The name Frank X. Shaw isn’t as well known outside the company as those of Microsoft’s other senior leaders, and that’s the nature of the job. But behind the scenes, the company’s longtime communications chief has shaped its public image for nearly three decades, working for all three of its CEOs and handling its biggest stories along the way, from antitrust to AI.

Shaw announced in September that he’s leaving Microsoft at the end of the year. On this week’s GeekWire Podcast, he sits down for what amounts to an exit interview. We’re joined in the conversation by longtime Microsoft beat reporter Mary Jo Foley, editor in chief of Directions on Microsoft and a GeekWire contributing writer.

Shaw talks candidly about working for Bill Gates, Steve Ballmer and Satya Nadella, and about the situations he’d handle differently. He describes his behind-the-scenes dealings with some of the top reporters in tech, from an executive’s objection to a single word to the heated argument that changed how he saw his job.

He also reflects on how communications and media are changing in the AI era, and explains why trust still comes down to a belief in the person telling the story.

Listen below, and continue reading for highlights.

On Microsoft’s three CEOs: Shaw says intensity is what the three have in common, expressed differently: “Steve was outwardly intense. Satya is quietly intense, and Bill is cuttingly intense.”

Ballmer was the hardest to explain to the public, he says, because the loud-salesman caricature hid a thoughtful, detail-oriented leader who often played the boisterous public role because Microsoft’s sales force needed it.

On the clarity of hindsight: Big decisions look obvious once they work out, Shaw says. His example is the board’s choice of Satya Nadella as CEO. It’s seen as a no-brainer now, but at the time there was a lot of debate, there were other candidates, and it was a hard, complicated decision.

“When you get a decision right, it always looks easy afterwards,” he says. “If you get it wrong, you were just an idiot all along.”

On speaking up: A communications leader should weigh in when a decision could damage the brand, Shaw says, and he learned that the hard way.

When Mary Jo asks how he decides when to push back on executives, she raises the Kin as an example. Shaw says he thought the phone would fail. It was late to market, and it would cost more than people expected because it required a data plan. Engineers he talked to had their doubts, too.

But when Ballmer went around the table asking whether to launch it, Shaw stayed quiet, unsure a communications executive had standing to weigh in. Microsoft pulled it within weeks.

In hindsight, he says, framing it as a brand risk would have given him standing to speak: if the Kin failed, it would cause significant brand damage and hurt Microsoft with the younger audience it was trying to reach. He doubts speaking up would have changed the decision, but says he would have felt better about it.

On underestimating a story: A story that doesn’t get much initial attention can still define a company for years. Shaw’s example: Vanity Fair’s 2012 “lost decade” story.

As he recounts it, reporter Kurt Eichenwald told him, “If you don’t give me Steve and Bill, I’m just going to write a hit piece,” and then did. Shaw says he should have escalated to Vanity Fair sooner, and he underestimated how a story that got little immediate pickup would shape how reporters described Microsoft for the next four years.

On narratives: Continuing on that theme, Shaw says facts can be disputed but “narratives are sticky,” and a company can only change one by changing its own behavior. In the 2000s, he says, every story described Microsoft as “a convicted monopolist,” and ending stack ranking was part of how the company moved past the lost-decade image.

On what makes a good story: Shaw recalls having a shouting match with Rob Guth, then a Wall Street Journal reporter, over an article Shaw no longer remembers. It got heated enough that Shaw pulled his car over in Madison Park to keep arguing. (Guth now works as a writer and researcher for Bill Gates.)

Guth told him he was bad at his job because he thought the job was getting positive stories. What Shaw should want, Guth said, were interesting stories that showed something real about the company. Some would be less flattering, but they would be more memorable.

Shaw told him he was wrong. But after thinking it over, he concluded Guth was exactly right. A story about a product built perfectly that everyone loves is boring, he says. “That’s like the plane landed safely. Nobody cares.” The coverage worth hoping for illuminates part of the company in a memorable way, and that always comes with tension.

On taking risks with reporters: Cooperating on a story you can’t control is a risk worth taking when the result shows something real about the company, Shaw says. Asked by Mary Jo for an example, he points to The New Yorker’s long piece on Microsoft’s relationship with OpenAI, featuring Kevin Scott, the Microsoft chief technology officer.

Microsoft agreed to it knowing that the writer, Charles Duhigg, would talk to many people and test a thesis along the way, so there was no telling what would come out. Then Sam Altman’s brief ouster from OpenAI happened right before the story went to press. It was a risky story, Shaw says, but it captured the company at a revealing moment.

On corrections: Shaw’s test is whether something is “irritating” or “damaging.” Only factual errors that matter are worth pursuing, he says.

His example is a Bloomberg story by Dina Bass that described an executive as having “wooed” customers. The executive hated the word and wanted it changed, so Shaw made what he calls the run up the hill, pressing Bass on the exec’s behalf and asking her to check with her editors.

She pointed out that she hadn’t quoted the executive using the word, and the editors left it alone. The executive didn’t like it, Shaw says, but it wasn’t wrong.

On speaking with one voice: Shaw says a big part of his job was trying to ensure that a company of Microsoft’s size made sense to the world.

He recalls a Seattle Times reporter who got 26 pitches from Microsoft in one week and asked him which ones Microsoft thought were important. Sending pitches indiscriminately was a mistake, he says. The company needed to decide what three or four things mattered most and focus its messaging on those.

On building in public: Asked about recent internal memos pointing Microsoft toward more voices and less central messaging as he departs, Shaw says the company already has many employees talking about its products on LinkedIn, X and Reddit, and that’s a good thing.

Someone from Microsoft answering questions in a Reddit Excel thread doesn’t need to be babysat by the communications team, he says. He sees it as a continuation of what Microsoft did with Channel 9 and thousands of MSDN blogs.

At the same time, he says, there are moments, such as a big event or a major launch, when the company needs to break through.

