Why AI could eat quantum computing’s lunch

Tech companies have been funneling billions of dollars into quantum computers for years. The hope is that they’ll be a game changer for fields as diverse as finance, drug discovery, and logistics.

Those expectations have been especially high in physics and chemistry, where the weird effects of quantum mechanics come into play. In theory, this is where quantum computers could have a huge advantage over conventional machines.

But while the field struggles with the realities of tricky quantum hardware, another challenger is making headway in some of these most promising use cases. AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all.

The scale and complexity of quantum systems that can be simulated using AI is advancing rapidly, says Giuseppe Carleo, a professor of computational physics at the Swiss Federal Institute of Technology (EPFL). Last month, he coauthored a paper published in Science showing that neural-network-based approaches are rapidly becoming the leading technique for modeling materials with strong quantum properties. Meta also recently unveiled an AI model trained on a massive new data set of materials that has jumped to the top of a leaderboard for machine-learning approaches to material discovery.

Given the pace of recent advances, a growing number of researchers are now asking whether AI could solve a substantial chunk of the most interesting problems in chemistry and materials science before large-scale quantum computers become a reality. 

“The existence of these new contenders in machine learning is a serious hit to the potential applications of quantum computers,” says Carleo “In my opinion, these companies will find out sooner or later that their investments are not justified.”

Exponential problems

The promise of quantum computers lies in their potential to carry out certain calculations much faster than conventional computers. Realizing this promise will require much larger quantum processors than we have today. The biggest devices have just crossed the thousand-qubit mark, but achieving an undeniable advantage over classical computers will likely require tens of thousands, if not millions. Once that hardware is available, though, a handful of quantum algorithms, like the encryption-cracking Shor’s algorithm, have the potential to solve problems exponentially faster than classical algorithms can. 

But for many quantum algorithms with more obvious commercial applications, like searching databases, solving optimization problems, or powering AI, the speed advantage is more modest. And last year, a paper coauthored by Microsoft’s head of quantum computing, Matthias Troyer, showed that these theoretical advantages disappear if you account for the fact that quantum hardware operates orders of magnitude slower than modern computer chips. The difficulty of getting large amounts of classical data in and out of a quantum computer is also a major barrier. 

So Troyer and his colleagues concluded that quantum computers should instead focus on problems in chemistry and materials science that require simulation of systems where quantum effects dominate. A computer that operates along the same quantum principles as these systems should, in theory, have a natural advantage here. In fact, this has been a driving idea behind quantum computing ever since the renowned physicist Richard Feynman first proposed the idea.

The rules of quantum mechanics govern many things with huge practical and commercial value, like proteins, drugs, and materials. Their properties are determined by the interactions of their constituent particles, in particular their electrons—and simulating these interactions in a computer should make it possible to predict what kinds of characteristics a molecule will exhibit. This could prove invaluable for discovering things like new medicines or more efficient battery chemistries, for example. 

But the intuition-defying rules of quantum mechanics—in particular, the phenomenon of entanglement, which allows the quantum states of distant particles to become intrinsically linked—can make these interactions incredibly complex. Precisely tracking them requires complicated math that gets exponentially tougher the more particles are involved. That can make simulating large quantum systems intractable on classical machines.

This is where quantum computers could shine. Because they also operate on quantum principles, they are able to represent quantum states much more efficiently than is possible on classical machines. They could also take advantage of quantum effects to speed up their calculations.

But not all quantum systems are the same. Their complexity is determined by the extent to which their particles interact, or correlate, with each other. In systems where these interactions are strong, tracking all these relationships can quickly explode the number of calculations required to model the system. But in most that are of practical interest to chemists and materials scientists, correlation is weak, says Carleo. That means their particles don’t affect each other’s behavior significantly, which makes the systems far simpler to model.

The upshot, says Carleo, is that quantum computers are unlikely to provide any advantage for most problems in chemistry and materials science. Classical tools that can accurately model weakly correlated systems already exist, the most prominent being density functional theory (DFT). The insight behind DFT is that all you need to understand a system’s key properties is its electron density, a measure of how its electrons are distributed in space. This makes for much simpler computation but can still provide accurate results for weakly correlated systems.

Simulating large systems using these approaches requires considerable computing power. But in recent years there’s been an explosion of research using DFT to generate data on chemicals, biomolecules, and materials—data that can be used to train neural networks. These AI models learn patterns in the data that allow them to predict what properties a particular chemical structure is likely to have, but they are orders of magnitude cheaper to run than conventional DFT calculations. 

This has dramatically expanded the size of systems that can be modeled—to as many as 100,000 atoms at a time—and how long simulations can run, says Alexandre Tkatchenko, a physics professor at the University of Luxembourg. “It’s wonderful. You can really do most of chemistry,” he says.

Olexandr Isayev, a chemistry professor at Carnegie Mellon University, says these techniques are already being widely applied by companies in chemistry and life sciences. And for researchers, previously out of reach problems such as optimizing chemical reactions, developing new battery materials, and understanding protein binding are finally becoming tractable.

As with most AI applications, the biggest bottleneck is data, says Isayev. Meta’s recently released materials data set was made up of DFT calculations on 118 million molecules. A model trained on this data achieved state-of-the-art performance, but creating the training material took vast computing resources, well beyond what’s accessible to most research teams. That means fulfilling the full promise of this approach will require massive investment.

Modeling a weakly correlated system using DFT is not an exponentially scaling problem, though. This suggests that with more data and computing resources, AI-based classical approaches could simulate even the largest of these systems, says Tkatchenko. Given that quantum computers powerful enough to compete are likely still decades away, he adds, AI’s current trajectory suggests it could reach important milestones, such as precisely simulating how drugs bind to a protein, much sooner.

Strong correlations

When it comes to simulating strongly correlated quantum systems—ones whose particles interact a lot—methods like DFT quickly run out of steam. While more exotic, these systems include materials with potentially transformative capabilities, like high-temperature superconductivity or ultra-precise sensing. But even here, AI is making significant strides.

In 2017, EPFL’s Carleo and Microsoft’s Troyer published a seminal paper in Science showing that neural networks could model strongly correlated quantum systems. The approach doesn’t learn from data in the classical sense. Instead, Carleo says, it is similar to DeepMind’s AlphaZero model, which mastered the games of Go, chess, and shogi using nothing more than the rules of each game and the ability to play itself.

In this case, the rules of the game are provided by Schrödinger’s equation, which can precisely describe a system’s quantum state, or wave function. The model plays against itself by arranging particles in a certain configuration and then measuring the system’s energy level. The goal is to reach the lowest energy configuration (known as the ground state), which determines the system’s properties. The model repeats this process until energy levels stop falling, indicating that the ground state—or something close to it—has been reached.

The power of these models is their ability to compress information, says Carleo. “The wave function is a very complicated mathematical object,” he says. “What has been shown by several papers now is that [the neural network] is able to capture the complexity of this object in a way that can be handled by a classical machine.”

Since the 2017 paper, the approach has been extended to a wide range of strongly correlated systems, says Carleo, and results have been impressive. The Science paper he published with colleagues last month put leading classical simulation techniques to the test on a variety of tricky quantum simulation problems, with the goal of creating a benchmark to judge advances in both classical and quantum approaches.

Carleo says that neural-network-based techniques are now the best approach for simulating many of the most complex quantum systems they tested. “Machine learning is really taking the lead in many of these problems,” he says.

These techniques are catching the eye of some big players in the tech industry. In August, researchers at DeepMind showed in a paper in Science that they could accurately model excited states in quantum systems, which could one day help predict the behavior of things like solar cells, sensors, and lasers. Scientists at Microsoft Research have also developed an open-source software suite to help more researchers use neural networks for simulation.

