As the heads of leading artificial intelligence companies came together last weekend to warn that runaway AI development could lead to catastrophic outcomes, momentum briefly seemed to be building for regulatory action from Washington.

But one connected and influential tech industry figure has become the leading voice opposing many proposals for government action — on his podcast, in media appearances, and in his private advice to President Donald Trump, who now calls AI doomsday fears a “hoax.”

David Sacks is a venture capitalist and part of the “PayPal Mafia” who, in recent years, has exemplified the rightward shift of some in the tech sector. He advised Elon Musk on his takeover of Twitter in 2022, and regularly unleashes a stream of scornful commentary about Democrats on social media and on the podcast All-In, which he co-hosts with three other venture capitalists. 

And for anyone wondering why Trump seems so newly dug-in against AI safety warnings — and against government action that might slow down the runaway technology before people might get hurt — Sacks is right at the center of the conversation. When Politico asked this week about Trump’s newly vocal hostility to AI safety, a senior White House official said: “David Sacks. It’s all coming from him.”

Yet Sacks’s role in the debate is an unusual one. Though he champions the AI industry, he’s no fan of the US’s two biggest AI companies. In his years in the tech industry, he’s made powerful allies, as well as many enemies — technology investor Paul Graham once posted that Sacks may be “the most evil person in Silicon Valley” (in response, Sacks called him a bully and made insinuations of antisemitism). His role, and ideology, are a window into how complex the politics and geopolitical stakes of the AI race really are — as well as how Trump administration policy is often shaped less by agency policy processes than by who has the president’s ear.

After endorsing and fundraising for Trump in 2024 — “I love David’s house, what a house,” Trump said after a fundraiser at Sacks’s $20 million San Francisco mansion) — Sacks was rewarded with a job as the White House’s “artificial intelligence and crypto czar.” There, he helped steer policy until stepping down this past March. He still co-chairs a presidential technology board, and his influence persists — in May, the Washington Post reported that a phone call from Sacks to Trump temporarily derailed the planned signing of an executive order on AI. (Vox reached out to Sacks for this article, but he declined to comment.)

Sacks is not the only Trump adviser thinking about AI: Other top administration officials, like Treasury Secretary Scott Bessent and Chief of Staff Susie Wiles have reportedly been more concerned about the risks of new AI tools for hacking, and have been trying to establish a government body that could help oversee the industry and address these risks.

But, so far, Trump has rejected this approach, and stayed much closer to the Sacks position. In a flurry of Truth Social posts Monday, he argued that AI would be “the Greatest Economic Development Engine in History,” said that the US had to “win” against China in the AI race, and dismissed concerns AI would “destroy the World” as a “hoax.”

To understand the implications of Sacks’s influence, you have to understand his position in the Silicon Valley AI ecosystem. In conversations with AI policy insiders, it becomes clear that many view his position through the lens of both his history as a tech investor, as well as his sharp-elbowed and take-no-prisoners style.

The split he embodies isn’t just about AI safety, but a fundamental divide over which business model of AI should succeed — one that pits Sacks directly against the biggest AI companies driving the safety debate.

Sacks is pro-AI — but he’s at odds with Anthropic and OpenAI

Sacks has generally been opposed to new regulation of AI companies by the government. His preferred approach, he’s frequently said, is to “let them cook.” He wants to let tech companies march ahead, and argues that government intervention would squelch innovation. “It’s this very libertarian approach to America winning in AI,” said Chris McGuire, a senior fellow at Council on Foreign Relations who worked on tech policy for President Joe Biden’s National Security Council, of Sacks’s approach.

That puts Sacks in opposition to AI safety advocates, who argue that regulation is necessary to rein in an increasingly dangerous technology. Indeed, even the leading AI company CEOs — Dario Amodei, Sam Altman, and even Sacks’s friend Elon Musk — have called for a slowdown in advanced AI development because of these dangers.

But Sacks has tended to dismiss warnings such as that of former Anthropic employee Jacob Coxon as “doomer histrionics.” Of late, he’s also argued that if Anthropic and OpenAI are worried that their products are dangerous, that’s on them. “If the unreleased models are scary enough that you think you should slow down, I support your decision to be responsible,” Sacks posted on X on Saturday. 

Indeed, though Sacks is often shorthanded as pro-AI, he’s in some key ways been quite critical of the leading AI labs, OpenAI and Anthropic, themselves.

That’s partly because he’s aligned with a different faction in Silicon Valley. Sacks and key allies like Nvidia CEO Jensen Huang and Meta CEO Mark Zuckerberg want much more AI development from many more places, by way of models that are “open weight” or “open source.” These models make their parameters (“weights”) publicly available, in contrast to the flagship proprietary models of OpenAI and Anthropic. 

“Keeping innovation decentralized and accessible is the key to avoiding an unsafe and dystopian future where advanced AI capabilities are centralized and controlled by only a few hands,” Sacks posted on X earlier month. He has accused companies like Anthropic of trying to “kneecap open source” with regulation.

