Satya Nadella warns companies using AI are paying twice and risking intellectual property theft

Satya Nadella has issued a shocking warning to companies using AI

Of all the debates raging about the potential downsides of AI, there is one worry causing the most hand-wringing among AI enthusiasts in Silicon Valley. Their fear is that the giant AI labs that sell proprietary models are somehow acting like Trojan horses. The concern is that, as startups and enterprises use AI models from labs like OpenAI and Anthropic, the labs gain ever-increasing access to those companies' most sensitive business information. The model makers can then use that knowledge for themselves, potentially becoming competitors to their own customers. Those issuing such warnings range from VCs like Jason Calacanis to Palantir CEO Alex Karp. Now, in a surprising blog post published on Monday, Microsoft CEO Satya Nadella has joined this crowd. Nadella warns that AI users (the "buyers" as he calls them) are paying twice. They knowingly spend for AI token usage but they also, obliviously, hand over valuable data in the process. "You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!" he writes. Most dangerously, enterprises are literally teaching the models about the nuances of their businesses, he argues. "Models learn from 'exhaust,' the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how," he writes. This is "the kind of knowledge a competitor could never buy," and yet enterprises are handing it over. Nadella argues that if AI companies get to freely scrape the internet to train their models, it's only fair that enterprises get to study -- or "distill" -- those models in return. "Distillation" is the practice of using a model's own outputs to learn how it works and to train a new, often cheaper, model based on those insights. In February Anthropic accused Chinese open source models of sending millions of prompts to Claude as a way to improve their own models, and urged the U.S. government crack down on export controls. Nadella's point is that model makers can't have it both ways. It's hypocritical for them to freely train on the world's data while restricting others from doing the same to their models. "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," the Microsoft CEO writes. Nadella is particularly concerned when model makers "reserve the right to learn from customer usage and interaction data." Nadella's solution is the kind of thing the CEO of a giant cloud provider would suggest. He wants companies to "retain ownership" of their data including prompts, feedback, etc. So he's urging them to build their own "proprietary learning environments" on the cloud (where their data is likely already stored anyway and, conveniently, which could mean Microsoft's cloud, Azure). He also wants companies to build in what he calls "orchestration layers" -- essentially, a way to easily switch between AI models from different providers rather than being locked into one. Tools like AI "gateways" that let companies do exactly this, have become increasingly popular. While Nadella never uses the words "open-source" as the method for retaining ownership, this is an obvious subtext. Yet, there's another subtext. Large companies, many of which still have some of their own data centers in addition to using the cloud, are already moving to open source models installed on their own premises ("on-prem," in industry jargon). Idit Levine, founder and CEO of Solo.io -- which makes networking and security software that helps enterprises manage AI systems -- says she's seeing exactly this shift play out with her own customers. After experimenting with proprietary model makers, they start asking themselves: "Can I take an open-source model and run it on-prem? It will do almost 90% of what the big one's doing. It will cost way less," she tells TechCrunch. "They understand that, and they can control it." Solo.io's technology was selected last year as the tech powering the Linux Foundation's Agentgateway project. Her company counts enterprises like T-Mobile, ADP and SAP as customers. She sees companies increasingly installing on-premise open source models and sees it as the next big wave in enterprise AI use. She's not alone. Vercel -- best known as a platform for building and hosting websites, which has recently added AI model-switching tools -- and OpenRouter, a company that helps developers route requests across different AI models -- are both seeing a surge in traffic to open-source models. In fact, open models accounted for 29% of all traffic routed through Vercel's gateway last month. With the CEO of Microsoft, a company that has invested in both OpenAI and Anthropic, now openly urging enterprises to be wary of using proprietary models, we'll bet this trend continues to grow. "In consuming intelligence, you are creating intelligence. And what you create should belong to you," Nadella writes.

