Microsoft has a new plan to prove what’s real and what’s AI online

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Microsoft has a new plan to prove what’s real and what’s AI online

AI-enabled deception now permeates our online lives. There are the high-profile cases you may easily spot, like when White House officials recently shared a manipulated image of a protester in Minnesota and then mocked those asking about it. Other times, it slips quietly into social media feeds and racks up views, like the videos that Russian influence campaigns are currently spreading to discourage Ukrainians from enlisting. It is into this mess that Microsoft has put forward a blueprint, shared with MIT Technology Review, for how to prove what’s real online. An AI safety research team at the company recently evaluated how methods for documenting digital manipulation are faring against today’s most worrying AI developments, like interactive deepfakes and widely accessible hyperrealistic models. It then recommended technical standards that can be adopted by AI companies and social media platforms. To understand the gold standard that Microsoft is pushing, imagine you have a Rembrandt painting and you are trying to document its authenticity. You might describe its provenance with a detailed manifest of where the painting came from and all the times it changed hands. You might apply a watermark that would be invisible to humans but readable by a machine. And you could digitally scan the painting and generate a mathematical signature, like a fingerprint, based on the brush strokes. If you showed the piece at a museum, a skeptical visitor could then examine these proofs to verify that it’s an original. All of these methods are already being used to varying degrees in the effort to vet content online. Microsoft evaluated 60 different combinations of them, modeling how each setup would hold up under different failure scenarios—from metadata being stripped to content being slightly altered or deliberately manipulated. The team then mapped which combinations produce sound results that platforms can confidently show to people online, and which ones are so unreliable that they may cause more confusion than clarification. Ask AIWhy it matters to you?BETAHere’s why this story might matter to you, according to AI. This is a beta feature and AI hallucinates—it might get weirdTell me why it matters The company’s chief scientific officer, Eric Horvitz, says the work was prompted by legislation—like California’s AI Transparency Act, which will take effect in August—and the speed at which AI has developed to combine video and voice with striking fidelity. “You might call this self-regulation,” Horvitz told MIT Technology Review. But it’s clear he sees pursuing the work as boosting Microsoft’s image: “We’re also trying to be a selected, desired provider to people who want to know what’s going on in the world.” Nevertheless, Horvitz declined to commit to Microsoft using its own recommendation across its platforms. The company sits at the center of a giant AI content ecosystem: It runs Copilot, which can generate images and text; it operates Azure, the cloud service through which customers can access OpenAI and other major AI models; it owns LinkedIn, one of the world’s largest professional platforms; and it holds a significant stake in OpenAI. But when asked about in-house implementation, Horvitz said in a statement, “Product groups and leaders across the company were involved in this study to inform product road maps and infrastructure, and our engineering teams are taking action on the report’s findings.” It’s important to note that there are inherent limits to these tools; just as they would not tell you what your Rembrandt means, they are not built to determine if content is accurate or not. They only reveal if it has been manipulated. It’s a point that Horvitz says he has to make to lawmakers and others who are skeptical of Big Tech as an arbiter of fact. “It’s not about making any decisions about what’s true and not true,” he said. “It’s about coming up with labels that just tell folks where stuff came from.” Hany Farid, a professor at UC Berkeley who specializes in digital forensics but wasn’t involved in the Microsoft research, says that if the industry adopted the company’s blueprint, it would be meaningfully more difficult to deceive the public with manipulated content. Sophisticated individuals or governments can work to bypass such tools, he says, but the new standard could eliminate a significant portion of misleading material. “I don’t think it solves the problem, but I think it takes a nice big chunk out of it,” he says. Still, there are reasons to see Microsoft’s approach as an example of somewhat naïve techno-optimism. There is growing evidence that people are swayed by AI-generated content even when they know that it is false. And in a recent study of pro-Russian AI-generated videos about the war in Ukraine, comments pointing out that the videos were made with AI received far less engagement than comments treating them as genuine. “Are there people who, no matter what you tell them, are going to believe what they believe?” Farid asks. “Yes.” But, he adds, “there are a vast majority of Americans and citizens around the world who I do think want to know the truth.” That desire has not exactly led to urgent action from tech companies. Google started adding a watermark to content generated by its AI tools in 2023, which Farid says has been helpful in his investigations. Some platforms use C2PA, a provenance standard Microsoft helped launch in 2021. But the full suite of changes that Microsoft suggests, powerful as they are, might remain only suggestions if they threaten the business models of AI companies or social media platforms. “If the Mark Zuckerbergs and the Elon Musks of the world think that putting ‘AI generated’ labels on something will reduce engagement, then of course they’re incentivized not to do it,” Farid says. Platforms like Meta and Google have already said they’d include labels for AI-generated content, but an audit conducted by Indicator last year found that only 30% of its test posts on Instagram, LinkedIn, Pinterest, TikTok, and YouTube were correctly labeled as AI-generated. More forceful moves toward content verification might come from the many pieces of AI regulation pending around the world. The European Union’s AI Act, as well as proposed rules in India and elsewhere, would all compel AI companies to require some form of disclosure that a piece of content was generated with AI. One priority from Microsoft is, unsurprisingly, to play a role in shaping these rules. The company waged a lobbying effort during the drafting of California’s AI Transparency Act, which Horvitz said made the legislation’s requirements on how tech companies must disclose AI-generated content “a bit more realistic.” But another is a very real concern about what could happen if the rollout of such content-verification technology is done poorly. Lawmakers are demanding tools that can verify what’s real, but the tools are fragile. If labeling systems are rushed out, inconsistently applied, or frequently wrong, people could come to distrust them altogether, and the entire effort would backfire. That’s why the researchers argue that it may be better in some cases to show nothing at all than a verdict that could be wrong. Inadequate tools could also create new avenues for what the researchers call sociotechnical attacks. Imagine that someone takes a real image of a fraught political event and uses an AI tool to change only an inconsequential share of pixels in the image. When it spreads online, it could be misleadingly classified by platforms as AI-manipulated. But combining provenance and watermark tools would mean platforms could clarify that the content was only partially AI generated, and point out where the changes were made. California’s AI Transparency Act will be the first major test of these tools in the US, but enforcement could be challenged by President Trump’s executive order from late last year seeking to curtail state AI regulations that are “burdensome” to the industry. The administration has also generally taken a posture against efforts to curb disinformation, and last year, via DOGE, it canceled grants related to misinformation. And, of course, official government channels in the Trump administration have shared content manipulated with AI (MIT Technology Review reported that the Department of Homeland Security, for example, uses video generators from Google and Adobe to make content it shares with the public). I asked Horvitz whether fake content from this source worries him as much as that coming from the rest of social media. He initially declined to comment, but then he said, “Governments have not been outside the sectors that have been behind various kinds of manipulative disinformation, and this is worldwide.”