Proponents of open models hope advanced AI becomes something more like a widely available resource that individual users and companies can download, run themselves, and customize to their internal specifications — rather than having to go through a handful of companies that control access. “The defining questions of our age are who will have access to superintelligence and what will we direct it towards,” Zuckerberg wrote in August. “Will it be centralized and restricted to a few institutions, or will it be a tool that empowers everyone?”

There’s a lot of money riding on this bet. Huang’s Nvidia manufactures chips used to train AI models and would like more customers (and just this month agreed to buy Hugging Face, a repository of open models). Zuckerberg’s Meta has hoped to be the US leader in open model development. 

More broadly, software startup founders and the venture capitalists who fund them can benefit from powerful open weight AI being widely available, since it’s a cheaper alternative to OpenAI and Anthropic’s expensive products. Sacks, as a co-founder of the venture capitalist firm Craft Ventures, and host of a popular podcast with three fellow venture capitalists, is more closely tied to this part of the tech ecosystem than any other.

Many believe that capable open weight AI presents a serious threat to the Big Two AI companies’ current dominance of the AI frontier, and thus their business model. The flip side is that some have argued that the Big Two present an “existential threat” to the software industry, predicting the frontier labs’ products could take over much of what today’s software companies do.

But regarding safety concerns, advocates have warned about the dangers of both closed “frontier” models and open models. “On the open source side in particular, it’s easier to have a bad actor — a terrorist group, a rogue nation, an extremist political group that wants to cause chaos,” said Brendan Steinhauser of the Alliance for Secure AI, an AI safety advocacy group. “They can strip out safety features and guardrails, they can do some fine tuning to basically make the models even more dangerous, and get them to help them develop biological weapons.”

The open weight AI faction argues, though, that intrusive government regulation on AI would end up mostly hurting open models and the companies that use them — entrenching the dominance of OpenAI and Anthropic, who would be better able to comply with onerous safety requirements given their size. They’ve publicly pooh-poohed AI’s existential risks, instead playing up what they say are the dangers that AI’s power will be controlled by a privileged few. Less regulation, Sacks argues, will let more flowers bloom.

These concerns aren’t only held by those with financial stakes. There’s also a school of thought on the left that is deeply suspicious of OpenAI and Anthropic’s intentions, and particularly around the companies’ desire for an exemption from antitrust enforcement, which they say will let them work together to address safety. 

Commentator Matt Stoller has argued that those warning about dangerous “superintelligence” are playing into the hands of tech “oligarchs.” And a recent American Prospect article said policymakers should “avoid codifying the preferences of the very companies who are generating the panic.” On the right, Sens. Josh Hawley (R-MO) and Ted Cruz (R-TX) have expressed similar concerns that large AI companies’ demands for restrictions are a cover for consolidating market dominance. 

At a Politico event Wednesday — and to give a sense of just how strange the bedfellows can be around this issue — Sacks stressed that he agreed with former Democratic FTC chair Lina Khan that existing liability law already applies to AI companies. But while Khan said she was open to adding new legal restrictions, Sacks is arguing that existing law is perfectly sufficient for addressing safety risks. “These CEOs are going to be subject to massive civil and criminal liability if they put out products that are unsafe,” he said.

In other comments at the event, Sacks did appear to have softened his tone a bit, saying he was “open to ideas” about AI safety, and that “we should have an intelligent conversation” about how to manage risks. The problem, he argued, was that “what’s going on right now is a fear campaign” that makes such an intelligent conversation impossible.

Former Rep. Brad Carson, who heads Americans for Responsible Innovation, an AI safety advocacy group, said that Sacks has been the one preventing such a conversation. “When it comes to AI policy the Trump administration has, through their unwillingness to even talk about reasonable guardrails, brought things to such a crisis point that the industry is in jeopardy,” Carson said. “In many ways, David Sacks is the author of that ill-fated intransigence.”

Sacks, for his part, has pointed to Anthropic’s funding of much of the AI safety advocacy world — for instance, the company has given $40 million to an advocacy group founded by Carson — and said the company is “running a sophisticated regulatory capture strategy based on fear-mongering.”

If we’re in an AI race with China, why are some of Trump’s policies friendly to Chinese AI?

The influence of Sacks and his worldview could help explain another seeming mystery of the administration: If Trump thinks it’s so crucial that the United States beats China in the AI race, why has he been so willing to bless the sale of AI chips abroad — including even to China?

National security hawks both inside and outside the Trump administration had warned against these sales and hoped to impose stringent controls on the export of Nvidia chips, fearing that if too many got out, the US would squander its AI lead over China.

Huang lobbied intensely against this, hoping to sell Nvidia’s most advanced chips to Saudi Arabia, and the United Arab Emirates — and trying to resume sales of a less advanced but still sophisticated AI chip to China itself. Sacks became Huang’s biggest champion inside the administration — and they won over the president. Sacks publicly argued that blocking US chip sales to China and other countries would provide an opening for Chinese companies like Huawei to dominate their domestic market and gain a greater global market share with competing chips, putting the US on weaker footing in the long run.

National security-minded critics disagreed, arguing that Sacks’s particular vision for AI will likely, in practice, strengthen China’s AI sector. “I think we can have a reasonable debate on domestic policy,” said McGuire of the Council on Foreign Relations. “But when we start from the premise that the American AI stack — models, cloud, and chips — should be dominant globally, then selling China the technology to advance its own capability obviously undercuts that.”