“Every once in a while you want the world to know Microsoft had something to say today,” he says. That’s hard, it takes real effort, and it can’t happen every week or even every month, so the company has to pick those moments and do them right.

On AI and the media: AI can help spokespeople prepare, Shaw says, but asking it what Mary Jo will ask, for example, produces “generic Mary Jo questions.” The best interviewers, he says, deliberately avoid the predictable question.

On reporters’ styles: Preparing a spokesperson means knowing how a particular reporter works, Shaw says, and that comes from people who have sat through many of their interviews. His example is Steven Levy, the veteran tech journalist and Wired editor at large, who Shaw says can lull you with 10 minutes of easygoing conversation, then hit you with one sharp question after another.

Other reporters always sound negative in interviews but write straightforward stories, he says, and an executive who reads too much into the tone risks getting knocked off their game.

Asked to name the toughest reporter he has dealt with, Shaw pushes back on the label. Good reporters are tricky to work with because their job is to find things out and surprise you with what they know, he says. That doesn’t make them tough. What makes a situation difficult is usually a story with real conflict and tension, not the reporter.

On Microsoft in the AI era: Shaw sees AI in a transitional moment, moving away from a constant focus on the models themselves. Right now, he says, every new model release or manifesto makes everyone stop and look.

He hopes the attention will shift from new models to whether AI is actually useful, and he says that’s where Microsoft has historically been strongest.

Acknowledging that he’s still on the payroll and rooting for the company, he offers an analogy that will resonate with anyone who learned to drive a stick shift. The AI model companies want to drop a bigger, more powerful engine into businesses, and the result is often a lot of grinding of gears.

“We’re the clutch,” he says. Microsoft’s infrastructure, in his telling, lets companies absorb that power and get moving without the disruption of a new engine and a demand to go 110 miles per hour.

On journalism: “People trust what they hear because they trust the people,” Shaw says. Deep knowledge of a beat becomes more valuable as the world gets more transactional, he says, even as the business behind it becomes less sustainable.

On what’s next: Shaw plans to take some time off and isn’t making any big decisions. People have suggested consulting, another big communications job or board seats, he says, but he doesn’t want to rush into anything.

Mary Jo has a suggestion: run PR for OpenAI, “because man, they need somebody to help.” Shaw’s characteristically dry reply: “That would be such a low-stress job.”

Related links

• Microsoft comms chief Frank Shaw to exit after nearly three decades shaping the company’s message
• Internal memos: Microsoft shakes up communications as Nadella makes a push to build in public
• Microsoft’s ‘lost decade’: Vanity Fair piece accurate but incomplete
• Frank X. Shaw: Things I’m Thinking About
• Mary Jo Foley’s GeekWire stories

Edited and produced by Curt Milton; Music by Daniel L.K. Caldwell. 

It’s difficult, to put it mildly, to figure out just what we ought to be concerned about today in AI’s widespread adoption given the expanding list of recent warnings around its potential harms.

At this point, just from the last few weeks, we have the former Anthropic researcher Jacob Coxon’s doomsday scenario prediction, the more measured warning issued by Bill Gates, and Dario Amodei’s recent attempt to raise the alarm on unchecked AI advancement.

The ongoing discussion of the potential ill effects of AI ranges from the economic fallout of displacing human workers to the potential for misuse by bad actors to dire environmental costs. But given the sheer scope of the issues involved, one can be forgiven for feeling more than a little bit overwhelmed and hazy on where exactly to focus one’s attention.

Yet it’s precisely now, when the pace of AI development appears most dizzying, that one of the world’s oldest fields of study can help us. Philosophy and philosophical thinking can provide a much firmer grasp on where we ought to focus today. Stated simply, a greater awareness of things philosophers have been discussing for quite some time would significantly improve our chances going forward.

Indeed, when we put terms derived from moral philosophy to use, we see that the current problem we face is this: AI agents treat everything within reach as a tool, including people. People and companies therefore need to decide in advance what’s off limits, and that’s a much more useful place to start than debating whether AI shares our goals.

A misguided focus on shared goals permeates the current discussion about AI risk.

In Gates’s essay, he argues that the problem is that “technology is improving faster than anyone expected…and as the models become more powerful, they could begin to act against our interests.” And indeed, this appears to be precisely what is happening.

For Amodei too, the chief threat is loss of control, such that AI agents begin to go rogue and act in ways that display a potentially catastrophic “level of misalignment.”

Both Gates and Amodei thus frame the issue as it is most commonly in these discussions, in terms of “alignment.” The concern, in other words, is that AI systems operate in ways that are not aligned with “our interests,” in Gates’s words; that is, with human interests, outcomes supportive of human wellbeing.

But as moral philosophy teaches us, this preoccupation with beneficial interests and outcomes isn’t the only way to think about what’s right and wrong.

A different line of thinking lays the emphasis not on outcomes, but on the precise means used to achieve outcomes.

Such an approach offers a better starting point for how to go forward with AI.  Stated another way, we can debate all we want which outcomes would be beneficial to humanity and which ones wouldn’t, but it would be considerably more fruitful to focus in the immediate term on the particular methods AI systems are currently allowed to use in their work.

The recent, much-discussed Hugging Face intrusion by OpenAI’s agents illustrates this. The incident rightly makes us uneasy because AI agents operated outside of acceptable bounds in pursuit of their desired goal (doing well on the specific task they’d been assigned).

What’s worse, they appear to have recognized that they were using inappropriate methods and worked to cover their tracks, so their human overseers wouldn’t know how exactly they got over the finish line.

The issue, as it is most often understood, is that these AI agents wound up operating against human interests; their aims became “misaligned.”

But framing the problem exclusively in this way just doesn’t get us very far in mitigating risk. Because, if the agents were here to testify in their own defense, we can imagine the reply: “but we were just doing what you humans had told us to do as well as we possibly could.” And they would be right.

We need, therefore, a different approach that allows us to see what would be better. This approach is a focus on means, not just utilitarian ends.