One of the main advantages of the approach is that it piggybacks on massive investments in AI software and hardware, says Filippo Vicentini, a professor of AI and condensed-matter physics at École Polytechnique in France, who was also a coauthor on the Science benchmarking paper: “Being able to leverage these kinds of technological advancements gives us a huge edge.”

There is a caveat: Because the ground states are effectively found through trial and error rather than explicit calculations, they are only approximations. But this is also why the approach could make progress on what has looked like an intractable problem, says Juan Carrasquilla, a researcher at ETH Zurich, and another coauthor on the Science benchmarking paper.

If you want to precisely track all the interactions in a strongly correlated system, the number of calculations you need to do rises exponentially with the system’s size. But if you’re happy with an answer that is just good enough, there’s plenty of scope for taking shortcuts. 

“Perhaps there’s no hope to capture it exactly,” says Carrasquilla. “But there’s hope to capture enough information that we capture all the aspects that physicists care about. And if we do that, it’s basically indistinguishable from a true solution.”

And while strongly correlated systems are generally too hard to simulate classically, there are notable instances where this isn’t the case. That includes some systems that are relevant for modeling high-temperature superconductors, according to a 2023 paper in Nature Communications.

“Because of the exponential complexity, you can always find problems for which you can’t find a shortcut,” says Frank Noe, research manager at Microsoft Research, who has led much of the company’s work in this area. “But I think the number of systems for which you can’t find a good shortcut will just become much smaller.”

No magic bullets

However, Stefanie Czischek, an assistant professor of physics at the University of Ottawa, says it can be hard to predict what problems neural networks can feasibly solve. For some complex systems they do incredibly well, but then on other seemingly simple ones, computational costs balloon unexpectedly. “We don’t really know their limitations,” she says. “No one really knows yet what are the conditions that make it hard to represent systems using these neural networks.”

Meanwhile, there have also been significant advances in other classical quantum simulation techniques, says Antoine Georges, director of the Center for Computational Quantum Physics at the Flatiron Institute in New York, who also contributed to the recent Science benchmarking paper. “They are all successful in their own right, and they are also very complementary,” he says. “So I don’t think these machine-learning methods are just going to completely put all the other methods out of business.”

Quantum computers will also have their niche, says Martin Roetteler, senior director of quantum solutions at IonQ, which is developing quantum computers built from trapped ions. While he agrees that classical approaches will likely be sufficient for simulating weakly correlated systems, he’s confident that some large, strongly correlated systems will be beyond their reach. “The exponential is going to bite you,” he says. “There are cases with strongly correlated systems that we cannot treat classically. I’m strongly convinced that that’s the case.”

In contrast, he says, a future fault-tolerant quantum computer with many more qubits than today’s devices will be able to simulate such systems. This could help find new catalysts or improve understanding of metabolic processes in the body—an area of interest to the pharmaceutical industry.

Neural networks are likely to increase the scope of problems that can be solved, says Jay Gambetta, who leads IBM’s quantum computing efforts, but he’s unconvinced they’ll solve the hardest challenges businesses are interested in.

“That’s why many different companies that essentially have chemistry as their requirement are still investigating quantum—because they know exactly where these approximation methods break down,” he says.

Gambetta also rejects the idea that the technologies are rivals. He says the future of computing is likely to involve a hybrid of the two approaches, with quantum and classical subroutines working together to solve problems. “I don’t think they’re in competition. I think they actually add to each other,” he says.

But Scott Aaronson, who directs the Quantum Information Center at the University of Texas, says machine-learning approaches are directly competing against quantum computers in areas like quantum chemistry and condensed-matter physics. He predicts that a combination of machine learning and quantum simulations will outperform purely classical approaches in many cases, but that won’t become clear until larger, more reliable quantum computers are available.

“From the very beginning, I’ve treated quantum computing as first and foremost a scientific quest, with any industrial applications as icing on the cake,” he says. “So if quantum simulation turns out to beat classical machine learning only rarely, I won’t be quite as crestfallen as some of my colleagues.”

One area where quantum computers look likely to have a clear advantage is in simulating how complex quantum systems evolve over time, says EPFL’s Carleo. This could provide invaluable insights for scientists in fields like statistical mechanics and high-energy physics, but it seems unlikely to lead to practical uses in the near term. “These are more niche applications that, in my opinion, do not justify the massive investments and the massive hype,” Carleo adds.

Nonetheless, the experts MIT Technology Review spoke to said a lack of commercial applications is not a reason to stop pursuing quantum computing, which could lead to fundamental scientific breakthroughs in the long run.

“Science is like a set of nested boxes—you solve one problem and you find five other problems,” says Vicentini. “The complexity of the things we study will increase over time, so we will always need more powerful tools.”

How ChatGPT search paves the way for AI agents

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

OpenAI’s Olivier Godement, head of product for its platform, and Romain Huet, head of developer experience, are on a whistle-stop tour around the world. Last week, I sat down with the pair in London before DevDay, the company’s annual developer conference. London’s DevDay is the first one for the company outside San Francisco. Godement and Huet are heading to Singapore next. 

It’s been a busy few weeks for the company. In London, OpenAI announced updates to its new Realtime API platform, which allows developers to build voice features into their applications. The company is rolling out new voices and a function that lets developers generate prompts, which will allow them to build apps and more helpful voice assistants more quickly. Meanwhile for consumers, OpenAI announced it was launching ChatGPT search, which allows users to search the internet using the chatbot. Read more here

Both developments pave the way for the next big thing in AI: agents. These are AI assistants that can complete complex chains of tasks, such as booking flights. (You can read my explainer on agents here.) 

“Fast-forward a few years—every human on Earth, every business, has an agent. That agent knows you extremely well. It knows your preferences,” Godement says. The agent will have access to your emails, apps, and calendars and will act like a chief of staff, interacting with each of these tools and even working on long-term problems, such as writing a paper on a particular topic, he says. 

OpenAI’s strategy is to both build agents itself and allow developers to use its software to build their own agents, says Godement. Voice will play an important role in what agents will look and feel like. 

“At the moment most of the apps are chat based … which is cool, but not suitable for all use cases. There are some use cases where you’re not typing, not even looking at the screen, and so voice essentially has a much better modality for that,” he says. 

But there are two big hurdles that need to be overcome before agents can become a reality, Godement says. 

The first is reasoning. Building AI agents requires us to be able to trust that they will be able to complete complex tasks and do the right things, says Huet. That’s where OpenAI “reasoning” feature comes in. Introduced in OpenAI’s o1 model last month, it uses reinforcement learning to teach the model how to process information using “chain of thought.” Giving the model more time to generate answers allows it to recognize and correct mistakes, break down problems into smaller ones, and try different approaches to answering questions, Godement says. 

But OpenAI’s claims about reasoning should be taken with a pinch of salt, says Chirag Shah, a computer science professor at the University of Washington. Large language models are not exhibiting true reasoning. It’s most likely that they have picked up what looks like logic from something they’ve seen in their training data.

“These models sometimes seem to be really amazing at reasoning, but it’s just like they’re really good at pretending, and it only takes a little bit of picking at them to break them,” he says.

There is still much more work to be done, Godement admits. In the short term, AI models such as o1 need to be much more reliable, faster, and cheaper. In the long term, the company needs to apply its chain-of-thought technique to a wider pool of use cases. OpenAI has focused on science, coding, and math. Now it wants to address other fields, such as law, accounting, and economics, he says. 

Second on the to-do list is the ability to connect different tools, Godement says. An AI model’s capabilities will be limited if it has to rely on its training data alone. It needs to be able to surf the web and look for up-to-date information. ChatGPT search is one powerful way OpenAI’s new tools can now do that. 