China also looms over the cause of open weight AI that Sacks champions — because it’s Chinese companies like DeepSeek and Moonshot that produce some of the leading open weight models that US software startups currently use.

OpenAI and Anthropic argue that these Chinese models present national security risks, claim they rely heavily on copying or “distilling” their own models, and want the US government to scrutinize them. Some Trump officials have reportedly been sympathetic to their concerns, but the administration hasn’t restricted these Chinese models’ use in the US. Sacks argues that a US ban on Chinese open weight models will just end up enshrining the OpenAI-Anthropic duopoly he so loathes.

Overall, though, on both national security risks and AI safety risks, Sacks and Huang still seem to have the president’s ear. 

On Monday, Sacks was onstage with Huang at the live All-In podcast event discussing the “doomer hoax” when a purportedly impromptu call came through for the Nvidia CEO: It was Trump. 

“The robots are not going to be taking over the world,” Trump said on speakerphone. “That’s not going to happen.”

Then, after speaking for several minutes, Trump added: “David has done an amazing job.”

Nearly two decades ago, a group of researchers at the Idaho National Laboratory ran a secret experiment, known as the Aurora Generator Test, on a massive emerald green diesel generator. Using 30 lines of code, they hacked into the generator’s virtual backroom, corrupting safeguards and flipping switches that left it out of sync with the rest of the power grid.

Before an audience of electrical utility executives and energy department officials watching from a nearby room, the 27-ton generator jolted violently, sputtering out a dark smoke as its rubber innards rapidly tore themselves apart. 

“The implication was that with just a few lines of code, you can create conditions that were physically going to be very damaging to the machines we rely on,” lead researcher Michael Assante later told the journalist Andy Greenberg for his book Sandworm: A New Era of Cyberwar and the Hunt for the Kremlin’s Most Dangerous Hackers. “I had a very real pit in my stomach. It was like a glimpse of the future.”

That future seems to have creeped much closer this week, with leading AI developers and executives casually admitting that they believe their creation is, to paraphrase one winking X post, as likely to end humanity as the New York Jets are to make the playoffs this season. Now, exactly how AI would pull off killing everyone is far murkier territory. But as Assante sensed all those years ago, AI may not need to be all-powerful to leap from its virtual cage and into the critical infrastructure that powers our hospitals, flushes our toilets, and pumps water to our faucets. It just needs to fall into the wrong hands. 

Almost any AI doomsday scenario you can think of begins with a swarm of shadowy bots slithering through cyberspace, poking their noses behind the scenes of power plants, toaster ovens, traffic lights, or even, in an utterly extreme case, our nuclear weapons arsenal. If destroying the Aurora generator, at the very least, required a human hacker to write the code, AI has since completely torn down the barrier to entry for wreaking havoc, as the Hugging Face incident demonstrated earlier this year. 

But AI need not go rogue to threaten the power grid or mess with your tap water. A new age of AI-powered, but human-directed hacking is already upon us, experts say. With some of the nation’s most sensitive infrastructure startlingly vulnerable to attack, the most likely AI apocalypse could in fact start with the water tower or generator idling in your backyard. In a worst-case scenario, this could lead to a cascade of scary failures in the systems we rely on to live comfortably modern lives, from running our AC in blazingly hot summers to geolocating our ships and planes.

“Before, you needed to have highly skilled technical expertise,” to pull off some version of a real-life Aurora Generator Test, said Alvaro Cardenas, a computer science professor at UC Santa Cruz. “And now, you just have to have a general idea of what’s possible.”

AI is making an old threat much worse

Not long after the Idaho National Laboratory hacked that poor generator into combustion, the federal government mandated that electrical utilities engage in basic cybersecurity hygiene. 

And yet much of America’s critical infrastructure — like gas pipelines, water desalination plants, or cargo terminals — remains woefully unprepared for even conventional cyber attacks, much less the coming onslaught of AI. 

That’s in part because cyberattacks like the one simulated in the Aurora Generator Test have long been — and still are, to some extent — exceedingly rare, unappealing to most criminal hackers because they’re optimized for disruption rather than financial gain. But in the process of making it extremely easy for anyone to code, AI has also made it extremely easy for almost anyone to vibe hack their way into your online bank account, or, in theory, a local reservoir or the power grid. AI agents can also vastly widen the scope on easy targets because, as Andy Bochman, an expert in infrastructure resilience at West Yost, put it — “they don’t sleep; they don’t get tired; and they don’t get sick.” 

In the past, even those who did want to launch large-scale attacks on infrastructure probably weren’t savvy enough to do so. And while other nations such as China, Iran, and Russia have indeed already breached many of our infrastructure systems (as we have theirs), they haven’t opted to make much of a ruckus once inside. 

But that norm is now changing, said Jason Healey, a cybersecurity scholar at Columbia University. “Geopolitics is eroding the idea that those with capability lack the intent, and AI is eroding the other part of it, that those with the intent lack the capability,” he told me. So, on the one hand, a nation like Russia might be much more inclined to actually disrupt our power grid now than it used to be. And, on the other hand, a nihilistic lone wolf or terrorist group could use AI to do much more damage than they ever could before. 

