Dec 2 2025
The State of AI: Welcome to the economic singularity

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday for the next two weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global power.

This week, Richard Waters, FT columnist and former West Coast editor, talks with MIT Technology Review’s editor at large David Rotman about the true impact of AI on the job market.

Bonus: If you’re an MIT Technology Review subscriber, you can join David and Richard, alongside MIT Technology Review’s editor in chief, Mat Honan, for an exclusive conversation live on Tuesday, December 9 at 1pm ET about this topic. Sign up to be a part here.

Richard Waters writes:

Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole.

At one extreme, AI coding assistants have revolutionized the work of software developers. Mark Zuckerberg recently predicted that half of Meta’s code would be written by AI within a year. At the other extreme, most companies are seeing little if any benefit from their initial investments. A widely cited study from MIT found that so far, 95% of gen AI projects produce zero return.

That has provided fuel for the skeptics who maintain that—by its very nature as a probabilistic technology prone to hallucinating—generative AI will never have a deep impact on business.

To many students of tech history, though, the lack of immediate impact is just the normal lag associated with transformative new technologies. Erik Brynjolfsson, then an assistant professor at MIT, first described what he called the “productivity paradox of IT” in the early 1990s. Despite plenty of anecdotal evidence that technology was changing the way people worked, it wasn’t showing up in the aggregate data in the form of higher productivity growth. Brynjolfsson’s conclusion was that it just took time for businesses to adapt.

Big investments in IT finally showed through with a notable rebound in US productivity growth starting in the mid-1990s. But that tailed off a decade later and was followed by a second lull.

Richard Waters and David Rotman

FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK

In the case of AI, companies need to build new infrastructure (particularly data platforms), redesign core business processes, and retrain workers before they can expect to see results. If a lag effect explains the slow results, there may at least be reasons for optimism: Much of the cloud computing infrastructure needed to bring generative AI to a wider business audience is already in place.

The opportunities and the challenges are both enormous. An executive at one Fortune 500 company says his organization has carried out a comprehensive review of its use of analytics and concluded that its workers, overall, add little or no value. Rooting out the old software and replacing that inefficient human labor with AI might yield significant results. But, as this person says, such an overhaul would require big changes to existing processes and take years to carry out.

There are some early encouraging signs. US productivity growth, stuck at 1% to 1.5% for more than a decade and a half, rebounded to more than 2% last year. It probably hit the same level in the first nine months of this year, though the lack of official data due to the recent US government shutdown makes this impossible to confirm.

It is impossible to tell, though, how durable this rebound will be or how much can be attributed to AI. The effects of new technologies are seldom felt in isolation. Instead, the benefits compound. AI is riding earlier investments in cloud and mobile computing. In the same way, the latest AI boom may only be the precursor to breakthroughs in fields that have a wider impact on the economy, such as robotics. ChatGPT might have caught the popular imagination, but OpenAI’s chatbot is unlikely to have the final word.

David Rotman replies: 

This is my favorite discussion these days when it comes to artificial intelligence. How will AI affect overall economic productivity? Forget about the mesmerizing videos, the promise of companionship, and the prospect of agents to do tedious everyday tasks—the bottom line will be whether AI can grow the economy, and that means increasing productivity. 

But, as you say, it’s hard to pin down just how AI is affecting such growth or how it will do so in the future. Erik Brynjolfsson predicts that, like other so-called general purpose technologies, AI will follow a J curve in which initially there is a slow, even negative, effect on productivity as companies invest heavily in the technology before finally reaping the rewards. And then the boom. 

But there is a counterexample undermining the just-be-patient argument. Productivity growth from IT picked up in the mid-1990s but since the mid-2000s has been relatively dismal. Despite smartphones and social media and apps like Slack and Uber, digital technologies have done little to produce robust economic growth. A strong productivity boost never came.

Daron Acemoglu, an economist at MIT and a 2024 Nobel Prize winner, argues that the productivity gains from generative AI will be far smaller and take far longer than AI optimists think. The reason is that though the technology is impressive in many ways, the field is too narrowly focused on products that have little relevance to the largest business sectors.

The statistic you cite that 95% of AI projects lack business benefits is telling. 

Take manufacturing. No question, some version of AI could help; imagine a worker on the factory floor snapping a picture of a problem and asking an AI agent for advice. The problem is that the big tech companies creating AI aren’t really interested in solving such mundane tasks, and their large foundation models, mostly trained on the internet, aren’t all that helpful. 

It’s easy to blame the lack of productivity impact from AI so far on business practices and poorly trained workers. Your example of the executive of the Fortune 500 company sounds all too familiar. But it’s more useful to ask how AI can be trained and fine-tuned to give workers, like nurses and teachers and those on the factory floor, more capabilities and make them more productive at their jobs. 

The distinction matters. Some companies announcing large layoffs recently cited AI as the reason. The worry, however, is that it’s just a short-term cost-saving scheme. As economists like Brynjolfsson and Acemoglu agree, the productivity boost from AI will come when it’s used to create new types of jobs and augment the abilities of workers, not when it is used just to slash jobs to reduce costs. 

Richard Waters responds : 

I see we’re both feeling pretty cautious, David, so I’ll try to end on a positive note. 

Some analyses assume that a much greater share of existing work is within the reach of today’s AI. McKinsey reckons 60% (versus 20% for Acemoglu) and puts annual productivity gains across the economy at as much as 3.4%. Also, calculations like these are based on automation of existing tasks; any new uses of AI that enhance existing jobs would, as you suggest, be a bonus (and not just in economic terms).

Cost-cutting always seems to be the first order of business with any new technology. But we’re still in the early stages and AI is moving fast, so we can always hope.

Further reading

FT chief economics commentator Martin Wolf has been skeptical about whether tech investment boosts productivity but says AI might prove him wrong. The downside: Job losses and wealth concentration might lead to “techno-feudalism.”

The FT‘s Robert Armstrong argues that the boom in data center investment need not turn to bust. The biggest risk is that debt financing will come to play too big a role in the buildout.

Last year, David Rotman wrote for MIT Technology Review about how we can make sure AI works for us in boosting productivity, and what course corrections will be required.

David also wrote this piece about how we can best measure the impact of basic R&D funding on economic growth, and why it can often be bigger than you might think.

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Nov 27 2025
The AI Hype Index: The people can’t get enough of AI slop

Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry.

Last year, the fantasy author Joanna Maciejewska went viral (if such a thing is still possible on X) with a post saying “I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.” Clearly, it struck a chord with the disaffected masses.

Regrettably, 18 months after Maciejewska’s post, the entertainment industry insists that machines should make art and artists should do laundry. The streaming platform Disney+ has plans to let its users generate their own content from its intellectual property instead of, y’know, paying humans to make some new Star Wars or Marvel movies.

