good.