Amid IP Theft Concerns, Microsoft CEO Floats New AI Patent Concept

Microsoft's CEO is pushing for a new kind of patent to address fears that leading AI models could become Trojan horses that steal intellectual property from corporate customers. In a Sunday X post, Satya Nadella questioned how firms should protect their core IP at a time when top AI companies are hungry for more data to improve their large language models. To keep their commercial customers at ease, the top AI providers, such as OpenAI and Anthropic, have strict policies to prevent training on copyrighted content. But earlier this month, Palantir CEO Alex Karp alleged that a growing number of businesses are worried that using top AI models could expose their IP to theft, all while paying hefty usage fees. "I'm going to get no value, and they're going to get my IP," Karp told CNBC. Businesses "want to know they own the means of production, [and that] it's not being transferred to someone else." Karp's comments sparked tech investor David Sacks, a White House advisor on AI, to point to the risk of an AI provider creating copycat products using data from their enterprise clients. "Don't think that can happen? Just look at Figma. According to The Information, Anthropic 'blindsided' its then-business partner with the launch of Claude Design," Sacks tweeted. "This isn't an isolated example. Anthropic has launched Claude Science, Claude Security, Claude Legal, and of course Claude Code -- each expanding into categories previously served by companies building on top of their models." The IP theft concerns could derail enterprise demand for the top AI models, the major cash cow for the leading providers, pushing companies to embrace open models. Surprisingly, the CEO of Microsoft -- a major partner and investor in OpenAI -- agrees with Karp. "AI creates the reverse problem. In the AI age, the buyer risks giving away knowledge, just in order to use what they bought," Nadella wrote. "You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!" The essay naturally raises questions about whether the enterprise customer data flowing through AI models can truly remain private. Nadella suggests that data training is inevitable. "Models learn from 'exhaust,' the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how," he said. We reached out to Microsoft for comment. In the meantime, Nadella describes the problem as the "reverse information paradox," where the user has to increasingly expose information to get the most value from the AI model -- but the model itself remains opaque because it's closed-source. In response, Nadella says we need an AI equivalent to patents that protect an inventor's idea when it's disclosed to the public, preventing infringement. Microsoft's CEO also threw some subtle shade at top AI labs that train their models on data from the internet, sometimes without consent. "I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation," he said, referring to competitors using a large AI model to transfer the skills to a smaller one. "If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it's imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop," he added.

Microsoft chief turns hostile on frontier AI labs, warns companies to guard their IP

Seemingly unaware of the concept of irony, Satya Nadella is warning AI-using enterprises to take care not to give away their business secrets alongside the massive piles of cash they're forking over to frontier labs every month. Writing in a long-form post on X over the weekend, the Microsoft CEO and chairman warned of what he called the "reverse information paradox," a situation in which purchasers of AI essentially pay for the intelligence product they're getting twice: once with cash, and again "with something even more valuable," namely the proprietary business knowledge one has to feed an AI model in order to make it worth using in the rare instance an AI investment actually pays off. "Over time, the information asymmetry becomes increasingly skewed," Nadella noted. "The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return." The irony is thick, given that Microsoft itself pushes AI that slurps up business data, and Redmond helped get this entire messy AI ball rolling by investing billions into early generative AI leader OpenAI. Azure was the former exclusive cloud home for ChatGPT, and Microsoft leadership arguably helped Altman get his job back when OpenAI ousted him in 2023. The pair's relationship grew strained in the intervening years, and they loosened several exclusivity provisions in early 2026. It also comes after a number of large organizations paused or restricted Microsoft Copilot deployments in 2024 over a related concern: weak data governance and sprawling internal access rights. Enterprise data security outfit Securiti told The Register in 2024 that about half of the more than 20 chief data officers it polled had grounded Copilot deployments, either switching the assistant off or severely restricting what it could access. The problem was particularly acute in organizations with years of accumulated SharePoint and Microsoft 365 permissions, where overly broad access rights risked exposing sensitive information through Copilot. Fast forward a couple of years, and now Nadella is warning that data protection measures aren't even enough for a business to stay safe in the AI age. "Models learn from 'exhaust,' the prompts people write, the tools agents use, and especially the corrections people make," Nadella warned. "It's the kind of knowledge a competitor could never buy, and the kind that leaks almost imperceptibly: trace by trace, correction by correction, eval by eval." Consuming intelligence through AI, Nadella added, creates more organizational intelligence. The Microsoft chief argued that the knowledge generated through those interactions ought to belong to the companies that create it. "Enterprises need a real trust boundary for their human capital and token capital to compound," Nadella wrote, describing his ideal solution as having "a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent." In other words, welcome to the post-cloud era when all your AI infrastructure will come home to roost inside your own network. If you think we're exaggerating, Nadella even mentions that one of the things enterprises need to do to solve the reverse information paradox is to build their own proprietary AI learning environments "within the tenant boundary." We asked Nadella and Microsoft whether solving the problem goes beyond good data governance, as Nadella suggested in his article, and a spokesperson told us yes, describing the matter as a structural problem with the current generally accepted model of AI business in which companies rely on hosted services. Anyone and everyone using AI for business is at risk, they explained. In addition to isolating learning environments, Nadella's X note also suggested AI-using businesses should create their own private evaluation systems and retain ownership of organizational AI memory and decouple their orchestration layer from any particular AI model, essentially creating "your own continuous learning loop." "A company should be able to use a model without giving up the knowledge that makes it unique," Nadella wrote. The Microsoft spokesperson argued that agent harnesses and memory should be independent of models, and called for enterprises to have the rights to their own usage data and model outputs, echoing Nadella's comments about the irony of leading AI firms crying foul about model distillation while reserving "the right to learn from customer usage and interaction data." As for whether this is a generic warning that something in the AI industry's got to give or a sales pitch with Microsoft positioned as the hero, the spokesperson made that clear, telling us that Copilot and Azure AI Foundry (a hosted solution, it's worth pointing out) are Redmond's solution to the problems Nadella outlined in his weekend post. Both separate context, memory, and agent harnesses from AI models themselves, giving businesses an additional layer of assurance that their data is safe, the company told us. It's debatable whether or not Microsoft is actually the AI data protection hero enterprises are looking for. But the bigger point is true: Frontier labs are rolling in valuable proprietary data, and that could come back to bite the businesses that forked it over for free. ®