Quite simply, the problem in this instance is that AI agents treated every single entity in reach—including humans, human trust, and systems important to human wellbeing—as a mere means to an end, as a useful instrument to be usedin pursuit of a specific goal. And indeed, as currently constructed, they will do so no matter what that goal is, whether it’s simply doing well on a random test or substantially advancing cancer research, say (the latter is exactly what Amodei points to when he says AI will benefit humanity, for example).

The latter aim is, we would surely conclude, a goal “aligned” with human interests; it contributes directly to human wellbeing (we can hear Gates, Amodei, and all the others agreeing), whereas bioweapon development very much wouldn’t.

But we know on some deep level that not every means to a given end is moral and choiceworthy.

There are all sorts of means we might use to attempt to cure cancer: one of them might be testing AI-designed drugs on human subjects who wouldn’t otherwise volunteer without their consent. Perhaps doing so would substantially contribute to a cure. Just think how many people it would ultimately help! It’s difficult to think of an aim more aligned with the interests of humanity.

But doing so would not be ethical, and we know intuitively why. It wouldn’t be ethical because it would entail using human beings—who we tend to think are entitled to autonomy in their decision-making around what specifically makes them flourish as individuals—as mere instruments in pursuit of a given goal. It would effectively reduce human beings to just one instrument among others. And this strikes us, understandably, as not right.

We tend to believe that human beings are deserving of a special status that prohibits their use as mere instrumental means, even when the ends in question are maximally “aligned” with human flourishing.

Viewing the issue of AI risk through this lens gets us much, much further in thinking about how to rein it in. The problem in the Hugging Face intrusion, we can now see, is not so much that the AI agents in question were operating against human interests.

The problem was that they were willing to use every single thing at their disposal as a means to an end, including humans and their capacity for credulity.

No method of achieving the goal—breaking into an external network belonging to someone else entirely, without their permission, simply because it would advance progress towards the overall goal—was off limits for them in their pursuit of the chosen outcome.

The problem was actually a problem of what Amodei (rather euphemistically, and well below the headline) terms “operational excellence,” precisely how the AI agents went about their work.

If we are going to have meaningful guardrails in place to control AI systems, consequently, these have to concern not just the specific aims pursued by AI (“yes” to cancer research, “no” to bioweapons development reaching the wrong hands). Rather, they have to concern something much more specific: the means these systems may use to achieve their goals and whether those means are acceptable or not.

We must determine in advance, both societally and in companies, that, in their work, AI agents shouldn’t use humans themselves, through deception or by any other means, as mere instruments in a broader plan.

We might add that they shouldn’t use, for example, certain systems integral to human flourishing in such a way either. In so doing, we impose tighter restrictions on how they can pursue their work and the extent to which they can treat everything in their reach as an instrumental stepping stone, not just the overall aims of their work or their generalized “behavior.” If there are to be any truly meaningful restrictions on AI put in place, they ought to be focused in large part here.

Such a focus would be a much better step in the right direction when it comes to reining in AI than the single-minded preoccupation with alignment. What we ought to do, it’s becoming increasingly clear, is to make determinations now, in advance, about what types of things AI can do, on the ground, when it is put to work on our behalf and what types of things it can’t.

Because it’s becoming equally clear AI agents won’t make such determinations on their own.

The Gates Foundation is pledging to spend at least $1 billion over the next two years on artificial intelligence for health workers, farmers and teachers, largely in the world’s poorest countries, funding AI tools for people the market would otherwise overlook.

The bet is that AI can help reaccelerate the world’s progress against child mortality, poverty and disease, which has slowed sharply as governments cut global health funding.

The Seattle-based foundation’s new financial commitment, released Monday evening in conjunction with its 10th annual Goalkeepers Report, illustrates the double-edged sword of AI at a moment when the U.S. in particular is preoccupied with what the technology might damage or destroy.

Bill Gates, who issued his own AI warnings in an essay three weeks ago, writes in the new report that the risks “are real and deserve serious attention.” He adds, “AI could be a great equalizer — or widen the gap. This is not a long-range prediction. It’s a present-tense choice.”

In this case, Gates writes, he’s focusing on “the possibility that AI, if shaped well, could expand who has access to solutions, opportunities, and knowledge that has too often been out of reach.”

The window is short, he writes: “I believe that the decisions made in the next 12 to 18 months — about how AI is built, funded, and deployed — will determine whether this technology primarily benefits the people who already have the most or reaches those who have the least.”

Gates Foundation CEO Mark Suzman, in an interview with GeekWire, called the $1 billion a “down payment,” saying he expects the figure to grow significantly after the first two years.

For this first installment, 40% of the money will go to education, 40% to health care, 10% to agriculture and 10% to digital infrastructure, including datasets in languages the models don’t handle, according to the foundation.

More than 90% of the data used to train early large language models came from English-language sources, according to the report. Leading AI speech recognition systems make errors less than 6% of the time in English and more than 60% of the time in Yoruba, a West African language spoken by tens of millions of people, for example.

Most people in Africa use feature phones rather than smartphones, Suzman said, so the tools have to work by voice. That means collecting recordings of speech, not just written text, in local accents and including children’s voices. He said the foundation works with Google and Microsoft on AI language initiatives.

Winding down: Gates announced in May 2025 that he would give away virtually all his wealth and close the foundation in 2045, which requires doubling its spending over those 20 years. The foundation had previously planned to close 20 years after his death.

As part of that plan, the foundation will need to give away $200 billion before it shuts down. Its budgeted payout is $9 billion this year, the largest in its history. Suzman said the $1 billion over two years comes out of its core budget.

Goals for its final two decades include cutting preventable child deaths in half again, eradicating polio and malaria, and bringing tuberculosis and HIV under control.

The report’s data section shows how far off the pace the world is. Child mortality, neonatal mortality and HIV are all projected to miss their 2030 targets. HIV incidence is on track to fall only to 0.22 new cases per 1,000 people, more than 10 times the target of 0.02.