It is into this mess that Microsoft has put forward a blueprint, shared with MIT Technology Review, for how to prove what’s real online.

An AI safety research team at the company recently evaluated how methods for documenting digital manipulation are faring against today’s most worrying AI developments, like interactive deepfakes and widely accessible hyperrealistic models. It then recommended technical standards that can be adopted by AI companies and social media platforms.

To understand the gold standard that Microsoft is pushing, imagine you have a Rembrandt painting and you are trying to document its authenticity. You might describe its provenance with a detailed manifest of where the painting came from and all the times it changed hands. You might apply a watermark that would be invisible to humans but readable by a machine. And you could digitally scan the painting and generate a mathematical signature, like a fingerprint, based on the brush strokes. If you showed the piece at a museum, a skeptical visitor could then examine these proofs to verify that it’s an original.

All of these methods are already being used to varying degrees in the effort to vet content online. Microsoft evaluated 60 different combinations of them, modeling how each setup would hold up under different failure scenarios—from metadata being stripped to content being slightly altered or deliberately manipulated. The team then mapped which combinations produce sound results that platforms can confidently show to people online, and which ones are so unreliable that they may cause more confusion than clarification.

The company’s chief scientific officer, Eric Horvitz, says the work was prompted by legislation—like California’s AI Transparency Act, which will take effect in August—and the speed at which AI has developed to combine video and voice with striking fidelity.

