What’s next for AI in 2025

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

For the last couple of years we’ve had a go at predicting what’s coming next in AI. A fool’s game given how fast this industry moves. But we’re on a roll, and we’re doing it again.

How did we score last time round? Our four hot trends to watch out for in 2024 included what we called customized chatbots—interactive helper apps powered by multimodal large language models (check: we didn’t know it yet, but we were talking about what everyone now calls agents, the hottest thing in AI right now); generative video (check: few technologies have improved so fast in the last 12 months, with OpenAI and Google DeepMind releasing their flagship video generation models, Sora and Veo, within a week of each other this December); and more general-purpose robots that can do a wider range of tasks (check: the payoffs from large language models continue to trickle down to other parts of the tech industry, and robotics is top of the list). 

We also said that AI-generated election disinformation would be everywhere, but here—happily—we got it wrong. There were many things to wring our hands over this year, but political deepfakes were thin on the ground. 

So what’s coming in 2025? We’re going to ignore the obvious here: You can bet that agents and smaller, more efficient, language models will continue to shape the industry. Instead, here are five alternative picks from our AI team.

1. Generative virtual playgrounds 

If 2023 was the year of generative images and 2024 was the year of generative video—what comes next? If you guessed generative virtual worlds (a.k.a. video games), high fives all round.

We got a tiny glimpse of this technology in February, when Google DeepMind revealed a generative model called Genie that could take a still image and turn it into a side-scrolling 2D platform game that players could interact with. In December, the firm revealed Genie 2, a model that can spin a starter image into an entire virtual world.

Other companies are building similar tech. In October, the AI startups Decart and Etched revealed an unofficial Minecraft hack in which every frame of the game gets generated on the fly as you play. And World Labs, a startup cofounded by Fei-Fei Li—creator of ImageNet, the vast data set of photos that kick-started the deep-learning boom—is building what it calls large world models, or LWMs.

One obvious application is video games. There’s a playful tone to these early experiments, and generative 3D simulations could be used to explore design concepts for new games, turning a sketch into a playable environment on the fly. This could lead to entirely new types of games. 

But they could also be used to train robots. World Labs wants to develop so-called spatial intelligence—the ability for machines to interpret and interact with the everyday world. But robotics researchers lack good data about real-world scenarios with which to train such technology. Spinning up countless virtual worlds and dropping virtual robots into them to learn by trial and error could help make up for that.   

—Will Douglas Heaven

2. Large language models that “reason”

The buzz was justified. When OpenAI revealed o1 in September, it introduced a new paradigm in how large language models work. Two months later, the firm pushed that paradigm forward in almost every way with o3—a model that just might reshape this technology for good.

Most models, including OpenAI’s flagship GPT-4, spit out the first response they come up with. Sometimes it’s correct; sometimes it’s not. But the firm’s new models are trained to work through their answers step by step, breaking down tricky problems into a series of simpler ones. When one approach isn’t working, they try another. This technique, known as “reasoning” (yes—we know exactly how loaded that term is), can make this technology more accurate, especially for math, physics, and logic problems.

Then it stalled; it couldn’t figure out what type of flour to pick. Goel watched as Mariner explained its steps in a chat window: “It says, ‘I will use the browser’s Back button to return to the recipe.’”

It was a remarkable moment. Instead of hitting a wall, the agent had broken the task down into separate actions and picked one that might resolve the problem. Figuring out you need to click the Back button may sound basic, but for a mindless bot it’s akin to rocket science. And it worked: Mariner went back to the recipe, confirmed the type of flour, and carried on filling Goel’s basket.

Google DeepMind is also building an experimental version of Gemini 2.0, its latest large language model, that uses this step-by-step approach to problem solving, called Gemini 2.0 Flash Thinking.