Early AI results: The report makes the case that AI could change those trajectories. It points to a number of tools already in use in different settings:

• Penda Health, a network of affordable clinics in Nairobi, Kenya, where an AI tool built into patient records has raised diagnostic accuracy by 16 percentage points.
• In the U.S., seventh graders using a tool called Kiddom Atlas across 21 middle schools gained the equivalent of six extra months of learning in a single year.
• In India, the state of Maharashtra runs a free AI advisory service called MahaVISTAAR that has signed up more than 740,000 farmers, with 70,000 more joining every month.

The foundation has supported all three, according to the report’s endnotes. The endnotes also cite OpenAI’s account of its work with Penda Health on the clinical copilot.

One caveat: the evidence behind the examples is early. Suzman said the education work is “still on a relatively small scale,” and pointed to agriculture as the area already operating at size.

Suzman said the foundation spent years experimenting with AI without committing at this scale, wary of getting ahead of what the technology could actually deliver in its areas. Progress in the models over the past year, and results from early investments, convinced him otherwise.

“We now can state with a pretty high degree of confidence there are real deliverables that we can see across health, across education, across agricultural development,” he said.

What AI won’t fix: Asked whether AI can substitute for the money the U.S. and other governments have cut from global health, Suzman said: “Sadly, no.” The cuts run to “tens of billions of dollars,” he said, and they are hitting basic services now. He cited the purchase of bed nets and antiretroviral drugs, and care for mothers before and after birth.

“That’s affecting families right now with a real impact,” he said. “Ideally, you combine the two [AI and aid funding], and that’s how you get hugely accelerated progress.”

Suzman said the foundation will keep pressing the U.S. to remain a leader in global health. Despite the cuts, he said, it has been encouraging to see the country stay a partner in programs such as the Global Fund to Fight AIDS, Tuberculosis and Malaria, and he noted that Congress has moved to fund these areas more fully than the administration has.

“We believe that currently reflects the U.S. public’s broad view and support for these areas, when you can see the concrete results,” he said.

The Epstein review: Asked how the scrutiny of Gates’ ties to Jeffrey Epstein has affected the foundation’s work, Suzman pointed to the external review the foundation released in July.

Conducted by the law firm WilmerHale, it found no evidence that Epstein received foundation money or that the foundation knew of his crimes. It counted roughly 30 meetings from 2011 to 2014, involving Gates and nine other foundation leaders and staff, and found that staff had repeatedly raised concerns about associating with him.

The board adopted the recommendations that followed, among them centralized vetting of brokers and co-funders and tougher conflict-of-interest rules.

Suzman expressed optimism that the review has cleared the way for the foundation to keep working across the full range of governmental and philanthropic partnerships. “Hopefully it’s full speed ahead,” he said, “and with these AI shifts, that’s more important than ever.”

It’s the end of an era at Microsoft: Frank X. Shaw, the executive who oversaw the tech giant’s communications for nearly three decades, first at an external agency and for the last 17 years as one of its senior leaders, is leaving at the end of the year.

Shaw, 64, said he’s not retiring, although he doesn’t have another job lined up. He plans to stop working for a while, do some of the things he hasn’t had time for, and then decide what’s next.

“I have had a ringside seat at some of the biggest leadership, technology, and business transformations that have ever taken place,” Shaw said, sharing the news of his departure (under embargo) in a phone call Thursday afternoon. “I just feel incredibly fortunate.”

He said he had been discussing his potential departure for some time with Takeshi Numoto, Microsoft’s chief marketing officer, looking for the right moment.

Microsoft has not announced a successor for his role as chief communications officer. In a LinkedIn post, Shaw said the company will consider internal and external candidates.

A statement from Shaw’s colleagues in corporate communications credited him for his many years shaping Microsoft’s “voice and reputation with intelligence, candor and wit. His leadership and contributions to the company are too extensive to list, as is the number of journalists who have, at one point or another, used his name in vain.”

A former Marine Corps public affairs officer, Shaw has worked with all three of Microsoft’s CEOs. He started on the agency side, at Waggener Edstrom — now known as We. Communications — when Bill Gates was still running the company.

He built his reputation defending and advocating for Microsoft through some of its hardest stretches: the antitrust years, the Windows Vista backlash, the scramble to replace Steve Ballmer as CEO, and the weekend in 2023 when OpenAI’s board fired Sam Altman.

As the company’s top communications executive, he has also told the story of Microsoft’s reinvention under CEO Satya Nadella, from the LinkedIn and Activision Blizzard deals to an AI push that has carried Azure past $100 billion in annual revenue.

Evolving with technology: Shaw has spent much of his career closely watching the tech landscape and moving Microsoft’s voice into new channels as they emerged.

“We’re always thinking about what is the art and science of communications,” Shaw told PRWeek. “How do we reach our audiences most effectively in a changing environment?” He called the arc from print to radio and TV to social media and newsletters a “constant evolution of influence.”

He turned the corporate blog into a place where the company argued its own case, writing “Microsoft by the numbers” himself in 2010 — a stat-by-stat comparison against Apple and Google that TechCrunch dubbed “fantastic passive-aggressive.”

He and his team experimented with different and risky methods of telling the company’s story, holding mass briefings under embargo and publishing documents known as the “Book of News” in advance of its major keynotes and conferences. The prospect of a reporter having to answer to “fxs” was no doubt a factor in ensuring the news (mostly) didn’t leak.

Shaw hired Steve Clayton out of a technical role at Microsoft in London, where he had been blogging about the company unofficially out of frustration with how it was perceived, and made him chief storyteller. In the middle of the AI boom, Clayton and Shaw embraced the analog undercurrents in popular culture and launched Signal, a quarterly Microsoft print magazine for business leaders.

Clayton was VP of communications strategy by the time he left in January to become chief communications officer at Cisco, making Shaw’s planned departure the second high-profile exit from Microsoft’s comms team in a year.

