What to expect from Neuralink in 2025

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What to expect from Neuralink in 2025

In November, a young man named Noland Arbaugh announced he’d be livestreaming from his home for three days straight. His broadcast was in some ways typical fare: a backyard tour, video games, meet mom. The difference is that Arbaugh, who is paralyzed, has thin electrode-studded wires installed in his brain, which he used to move a computer mouse on a screen, click menus, and play chess. The implant, called N1, was installed last year by neurosurgeons working with Neuralink, Elon Musk’s brain-interface company. The possibility of listening to neurons and using their signals to move a computer cursor was first demonstrated more than 20 years ago in a lab setting. Now, Arbaugh’s livestream is an indicator that Neuralink is a whole lot closer to creating a plug-and-play experience that can restore people’s daily ability to roam the web and play games, giving them what the company has called “digital freedom.” But this is not yet a commercial product. The current studies are small-scale—they are true experiments, explorations of how the device works and how it can be improved. For instance, at some point last year, more than half the electrode-studded “threads” inserted into Aurbaugh’s brain retracted, and his control over the device worsened; Neuralink rushed to implement fixes so he could use his remaining electrodes to move the mouse. Neuralink did not reply to emails seeking comment, but here is what our analysis of its public statements leads us to expect from the company in 2025. More patients How many people will get these implants? Elon Musk keeps predicting huge numbers. In August, he posted on X: “If all goes well, there will be hundreds of people with Neuralinks within a few years, maybe tens of thousands within five years, millions within 10 years.” In reality, the actual pace is slower—a lot slower. That’s because in a study of a novel device, it’s typical for the first patients to be staged months apart, to allow time to monitor for problems. Neuralink has publicly announced that two people have received an implant: Arbaugh and a man referred to only as “Alex,” who received his in July or August. Then, on January 8, Musk disclosed during an online interview that there was now a third person with an implant. “We’ve got now three patients, three humans with Neuralinks implanted, and they are all working …well,” Musk said. During 2025, he added, “we expect to hopefully do, I don’t know, 20 or 30 patients.” Barring major setbacks, expect the pace of implants to increase—although perhaps not as fast as Musk says. In November, Neuralink updated its US trial listing to include space for five volunteers (up from three), and it also opened a trial in Canada with room for six. Considering these two studies only, Neuralink would carry out at least two more implants by the end of 2025 and eight by the end of 2026. However, by opening further international studies, Neuralink could increase the pace of the experiments. Better control So how good is Arbaugh’s control over the mouse? You can get an idea by trying a game called Webgrid, where you try to click quickly on a moving target. The program translates your speed into a measure of information transfer: bits per second. Neuralink claims Arbaugh reached a rate of over nine bits per second, doubling the old brain-interface record. The median able-bodied user scores around 10 bits per second, according to Neuralink. And yet during his livestream, Arbaugh complained that his mouse control wasn’t very good because his “model” was out of date. It was a reference to how his imagined physical movements get mapped to mouse movements. That mapping degrades over hours and days, and to recalibrate it, he has said, he spends as long as 45 minutes doing a set of retraining tasks on his monitor, such as imagining moving a dot from a center point to the edge of a circle. Noland Arbaugh stops to calibrate during a livestream on X@MODDEDQUAD VIA X Improving the software that sits between Arbaugh’s brain and the mouse is a big area of focus for Neuralink—one where the company is still experimenting and making significant changes. Among the goals: cutting the recalibration time to a few minutes. “We want them to feel like they are in the F1 [Formula One] car, not the minivan,” Bliss Chapman, who leads the BCI software team, told the podcaster Lex Fridman last year. Device changes Before Neuralink ever seeks approval to sell its brain interface, it will have to lock in a final device design that can be tested in a “pivotal trial” involving perhaps 20 to 40 patients, to show it really works as intended. That type of study could itself take a year or two to carry out and hasn’t yet been announced. In fact, Neuralink is still tweaking its implant in significant ways—for instance, by trying to increase the number of electrodes or extend the battery life. This month, Musk said the next human tests would be using an “upgraded Neuralink device.” The company is also still developing the surgical robot, called R1, that’s used to implant the device. It functions like a sewing machine: A surgeon uses R1 to thread the electrode wires into people’s brains. According to Neuralink’s job listings, improving the R1 robot and making the implant process entirely automatic is a major goal of the company. That’s partly to meet Musk’s predictions of a future where millions of people have an implant, since there wouldn’t be enough neurosurgeons in the world to put them all in manually. “We want to get to the point where it’s one click,” Neuralink president Dongjin Seo told Fridman last year. Robot arm Late last year, Neuralink opened a companion study through which it says some of its existing implant volunteers will get to try using their brain activity to control not only a computer mouse but other types of external devices, including an “assistive robotic arm.” We haven’t yet seen what Neuralink’s robotic arm looks like—whether it’s a tabletop research device or something that could be attached to a wheelchair and used at home to complete daily tasks. But it’s clear such a device could be helpful. During Aurbaugh’s livestream he frequently asked other people to do simple things for him, like brush his hair or put on his hat. Arbaugh demonstrates the use of Imagined Movement Control.@MODDEDQUAD VIA X And using brains to control robots is definitely possible—although so far only in a controlled research setting. In tests using a different brain implant, carried out at the University of Pittsburgh in 2012, a paralyzed woman named Jan Scheuermann was able to use a robot arm to stack blocks and plastic cups about as well as a person who’d had a severe stroke—impressive, since she couldn’t actually move her own limbs. There are several practical obstacles to using a robot arm at home. One is developing a robot that’s safe and useful. Another, as noted by Wired, is that the calibration steps to maintain control over an arm that can make 3D movements and grasp objects could be onerous and time consuming. Vision implant In September, Neuralink said it had received “breakthrough device designation” from the FDA for a version of its implant that could be used to restore limited vision to blind people. The system, which it calls Blindsight, would work by sending electrical impulses directly into a volunteer’s visual cortex, producing spots of light called phosphenes. If there are enough spots, they can be organized into a simple, pixelated form of vision, as previously demonstrated by academic researchers. The FDA designation is not the same as permission to start the vision study. Instead, it’s a promise by the agency to speed up review steps, including agreements around what a trial should look like. Right now, it’s impossible to guess when a Neuralink vision trial could start, but it won’t necessarily be this year. More money Neuralink last raised money in 2003, collecting around $325 million from investors in a funding round that valued the company at over $3 billion, according to Pitchbook. Ryan Tanaka, who publishes a podcast about the company, Neura Pod, says he thinks Neuralink will raise more money this year and that the valuation of the private company could triple. Fighting regulators Neuralink has attracted plenty of scrutiny from news reporters, animal-rights campaigners, and even fraud investigators at the Securities and Exchange Commission. Many of the questions surround its treatment of test animals and whether it rushed to try the implant in people. More recently, Musk has started using his X platform to badger and bully heads of state and was named by Donald Trump to co-lead a so-called Department of Government Efficiency, which Musk says will “get rid of nonsensical regulations” and potentially gut some DC agencies. During 2025, watch for whether Musk uses his digital bullhorn to give health regulators pointed feedback on how they’re handling Neuralink. Other efforts Don’t forget that Neuralink isn’t the only company working on brain implants. A company called Synchron has one that’s inserted into the brain through a blood vessel, which it’s also testing in human trials of brain control over computers. Other companies, including Paradromics, Precision Neuroscience, and BlackRock Neurotech, are also developing advanced brain-computer interfaces. Special thanks to Ryan Tanaka of Neura Pod for pointing us to Neuralink’s public announcements and projections.