“You might call this self-regulation,” Horvitz told MIT Technology Review. But it’s clear he sees pursuing the work as boosting Microsoft’s image: “We’re also trying to be a selected, desired provider to people who want to know what’s going on in the world.”

Nevertheless, Horvitz declined to commit to Microsoft using its own recommendation across its platforms. The company sits at the center of a giant AI content ecosystem: It runs Copilot, which can generate images and text; it operates Azure, the cloud service through which customers can access OpenAI and other major AI models; it owns LinkedIn, one of the world’s largest professional platforms; and it holds a significant stake in OpenAI. But when asked about in-house implementation, Horvitz said in a statement, “Product groups and leaders across the company were involved in this study to inform product road maps and infrastructure, and our engineering teams are taking action on the report’s findings.”

It’s important to note that there are inherent limits to these tools; just as they would not tell you what your Rembrandt means, they are not built to determine if content is accurate or not. They only reveal if it has been manipulated. It’s a point that Horvitz says he has to make to lawmakers and others who are skeptical of Big Tech as an arbiter of fact.

“It’s not about making any decisions about what’s true and not true,” he said. “It’s about coming up with labels that just tell folks where stuff came from.”

Hany Farid, a professor at UC Berkeley who specializes in digital forensics but wasn’t involved in the Microsoft research, says that if the industry adopted the company’s blueprint, it would be meaningfully more difficult to deceive the public with manipulated content. Sophisticated individuals or governments can work to bypass such tools, he says, but the new standard could eliminate a significant portion of misleading material.

“I don’t think it solves the problem, but I think it takes a nice big chunk out of it,” he says.

Still, there are reasons to see Microsoft’s approach as an example of somewhat naïve techno-optimism. There is growing evidence that people are swayed by AI-generated content even when they know that it is false. And in a recent study of pro-Russian AI-generated videos about the war in Ukraine, comments pointing out that the videos were made with AI received far less engagement than comments treating them as genuine.

“Are there people who, no matter what you tell them, are going to believe what they believe?” Farid asks. “Yes.” But, he adds, “there are a vast majority of Americans and citizens around the world who I do think want to know the truth.”

That desire has not exactly led to urgent action from tech companies. Google started adding a watermark to content generated by its AI tools in 2023, which Farid says has been helpful in his investigations. Some platforms use C2PA, a provenance standard Microsoft helped launch in 2021. But the full suite of changes that Microsoft suggests, powerful as they are, might remain only suggestions if they threaten the business models of AI companies or social media platforms.

“If the Mark Zuckerbergs and the Elon Musks of the world think that putting ‘AI generated’ labels on something will reduce engagement, then of course they’re incentivized not to do it,” Farid says. Platforms like Meta and Google have already said they’d include labels for AI-generated content, but an audit conducted by Indicator last year found that only 30% of its test posts on Instagram, LinkedIn, Pinterest, TikTok, and YouTube were correctly labeled as AI-generated.

More forceful moves toward content verification might come from the many pieces of AI regulation pending around the world. The European Union’s AI Act, as well as proposed rules in India and elsewhere, would all compel AI companies to require some form of disclosure that a piece of content was generated with AI.

One priority from Microsoft is, unsurprisingly, to play a role in shaping these rules. The company waged a lobbying effort during the drafting of California’s AI Transparency Act, which Horvitz said made the legislation’s requirements on how tech companies must disclose AI-generated content “a bit more realistic.”

But another is a very real concern about what could happen if the rollout of such content-verification technology is done poorly. Lawmakers are demanding tools that can verify what’s real, but the tools are fragile. If labeling systems are rushed out, inconsistently applied, or frequently wrong, people could come to distrust them altogether, and the entire effort would backfire. That’s why the researchers argue that it may be better in some cases to show nothing at all than a verdict that could be wrong.

Inadequate tools could also create new avenues for what the researchers call sociotechnical attacks. Imagine that someone takes a real image of a fraught political event and uses an AI tool to change only an inconsequential share of pixels in the image. When it spreads online, it could be misleadingly classified by platforms as AI-manipulated. But combining provenance and watermark tools would mean platforms could clarify that the content was only partially AI generated, and point out where the changes were made.