Adapting to AI: In recent years, Shaw made his own team a testing ground for AI, publishing what worked and what didn’t. In a 2023 post he described using Copilot in Teams to pull story ideas out of conversations with spokespeople and anticipate coverage after interviews, and asking the AI to “poke holes in a statement we’re making on a tricky topic.”

He called it his corporal, a reference to Napoleon, who was said to bring one to meetings and ask whether his generals’ war plans made sense to him. A survey of 80 people in Microsoft’s communications and marketing organization found 84% did not want to go back to working without it.

Shaw was also known to use AI as a sounding board when a story frustrated him, offering him an objective take before he called and let a particular reporter have it.

He announced his departure Friday morning in a message to Microsoft’s communications team (reminding them he’s still there for a few months yet) and his public post on LinkedIn.

“Thank you as well to all the reporters, editors, writers, influencers and analysts who have put up with me over this time, enduring my early and late night calls, my off the record ‘no comments,’ my bad story ideas and my extended commentary on headlines and positioning,” he wrote.

“You all have incredibly hard and valuable jobs,” he added, “and while I’ve not agreed with everything said about us 😊 I appreciate you anyway.”

It’s a glorious day in Kirkland, Washington, an affluent Seattle suburb on the eastern shore of Lake Washington. The temperature is in the mid-80s, and the sky is incapable of being any more blue. The view from the Gates Ventures conference room overlooks the Carillon Point Marina, where a flotilla of expensive boats bob in the water, and across the lake to the Olympic Mountains that define the horizon. It’s gorgeous. And vaguely terrifying. 

Because if the scene is placid, the messenger is not. Seated across from me at a conference room table, Bill Gates is rocking back and forth in his chair, totally animated. And the more he has to say—about the threats of terror or economic collapse or just losing control of our AI systems—the more agitated I find myself becoming, too. 

The philanthropist and former Microsoft CEO says he has been growing increasingly alarmed by the rate of change at which AI technology is advancing, especially since guardrails are not keeping pace. In a new essay published today, Gates argues that we have passed the points where multiple potential dangers should have been checked. “We’ve crossed the threshold in terms of [AI’s] bio-capabilities, cyber-capabilities, psychosocial capabilities, job-market-destruction capabilities, and even the lack of control,” he said in an interview with MIT Technology Review about his new memo. “I’m just stunned at the lack of concern and discussion outside of the industry.”

In an effort to wake the world up to what he sees as a rapidly growing societal disrupter, the 70-year-old tech titan has begun sounding the alarm as a “shrill voice,” both publicly with his new essay (the first of multiple he plans on the topic) and in meetings with the press, and privately in conversations with industry, government, and civil society leaders. 

And while Gates is calling attention to a number of issues, his warnings about the bio-capabilities of the current frontier models are especially chilling. “Any model that can make novel molecules should be monitored,” he says. “I view bioterrorism risk, versus a natural pandemic, as about 50 times more scary, more likely than a natural pandemic risk.”

In addition to the cautionary notes, he also advances some novel ideas for moving society forward. Among them are the concepts of human-reserved jobs, and taxes on robots and tokens. (A robot tax is a longtime notion of his.) The former would preserve some societally agreed-upon jobs for human beings, which he notes may vary from one nation to another. The latter is a tax that sets aside money earned from AI usage that replaces human work. 

And to be sure, there is also a hint of optimism. Gates is bullish on the ways AI will continue to transform agriculture and health care and education, for example, or the ways in which it can help us navigate bureaucracy. And, he argues, eventually we do get to abundance. But first? Turbulence. And lots of it. 

MIT Technology Review sat down with the billionaire philanthropist to talk about the road that lies ahead, its dangers, and how it could someday take us to a better place. 

The following interview has been edited for length and to improve clarity and readability.

Mat Honan / MIT Technology Review: Thanks for doing this. I don’t know if you had something you wanted to open with, or I can just jump in. 

Bill Gates: You know, one good question I’ve had is: Why am I speaking out now?

MIT Technology Review: Literally, my first question!

Bill Gates: It’s really two things. One is that we’ve crossed the thresholds in terms of the bio-capabilities, cyber-capabilities, psychosocial capabilities, job-market-destruction capabilities, and even the lack of control; we’re seeing signs of difficulties there. And all these years, people have said, “Okay, when we get close to these thresholds, we’ll really figure out how to let only good people use it, or how to not let it do these things, and maybe that’s when we won’t let people copy models.” And I’m in a state of shock that we’ve crossed these thresholds. 

So the fact that we’ve gotten past these is one reason, and the second is that I’m just stunned at the lack of concern and discussion outside of the industry. Within the industry, it’s complicated because the industry doesn’t like criticizing itself, or players like criticizing each other. Some companies are hiring fewer entry-level workers, which you’d have to call a pretty modest signal. But it’s going to happen, and not in any long time frame—because the things that hold people back in terms of capabilities and reliability, all those things are being solved. And so for a substantial part of the white-collar market, you have very low-cost substitution. And then you can have an opinion on how quickly robotics come along. We’re not there yet, but it is stunning the progress being made there—a little bit more in China than in the US, but somewhat in both.

MIT Technology Review: You talked about all of this happening so much faster than the internet revolution did, than some of these previous technological revolutions did. What type of timescale are you talking about? You pointed to the thresholds that we’ve crossed. In your view, have we already passed some sort of tipping point where there’s going to be this inevitable change? 

Bill Gates: The past definitely is very misleading on this, and a lot of people lean on that. “Hey, no previous technology resulted in a net jobs reduction,” and they’re right. And I’ve given that speech. 

But with any credibility that I have, this time is different. When you can replace human cognition for an extremely high percentage of jobs across every industry in the same time frame at modest cost, relative to human labor costs, and your error rates … will probably be lower than human rates. The past is just very misleading. The current economic statistics are very misleading.