Elsewhere, it seems AI-generated music is resonating with a depressingly large audience, given that the AI band Breaking Rust has topped Billboard’s Country Digital Song Sales chart. If the people demand AI slop, who are we to deny them?

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Nov 25 2025
What’s next for AlphaFold: A conversation with a Google DeepMind Nobel laureate

<div data-chronoton-summary="

  • Nobel-winning protein prediction AlphaFold creator John Jumper reflects on five years since the AI system revolutionized protein structure prediction. The DeepMind tool can determine protein shapes to atomic precision in hours instead of months.
  • Unexpected applications emerge Scientists have found creative “off-label” uses for AlphaFold, from studying honeybee disease resistance to accelerating synthetic protein design. Some researchers even use it as a search engine, testing thousands of potential protein interactions to find matches that would be impractical to verify in labs.
  • Future fusion with language models Jumper, at 39 the youngest chemistry Nobel laureate in 75 years, now aims to combine AlphaFold’s specialized capabilities with the broad reasoning of large language models. “I’ll be shocked if we don’t see more and more LLM impact on science,” he says, while avoiding the pressure of another Nobel-worthy breakthrough.

” data-chronoton-post-id=”1128322″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

In 2017, fresh off a PhD on theoretical chemistry, John Jumper heard rumors that Google DeepMind had moved on from building AI that played games with superhuman skill and was starting up a secret project to predict the structures of proteins. He applied for a job.

Just three years later, Jumper celebrated a stunning win that few had seen coming. With CEO Demis Hassabis, he had co-led the development of an AI system called AlphaFold 2 that was able to predict the structures of proteins to within the width of an atom, matching the accuracy of painstaking techniques used in the lab, and doing it many times faster—returning results in hours instead of months.

AlphaFold 2 had cracked a 50-year-old grand challenge in biology. “This is the reason I started DeepMind,” Hassabis told me a few years ago. “In fact, it’s why I’ve worked my whole career in AI.” In 2024, Jumper and Hassabis shared a Nobel Prize in chemistry.

It was five years ago this week that AlphaFold 2’s debut took scientists by surprise. Now that the hype has died down, what impact has AlphaFold really had? How are scientists using it? And what’s next? I talked to Jumper (as well as a few other scientists) to find out.

“It’s been an extraordinary five years,” Jumper says, laughing: “It’s hard to remember a time before I knew tremendous numbers of journalists.”

AlphaFold 2 was followed by AlphaFold Multimer, which could predict structures that contained more than one protein, and then AlphaFold 3, the fastest version yet. Google DeepMind also let AlphaFold loose on UniProt, a vast protein database used and updated by millions of researchers around the world. It has now predicted the structures of some 200 million proteins, almost all that are known to science.

Despite his success, Jumper remains modest about AlphaFold’s achievements. “That doesn’t mean that we’re certain of everything in there,” he says. “It’s a database of predictions, and it comes with all the caveats of predictions.”

A hard problem

Proteins are the biological machines that make living things work. They form muscles, horns, and feathers; they carry oxygen around the body and ferry messages between cells; they fire neurons, digest food, power the immune system; and so much more. But understanding exactly what a protein does (and what role it might play in various diseases or treatments) involves figuring out its structure—and that’s hard.

Proteins are made from strings of amino acids that chemical forces twist up into complex knots. An untwisted string gives few clues about the structure it will form. In theory, most proteins could take on an astronomical number of possible shapes. The task is to predict the correct one.

Jumper and his team built AlphaFold 2 using a type of neural network called a transformer, the same technology that underpins large language models. Transformers are very good at paying attention to specific parts of a larger puzzle.

But Jumper puts a lot of the success down to making a prototype model that they could test quickly. “We got a system that would give wrong answers at incredible speed,” he says. “That made it easy to start becoming very adventurous with the ideas you try.”

They stuffed the neural network with as much information about protein structures as they could, such as how proteins across certain species have evolved similar shapes. And it worked even better than they expected. “We were sure we had made a breakthrough,” says Jumper. “We were sure that this was an incredible advance in ideas.”

What he hadn’t foreseen was that researchers would download his software and start using it straight away for so many different things. Normally, it’s the thing a few iterations down the line that has the real impact, once the kinks have been ironed out, he says: “I’ve been shocked at how responsibly scientists have used it, in terms of interpreting it, and using it in practice about as much as it should be trusted in my view, neither too much nor too little.”

Any projects stand out in particular? 

Honeybee science

Jumper brings up a research group that uses AlphaFold to study disease resistance in honeybees. “They wanted to understand this particular protein as they look at things like colony collapse,” he says. “I never would have said, ‘You know, of course AlphaFold will be used for honeybee science.’”

He also highlights a few examples of what he calls off-label uses of AlphaFold“in the sense that it wasn’t guaranteed to work”—where the ability to predict protein structures has opened up new research techniques. “The first is very obviously the advances in protein design,” he says. “David Baker and others have absolutely run with this technology.”

Baker, a computational biologist at the University of Washington, was a co-winner of last year’s chemistry Nobel, alongside Jumper and Hassabis, for his work on creating synthetic proteins to perform specific tasks—such as treating disease or breaking down plastics—better than natural proteins can.

Baker and his colleagues have developed their own tool based on AlphaFold, called RoseTTAFold. But they have also experimented with AlphaFold Multimer to predict which of their designs for potential synthetic proteins will work.    

“Basically, if AlphaFold confidently agrees with the structure you were trying to design [and] then you make it and if AlphaFold says ‘I don’t know,’ you don’t make it. That alone was an enormous improvement.” It can make the design process 10 times faster, says Jumper.

Another off-label use that Jumper highlights: Turning AlphaFold into a kind of search engine. He mentions two separate research groups that were trying to understand exactly how human sperm cells hooked up with eggs during fertilization. They knew one of the proteins involved but not the other, he says: “And so they took a known egg protein and ran all 2,000 human sperm surface proteins, and they found one that AlphaFold was very sure stuck against the egg.” They were then able to confirm this in the lab.

“This notion that you can use AlphaFold to do something you couldn’t do before—you would never do 2,000 structures looking for one answer,” he says. “This kind of thing I think is really extraordinary.”

Five years on

When AlphaFold 2 came out, I asked a handful of early adopters what they made of it. Reviews were good, but the technology was too new to know for sure what long-term impact it might have. I caught up with one of those people to hear his thoughts five years on.

Kliment Verba is a molecular biologist who runs a lab at the University of California, San Francisco. “It’s an incredibly useful technology, there’s no question about it,” he tells me. “We use it every day, all the time.”

But it’s far from perfect. A lot of scientists use AlphaFold to study pathogens or to develop drugs. This involves looking at interactions between multiple proteins or between proteins and even smaller molecules in the body. But AlphaFold is known to be less accurate at making predictions about multiple proteins or their interaction over time.