Nadella's Reverse Information Paradox: AI's hidden cost

Satya Nadella has coined a term for the quiet cost of enterprise AI: the Reverse Information Paradox. Use a model, he argues, and you pay twice, once in cash and once in the proprietary know-how you feed it. The awkward part is that Microsoft helped build the machine he is warning you about. Microsoft's Satya Nadella says every firm using AI is paying for it twice, once in cash, and once in the secrets it hands over to make the thing useful. He calls it the Reverse Information Paradox. He also runs the company that helped build the trap. Satya Nadella has a warning for everyone buying AI. You are paying for it twice. And the second payment is your crown jewels. In a long essay on X that drew 10 million views, the Microsoft chief laid out an idea he calls the Reverse Information Paradox. It is sharp, a little wonky, and more than a little awkward coming from him. Pay once in cash, once in secrets The name is a riff on the Nobel economist Kenneth Arrow. Arrow's original paradox was the seller's problem. To sell information, you often have to reveal it, and once it is revealed, why would anyone pay? Nadella flips it. In the AI age, he argues, the risk sits with the buyer. To make a model genuinely useful, you have to feed it your proprietary knowledge. The better you want it to work, the more you feed it. So you pay in money, then again in something worth more: the know-how that makes your company yours. "The seller learns more and more about you as you use what you purchased", he wrote, "while you learn very little about what the seller is learning in return." The leak you cannot see The clever part is where he says the knowledge escapes. Not through some obvious breach, but through what he calls "exhaust": the prompts you write, the tools your agents use, and above all the corrections you make when the model gets something wrong. Every fix teaches the model. "It's the kind of knowledge a competitor could never buy", Nadella wrote, "and the kind that leaks almost imperceptibly: trace by trace, correction by correction, eval by eval." His verdict is blunt. If learning only flows one way, the money flows with it, toward whoever owns the AI, not whoever owns the knowledge. The irony is doing a lot of work Here is the catch. This is Microsoft talking. Redmond poured billions into OpenAI and hosted ChatGPT on Azure. Its Copilot assistant is built to reach deep into a company's email, files and chat. Back in 2024, roughly half of the data chiefs in one survey had paused or curbed Copilot over exactly this fear, as the Register noted. To his credit, Nadella names his own side's double standard. AI labs demand fair-use rights to train on the public web, then restrict customers from doing the same with model outputs. He is not wrong. He is also selling the fix. Nadella's answer, and his pitch The solution, he says, is a hard "trust boundary" around a company's data, evals and memory. Nothing crosses it, "not even the intelligence exhaust, without consent." He borrows a line from Palantir's Alex Karp about wanting to own the means of production. His checklist runs to five points. Own your evals. Build learning environments inside your own tenant boundary. Keep the orchestration layer free of any single model. Then let it all compound. Microsoft, naturally, sells products that do each of these things. Strip out the pitch and the core point still holds. This is the same executive who turned on the AI giants he helped build. The frontier labs are quietly amassing a fortune in other companies' know-how. And the firms handing it over are, for now, doing it for free.