The difference is that Arbaugh, who is paralyzed, has thin electrode-studded wires installed in his brain, which he used to move a computer mouse on a screen, click menus, and play chess. The implant, called N1, was installed last year by neurosurgeons working with Neuralink, Elon Musk’s brain-interface company.

The possibility of listening to neurons and using their signals to move a computer cursor was first demonstrated more than 20 years ago in a lab setting. Now, Arbaugh’s livestream is an indicator that Neuralink is a whole lot closer to creating a plug-and-play experience that can restore people’s daily ability to roam the web and play games, giving them what the company has called “digital freedom.”

But this is not yet a commercial product. The current studies are small-scale—they are true experiments, explorations of how the device works and how it can be improved. For instance, at some point last year, more than half the electrode-studded “threads” inserted into Aurbaugh’s brain retracted, and his control over the device worsened; Neuralink rushed to implement fixes so he could use his remaining electrodes to move the mouse.

Neuralink did not reply to emails seeking comment, but here is what our analysis of its public statements leads us to expect from the company in 2025.

More patients

How many people will get these implants? Elon Musk keeps predicting huge numbers. In August, he posted on X: “If all goes well, there will be hundreds of people with Neuralinks within a few years, maybe tens of thousands within five years, millions within 10 years.”

In reality, the actual pace is slower—a lot slower. That’s because in a study of a novel device, it’s typical for the first patients to be staged months apart, to allow time to monitor for problems.