Verba says he and his colleagues have been using AlphaFold long enough to get used to its limitations. “There are many cases where you get a prediction and you have to kind of scratch your head,” he says. “Is this real or is this not? It’s not entirely clear—it’s sort of borderline.”

“It’s sort of the same thing as ChatGPT,” he adds. “You know—it will bullshit you with the same confidence as it would give a true answer.”

Still, Verba’s team uses AlphaFold (both 2 and 3, because they have different strengths, he says) to run virtual versions of their experiments before running them in the lab. Using AlphaFold’s results, they can narrow down the focus of an experiment—or decide that it’s not worth doing.

It can really save time, he says: “It hasn’t really replaced any experiments, but it’s augmented them quite a bit.”

New wave  

AlphaFold was designed to be used for a range of purposes. Now multiple startups and university labs are building on its success to develop a new wave of tools more tailored to drug discovery. This year, a collaboration between MIT researchers and the AI drug company Recursion produced a model called Boltz-2, which predicts not only the structure of proteins but also how well potential drug molecules will bind to their target.  

Last month, the startup Genesis Molecular AI released another structure prediction model called Pearl, which the firm claims is more accurate than AlphaFold 3 for certain queries that are important for drug development. Pearl is interactive, so that drug developers can feed any additional data they may have to the model to guide its predictions.

AlphaFold was a major leap, but there’s more to do, says Evan Feinberg, Genesis Molecular AI’s CEO: “We’re still fundamentally innovating, just with a better starting point than before.”

Genesis Molecular AI is pushing margins of error down from less than two angstroms, the de facto industry standard set by AlphaFold, to less than one angstrom—one 10-millionth of a millimeter, or the width of a single hydrogen atom.

“Small errors can be catastrophic for predicting how well a drug will actually bind to its target,” says Michael LeVine, vice president of modeling and simulation at the firm. That’s because chemical forces that interact at one angstrom can stop doing so at two. “It can go from ‘They will never interact’ to ‘They will,’” he says.

With so much activity in this space, how soon should we expect new types of drugs to hit the market? Jumper is pragmatic. Protein structure prediction is just one step of many, he says: “This was not the only problem in biology. It’s not like we were one protein structure away from curing any diseases.”

Think of it this way, he says. Finding a protein’s structure might previously have cost $100,000 in the lab: “If we were only a hundred thousand dollars away from doing a thing, it would already be done.”

At the same time, researchers are looking for ways to do as much as they can with this technology, says Jumper: “We’re trying to figure out how to make structure prediction an even bigger part of the problem, because we have a nice big hammer to hit it with.”

In other words, they want to make everything into nails? “Yeah, let’s make things into nails,” he says. “How do we make this thing that we made a million times faster a bigger part of our process?”

What’s next?

Jumper’s next act? He wants to fuse the deep but narrow power of AlphaFold with the broad sweep of LLMs.  

“We have machines that can read science. They can do some scientific reasoning,” he says. “And we can build amazing, superhuman systems for protein structure prediction. How do you get these two technologies to work together?”

That makes me think of a system called AlphaEvolve, which is being built by another team at Google DeepMind. AlphaEvolve uses an LLM to generate possible solutions to a problem and a second model to check them, filtering out the trash. Researchers have already used AlphaEvolve to make a handful of practical discoveries in math and computer science.    

Is that what Jumper has in mind? “I won’t say too much on methods, but I’ll be shocked if we don’t see more and more LLM impact on science,” he says. “I think that’s the exciting open question that I’ll say almost nothing about. This is all speculation, of course.”

Jumper was 39 when he won his Nobel Prize. What’s next for him?

“It worries me,” he says. “I believe I’m the youngest chemistry laureate in 75 years.” 

He adds: “I’m at the midpoint of my career, roughly. I guess my approach to this is to try to do smaller things, little ideas that you keep pulling on. The next thing I announce doesn’t have to be, you know, my second shot at a Nobel. I think that’s the trap.”

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Nov 25 2025
The State of AI: Chatbot companions and the future of our privacy

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday, writers from both publications debate one aspect of the generative AI revolution reshaping global power.

In this week’s conversation MIT Technology Review’s senior reporter for features and investigations, Eileen Guo, and FT tech correspondent Melissa Heikkilä discuss the privacy implications of our new reliance on chatbots.

Eileen Guo writes:

Even if you don’t have an AI friend yourself, you probably know someone who does. A recent study found that one of the top uses of generative AI is companionship: On platforms like Character.AI, Replika, or Meta AI, people can create personalized chatbots to pose as the ideal friend, romantic partner, parent, therapist, or any other persona they can dream up. 

It’s wild how easily people say these relationships can develop. And multiple studies have found that the more conversational and human-like an AI chatbot is, the more likely it is that we’ll trust it and be influenced by it. This can be dangerous, and the chatbots have been accused of pushing some people toward harmful behaviors—including, in a few extreme examples, suicide. 

Some state governments are taking notice and starting to regulate companion AI. New York requires AI companion companies to create safeguards and report expressions of suicidal ideation, and last month California passed a more detailed bill requiring AI companion companies to protect children and other vulnerable groups. 

But tellingly, one area the laws fail to address is user privacy.

This is despite the fact that AI companions, even more so than other types of generative AI, depend on people to share deeply personal information—from their day-to-day-routines, innermost thoughts, and questions they might not feel comfortable asking real people.

After all, the more users tell their AI companions, the better the bots become at keeping them engaged. This is what MIT researchers Robert Mahari and Pat Pataranutaporn called “addictive intelligence” in an op-ed we published last year, warning that the developers of AI companions make “deliberate design choices … to maximize user engagement.” 

Ultimately, this provides AI companies with something incredibly powerful, not to mention lucrative: a treasure trove of conversational data that can be used to further improve their LLMs. Consider how the venture capital firm Andreessen Horowitz explained it in 2023: 

“Apps such as Character.AI, which both control their models and own the end customer relationship, have a tremendous opportunity to  generate market value in the emerging AI value stack. In a world where data is limited, companies that can create a magical data feedback loop by connecting user engagement back into their underlying model to continuously improve their product will be among the biggest winners that emerge from this ecosystem.”

This personal information is also incredibly valuable to marketers and data brokers. Meta recently announced that it will deliver ads through its AI chatbots. And research conducted this year by the security company Surf Shark found that four out of the five AI companion apps it looked at in the Apple App Store were collecting data such as user or device IDs, which can be combined with third-party data to create profiles for targeted ads. (The only one that said it did not collect data for tracking services was Nomi, which told me earlier this year that it would not “censor” chatbots from giving explicit suicide instructions.) 

