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Encharge AI unveils EN100 AI accelerator chip with analog memory

EnCharge AI, an AI chip startup that raised $144 million to date, announced the EnCharge EN100, an AI accelerator built on precise and scalable analog in-memory computing. Designed to bring advanced AI capabilities to laptops, workstations, and edge devices, EN100leverages transformational efficiency to deliver 200-plus TOPS (a measure of AI performance) of total compute power within the power constraints of edge and client platforms such as laptops. The company spun out of Princeton University on the bet that its analog memory chips will speed up AI processing and cut costs too. “EN100 represents a fundamental shift in AI computing architecture, rooted in hardware and software innovations that have been de-risked through fundamental research spanning multiple generations of silicon development,” said Naveen Verma, CEO at EnCharge AI, in a statement. “These innovations are now being made available as products for the industry to use, as scalable, programmable AI inference solutions that break through the energy efficient limits of today’s digital solutions. This means advanced, secure, and personalized AI can run locally, without relying on cloud infrastructure. We hope this will radically expand what you can do with AI.” Previously, models driving the next generation of AI economy—multimodal and reasoning systems—required massive data center processing power. Cloud dependency’s cost, latency, and security drawbacks made countless AI applications impossible. EN100 shatters these limitations. By fundamentally reshaping where AI inference happens, developers can now deploy sophisticated, secure, personalized applications locally. This breakthrough enables organizations to rapidly integrate advanced capabilities into existing products—democratizing powerful AI technologies and bringing high-performance inference directly to end-users, the company said. EN100, the first of the EnCharge EN series of chips, features an optimized architecture that efficiently processes AI tasks while minimizing energy. Available in two form factors – M.2 for laptops and PCIe for workstations – EN100 is engineered to transform on-device capabilities: ● M.2 for Laptops: Delivering up to 200+ TOPS of AI compute power in an 8.25W power envelope, EN100 M.2 enables sophisticated AI applications on laptops without compromising battery life or portability. ● PCIe for Workstations: Featuring four NPUs reaching approximately 1 PetaOPS, the EN100 PCIe card delivers GPU-level compute capacity at a fraction of the cost and power consumption, making it ideal for professional AI applications utilizing complex models and large datasets. EnCharge AI’s comprehensive software suite delivers full platform support across the evolving model landscape with maximum efficiency. This purpose-built ecosystem combines specialized optimization tools, high-performance compilation, and extensive development resources—all supporting popular frameworks like PyTorch and TensorFlow. Compared to competing solutions, EN100 demonstrates up to ~20x better performance per watt across various AI workloads. With up to 128GB of high-density LPDDR memory and bandwidth reaching 272 GB/s, EN100 efficiently handles sophisticated AI tasks, such as generative language models and real-time computer vision, that typically require specialized data center hardware. The programmability of EN100 ensures optimized performance of AI models today and the ability to adapt for the AI models of tomorrow. “The real magic of EN100 is that it makes transformative efficiency for AI inference easily accessible to our partners, which can be used to help them achieve their ambitious AI roadmaps,” says Ram Rangarajan, Senior Vice President of Product and Strategy at EnCharge AI. “For client platforms, EN100 can bring sophisticated AI capabilities on device, enabling a new generation of intelligent applications that are not only faster and more responsive but also more secure and personalized.” Early adoption partners have already begun working closely with EnCharge to map out how EN100 will deliver transformative AI experiences, such as always-on multimodal AI agents and enhanced gaming applications that render realistic environments in real-time. While the first round of EN100’’s Early Access Program is currently full, interested developers and OEMs can sign up to learn more about the upcoming Round 2 Early Access Program, which provides a unique opportunity to gain a competitive advantage by being among the first to leverage EN100’s capabilities for commercial applications at www.encharge.ai/en100. Competition EnCharge doesn’t directly compete with many of the big players, as we have a slightly different focus and strategy. Our approach prioritizes the rapidly growing AI PC and edge device market, where our energy efficiency advantage is most compelling, rather than competing directly in data center markets. That said, EnCharge does have a few differentiators that make it uniquely competitive within the chip landscape. For one, EnCharge’s chip has dramatically higher energy efficiency (approximately 20 times greater) than the leading players. The chip can run the most advanced AI models using about as much energy as a light bulb, making it an extremely competitive offering for any use case that can’t be confined to a data center. Secondly, EnCharge’s analog in-memory computing