3. It’s boom time for AI in science 

One of the most exciting uses for AI is speeding up discovery in the natural sciences. Perhaps the greatest vindication of AI’s potential on this front came last October, when the Royal Swedish Academy of Sciences awarded the Nobel Prize for chemistry to Demis Hassabis and John M. Jumper from Google DeepMind for building the AlphaFold tool, which can solve protein folding, and to David Baker for building tools to help design new proteins.

Expect this trend to continue next year, and to see more data sets and models that are aimed specifically at scientific discovery. Proteins were the perfect target for AI, because the field had excellent existing data sets that AI models could be trained on. 

The hunt is on to find the next big thing. One potential area is materials science. Meta has released massive data sets and models that could help scientists use AI to discover new materials much faster, and in December, Hugging Face, together with the startup Entalpic, launched LeMaterial, an open-source project that aims to simplify and accelerate materials research. Their first project is a data set that unifies, cleans, and standardizes the most prominent material data sets. 

AI model makers are also keen to pitch their generative products as research tools for scientists. OpenAI let scientists test its latest o1 model and see how it might support them in research. The results were encouraging. 

4. AI companies get cozier with national security

There is a lot of money to be made by AI companies willing to lend their tools to border surveillance, intelligence gathering, and other national security tasks. 

The US military has launched a number of initiatives that show it’s eager to adopt AI, from the Replicator program—which, inspired by the war in Ukraine, promises to spend $1 billion on small drones—to the Artificial Intelligence Rapid Capabilities Cell, a unit bringing AI into everything from battlefield decision-making to logistics. European militaries are under pressure to up their tech investment, triggered by concerns that Donald Trump’s administration will cut spending to Ukraine. Rising tensions between Taiwan and China weigh heavily on the minds of military planners, too. 

In 2025, these trends will continue to be a boon for defense-tech companies like Palantir, Anduril, and others, which are now capitalizing on classified military data to train AI models. 

The defense industry’s deep pockets will tempt mainstream AI companies into the fold too. OpenAI in December announced it is partnering with Anduril on a program to take down drones, completing a year-long pivot away from its policy of not working with the military. It joins the ranks of Microsoft, Amazon, and Google, which have worked with the Pentagon for years. 

Other AI competitors, which are spending billions to train and develop new models, will face more pressure in 2025 to think seriously about revenue. It’s possible that they’ll find enough non-defense customers who will pay handsomely for AI agents that can handle complex tasks, or creative industries willing to spend on image and video generators. 

5. Nvidia sees legitimate competition

For much of the current AI boom, if you were a tech startup looking to try your hand at making an AI model, Jensen Huang was your man. As CEO of Nvidia, the world’s most valuable corporation, Huang helped the company become the undisputed leader of chips used both to train AI models and to ping a model when anyone uses it, called “inferencing.”

A number of forces could change that in 2025. For one, behemoth competitors like Amazon, Broadcom, AMD, and others have been investing heavily in new chips, and there are early indications that these could compete closely with Nvidia’s—particularly for inference, where Nvidia’s lead is less solid. 

A growing number of startups are also attacking Nvidia from a different angle. Rather than trying to marginally improve on Nvidia’s designs, startups like Groq are making riskier bets on entirely new chip architectures that, with enough time, promise to provide more efficient or effective training. In 2025 these experiments will still be in their early stages, but it’s possible that a standout competitor will change the assumption that top AI models rely exclusively on Nvidia chips.

Underpinning this competition, the geopolitical chip war will continue. That war thus far has relied on two strategies. On one hand, the West seeks to limit exports to China of top chips and the technologies to make them. On the other, efforts like the US CHIPS Act aim to boost domestic production of semiconductors.

Donald Trump may escalate those export controls and has promised massive tariffs on any goods imported from China. In 2025, such tariffs would put Taiwan—on which the US relies heavily because of the chip manufacturer TSMC—at the center of the trade wars. That’s because Taiwan has said it will help Chinese firms relocate to the island to help them avoid the proposed tariffs. That could draw further criticism from Trump, who has expressed frustration with US spending to defend Taiwan from China. 