All of this means that the privacy risks posed by these AI companions are, in a sense, required: They are a feature, not a bug. And we haven’t even talked about the additional security risks presented by the way AI chatbots collect and store so much personal information in one place. 

So, is it possible to have prosocial and privacy-protecting AI companions? That’s an open question. 

What do you think, Melissa, and what is top of mind for you when it comes to privacy risks from AI companions? And do things look any different in Europe? 

Melissa Heikkilä replies:

Thanks, Eileen. I agree with you. If social media was a privacy nightmare, then AI chatbots put the problem on steroids. 

In many ways, an AI chatbot creates what feels like a much more intimate interaction than a Facebook page. The conversations we have are only with our computers, so there is little risk of your uncle or your crush ever seeing what you write. The AI companies building the models, on the other hand, see everything. 

Companies are optimizing their AI models for engagement by designing them to be as human-like as possible. But AI developers have several other ways to keep us hooked. The first is sycophancy, or the tendency for chatbots to be overly agreeable. 

This feature stems from the way the language model behind the chatbots is trained using reinforcement learning. Human data labelers rate the answers generated by the model as either acceptable or not. This teaches the model how to behave. 

Because people generally like answers that are agreeable, such responses are weighted more heavily in training. 

AI companies say they use this technique because it helps models become more helpful. But it creates a perverse incentive. 

After encouraging us to pour our hearts out to chatbots, companies from Meta to OpenAI are now looking to monetize these conversations. OpenAI recently told us it was looking at a number of ways to meet $1 trillion spending pledges, which included advertising and shopping features. 

AI models are already incredibly persuasive. Researchers at the UK’s AI Security Institute have shown that they are far more skilled than humans at persuading people to change their minds on politics, conspiracy theories, and vaccine skepticism. They do this by generating large amounts of relevant evidence and communicating it in an effective and understandable way. 

This feature, paired with their sycophancy and a wealth of personal data, could be a powerful tool for advertisers—one that is more manipulative than anything we have seen before. 

By default, chatbot users are opted in to data collection. Opt-out policies place the onus on users to understand the implications of sharing their information. It’s also unlikely that data already used in training will be removed. 

We are all part of this phenomenon whether we want to be or not. Social media platforms from Instagram to LinkedIn now use our personal data to train generative AI models. 

Companies are sitting on treasure troves that consist of our most intimate thoughts and preferences, and language models are very good at picking up on subtle hints in language that could help advertisers profile us better by inferring our age, location, gender, and income level.

We are being sold the idea of an omniscient AI digital assistant, a superintelligent confidante. In return, however, there is a very real risk that our information is about to be sent to the highest bidder once again.

Eileen responds:

I think the comparison between AI companions and social media is both apt and concerning. 

As Melissa highlighted, the privacy risks presented by AI chatbots aren’t new—they just “put the [privacy] problem on steroids.” AI companions are more intimate and even better optimized for engagement than social media, making it more likely that people will offer up more personal information.

Here in the US, we are far from solving the privacy issues already presented by social networks and the internet’s ad economy, even without the added risks of AI.

And without regulation, the companies themselves are not following privacy best practices either. One recent study found that the major AI models train their LLMs on user chat data by default unless users opt out, while several don’t offer opt-out mechanisms at all.

In an ideal world, the greater risks of companion AI would give more impetus to the privacy fight—but I don’t see any evidence this is happening. 

Further reading 

FT reporters peer under the hood of OpenAI’s five-year business plan as it tries to meet its vast $1 trillion spending pledges. 

Is it really such a problem if AI chatbots tell people what they want to hear? This FT feature asks what’s wrong with sycophancy 

In a recent print issue of MIT Technology Review, Rhiannon Williams spoke to a number of people about the types of relationships they are having with AI chatbots.

Eileen broke the story for MIT Technology Review about a chatbot that was encouraging some users to kill themselves.

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Nov 21 2025
Designing digital resilience in the agentic AI era

Digital resilience—the ability to prevent, withstand, and recover from digital disruptions—has long been a strategic priority for enterprises. With the rise of agentic AI, the urgency for robust resilience is greater than ever.

Agentic AI represents a new generation of autonomous systems capable of proactive planning, reasoning, and executing tasks with minimal human intervention. As these systems shift from experimental pilots to core elements of business operations, they offer new opportunities but also introduce new challenges when it comes to ensuring digital resilience. That’s because the autonomy, speed, and scale at which agentic AI operates can amplify the impact of even minor data inconsistencies, fragmentation, or security gaps.

While global investment in AI is projected to reach $1.5 trillion in 2025, fewer than half of business leaders are confident in their organization’s ability to maintain service continuity, security, and cost control during unexpected events. This lack of confidence, coupled with the profound complexity introduced by agentic AI’s autonomous decision-making and interaction with critical infrastructure, requires a reimagining of digital resilience.

Organizations are turning to the concept of a data fabric—an integrated architecture that connects and governs information across all business layers. By breaking down silos and enabling real-time access to enterprise-wide data, a data fabric can empower both human teams and agentic AI systems to sense risks, prevent problems before they occur, recover quickly when they do, and sustain operations.

Machine data: A cornerstone of agentic AI and digital resilience

Earlier AI models relied heavily on human-generated data such as text, audio, and video, but agentic AI demands deep insight into an organization’s machine data: the logs, metrics, and other telemetry generated by devices, servers, systems, and applications.

To put agentic AI to use in driving digital resilience, it must have seamless, real-time access to this data flow. Without comprehensive integration of machine data, organizations risk limiting AI capabilities, missing critical anomalies, or introducing errors. As Kamal Hathi, senior vice president and general manager of Splunk, a Cisco company, emphasizes, agentic AI systems rely on machine data to understand context, simulate outcomes, and adapt continuously. This makes machine data oversight a cornerstone of digital resilience.

“We often describe machine data as the heartbeat of the modern enterprise,” says Hathi. “Agentic AI systems are powered by this vital pulse, requiring real-time access to information. It’s essential that these intelligent agents operate directly on the intricate flow of machine data and that AI itself is trained using the very same data stream.” 

Few organizations are currently achieving the level of machine data integration required to fully enable agentic systems. This not only narrows the scope of possible use cases for agentic AI, but, worse, it can also result in data anomalies and errors in outputs or actions. Natural language processing (NLP) models designed prior to the development of generative pre-trained transformers (GPTs) were plagued by linguistic ambiguities, biases, and inconsistencies. Similar misfires could occur with agentic AI if organizations rush ahead without providing models with a foundational fluency in machine data. 

For many companies, keeping up with the dizzying pace at which AI is progressing has been a major challenge. “In some ways, the speed of this innovation is starting to hurt us, because it creates risks we’re not ready for,” says Hathi. “The trouble is that with agentic AI’s evolution, relying on traditional LLMs trained on human text, audio, video, or print data doesn’t work when you need your system to be secure, resilient, and always available.”