approach makes its chips far more compute dense than conventional digital architectures, with roughly 30 TOPS/mm2 versus 3. This allows customers to pack significantly more AI processing power into the same physical space, something that’s particularly valuable for laptops, smartphones, and other portable devices where space is at a premium. OEMs can integrate powerful AI capabilities without compromising on device size, weight, or form factor, enabling them to create sleeker, more compact products while still delivering advanced AI features. Origins Encharge AI has raised $144 million. In March 2024, EnCharge partnered with Princeton University to secure an $18.6 million grant from DARPA Optimum Processing Technology Inside Memory Arrays (OPTIMA) program Optima is a $78 million effort to develop fast, power-efficient, and scalable compute-in-memory accelerators that can unlock new possibilities for commercial and defense-relevant AI workloads not achievable with current technology. EnCharge’s inspiration came from addressing a critical challenge in AI: the inability of traditional computing architectures to meet the needs of AI. The company was founded to solve the problem that, as AI models grow exponentially in size and complexity, traditional chip architectures (like GPUs) struggle to keep pace, leading to both memory and processing bottlenecks, as well as associated skyrocketing energy demands. (For example, training a single large language model can consume as much electricity as 130 U.S. households use in a year.) The specific technical inspiration originated from the work of EnCharge ‘s founder, Naveen Verma, and his research at Princeton University in next generation computing architectures. He and his collaborators spent over seven years exploring a variety of innovative computing architectures, leading to a breakthrough in analog in-memory computing. This approach aimed to significantly enhance energy efficiency for AI workloads while mitigating the noise and other challenges that had hindered past analog computing efforts. This technical achievement, proven and de-risked over multiple generations of silicon, was the basis for founding EnCharge AI to commercialize analog in-memory computing solutions for AI inference. Encharge AI launched in 2022, led by a team with semiconductor and AI system experience. The team spun out of Princeton University, with a focus on a robust and scalable analog in-memory AI inference chip and accompanying software. The company was able to overcome previous hurdles to analog and in-memory chip architectures by leveraging precise metal-wire switch capacitors instead of noise-prone transistors. The result is a full-stack architecture that is up to 20 times more energy efficient than currently available or soon-to-be-available leading digital AI chip solutions. With this tech, EnCharge is fundamentally changing how and where AI computation happens. Their technology dramatically reduces the energy requirements for AI computation, bringing advanced AI workloads out of the data center and onto laptops, workstations, and edge devices. By moving AI inference closer to where data is generated and used, EnCharge enables a new generation of AI-enabled devices and applications that were previously impossible due to energy, weight, or size constraints while improving security, latency, and cost. Why it matters Encharge AI is striving to get rid of memory bottlenecks in AI computing. As AI models have grown exponentially in size and complexity, their chip and associated energy demands have skyrocketed. Today, the vast majority of AI inference computation is accomplished with massive clusters of energy-intensive chips warehoused in cloud data centers. This creates cost, latency, and security barriers for applying AI to use cases that require on-device computation. Only with transformative increases in compute efficiency will AI be able to break out of the data center and address on-device AI use-cases that are size, weight, and power constrained or have latency or privacy requirements that benefit from keeping data local. Lowering the cost and accessibility barriers of advanced AI can have dramatic downstream effects on a broad range of industries, from consumer electronics to aerospace and defense. The reliance on data centers also present supply chain bottleneck risks. The AI-driven surge in demand for high-end graphics processing units (GPUs) alone could increase total demand for certain upstream components by 30% or more by 2026. However, a demand increase of about 20% or more has a high likelihood of upsetting the equilibrium and causing a chip shortage. The company is already seeing this in the massive costs for the latest GPUs and years-long wait lists as a small number of dominant AI companies buy up all available stock. The environmental and energy demands of these data centers are also unsustainable with current technology. The energy use of a single Google search has increased over 20x from 0.3 watt-hours to 7.9 watt-hours with the addition of AI to power search. In aggregate, the International Energy Agency (IEA) projects that data centers’ electricity consumption in 2026 will be double that of 2022 — 1K terawatts, roughly equivalent to Japan’s current total consumption. Investors include Tiger Global Management, Samsung Ventures, IQT, RTX Ventures, VentureTech Alliance, Anzu Partners, VentureTech Alliance, AlleyCorp and ACVC Partners. The company has 66 people.