These tools need to be able not only to retrieve information but to take actions in the real world. Competitor Anthropic announced a new feature where its Claude chatbot can “use” a computer by interacting with its interface to click on things, for example. This is an important feature for agents if they are going to be able to execute tasks like booking flights. Godement says o1 can “sort of” use tools, though not very reliably, and that research on tool use is a “promising development.” 

In the next year, Godemont says, he expects the adoption of AI for customer support and other assistant-based tasks to grow. However, he says that it can be hard to predict how people will adopt and use OpenAI’s technology. 

“Frankly, looking back every year, I’m surprised by use cases that popped up that I did not even anticipate,” he says. “I expect there will be quite a few surprises that you know none of us could predict.” 


Now read the rest of The Algorithm

Deeper Learning

This AI-generated version of Minecraft may represent the future of real-time video generation

When you walk around in a version of the video game Minecraft from the AI companies Decart and Etched, it feels a little off. Sure, you can move forward, cut down a tree, and lay down a dirt block, just like in the real thing. If you turn around, though, the dirt block you just placed may have morphed into a totally new environment. That doesn’t happen in Minecraft. But this new version is entirely AI-generated, so it’s prone to hallucinations. Not a single line of code was written.

Ready, set, go: This version of Minecraft is generated in real time, using a technique known as next-frame prediction. The AI companies behind it did this by training their model, Oasis, on millions of hours of Minecraft game play and recordings of the corresponding actions a user would take in the game. The AI is able to sort out the physics, environments, and controls of Minecraft from this data alone. Read more from Scott J. Mulligan.

Bits and Bytes

AI search could break the web
At its best, AI search can better infer a user’s intent, amplify quality content, and synthesize information from diverse sources. But if AI search becomes our primary portal to the web, it threatens to disrupt an already precarious digital economy, argues Benjamin Brooks, a fellow at the Berkman Klein Center at Harvard University, who used to lead public policy for Stability AI. (MIT Technology Review

AI will add to the e-waste problem. Here’s what we can do about it.
Equipment used to train and run generative AI models could produce up to 5 million tons of e-waste by 2030, a relatively small but significant fraction of the global total. (MIT Technology Review

How an “interview” with a dead luminary exposed the pitfalls of AI
A state-funded radio station in Poland fired its on-air talent and brought in AI-generated presenters. But the experiment caused an outcry and was stopped when tone of them  “interviewed” a dead Nobel laureate. (The New York Times

Meta says yes, please, to more AI-generated slop
In Meta’s latest earnings call, CEO Mark Zuckerberg said we’re likely to see 
“a whole new category of content, which is AI generated or AI summarized content or kind of existing content pulled together by AI in some way.” Zuckerberg added that he thinks “that’s going to be just very exciting.” (404 Media

Chasing AI’s value in life sciences

Inspired by an unprecedented opportunity, the life sciences sector has gone all in on AI. For example, in 2023, Pfizer introduced an internal generative AI platform expected to deliver $750 million to $1 billion in value. And Moderna partnered with OpenAI in April 2024, scaling its AI efforts to deploy ChatGPT Enterprise, embedding the tool’s capabilities across business functions from legal to research.

In drug development, German pharmaceutical company Merck KGaA has partnered with several AI companies for drug discovery and development. And Exscientia, a pioneer in using AI in drug discovery, is taking more steps toward integrating generative AI drug design with robotic lab automation in collaboration with Amazon Web Services (AWS).

Given rising competition, higher customer expectations, and growing regulatory challenges, these investments are crucial. But to maximize their value, leaders must carefully consider how to balance the key factors of scope, scale, speed, and human-AI collaboration.

The early promise of connecting data

The common refrain from data leaders across all industries—but specifically from those within data-rich life sciences organizations—is “I have vast amounts of data all over my organization, but the people who need it can’t find it.” says Dan Sheeran, general manager of health care and life sciences for AWS. And in a complex healthcare ecosystem, data can come from multiple sources including hospitals, pharmacies, insurers, and patients.

“Addressing this challenge,” says Sheeran, “means applying metadata to all existing data and then creating tools to find it, mimicking the ease of a search engine. Until generative AI came along, though, creating that metadata was extremely time consuming.”

ZS’s global head of the digital and technology practice, Mahmood Majeed notes that his teams regularly work on connected data programs, because “connecting data to enable connected decisions across the enterprise gives you the ability to create differentiated experiences.”

Majeed points to Sanofi’s well-publicized example of connecting data with its analytics app, plai, which streamlines research and automates time-consuming data tasks. With this investment, Sanofi reports reducing research processes from weeks to hours and the potential to improve target identification in therapeutic areas like immunology, oncology, or neurology by 20% to 30%.

Achieving the payoff of personalization

Connected data also allows companies to focus on personalized last-mile experiences. This involves tailoring interactions with healthcare providers and understanding patients’ individual motivations, needs, and behaviors.

Early efforts around personalization have relied on “next best action” or “next best engagement” models to do this. These traditional machine learning (ML) models suggest the most appropriate information for field teams to share with healthcare providers, based on predetermined guidelines.

When compared with generative AI models, more traditional machine learning models can be inflexible, unable to adapt to individual provider needs, and they often struggle to connect with other data sources that could provide meaningful context. Therefore, the insights can be helpful but limited.  

Sheeran notes that companies have a real opportunity to improve their ability to gain access to connected data for better decision-making processes, “Because the technology is generative, it can create context based on signals. How does this healthcare provider like to receive information? What insights can we draw about the questions they’re asking? Can their professional history or past prescribing behavior help us provide a more contextualized answer? This is exactly what generative AI is great for.”

Beyond this, pharmaceutical companies spend millions of dollars annually to customize marketing materials. They must ensure the content is translated, tailored to the audience and consistent with regulations for each location they offer products and services. A process that usually takes weeks to develop individual assets has become a perfect use case for generative copy and imagery. With generative AI, the process is reduced to from weeks to minutes and creates competitive advantage with lower costs per asset, Sheeran says.

Accelerating drug discovery with AI, one step at a time

Perhaps the greatest hope for AI in life sciences is its ability to generate insights and intellectual property using biology-specific foundation models. Sheeran says, “our customers have seen the potential for very, very large models to greatly accelerate certain discrete steps in the drug discovery and development processes.” He continues, “Now we have a much broader range of models available, and an even larger set of models coming that tackle other discrete steps.”

By Sheeran’s count, there are approximately six major categories of biology-specific models, each containing five to 25 models under development or already available from universities and commercial organizations.

The intellectual property generated by biology-specific models is a significant consideration, supported by services such as Amazon Bedrock, which ensures customers retain control over their data, with transparency and safeguards to prevent unauthorized retention and misuse.

Finding differentiation in life sciences with scope, scale, and speed

Organizations can differentiate with scope, scale, and speed, while determining how AI can best augment human ingenuity and judgment. “Technology has become so easy to access. It’s omnipresent. What that means is that it’s no longer a differentiator on its own,” says Majeed. He suggests that life sciences leaders consider:

Scope: Have we zeroed in on the right problem? By clearly articulating the problem relative to the few critical things that could drive advantage, organizations can identify technology and business collaborators and set standards for measuring success and driving tangible results.

Scale: What happens when we implement a technology solution on a large scale? The highest-priority AI solutions should be the ones with the most potential for results.Scale determines whether an AI initiative will have a broader, more widespread impact on a business, which provides the window for a greater return on investment, says Majeed.

By thinking through the implications of scale from the beginning, organizations can be clear on the magnitude of change they expect and how bold they need to be to achieve it. The boldest commitment to scale is when companies go all in on AI, as Sanofi is doing, setting goals to transform the entire value chain and setting the tone from the very top.