'You essentially pay for intelligence twice, once with money, and again with something even more valuable': Microsoft CEO Satya Nadella warns AI users not to give away too much

* Microsoft CEO Satya Nadella has warned AI companies are training their models on the business secrets of their customers * These secrets are then used to train new, more powerful models, that are sold to their customer's competitors * But, Nadella says there is a way to remain competitive without being locked in to one AI vendor Microsoft CEO Satya Nadella has warned the big players in the AI industry are using their proprietary models to learn the business secrets of their customers, which they can then use to train and deploy more advanced AI models. The crux of the issue, Nadella said in a blog post, is that, "You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!" What Nadella is saying in essence, is that AI companies are harvesting sensitive business data from their customers, using it to make training their models cheaper, and then launching these models for use by their own customer's competition. "The kind of knowledge a competitor could never buy" "Models learn from 'exhaust,' the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how," Nadella explained. Nadella also criticized how AI companies are increasingly complaining about how their models are being distilled by their own competition. For example, Anthropic accused retailer and e-commerce company Alibaba for using thousands of Claude prompts to distill their own models. By figuring out how a proprietary model works, you don't have to spend the enormous amount of capital needed to source training data and create your own AI model. This, for Nadella, is a major contradiction in how AI companies work. "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," he said. It is also therefore hypocritical for AI companies to accuse other companies of distilling their own product, and then include within their AI usage contracts clauses that allow AI companies to "reserve the right to learn from customer usage and interaction data." "In consuming intelligence, you are creating intelligence. And what you create should belong to you," Nadella added. On-prem is back in fashion Nadella's fix for this growing problem? It's time to move back to on-prem. Nadella encourages businesses to "retain ownership" of the data they feed AI models by switching to the use of "proprietary learning environments" built on the cloud. The added benefit of moving to these environments is that they allow businesses to switch between different AI models provided by different companies using "orchestration layers" and AI gateways. There is also a growing trend of businesses switching to using open source technologies, which goes hand in hand with businesses operating in the cloud. Businesses can train open source AI models using their data that is already available in cloud environments to do much of what the proprietary models do, for far cheaper -- and without handing over that same sensitive data to be used by AI companies to train their own models. The on-prem solution also has additional benefits. AI models operated on-site within manufacturing plants, stores, and other premises are far cheaper and require less specialized hardware. Businesses that operate using a centralized cloud are increasingly encountering issues with data egress fees, storage bloat, and idle specialized hardware. Google Cloud recently released a report about these very issues, and also encouraged businesses to move towards using AI gateways and on-prem models to reduce latency, improve resilience, and cut per-token costs by switching to local, highly optimized models. Via TechCrunch Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.

Tokenomics - five C-words from Satya Nadella as the Microsoft CEO argues organizations must be able to benefit from AI models without paying twice!

You essentially pay for intelligence twice. A grim warning from Microsoft CEO Satya Nadella, part of a thesis that mirrors the comments from Palantir CEO Alex Karp earlier this month, albeit delivered in a less 'excitable' manner and all the more convincing as a result. Karp launched into a denunciation of frontier model AI firms and their "'effing insane" business models in one of the most colorful contributions to the wider tokenomics debate to date, ranting in a televised interview: Our clients just say they're unhappy with the frontier labs...I'm telling you, in this country every single enterprise I deal with, these people are livid. They're like, 'I am paying for tokens that create no value. These people are stealing the weights and alpha of my business...these models have been completely and irresponsibly over-sold. Essentially Nadella is making the same basic point in a blog posting, arguing: You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it! Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return. Paradox re-born This is, he suggests, an example of the Reverse Information Paradox in action, an AI-motived spin on the Information Paradox proposed by Nobel Prize winning economist Kenneth Arrow, who posited: [Information's] value for the purchaser is not known until he has the information, but then he has in effect acquired it without cost. AI creates the reverse of this situation, suggests Nadella, to the extent that: In the AI age, the buyer risks giving away knowledge, just in order to use what they bought. What to do? So what can be done here? Nadella notes that: In consuming intelligence, you are creating intelligence. And what you create should belong to you. This is your particular intelligence, in Hayek's sense: the knowledge of time, place, and circumstance that no one else can hold. It knows what you think, what you value, and how you measure success. Part of the issues raised by Arrow's paradox can be addressed through the mechanism of patents, which allow the owners of ideas to disclose them without simply giving them away to all and sundry. Something similar, but more sophisticated is needed to deal with the posited Reverse Information Paradox, he argues: Models learn from 'exhaust', the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how. It's the kind of knowledge a competitor could never buy, and the kind that leaks almost imperceptibly: trace by trace, correction by correction, eval by eval. Now, Nadella isn't turning on frontier model firms, such as OpenAI in which Microsoft has a significant financial interest, in the way that Karp did, Indeed he argues: The great innovation that comes from model providers having fair use rights to train models on public data is needed. But he adds: 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. If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it's imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop. Five 'C's Enterprises need a genuine 'trust boundary' for their token capital and its human equivalent to compound he explains: It is where an organization's data, traces, evals, adapted weights, and memory accumulate and improve together. And it is a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent. Enterprises will demand the rights to use model outputs to fine tune and/or train their own models. I think of this as every firm's right to align models to their enterprise accountability obligations. This 'trust boundary' must evolve from protecting information to protecting the mechanisms through which organizations learn, adapt, and compound intelligence, he adds, suggesting a number of key recommendations around five 'C's. These are: * Control - enterprises need to create private evals, because evals define what "good" looks like inside the organization. Nadella warns: Retain ownership of your organization's memory, traces, feedbacks, decisions, and institutional context, and ability to use outputs of models from your own tasks and queries. * Capability - build proprietary learning environments within the tenant boundary to train or tune models, where models learn against real workflows without exposing the company's knowledge. * Choice - make sure the orchestration layer is de-coupled from any single model, urges Nadella: Ask yourself: If any one model you are using is taken away, do you still have the ability to operate and optimize for your evals using other models? Does your company's 'veteran' capability remain with you even if a given 'generalist' model is taken away? * Cost - de-coupling the orchestration layer allows enterprises to bring together context, models, and tasks in the most efficient and cost-effective way without sacrificing quality. * Finally, Compound - in other words, bring the above four ideas together and, says Nadella: You create your own continuous learning loop (i.e. hill climbing machine) that will allow your AI investments to compound the value of your firm. My take A company should be able to use a model without giving up the knowledge that makes it unique. An important bottom line, adding to an overall compelling thesis from the Microsoft CEO - and so much more convincingly persuasive than wild ranting on TV!