EnCharge AI, an AI chip startup that raised $144 million to date, announced the EnCharge EN100,
an AI accelerator built on precise and scalable analog in-memory computing.

Designed to bring advanced AI capabilities to laptops, workstations, and edge devices, EN100
leverages transformational efficiency to deliver 200-plus TOPS (a measure of AI performance) of total compute power within the power constraints of edge and client platforms such as laptops.

The company spun out of Princeton University on the bet that its analog memory chips will speed up AI processing and cut costs too.

“EN100 represents a fundamental shift in AI computing architecture, rooted in hardware and software innovations that have been de-risked through fundamental research spanning multiple generations of silicon development,” said Naveen Verma, CEO at EnCharge AI, in a statement. “These innovations are now being made available as products for the industry to use, as scalable, programmable AI inference solutions that break through the energy efficient limits of today’s digital solutions. This means advanced, secure, and personalized AI can run locally, without relying on cloud infrastructure. We hope this will radically expand what you can do with AI.”

Previously, models driving the next generation of AI economy—multimodal and reasoning systems—required massive data center processing power. Cloud dependency’s cost, latency, and security drawbacks made countless AI applications impossible.

EN100 shatters these limitations. By fundamentally reshaping where AI inference happens, developers can now deploy sophisticated, secure, personalized applications locally.

This breakthrough enables organizations to rapidly integrate advanced capabilities into existing products—democratizing powerful AI technologies and bringing high-performance inference directly to end-users, the company said.

EN100, the first of the EnCharge EN series of chips, features an optimized architecture that efficiently processes AI tasks while minimizing energy. Available in two form factors – M.2 for laptops and PCIe for workstations – EN100 is engineered to transform on-device capabilities:

● M.2 for Laptops: Delivering up to 200+ TOPS of AI compute power in an 8.25W power envelope, EN100 M.2 enables sophisticated AI applications on laptops without compromising battery life or portability.

● PCIe for Workstations: Featuring four NPUs reaching approximately 1 PetaOPS, the EN100 PCIe card delivers GPU-level compute capacity at a fraction of the cost and power consumption, making it ideal for professional AI applications utilizing complex models and large datasets.

EnCharge AI’s comprehensive software suite delivers full platform support across the evolving model landscape with maximum efficiency. This purpose-built ecosystem combines specialized optimization tools, high-performance compilation, and extensive development resources—all supporting popular frameworks like PyTorch and TensorFlow.

Compared to competing solutions, EN100 demonstrates up to ~20x better performance per watt across various AI workloads. With up to 128GB of high-density LPDDR memory and bandwidth reaching 272 GB/s, EN100 efficiently handles sophisticated AI tasks, such as generative language models and real-time computer vision, that typically require specialized data center hardware. The programmability of EN100 ensures optimized performance of AI models today and the ability to adapt for the AI models of tomorrow.

“The real magic of EN100 is that it makes transformative efficiency for AI inference easily accessible to our partners, which can be used to help them achieve their ambitious AI roadmaps,” says Ram Rangarajan, Senior Vice President of Product and Strategy at EnCharge AI. “For client platforms, EN100 can bring sophisticated AI capabilities on device, enabling a new generation of intelligent applications that are not only faster and more responsive but also more secure and personalized.”

Early adoption partners have already begun working closely with EnCharge to map out how EN100 will deliver transformative AI experiences, such as always-on multimodal AI agents and enhanced gaming applications that render realistic environments in real-time.

While the first round of EN100’’s Early Access Program is currently full, interested developers and OEMs can sign up to learn more about the upcoming Round 2 Early Access Program, which provides a unique opportunity to gain a competitive advantage by being among the first to leverage EN100’s capabilities for commercial applications at www.encharge.ai/en100.

Competition

EnCharge doesn’t directly compete with many of the big players, as we have a slightly different focus and strategy. Our approach prioritizes the rapidly growing AI PC and edge device market, where our energy efficiency advantage is most compelling, rather than competing directly in data center markets.

That said, EnCharge does have a few differentiators that make it uniquely competitive within the chip landscape. For one, EnCharge’s chip has dramatically higher energy efficiency (approximately 20 times greater) than the leading players. The chip can run the most advanced AI models using about as much energy as a light bulb, making it an extremely competitive offering for any use case that can’t be confined to a data center.

Secondly, EnCharge’s analog in-memory computing approach makes its chips far more compute dense than conventional digital architectures, with roughly 30 TOPS/mm2 versus 3. This allows customers to pack significantly more AI processing power into the same physical space, something that’s particularly valuable for laptops, smartphones, and other portable devices where space is at a premium. OEMs can integrate powerful AI capabilities without compromising on device size, weight, or form factor, enabling them to create sleeker, more compact products while still delivering advanced AI features.

Origins

Encharge AI has raised $144 million.

