When Donald Trump was last President, ChatGPT had not yet been launched. Now, as he prepares to return to the White House after defeating Vice President Kamala Harris in the 2024 election, the artificial intelligence landscape looks quite different.

AI systems are advancing so rapidly that some leading executives of AI companies, such as Anthropic CEO Dario Amodei and Elon Musk, the Tesla CEO and a prominent Trump backer, believe AI may become smarter than humans by 2026. Others offer a more general timeframe. In an essay published in September, OpenAI CEO Sam Altman said, “It is possible that we will have superintelligence in a few thousand days,” but also noted that “it may take longer.” Meanwhile, Meta CEO Mark Zuckerberg sees the arrival of these systems as more of a gradual process rather than a single moment.

[time-brightcove not-tgx=”true”]

Either way, such advances could have far-reaching implications for national security, the economy, and the global balance of power.

Read More: When Might AI Outsmart Us? It Depends Who You Ask

Trump’s own pronouncements on AI have fluctuated between awe and apprehension. In a June interview on Logan Paul’s Impaulsive podcast, he described AI as a “superpower” and called its capabilities “alarming.” And like many in Washington, he views the technology through the lens of competition with China, which he sees as the “primary threat” in the race to build advanced AI.

Yet even his closest allies are divided on how to govern the technology: Musk has long voiced concerns about AI’s existential risks, while J.D. Vance, Trump’s Vice President, sees such warnings from industry as a ploy to usher regulations that would “entrench the tech incumbents.” These divisions among Trump’s confidants hint at the competing pressures that will shape AI policy during Trump’s second term.

Undoing Biden’s AI legacy

Trump’s first major AI policy move will likely be to repeal President Joe Biden’s Executive Order on AI. The sweeping order, signed in October 2023, sought to address threats the technology could pose to civil rights, privacy, and national security, while promoting innovation, competition, and the use of AI for public services.

Trump promised to repeal the Executive Order on the campaign trail in December 2023, and this position was reaffirmed in the Republican Party platform in July, which criticized the executive order for hindering innovation and imposing “radical leftwing ideas” on the technology’s development.

Read more: Republicans’ Vow to Repeal Biden’s AI Executive Order Has Some Experts Worried

Sections of the Executive Order which focus on racial discrimination or inequality are “not as much Trump’s style,” says Dan Hendrycks, executive and research director of the Center for AI Safety. While experts have criticized any rollback of bias protections, Hendrycks says the Trump Administration may preserve other aspects of Biden’s approach. “I think there’s stuff in [the Executive Order] that’s very bipartisan, and then there’s some other stuff that’s more specifically Democrat-flavored,” Hendrycks says.

“It would not surprise me if a Trump executive order on AI maintained or even expanded on some of the core national security provisions within the Biden Executive Order, building on what the Department of Homeland Security has done for evaluating cybersecurity, biological, and radiological risks associated with AI,” says Samuel Hammond, a senior economist at the Foundation for American Innovation, a technology-focused think tank.

The fate of the U.S. AI Safety Institute (AISI), an institution created last November by the Biden Administration to lead the government’s efforts on AI safety, also remains uncertain. In August, the AISI signed agreements with OpenAI and Anthropic to formally collaborate on AI safety research, and on the testing and evaluation of new models. “Almost certainly, the AI Safety Institute is viewed as an inhibitor to innovation, which doesn’t necessarily align with the rest of what appears to be Trump’s tech and AI agenda,” says Keegan McBride, a lecturer in AI, government, and policy at the Oxford Internet Institute. But Hammond says that while some fringe voices would move to shutter the institute, “most Republicans are supportive of the AISI. They see it as an extension of our leadership in AI.”

Read more: What Trump’s Win Means for Crypto

Congress is already working on protecting the AISI. In October, a broad coalition of companies, universities, and civil society groups—including OpenAI, Lockheed Martin, Carnegie Mellon University, and the nonprofit Encode Justice—signed a letter calling on key figures in Congress to urgently establish a legislative basis for the AISI. Efforts are underway in both the Senate and the House of Representatives, and both reportedly have “pretty wide bipartisan support,” says Hamza Chaudhry, U.S. policy specialist at the nonprofit Future of Life Institute.

