All of the major AI providers want you to use, and ideally stay within, their super apps, and now Microsoft is looking to capture that attention, too.

During an earnings call this week, CEO Satya Nadella confirmed that the tech giant is building a Copilot ‘super app’ that will be rolled out this quarter. The new platform will bring together various Copilot tools, including chat, Cowork, long-running Autopilot agents, and the always-on Microsoft Scout, powered by OpenClaw.

Microsoft said the super app will be wired into many of its other governance platforms, including Agent 365, IT Ops, SecOps, FinOps, and business processes. And, it said, CRM and ERP systems will “serve as skills and plug-ins that go into core work.”

“You’re able to take that enterprise-wide workflow and wire it into the super app,” Nadella said, describing it as “the coming together of a new way to work.”

With this move, Microsoft will compete with OpenAI’s ChatGPT Work, Claude Cowork, and a growing number of others trying to capture as much of a user’s workflow as possible. It could prove a strong contender, as everyday Copilot “usage intensity” is at the same level as that of Outlook or Teams, Nadella said, and paid seats now surpass 30 million.

Every model should be ‘swappable’

Even as it builds a super app to bridge workflows, Microsoft is acknowledging enterprise demand for model choice. Customers are making it clear that they don’t want to be locked into one model; they want the ability to move between open, closed, and frontier options based on the best tool for the job.

This trend is reflected in Redmond’s own usage statistics: Since the beginning of the year, it has tracked a 5x increase in the number of customers building with models from multiple providers featured on its platform.

The company claims it has the broadest model catalog in the cloud, offering more than 11,000 models from OpenAI, Anthropic, Mistral, its own MAI family, and others.

“We are building a new model system, where the harness, context, memory, and action space are separate from any one model family, thereby moving the frontier on the cost-to-outcome curve,” Nadella said. “That’s really the enterprise design architecture that we are going to evangelize.”

He described enterprises as “learning machines” that need their own internal learning machines, and said that they will be evaluating how providers are helping them reach their business goals and support knowledge creation.

“The models are an input, not some extraction of the knowledge of the enterprise,” Nadella said. “This is not going to be about, ‘come in and take all my knowledge and benefit yourself, [and] I am not getting anything out of it.’”

The key is in balancing the advantages of frontier models with lower-cost options, open weights with closed weights, and having the ability to train internal models based on outputs, traces, and context. “You should and you can use frontier models,” Nadella said. “There’s no reason not to.”

However, he said, any given model at any given time should be swappable to democratize design.

For instance, data from cybersecurity evaluation framework CyberGym showed that Microsoft’s new MAI-Cyber-1-Flash coding agent achieved Claude Mythos-level performance at 50% of the cost. This is because 90% of tasks were completed by Cyber-1-Flash and 10% by frontier models from OpenAI, Anthropic, and others. 

This ability to use the right model for the right task in what is essentially a pipeline job is a “super important characteristic,” Nadella said. Microsoft Copilot, Security Copilot, and GitHub Copilot are all built to support movement between different models based on the task.

This strategy is also reflected in the company’s new Project Perception cybersecurity offering. The platform features three specialized types of agent (red, blue, and green), and the underlying harness decides which AI model is best suited for a given task. Guided by specialized playbooks, red team agents discover vulnerabilities, blue team agents triage, and green team agents propose remediation plans.

“You create your own agentic system that’s continuously operating to create the cyber defense you need,” Nadella explained. “Especially in cyber[security], it becomes critical to have that multi-model approach.”

Nadella also pointed to the recent Hugging Face incident, in which an OpenAI model went rogue, broke out of its sandbox, and launched an attack against the popular open-source platform, noting that enterprises will likely need to use multiple models to offset and remediate the various challenges of each, and should not be “subject to the refusals of one model.”

“We talk about the frontier as if it’s one thing,” Nadella said. “The frontier is about every firm having a frontier, the choice, the cost control, and the capability that they need in order to be able to control their destiny.”

Increased push to usage-based pricing, closing demand gaps

As Microsoft emphasizes its model-agnostic architecture, it is also shifting from per-seat to per-seat-plus-consumption pricing; the company recently added usage-based billing to Cowork and Agent 365, and plans to continue that trend across its products.

While these moves have resulted in sticker shock and ‘tokenmaxxing’ at many companies, Nadella framed it as a revenue driver. “We are advancing the frontier on the cost-to-outcome curve, ensuring every customer can turn tokens into business results.”

Meanwhile, Microsoft said it will continue to close data demand-capacity gaps.

The company added 88 data centers in FY 2026, including 31 across five continents this past quarter. It contended that it is bringing capacity online “faster than ever,” reducing dock-to-live times for new GPUs in its largest regions by nearly 50% over the fiscal year.

However, CFO Amy Hood acknowledged during the earnings call, “the situation is obviously that demand exceeds available supply in a relatively extreme moment.”

Reflecting this, revenue for Azure and other cloud services grew by 43% in Microsoft’s fiscal year ended June 30, and the company expects similar revenue growth (45%) in fiscal year ‘27.

