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.

Ecommerce MGMT 0 Comments
Nov 18 2025
The State of AI: How war will be changed forever

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 conversation, Helen Warrell, FT investigations reporter and former defense and security editor, and James O’Donnell, MIT Technology Review’s senior AI reporter, consider the ethical quandaries and financial incentives around AI’s use by the military.

Helen Warrell, FT investigations reporter 

It is July 2027, and China is on the brink of invading Taiwan. Autonomous drones with AI targeting capabilities are primed to overpower the island’s air defenses as a series of crippling AI-generated cyberattacks cut off energy supplies and key communications. In the meantime, a vast disinformation campaign enacted by an AI-powered pro-Chinese meme farm spreads across global social media, deadening the outcry at Beijing’s act of aggression.

Scenarios such as this have brought dystopian horror to the debate about the use of AI in warfare. Military commanders hope for a digitally enhanced force that is faster and more accurate than human-directed combat. But there are fears that as AI assumes an increasingly central role, these same commanders will lose control of a conflict that escalates too quickly and lacks ethical or legal oversight. Henry Kissinger, the former US secretary of state, spent his final years warning about the coming catastrophe of AI-driven warfare.

Grasping and mitigating these risks is the military priority—some would say the “Oppenheimer moment”—of our age. One emerging consensus in the West is that decisions around the deployment of nuclear weapons should not be outsourced to AI. UN secretary-general António Guterres has gone further, calling for an outright ban on fully autonomous lethal weapons systems. It is essential that regulation keep pace with evolving technology. But in the sci-fi-fueled excitement, it is easy to lose track of what is actually possible. As researchers at Harvard’s Belfer Center point out, AI optimists often underestimate the challenges of fielding fully autonomous weapon systems. It is entirely possible that the capabilities of AI in combat are being overhyped.

Anthony King, Director of the Strategy and Security Institute at the University of Exeter and a key proponent of this argument, suggests that rather than replacing humans, AI will be used to improve military insight. Even if the character of war is changing and remote technology is refining weapon systems, he insists, “the complete automation of war itself is simply an illusion.”

Of the three current military use cases of AI, none involves full autonomy. It is being developed for planning and logistics, cyber warfare (in sabotage, espionage, hacking, and information operations; and—most controversially—for weapons targeting, an application already in use on the battlefields of Ukraine and Gaza. Kyiv’s troops use AI software to direct drones able to evade Russian jammers as they close in on sensitive sites. The Israel Defense Forces have developed an AI-assisted decision support system known as Lavender, which has helped identify around 37,000 potential human targets within Gaza. 

Helen Warrell and James O'Donnell

FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK

There is clearly a danger that the Lavender database replicates the biases of the data it is trained on. But military personnel carry biases too. One Israeli intelligence officer who used Lavender claimed to have more faith in the fairness of a “statistical mechanism” than that of a grieving soldier.

Tech optimists designing AI weapons even deny that specific new controls are needed to control their capabilities. Keith Dear, a former UK military officer who now runs the strategic forecasting company Cassi AI, says existing laws are more than sufficient: “You make sure there’s nothing in the training data that might cause the system to go rogue … when you are confident you deploy it—and you, the human commander, are responsible for anything they might do that goes wrong.”

It is an intriguing thought that some of the fear and shock about use of AI in war may come from those who are unfamiliar with brutal but realistic military norms. What do you think, James? Is some opposition to AI in warfare less about the use of autonomous systems and really an argument against war itself? 

James O’Donnell replies:

Hi Helen, 

One thing I’ve noticed is that there’s been a drastic shift in attitudes of AI companies regarding military applications of their products. In the beginning of 2024, OpenAI unambiguously forbade the use of its tools for warfare, but by the end of the year, it had signed an agreement with Anduril to help it take down drones on the battlefield. 

This step—not a fully autonomous weapon, to be sure, but very much a battlefield application of AI—marked a drastic change in how much tech companies could publicly link themselves with defense. 

What happened along the way? For one thing, it’s the hype. We’re told AI will not just bring superintelligence and scientific discovery but also make warfare sharper, more accurate and calculated, less prone to human fallibility. I spoke with US Marines, for example, who tested a type of AI while patrolling the South Pacific that was advertised to analyze foreign intelligence faster than a human could. 

Secondly, money talks. OpenAI and others need to start recouping some of the unimaginable amounts of cash they’re spending on training and running these models. And few have deeper pockets than the Pentagon. And Europe’s defense heads seem keen to splash the cash too. Meanwhile, the amount of venture capital funding for defense tech this year has already doubled the total for all of 2024, as VCs hope to cash in on militaries’ newfound willingness to buy from startups. 