Neuralink has publicly announced that two people have received an implant: Arbaugh and a man referred to only as “Alex,” who received his in July or August.

Then, on January 8, Musk disclosed during an online interview that there was now a third person with an implant. “We’ve got now three patients, three humans with Neuralinks implanted, and they are all working …well,” Musk said. During 2025, he added, “we expect to hopefully do, I don’t know, 20 or 30 patients.”

Barring major setbacks, expect the pace of implants to increase—although perhaps not as fast as Musk says. In November, Neuralink updated its US trial listing to include space for five volunteers (up from three), and it also opened a trial in Canada with room for six. Considering these two studies only, Neuralink would carry out at least two more implants by the end of 2025 and eight by the end of 2026.

However, by opening further international studies, Neuralink could increase the pace of the experiments.

Better control

So how good is Arbaugh’s control over the mouse? You can get an idea by trying a game called Webgrid, where you try to click quickly on a moving target. The program translates your speed into a measure of information transfer: bits per second.

Neuralink claims Arbaugh reached a rate of over nine bits per second, doubling the old brain-interface record. The median able-bodied user scores around 10 bits per second, according to Neuralink.

And yet during his livestream, Arbaugh complained that his mouse control wasn’t very good because his “model” was out of date. It was a reference to how his imagined physical movements get mapped to mouse movements. That mapping degrades over hours and days, and to recalibrate it, he has said, he spends as long as 45 minutes doing a set of retraining tasks on his monitor, such as imagining moving a dot from a center point to the edge of a circle.

Noland Arbaugh stops to calibrate during a livestream on X

@MODDEDQUAD VIA X

Improving the software that sits between Arbaugh’s brain and the mouse is a big area of focus for Neuralink—one where the company is still experimenting and making significant changes. Among the goals: cutting the recalibration time to a few minutes. “We want them to feel like they are in the F1 [Formula One] car, not the minivan,” Bliss Chapman, who leads the BCI software team, told the podcaster Lex Fridman last year.

Device changes

Before Neuralink ever seeks approval to sell its brain interface, it will have to lock in a final device design that can be tested in a “pivotal trial” involving perhaps 20 to 40 patients, to show it really works as intended. That type of study could itself take a year or two to carry out and hasn’t yet been announced.

In fact, Neuralink is still tweaking its implant in significant ways—for instance, by trying to increase the number of electrodes or extend the battery life. This month, Musk said the next human tests would be using an “upgraded Neuralink device.”

The company is also still developing the surgical robot, called R1, that’s used to implant the device. It functions like a sewing machine: A surgeon uses R1 to thread the electrode wires into people’s brains. According to Neuralink’s job listings, improving the R1 robot and making the implant process entirely automatic is a major goal of the company. That’s partly to meet Musk’s predictions of a future where millions of people have an implant, since there wouldn’t be enough neurosurgeons in the world to put them all in manually.

“We want to get to the point where it’s one click,” Neuralink president Dongjin Seo told Fridman last year.

Robot arm

Late last year, Neuralink opened a companion study through which it says some of its existing implant volunteers will get to try using their brain activity to control not only a computer mouse but other types of external devices, including an “assistive robotic arm.”

We haven’t yet seen what Neuralink’s robotic arm looks like—whether it’s a tabletop research device or something that could be attached to a wheelchair and used at home to complete daily tasks.

But it’s clear such a device could be helpful. During Aurbaugh’s livestream he frequently asked other people to do simple things for him, like brush his hair or put on his hat.

Arbaugh demonstrates the use of Imagined Movement Control.

@MODDEDQUAD VIA X

And using brains to control robots is definitely possible—although so far only in a controlled research setting. In tests using a different brain implant, carried out at the University of Pittsburgh in 2012, a paralyzed woman named Jan Scheuermann was able to use a robot arm to stack blocks and plastic cups about as well as a person who’d had a severe stroke—impressive, since she couldn’t actually move her own limbs.

There are several practical obstacles to using a robot arm at home. One is developing a robot that’s safe and useful. Another, as noted by Wired, is that the calibration steps to maintain control over an arm that can make 3D movements and grasp objects could be onerous and time consuming.

Vision implant

In September, Neuralink said it had received “breakthrough device designation” from the FDA for a version of its implant that could be used to restore limited vision to blind people. The system, which it calls Blindsight, would work by sending electrical impulses directly into a volunteer’s visual cortex, producing spots of light called phosphenes. If there are enough spots, they can be organized into a simple, pixelated form of vision, as previously demonstrated by academic researchers.