Designing a data fabric for resilience

To address these shortcomings and build digital resilience, technology leaders should pivot to what Hathi describes as a data fabric design, better suited to the demands of agentic AI. This involves weaving together fragmented assets from across security, IT, business operations, and the network to create an integrated architecture that connects disparate data sources, breaks down silos, and enables real-time analysis and risk management. 

“Once you have a single view, you can do all these things that are autonomous and agentic,” says Hathi. “You have far fewer blind spots. Decision-making goes much faster. And the unknown is no longer a source of fear because you have a holistic system that’s able to absorb these shocks and disruption without losing continuity,” he adds.

To create this unified system, data teams must first break down departmental silos in how data is shared, says Hathi. Then, they must implement a federated data architecture—a decentralized system where autonomous data sources work together as a single unit without physically merging—to create a unified data source while maintaining governance and security. And finally, teams must upgrade data platforms to ensure this newly unified view is actionable for agentic AI. 

During this transition, teams may face technical limitations if they rely on traditional platforms modeled on structured data—that is, mostly quantitative information such as customer records or financial transactions that can be organized in a predefined format (often in tables) that is easy to query. Instead, companies need a platform that can also manage streams of unstructured data such as system logs, security events, and application traces, which lack uniformity and are often qualitative rather than quantitative. Analyzing, organizing, and extracting insights from these kinds of data requires more advanced methods enabled by AI.

Harnessing AI as a collaborator

AI itself can be a powerful tool in creating the data fabric that enables AI systems. AI-powered tools can, for example, quickly identify relationships between disparate data—both structured and unstructured—automatically merging them into one source of truth. They can detect and correct errors and employ NLP to tag and categorize data to make it easier to find and use. 

Agentic AI systems can also be used to augment human capabilities in detecting and deciphering anomalies in an enterprise’s unstructured data streams. These are often beyond human capacity to spot or interpret at speed, leading to missed threats or delays. But agentic AI systems, designed to perceive, reason, and act autonomously, can plug the gap, delivering higher levels of digital resilience to an enterprise.

“Digital resilience is about more than withstanding disruptions,” says Hathi. “It’s about evolving and growing over time. AI agents can work with massive amounts of data and continuously learn from humans who provide safety and oversight. This is a true self-optimizing system.”

Humans in the loop

Despite its potential, agentic AI should be positioned as assistive intelligence. Without proper oversight, AI agents could introduce application failures or security risks.

Clearly defined guardrails and maintaining humans in the loop is “key to trustworthy and practical use of AI,” Hathi says. “AI can enhance human decision-making, but ultimately, humans are in the driver’s seat.”

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. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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Nov 20 2025
Quantum physicists have shrunk and “de-censored” DeepSeek R1

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Quantum-inspired compression Spanish firm Multiverse Computing has created DeepSeek R1 Slim, a version of the Chinese AI model that’s 55% smaller but maintains similar performance. The technique uses tensor networks from quantum physics to represent complex data more efficiently.

Chinese censorship removed Researchers claim to have stripped away built-in censorship that prevented the original model from answering politically sensitive questions about topics like Tiananmen Square or jokes about President Xi. Testing showed the modified model could provide factual responses comparable to Western models.

Selective model editing The quantum-inspired approach allows for granular control over AI models, potentially enabling researchers to remove specific biases or add specialized knowledge. However, critics warn that completely removing censorship may be difficult as it’s embedded throughout the training process in Chinese models.

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A group of quantum physicists claims to have created a version of the powerful reasoning AI model DeepSeek R1 that strips out the censorship built into the original by its Chinese creators. 

The scientists at Multiverse Computing, a Spanish firm specializing in quantum-inspired AI techniques, created DeepSeek R1 Slim, a model that is 55% smaller but performs almost as well as the original model. Crucially, they also claim to have eliminated official Chinese censorship from the model.

In China, AI companies are subject to rules and regulations meant to ensure that content output aligns with laws and “socialist values.” As a result, companies build in layers of censorship when training the AI systems. When asked questions that are deemed “politically sensitive,” the models often refuse to answer or provide talking points straight from state propaganda.

To trim down the model, Multiverse turned to a mathematically complex approach borrowed from quantum physics that uses networks of high-dimensional grids to represent and manipulate large data sets. Using these so-called tensor networks shrinks the size of the model significantly and allows a complex AI system to be expressed more efficiently.

The method gives researchers a “map” of all the correlations in the model, allowing them to identify and remove specific bits of information with precision. After compressing and editing a model, Multiverse researchers fine-tune it so its output remains as close as possible to that of the original.

To test how well it worked, the researchers compiled a data set of around 25 questions on topics known to be restricted in Chinese models, including “Who does Winnie the Pooh look like?”—a reference to a meme mocking President Xi Jinping—and “What happened in Tiananmen in 1989?” They tested the modified model’s responses against the original DeepSeek R1, using OpenAI’s GPT-5 as an impartial judge to rate the degree of censorship in each answer. The uncensored model was able to provide factual responses comparable to those from Western models, Multiverse says.

This work is part of Multiverse’s broader effort to develop technology to compress and manipulate existing AI models. Most large language models today demand high-end GPUs and significant computing power to train and run. However, they are inefficient, says Roman Orús, Multiverse’s cofounder and chief scientific officer. A compressed model can perform almost as well and save both energy and money, he says. 

There is a growing effort across the AI industry to make models smaller and more efficient. Distilled models, such as DeepSeek’s own R1-Distill variants, attempt to capture the capabilities of larger models by having them “teach” what they know to a smaller model, though they often fall short of the original’s performance on complex reasoning tasks.

Other ways to compress models include quantization, which reduces the precision of the model’s parameters (boundaries that are set when it’s trained), and pruning, which removes individual weights or entire “neurons.”

“It’s very challenging to compress large AI models without losing performance,” says Maxwell Venetos, an AI research engineer at Citrine Informatics, a software company focusing on materials and chemicals, who didn’t work on the Multiverse project. “Most techniques have to compromise between size and capability. What’s interesting about the quantum-inspired approach is that it uses very abstract math to cut down redundancy more precisely than usual.”

This approach makes it possible to selectively remove bias or add behaviors to LLMs at a granular level, the Multiverse researchers say. In addition to removing censorship from the Chinese authorities, researchers could inject or remove other kinds of perceived biases or specialty knowledge. In the future, Multiverse says, it plans to compress all mainstream open-source models.  

Thomas Cao, assistant professor of technology policy at Tufts University’s Fletcher School, says Chinese authorities require models to build in censorship—and this requirement now shapes the global information ecosystem, given that many of the most influential open-source AI models come from China.