Satya Nadella's 'Reverse Information Paradox' post draws reactions from AI leaders

Microsoft CEO Satya Nadella highlighted risks businesses face with AI adoption. He warned companies could lose valuable organizational knowledge through AI interactions. Tech leaders responded to Nadella's "Reverse Information Paradox" concerns. Some executives agreed, while others noted existing protections. Discussions are ongoing regarding AI's impact on intellectual property and data control. Microsoft CEO Satya Nadella's recent post on X, in which he warned that businesses risk giving away valuable organisational knowledge as they adopt artificial intelligence (AI), has prompted responses from technology leaders. In the post, titled The Reverse Information Paradox, Nadella argued that companies need to protect not only their proprietary data but also the learning created through prompts, feedback, workflows, and interactions with AI systems. Nadella's comments drew responses from many executives including those at Perplexity, Microsoft, Glean, Google Cloud, etc. Sharing Nadella's quote in a post on X, Perplexity cofounder and CEO Aravind Srinivas wrote, "Well said." Among other things, the Microsoft CEO noted, "If learning flows in only one direction,economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it's imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop." Brad Smith, vice chair and president of Microsoft, said in a post, "Every new generation of digital technology creates a new generation of IP issues. The 'Reverse Information Paradox,' as Satya describes here, may well create even broader and more profound issues than we've seen in recent decades. It will be vital for businesses and jobs across the economy that we discuss and address these effectively." Arvind Jain, founder and CEO of enterprise AI startup Glean, wrote: "In the AI era, firms need to protect more than data; they need to protect how they learn from their work. Prompts,corrections, evals, and memory capture the know-how that makes a company better over time. The right architecture keeps that know-how in the company's hands, not tied to any single model." Priyanka Vergadia, head of developer relations at Google Cloud AI, wrote: "Most enterprise API tiers already ship zero-retention, no-training terms. This feels like this is still fighting 2023 ChatGPT." She added, "The conversation mashes two different debates together -- public data fair use vs. private interaction data like they're the same argument. They're not." Vergadia also wrote that, "Building your own tenant-boundary learning environment is advice for companies with nine-figure AI budgets, not the rest." The responses came after Nadella argued that AI is creating a "reverse information paradox," where companies "pay for intelligence twice", once with money and again by revealing the proprietary knowledge needed to make AI useful. In his post, he also said that the companies that succeed in the AI era will be those that retain ownership of the learning generated through their use of AI systems.