I do think the opposition to AI warfare falls into a few camps, one of which simply rejects the idea that more precise targeting (if it’s actually more precise at all) will mean fewer casualties rather than just more war. Consider the first era of drone warfare in Afghanistan. As drone strikes became cheaper to implement, can we really say it reduced carnage? Instead, did it merely enable more destruction per dollar?

But the second camp of criticism (and now I’m finally getting to your question) comes from people who are well versed in the realities of war but have very specific complaints about the technology’s fundamental limitations. Missy Cummings, for example, is a former fighter pilot for the US Navy who is now a professor of engineering and computer science at George Mason University. She has been outspoken in her belief that large language models, specifically, are prone to make huge mistakes in military settings.

The typical response to this complaint is that AI’s outputs are human-checked. But if an AI model relies on thousands of inputs for its conclusion, can that conclusion really be checked by one person?

Tech companies are making extraordinarily big promises about what AI can do in these high-stakes applications, all while pressure to implement them is sky high. For me, this means it’s time for more skepticism, not less. 

Helen responds:

Hi James, 

We should definitely continue to question the safety of AI warfare systems and the oversight to which they’re subjected—and hold political leaders to account in this area. I am suggesting that we also apply some skepticism to what you rightly describe as the “extraordinarily big promises” made by some companies about what AI might be able to achieve on the battlefield. 

There will be both opportunities and hazards in what the military is being offered by a relatively nascent (though booming) defense tech scene. The danger is that in the speed and secrecy of an arms race in AI weapons, these emerging capabilities may not receive the scrutiny and debate they desperately need.

Further reading:

Michael C. Horowitz, director of Perry World House at the University of Pennsylvania, explains the need for responsibility in the development of military AI systems in this FT op-ed.

The FT’s tech podcast asks what Israel’s defense tech ecosystem can tell us about the future of warfare 

This MIT Technology Review story analyzes how OpenAI completed its pivot to allowing its technology on the battlefield.

MIT Technology Review also uncovered how US soldiers are using generative AI to help scour thousands of pieces of open-source intelligence.

Ecommerce MGMT 0 Comments
Nov 14 2025
Google DeepMind is using Gemini to train agents inside Goat Simulator 3

Google DeepMind has built a new video-game-playing agent called SIMA 2 that can navigate and solve problems in a wide range of 3D virtual worlds. The company claims it’s a big step toward more general-purpose agents and better real-world robots.   

Google DeepMind first demoed SIMA (which stands for “scalable instructable multiworld agent”) last year. But SIMA 2 has been built on top of Gemini, the firm’s flagship large language model, which gives the agent a huge boost in capability.

The researchers claim that SIMA 2 can carry out a range of more complex tasks inside virtual worlds, figure out how to solve certain challenges by itself, and chat with its users. It can also improve itself by tackling harder tasks multiple times and learning through trial and error.

“Games have been a driving force behind agent research for quite a while,” Joe Marino, a research scientist at Google DeepMind, said in a press conference this week. He noted that even a simple action in a game, such as lighting a lantern, can involve multiple steps: “It’s a really complex set of tasks you need to solve to progress.”

The ultimate aim is to develop next-generation agents that are able to follow instructions and carry out open-ended tasks inside more complex environments than a web browser. In the long run, Google DeepMind wants to use such agents to drive real-world robots. Marino claimed that the skills SIMA 2 has learned, such as navigating an environment, using tools, and collaborating with humans to solve problems, are essential building blocks for future robot companions.

Unlike previous work on game-playing agents such as AlphaZero, which beat a Go grandmaster in 2016, or AlphaStar, which beat 99.8% of ranked human competition players at the video game StarCraft 2 in 2019, the idea behind SIMA is to train an agent to play an open-ended game without preset goals. Instead, the agent learns to carry out instructions given to it by people.

Humans control SIMA 2 via text chat, by talking to it out loud, or by drawing on the game’s screen. The agent takes in a video game’s pixels frame by frame and figures out what actions it needs to take to carry out its tasks.

Like its predecessor, SIMA 2 was trained on footage of humans playing eight commercial video games, including No Man’s Sky and Goat Simulator 3, as well as three virtual worlds created by the company. The agent learned to match keyboard and mouse inputs to actions.

Hooked up to Gemini, the researchers claim, SIMA 2 is far better at following instructions (asking questions and providing updates as it goes) and figuring out for itself how to perform certain more complex tasks.  