Just last month, we saw a glimpse of what this could look like when dozens of water and wastewater systems in small towns across the nation were attacked by a group of hackers most likely associated with Iran, leading to temporary water stoppages and flooding. These attacks have not been definitively linked to AI, but the National Security Agency warned soon afterward that hackers have been actively using AI to target US infrastructure like it. A few weeks later, President Donald Trump declared a national emergency over foreign interference in the power grid, which referenced the growing threat of cybersecurity.  

Preparing for the worst

Most of the nation’s water systems are extremely local and absurdly exposed to such attacks, so much so that “it’s almost like having a welcome mat” for a would-be hacker, said Bochman, mostly because these utilities are too small and underfunded to do much to stop them. “You have a million other problems to take care of, like aging infrastructure — your stuff’s falling apart because it’s been in the ground for 100 years,” he said. Addressing that deferred maintenance often feels far more urgent than doing “something more on cybersecurity.”

Whether it’s a rogue cluster of agents or a hostile nation-state trying to mess with our electricity, we should prepare our most important infrastructure for the worst. In Bochman’s view, that may mean rejecting the pressure to digitize everything in the first place and moving toward something that resembles the days when “people manned things like substations, communicated by a landline telephone, and read gauges, the analog things,” he said. “The screen is the thing that is infinitely manipulatable.”

Healey, who advised the Biden administration heavily on cybersecurity and infrastructure, believes that the nation desperately needs a coordinated response, between the federal government and AI companies, but also between the US and other countries like China, where similarly powerful models are being developed. And utility systems need to assume they will be a target and improve their cybersecurity practices accordingly, Healey says, ideally with funding from the federal government or, perhaps more appropriately, AI companies. 

Nobody knows precisely how vulnerable America’s infrastructure is to AI right now, in part because policymakers — and perhaps more disturbingly, AI’s creators themselves — seem woefully unsure of how to stop the technology from acting badly (or obeying bad orders) in the first place. For now at least, the solution might be, as Bochman suggests, to pull as many of our critical systems offline as possible, the better to hide it from AI and those who would misuse it.

“When bad things start to happen, no one will know what to do or why because they’re black boxes; the people that make them don’t know what’s going on inside,” Bochman said. “And the poor utility people who ultimately own the risk when someone gets hurt from a system” won’t understand what went wrong either, “and they’ll wish to God they’d never taken it on board.”

Throughout the Trump era, Democrats have wrung their hands about how they can win over working-class voters.

Why not ask the Democrats who’ve actually done it?

That’s what Robin Johnson — a political consultant based in rural Illinois, and a part-time political science professor at Monmouth College — set out to do after Donald Trump first won the presidency, asking Democratic state legislators who won tough districts how they pulled it off.

But after Trump’s shocking second win in 2024 revived concern about Democrats’ working-class woes, the time seemed ripe to ask legislators about how they’ve managed to cope with the challenges of a new decade.

So, working with the center-left Progressive Policy Institute and co-authors Stuart Malec and Jolie Libert, Johnson interviewed 28 state legislators who won in heavily working-class areas in the seven main presidential battleground states. 

They include Nevada state Assemblymember Max Carter, a retired union electrician; Michigan state Rep. Tullio Liberati, a construction small business owner; Arizona state Rep. Lorena Austin, a self-described “five-time college dropout” with sleeve tattoos; and Wisconsin state Rep. Karen DeSanto, a nonprofit executive and former professional circus clown.

The report, which Vox reviewed exclusively ahead of its publication, comes amid a factional debate within the party between rising left-wing and socialist candidates who have argued for more far-reaching policy proposals, and more moderate Democrats who have pushed for concessions to working-class cultural sensibilities. In the report, the authors said that lawmakers they focused on “could hardly be more different from the left-wing populists who’ve captivated the national media this summer.” 

Generally, the legislators in the report focused on pocketbook issues, though in different ways — some embraced populist causes, while others positioned themselves as business-friendly. And they broadly agreed that cultural issues were Democrats’ biggest challenge in reaching the working class.

“It’s really interesting how common the themes and the challenges are from rural Georgia, right down to the border of Arizona, to Las Vegas, northern North Carolina, and then up in the ‘Blue Wall’ states,” Johnson told me. 

I spoke to Johnson last week about his findings, and about what these state legislators’ experience has to tell us about the debate over the Democratic Party’s future. Our conversation, which has been condensed and edited, is below.

What’s changed in 10 years of interviewing legislators facing tough races

Walk me through where the idea for this project came from.

I’ve been working on this for 10 years. It started at a conversation at a little diner in Maquoketa, Iowa — a little “choke and puke,” as we call those places. I was with a former state senator from Iowa named Jeff Danielson, and it was right after Trump won. Most of the focus was on what happened at the congressional or presidential level, and we thought, why not take a look at state legislators competing in these tough districts?

I approached my friend and my congresswoman, Cheri Bustos, who I’ve known really well and helped on her campaigns, and said, would you be interested in working with us and sponsoring it? She said, absolutely, because her district, the 17th in Illinois, is rural working-class. And she was able to win these voters.