Microsoft CEO Satya Nadella Says Businesses 'Pay for Intelligence Twice,' Warns AI Can Cost Companies Pro

In an article titled 'The Reverse Information Paradox' posted on X, Nadella drew on economist Kenneth Arrow's Information Paradox, saying AI has flipped the traditional relationship between buyers and sellers of information. "You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful," he added. Calls For Greater Enterprise Control Nadella said protecting enterprise knowledge requires more than safeguarding data because AI models also learn from prompts, workflows, evaluations, and the corrections users make over time. He added that those interactions gradually become institutional know-how that competitors cannot easily replicate. "In consuming intelligence, you are creating intelligence. And what you create should belong to you," Nadella said. He added that companies should be able to use AI without transferring the knowledge that makes them unique to model providers. Microsoft holds a roughly 27% stake in OpenAI and has integrated the startup's models into products including Azure AI, Microsoft 365 Copilot and GitHub Copilot. In June, the Microsoft CEO said the AI future dominated by a handful of models could concentrate economic value and weaken businesses' competitive advantages, calling instead for a broader AI ecosystem. The Essay Draws Attention Microsoft AI executive Nicolas Bustamante expanded on Nadella's thesis, saying enterprises are increasingly accumulating learning, not just data. He said organizations will increasingly focus on owning the intelligence created through those interactions rather than allowing it to become part of someone else's learning loop. Benzinga edge rankings indicate MSFT has a Momentum score in the 12th percentile and a Growth score in the 54th percentile. Disclaimer: This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors. Photo courtesy: Shutterstock Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

Satya Nadella AI: Microsoft CEO Satya Nadella warns of 'reverse information paradox' facing businesses in AI Age

Microsoft Chairman and CEO Satya Nadella explained that enterprises need a real trust boundary for their human capital and token capital to compound, as a company should be able to use a model without giving up the knowledge that makes it unique. This "reverse information paradox" is the central challenge that businesses need to confront in the age of intelligence. Nadella, on X, stated that while Nobel Prize-winning economist Kenneth Arrow described a paradox where a seller risks giving away knowledge to sell it, artificial intelligence creates the opposite problem. "In the AI age, the buyer risks giving away knowledge, just in order to use what they bought," Nadella said. "You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it! .....That is what I think of as the Reverse Information Paradox." He noted that over time, the information asymmetry becomes increasingly skewed because the seller learns more about the buyer, while the buyer learns very little about what the seller learns in return. According to Nadella, resolving this issue requires more than standard data protection. Models learn continuously from "exhaust," which includes user prompts, agent tools, and corrections made when a model is wrong. "Every correction is distilled into institutional know-how," Nadella stated. "It's the kind of knowledge a competitor could never buy, and the kind that leaks almost imperceptibly: trace by trace, correction by correction, eval by eval. In consuming intelligence, you are creating intelligence. And what you create should belong to you." He pointed out the irony in the current status quo, where model providers utilize fair use rights to train on public data but then impose restrictive terms on distillation and reserve the right to learn from customer usage. Nadella stated that if learning flows in only one direction, economic value converges toward the infrastructure owners rather than the knowledge creators. Consequently, distributing learning infrastructure to every firm is imperative so they can control their own learning loop. To secure this boundary, Nadella outlined that enterprises must focus on control, capability, choice, cost, and compounding. This involves creating private evaluations, retaining ownership of organizational memory, building proprietary learning environments within a tenant boundary, and decoupling the orchestration layer from any single model to ensure long-term cost efficiency and value compounding. Quoting Palantir CEO Alex Karp, Nadella highlighted the growing demand among technical customers for absolute autonomy over their proprietary systems. "What the technical customers want is control over their compute, their models, their data stack, and their alpha," Nadella quoted Karp. "They want to know they own the means of production, and it's not being transferred to someone else."