The FDA designation is not the same as permission to start the vision study. Instead, it’s a promise by the agency to speed up review steps, including agreements around what a trial should look like. Right now, it’s impossible to guess when a Neuralink vision trial could start, but it won’t necessarily be this year.

More money

Neuralink last raised money in 2003, collecting around $325 million from investors in a funding round that valued the company at over $3 billion, according to Pitchbook. Ryan Tanaka, who publishes a podcast about the company, Neura Pod, says he thinks Neuralink will raise more money this year and that the valuation of the private company could triple.

Fighting regulators

Neuralink has attracted plenty of scrutiny from news reporters, animal-rights campaigners, and even fraud investigators at the Securities and Exchange Commission. Many of the questions surround its treatment of test animals and whether it rushed to try the implant in people.

More recently, Musk has started using his X platform to badger and bully heads of state and was named by Donald Trump to co-lead a so-called Department of Government Efficiency, which Musk says will “get rid of nonsensical regulations” and potentially gut some DC agencies.

During 2025, watch for whether Musk uses his digital bullhorn to give health regulators pointed feedback on how they’re handling Neuralink.

Other efforts

Don’t forget that Neuralink isn’t the only company working on brain implants. A company called Synchron has one that’s inserted into the brain through a blood vessel, which it’s also testing in human trials of brain control over computers. Other companies, including Paradromics, Precision Neuroscience, and BlackRock Neurotech, are also developing advanced brain-computer interfaces.

Special thanks to Ryan Tanaka of Neura Pod for pointing us to Neuralink’s public announcements and projections.

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Cost issues: 76% Forecasting future data center capacity requirements: 71% Improving energy performance for facilities equipment: 67% Power availability: 63% Supply chain disruptions: 65% A lack of qualified staff: 67% With respect to capacity planning, there’s been a notable increase in the number of operators who describe themselves as “very concerned” about forecasting future data center capacity requirements. Andy Lawrence, Uptime’s executive director of research, said two factors are contributing to this concern: ongoing strong growth for IT demand, and the often-unpredictable demand that AI workloads are creating. “There’s great uncertainty about … what the impact of AI is going to be, where it’s going to be located, how much of the power is going to be required, and even for things like space and cooling, how much of the infrastructure is going to be sucked up to support AI, whether it’s in a colocation, whether it’s in an enterprise or even in a hyperscale facility,” Lawrence said during a webinar sharing the survey results. The survey found that roughly one-third of data center owners and operators currently perform some AI training or inference, with significantly more planning to do so in the future. As the number of AI-based software deployments increases, information about the capabilities and limitations of AI in the workplace is becoming available. The awareness is also revealing AI’s suitability for certain tasks. According to the report, “the data center industry is entering a period of careful adoption, testing, and validation. Data centers are slow and careful in adopting new technologies, and AI will not be an exception.”

Micron unveils PCIe Gen6 SSD to power AI data center workloads

Competitive positioning With the launch of the 9650 SSD PCIe Gen 6, Micron competes with Samsung and SK Hynix enterprise SSD offerings, which are the dominant players in the SSD market. In December last year, SK Hynix announced the development of PS1012 U.2 Gen5 PCIe SSD, for massive high-capacity storage for AI data centers. The PM1743 is Samsung’s PCIe Gen5 offering in the market, with 14,000 MBps sequential read, designed for high-performance enterprise workloads. According to Faruqui, PCIe Gen6 data center SSDs are best suited for AI inference performance enhancement. However, we’re still months away from large-scale adoption as no current CPU platforms are available with PCIe 6.0 support. Only Nvidia’s Blackwell-based GPUs have native PCIe 6.0 x16 support with interoperability tests in progress. He added that PCIe Gen 6 SSDs will see very delayed adoption in the PC segment and imminent 2025 2H adoption in AI, data centers, high-performance computing (HPC), and enterprise storage solutions. Micron has also introduced two additional SSDs alongside the 9650. The 6600 ION SSD delivers 122TB in an E3.S form factor and is targeted at hyperscale and enterprise data centers looking to consolidate server infrastructure and build large AI data lakes. A 245TB variant is on the roadmap. The 7600 PCIe Gen5 SSD, meanwhile, is aimed at mixed workloads that require lower latency.