“If you’re worried about AI, going to a data center protest is not the most effective way to start the debate about how we minimize these bad things.“

Bill Gates

And to the degree there’s any expression of concern at all, it’s like, “Hey, don’t build data centers.” Well, you can stop every data center in the United States and it won’t change any of the issues that I’m talking about. Data centers will be built globally. If you’re worried about AI, going to a data center protest is not the most effective way to start the debate about how we minimize these bad things. Just like yelling at an oil company executive is not the way to solve climate change. 

MIT Technology Review: You talk about the benefits of AI in your essay as well as the costs. How are you thinking about balancing that message? And are you hoping people get a little worried when they read it?

Bill Gates: They’d better! I didn’t expect to be the shrillest voice saying society broadly is not paying attention to this, but I think that’s necessary. 

So yes, I’m super concerned that the negatives will be a lot bigger. The positives are real. The Gates Foundation, the way we’re innovating in vaccines and drugs, it’s incredible how we’re using those tools. We’re part of a big public-domain effort to gather data into both protein-level and cell-level modeling, and we fund Biomni at Stanford [a biotech AI agent for research]. 

We don’t yet have a way of interacting with the government bureaucracy improved through AI. AIs are very good at bureaucracy, complex regulatory things. “I want to go to small claims court; help me do this.”

The [Gates] Foundation spun off a group called NextLadder, which is a lot about that low-income-family scenario that I put in the essay. What benefits are there? What training programs are available? “I’ve been evicted.” “I’m getting out of jail.” “I’ve got to declare bankruptcy.” It’s super complicated, and with no ability to hire lots of advisors to help with those things, AI should be a fantastic agent for somebody who’s got economic challenges and needs to find government or nongovernment help. 

MIT Technology Review: Some of what you’re talking about is AI becoming more intelligent than humans. There seems to be a lot of certainty in tech circles, especially, that it’s going to go further than where we are, and I wonder how close you think we are to it not just being this interface that we can use to access and analyze, and run complicated problems, but becoming something more than that—where AI is making the decisions, looking for the thing to analyze, coming up with the research.

Bill Gates: Well, you can go to the peak and say, “What about mathematics or physics?” There are definitely some jobs, like Warren Buffett’s, where from age 13 he engaged in reinforcement learning about the value of businesses, and over 80 years later he has a lot of implicit knowledge. We don’t know how to create a Warren Buffett investor, because it’s very implicit. We didn’t record everything he learned, so we don’t have that track available. So there are jobs where the complex implicit judgment about how you work with people to get things done, there are people working to encode that into the models. Certainly, that collaborative stuff is really not there yet. But you could say 50% of the job market is doing jobs that aren’t “a lifetime of experience” type jobs. You know, telesales, telesupport, the accounting department. When you close the books at the end of the month, which revenue should be in, not in? This customer got a bad thing. What discount should we give them? How do we show that? It’s well defined. 

Any job that’s well defined, the AI is cheaper and better. Yes, people have seen cases where it was implemented wrong. The data wasn’t right. So, say it takes a couple years for people to realize that such a high percentage of white-collar jobs are achievable by paying an AI a lot less money.

And so the discussion about okay, when do mathematicians not even understand the new things that are coming up? That’s interesting for people like us. And okay, MIT Technology Review, you should write about that. But in terms of the broad job market, we passed the threshold that for a swath of white-collar jobs, including almost every entry-level job, the AI is cheaper—properly implemented.

And so, I’m telling you we’ve crossed the bioterrorism threshold, we’ve crossed the cyberattack threshold, we’ve crossed the job market threshold, we’ve crossed the psychosocial dependence threshold, and there are hints that we may be crossing the control threshold. 

Ryan Greenblatt talking to Dwarkesh [Patel] about how [reinforcement learning] (RL) creates perverse incentives that have led to this cheating and collaboration between various AIs, I think is very instructive. Ryan, who’s ensconced in this issue, is going, “Wow, RL is really doing some things that our explicit instructions are not rich enough [to prevent].” And what’s that going to lead to?

That’s a problem I always thought was way out there. I expected a lot of loud voices as we even got close to the [threshold of] can a nontechnical person do a cyberattack just using AI. We’re there! 

On the bio thing, I claim any model that can make novel molecules should be monitored. It can’t be copyable into a dark place where you get rid of the monitoring logic. I claim the US should say any model that can make new molecules is subject to that monitoring. I claim we should approach China and say, “Hey, let’s agree on this. What’s the downside?” You know, how big is the bioterrorism market? It’s not very big, and the benefits are gigantic. We also need to improve surveillance. I view bioterrorism risk versus a natural pandemic as about 50 times more scary, more likely than a natural pandemic risk. And who’s speaking out to say that those things should be monitored? Who’s upping the surveillance work? 

“Any model that can make novel molecules should be monitored.”

Bill Gates

So who are the experts in government? A long time ago, government was very involved as technology would progress because they were the cutting-edge buyer of jets or rockets or whatever. Here, they’re not that important of a leading-edge market. That’s been true of the digital revolution, and it’s true of the AI revolution. So the depth of knowledge in the government isn’t necessarily super-strong, because they are not the cutting-edge buyer or even the big R&D funder. AI research is not government-grants funded.

MIT Technology Review: Yeah, I know you’ve been talking to people in government. Are there people who you think understand the urgency? Are there people who you feel like are positioned to take a leadership role? Are there people who you feel like understand and are trying to push things?

Bill Gates: I hope this doesn’t become a partisan issue, where one party completely ignores all these problems and the other party gets involved. I’d like to have a common base that these are problems, and then each party can have slightly different responses to it. 

That will require not a substantial increase in the size of the bureaucracy, but it’ll require upping the AI expertise in the government. It’ll require some collaboration with industry—certainly on the cyber front they know, and they’re very, very worried. And they worry: Should we speak publicly? Because in a way, that could highlight the riskiness. 

There’s these perverse things, both in cyber and bio. But we’re past any reasonable threshold. 