It’s unclear how these forces will play out, but it will only further incentivize chipmakers to reduce reliance on Taiwan, which is the entire purpose of the CHIPS Act. As spending from the bill begins to circulate, next year could bring the first evidence of whether it’s materially boosting domestic chip production. 

—James O’Donnell

What’s next for drones

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

Drones have been a mainstay technology among militaries, hobbyists, and first responders alike for more than a decade, and in that time the range available has skyrocketed. No longer limited to small quadcopters with insufficient battery life, drones are aiding search and rescue efforts, reshaping wars in Ukraine and Gaza, and delivering time-sensitive packages of medical supplies. And billions of dollars are being plowed into building the next generation of fully autonomous systems. 

These developments raise a number of questions: Are drones safe enough to be flown in dense neighborhoods and cities? Is it a violation of people’s privacy for police to fly drones overhead at an event or protest? Who decides what level of drone autonomy is acceptable in a war zone?

Those questions are no longer hypothetical. Advancements in drone technology and sensors, falling prices, and easing regulations are making drones cheaper, faster, and more capable than ever. Here’s a look at four of the biggest changes coming to drone technology in the near future.

Police drone fleets

Today more than 1,500 US police departments have drone programs, according to tracking conducted by the Atlas of Surveillance. Trained police pilots use drones for search and rescue operations, monitoring events and crowds, and other purposes. The Scottsdale Police Department in Arizona, for example, successfully used a drone to locate a lost elderly man with dementia, says Rich Slavin, Scottsdale’s assistant chief of police. He says the department has had useful but limited experiences with drones to date, but its pilots have often been hamstrung by the “line of sight” rule from the Federal Aviation Administration (FAA). The rule stipulates that pilots must be able to see their drones at all times, which severely limits the drone’s range.

Soon, that will change. On a rooftop somewhere in the city, Scottsdale police will in the coming months install a new police drone capable of autonomous takeoff, flight, and landing. Slavin says the department is seeking a waiver from the FAA to be able to fly its drone past the line of sight. (Hundreds of police agencies have received a waiver from the FAA since the first was granted in 2019.) The drone, which can fly up to 57 miles per hour, will go on missions as far as three miles from its docking station, and the department says it will be used for things like tracking suspects or providing a visual feed of an officer at a traffic stop who is waiting for backup. 

“The FAA has been much more progressive in how we’re moving into this space,” Slavin says. That could mean that around the country, the sight (and sound) of a police drone soaring overhead will become much more common. 

The Scottsdale department says the drone, which it is purchasing from Aerodome, will kick off its drone-as-first-responder program and will play a role in the department’s new “real-time crime center.” These sorts of centers are becoming increasingly common in US policing, and allow cities to connect cameras, license plate readers, drones, and other monitoring methods to track situations on the fly. The rise of the centers, and their associated reliance on drones, has drawn criticism from privacy advocates who say they conduct a great deal of surveillance with little transparency about how footage from drones and other sources will be used or shared. 

In 2019, the police department in Chula Vista, California, was the first to receive a waiver from the FAA to fly beyond line of sight. The program sparked criticism from members of the community who alleged the department was not transparent about the footage it collected or how it would be used. 

Jay Stanley, a senior policy analyst at the American Civil Liberties Union’s Speech, Privacy, and Technology Project, says the waivers exacerbate existing privacy issues related to drones. If the FAA continues to grant them, police departments will be able to cover far more of a city with drones than ever, all while the legal landscape is murky about whether this would constitute an invasion of privacy. 

“If there’s an accumulation of different uses of this technology, we’re going to end up in a world where from the moment you step out of your front door, you’re going to feel as though you’re under the constant eye of law enforcement from the sky,” he says. “It may have some real benefits, but it is also in dire need of strong checks and balances.”

Scottsdale police say the drone could be used in a variety of scenarios, such as responding to a burglary in progress or tracking a driver with suspected connection to a kidnapping. But the real benefit, Slavin says, will come from pairing it with other existing technologies, like automatic license plate readers and hundreds of cameras placed around the city. “It can get to places very, very quickly,” he says. “It gives us real-time intelligence and helps us respond faster and smarter.”