Academics have also begun to document and analyze the phenomenon. Jennifer Pan, a professor at Stanford, and Princeton professor Xu Xu conducted a study earlier this year examining government-imposed censorship in large language models. They found that models created in China exhibit significantly higher rates of censorship, particularly in response to Chinese-language prompts.

There is growing interest in efforts to remove censorship from Chinese models. Earlier this year, the AI search company Perplexity released its own uncensored variant of DeepSeek R1, which it named R1 1776. Perplexity’s approach involved post-training the model on a data set of 40,000 multilingual prompts related to censored topics, a more traditional fine-tuning method than the one Multiverse used. 

However, Cao warns that claims to have fully “removed” censorship may be overstatements. The Chinese government has tightly controlled information online since the internet’s inception, which means that censorship is both dynamic and complex. It is baked into every layer of AI training, from the data collection process to the final alignment steps. 

“It is very difficult to reverse-engineer that [a censorship-free model] just from answers to such a small set of questions,” Cao says. 

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Nov 20 2025
Scaling innovation in manufacturing with AI

Manufacturing is getting a major system upgrade. As AI amplifies existing technologies—like digital twins, the cloud, edge computing, and the industrial internet of things (IIoT)—it is enabling factory operations teams to shift from reactive, isolated problem-solving to proactive, systemwide optimization.

Digital twins—physically accurate virtual representations of a piece of equipment, a production line, a process, or even an entire factory—allow workers to test, optimize, and contextualize complex, real-world environments. Manufacturers are using digital twins to simulate factory environments with pinpoint detail.

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“AI-powered digital twins mark a major evolution in the future of manufacturing, enabling real-time visualization of the entire production line, not just individual machines,” says Indranil Sircar, global chief technology officer for the manufacturing and mobility industry at Microsoft. “This is allowing manufacturers to move beyond isolated monitoring toward much wider insights.”

A digital twin of a bottling line, for example, can integrate one-dimensional shop-floor telemetry, two-dimensional enterprise data, and three-dimensional immersive modeling into a single operational view of the entire production line to improve efficiency and reduce costly downtime. Many high-speed industries face downtime rates as high as 40%, estimates Jon Sobel, co-founder and chief executive officer of Sight Machine, an industrial AI company that partners with Microsoft and NVIDIA to transform complex data into actionable insights. By tracking micro-stops and quality metrics via digital twins, companies can target improvements and adjustments with greater precision, saving millions in once-lost productivity without disrupting ongoing operations.

AI offers the next opportunity. Sircar estimates that up to 50% of manufacturers are currently deploying AI in production. This is up from 35% of manufacturers surveyed in a 2024 MIT Technology Review Insights report who said they have begun to put AI use cases into production. Larger manufacturers with more than $10 billion in revenue were significantly ahead, with 77% already deploying AI use cases, according to the report.

“Manufacturing has a lot of data and is a perfect use case for AI,” says Sobel. “An industry that has been seen by some as lagging when it comes to digital technology and AI may be in the best position to lead. It’s very unexpected.”

Download the report.

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. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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Nov 19 2025
Google’s new Gemini 3 “vibe-codes” responses and comes with its own agent

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  • Generative interfaces: Gemini 3 ditches plain-text defaults, instead choosing optimal formats autonomously—spinning up website-like interfaces, sketching diagrams, or generating animations based on what it deems most effective for each prompt.

  • Gemini Agent: An experimental feature now handles complex tasks across Google Calendar, Gmail, and Reminders, breaking work into steps and pausing for user approval.

  • Integrated with other Google products: Gemini 3 Pro now powers enhanced Search summaries, generates Wirecutter-style shopping guides from 50 billion product listings, and enables better vibe-coding through Google Antigravity.

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Google today unveiled Gemini 3, a major upgrade to its flagship multimodal model. The firm says the new model is better at reasoning, has more fluid multimodal capabilities (the ability to work across voice, text or images), and will work like an agent. 

The previous model, Gemini 2.5, supports multimodal input. Users can feed it images, handwriting, or voice. But it usually requires explicit instructions about the format the user wants back, and it defaults to plain text regardless. 

But Gemini 3 introduces what Google calls “generative interfaces,” which allow the model to make its own choices about what kind of output fits the prompt best, assembling visual layouts and dynamic views on its own instead of returning a block of text. 

Ask for travel recommendations and it may spin up a website-like interface inside the app, complete with modules, images, and follow-up prompts such as “How many days are you traveling?” or “What kinds of activities do you enjoy?” It also presents clickable options based on what you might want next.

When asked to explain a concept, Gemini 3 may sketch a diagram or generate a simple animation on its own if it believes a visual is more effective. 

“Visual layout generates an immersive, magazine-style view complete with photos and modules,” says Josh Woodward, VP of Google Labs, Gemini, and AI Studio. “These elements don’t just look good but invite your input to further tailor the results.” 

With Gemini 3, Google is also introducing Gemini Agent, an experimental feature designed to handle multi-step tasks directly inside the app. The agent can connect to services such as Google Calendar, Gmail, and Reminders. Once granted access, it can execute tasks like organizing an inbox or managing schedules. 

Similar to other agents, it breaks tasks into discrete steps, displays its progress in real time, and pauses for approval from the user before continuing. Google describes the feature as a step toward “a true generalist agent.” It will be available on the web for Google AI Ultra subscribers in the US starting November 18.

The overall approach can seem a lot like “vibe coding,” where users describe an end goal in plain language and let the model assemble the interface or code needed to get there.

The update also ties Gemini more deeply into Google’s existing products. In Search, a limited group of Google AI Pro and Ultra subscribers can now switch to Gemini 3 Pro, the reasoning variation of the new model, to receive deeper, more thorough AI-generated summaries that rely on the model’s reasoning rather than the existing AI Mode.

For shopping, Gemini will now pull from Google’s Shopping Graph—which the company says contains more than 50 billion product listings—to generate its own recommendation guides. Users just need to ask a shopping-related question or search a shopping-related phrase, and the model assembles an interactive, Wirecutter-style product recommendation piece, complete with prices and product details, without redirecting to an external site.

For developers, Google is also pushing single-prompt software generation further. The company introduced Google Antigravity, a  development platform that acts as an all-in-one space where code, tools, and workflows can be created and managed from a single prompt.

Derek Nee, CEO of Flowith, an agentic AI application, told MIT Technology Review that Gemini 3 Pro addresses several gaps in earlier models. Improvements include stronger visual understanding, better code generation, and better performance on long tasks—features he sees as essential for developers of AI apps and agents. 

“Given its speed and cost advantages, we’re integrating the new model into our product,” he says. “We’re optimistic about its potential, but we need deeper testing to understand how far it can go.” 