We talked to probably 75 current and former legislators for that first report. We did another one in 2021, focused on members of Congress and state legislators who win Trump districts. Then we did a third one last year, on Democrats in working-class districts in the heartland. Then she retired from Congress and I went to a PPI conference this past February, and PPI agreed to partner on this with a different focus, slightly — on the seven battleground states, on districts that are really close and have more working-class voters than the national average.

So, having done this several times before, what’s new in the latest version? What are people saying now that they weren’t, necessarily, in previous rounds of this?

In the past, when we focused on the Midwest, it was mostly all white, mostly men, some women. Now, we went to North Carolina and Georgia, and there were African American legislators, men and women. And we went to the West, and there were Latinos.

But what was interesting to me was: Nobody brought up their identity in these interviews. I had to bring it up to one woman from Nevada, mentioning, “Well, you’re Native American.” “Yeah, but I don’t really talk about that much,” was the response. 

That was one big takeaway: Nobody talked about identity. Their focus was totally on their constituents, their working-class constituents. 

What were some of the common themes you saw in the responses?

There’s no secret sauce. They’re deeply rooted in their communities, they know the people that live there, they know the language, they work hard.

But they focus on economic issues. If somebody asks them about cultural issues, they’ll respond, but they try to get back to economic issues, because that’s what they’re hearing about from voters, mostly.

These folks are from a variety of backgrounds. Some are true working-class, they don’t have college degrees. One guy near Detroit runs a construction company, didn’t get a degree. Another woman we focused on from Arizona had tattoos all over and took 14 years to get her BA. Some are the traditional route — attorneys, some small business people.

But it’s a lot of different backgrounds, and we want to contrast that with the consultants who tried to make the perfect Frankenstein candidate out of [former Maine Senate candidate Graham] Platner. These people are out there — you don’t need to create it. They’re out there.

Cultural issues

On how politics has changed over the years, there’s a particularly vivid quote in the report from state Assemblymember Elaine Marzola from Nevada.  

She tells you: “My campaign’s biggest adjustment in 2024 was shifting away from leading with social and cultural issues and instead focusing more directly on affordability and economic concerns.” And she adds, “While I remain progressive on many social issues, voters across backgrounds increasingly want leaders focused on affordability, education, and public safety rather than endless culture war debates.”

How widespread was that sentiment?

It’s very widespread. Their focus is on the issues that matter, that they’re picking up from their constituents. A lot of times they reported that some of the people in their campaigns told them they should talk more about abortion or other social issues — mainly abortion — and they refused to do it. 

A lot of them talked about a major problem in the party being these litmus tests that groups have, that you can vote with them on nine out of 10 issues, but on the tenth issue, if you’re not with them, you’re an enemy. 

If you’re going to have a true big-tent party, you’ve got to have some room for disagreements on some of these issues that reflect the local voters that people have in their districts. Several in Georgia told me pointedly that their leadership understood and would give them passes on votes that they knew they couldn’t be with them on. But in other cases, I heard a lot of pointed comments about these groups demanding 100 percent allegiance, that it’s all or nothing.

In a lot of districts down South, I heard from a couple gentlemen; it’s the Bible Belt. People go to church regularly. African Americans, the older generations of African Americans, are more middle-of-the-road, if not conservative on some of these cultural issues. So there’s got to be room for people to vote their districts on some of these cultural issues.

But on the economic issues, they’re there. They’re unified with the rest of the party. 

Economic issues

Well, it’s interesting on economic issues, because there’s unity on focusing on affordability and pocketbook issues in some form. But you also describe how some people take a more populist approach, and others have a more business-friendly approach. Is there tension there?

We asked people, how would they describe themselves? And most of them describe themselves as moderate. But the range of issues you have at the state level may differ some compared to the federal issues. A true populist approach at the state level may not have the same meaning as a populist at the federal level.

In fact, some of the folks with business backgrounds were pretty aggressive in some of their policy ideas. I think [Assemblymember] Joe Dalia from Nevada is one, he actually combined with [Rep.] Ro Khanna on a policy idea, the congressman from Silicon Valley who’s definitely a progressive. Dalia’s background is in business entrepreneurship.

Is all politics local?

The dynamics are a little different in state legislative races, even tough competitive ones, because there’s just so much less national money, national interest, group interest, virality, all of the dynamics that are so important to national politics. These are truly local races. So do you think these lessons can apply to national politics?

These state legislators are easy to overlook. They’re close enough to the people that they’re in, they can knock doors more than like a member of Congress. You can’t really knock doors in a congressional race — you can do some and have volunteers do it, but these people are out doing it individually. 

But they still get hit on national issues. Some of them didn’t do much TV — Georgia’s got smaller districts — but they’re on TV in Wisconsin. And they’ve got to raise money and then have the party help them out. They’re used to getting hit on national issues because the Republicans try to tie them to some of the more far-left ideas. 

They also feel that the deep roots they have in the community help overcome that because they’re better known, they’re battle-tested in that regard. A lot of times, especially with social media, you think you’re going to come up with some neat little line to put on social media that’s going to carry you to a victory, or some pithy slogan or whatever. It’s really not. For these folks anyway, it just comes down to doing the hard work. 