While police departments might indeed benefit from drones in those situations, Stanley says the ACLU has found that many deploy them for far more ordinary cases, like reports of a kid throwing a ball against a garage or of “suspicious persons” in an area.

“It raises the question about whether these programs will just end up being another way in which vulnerable communities are over-policed and nickeled and dimed by law enforcement agencies coming down on people for all kinds of minor transgressions,” he says.

Drone deliveries, again

Perhaps no drone technology is more overhyped than home deliveries. For years, tech companies have teased futuristic renderings of a drone dropping off a package on your doorstep just hours after you ordered it. But they’ve never managed to expand them much beyond small-scale pilot projects, at least in the US, again largely due to the FAA’s line of sight rules. 

But this year, regulatory changes are coming. Like police departments, Amazon’s Prime Air program was previously limited to flying its drones within the pilot’s line of sight. That’s because drone pilots don’t have radar, air traffic controllers, or any of the other systems commercial flight relies on to monitor airways and keep them safe. To compensate, Amazon spent years developing an onboard system that would allow its drones to detect nearby objects and avoid collisions. The company says it showed the FAA in demonstrations that its drones could fly safely in the same airspace as helicopters, planes, and hot air balloons. 

In May, Amazon announced the FAA had granted the company a waiver and permission to expand operations in Texas, more than a decade after the Prime Air project started. And in July, the FAA cleared one more roadblock by allowing two companies—Zipline as well as Google’s Wing Aviation—to fly in the same airspace simultaneously without the need for visual observers. 

While all this means your chances of receiving a package via drone have ticked up ever so slightly, the more compelling use case might be medical deliveries. Shakiba Enayati, an assistant professor of supply chains at the University of Missouri–St. Louis, has spent years researching how drones could conduct last-mile deliveries of vaccines, antivenom, organs, and blood in remote places. She says her studies have found drones to be game changers for getting medical supplies to underserved populations, and if the FAA extends these regulatory changes, it could have a real impact. 

That’s especially true in the steps leading up to an organ transplant, she says. Before an organ can be transmitted to a recipient, a number of blood tests must be sent back-and-forth to make sure the recipient can accept it, which takes a time if the blood is being transferred by car or even helicopter. “In these cases, the clock is ticking,” Enayati says. If drones were allowed to be used in this step at scale, it would be a significant improvement.

“If the technology is supporting the needs of organ delivery, it’s going to make a big change in such an important arena,” she says.

That development could come sooner than using drones for delivery of the actual organs, which have to be transported under very tightly controlled conditions to preserve them.

Domesticating the drone supply chain

Signed into law last December, the American Security Drone Act bars federal agencies from buying drones from countries thought to pose a threat to US national security, such as Russia and China. That’s significant. China is the undisputed leader when it comes to manufacturing drones and drone parts, with over 90% of law enforcement drones in the US made by Shenzhen-based DJI, and many drones used by both sides of the war in Ukraine are made by Chinese companies. 

The American Security Drone Act is part of an effort to curb that reliance on China. (Meanwhile, China is stepping up export restrictions on drones with military uses.) As part of the act, the US Department of Defense’s Defense Innovation Unit has created the Blue UAS Cleared List, a list of drones and parts the agency has investigated and approved for purchase. The list applies to federal agencies as well as programs that receive federal funding, which often means state police departments or other non-federal agencies. 

Since the US is set to spend such significant sums on drones—with $1 billion earmarked for the Department of Defense’s Replicator initiative alone—getting on the Blue List is a big deal. It means those federal agencies can make large purchases with little red tape. 

Allan Evans, CEO of US-based drone part maker Unusual Machine, says the list has sparked a significant rush of drone companies attempting to conform to the US standards. His company manufactures a first-person view flight controller that he hopes will become the first of its kind to be approved for the Blue List.