Google DeepMind tested the agent inside environments it had never seen before. In one set of experiments, researchers asked Genie 3, the latest version of the firm’s world model, to produce environments from scratch and dropped SIMA 2 into them. They found that the agent was able to navigate and carry out instructions there.

The researchers also used Gemini to generate new tasks for SIMA 2. If the agent failed, at first Gemini generated tips that SIMA 2 took on board when it tried again. Repeating a task multiple times in this way often allowed SIMA 2 to improve by trial and error until it succeeded, Marino said.

Git gud

SIMA 2 is still an experiment. The agent struggles with complex tasks that require multiple steps and more time to complete. It also remembers only its most recent interactions (to make SIMA 2 more responsive, the team cut its long-term memory). It’s also still nowhere near as good as people at using a mouse and keyboard to interact with a virtual world.

Julian Togelius, an AI researcher at New York University who works on creativity and video games, thinks it’s an interesting result. Previous attempts at training a single system to play multiple games haven’t gone too well, he says. That’s because training models to control multiple games just by watching the screen isn’t easy: “Playing in real time from visual input only is ‘hard mode,’” he says.

In particular, Togelius calls out GATO, a previous system from Google DeepMind, which—despite being hyped at the time—could not transfer skills across a significant number of virtual environments.  

Still, he is open-minded about whether or not SIMA 2 could lead to better robots. “The real world is both harder and easier than video games,” he says. It’s harder because you can’t just press A to open a door. At the same time, a robot in the real world will know exactly what its body can and can’t do at any time. That’s not the case in video games, where the rules inside each virtual world can differ.

Others are more skeptical. Matthew Guzdial, an AI researcher at the University of Alberta, isn’t too surprised that SIMA 2 can play many different video games. He notes that most games have very similar keyboard and mouse controls: Learn one and you learn them all. “If you put a game with weird input in front of it, I don’t think it’d be able to perform well,” he says.

Guzdial also questions how much of what SIMA 2 has learned would really carry over to robots. “It’s much harder to understand visuals from cameras in the real world compared to games, which are designed with easily parsable visuals for human players,” he says.

Still, Marino and his colleagues hope to continue their work with Genie 3 to allow the agent to improve inside a kind of endless virtual training dojo, where Genie generates worlds for SIMA to learn in via trial and error guided by Gemini’s feedback. “We’ve kind of just scratched the surface of what’s possible,” he said at the press conference.  

Ecommerce MGMT 0 Comments
Nov 14 2025
OpenAI’s new LLM exposes the secrets of how AI really works

ChatGPT maker OpenAI has built an experimental large language model that is far easier to understand than typical models.

That’s a big deal, because today’s LLMs are black boxes: Nobody fully understands how they do what they do. Building a model that is more transparent sheds light on how LLMs work in general, helping researchers figure out why models hallucinate, why they go off the rails, and just how far we should trust them with critical tasks.

“As these AI systems get more powerful, they’re going to get integrated more and more into very important domains,” Leo Gao, a research scientist at OpenAI, told MIT Technology Review in an exclusive preview of the new work. “It’s very important to make sure they’re safe.”

This is still early research. The new model, called a weight-sparse transformer, is far smaller and far less capable than top-tier mass-market models like the firm’s GPT-5, Anthropic’s Claude, and Google DeepMind’s Gemini. At most it’s as capable as GPT-1, a model that OpenAI developed back in 2018, says Gao (though he and his colleagues haven’t done a direct comparison).    

But the aim isn’t to compete with the best in class (at least, not yet). Instead, by looking at how this experimental model works, OpenAI hopes to learn about the hidden mechanisms inside those bigger and better versions of the technology.

It’s interesting research, says Elisenda Grigsby, a mathematician at Boston College who studies how LLMs work and who was not involved in the project: “I’m sure the methods it introduces will have a significant impact.” 

Lee Sharkey, a research scientist at AI startup Goodfire, agrees. “This work aims at the right target and seems well executed,” he says.

Why models are so hard to understand

OpenAI’s work is part of a hot new field of research known as mechanistic interpretability, which is trying to map the internal mechanisms that models use when they carry out different tasks.

That’s harder than it sounds. LLMs are built from neural networks, which consist of nodes, called neurons, arranged in layers. In most networks, each neuron is connected to every other neuron in its adjacent layers. Such a network is known as a dense network.