But this is the bench that the Democrats need. You want to pick some people down the road that have proven ability to win these tough districts — here they are. 

The company that operates the vast cold storage warehouse scorched in a June blaze that plagued neighbors in Boyle Heights with smoke and stink said it doesn't deserve the pummeling it got for how it contained the catastrophe.

On stage at Davos in the Swiss Alps earlier this year, Bill Gates, before an audience of the global do-gooding elite, announced a $50 million partnership between his foundation and an unlikely new ally, OpenAI, aimed at improving healthcare in Rwanda.

Just a month earlier, the Gates Foundation had released a harrowing report about widespread global health cuts and collapsing foreign aid infrastructure, documenting a rise in child deaths for the first time this century. But at Davos, even as the US hemorrhaged funding for vaccines, maternal healthcare, and HIV treatment, the Microsoft co-founder struck an optimistic tone. 

“Using innovation, using AI,” Gates told Reuters, “I think we can get back on track.”

As it turns out, the partnership was just an opening salvo for what’s become a wholesale embrace of AI by the world’s largest global health philanthropy. A $200 million partnership with Anthropic came just a few months later. And then, on Tuesday, the Gates Foundation announced a plan to dole out a jaw-dropping $1 billion over the next two years for scaling up AI interventions across healthcare, education, and agriculture around the world. 

The sense that AI will soon change everything, and mostly for the worse, has perhaps never felt so pressing and alarming as it does right now. But this hefty new commitment is premised on the idea that AI could eventually make our lives much better too. In a country like America, AI requires massive investments and buildout — in the grid, in power, in technology, in water. In many parts of Africa, already infrastructure-poor, the foundation is effectively betting that AI can compensate for some of the infrastructure that isn’t in place or that has been lost — and offer a new layer that can help in education, in delivering healthcare, and more. 

The Gates Foundation’s new commitments include projects to improve how farmers analyze their crops, how doctors diagnose patients, and how students learn. Some have already proven to be quite effective, according to the foundation, with one tool improving the accuracy of medical diagnoses by 16 percent across several Kenyan clinics. “In poorer countries with enormous health worker shortages and lack of health systems infrastructure,” Gates wrote on his blog earlier this year, “AI can be a game-changer in expanding access to quality care.” 

Last year’s foreign aid cuts were unfathomably destructive for some of the world’s poorest countries, shuttering thousands of clinics and perhaps hundreds of thousands of classrooms in places where critical workers and supplies were already stretched thin. No, AI cannot deliver a baby, administer a vaccine, or manage a classroom — not yet, at least — but it can, the Gates Foundation hopes, help ease the burden and augment the quality of care for nurses and teachers now tasked with serving far more people than any one person should. 

“This is the most transformative technology of our lifetime,” Mark Suzman, CEO of the Gates Foundation, told me, and while there are “very real risks and challenges” surrounding AI, it also presents genuine opportunities to tangibly improve many people’s lives.  

It is a tantalizing premise: We can’t afford to build clinics, hire teachers, or lay new power lines, but perhaps AI could create the digital infrastructure needed to help billions of people do more with what they have — or rather, with what’s left. The question is, can the right layer of innovation help bolster a buckling system, even as the physical and logistical infrastructure surrounding it crumbles?

Is AI really what global health needs right now? 

Most Americans have yet to see AI improve their lives in meaningful ways, much less those living in poverty or in countries where only a sliver of the population has ever used ChatGPT. 

But those numbers in the developing world are slowly changing, a shift enabled in part by other global health funders convinced that as foreign aid dries up, AI could soon help the world do more with less. The Trump administration itself is on board with this thinking, so long as it benefits American tech companies. Last year, the State Department announced a $150 million partnership with the drone company Zipline to transport medical supplies in several countries.

To Zipline’s credit, an NPR reporter in Rwanda last month found that the company’s drones were already saving lives in places with poor road infrastructure, where driving to the nearest hospital can take hours on winding hills, but where drones can deliver crucial blood transfusions within minutes. 

And yet, for Rwandans to benefit fully from these high-tech deliveries, they need staffed clinics to visit. As a result of foreign aid cuts, some of those literal landing spots are now on the verge of closing in some of the nation’s most vulnerable rural corridors. “Without the underlying infrastructure, like roads, energy and power and unless there is accessibility, rural and otherwise,” Enoh Ebong, president of the global development department at the Center for Strategic and International Studies, told NPR, “then all of these solutions will stop short.”

But just as medical delivery drones still need a clinic to fly to, most of these interventions require some layer of physical infrastructure — roads, schools, power, and quite obviously, broadband  — to function. Satellite imagery shows that some rural communities in sub-Saharan Africa quite literally went dark in the aftermath of USAID cuts. It will be much harder for their kids to prompt an AI math tutor, or for a local farmer to download a weather app. 

“We’re seeing this very much as a both/and,” Suzman told me, calling more traditional funding for education and healthcare “the bedrock for long-term economic growth,” which AI is meant to supplement, not replace. But, in practice, foreign aid cuts have left serious cracks across much of that bedrock, which could make it harder for state-of-the-art solutions to function as intended.  