The American Security Drone Act is unlikely to affect private purchases in the US of drones used by videographers, drone racers, or hobbyists, which will overwhelmingly still be made by China-based companies like DJI. That means any US-based drone companies, at least in the short term, will only survive by catering to the US defense market.  

“Basically any US company that isn’t willing to have ancillary involvement in defense work will lose,” Evans says. 

The coming months will show the law’s true impact: Because the US fiscal year ends in September, Evans says he expects to see a host of agencies spending their use-it-or-lose-it funding on US-made drones and drone components in the next month. “That will indicate whether the marketplace is real or not, and how much money is actually being put toward it,” he says.

Autonomous weapons in Ukraine

The drone war in Ukraine has largely been one of attrition. Drones have been used extensively for surveying damage, finding and tracking targets, or dropping weapons since the war began, but on average these quadcopter drones last just three flights before being shot down or rendered unnavigable by GPS jamming. As a result, both Ukraine and Russia prioritized accumulating high volumes of drones with the expectation that they wouldn’t last long in battle. 

Now they’re having to rethink that approach, according to Andriy Dovbenko, founder of the UK-Ukraine Tech Exchange, a nonprofit that helps startups involved in Ukraine’s war effort and eventual reconstruction raise capital. While working with drone makers in Ukraine, he says, he has seen the demand for technology shift from big shipments of simple commercial drones to a pressing need for drones that can navigate autonomously in an environment where GPS has been jammed. With 70% of the front lines suffering from jamming, according to Dovbenko, both Russian and Ukrainian drone investment is now focused on autonomous systems. 

That’s no small feat. Drone pilots usually rely on video feeds from the drone as well as GPS technology, neither of which is available in a jammed environment. Instead, autonomous drones operate with various types of sensors like LiDAR to navigate, though this can be tricky in fog or other inclement weather. Autonomous drones are a new and rapidly changing technology, still being tested by US-based companies like Shield AI. The evolving war in Ukraine is raising the stakes and the pressure to deploy affordable and reliable autonomous drones.  

The transition toward autonomous weapons also raises serious yet largely unanswered questions about how much humans should be taken out of the loop in decision-making. As the war rages on and the need for more capable weaponry rises, Ukraine will likely be the testing ground for if and how the moral line is drawn. But Dovbenko says stopping to find that line during an ongoing war is impossible. 

“There is a moral question about how much autonomy you can give to the killing machine,” Dovbenko says. “This question is not being asked right now in Ukraine because it’s more of a matter of survival.”

What’s next for generative video

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

When OpenAI revealed its new generative video model, Sora, last month, it invited a handful of filmmakers to try it out. This week the company published the results: seven surreal short films that leave no doubt that the future of generative video is coming fast. 

The first batch of models that could turn text into video appeared in late 2022, from companies including Meta, Google, and video-tech startup Runway. It was a neat trick, but the results were grainy, glitchy, and just a few seconds long.

Fast-forward 18 months, and the best of Sora’s high-definition, photorealistic output is so stunning that some breathless observers are predicting the death of Hollywood. Runway’s latest models can produce short clips that rival those made by blockbuster animation studios. Midjourney and Stability AI, the firms behind two of the most popular text-to-image models, are now working on video as well.

A number of companies are racing to make a business on the back of these breakthroughs. Most are figuring out what that business is as they go. “I’ll routinely scream, ‘Holy cow, that is wicked cool’ while playing with these tools,” says Gary Lipkowitz, CEO of Vyond, a firm that provides a point-and-click platform for putting together short animated videos. “But how can you use this at work?”

Whatever the answer to that question, it will probably upend a wide range of businesses and change the roles of many professionals, from animators to advertisers. Fears of misuse are also growing. The widespread ability to generate fake video will make it easier than ever to flood the internet with propaganda and nonconsensual porn. We can see it coming. The problem is, nobody has a good fix.