Speed: Are we set up to quickly learn and correct course? Organizations that can rapidly learn from their data and AI experiments, adjust based on those learnings, and continuously iterate are the ones that will see the most success. Majeed emphasizes, “Don’t underestimate this component; it’s where most of the work happens. A good partner will set you up for quick wins, keeping your teams learning and maintaining momentum.”

Sheeran adds, “ZS has become a trusted partner for AWS because our customers trust that they have the right domain expertise. A company like ZS has the ability to focus on the right uses of AI because they’re in the field and on the ground with medical professionals giving them the ability to constantly stay ahead of the curve by exploring the best ways to improve their current workflows.”

Human-AI collaboration at the heart

Despite the allure of generative AI, the human element is the ultimate determinant of how it’s used. In certain cases, traditional technologies outperform it, with less risk, so understanding what it’s good for is key. By cultivating broad technology and AI fluency throughout the organization, leaders can teach their people to find the most powerful combinations of human-AI collaboration for technology solutions that work. After all, as Majeed says, “it’s all about people—whether it’s customers, patients, or our own employees’ and users’ experiences.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

OpenAI brings a new web search tool to ChatGPT

ChatGPT can now search the web for up-to-date answers to a user’s queries, OpenAI announced today. 

Until now, ChatGPT was mostly restricted to generating answers from its training data, which is current up to October 2023 for GPT-4o, and had limited web search capabilities. Searches about generalized topics will still draw on this information from the model itself, but now ChatGPT will automatically search the web in response to queries about recent information such as sports, stocks, or news of the day, and can deliver rich multi-media results. Users can also manually trigger a web search, but for the most part, the chatbot will make its own decision about when an answer would benefit from information taken from the web, says Adam Fry, OpenAI’s product lead for search.

“Our goal is to make ChatGPT the smartest assistant, and now we’re really enhancing its capabilities in terms of what it has access to from the web,” Fry tells MIT Technology Review. The feature is available today for the chatbot’s paying users. 

ChatGPT triggers a web search when the user asks about local restaurants in this example

While ChatGPT search, as it is known, is initially available to paying customers, OpenAI intends to make it available for free later, even when people are logged out. The company also plans to combine search with its voice features and Canvas, its interactive platform for coding and writing, although these capabilities will not be available in today’s initial launch.

The company unveiled a standalone prototype of web search in July. Those capabilities are now built directly into the chatbot. OpenAI says it has “brought the best of the SearchGPT experience into ChatGPT.” 

OpenAI is the latest tech company to debut an AI-powered search assistant, challenging similar tools from competitors such as Google, Microsoft, and startup Perplexity. Meta, too, is reportedly developing its own AI search engine. As with Perplexity’s interface, users of ChatGPT search can interact with the chatbot in natural language, and it will offer an AI-generated answer with sources and links to further reading. In contrast, Google’s AI Overviews offer a short AI-generated summary at the top of the website, as well as a traditional list of indexed links. 

These new tools could eventually challenge Google’s 90% market share in online search. AI search is a very important way to draw more users, says Chirag Shah, a professor at the University of Washington, who specializes in online search. But he says it is unlikely to chip away at Google’s search dominance. Microsoft’s high-profile attempt with Bing barely made a dent in the market, Shah says. 

Instead, OpenAI is trying to create a new market for more powerful and interactive AI agents, which can take complex actions in the real world, Shah says. 

The new search function in ChatGPT is a step toward these agents. 

It can also deliver highly contextualized responses that take advantage of chat histories, allowing users to go deeper in a search. Currently, ChatGPT search is able to recall conversation histories and continue the conversation with questions on the same topic. 

ChatGPT itself can also remember things about users that it can use later —sometimes it does this automatically, or you can ask it to remember something. Those “long-term” memories affect how it responds to chats. Search doesn’t have this yet—a new web search starts from scratch— but it should get this capability in the “next couple of quarters,” says Fry. When it does, OpenAI says it will allow it to deliver far more personalized results based on what it knows.

“Those might be persistent memories, like ‘I’m a vegetarian,’ or it might be contextual, like ‘I’m going to New York in the next few days,’” says Fry. “If you say ‘I’m going to New York in four days,’ it can remember that fact and the nuance of that point,” he adds. 

To help develop ChatGPT’s web search, OpenAI says it leveraged its partnerships with news organizations such as Reuters, the Atlantic, Le Monde, the Financial Times, Axel Springer, Condé Nast, and Time. However, its results include information not only from these publishers, but any other source online that does not actively block its search crawler.   

It’s a positive development that ChatGPT will now be able to retrieve information from these reputable online sources and generate answers based on them, says Suzan Verberne, a professor of natural-language processing at Leiden University, who has studied information retrieval. It also allows users to ask follow-up questions.

But despite the enhanced ability to search the web and cross-check sources, the tool is not immune from the persistent tendency of AI language models to make things up or get it wrong. When MIT Technology Review tested the new search function and asked it for vacation destination ideas, ChatGPT suggested “luxury European destinations” such as Japan, Dubai, the Caribbean islands, Bali, the Seychelles, and Thailand. It offered as a source an article from the Times, a British newspaper, which listed these locations as well as those in Europe as luxury holiday options.

“Especially when you ask about untrue facts or events that never happened, the engine might still try to formulate a plausible response that is not necessarily correct,” says Verberne. There is also a risk that misinformation might seep into ChatGPT’s answers from the internet if the company has not filtered its sources well enough, she adds. 

Another risk is that the current push to access the web through AI search will disrupt the internet’s digital economy, argues Benjamin Brooks, a fellow at Harvard University’s Berkman Klein Center, who previously led public policy for Stability AI, in an op-ed published by MIT Technology Review today.

“By shielding the web behind an all-knowing chatbot, AI search could deprive creators of the visits and ‘eyeballs’ they need to survive,” Brooks writes.

This AI-generated version of Minecraft may represent the future of real-time video generation

When you walk around in a version of the video game Minecraft from the AI companies Decart and Etched, it feels a little off. Sure, you can move forward, cut down a tree, and lay down a dirt block, just like in the real thing. If you turn around, though, the dirt block you just placed may have morphed into a totally new environment. That doesn’t happen in Minecraft. But this new version is entirely AI-generated, so it’s prone to hallucinations. Not a single line of code was written.

For Decart and Etched, this demo is a proof of concept. They imagine that the technology could be used for real-time generation of videos or video games more generally. “Your screen can turn into a portal—into some imaginary world that doesn’t need to be coded, that can be changed on the fly. And that’s really what we’re trying to target here,” says Dean Leitersdorf, cofounder and CEO of Decart, which came out of stealth this week.

Their version of Minecraft is generated in real time, in a technique known as next-frame prediction. They did this by training their model, Oasis, on millions of hours of Minecraft gameplay and recordings of the corresponding actions a user would take in the game. The AI is able to sort out the physics, environments, and controls of Minecraft from this data alone. 

The companies acknowledge that their version of Minecraft is a little wonky. The resolution is quite low, you can only play for minutes at a time, and it’s prone to hallucinations like the one described above. But they believe that with innovations in chip design and further improvements, there’s no reason they can’t develop a high-fidelity version of Minecraft, or really any game. 

“What if you could say ‘Hey, add a flying unicorn here’? Literally, talk to the model. Or ‘Turn everything here into medieval ages,’ and then, boom, it’s all medieval ages. Or ‘Turn this into Star Wars,’ and it’s all Star Wars,” says Leitersdorf.