America-first AI and the race against China

Trump’s previous comments suggest that maintaining the U.S.’s lead in AI development will be a key focus for his Administration.“We have to be at the forefront,” he said on the Impaulsive podcast in June. “We have to take the lead over China.” Trump also framed environmental concerns as potential obstacles, arguing they could “hold us back” in what he views as the race against China.

Trump’s AI policy could include rolling back regulations to accelerate infrastructure development, says Dean Ball, a research fellow at George Mason University. “There’s the data centers that are going to have to be built. The energy to power those data centers is going to be immense. I think even bigger than that: chip production,” he says. “We’re going to need a lot more chips.” While Trump’s campaign has at times attacked the CHIPS Act, which provides incentives for chip makers manufacturing in the U.S, leading some analysts to believe that he is unlikely to repeal the act. 

Read more: What Donald Trump’s Win Means for the Economy

Chip export restrictions are likely to remain a key lever in U.S. AI policy. Building on measures he initiated during his first term—which were later expanded by Biden—Trump may well  strengthen controls that curb China’s access to advanced semiconductors. “It’s fair to say that the Biden Administration has been pretty tough on China, but I’m sure Trump wants to be seen as tougher,” McBride says. It is “quite likely” that Trump’s White House will “double down” on export controls in an effort to close gaps that have allowed China to access chips, says Scott Singer, a visiting scholar in the Technology and International Affairs Program at the Carnegie Endowment for International Peace. “The overwhelming majority of people on both sides think that the export controls are important,” he says.

The rise of open-source AI presents new challenges. China has shown it can leverage U.S. systems, as demonstrated when Chinese researchers reportedly adapted an earlier version of Meta’s Llama model for military applications. That’s created a policy divide. “You’ve got people in the GOP that are really in favor of open-source,” Ball says. “And then you have people who are ‘China hawks’ and really want to forbid open-source at the frontier of AI.”

“My sense is that because a Trump platform has so much conviction in the importance and value of open-source I’d be surprised to see a movement towards restriction,” Singer says.

Despite his tough talk, Trump’s deal-making impulses could shape his policy towards China. “I think people misunderstand Trump as a China hawk. He doesn’t hate China,” Hammond says, describing Trump’s “transactional” view of international relations. In 2018, Trump lifted restrictions on Chinese technology company ZTE in exchange for a $1.3 billion fine and increased oversight. Singer sees similar possibilities for AI negotiations, particularly if Trump accepts concerns held by many experts about AI’s more extreme risks, such as the chance that humanity may lose control over future systems.

Read more: U.S. Voters Value Safe AI Development Over Racing Against China, Poll Shows

Trump’s coalition is divided over AI

Debates over how to govern AI reveal deep divisions within Trump’s coalition of supporters. Leading figures, including Vance, favor looser regulations of the technology. Vance has dismissed AI risk as an industry ploy to usher in new regulations that would “make it actually harder for new entrants to create the innovation that’s going to power the next generation of American growth.”

Silicon Valley billionaire Peter Thiel, who served on Trump’s 2016 transition team, recently cautioned against movements to regulate AI. Speaking at the Cambridge Union in May, he said any government with the authority to govern the technology would have a “global totalitarian character.” Marc Andreessen, the co-founder of prominent venture capital firm Andreessen Horowitz, gave $2.5 million to a pro-Trump super political action committee, and an additional $844,600 to Trump’s campaign and the Republican Party.

Yet, a more safety-focused perspective has found other supporters in Trump’s orbit. Hammond, who advised on the AI policy committee for Project 2025, a proposed policy agenda led by right-wing think tank the Heritage Foundation, and not officially endorsed by the Trump campaign, says that “within the people advising that project, [there was a] very clear focus on artificial general intelligence and catastrophic risks from AI.”