Dense networks are relatively efficient to train and run, but they spread what they learn across a vast knot of connections. The result is that simple concepts or functions can be split up between neurons in different parts of a model. At the same time, specific neurons can also end up representing multiple different features, a phenomenon known as superposition (a term borrowed from quantum physics). The upshot is that you can’t relate specific parts of a model to specific concepts.

“Neural networks are big and complicated and tangled up and very difficult to understand,” says Dan Mossing, who leads the mechanistic interpretability team at OpenAI. “We’ve sort of said: ‘Okay, what if we tried to make that not the case?’”

Instead of building a model using a dense network, OpenAI started with a type of neural network known as a weight-sparse transformer, in which each neuron is connected to only a few other neurons. This forced the model to represent features in localized clusters rather than spread them out.

Their model is far slower than any LLM on the market. But it is easier to relate its neurons or groups of neurons to specific concepts and functions. “There’s a really drastic difference in how interpretable the model is,” says Gao.

Gao and his colleagues have tested the new model with very simple tasks. For example, they asked it to complete a block of text that opens with quotation marks by adding matching marks at the end.  

It’s a trivial request for an LLM. The point is that figuring out how a model does even a straightforward task like that involves unpicking a complicated tangle of neurons and connections, says Gao. But with the new model, they were able to follow the exact steps the model took.

“We actually found a circuit that’s exactly the algorithm you would think to implement by hand, but it’s fully learned by the model,” he says. “I think this is really cool and exciting.”

Where will the research go next? Grigsby is not convinced the technique would scale up to larger models that have to handle a variety of more difficult tasks.    

Gao and Mossing acknowledge that this is a big limitation of the model they have built so far and agree that the approach will never lead to models that match the performance of cutting-edge products like GPT-5. And yet OpenAI thinks it might be able to improve the technique enough to build a transparent model on a par with GPT-3, the firm’s breakthrough 2021 LLM. 

“Maybe within a few years, we could have a fully interpretable GPT-3, so that you could go inside every single part of it and you could understand how it does every single thing,” says Gao. “If we had such a system, we would learn so much.”

Ecommerce MGMT 0 Comments
Nov 13 2025
Improving VMware migration workflows with agentic AI

For years, many chief information officers (CIOs) looked at VMware-to-cloud migrations with a wary pragmatism. Manually mapping dependencies and rewriting legacy apps mid-flight was not an enticing, low-lift proposition for enterprise IT teams.

But the calculus for such decisions has changed dramatically in a short period of time. Following recent VMware licensing changes, organizations are seeing greater uncertainty around the platform’s future. At the same time, cloud-native innovation is accelerating. According to the CNCF’s 2024 Annual Survey, 89% of organizations have already adopted at least some cloud-native techniques, and the share of companies reporting nearly all development and deployment as cloud-native grew sharply from 2023 to 2024 (20% to 24%). And market research firm IDC reports that cloud providers have become top strategic partners for generative AI initiatives.

DOWNLOAD THE ARTICLE

This is all happening amid escalating pressure to innovate faster and more cost-effectively to meet the demands of an AI-first future. As enterprises prepare for that inevitability, they are facing compute demands that are difficult, if not prohibitively expensive, to maintain exclusively on-premises.

Download the full article.

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.

This content 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.

Ecommerce MGMT 0 Comments
Nov 11 2025
Reimagining cybersecurity in the era of AI and quantum

AI and quantum technologies are dramatically reconfiguring how cybersecurity functions, redefining the speed and scale with which digital defenders and their adversaries can operate.

The weaponization of AI tools for cyberattacks is already proving a worthy opponent to current defenses. From reconnaissance to ransomware, cybercriminals can automate attacks faster than ever before with AI. This includes using generative AI to create social engineering attacks at scale, churning out tens of thousands of tailored phishing emails in seconds, or accessing widely available voice cloning software capable of bypassing security defenses for as little as a few dollars. And now, agentic AI raises the stakes by introducing autonomous systems that can reason, act, and adapt like human adversaries.

But AI isn’t the only force shaping the threat landscape. Quantum computing has the potential to seriously undermine current encryption standards if developed unchecked. Quantum algorithms can solve the mathematical problems underlying most modern cryptography, particularly public-key systems like RSA and Elliptic Curve, widely used for secure online communication, digital signatures, and cryptocurrency.

“We know quantum is coming. Once it does, it will force a change in how we secure data across everything, including governments, telecoms, and financial systems,” says Peter Bailey, senior vice president and general manager of Cisco’s security business.

“Most organizations are understandably focused on the immediacy of AI threats,” says Bailey. “Quantum might sound like science fiction, but those scenarios are coming faster than many realize. It’s critical to start investing now in defenses that can withstand both AI and quantum attacks.”