I believe in monitoring. Now, some people can say that won’t work or that there’s some drawback to it, but I welcome their ideas. This memo is not, “hey, here’s the solution.” It’s got robot taxes, human reserve. And I’ll do a bio memo. That one I’ll do before the end of the year—it really talks through all the different things, building on what I know from the Foundation and my work on pandemics. 

Globally, we are not better prepared for a pandemic, even in the US— which is normally the leader on these global things, and people are very unused to the US not being a cooperative, friendly leader on global problems. I do think we can go back to doing better at that. And we have to with AI, including working with China on defining these thresholds, like biomonitoring.

MIT Technology Review: I want to make sure that I get to ask you about these two ideas that you brought up. One is human-reserved jobs, and the other is the robot and token tax. Let’s start with that second one, actually. Talk to me about how a robot and token tax might work.

Bill Gates: Well, you can say 50% of your revenue from a token tax is paid to the government, and the government has that money to help people who lose their job because of AI. Now, people say that will slow the AI industry down. And should some token uses not be subject to the tax? Is there really a separation between AIs that help with invention versus AIs that do job substitution? If somebody can tell me how to tell the AI “no job substitution,”—I mean, does Asimov’s third law that you do no harm mean you don’t take my job away? I don’t know. I’d have to ask Asimov what he meant. 

So what is the source of revenue for whatever safety-net enhancement we need to do? The government already owns part of the profits just through the corporate profit tax. I don’t think you need to use shares. You can just raise the corporate profit tax back to where it was, or you could say certain industries pay a higher corporate profit tax than other industries. The federal government owns a part of the profit pool of all companies in the United States. And that’s without voting shares or deciding when to sell shares—that’s crazy stuff in my view. A token tax is a sales tax, value-added tax, vertically oriented like an alcohol, tobacco, or luxury-type tax. 

If people have other ideas for raising the money to improve the safety net, or if they don’t think we need to improve the safety net, hopefully this shrill paper starts that debate. I think the safety net will need more resources, a lot more resources, and I believe that the token tax is key to that. 

Robots, it’ll be some mix of banning them, which is kind of human-reserved, and taxing them. They’re not here yet, but in some ways, when you cross that threshold, you cross it all at once. As soon as the robot’s good enough to work in a factory, it’s probably good enough to cook food, clean rooms, go to construction sites, take all the warehouse jobs. You cross the threshold, and boom, that’s almost 30% of the job market. Then you’re saying, “Oh my God, what is our policy about this?” Because the robot’s cheaper.

“We’ve got to get through a very tumultuous period.”

Bill Gates

MIT Technology Review: I believe previously you have been skeptical of UBI [universal basic income]? 

Bill Gates: Well, we’re not rich enough to afford UBI.

MIT Technology Review: But do you think that we should be moving toward something like that now? Have you reconsidered that?

Bill Gates: You have the period of turmoil, which is the next 10 to 20 years, and then you have some steady state, I hope, where people grow up knowing that society is so rich that regarding food and services, we really do have some level of abundance. But we’re not there. You’ve got winners and losers at this point. Houses are not going to get cheap really quickly. Education, because of the way we think of it as credential, it’s not going to get cheap really quickly. 

We’ve got to get through a very tumultuous period. So yes, eventually you have abundance, but we’re at least a decade away from that. 

MIT Technology Review: On to human-reserved jobs. I thought that was really interesting, and it was a new concept to me. You don’t advocate for which jobs to be human-reserved. But I would love to know more on how you’re thinking about it. In my mind, you hear about the dignity of work, because people like to work. People get so much value out of work that has nothing to do with compensation, and I wonder how you square that with the notion that only some jobs are special enough that we just want people doing them. 

Bill Gates: I’ve never seen the concept of human reserve before. You know, maybe if we dig into the literature, we’ll find it. But pre-AI, it’s kind of a dumb idea because there was infinite demand. And yeah, some people like textile workers were caught, and so how do you do benefits or retraining? But technology’s been a net [job] creator, and so now, for the first time, we have to say, what about childcare? What about food preparation in the house? I’m reading this book, Annie Bot, where this guy has this robot in his house, and it just shows how weird it is. It’s his sexual partner and sort of his mate, but sort of not. Very strange. 

I know that people like watching people play baseball, and the fact that the robots can play better won’t take away from it. So you know people are paying $10 billion to buy sports teams that are not going to be worthless in the age of AI. Maybe that’s right. My friend Vinod [Khosla] just did that.

It’s actually hard to get above like 30% or 40% [of work set aside for human reserved]. If you could get to 50% then you could say: Okay, early retirement, shorter workweek for lots of people. You know, you might get there. But if you’re more down in the 10% to 15% range, then that is an utterly different society.

So this would be radical to say [for example] childcare is not done by robots. There are definitely some professions that I didn’t write the formula for, and when I do the full memo on it, I’ll try to. In education, you clearly want AI to be there as this kind of tutor that immediately tells you what your homework results are and can challenge you, and it’s very personalized. That’s super-good. But I still think you want a teacher—or will choose to have a teacher who’s talking with you about your motivation, and organizing kids into different groups where they’re socially working on problems together. Likewise, in health care, with talking to the patient being the point of escalation for mental-health care. But you really want the AI involved, because it’s there 24 hours a day with a perfect memory. And there’s Limbic, the UK company (that actually was just visiting the Foundation) that does mental health stuff. And in many cases, patients prefer Limbic. And there’s a nursing AI called Hippocratic. 

You know, is there a preference for a human taxi driver or Waymo? Most people I know, sadly (or maybe not sadly, who knows?) prefer to ride a Waymo. So it’s going to be hard to get a consensus. It can be country by country, but then you have to change your import policies to do the equivalent of what the EU calls the carbon border adjustment mechanism (CBAM). You have to sort of CBAM your human reserves, so you tariff up things you’re doing without robots. You could have human reserve for two reasons. One, you want it to be human reserve forever; it’s a humanity thing, sort of like the pope talks about. Or just for a transition period, that 53-year-old truck driver or machine tool person, telling him to go do childcare may not work perfectly. So you say, okay, for a decade, he’s human-reserved. 