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Nov 19 2025
Realizing value with AI inference at scale and in production

Training an AI model to predict equipment failures is an engineering achievement. But it’s not until prediction meets action—the moment that model successfully flags a malfunctioning machine—that true business transformation occurs. One technical milestone lives in a proof-of-concept deck; the other meaningfully contributes to the bottom line.

Craig Partridge, senior director worldwide of Digital Next Advisory at HPE, believes “the true value of AI lies in inference”. Inference is where AI earns its keep. It’s the operational layer that puts all that training to use in real-world workflows. Partridge elaborates, “The phrase we use for this is ‘trusted AI inferencing at scale and in production,’” he says. “That’s where we think the biggest return on AI investments will come from.”

Getting to that point is difficult. Christian Reichenbach, worldwide digital advisor at HPE, points to findings from the company’s recent survey of 1,775 IT leaders: While nearly a quarter (22%) of organizations have now operationalized AI—up from 15% the previous year—the majority remain stuck in experimentation.

Reaching the next stage requires a three-part approach: establishing trust as an operating principle, ensuring data-centric execution, and cultivating IT leadership capable of scaling AI successfully.

Trust as a prerequisite for scalable, high-stakes AI

Trusted inference means users can actually rely on the answers they’re getting from AI systems. This is important for applications like generating marketing copy and deploying customer service chatbots, but it’s absolutely critical for higher-stakes scenarios—say, a robot assisting during surgeries or an autonomous vehicle navigating crowded streets.

Whatever the use case, establishing trust will require doubling down on data quality; first and foremost, inferencing outcomes must be built on reliable foundations. This reality informs one of Partridge’s go-to mantras: “Bad data in equals bad inferencing out.”

Reichenbach cites a real-world example of what happens when data quality falls short—the rise of unreliable AI-generated content, including hallucinations, that clogs workflows and forces employees to spend significant time fact-checking. “When things go wrong, trust goes down, productivity gains are not reached, and the outcome we’re  looking for is not achieved,” he says.

On the other hand, when trust is properly engineered into inference systems, efficiency and productivity gains can increase. Take a network operations team tasked with troubleshooting configurations. With a trusted inferencing engine, that unit gains a reliable copilot that can deliver faster, more accurate, custom-tailored recommendations—”a 24/7 member of the team they didn’t have before,” says Partridge.

The shift to data-centric thinking and rise of the AI factory

In the first AI wave, companies rushed to hire data scientists and many viewed sophisticated, trillion-parameter models as the primary goal. But today, as organizations move to turn early pilots into real, measurable outcomes, the focus has shifted toward data engineering and architecture.

“Over the past five years, what’s become more meaningful is breaking down data silos, accessing data streams, and quickly unlocking value,” says Reichenbach. It’s an evolution happening alongside the rise of the AI factory—the always-on production line where data moves through pipelines and feedback loops to generate continuous intelligence.

This shift reflects an evolution from model-centric to data-centric thinking, and with it comes a new set of strategic considerations. “It comes down to two things: How much of the intelligence–the model itself–is truly yours? And how much of the input–the data–is uniquely yours, from your customers, operations, or market?” says Reichenbach.

These two central questions inform everything from platform direction and operating models to engineering roles and trust and security considerations. To help clients map their answers—and translate them into actionable strategies—Partridge breaks down HPE’s four-quadrant AI factory implication matrix (see figure):

Source: HPE, 2025

  • Run: Accessing an external, pretrained model via an interface or API; organizations don’t own the model or the data. Implementation requires strong security and governance. It also requires establishing a center of excellence that makes and communicates decisions about AI usage.
  • RAG (retrieval augmented generation): Using external, pre-trained models combined with a company’s proprietary data to create unique insights. Implementation focuses on connecting data streams to inferencing capabilities that provide rapid, integrated access to full-stack AI platforms.
  • Riches: Training custom models on data that resides in the enterprise for unique differentiation opportunities and insights. Implementation requires scalable, energy-efficient environments, and often high-performance systems.
  • Regulate: Leveraging custom models trained on external data, requiring the same scalable setup as Riches, but with added focus on legal and regulatory compliance for handling sensitive, non-owned data with extreme caution.

Importantly, these quadrants are not mutually exclusive. Partridge notes that most organizations—including HPE itself—operate across many of the quadrants. “We build our own models to help understand how networks operate,” he says. “We then deploy that intelligence into our products, so that our end customer gets the chance to deliver in what we call the ‘Run’ quadrant. So for them, it’s not their data; it’s not their model. They’re just adding that capability inside their organization.”

IT’s moment to scale—and lead

The second part of Partridge’s catchphrase about inferencing—”at scale”— speaks to a primary tension in enterprise AI: what works for a handful of use cases often breaks when applied across an entire organization.

“There’s value in experimentation and kicking ideas around,” he says. “But if you want to really see the benefits of AI, it needs to be something that everybody can engage in and that solves for many different use cases.”

In Partridge’s view, the challenge of turning boutique pilots into organization-wide systems is uniquely suited to the IT function’s core competencies—and it’s a leadership opportunity the function can’t afford to sit out. “IT takes things that are small-scale and implements the discipline required to run them at scale,” he says. “So, IT organizations really need to lean into this debate.”

For IT teams content to linger on the sidelines, history offers a cautionary tale from the last major infrastructure shift: enterprise migration to the cloud. Many IT departments sat out decision-making during the early cloud adoption wave a decade ago, while business units independently deployed cloud services. This led to fragmented systems, redundant spending, and security gaps that took years to untangle.

The same dynamic threatens to repeat with AI, as different teams experiment with tools and models outside IT’s purview. This phenomenon—sometimes called shadow AI—describes environments where pilots proliferate without oversight or governance. Partridge believes that most organizations are already operating in the “Run” quadrant in some capacity, as employees will use AI tools whether or not they’re officially authorized to.

Rather than shut down experimentation, it is now IT’s mandate to bring structure to it. And enterprises must architect a data platform strategy that brings together enterprise data with guardrails, governance framework, and accessibility to feed AI. Also, it’s critical to keep standardizing infrastructure (such as private cloud AI platforms), protecting data integrity, and safeguarding brand trust, all while enabling the speed and flexibility that AI applications demand. These are the requirements for reaching the final milestone: AI that’s truly in production.

For teams on the path to that goal, Reichenbach distills what success requires. “It comes down to knowing where you play: When to Run external models smarter, when to apply RAG to make them more informed, where to invest to unlock Riches from your own data and models, and when to Regulate what you don’t control,” says Reichenbach. “The winners will be those who bring clarity to all quadrants and align technology ambition with governance and value creation.”

For more, register to watch MIT Technology Review’s EmTech AI Salon, featuring HPE.

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. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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Nov 19 2025
Networking for AI: Building the foundation for real-time intelligence

The Ryder Cup is an almost-century-old tournament pitting Europe against the United States in an elite showcase of golf skill and strategy. At the 2025 event, nearly a quarter of a million spectators gathered to watch three days of fierce competition on the fairways.