Critical to this defense is a zero trust approach to cybersecurity, which assumes no user or device can be inherently trusted. By enforcing continuous verification, zero trust enables constant monitoring and ensures that any attempts to exploit vulnerabilities are quickly detected and addressed in real time. This approach is technology-agnostic and creates a resilient framework even in the face of an ever-changing threat landscape.

Putting up AI defenses 

AI is lowering the barrier to entry for cyberattacks, enabling hackers even with limited skills or resources to infiltrate, manipulate, and exploit the slightest digital vulnerability.

Nearly three-quarters (74%) of cybersecurity professionals say AI-enabled threats are already having a significant impact on their organization, and 90% anticipate such threats in the next one to two years. 

“AI-powered adversaries have advanced techniques and operate at machine speed,” says Bailey. “The only way to keep pace is to use AI to automate response and defend at machine speed.”

To do this, Bailey says, organizations must modernize systems, platforms, and security operations to automate threat detection and response—processes that have previously relied on human rule-writing and reaction times. These systems must adapt dynamically as environments evolve and criminal tactics change.

At the same time, companies must strengthen the security of their AI models and data to reduce exposure to manipulation from AI-enabled malware. Such risks could include, for instance, prompt injections, where a malicious user crafts a prompt to manipulate an AI model into performing unintended actions, bypassing its original instructions and safeguards.

Agentic AI further ups the ante, with hackers able to use AI agents to automate attacks and make tactical decisions without constant human oversight. “Agentic AI has the potential to collapse the cost of the kill chain,” says Bailey. “That means everyday cybercriminals could start executing campaigns that today only well-funded espionage operations can afford.”

Organizations, in turn, are exploring how AI agents can help them stay ahead. Nearly 40% of companies expect agentic AI to augment or assist teams over the next 12 months, especially in cybersecurity, according to Cisco’s 2025 AI Readiness Index. Use cases include AI agents trained on telemetry, which can identify anomalies or signals from machine data too disparate and unstructured to be deciphered by humans. 

Calculating the quantum threat

As many cybersecurity teams focus on the very real AI-driven threat, quantum is waiting on the sidelines. Almost three-quarters (73%) of US organizations surveyed by KPMG say they believe it is only a matter of time before cybercriminals are using quantum to decrypt and disrupt today’s cybersecurity protocols. And yet, the majority (81%) also admit they could do more to ensure that their data remains secure.

Companies are right to be concerned. Threat actors are already carrying out harvest now, decrypt later attacks, stockpiling sensitive encrypted data to crack once quantum technology matures. Examples include state-sponsored actors intercepting government communications and cybercriminal networks storing encrypted internet traffic or financial records. 

Large technology companies are among the first to roll out quantum defenses. For example, Apple is using cryptography protocol PQ3 to defend against harvest now, decrypt later attacks on its iMessage platform. Google is testing post-quantum cryptography (PQC)—which is resistant to attacks from both quantum and classical computers—in its Chrome browser. And Cisco “has made significant investments in quantum-proofing our software and infrastructure,” says Bailey. “You’ll see more enterprises and governments taking similar steps over the next 18 to 24 months,” he adds. 

As regulations like the US Quantum Computing Cybersecurity Preparedness Act lay out requirements for mitigating against quantum threats, including standardized PQC algorithms by the National Institute of Standards and Technology, a wider range of organizations will start preparing their own quantum defenses. 

For organizations beginning that journey, Bailey outlines two key actions. First, establish visibility. “Understand what data you have and where it lives,” he says. “Take inventory, assess sensitivity, and review your encryption keys, rotating out any that are weak or outdated.”

Second, plan for migration. “Next, assess what it will take to support post-quantum algorithms across your infrastructure. That means addressing not just the technology, but also the process and people implications,” Bailey says.

Adopting proactive defense 

Ultimately, the foundation for building resilience against both AI and quantum is a zero trust approach, says Bailey. By embedding zero trust access controls across users, devices, business applications, networks, and clouds, this approach grants only the minimum access required to complete a task and enables continuous monitoring. It can also minimize the attack surface by confining a potential threat to an isolated zone, preventing it from accessing other critical systems.

Into this zero trust architecture, organizations can integrate specific measures to defend against AI and quantum risks. For instance, quantum-immune cryptography and AI-powered analytics and security tools can be used to identify complex attack patterns and automate real-time responses. 