Microsoft CEO Echoes Palantir CEO's Warning on Enterprise AI

It turns out that Palantir (PLTR) CEO Alex Karp's thunderous warning about the AI industry wasn't a one-off rant. Over the past couple of years, the word "AI" has become like a broken record, heard at least once almost every day, often followed by a wave of anxiety. What has happened amid all the FOMO and paranoia is that users have begun sharing virtually everything deemed "confidential" under the sun in search of answers. Microsoft (MSFT) CEO Satya Nadella has now raised a strikingly similar concern in a recent blog post on Sn Scratchpad. Businesses pay for intelligence, but for that to be useful, you need to present the AI model companies with proprietary data, workflows, and corrections that give them a competitive edge. It's actually the reverse of what Nobel Prize-winning economist Kenneth Arrow described as the information paradox. The buyer is essentially giving up their knowledge simply to make use of what they have purchased. Nadella's concern is that companies ultimately pay twice, once in cash and again with institutional know-how over time. Satya Nadella says companies may be paying for AI twice Microsoft CEO Satya Nadella argued that the visible cost of AI might just be the beginning. "You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful," Nadella wrote in a recent blog post. For AI systems to perform better, there needs to be higher-quality internal context, which likely includes employee prompts, operational procedures, agentic activity, and corrections. "Models learn 'from exhaust,' the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong," Nadella said. "Every correction is distilled into institutional know-how." Interestingly, TheStreet's top tech contributor, Vuk Zdinjak, recently covered Palantir CEO Alex Karp's explosive tirade against frontier-model providers. "I am paying for tokens that create no value," Karp said in his most recent appearance on CNBC's "Squawk Box," describing the frustration he hears from enterprise customers. "These people are stealing the weights and alpha of my business." Additionally, Karp also challenged the industry's basic pricing model: "If I can make you $1 billion tomorrow, wouldn't I say I'll make you $1 billion, and I want 30%? Why are they charging for tokens if it's so valuable?" Nadella's version feels a lot less confrontational, but far more coherent, than Karp's. Still, the underlying warning remains the same. Businesses are effectively renting models while donating the knowledge that makes them much more capable. "In consuming intelligence, you are creating intelligence, and what you create should belong to you," as Nadella puts it. Nadella's warning strengthens Palantir's core AI pitch For Palantir (PLTR) stock investors, Nadella's warning is important and may have indirectly validated the problem Karp says Palantir was built to solve. The CEO of the controversial tech firm Karp argued that enterprises should not expose their proprietary data, workflows, and operational knowledge directly to large language models outside their organizations. Palantir's answer is Ontology, an application layer that connects models to company operations while controlling what models can access and retain. Karp said Ontology makes AI "safe and useful and precise," preventing models from caching customer data, replicating the business, or transferring sensitive intellectual property. He went a step further in his interview with podcaster Mathias Döpfner, saying businesses need an application layer that "protects your data from being essentially abused by large language model providers." If customers become more wary of the data they give up, Palantir could be in line for a massive long-term windfall, but it could also create valuation risks elsewhere in the AI sector. Palantir needs to prove Ontology can turn that strategic concern into durable contracts, expanding margins, and measurable customer returns. It's worth mentioning that the stock is down 27% in the past six months and more than 26% year-to-date, according to Seeking Alpha data. Still, Palantir stock is changing hands at 88 times non-GAAP forward earnings, a steep premium, to say the least, compared to the sector median of around 25 times. Nadella's warning raises the stakes for the AI trade The interesting part is that the broader AI trade is already up against the uncomfortable question that Wall Street hasn't answered: Who will earn enough money to justify the extraordinary spending? For perspective, Amazon, Microsoft, Alphabet, and Meta are projected to spend about $630 billion on data centers and AI chips in 2026 alone, according to Reuters, more than 4 times their 2023 guidance. However, with recent developments, it seems the chickens are finally coming home to roost as the AI trade undergoes a shakeout. Bank of America's latest survey found that 45% of fund managers view an AI bubble as the market's biggest tail risk, Reuters also reported. Yet investors remain heavily committed to the chip stock trade. Moreover, several of Wall Street's most popular personalities have sounded alarms. Ray Dalio says AI is "now in the early stages of a bubble," while Jeremy Grantham warns that "sooner or later, the bubble will burst." "Big Short" investor Michael Burry has long been skeptical of the AI boom, calling semiconductor valuations "a pure form of overvaluation" and warning that the end may be near. Nadella's argument adds to those vulnerabilities. The reverse information paradox may lead customers to redirect spending toward private, model-agnostic systems, weighing on the biggest names in AI and calling their nosebleed valuations into question. The Arena Media Brands, LLC THESTREET is a registered trademark of TheStreet, Inc. This story was originally published July 14, 2026 at 8:07 PM.

Satya Nadella warns: if you use AI, you pay twice

News Satya Nadella warns: if you use AI, you pay twice First with fees, then with the company data you share To say that artificial intelligence is changing the way we work would be a huge understatement. According to Satya Nadella, CEO of Microsoft, however, we must be careful, because he states that when we use enterprise AI models we end up paying twice: first with money, by using those services, and then with the most valuable thing we have, the internal information we share. Corporate data is the real value Nadella explains that every interaction with a model teaches the system about the company's internal processes, from the prompts we write to the corrections we make. This information has enormous value, think about how much we'd charge a competitor for that information, and how we hand it over free of charge to various AI tools. In trying to get the best results, we end up continually offering strategic details about our business. Microsoft's CEO urges companies to retain ownership of their data and build their own learning environments in the cloud. With orchestration tools or AI gateways that let us switch models easily without getting locked into a single provider, thus maintaining control over sensitive information. More and more companies are choosing to install open-source models on their own servers. With this strategy you can get performance close to that of the major platforms, at a lower cost and with almost complete control. Following Nadella's line of reasoning, using AI also means helping to train it, and what we create should stay ours.