As we continue to get to grips what’s ahead—good and bad—here are four things to think about. We’ve also curated a selection of the best videos filmmakers have made using this technology, including an exclusive reveal of “Somme Requiem,” an experimental short film by Los Angeles–based production company Myles. Read on for a taste of where AI moviemaking is headed. 

1. Sora is just the start

OpenAI’s Sora is currently head and shoulders above the competition in video generation. But other companies are working hard to catch up. The market is going to get extremely crowded over the next few months as more firms refine their technology and start rolling out Sora’s rivals.

The UK-based startup Haiper came out of stealth this month. It was founded in 2021 by former Google DeepMind and TikTok researchers who wanted to work on technology called neural radiance fields, or NeRF, which can transform 2D images into 3D virtual environments. They thought a tool that turned snapshots into scenes users could step into would be useful for making video games.

But six months ago, Haiper pivoted from virtual environments to video clips, adapting its technology to fit what CEO Yishu Miao believes will be an even bigger market than games. “We realized that video generation was the sweet spot,” says Miao. “There will be a super-high demand for it.”

Like OpenAI’s Sora, Haiper’s generative video tech uses a diffusion model to manage the visuals and a transformer (the component in large language models like GPT-4 that makes them so good at predicting what comes next), to manage the consistency between frames. “Videos are sequences of data, and transformers are the best model to learn sequences,” says Miao.

Consistency is a big challenge for generative video and the main reason existing tools produce just a few seconds of video at a time. Transformers for video generation can boost the quality and length of the clips. The downside is that transformers make stuff up, or hallucinate. In text, this is not always obvious. In video, it can result in, say, a person with multiple heads. Keeping transformers on track requires vast silos of training data and warehouses full of computers.

That’s why Irreverent Labs, founded by former Microsoft researchers, is taking a different approach. Like Haiper, Irreverent Labs started out generating environments for games before switching to full video generation. But the company doesn’t want to follow the herd by copying what OpenAI and others are doing. “Because then it’s a battle of compute, a total GPU war,” says David Raskino, Irreverent’s cofounder and CTO. “And there’s only one winner in that scenario, and he wears a leather jacket.” (He’s talking about Jensen Huang, CEO of the trillion-dollar chip giant Nvidia.)

Instead of using a transformer, Irreverent’s tech combines a diffusion model with a model that predicts what’s in the next frame on the basis of common-sense physics, such as how a ball bounces or how water splashes on the floor. Raskino says this approach reduces both training costs and the number of hallucinations. The model still produces glitches, but they are distortions of physics (like a bouncing ball not following a smooth curve, for example) with known mathematical fixes that can be applied to the video after it is generated, he says.

Which approach will last remains to be seen. Miao compares today’s technology to large language models circa GPT-2. Five years ago, OpenAI’s groundbreaking early model amazed people because it showed what was possible. But it took several more years for the technology to become a game-changer.

It’s the same with video, says Miao: “We’re all at the bottom of the mountain.”

2. What will people do with generative video? 

Video is the medium of the internet. YouTube, TikTok, newsreels, ads: expect to see synthetic video popping up everywhere there’s video already.

The marketing industry is one of the most enthusiastic adopters of generative technology. Two-thirds of marketing professionals have experimented with generative AI in their jobs, according to a recent survey Adobe carried out in the US, with more than half saying they have used the technology to produce images.

Generative video is next. A few marketing firms have already put out short films to demonstrate the technology’s potential. The latest example is the 2.5-minute-long “Somme Requiem,” made by Myles. You can watch the film below in an exclusive reveal from MIT Technology Review.

“Somme Requiem” depicts snowbound soldiers during the World War I Christmas ceasefire in 1914. The film is made up of dozens of different shots that were produced using a generative video model from Runway, then stitched together, color-corrected, and set to music by human video editors at Myles. “The future of storytelling will be a hybrid workflow,” says founder and CEO Josh Kahn.