A major limitation right now is hardware. They relied on Nvidia cards for their current demo, but in the future, they plan to use Sohu, a new card that Etched has in development, which the firm claims will improve performance by a factor of 10. This gain would significantly cut down on the cost and energy needed to produce real-time interactive video. It would allow Decart and Etched to make a better version of their current demo, allowing the game to run longer, with fewer hallucinations, and at higher resolution. They say the new chip would also make it possible for more players to use the model at once.

“Custom chips for AI hold the potential to unlock significant performance gains and energy efficiency gains,” says Siddharth Garg, a professor of electrical and computer engineering at NYU Tandon, who is not associated with Etched or Decart.

Etched says that its gains come from designing their cards specifically for AI development. For example, the chip uses a single core, which it says makes it possible to handle complicated mathematical operations with more efficiency. The chip also focuses on inference (where an AI makes predictions) over training (where an AI learns from data).

“We are building something much more specialized than all of the chips out on the market today,” says Robert Wachen, cofounder and COO of Etched. They plan to run projects on the new card next year. Until the chip is deployed or its capabilities are verified, Etched’s claims are yet to be substantiated. And given the extent of AI specialization already in the top GPUs on the market, Garg is “very skeptical about a 10x improvement just from smarter or more specialized design.”

But the two companies have big ambitions. If the efficiency gains are close to what Etched claims, they believe, they will be able to generate real-time virtual doctors or tutors. “All of that is coming down the pipe, and it comes from having a better architecture and better hardware to power it. So that’s what we’re really trying to get people to realize with the proof of concept here,” says Wachen.

For the time being, you can try out the demo of their version of Minecraft here.

Palmer Luckey’s vision for the future of mixed reality

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War is a catalyst for change, an expert in AI and warfare told me in 2022. At the time, the war in Ukraine had just started, and the military AI business was booming. Two years later, things have only ramped up as geopolitical tensions continue to rise.

Silicon Valley players are poised to benefit. One of them is Palmer Luckey, the founder of the virtual-reality headset company Oculus, which he sold to Facebook for $2 billion. After Luckey’s highly public ousting from Meta, he founded Anduril, which focuses on drones, cruise missiles, and other AI-enhanced technologies for the US Department of Defense. The company is now valued at $14 billion. My colleague James O’Donnell interviewed Luckey about his new pet project: headsets for the military. 

Luckey is increasingly convinced that the military, not consumers, will see the value of mixed-reality hardware first: “You’re going to see an AR headset on every soldier, long before you see it on every civilian,” he says. In the consumer world, any headset company is competing with the ubiquity and ease of the smartphone, but he sees entirely different trade-offs in defense. Read the interview here

The use of AI for military purposes is controversial. Back in 2018, Google pulled out of the Pentagon’s Project Maven, an attempt to build image recognition systems to improve drone strikes, following staff walkouts over the ethics of the technology. (Google has since returned to offering services for the defense sector.) There has been a long-standing campaign to ban autonomous weapons, also known as “killer robots,” which powerful militaries such as the US have refused to agree to.  

But the voices that boom even louder belong to an influential faction in Silicon Valley, such as Google’s former CEO Eric Schmidt, who has called for the military to adopt and invest more in AI to get an edge over adversaries. Militaries all over the world have been very receptive to this message.

That’s good news for the tech sector. Military contracts are long and lucrative, for a start. Most recently, the Pentagon purchased services from Microsoft and OpenAI to do search, natural-language processing, machine learning, and data processing, reports The Intercept. In the interview with James, Palmer Luckey says the military is a perfect testing ground for new technologies. Soldiers do as they are told and aren’t as picky as consumers, he explains. They’re also less price-sensitive: Militaries don’t mind spending a premium to get the latest version of a technology.

But there are serious dangers in adopting powerful technologies prematurely in such high-risk areas. Foundation models pose serious national security and privacy threats by, for example, leaking sensitive information, argue researchers at the AI Now Institute and Meredith Whittaker, president of the communication privacy organization Signal, in a new paper. Whittaker, who was a core organizer of the Project Maven protests, has said that the push to militarize AI is really more about enriching tech companies than improving military operations. 

Despite calls for stricter rules around transparency, we are unlikely to see governments restrict their defense sectors in any meaningful way beyond voluntary ethical commitments. We are in the age of AI experimentation, and militaries are playing with the highest stakes of all. And because of the military’s secretive nature, tech companies can experiment with the technology without the need for transparency or even much accountability. That suits Silicon Valley just fine. 


Now read the rest of The Algorithm

Deeper Learning

How Wayve’s driverless cars will meet one of their biggest challenges yet

The UK driverless-car startup Wayve is headed west. The firm’s cars learned to drive on the streets of London. But Wayve has announced that it will begin testing its tech in and around San Francisco as well. And that brings a new challenge: Its AI will need to switch from driving on the left to driving on the right.

Full speed ahead: As visitors to or from the UK will know, making that switch is harder than it sounds. Your view of the road, how the vehicle turns—it’s all different. The move to the US will be a test of Wayve’s technology, which the company claims is more general-purpose than what many of its rivals are offering. Across the Atlantic, the company will now go head to head with the heavyweights of the growing autonomous-car industry, including Cruise, Waymo, and Tesla. Join Will Douglas Heaven on a ride in one of its cars to find out more

Bits and Bytes

Kids are learning how to make their own little language models
Little Language Models is a new application from two PhD researchers at MIT’s Media Lab that helps children understand how AI models work—by getting to build small-scale versions themselves. (MIT Technology Review

Google DeepMind is making its AI text watermark open source
Google DeepMind has developed a tool for identifying AI-generated text called SynthID, which is part of a larger family of watermarking tools for generative AI outputs. The company is applying the watermark to text generated by its Gemini models and making it available for others to use too. (MIT Technology Review

Anthropic debuts an AI model that can “use” a computer
The tool enables the company’s Claude AI model to interact with computer interfaces and take actions such as moving a cursor, clicking on things, and typing text. It’s a very cumbersome and error-prone version of what some have said AI agents will be able to do one day. (Anthropic

Can an AI chatbot be blamed for a teen’s suicide?
A 14-year-old boy committed suicide, and his mother says it was because he was obsessed with an AI chatbot created by Character.AI. She is suing the company. Chatbots have been touted as cures for loneliness, but critics say they actually worse isolation.  (The New York Times

Google, Microsoft, and Perplexity are promoting scientific racism in search results
The internet’s biggest AI-powered search engines are featuring the widely debunked idea that white people are genetically superior to other races. (Wired

Cultivating the next generation of AI innovators in a global tech hub

A few years ago, I had to make one of the biggest decisions of my life: continue as a professor at the University of Melbourne or move to another part of the world to help build a brand new university focused entirely on artificial intelligence.

With the rapid development we have seen in AI over the past few years, I came to the realization that educating the next generation of AI innovators in an inclusive way and sharing the benefits of technology across the globe is more important than maintaining the status quo. I therefore packed my bags for the Mohammed bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi.

The world in all its complexity

Today, the rewards of AI are mostly enjoyed by a few countries in what the Oxford Internet Institute dubs the “Compute North.” These countries, such as the US, the U.K., France, Canada, and China, have dominated research and development, and built state of the art AI infrastructure capable of training foundational models. This should come as no surprise, as these countries are home to many of the world’s top universities and large tech corporations.

But this concentration of innovation comes at a cost for the billions of people who live outside these dominant countries and have different cultural backgrounds.

Large language models (LLMs) are illustrative of this disparity. Researchers have shown that many of the most popular multilingual LLMs perform poorly with languages other than English, Chinese, and a handful of other (mostly) European languages. Yet, there are approximately 6,000 languages spoken today, many of them in communities in Africa, Asia, and South America. Arabic alone is spoken by almost 400 million people and Hindi has 575 million speakers around the world.