California’s AI Transparency Act will be the first major test of these tools in the US, but enforcement could be challenged by President Trump’s executive order from late last year seeking to curtail state AI regulations that are “burdensome” to the industry. The administration has also generally taken a posture against efforts to curb disinformation, and last year, via DOGE, it canceled grants related to misinformation. And, of course, official government channels in the Trump administration have shared content manipulated with AI (MIT Technology Review reported that the Department of Homeland Security, for example, uses video generators from Google and Adobe to make content it shares with the public).

I asked Horvitz whether fake content from this source worries him as much as that coming from the rest of social media. He initially declined to comment, but then he said, “Governments have not been outside the sectors that have been behind various kinds of manipulative disinformation, and this is worldwide.”

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Across many conversations at the start of this year, at PTC and other conferences alike, the word on everyone’s lips seems to be “community.” For the data center industry, that single word now captures a turning point from just a few short years ago: we are no longer a niche, back‑of‑house utility, but a front‑page presence in local politics, school board budgets, and town hall debates. That visibility is forcing a choice in how we tell our story—either accept a permanent NIMBY-reactive framework, or actively build a YIMBY narrative that portrays the real value digital infrastructure brings to the markets and surrounding communities that host it. Speaking regularly with Ilissa Miller, CEO of iMiller Public Relations about this topic, there is work to be done across the ecosystem to build communications. Miller recently reflected: “What we’re seeing in communities isn’t a rejection of digital infrastructure, it’s a rejection of uncertainty driven by anxiety and fear. Most local leaders have never been given a framework to evaluate digital infrastructure developments the way they evaluate roads, water systems, or industrial parks. When there’s no shared planning language, ‘no’ becomes the safest answer.” A Brief History of “No” Community pushback against data centers is no longer episodic; it has become organized, media‑savvy, and politically influential in key markets. In Northern Virginia, resident groups and environmental organizations have mobilized against large‑scale campuses, pressing counties like Loudoun and Prince William to tighten zoning, question incentives, and delay or reshape projects.1 Loudoun County’s move in 2025 to end by‑right approvals for new facilities, requiring public hearings and board votes, marked a watershed moment as the world’s densest data center market signaled that communities now expect more say over where and how these campuses are built. Prince William County’s decision to sharply increase its tax rate on

Nomads at the Frontier: PTC 2026 Signals the Digital Infrastructure Industry’s Moment of Execution

Each January, the Pacific Telecommunications Council conference serves as a barometer for where digital infrastructure is headed next. And according to Nomad Futurist founders Nabeel Mahmood and Phillip Koblence, the message from PTC 2026 was unmistakable: The industry has moved beyond hype. The hard work has begun. In the latest episode of The DCF Show Podcast, part of our ongoing ‘Nomads at the Frontier’ series, Mahmood and Koblence joined Data Center Frontier to unpack the tone shift emerging across the AI and data center ecosystem. Attendance continues to grow year over year. Conversations remain energetic. But the character of those conversations has changed. As Mahmood put it: “The hype that the market started to see is actually resulting a bit more into actions now, and those conversations are resulting into some good progress.” The difference from prior years? Less speculation. More execution. From Data Center Cowboys to Real Deployments Koblence offered perhaps the sharpest contrast between PTC conversations in 2024 and those in 2026. Two years ago, many projects felt speculative. Today, developers are arriving with secured power, customers, and construction underway. “If 2024’s PTC was data center cowboys — sites that in someone’s mind could be a data center — this year was: show me the money, show me the power, give me accurate timelines.” In other words, the market is no longer rewarding hypothetical capacity. It is demanding delivered capacity. Operators now speak in terms of deployments already underway, not aspirational campuses still waiting on permits and power commitments. And behind nearly every conversation sits the same gating factor. Power. Power Has Become the Industry’s Defining Constraint Whether discussions centered on AI factories, investment capital, or campus expansion, Mahmood and Koblence noted that every conversation eventually returned to energy availability. “All of those questions are power,” Koblence said.

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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