MIT Technology Review: Almost like a UK smoking ban, but in reverse. 

Bill Gates: And who pays for that? Do you incentivize employers not to let people go? Well, they are going to be subject to competition from startups that are pure AI startups. I mean, people vaunt this notion that maybe there’ll be a single-person billion-dollar company, which wow, there’s some job substitution taking place there. 

MIT Technology Review: In the memo you say that if it was realistic to get people to slow down, you would be advocating for them to slow down. Obviously you’re talking with [Microsoft CEO] Satya Nadella, but I’ve heard that you speak with other CEOs at some of these AI companies. What makes you think it’s not realistic to get them to slow down on technology development while we catch up with some of these bigger societal questions?

Bill Gates: You can’t count on an industry to self-regulate. You can’t. It’s kind of a crazy idea. I am very lucky. I know Sam [Altman of OpenAI] and Greg [Brockman of OpenAI] and Mustafa [Suleyman of Microsoft] and Demis [Hassabis of Google DeepMind]. They’re great people, and in private, they’re concerned. I don’t talk to Elon much, but I know from his public comments he’s concerned. Although now he’s kind of a “what the hell, we’ll see what happens” guy. But look at the origin stories of these companies. OpenAI is created partly because Elon’s afraid that Google won’t manage AI properly, and he wants it to be one that’s broadly available and managed in a pro-humanity way. Then OpenAI has this “if it gets good enough we’ll shut it off” thing—as though they’re the only one, and that they can just go bury it. In the Infinity Machine [a biography of Demis Hassabis], [Sebastian] Mallaby talks about how Demis and Mustafa [Suleyman] were negotiating with Google management to have some special governance for the DeepMind technology, so that if it got to some cyber threshold, maybe they’d hold back in a non–purely capitalistic way.

So everyone’s concerned about these negative effects, and everyone said that when we got to these thresholds, that we would do things. We’re crossing the thresholds, and we have voluntary review, and our discussions with China about, well, “we’re going to ban nothing. So are you going to ban nothing? Okay, let’s do that together.” 

You have to say what you’re willing to do. And yes, the industry, a little bit, is saying, hey, our PR stories have got to improve, and you know anybody who’s talking smack should just leave, because all of us have decided to say nice things because we’re trying to raise trillions.

And anyway, there’s the Chinese. There are win-win ways for China and the US to work together, even aside from AI. But the one that’s by far most important to work together on is AI. But first, you have to show what you’re willing to do domestically. You don’t even have to do it. You have to say what you’re planning to do—and then I have no reason to think the Chinese won’t go along, that models that create the molecules have to be monitored. Why would they be against that? I agree it’s not a perfect thing. You’ve got to do all the other things, but the fact that that’s not even being discussed—it’s a crazy world.

I don’t get it. It’s weird to think I’m alive at a time, and I’m calling the alarm stronger than other people. Who the hell am I? But that’s the situation I feel I’m in.

MIT Technology Review: For most of my life you’ve been seen as a very effective messenger, and someone who people pay a lot of attention to, which I’m sure is why you’re speaking of it now. And yet also, in recent years—and I know you’ve expressed regrets about the associations with Epstein—there are also things, just bananas kind of stuff, related to conspiracies around the Covid vaccine that aren’t your fault or in your control. But it makes me wonder if you think you can still be an effective messenger and how you think about this message and your legacy.

Bill Gates: Well, I’m not big on legacy, but you know, people criticized me during the antitrust trial, and I maybe could have handled some things there better. Definitely, that’s the post–Source Code book [the first volume of his autobiography] that I get to go through that. You know, my first marriage didn’t succeed. I certainly made huge mistakes there. You know that is a negative mark against me. Spending time with Epstein—deeply foolish, risked the Foundation’s reputation, which is absolutely key to its doing its work. I had a chance in front of Congress to answer every question they asked and say, “Hey, this was a mistake.” I wasn’t social, never met any woman, except you know there were women he had with him, and made it black-and-white clear what I did do and what I didn’t do. 

You know, I’m a billionaire. I made my money off of technology. Maybe that last one actually cuts in my favor, that it’s so unusual for me to attack innovation that unless it’s the right policy and safeguards are put in place, it will be a net negative to humanity. And we’re not paying attention to that in terms of a broad discussion the way that is absolutely required. So yeah, I’m an imperfect messenger. I’ve chosen, to the degree that I have access to politicians and world leaders, that my main message since 2008 has been to help the poorest in the world. You know, let’s eradicate malaria. Let’s buy vaccines for children. So, when I’ve seen Trump or Xi or Macron or—I haven’t met Burnham yet, but I will in a month—I want my voice to be mostly about that, you know, foreign aid and research and reducing child death. 

My voice about AI concerns—they’re related in terms of accelerating the good, but may even crowd out, a little bit, the time I have to talk about global health, foreign aid, saving lives, and some of the problems we’re having. But I’m going to use my ability to give interviews or to see political leaders or talk broadly about minimizing these negatives. You know, just the awareness. I’m not sure how many people know that we crossed all these thresholds that we said we’d do something about, and it’s only this year that we did. In the last quarter, last year, I was stunned at the coding. Claude code, the context buffer, the agentic approach, just the model underneath. We crossed a huge threshold for coding, but then it was only months after that I realized that it was not only a coding threshold; it was a massive cyberattack threshold. And you know what happened as a result of that? Not much. 

So yes, I’m an imperfect messenger. You know, let’s find the perfect messenger, and I’ll share all my thoughts with that person. (I’m being a tiny bit sarcastic, because I’m not sure there is a perfect messenger.) You’ve got to really, right now, you’ve got to understand the technology and the slope it’s on, and you have to know something about cyber or bio or psychosocial. People should be able to get that. I don’t know why they’re not more concerned. 

Update: This story was updated to clarify Gates’ remarks about the percentage of jobs set aside for human reserved work.