For example, LLaMA 2 performs up to 50% better in English compared to Arabic, when measured using the LM-Evaluation-Harness framework. Meanwhile, Jais, an LLM co-developed by MBZUAI, exceeds LLaMA 2 in Arabic and is comparable to Meta’s model in English (see table below).

The chart shows that the only way to develop AI applications that work for everyone is by creating new institutions outside the Compute North that consistently and conscientiously invest in building tools designed for the thousands of language communities across the world.

Environments of innovation

One way to design new institutions is to study history and understand how today’s centers of gravity in AI research emerged decades ago. Before Silicon Valley earned its reputation as the center of global technological innovation, it was called Santa Clara Valley and was known for its prune farms. However, the main catalyst was Stanford University, which had built a reputation as one of the best places in the world to study electrical engineering. Over the years, through a combination of government-led investment through grants and focused research, the university birthed countless inventions that advanced computing and created a culture of entrepreneurship. The results speak for themselves: Stanford alumni have founded companies such as Alphabet, NVIDIA, Netflix, and PayPal, to name a few.

Today, like MBZUAI’s predecessor in Santa Clara Valley, we have an opportunity to build a new technology hub centered around a university.

And that’s why I chose to join MBZUAI, the world’s first research university focused entirely on AI. From MBZUAI’s position at the geographical crossroads of East and West, our goal is to attract the brightest minds from around the world and equip them with the tools they need to push the boundaries of AI research and development.

A community for inclusive AI

MBZUAI’s student body comes from more than 50 different countries around the globe. It has attracted top researchers such as Monojit Choudhury from Microsoft, Elizabeth Churchill from Google, Ted Briscoe from the University of Cambridge, Sami Haddin from the Technical University of Munich, and Yoshihiko Nakamura from the University of Tokyo, just to name a few.

These scientists may be from different places but they’ve found a common purpose at MBZUAI with our interdisciplinary nature, relentless focus on making AI a force for global progress, and emphasis on collaboration across disciplines such as robotics, NLP, machine learning, and computer vision.

In addition to traditional AI disciplines, MBZUAI has built departments in sibling areas that can both contribute to and benefit from AI, including human computer interaction, statistics and data science, and computational biology.

Abu Dhabi’s commitment to MBZUAI is part of a broader vision for AI that extends beyond academia. MBZUAI’s scientists have collaborated with G42, an Abu Dhabi-based tech company, on Jais, an Arabic-centric LLM that is the highest-performing open-weight Arabic LLM; and also NANDA, an advanced Hindi LLM. MBZUAI’s Institute of Foundational Models has created LLM360, an initiative designed to level the playing field of large model research and development by publishing fully open source models and datasets that are competitive with closed source or open weights models available from tech companies in North America or China.

MBZUAI is also developing language models that specialize in Turkic languages, which have traditionally been underrepresented in NLP, yet are spoken by millions of people.

Another recent project has brought together native speakers of 26 languages from 28 different countries to compile a benchmark dataset that evaluates the performance of vision language models and their ability to understand cultural nuances in images.

These kinds of efforts to expand the capabilities of AI to broader communities are necessary if we want to maintain the world’s cultural diversity and provide everyone with AI tools that are useful to them. At MBZUAI, we have created a unique mix of students and faculty to drive globally-inclusive AI innovation for the future. By building a broad community of scientists, entrepreneurs, and thinkers, the university is increasingly establishing itself as a driving force in AI innovation that extends far beyond Abu Dhabi, with the goal of developing technologies that are inclusive for the world’s diverse languages and culture.

This content was produced by the Mohamed bin Zayed University of Artificial Intelligence. It was not written by MIT Technology Review’s editorial staff.

This AI system makes human tutors better at teaching children math

The US has a major problem with education inequality. Children from low-income families are less likely to receive high-quality education, partly because poorer districts struggle to retain experienced teachers. 

Artificial intelligence could help, by improving the one-on-one tutoring sometimes used to supplement class instruction in these schools. With help from an AI tool, tutors could tap into more experienced teachers’ expertise during virtual tutoring sessions. 

Researchers from Stanford University developed an AI system calledTutor CoPilot on top of OpenAI’s GPT-4 and integrated it into a platform called FEV Tutor, which connects students with tutors virtually. Tutors and students type messages to one another through a chat interface, and a tutor who needs help explaining how and why a student went wrong can press a button to generate suggestions from Tutor CoPilot. 

The researchers created the model by training GPT-4 on a database of 700 real tutoring sessions in which experienced teachers worked on on one with first- to fifth-grade students on math lessons, identifying the students’ errors and then working with them to correct the errors in such a way that they learned to understand the broader concepts being taught. From this, the model generates responses that tutors can customize to help their online students.

“I’m really excited about the future of human-AI collaboration systems,” says Rose Wang, a PhD student at Stanford University who worked on the project, which was published on arXiv and has not yet been peer-reviewed “I think this technology is a huge enabler, but only if it’s designed well.”

The tool isn’t designed to actually teach the students math—instead, it offers tutors helpful advice on how to nudge students toward correct answers while encouraging deeper learning. 

For example, it can suggest that the tutor ask how the student came up with an answer, or propose questions that could point to a different way to solve a problem. 

To test its efficacy, the team examined the interactions of 900 tutors virtually teaching math to 1,787 students between five and 13 years old from historically underserved communities in the US South. Half the tutors had the option to activate Tutor CoPilot, while the other half did not. 

The students whose tutors had access to Tutor CoPilot were 4 percentage points more likely to pass their exit ticket—an assessment of whether a student has mastered a subject—than those whose tutors did not have access to it. (Pass rates were 66% and 62%, respectively.)

The tool works as well as it does because it’s being used to teach relatively basic mathematics, says Simon Frieder, a machine-learning researcher at the University of Oxford, who did not work on the project. “You couldn’t really do a study with much more advanced mathematics at this current point in time,” he says.

The team estimates that the tool could improve student learning at a cost of around $20 per tutor annually to the tutoring provider, which is significantly cheaper than the thousands of dollars it usually takes to train educators in person. 

It has the potential to improve the relationship between novice tutors and their students by training them to approach problems the way experienced teachers do, says Mina Lee, an assistant professor of computer science at the University of Chicago, who was not involved in the project.

“This work demonstrates that the tool actually does work in real settings,” she says. “We want to facilitate human connection, and this really highlights how AI can augment human-to-human interaction.”

As a next step, Wang and her colleagues are interested in exploring how well novice tutors remember the teaching methods imparted by Tutor CoPilot. This could help them gain a sense of how long the effects of these kinds of AI interventions might last. They also plan to try to work out which other school subjects or age groups could benefit from such an approach.

“There’s a lot of substantial ways in which the underlying technology can get better,” Wang says. “But we’re not deploying an AI technology willy-nilly without pre-validating it—we want to be sure we’re able to rigorously evaluate it before we actually send it out into the wild. For me, the worst fear is that we’re wasting the students’ time.”

Palmer Luckey on the Pentagon’s future of mixed reality

Palmer Luckey has, in some ways, come full circle. 

His first experience with virtual-reality headsets was as a teenage lab technician at a defense research center in Southern California, studying their potential to curb PTSD symptoms in veterans. He then built Oculus, sold it to Facebook for $2 billion, left Facebook after a highly public ousting, and founded Anduril, which focuses on drones, cruise missiles, and other AI-enhanced technologies for the US Department of Defense. The company is now valued at $14 billion.

Now Luckey is redirecting his energy again, to headsets for the military. In September, Anduril announced it would partner with Microsoft on the US Army’s Integrated Visual Augmentation System (IVAS), arguably the military’s largest effort to develop a headset for use on the battlefield. Luckey says the IVAS project is his top priority at Anduril.