Hood said that Microsoft remains “focused on delivering efficiencies,” including in its CPU and GPU fleets, and engineers are also working on process improvements. The company added another gigawatt of capacity this quarter and is on track to roughly double its overall capacity in two years.

“We are also getting more from the infrastructure we already have by optimizing across silicon, systems, and software,” Hood said.

This article originally appeared on CIO.com.

The 19^th-century French novelist Honore de Balzac is believed to have said that behind every great fortune lies a great crime.

That’s even more true today than it was 200 years ago — just look at how Big AI, including Anthropic, OpenAI, Google, and others have built their trillion-dollar fortunes. 

They all use vast amounts of copyrighted material to train their large language models (LLMs) without paying the copyright holders. In other words, they steal it. They don’t call it stealing, though. They call it “fair use,” which in this case amounts to the same thing.

Generative AI (genAI) training requires massive amounts of text. The better-written and more information-dense that text is, the more it helps. AI gets a lot smarter a lot faster when it’s trained on well-written books and magazine and newspaper articles than when it’s trained on social media banter (or most everything else you find on the internet).

Since the dawn of AI, companies have been hoovering up copyrighted material wherever they find it — on the open web, behind paywalls, even in manually scanned books — and then used the scanned text. And they do it all without asking authors’ or publishers’ permissions, and without paying them.

It’s the greatest intellectual property theft in history by a long shot — billions and billions of dollars worth. Books, articles, music, photographs, artwork, you name it. If a human being has created it, Big AI has likely grabbed it and ingested it without asking, then made big profits off it.

I know this from personal experience; I’ve got skin in the game. Big AI companies have used at least 30 of my books to train their models without asking.  And that’s only what I’ve confirmed. Big AI might well have stolen even more. And it also might have stolen many of the thousands of articles I’ve written through the years.

For the AI bigwigs, it’s standard operating procedure. So it was surprising to see Microsoft CEO Satya Nadella calling out Big AI for its hypocrisy in using other people’s intellectual property without paying, then crying foul when small AI companies use Big AI’s work to train their own models using a technique called distillation.

He wrote on X: “While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data.” 

One important note here: Nadella believes Big AI should be allowed to steal copyrighted material for training purposes. He makes that clear by his mention of “fair use.” His issue is that he believes smaller AI companies should be able to use Big AI’s work to train their models — and Big AI doesn’t want to let them do it.

Alistar Barr, in Business Insider, makes a related point: “Anthropic, OpenAI, and Google are discovering what the rest of the internet has already learned through painful experience: once you put something online, people will find ways to use it in ways you don’t like and can’t stop.” 

Before we look at whether Nadella is right in calling Big AI’s actions hypocritical, let’s examine the technique at the heart of the issue: distillation.

The lowdown on distillation

Distillation refers to an AI training technique in which you take outputs from one AI model, such as the answer to a prompt, and use that output to train your own model. AI companies do that quite frequently and have been doing so for a long time.

Barr explains distillation this way: “Distillation looks an awful lot like what AI companies have been doing to the rest of the internet. Scrape web content for free and without permission. Turn it into a product you sell. Argue it’s fair use. Hope the lawyers sort out the details later.”

Elon Musk has admitted his company xAI used distillation techniques to take output from competitor OpenAI to train xAI’s Grok chatbot. He explained, “Generally AI companies distill other AI companies.”

Analysts point out the AI industry is built on distillation. Neil Shah, vice president of research at Counterpoint Research says, “The reality is none of the models is an island and the entire industry has mostly evolved based on recursive learning. The newer entrants are in many instances going through the same routes of ‘distillation’ and ‘optimization.’”

It’s only become an issue now because Chinese AI companies have been using distillation techniques to catch up to American AI companies.

OpenAI CEO Sam Altman has been pressuring the US to take legal action against the Chinese companies.  Anthropic has piled on as well, saying the fight against distillation requires a coordinated response across the AI industry, cloud providers, and policymakers.

Hypocrisy or fair use?

So are OpenAI, Anthropic and other big US AI firms hypocritical, as Nadella claims? Absolutely. They’ve built their businesses on the theft of billions of dollars of stolen intellectual property and call it fair use. Now, they’re playing the victims when competitors do the same to them. (They’re also in some cases paying up; Anthropic just last week settled a copyright suit, paying out $1.5 billion.)

It’s good to see Nadella call out their hypocrisy. But Microsoft is as guilty of intellectual theft as the others — its AI Copilot is powered by OpenAI’s and Anthropic’s chatbots. The company faces major lawsuits for intellectual property theft. Among them is one by the New York Times, New York Daily News, and Center for Investigating Reporting, another by 400 local and regional newspapers, and another by 11 authors, including Pulitzer Prize winners Kai Bird, Jia Tolentino, and Daniel Okrent.

So, don’t praise Nadella for speaking out. Criticize him (and other AI tech leaders) for stealing billions of dollars in intellectual property from countless writers and publishers and calling it fair use.