“Zero trust slows down attacks and builds resilience,” Bailey says. “It ensures that even if a breach occurs, the crown jewels stay protected and operations can recover quickly.”

Ultimately, companies should not wait for threats to emerge and evolve. They must get ahead now. “This isn’t a what-if scenario; it’s a when,” says Bailey. “Organizations that invest early will be the ones setting the pace, not scrambling to catch up.”

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.

Ecommerce MGMT 0 Comments
Oct 31 2025
Chatbots are surprisingly effective at debunking conspiracy theories

It’s become a truism that facts alone don’t change people’s minds. Perhaps nowhere is this more clear than when it comes to conspiracy theories: Many people believe that you can’t talk conspiracists out of their beliefs. 

But that’s not necessarily true. It turns out that many conspiracy believers do respond to evidence and arguments—information that is now easy to deliver in the form of a tailored conversation with an AI chatbot.

In research we published in the journal Science this year, we had over 2,000 conspiracy believers engage in a roughly eight-minute conversation with DebunkBot, a model we built on top of OpenAI’s GPT-4 Turbo (the most up-to-date GPT model at that time). Participants began by writing out, in their own words, a conspiracy theory that they believed and the evidence that made the theory compelling to them. Then we instructed the AI model to persuade the user to stop believing in that conspiracy and adopt a less conspiratorial view of the world. A three-round back-and-forth text chat with the AI model (lasting 8.4 minutes on average) led to a 20% decrease in participants’ confidence in the belief, and about one in four participants—all of whom believed the conspiracy theory beforehand—indicated that they did not believe it after the conversation. This effect held true for both classic conspiracies (think the JFK assassination or the moon landing hoax) and more contemporary politically charged ones (like those related to the 2020 election and covid-19).

This story is part of MIT Technology Review’s series “The New Conspiracy Age,” on how the present boom in conspiracy theories is reshaping science and technology.

This is good news, given the outsize role that unfounded conspiracy theories play in today’s political landscape. So while there are widespread and legitimate concerns that generative AI is a potent tool for spreading disinformation, our work shows that it can also be part of the solution. 

Even people who began the conversation absolutely certain that their conspiracy was true, or who indicated that it was highly important to their personal worldview, showed marked decreases in belief. Remarkably, the effects were very durable; we followed up with participants two months later and saw just as big a reduction in conspiracy belief as we did immediately after the conversations. 

Our experiments indicate that many believers are relatively rational but misinformed, and getting them timely, accurate facts can have a big impact. Conspiracy theories can make sense to reasonable people who have simply never heard clear, non-conspiratorial explanations for the events they’re fixated on. This may seem surprising. But many conspiratorial claims, while wrong, seem reasonable on the surface and require specialized, esoteric knowledge to evaluate and debunk. 

For example, 9/11 deniers often point to the claim that jet fuel doesn’t burn hot enough to melt steel as evidence that airplanes were not responsible for bringing down the Twin Towers—but the chatbot responds by pointing out that although this is true, the American Institute of Steel Construction says jet fuel does burn hot enough to reduce the strength of steel by over 50%, which is more than enough to cause such towers to collapse. 

Although we have greater access to factual information than ever before, it is extremely difficult to search that vast corpus of knowledge efficiently. Finding the truth that way requires knowing what to google—or who to listen to—and being sufficiently motivated to seek out conflicting information. There are large time and skill barriers to conducting such a search every time we hear a new claim, and so it’s easy to take conspiratorial content you stumble upon at face value. And most would-be debunkers at the Thanksgiving table make elementary mistakes that AI avoids: Do you know the melting point and tensile strength of steel offhand? And when your relative calls you an idiot while trying to correct you, are you able to maintain your composure? 

With enough effort, humans would almost certainly be able to research and deliver facts like the AI in our experiments. And in a follow-up experiment, we found that the AI debunking was just as effective if we told participants they were talking to an expert rather than an AI. So it’s not that the debunking effect is AI-specific. Generally speaking, facts and evidence delivered by humans would also work. But it would require a lot of time and concentration for a human to come up with those facts. Generative AI can do the cognitive labor of fact-checking and rebutting conspiracy claims much more efficiently. 

In another large follow-up experiment, we found that what drove the debunking effect was specifically the facts and evidence the model provided: Factors like letting people know the chatbot was going to try to talk them out of their beliefs didn’t reduce its efficacy, whereas telling the model to try to persuade its chat partner without using facts and evidence totally eliminated the effect. 