“There is going to be a heads-up display on every soldier within a pretty short period of time,” he told MIT Technology Review in an interview last week on his work with the IVAS goggles. “The stuff that we’re building—it’s going to be a big part of that.”

Though few would bet against Luckey’s expertise in the realm of mixed reality, few observers share his optimism for the IVAS program. They view it, thus far, as an avalanche of failures. 

IVAS was first approved in 2018 as an effort to build state-of-the-art mixed-reality headsets for soldiers. In March 2021, Microsoft was awarded nearly $22 billion over 10 years to lead the project, but it quickly became mired in delays. Just a year later, a Pentagon audit criticized the program for not properly testing the goggles, saying its choices “could result in wasting up to $21.88 billion in taxpayer funds to field a system that soldiers may not want to use or use as intended.” The first two variants of the goggles—of which the army purchased 10,000 units—gave soldiers nausea, neck pain, and eye strain, according to internal documents obtained by Bloomberg. 

Such reports have left IVAS on a short leash with members of the Senate Armed Services Committee, which helps determine how much money should be spent on the program. In a subcommittee meeting in May, Senator Tom Cotton, an Arkansas Republican and ranking member, expressed frustration at IVAS’s slow pace and high costs, and in July the committee suggested a $200 million cut to the program. 

Meanwhile, Microsoft has for years been cutting investments into its HoloLens headset—the hardware on which the IVAS program is based—for lack of adoption. In June, Microsoft announced layoffs to its HoloLens teams, suggesting the project is now focused solely on serving the Department of Defense. The company received a serious blow in August, when reports revealed that the Army is considering reopening bidding for the contract to oust Microsoft entirely. 

This is the catastrophe that Luckey’s stepped into. Anduril’s contribution to the project will be Lattice, an AI-powered system that connects everything from drones to radar jammers to surveil, detect objects, and aid in decision-making. Lattice is increasingly becoming Anduril’s flagship offering. It’s a tool that allows soldiers to receive instantaneous information not only from Anduril’s hardware, but also from radars, vehicles, sensors, and other equipment not made by Anduril. Now it will be built into the IVAS goggles. “It’s not quite a hive mind, but it’s certainly a hive eye” is how Luckey described it to me. 

Palmer Luckey holding an autonomous drone interceptor
Anvil, seen here held by Luckey in Anduril’s Costa Mesa Headquarters, integrates with the Lattice OS and can navigate autonomously to intercept hostile drones.
PHILIP CHEUNG

Boosted by Lattice, the IVAS program aims to produce a headset that can help soldiers “rapidly identify potential threats and take decisive action” on the battlefield, according to the Army. If designed well, the device will automatically sort through countless pieces of information—drone locations, vehicles, intelligence—and flag the most important ones to the wearer in real time. 

Luckey defends the IVAS program’s bumps in the road as exactly what one should expect when developing mixed reality for defense. “None of these problems are anything that you would consider insurmountable,” he says. “It’s just a matter of if it’s going to be this year or a few years from now.” He adds that delaying a product is far better than releasing an inferior product, quoting Shigeru Miyamoto, the game director of Nintendo: “A delayed game is delayed only once, but a bad game is bad forever.”

He’s increasingly convinced that the military, not consumers, will be the most important testing ground for mixed-reality hardware: “You’re going to see an AR headset on every soldier, long before you see it on every civilian,” he says. In the consumer world, any headset company is competing with the ubiquity and ease of the smartphone, but he sees entirely different trade-offs in defense.

“The gains are so different when we talk about life-or-death scenarios. You don’t have to worry about things like ‘Oh, this is kind of dorky looking,’ or ‘Oh, you know, this is slightly heavier than I would prefer,’” he says. “Because the alternatives of, you know, getting killed or failing your mission are a lot less desirable.”

Those in charge of the IVAS program remain steadfast in the expectation that it will pay off with huge gains for those on the battlefield. “If it works,” James Rainey, commanding general of the Army Futures Command, told the Armed Services Committee in May, “it is a legitimate 10x upgrade to our most important formations.” That’s a big “if,” and one that currently depends on Microsoft’s ability to deliver. Luckey didn’t get specific when I asked if Anduril was positioning itself to bid to become IVAS’s primary contractor should the opportunity arise. 

If that happens, US troops may, willingly or not, become the most important test subjects for augmented- and virtual-reality technology as it is developed in the coming decades. The commercial sector doesn’t have thousands of individuals within a single institution who can test hardware in physically and mentally demanding situations and provide their feedback on how to improve it. 

That’s one of the ways selling to the defense sector is very different from selling to consumers, Luckey says: “You don’t actually have to convince every single soldier that they personally want to use it. You need to convince the people in charge of him, his commanding officer, and the people in charge of him that this is a thing that is worth wearing.” The iterations that eventually come from IVAS—if it keeps its funding—could signal what’s coming next for the commercial market. 

When I asked Luckey if there were lessons from Oculus he had to unlearn when working with the Department of Defense, he said there’s one: worrying about budgets. “I prided myself for years, you know—I’m the guy who’s figured out how to make VR accessible to the masses by being absolutely brutal at every part of the design process, trying to get costs down. That isn’t what the DOD wants,” he says. “They don’t want the cheapest headset in a vacuum. They want to save money, and generally, spending a bit more money on a headset that is more durable or that has better vision—and therefore allows you to complete a mission faster—is definitely worth the extra few hundred dollars.”

I asked if he’s impressed by the progress that’s been made during his eight-year hiatus from mixed reality. Since he left Facebook in 2017, Apple, Magic Leap, Meta, Snap, and a cascade of startups have been racing to move the technology from the fringe to the mainstream. Everything in mixed reality is about trade-offs, he says. Would you like more computing power, or a lighter and more comfortable headset? 

With more time at Meta, “I would have made different trade-offs in a way that I think would have led to greater adoption,” he says. “But of course, everyone thinks that.” While he’s impressed with the gains, “having been on the inside, I also feel like things could be moving faster.”

Years after leaving, Luckey remains noticeably annoyed by one specific decision he thinks Meta got wrong: not offloading the battery. Dwelling on technical details is unsurprising from someone who spent his formative years living in a trailer in his parents’ driveway posting in obscure forums and obsessing over goggle prototypes. He pontificated on the benefits of packing the heavy batteries and chips in removable pucks that the user could put in a pocket, rather than in the headset itself. Doing so makes the headset lighter and more comfortable. He says he was pushing Facebook to go that route before he was ousted, but when he left, it abandoned the idea. Apple chose to have an external battery for its Vision Pro, which Luckey praised. 

“Anyway,” he told me. “I’m still sore about it eight years later.”

Speaking of soreness, Luckey’s most public professional wound, his ouster from Facebook in 2017, was partially healed last month. The story—involving countless Twitter threads, doxxing, retractions and corrections to news articles, suppressed statements, and a significant segment in Blake Harris’s 2020 book The History of the Future—is difficult to boil down. But here’s the short version: A donation by Luckey to a pro-Trump group called Nimble America in late 2016 led to turmoil within Facebook after it was reported by the Daily Beast. That turmoil grew, especially after Ars Technica wrote that his donation was funding racist memes (the founders of Nimble America were involved in the subreddit r/TheDonald, but the organization itself was focused on creating pro-Trump billboards). Luckey left in March 2017, but Meta has never disclosed why. 

This April, Oculus’s former CTO John Carmack posted on X that he regretted not supporting Luckey more. Meta’s CTO, Andrew Bosworth, argued with Carmack, largely siding with Meta. In response, Luckey said, “You publicly told everyone my departure had nothing to do with politics, which is absolutely insane and obviously contradicted by reams of internal communications.” The two argued. In the X argument, Bosworth cautioned that there are “limits on what can be said here,” to which Luckey responded, “I am down to throw it all out there. We can make everything public and let people judge for themselves. Just say the word.” 