Musk, who has emerged as a prominent Trump campaign ally through both his donations and his promotion of Trump on his platform X (formerly Twitter), has long been concerned that AI could pose an existential threat to humanity. Recently, Musk said he believes there’s a 10% to 20% chance that AI “goes bad.” In August, Musk posted on X supporting the now-vetoed California AI safety bill that would have put guardrails on AI developers. Hendrycks, whose organization co-sponsored the California bill, and who serves as safety adviser at xAI, Musk’s AI company, says “If Elon is making suggestions on AI stuff, then I expect it to go well.” However, “there’s a lot of basic appointments and groundwork to do, which makes it a little harder to predict,” he says.

Trump has acknowledged some of the national security risks of AI. In June, he said he feared deepfakes of a U.S. President threatening a nuclear strike could prompt another state to respond, sparking a nuclear war. He also gestured to the idea that an AI system could “go rogue” and overpower humanity, but took care to distinguish this position from his personal view. However, for Trump, competition with China appears to remain the primary concern.

Read more: Trump Worries AI Deepfakes Could Trigger Nuclear War

But these priorities aren’t necessarily at odds and AI safety regulation does not inherently entail ceding ground to China, Hendrycks says. He notes that safeguards against malicious use require minimal investment from developers. “You have to hire one person to spend, like, a month or two on engineering, and then you get your jailbreaking safeguards,” he says. But with these competing voices shaping Trump’s AI agenda, the direction of Trump’s AI policy agenda remains uncertain.

“In terms of which viewpoint President Trump and his team side towards, I think that is an open question, and that’s just something we’ll have to see,” says Chaudhry. “Now is a pivotal moment.”

The number of people living in urban areas has tripled in the last 50 years, meaning when a major natural disaster such as an earthquake strikes a city, more lives are in danger. Meanwhile, the strength and frequency of extreme weather events has increased—a trend set to continue as the climate warms. That is spurring efforts around the world to develop a new generation of earthquake monitoring and climate forecasting systems to make detecting and responding to disasters quicker, cheaper, and more accurate than ever.

[time-brightcove not-tgx=”true”]

On Nov. 6, at the Barcelona Supercomputing Center​ in Spain, the Global Initiative on Resilience to Natural Hazards through AI Solutions will meet for the first time. The new United Nations initiative aims to guide governments, organizations, and communities in using AI for disaster management.

The initiative builds on nearly four years of groundwork laid by the International Telecommunications Union, the World Meteorological Organization (WMO) and the U.N. Environment Programme, which in early 2021 collectively convened a focus group to begin developing best practices for AI use in disaster management. These include enhancing data collection, improving forecasting, and streamlining communications.

Read more: Cities Are on the Front Line of the ‘Climate-Health Crisis.’ A New Report Provides a Framework for Tackling Its Effects

“What I find exciting is, for one type of hazard, there are so many different ways that AI can be applied and this creates a lot of opportunities,” says Monique Kuglitsch, who chaired the focus group. Take hurricanes for example: In 2023, researchers showed AI could help policymakers identify the best places to put traffic sensors to detect road blockages after tropical storms in Tallahassee, Fla. And in October, meteorologists used AI weather forecasting models to accurately predict that Hurricane Milton would land near Siesta Key, Florida. AI is also being used to alert members of the public more efficiently. Last year, The National Weather Service announced a partnership with AI translation company Lilt to help deliver forecasts in Spanish and simplified Chinese, which it says can reduce the time to translate a hurricane warning from an hour to 10 minutes.

Besides helping communities prepare for disasters, AI is also being used to coordinate response efforts. Following both Hurricane Milton and Hurricane Ian, non-profit GiveDirectly used Google’s machine learning models to analyze pre- and post-satellite images to identify the worst affected areas, and prioritize cash grants accordingly. Last year AI analysis of aerial images was deployed in cities like Quelimane, Mozambique, after Cyclone Freddy and Adıyaman, Turkey, after a 7.8 magnitude earthquake, to aid response efforts.