Kahn picked the period wartime setting to make a point. He notes that the Apple TV+ series Masters of the Air, which follows a group of World War II airmen, cost $250 million. The team behind Peter Jackson’s World War I documentary They Shall Not Grow Old spent four years curating and restoring more than 100 hours of archival film. “Most filmmakers can only dream of ever having an opportunity to tell a story in this genre,” says Kahn.

“Independent filmmaking has been kind of dying,” he adds. “I think this will create an incredible resurgence.”

Raskino hopes so. “The horror movie genre is where people test new things, to try new things until they break,” he says. “I think we’re going to see a blockbuster horror movie created by, like, four people in a basement somewhere using AI.”

So is generative video a Hollywood-killer? Not yet. The scene-setting shots in ”Somme Requiem”—empty woods, a desolate military camp—look great. But the people in it are still afflicted with mangled fingers and distorted faces, hallmarks of the technology. Generative video is best at wide-angle pans or lingering close-ups, which creates an eerie atmosphere but little action. If ”Somme Requiem” were any longer it would get dull.

But scene-setting shots pop up all the time in feature-length movies. Most are just a few seconds long, but they can take hours to film. Raskino suggests that generative video models could soon be used to produce those in-between shots for a fraction of the cost. This could also be done on the fly in later stages of production, without requiring a reshoot.

Michal Pechoucek, CTO at Gen Digital, the cybersecurity giant behind a range of antivirus brands including Norton and Avast, agrees. “I think this is where the technology is headed,” he says. “We’ll see many different models, each specifically trained in a certain domain of movie production. These will just be tools used by talented video production teams.”

We’re not there quite yet. A big problem with generative video is the lack of control users have over the output. Producing still images can be hit and miss; producing a few seconds of video is even more risky.

“Right now it’s still fun, you get a-ha moments,” says Miao. “But generating video that is exactly what you want is a very hard technical problem. We are some way off generating long, consistent videos from a single prompt.”

That’s why Vyond’s Lipkowitz thinks the technology isn’t yet ready for most corporate clients. These users want a lot more control over the look of a video than current tools give them, he says.

Thousands of companies around the world, including around 65% of the Fortune 500 firms, use Vyond’s platform to create animated videos for in-house communications, training, marketing, and more. Vyond draws on a range of generative models, including text-to-image and text-to-voice, but provides a simple drag-and-drop interface that lets users put together a video by hand, piece by piece, rather than generate a full clip with a click.

Running a generative model is like rolling dice, says Lipkowitz. “This is a hard no for most video production teams, particularly in the enterprise sector where everything must be pixel-perfect and on brand,” he says. “If the video turns out bad—maybe the characters have too many fingers, or maybe there is a company logo that is the wrong color—well, unlucky, that’s just how gen AI works.”

The solution? More data, more training, repeat. “I wish I could point to some sophisticated algorithms,” says Miao. “But no, it’s just a lot more learning.”

3. Misinformation isn’t new, but deepfakes will make it worse.

Online misinformation has been undermining our faith in the media, in institutions, and in each other for years. Some fear that adding fake video to the mix will destroy whatever pillars of shared reality we have left.

“We are replacing trust with mistrust, confusion, fear, and hate,” says Pechoucek. “Society without ground truth will degenerate.”

Pechoucek is especially worried about the malicious use of deepfakes in elections. During last year’s elections in Slovakia, for example, attackers shared a fake video that showed the leading candidate discussing plans to manipulate voters. The video was low quality and easy to spot as a deepfake. But Pechoucek believes it was enough to turn the result in favor of the other candidate.

John Wissinger, who leads the strategy and innovation teams at Blackbird AI, a firm that tracks and manages the spread of misinformation online, believes fake video will be most persuasive when it blends real and fake footage. Take two videos showing President Joe Biden walking across a stage. In one he stumbles, in the other he doesn’t. Who is to say which is real?

“Let’s say an event actually occurred, but the way it’s presented to me is subtly different,” says Wissinger. “That can affect my emotional response to it.” As Pechoucek noted, a fake video doesn’t even need to be that good to make an impact. A bad fake that fits existing biases will do more damage than a slick fake that doesn’t, says Wissinger.