AI Deployments are Reshaping Intra-Data Center Fiber and Communications

Artificial Intelligence is fundamentally changing the way data centers are architected, with a particular focus on the demands placed on internal fiber and communications infrastructure. While much attention is paid to the fiber connections between data centers or to end-users, the real transformation is happening inside the data center itself, where AI workloads are driving unprecedented requirements for bandwidth, low latency, and scalable networking. Network Segmentation and Specialization Inside the modern AI data center, the once-uniform network is giving way to a carefully divided architecture that reflects the growing divergence between conventional cloud services and the voracious needs of AI. Where a single, all-purpose network once sufficed, operators now deploy two distinct fabrics, each engineered for its own unique mission. The front-end network remains the familiar backbone for external user interactions and traditional cloud applications. Here, Ethernet still reigns, with server-to-leaf links running at 25 to 50 gigabits per second and spine connections scaling to 100 Gbps. Traffic is primarily north-south, moving data between users and the servers that power web services, storage, and enterprise applications. This is the network most people still imagine when they think of a data center: robust, versatile, and built for the demands of the internet age. But behind this familiar façade, a new, far more specialized network has emerged, dedicated entirely to the demands of GPU-driven AI workloads. In this backend, the rules are rewritten. Port speeds soar to 400 or even 800 gigabits per second per GPU, and latency is measured in sub-microseconds. The traffic pattern shifts decisively east-west, as servers and GPUs communicate in parallel, exchanging vast datasets at blistering speeds to train and run sophisticated AI models. The design of this network is anything but conventional: fat-tree or hypercube topologies ensure that no single link becomes a bottleneck, allowing thousands of

ABB and Applied Digital Build a Template for AI-Ready Data Centers

Toward the Future of AI Factories The ABB–Applied Digital partnership signals a shift in the fundamentals of data center development, where electrification strategy, hyperscale design and readiness, and long-term financial structuring are no longer separate tracks but part of a unified build philosophy. As Applied Digital pushes toward REIT status, the Ellendale campus becomes not just a development milestone but a cornerstone asset: a long-term, revenue-generating, AI-optimized property underpinned by industrial-grade power architecture. The 250 MW CoreWeave lease, with the option to expand to 400 MW, establishes a robust revenue base and validates the site’s design as AI-first, not cloud-retrofitted. At the same time, ABB is positioning itself as a leader in AI data center power architecture, setting a new benchmark for scalable, high-density infrastructure. Its HiPerGuard Medium Voltage UPS, backed by deep global manufacturing and engineering capabilities, reimagines power delivery for the AI era, bypassing the limitations of legacy low-voltage systems. More than a component provider, ABB is now architecting full-stack electrification strategies at the campus level, aiming to make this medium-voltage model the global standard for AI factories. What’s unfolding in North Dakota is a preview of what’s coming elsewhere: AI-ready campuses that marry investment-grade real estate with next-generation power infrastructure, built for a future measured in megawatts per rack, not just racks per row. As AI continues to reshape what data centers are and how they’re built, Ellendale may prove to be one of the key locations where the new standard was set.

Amazon’s Project Rainier Sets New Standard for AI Supercomputing at Scale

Supersized Infrastructure for the AI Era As AWS deploys Project Rainier, it is scaling AI compute to unprecedented heights, while also laying down a decisive marker in the escalating arms race for hyperscale dominance. With custom Trainium2 silicon, proprietary interconnects, and vertically integrated data center architecture, Amazon joins a trio of tech giants, alongside Microsoft’s Project Stargate and Google’s TPUv5 clusters, who are rapidly redefining the future of AI infrastructure. But Rainier represents more than just another high-performance cluster. It arrives in a moment where the size, speed, and ambition of AI infrastructure projects have entered uncharted territory. Consider the past several weeks alone: On June 24, AWS detailed Project Rainier, calling it “a massive, one-of-its-kind machine” and noting that “the sheer size of the project is unlike anything AWS has ever attempted.” The New York Times reports that the primary Rainier campus in Indiana could include up to 30 data center buildings. Just two days later, Fermi America unveiled plans for the HyperGrid AI campus in Amarillo, Texas on a sprawling 5,769-acre site with potential for 11 gigawatts of power and 18 million square feet of AI data center capacity. And on July 1, Oracle projected $30 billion in annual revenue from a single OpenAI cloud deal, tied to the Project Stargate campus in Abilene, Texas. As Data Center Frontier founder Rich Miller has observed, the dial on data center development has officially been turned to 11. Once an aspirational concept, the gigawatt-scale campus is now materializing—15 months after Miller forecasted its arrival. “It’s hard to imagine data center projects getting any bigger,” he notes. “But there’s probably someone out there wondering if they can adjust the dial so it goes to 12.” Against this backdrop, Project Rainier represents not just financial investment but architectural intent. Like Microsoft’s Stargate buildout in

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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