It is obviously not incumbent on the Gates Foundation to pay for all of this alone – nor would it even be feasible for them to do so. And for the record, plenty of the foundation’s other grant programs are not AI-related at all. But this tension does speak to a broader question that may be felt most acutely in a context like global health, but ultimately one that touches us all: Do we spend our scarce resources on the digital infrastructure of the future at the peril of our needs right now? Could you justify buying the new $1,999 folding iPhone if you can barely afford to buy groceries? 

The answer is not actually always an easy one. It is not for us to say, for example, that just because a community lacks reliable running water, its people should be excluded from the benefits (and encumbrances) of the smartphone, especially if that smartphone can connect them to health advice they could never access otherwise, or guidance that could help them survive a drought. 

In some parts of the world, digital forms of banking arrived long before local brick-and-mortar branches did, instantaneously connecting countless people to a cheap and secure way to save and send their cash to family and friends in need. It is certainly possible that AI, or at least the kind of applications the Gates Foundation funds, could one day have a similarly transformative effect on how people access personalized levels of care, education, and other services in places without the resources to do so otherwise. 

That is the hope, but it is also hard right now to believe uncritically in a technology that, as Gates himself blogged just last month, “will either be the greatest equalizer ever invented, or the worst source of injustice,” ushering in “one of the most turbulent times in human history.” 

In an interview with the New York Times soon after, he seemed convinced that AI will likely do more harm than good. “I don’t like bringing bad news to people, and I don’t like saying that innovation may be a net negative,” he said. “But that’s where we are.”

Now he wants billions of people around the world to use it? Yes, Suzman said, because if not, then inevitably the best parts of AI — the potential for exponentially better cancer treatments, for example — will go only “to those who can pay for it,” while the worst parts of it will fall, as they so often do, on those who benefit the least. “These decisions are being made now,” he said, “and the decisions being made now will then probably shape the following two decades.” Here’s hoping they’re good ones.

The image, made to look like a movie poster for "Transformers," urged the man to self-deport. The agency later deleted it.

Thousands of Salvadoran immigrants nationwide faced uncertainty after the federal government failed to decide by a Wednesday deadline whether to extend or terminate their longtime temporary protections against deportation.

The Department of Homeland Security has not yet announced whether it will end or extend Temporary Protected Status for some 170,000 Salvadorans, including 36,000 in California.

During a tense vote on a data center being built in her drought-stricken Arizona district last summer, Tucson City Councilmember Nikki Lee made an unlikely pitch: “We’re going to get a little out-of-this-world for a second, so just bear with me.” 

Like many of her constituents, Lee did not want Project Blue, a sprawling 290-acre data center, gobbling up an ounce of her local drinking water. “We need to put data centers where they belong,” she said, and “we don’t think data centers belong in the desert.”  

President Donald Trump’s recent bluster aside, it’s possible that the only thing as bipartisan as loving Dolly Parton is hating data centers, whose construction has become so controversial, so costly, and so backlogged here on Earth that companies like Google, Meta, and of course, SpaceX, are actively flirting with the idea of flinging them into orbit instead. Soon after the Tucson City Council rejected Project Blue’s attempt to connect to city utilities, Amazon Web Services, the secretive intended beneficiary, pulled out of the project, and another Jeff Bezos company, Blue Origin, filed a plan with regulators for tens of thousands of possible new data centers, this time, destined for orbit. 

Sure, launching a hyperscale AI data center into space is still unprecedented engineering-wise, carries an unfathomably large price tag, could engender its own severe environmental risks, and may or may not draw unwelcome attention from the hunters among the stars. But for AI companies, such trade-offs might still be preferable to the terrestrial roadblocks, among them an explosive populist backlash and a 12-year wait to connect to the power grid. 

Next Thursday, Google plans to launch a pilot satellite capable of processing basic AI queries into orbit, the most significant test yet in what’s become a burgeoning race to power AI from space. For now, the satellite won’t approach the levels of computing power at a hulking terrestrial data center like Project Blue. But it is a major step toward a future in which many or even most of our AI queries will be powered not from the outskirts of a city like Tucson, but from a blinking data center circling the skies.

SpaceX’s gargantuan initial public offering earlier this summer was predicated on the idea that it would launch a thus-far-unrealized fleet of orbital data centers to fuel much of the AI economy. The company — whose rockets Google will use to launch its pilot — claims that by the end of this decade, possibly as early as 2028, it will have successfully assembled an AI data center in orbit, with plans to eventually harvest a staggering 100 gigawatts of new solar energy for AI annually, equivalent to one-fifth of all US electricity consumption last year. (As always, that’s a very iffy promise.)

Still, as Google’s overtures clearly demonstrate, it’s no longer a question of if large-scale space-based data centers could be technically feasible, but rather when they’ll be ready to launch, and perhaps more importantly, whether the math of using them to power something like AI will make much financial sense anytime soon. As of now, building a one-gigawatt data center in space would cost about $51.5 billion, according to an analysis by aerospace engineer Andrew McCalip, compared with $16 billion for one on land.