Read more: How Meteorologists Are Using AI to Forecast Hurricane Milton and Other Storms

Operating early warning systems is primarily a governmental responsibility, but AI climate modeling—and, to a lesser extent, earthquake detection—has become a burgeoning private industry. Start-up SeismicAI says it’s working with the civil protection agencies in the Mexican states of Guerrero and Jalisco to deploy an AI-enhanced network of sensors, which would detect earthquakes in real-time. Tech giants Google, Nvidia, and Huawei are partnering with European forecasters and say their AI-driven models can generate accurate medium-term forecasts thousands of times more quickly than traditional models, while being less computationally intensive. And in September, IBM partnered with NASA to release a general-purpose open-source model that can be used for various climate-modeling cases, and which runs on a desktop.

AI advances

While machine learning techniques have been incorporated into weather forecasting models for many years, recent advances have allowed many new models to be built using AI from the ground-up, improving the accuracy and speed of forecasting. Traditional models, which rely on complex physics-based equations to simulate interactions between water and air in the atmosphere and require supercomputers to run, can take hours to generate a single forecast. In contrast, AI weather models learn to spot patterns by training on decades of climate data, most of which was collected via satellites and ground-based sensors and shared through intergovernmental collaboration.

Both AI and physics-based forecasts work by dividing the world into a three-dimensional grid of boxes and then determining variables like temperature and wind speed. But because AI models are more computationally efficient, they can create much finer-grained grids. For example, the the European Centre for Medium-Range Weather Forecasts’ highest resolution model breaks the world into 5.5 mile boxes, whereas forecasting startup Atmo offers models finer than one square mile. This bump in resolution can allow for more efficient allocation of resources during extreme weather events, which is particularly important for cities, says Johan Mathe, co-founder and CTO of the company, which earlier this year inked deals with the Philippines and the island nation of Tuvalu.

Limitations

AI-driven models are typically only as good as the data they are trained on, which can be a limiting factor in some places. “When you’re in a really high stakes situation, like a disaster, you need to be able to rely on the model output,” says Kuglitsch. Poorer regions—often on the frontlines of climate-related disasters—typically have fewer and worse-maintained weather sensors, for example, creating gaps in meteorological data. AI systems trained on this skewed data can be less accurate in the places most vulnerable to disasters. And unlike physics-based models, which follow set rules, as AI models become more complex, they increasingly operate as sophisticated ‘black boxes,’ where the path from input to output becomes less transparent. The U.N. initiative’s focus is on developing guidelines for using AI responsibly. Kuglitsch says standards could, for example, encourage developers to disclose a model’s limitations or ensure systems work across regional boundaries.

The initiative will test its recommendations in the field by collaborating with the Mediterranean and pan-European forecast and Early Warning System Against natural hazards (MedEWSa), a project that spun out of the focus group. “We’re going to be applying the best practices from the focus group and getting a feedback loop going, to figure out which of the best practices are easiest to follow,” Kuglitsch says. One MedEWSa pilot project will explore machine learning to predict the occurrence of wildfires an area around Athens, Greece. Another will use AI to improve flooding and landslide warnings in the area surrounding Tbilisi city, Georgia.

Read more: How the Cement Industry Is Creating Carbon-Negative Building Materials

Meanwhile, private companies like Tomorrow.io are seeking to plug these gaps by collecting their own data. The AI weather forecasting start-up has launched satellites with radar and other meteorological sensors to collect data from regions that lack ground-based sensors, which it combines with historical data to train its models. Tomorrow.io’s technology is being used by New England cities including Boston, to help city officials decide when to salt the roads ahead of snowfall. It’s also used by Uber and Delta Airlines.

Another U.N. initiative, the Systematic Observations Financing Facility (SOFF), also aims to close the weather data gap by providing financing and technical assistance in poorer countries. Johan Stander, director of services for the WMO, one of SOFF’s partners, says the WMO is working with private AI developers including Google and Microsoft, but stresses the importance of not handing off too much responsibility to AI systems.

“You can’t go to a machine and say, ‘OK, you were wrong. Answer me, what’s going on?’ You still need somebody to take that ownership,” he says. He sees private companies’ role as “supporting the national met services, instead of trying to take them over.”