Although the foibles and hallucinations of these models are well documented, our results suggest that debunking efforts are widespread enough on the internet to keep the conspiracy-focused conversations roughly accurate. When we hired a professional fact-checker to evaluate GPT-4’s claims, they found that over 99% of the claims were rated as true (and not politically biased). Also, in the few cases where participants named conspiracies that turned out to be true (like MK Ultra, the CIA’s human experimentation program from the 1950s), the AI chatbot confirmed their accurate belief rather than erroneously talking them out of it.

To date, largely by necessity, interventions to combat conspiracy theorizing have been mainly prophylactic—aiming to prevent people from going down the rabbit hole rather than trying to pull them back out. Now, thanks to advances in generative AI, we have a tool that can change conspiracists’ minds using evidence. 

Bots prompted to debunk conspiracy theories could be deployed on social media platforms to engage with those who share conspiratorial content—including other AI chatbots that spread conspiracies. Google could also link debunking AI models to search engines to provide factual answers to conspiracy-related queries. And instead of arguing with your conspiratorial uncle over the dinner table, you could just pass him your phone and have him talk to AI. 

Of course, there are much deeper implications here for how we as humans make sense of the world around us. It is widely argued that we now live in a “post-truth” world, where polarization and politics have eclipsed facts and evidence. By that account, our passions trump truth, logic-based reasoning is passé, and the only way to effectively change people’s minds is via psychological tactics like presenting compelling personal narratives or changing perceptions of the social norm. If so, the typical, discourse-based work of living together in a democracy is fruitless.

But facts aren’t dead. Our findings about conspiracy theories are the latest—and perhaps most extreme—in an emerging body of research demonstrating the persuasive power of facts and evidence. For example, while it was once believed that correcting falsehoods that aligns with one’s politics would just cause people to dig in and believe them even more, this idea of a “backfire” has itself been debunked: Many studies consistently find that corrections and warning labels reduce belief in, and sharing of, falsehoods—even among those who most distrust the fact-checkers making the corrections. Similarly, evidence-based arguments can change partisans’ minds on political issues, even when they are actively reminded that the argument goes against their party leader’s position. And simply reminding people to think about whether content is accurate before they share it can substantially reduce the spread of misinformation. 

And if facts aren’t dead, then there’s hope for democracy—though this arguably requires a consensus set of facts from which rival factions can work. There is indeed widespread partisan disagreement on basic facts, and a disturbing level of belief in conspiracy theories. Yet this doesn’t necessarily mean our minds are inescapably warped by our politics and identities. When faced with evidence—even inconvenient or uncomfortable evidence—many people do shift their thinking in response. And so if it’s possible to disseminate accurate information widely enough, perhaps with the help of AI, we may be able to reestablish the factual common ground that is missing from society today.

You can try our debunking bot yourself at at debunkbot.com. 

Thomas Costello is an assistant professor in social and decision sciences at Carnegie Mellon University. His research integrates psychology, political science, and human-computer interaction to examine where our viewpoints come from, how they differ from person to person, and why they change—as well as the sweeping impacts of artificial intelligence on these processes.

Gordon Pennycook is the Dorothy and Ariz Mehta Faculty Leadership Fellow and associate professor of psychology at Cornell University. He examines the causes and consequences of analytic reasoning, exploring how intuitive versus deliberative thinking shapes decision-making to understand errors underlying issues such as climate inaction, health behaviors, and political polarization.

David Rand is a professor of information science, marketing and management communication, and psychology at Cornell University. He uses approaches from computational social science and cognitive science to explore how human-AI dialogue can correct inaccurate beliefs, why people share falsehoods, and how to reduce political polarization and promote cooperation.

Ecommerce MGMT 0 Comments
Oct 30 2025
DeepSeek may have found a new way to improve AI’s ability to remember

<div data-chronoton-summary="

  • Memory Through Images: DeepSeek’s new OCR model stores information as visual rather than text tokens, a technique that allows it to retain more data. This approach could drastically reduce computing costs and carbon footprint while improving AI’s ability to ‘remember’.

  • Addressing Context Rot: The model works a bit like human memory, storing older or less critical information in slightly blurred form to save space. This could help address the fact current AI systems forget or muddle information over long conversations, a problem dubbed “context rot.”

  • DeepSeek Disruption: DeepSeek shocked the AI industry with its efficient DeepSeek-R1 reasoning model in January, and is again pushing boundaries. The OCR system can generate over 200,000 training data pages daily on a single GPU, potentially addressing the industry’s severe shortage of quality training text.

” data-chronoton-post-id=”1126932″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

An AI model released by the Chinese AI company DeepSeek uses new techniques that could significantly improve AI’s ability to “remember.”