AI has a hidden cost, warns Satya Nadella: What does he mean by that?

CEO Satya Nadella just went against all norms as he publicly admitted that it costs more to use artificial intelligence than what it says in the bill. In a lengthy blog on X published on July 12, the chairman and CEO of Microsoft said that all enterprises utilizing AI effectively are paying for it not once but twice. Once through cash, and once through something he deems far more valuable: the company's very own expertise. Also read: Meta keeps launching AI features that assume consent: Why it keeps backfiring According to him, to obtain meaningful responses from an AI model, one needs to supply the system with the context of the business, its processes, errors, and corrections. This is called "intelligence exhaust," according to Nadella, referring to the byproducts of prompts, corrections, and evaluations that workers make when employing an AI system. He says that such intelligence exhaust is not garbage information. It's actually institutional knowledge that cannot be easily purchased from a competitor and is leaking out gradually with each correction in the model. To drive home the point, he turned to economics. Economics Nobel laureate Kenneth Arrow once talked about an "Information Paradox": it is impossible for the seller of information to establish its value without disclosing it, while once disclosed, there is no incentive for the buyer to pay. But Nadella says the reverse is true for AI; it is actually the buyer, the enterprise, who ends up disclosing its knowledge. The more efficient you need your model to be, the more institutional knowledge you have to put in. Also read: Can AI make you a better cricketer? Str8bat co-founder Gagan Daga thinks so It is disturbing in how it is lopsided. While the AI firm gains knowledge from all the enterprises at once and builds on that knowledge for its customer base, each single enterprise gains only minimal knowledge for itself while losing control of what it has contributed to the machine-learning process. So what is his solution? He has come up with a solution that he calls the "five Cs." The first one is Control, which suggests that organizations should have control of their evaluations and institutional knowledge rather than leave this in the hands of a third party vendor infrastructure. The second is Capability; which suggests the need to create private, tenant-aware environments in which models are trained using actual organizational data, keeping them from leaking out. Third is Choice, which entails not getting locked down into a particular model or vendor. Fourth is Cost, which involves making use of different models and workflows in an efficient way rather than just choosing the most expensive option for everything. It's worth noting the timing. Nadella has spent much of 2026 talking publicly about AI's economics turning uncomfortable, from telling Microsoft staff to stop defaulting to the most expensive models, to warning in an earlier essay that AI could hollow out entire industries the way outsourcing once did. This latest post arrived the same week Microsoft was hit with a shareholder lawsuit over its AI spending disclosures. Whether the Reverse Information Paradox is a genuine strategic warning or a bit of framing to explain why enterprises should build more on Microsoft's own tools remains an open question. Either way, it's a rare admission from a hyperscaler CEO that AI's price tag doesn't end at the subscription fee.

Satya Nadella flags hidden risk of using AI, says you pay for intelligence twice

"You essentially pay for intelligence twice, once with money, and again with something even more valuable," he wrote. AI is changing how companies operate. Businesses are using AI to write code, analyse large amounts of data, automate everyday tasks and make important decisions. These tools can save time and improve productivity. However, Microsoft CEO Satya Nadella believes there is another side to the growing use of AI that companies should be aware of. In a detailed post on X, Nadella warned that businesses may be giving away valuable knowledge while using AI tools. Nadella calls this the "Reverse Information Paradox." He argues that companies pay for AI intelligence twice. "You essentially pay for intelligence twice, once with money, and again with something even more valuable," he wrote. Also read: Meta discontinues Muse Image AI feature after backlash, says it missed the mark The first cost is which a business pays an AI company to use its models and services. The second cost is less obvious. To get better answers from AI, companies provides more information about their work, processes and problems. According to him, AI models can learn from much more than the data directly provided to them. Prompts, tools used by AI agents and corrections made when a model gives a wrong answer can also reveal valuable information. "Every correction is distilled into institutional know-how. It's the kind of knowledge a competitor could never buy, and the kind that leaks almost imperceptibly: trace by trace, correction by correction, eval by eval," Nadella said. He further added, "In consuming intelligence, you are creating intelligence. And what you create should belong to you." The Microsoft CEO believes companies need stronger boundaries around their AI systems. Businesses should retain control of their data, AI memory, feedback, internal tests and other information created while using models. Also read: Apple accuses OpenAI of stealing trade secrets, ChatGPT maker responds He also advised organisations to avoid becoming completely dependent on one AI model. "In the cloud era, enterprises accumulated data. In the AI era, they accumulate learning," Nadella wrote. While concluding his post, he said, "a company should be able to use a model without giving up the knowledge that makes it unique."