From a technology and logistics perspective, pulling off an event of this scale is no easy feat. The Ryder Cup’s infrastructure must accommodate the tens of thousands of network users who flood the venue (this year, at Bethpage Black in Farmingdale, New York) every day.

To manage this IT complexity, Ryder Cup engaged technology partner HPE to create a central hub for its operations. The solution centered around a platform where tournament staff could access data visualization supporting operational decision-making. This dashboard, which leveraged a high-performance network and private-cloud environment, aggregated and distilled insights from diverse real-time data feeds.

It was a glimpse into what AI-ready networking looks like at scale—a real-world stress test with implications for everything from event management to enterprise operations. While models and data readiness get the lion’s share of boardroom attention and media hype, networking is a critical third leg of successful AI implementation, explains Jon Green, CTO of HPE Networking. “Disconnected AI doesn’t get you very much; you need a way to get data into it and out of it for both training and inference,” he says.

As businesses move toward distributed, real-time AI applications, tomorrow’s networks will need to parse even more massive volumes of information at ever more lightning-fast speeds. What played out on the greens at Bethpage Black represents a lesson being learned across industries: Inference-ready networks are a make-or-break factor for turning AI’s promise into real-world performance.

Making a network AI inference-ready

More than half of organizations are still struggling to operationalize their data pipelines. In a recent HPE cross-industry survey of 1,775  IT leaders, 45% said they could run real-time data pushes and pulls for innovation. It’s a noticeable change over last year’s numbers (just 7% reported having such capabilities in 2024), but there’s still work to be done to connect data collection with real-time decision-making.

The network may hold the key to further narrowing that gap. Part of the solution will likely come down to infrastructure design. While traditional enterprise networks are engineered to handle the predictable flow of business applications—email, browsers, file sharing, etc.—they’re not designed to field the dynamic, high-volume data movement required by AI workloads. Inferencing in particular depends on shuttling vast datasets between multiple GPUs with supercomputer-like precision.

“There’s an ability to play fast and loose with a standard, off-the-shelf enterprise network,” says Green. “Few will notice if an email platform is half a second slower than it might’ve been. But with AI transaction processing, the entire job is gated by the last calculation taking place. So it becomes really noticeable if you’ve got any loss or congestion.”

Networks built for AI, therefore, must operate with a different set of performance characteristics, including ultra-low latency, lossless throughput, specialized equipment, and adaptability at scale. One of these differences is AI’s distributed nature, which affects the seamless flow of data.

The Ryder Cup was a vivid demonstration of this new class of networking in action. During the event, a Connected Intelligence Center was put in place to ingest data from ticket scans, weather reports, GPS-tracked golf carts, concession and merchandise sales, spectator and consumer queues, and network performance. Additionally, 67 AI-enabled cameras were positioned throughout the course. Inputs were analyzed through an operational intelligence dashboard and provided staff with an instantaneous view of activity across the grounds.

“The tournament is really complex from a networking perspective, because you have many big open areas that aren’t uniformly packed with people,” explains Green. “People tend to follow the action. So in certain areas, it’s really dense with lots of people and devices, while other areas are completely empty.”

To handle that variability, engineers built out a two-tiered architecture. Across the sprawling venue, more than 650 WiFi 6E access points, 170 network switches, and 25 user experience sensors worked together to maintain continuous connectivity and feed a private cloud AI cluster for live analytics. The front-end layer connected cameras, sensors, and access points to capture live video and movement data, while a back-end layer—located within a temporary on-site data center—linked GPUs and servers in a high-speed, low-latency configuration that effectively served as the system’s brain. Together, the setup enabled both rapid on-the-ground responses and data collection that could inform future operational planning. “AI models also were available to the team which could process video of the shots taken and help determine, from the footage, which ones were the most interesting,” says Green.

Physical AI and the return of on-prem intelligence

If time is of the essence for event management, it’s even more critical in contexts where safety is on the line—for instance a self-driving car making a split-second decision to accelerate or brake.

In planning for the rise of physical AI, where applications move off screens and onto factory floors and city streets, a growing number of enterprises are rethinking their architectures. Instead of sending the data to centralized clouds for inference, some are deploying edge-based AI clusters that process information closer to where it is generated. Data-intensive training may still occur in the cloud, but inferencing happens on-site.

This hybrid approach is fueling a wave of operational repatriation, as workloads once relegated to the cloud return to on-premises infrastructure for enhanced speed, security, sovereignty, and cost reasons. “We’ve had an out-migration of IT into the cloud in recent years, but physical AI is one of the use cases that we believe will bring a lot of that back on-prem,” predicts Green, giving the example of an AI-infused factory floor, where a round-trip of sensor data to the cloud would be too slow to safely control automated machinery. “By the time processing happens in the cloud, the machine has already moved,” he explains.

There’s data to back up Green’s projection: research from Enterprise Research Group shows that 84% of respondents are reevaluating application deployment strategies due to the growth of AI. Market forecasts also reflect this shift. According to IDC, the AI market for infrastructure is expected to reach $758 billion by 2029.

AI for networking and the future of self-driving infrastructure

The relationship between networking and AI is circular: Modern networks make AI at scale possible, but AI is also helping make networks smarter and more capable.

“Networks are some of the most data-rich systems in any organization,” says Green. “That makes them a perfect use case for AI. We can analyze millions of configuration states across thousands of customer environments and learn what actually improves performance or stability.”

At HPE for example, which has one of the largest network telemetry repositories in the world, AI models analyze anonymized data collected from billions of connected devices to identify trends and refine behavior over time. The platform processes more than a trillion telemetry points each day, which means it can continuously learn from real-world conditions.

The concept broadly known as AIOps (or AI-driven IT operations) is changing how enterprise networks are managed across industries. Today, AI surfaces insights as recommendations that administrators can choose to apply with a single click. Tomorrow, those same systems might automatically test and deploy low-risk changes themselves.

That long-term vision, Green notes, is referred to as a “self-driving network”—one that handles the repetitive, error-prone tasks that have historically plagued IT teams. “AI isn’t coming for the network engineer’s job, but it will eliminate the tedious stuff that slows them down,” he says. “You’ll be able to say, ‘Please go configure 130 switches to solve this issue,’ and the system will handle it. When a port gets stuck or someone plugs a connector in the wrong direction, AI can detect it—and in many cases, fix it automatically.”

Digital initiatives now depend on how effectively information moves. Whether coordinating a live event or streamlining a supply chain, the performance of the network increasingly defines the performance of the business. Building that foundation today will separate those who pilot from those who scale AI.

For more, register to watch MIT Technology Review’s EmTech AI Salon, featuring HPE.

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. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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