Released last week, the optical character recognition (OCR) model works by extracting text from an image and turning it into machine-readable words. This is the same technology that powers scanner apps, translation of text in photos, and many accessibility tools. 

OCR is already a mature field with numerous high-performing systems, and according to the paper and some early reviews, DeepSeek’s new model performs on par with top models on key benchmarks.

But researchers say the model’s main innovation lies in how it processes information—specifically, how it stores and retrieves memories. Improving how AI models “remember” information could reduce the computing power they need to run, thus mitigating AI’s large (and growing) carbon footprint. 

Currently, most large language models break text down into thousands of tiny units called tokens. This turns the text into representations that models can understand. However, these tokens quickly become expensive to store and compute with as conversations with end users grow longer. When a user chats with an AI for lengthy periods, this challenge can cause the AI to forget things it’s been told and get information muddled, a problem some call “context rot.”

The new methods developed by DeepSeek (and published in its latest paper) could help to overcome this issue. Instead of storing words as tokens, its system packs written information into image form, almost as if it’s taking a picture of pages from a book. This allows the model to retain nearly the same information while using far fewer tokens, the researchers found. 

Essentially, the OCR model is a test bed for these new methods that permit more information to be packed into AI models more efficiently. 

Besides using visual tokens instead of just text tokens, the model is built on a type of tiered compression that is not unlike how human memories fade: Older or less critical content is stored in a slightly more blurry form in order to save space. Despite that, the paper’s authors argue, this compressed content can still remain accessible in the background while maintaining a high level of system efficiency.

Text tokens have long been the default building block in AI systems. Using visual tokens instead is unconventional, and as a result, DeepSeek’s model is quickly capturing researchers’ attention. Andrej Karpathy, the former Tesla AI chief and a founding member of OpenAI, praised the paper on X, saying that images may ultimately be better than text as inputs for LLMs. Text tokens might be “wasteful and just terrible at the input,” he wrote. 

Manling Li, an assistant professor of computer science at Northwestern University, says the paper offers a new framework for addressing the existing challenges in AI memory. “While the idea of using image-based tokens for context storage isn’t entirely new, this is the first study I’ve seen that takes it this far and shows it might actually work,” Li says.

The method could open up new possibilities in AI research and applications, especially in creating more useful AI agents, says Zihan Wang, a PhD candidate at Northwestern University. He believes that since conversations with AI are continuous, this approach could help models remember more and assist users more effectively.

The technique can also be used to produce more training data for AI models. Model developers are currently grappling with a severe shortage of quality text to train systems on. But the DeepSeek paper says that the company’s OCR system can generate over 200,000 pages of training data a day on a single GPU.

The model and paper, however, are only an early exploration of using image tokens rather than text tokens for AI memorization. Li says she hopes to see visual tokens applied not just to memory storage but also to reasoning. Future work, she says, should explore how to make AI’s memory fade in a more dynamic way, akin to how we can recall a life-changing moment from years ago but forget what we ate for lunch last week. Currently, even with DeepSeek’s methods, AI tends to forget and remember in a very linear way—recalling whatever was most recent, but not necessarily what was most important, she says. 

Despite its attempts to keep a low profile, DeepSeek, based in Hangzhou, China, has built a reputation for pushing the frontier in AI research. The company shocked the industry at the start of this year with the release of DeepSeek-R1, an open-source reasoning model that rivaled leading Western systems in performance despite using far fewer computing resources. 

Ecommerce MGMT 0 Comments
Oct 30 2025
The AI Hype Index: Data centers’ neighbors are pivoting to power blackouts

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.

Just about all businesses these days seem to be pivoting to AI, even when they don’t seem to know exactly why they’re investing in it—or even what it really does. “Optimization,” “scaling,” and “maximizing efficiency” are convenient buzzwords bandied about to describe what AI can achieve in theory, but for most of AI companies’ eager customers, the hundreds of billions of dollars they’re pumping into the industry aren’t adding up. And maybe they never will.

This month’s news doesn’t exactly cast the technology in a glowing light either. A bunch of NGOs and aid agencies are using AI models to generate images of fake suffering people to guilt their Instagram followers. AI translators are pumping out low-quality Wikipedia pages in the languages most vulnerable to going extinct. And thanks to the construction of new AI data centers, lots of neighborhoods living in their shadows are getting forced into their own sort of pivots—fighting back against the power blackouts and water shortages the data centers cause. How’s that for optimization?

Ecommerce MGMT 0 Comments