Six months later, Bosworth apologized to Luckey for the comments. Luckey responded, writing that although he is “infamously good at holding grudges,” neither Bosworth nor current leadership at Meta was involved in the incident. 

By now Luckey has spent years mulling over how much of his remaining anger is irrational or misplaced, but one thing is clear. He has a grudge left, but it’s against people behind the scenes—PR agents, lawyers, reporters—who, from his perspective, created a situation that forced him to accept and react to an account he found totally flawed. He’s angry about the steps Facebook took to keep him from communicating his side (Luckey has said he wrote versions of a statement at the time but that Facebook threatened further escalation if he posted it).

“What am I actually angry at? Am I angry that my life went in that direction? Absolutely,” he says.

“I have a lot more anger for the people who lied in a way that ruined my entire life and that saw my own company ripped out from under me that I’d spent my entire adult life building,” he says. “I’ve got plenty of anger left, but it’s not at Meta, the corporate entity. It’s not at Zuck. It’s not at Boz. Those are not the people who wronged me.”

While various subcommittees within the Senate and House deliberate how many millions to spend on IVAS each year, what is not in question is the Pentagon is investing to prepare for a potential conflict in the Pacific between China and Taiwan. The Pentagon requested nearly $10 billion for the Pacific Deterrence Initiative in its latest budget. The prospect of such a conflict is something Luckey considers often. 

He told the authors of Unit X: How the Pentagon and Silicon Valley Are Transforming the Future of War that Anduril’s “entire internal road map” has been organized around the question “How do you deter China? Not just in Taiwan, but Taiwan and beyond?”

At this point, nothing about IVAS is geared specifically toward use in the South Pacific as opposed to Ukraine or anywhere else. The design is in early stages. According to transcripts of a Senate Armed Services Subcommittee meeting in May, the military was scheduled to receive the third iteration of IVAS goggles earlier this summer. If they were on schedule, they’re currently in testing. That version is likely to change dramatically before it approaches Luckey’s vision for the future of mixed-reality warfare, in which “you have a little bit of an AI guardian angel on your shoulder, helping you out and doing all the stuff that is easy to miss in the midst of battle.”

Palmer Luckey sitting on yellow metal staircase
Designs for IVAS will have to adapt amid a shifting landscape of global conflict.
PHILIP CHEUNG

But will soldiers ever trust such a “guardian angel”? If the goggles of the future rely on AI-powered software like Lattice to identify threats—say, an enemy drone ahead or an autonomous vehicle racing toward you—Anduril is making the promise that it can sort through the false positives, recognize threats with impeccable accuracy, and surface critical information when it counts most. 

Luckey says the real test is how the technology compares with the current abilities of humans. “In a lot of cases, it’s already better,” he says, referring to Lattice, as measured by Anduril’s internal tests (it has not released these, and they have not been assessed by any independent external experts). “People are fallible in ways that machines aren’t necessarily,” he adds.

Still, Luckey admits he does worry about the threats Lattice will miss.

“One of the things that really worries me is there’s going to be people who die because Lattice misunderstood something, or missed a threat to a soldier that it should have seen,” he says. “At the same time, I can recognize that it’s still doing far better than people are doing today.”

When Lattice makes a significant mistake, it’s unlikely the public will know. Asked about the balance between transparency and national security in disclosing these errors, Luckey said that Anduril’s customer, the Pentagon, will receive complete information about what went wrong. That’s in line with the Pentagon’s policies on responsible AI adoption, which require that AI-driven systems be “developed with methodologies, data sources, design procedures, and documentation that are transparent to and auditable by their relevant defense personnel.” 

However, the policies promise nothing about disclosure to the public, a fact that’s led some progressive think tanks, like the Brennan Center for Justice, to call on federal agencies to modernize public transparency efforts for the age of AI. 

“It’s easy to say, Well, shouldn’t you be honest about this failure of your system to detect something?” Luckey says, regarding Anduril’s obligations. “Well, what if the failure was because the Chinese figured out a hole in the system and leveraged that to speed past our defenses of some military base? I’d say there’s not very much public good served in saying, ‘Attention, everyone—there is a way to get past all of the security on every US military base around the world.’ I would say that transparency would be the worst thing you could do.”

AI will add to the e-waste problem. Here’s what we can do about it.

Generative AI could account for up to 5 million metric tons of e-waste by 2030, according to a new study.

That’s a relatively small fraction of the current global total of over 60 million metric tons of e-waste each year. However, it’s still a significant part of a growing problem, experts warn. 

E-waste is the term to describe things like air conditioners, televisions, and personal electronic devices such as cell phones and laptops when they are thrown away. These devices often contain hazardous or toxic materials that can harm human health or the environment if they’re not disposed of properly. Besides those potential harms, when appliances like washing machines and high-performance computers wind up in the trash, the valuable metals inside the devices are also wasted—taken out of the supply chain instead of being recycled.

Depending on the adoption rate of generative AI, the technology could add 1.2 million to 5 million metric tons of e-waste in total by 2030, according to the study, published today in Nature Computational Science

“This increase would exacerbate the existing e-waste problem,” says Asaf Tzachor, a researcher at Reichman University in Israel and a co-author of the study, via email.

The study is novel in its attempts to quantify the effects of AI on e-waste, says Kees Baldé, a senior scientific specialist at the United Nations Institute for Training and Research and an author of the latest Global E-Waste Monitor, an annual report.

The primary contributor to e-waste from generative AI is high-performance computing hardware that’s used in data centers and server farms, including servers, GPUs, CPUs, memory modules, and storage devices. That equipment, like other e-waste, contains valuable metals like copper, gold, silver, aluminum, and rare earth elements, as well as hazardous materials such as lead, mercury, and chromium, Tzachor says.

One reason that AI companies generate so much waste is how quickly hardware technology is advancing. Computing devices typically have lifespans of two to five years, and they’re replaced frequently with the most up-to-date versions. 

While the e-waste problem goes far beyond AI, the rapidly growing technology represents an opportunity to take stock of how we deal with e-waste and lay the groundwork to address it. The good news is that there are strategies that can help reduce expected waste.

Expanding the lifespan of technologies by using equipment for longer is one of the most significant ways to cut down on e-waste, Tzachor says. Refurbishing and reusing components can also play a significant role, as can designing hardware in ways that makes it easier to recycle and upgrade. Implementing these strategies could reduce e-waste generation by up to 86% in a best-case scenario, the study projected. 

Only about 22% of e-waste is being formally collected and recycled today, according to the 2024 Global E-Waste Monitor. Much more is collected and recovered through informal systems, including in low- and lower-middle-income countries that don’t have established e-waste management infrastructure in place. Those informal systems can recover valuable metals but often don’t include safe disposal of hazardous materials, Baldé says.

Another major barrier to reducing AI-related e-waste is concerns about data security. Destroying equipment ensures information doesn’t leak out, while reusing or recycling equipment will require using other means to secure data. Ensuring that sensitive information is erased from hardware before recycling is critical, especially for companies handling confidential data, Tzachor says.

More policies will likely be needed to ensure that e-waste, including from AI, is recycled or disposed of properly. Recovering valuable metals (including iron, gold, and silver) can help make the economic case. However, e-waste recycling will likely still come with a price, since it’s costly to safely handle the hazardous materials often found inside the devices, Baldé says. 

“For companies and manufacturers, taking responsibility for the environmental and social impacts of their products is crucial,” Tzachor says. “This way, we can make sure that the technology we rely on doesn’t come at the expense of human and planetary health.”