“They’re not breaking any rules of physics. Yes, there are going to be engineering challenges, but they are all solvable with enough time and money,” said Jason Aspiotis, a leading space systems engineer who now leads an orbital data center startup. “The gray area is, well, what about the economics?” 

But even if this astronomically kooky plan does take off, it may not change much for those of us left on terra firma: Our energy bills will remain high, even if data centers aren’t there to drive them up. Our earthly infrastructure — affordable housing, public transit, and more — may be just as hard to build as ever. Harder even, maybe, given that crucial resources and untapped tax revenue will be literally rocketing up out of our reach.

No, if these tech giants succeed, it will not fix what’s wrong here on Earth, but merely prove once again their exemption to the gravitational forces holding the rest of us down.  

Space-based data centers, explained

My father spent most of his career working in the satellite industry, and would sometimes let me waltz inside the quaint little data center housed in his office, a room I remember as roughly the size of my school’s computer lab, hosting rows upon rows of servers that I couldn’t touch, churning with a cool, mechanical hum as they crunched zeroes and ones.

AI data centers work similarly to that server room but on an exponentially larger scale, requiring far more land and energy to run, and much more extravagant cooling systems. Sending his office’s data center into orbit would have been extraordinarily difficult, and getting one of the facilities that powers modern LLMs aloft would be an even larger, more ambitious undertaking. But the benefits — namely, “cheap space (no pun intended) and power,” as my dad put it to me recently — are even more enticing.

An orbital data center need not negotiate with an elected official like Lee over precious water rights to the Colorado River, though it would need to install bigger-than-football-field-sized heat radiators to keep itself cool. You can’t simply launch something that big into orbit, meaning a space-based hyperscale data center — the size of which would likely rival that of the International Space Station — would need to be assembled piece by piece, probably by robots. And while the sun would provide an endless supply of solar power to process your ChatGPT queries, it would also inflict incessant radiation on delicate circuitry. If something breaks, you can’t send a technician up to fix it. Many experts are also concerned that if an excess of space junk, which extraterrestrial data centers would inevitably produce, begins colliding like bumper cars in Earth’s low orbit, it could knock out the orbital infrastructure we already rely on, like GPS navigation and weather satellites. 

Even plastic decays in space, said Sven Bilén, a professor of aerospace engineering at Penn State, who, like Aspiotis, believes that early orbital data centers will probably be “processing data from space, like spatial imaging satellites or weather satellites, as opposed to, you know, cat videos” or AI-generated reality TV slop. 

But even if AI is not the lowest-hanging fruit for deploying compute into space, it is certainly the deepest-pocketed, meaning that many investors and companies like SpaceX and Google seem quite willing to shoulder the start-up costs anyway. And considering how difficult it’s gotten to build data centers on Earth, or at least in the United States, it’s indeed possible that space could become the cheaper, or at least most convenient option, more quickly than one might think. 

“In some form or another, in five years, we are going to have some orbital data centers in space for sure,” Krishna Muralidharan, who leads the Center for Semiconductor Manufacturing at the University of Arizona, told me, but it’s hard to say right now whether those consist of just a few expensive prototypes, or the beginning of a much bigger swing toward orbital compute. “Only time will tell whether you’re going to have a business case for it.”

Can we send our data center problem into space?

So, is it better to have a data center in space or in your backyard?

David Reid, a member of Virginia’s House of Delegates representing Loudoun County, home to more data centers than anywhere else on Earth, says the question has repeatedly come up at his town hall meetings. Reid is the rare politician — apart from the president, anyway — willing to defend terrestrial data centers, calling them a “money-printing machine” for local schools and infrastructure. Having spent decades working in the defense and technology sectors, he is doubtful that the math on orbital data centers will ever make sense.

“I believe this is, for lack of a better term, fictional nonsense,” he said, and is “creating a distraction from the fact that local officials here on Earth still need to make better zoning decisions.”

If most of the AI industry can eventually be powered by space, as SpaceX claims, it will require millions of sprawling new satellites, in an orbit currently dotted by fewer than 20,000 of them. Though “space is really big,” as Elon Musk has put it, one million satellites would still profoundly and, likely irrevocably, transform our night sky into one in which data centers outnumber visible stars.

“Will humanity notice if there are fewer stars in the sky? Well, not so much right now,” given light pollution in many cities, said John Barentine, an astronomer and environmental advocate for the night sky. “But what if they’re replaced by these artificial stars in the night sky? Is that the point at which people will say, ‘Wait a minute, we don’t want this. We want the benefits, but we don’t want this as a consequence.’”

This is, of course, the tension also at the heart of building data centers here on Earth. To some extent, they’re the most tangible representations of how little power we have to shape the course of a transformative new technology that few trust, and even fewer asked for. It’s one thing to object to a hulking data center, but it might be another altogether to see the sigils of our digital overlords illuminating the firmament. Would a place like Tucson, one of the most beautiful places to stargaze in the world, prefer a Project Blue to a starscape marred by blinking servers in the sky?

Update, September 24, 10:35 am ET: This post was originally published on September 3 and has been updated to include information about a pilot satellite capable of processing basic AI queries that Google is launching soon.

Federal officials said the targeted schools were connected to more than 5,000 drivers who had failed English proficiency tests.