That’s why Blackbird focuses on who is sharing what with whom. In some sense, whether something is true or false is less important than where it came from and how it is being spread, says Wissinger. His company already tracks low-tech misinformation, such as social media posts showing real images out of context. Generative technologies make things worse, but the problem of people presenting media in misleading ways, deliberately or otherwise, is not new, he says.

Throw bots into the mix, sharing and promoting misinformation on social networks, and things get messy. Just knowing that fake media is out there will sow seeds of doubt into bad-faith discourse. “You can see how pretty soon it could become impossible to discern between what’s synthesized and what’s real anymore,” says Wissinger.

4. We are facing a new online reality.

Fakes will soon be everywhere, from disinformation campaigns, to ad spots, to Hollywood blockbusters. So what can we do to figure out what’s real and what’s just fantasy? There are a range of solutions, but none will work by themselves.

The tech industry is working on the problem. Most generative tools try to enforce certain terms of use, such as preventing people from creating videos of public figures. But there are ways to bypass these filters, and open-source versions of the tools may come with more permissive policies.

Companies are also developing standards for watermarking AI-generated media and tools for detecting it. But not all tools will add watermarks, and watermarks can be stripped from a video’s metadata. No reliable detection tool exists either. Even if such tools worked, they would become part of a cat-and-mouse game of trying to keep up with advances in the models they are designed to police.

Online platforms like X and Facebook have poor track records when it comes to moderation. We should not expect them to do better once the problem gets harder. Miao used to work at TikTok, where he helped build a moderation tool that detects video uploads that violate TikTok’s terms of use. Even he is wary of what’s coming: “There’s real danger out there,” he says. “Don’t trust things that you see on your laptop.” 

Blackbird has developed a tool called Compass, which lets you fact check articles and social media posts. Paste a link into the tool and a large language model generates a blurb drawn from trusted online sources (these are always open to review, says Wissinger) that gives some context for the linked material. The result is very similar to the community notes that sometimes get attached to controversial posts on sites like X, Facebook, and Instagram. The company envisions having Compass generate community notes for anything. “We’re working on it,” says Wissinger.

But people who put links into a fact-checking website are already pretty savvy—and many others may not know such tools exist, or may not be inclined to trust them. Misinformation also tends to travel far wider than any subsequent correction.

In the meantime, people disagree on whose problem this is in the first place. Pechoucek says tech companies need to open up their software to allow for more competition around safety and trust. That would also let cybersecurity firms like his develop third-party software to police this tech. It’s what happened 30 years ago when Windows had a malware problem, he says: “Microsoft let antivirus firms in to help protect Windows. As a result, the online world became a safer place.”

But Pechoucek isn’t too optimistic. “Technology developers need to build their tools with safety as the top objective,” he says. “But more people think about how to make the technology more powerful than worry about how to make it more safe.”

There’s a common fatalistic refrain in the tech industry: change is coming, deal with it. “Generative AI is not going to get uninvented,” says Raskino. “This may not be very popular, but I think it’s true: I don’t think tech companies can bear the full burden. At the end of the day, the best defense against any technology is a very well-educated public. There’s no shortcut.”

Miao agrees. “It’s inevitable that we will massively adopt generative technology,” he says. “But it’s also the responsibility of the whole of society. We need to educate people.” 

“Technology will move forward, and we need to be prepared for this change,” he adds. “We need to remind our parents, our friends, that the things they see on their screen might not be authentic.” This is especially true for older generations, he says: “Our parents need to be aware of this kind of danger. I think everyone should work together.”

We’ll need to work together quickly. When Sora came out a month ago, the tech world was stunned by how quickly generative video had progressed. But the vast majority of people have no idea this kind of technology even exists, says Wissinger: “They certainly don’t understand the trend lines that we’re on. I think it’s going to catch the world by storm.”