In March 2024, EnCharge partnered with Princeton University to secure an $18.6 million grant from DARPA Optimum Processing Technology Inside Memory Arrays (OPTIMA) program Optima is a $78 million effort to develop fast, power-efficient, and scalable compute-in-memory accelerators that can unlock new possibilities for commercial and defense-relevant AI workloads not achievable with current technology.

EnCharge’s inspiration came from addressing a critical challenge in AI: the inability of traditional computing architectures to meet the needs of AI. The company was founded to solve the problem that, as AI models grow exponentially in size and complexity, traditional chip architectures (like GPUs) struggle to keep pace, leading to both memory and processing bottlenecks, as well as associated skyrocketing energy demands. (For example, training a single large language model can consume as much electricity as 130 U.S. households use in a year.)

The specific technical inspiration originated from the work of EnCharge ‘s founder, Naveen Verma, and his research at Princeton University in next generation computing architectures. He and his collaborators spent over seven years exploring a variety of innovative computing architectures, leading to a breakthrough in analog in-memory computing.

This approach aimed to significantly enhance energy efficiency for AI workloads while mitigating the noise and other challenges that had hindered past analog computing efforts. This technical achievement, proven and de-risked over multiple generations of silicon, was the basis for founding EnCharge AI to commercialize analog in-memory computing solutions for AI inference.

Encharge AI launched in 2022, led by a team with semiconductor and AI system experience. The team spun out of Princeton University, with a focus on a robust and scalable analog in-memory AI inference chip and accompanying software.

The company was able to overcome previous hurdles to analog and in-memory chip architectures by leveraging precise metal-wire switch capacitors instead of noise-prone transistors. The result is a full-stack architecture that is up to 20 times more energy efficient than currently available or soon-to-be-available leading digital AI chip solutions.

With this tech, EnCharge is fundamentally changing how and where AI computation happens. Their technology dramatically reduces the energy requirements for AI computation, bringing advanced AI workloads out of the data center and onto laptops, workstations, and edge devices. By moving AI inference closer to where data is generated and used, EnCharge enables a new generation of AI-enabled devices and applications that were previously impossible due to energy, weight, or size constraints while improving security, latency, and cost.

Why it matters

Encharge AI is striving to get rid of memory bottlenecks in AI computing.

As AI models have grown exponentially in size and complexity, their chip and associated energy demands have skyrocketed. Today, the vast majority of AI inference computation is accomplished with massive clusters of energy-intensive chips warehoused in cloud data centers. This creates cost, latency, and security barriers for applying AI to use cases that require on-device computation.

Only with transformative increases in compute efficiency will AI be able to break out of the data center and address on-device AI use-cases that are size, weight, and power constrained or have latency or privacy requirements that benefit from keeping data local. Lowering the cost and accessibility barriers of advanced AI can have dramatic downstream effects on a broad range of industries, from consumer electronics to aerospace and defense.

The reliance on data centers also present supply chain bottleneck risks. The AI-driven surge in demand for high-end graphics processing units (GPUs) alone could increase total demand for certain upstream components by 30% or more by 2026. However, a demand increase of about 20% or more has a high likelihood of upsetting the equilibrium and causing a chip shortage. The company is already seeing this in the massive costs for the latest GPUs and years-long wait lists as a small number of dominant AI companies buy up all available stock.

The environmental and energy demands of these data centers are also unsustainable with current technology. The energy use of a single Google search has increased over 20x from 0.3 watt-hours to 7.9 watt-hours with the addition of AI to power search. In aggregate, the International Energy Agency (IEA) projects that data centers’ electricity consumption in 2026 will be double that of 2022 — 1K terawatts, roughly equivalent to Japan’s current total consumption.

Investors include Tiger Global Management, Samsung Ventures, IQT, RTX Ventures, VentureTech Alliance, Anzu Partners, VentureTech Alliance, AlleyCorp and ACVC Partners. The company has 66 people.

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Platform approach gains steam among network teams

Revisting the platform vs. point solutions debate The dilemma of whether to deploy an assortment of best-of-breed products from multiple vendors or go with a unified platform of “good enough” tools from a single vendor has vexed IT execs forever. Today, the pendulum is swinging toward the platform approach for three key reasons. First, complexity, driven by the increasingly distributed nature of enterprise networks, has emerged as a top challenge facing IT execs. Second, the lines between networking and security are blurring, particularly as organizations deploy zero trust network access (ZTNA). And third, to reap the benefits of AIOps, generative AI and agentic AI, organizations need a unified data store. “The era of enterprise connectivity platforms is upon us,” says IDC analyst Brandon Butler. “Organizations are increasingly adopting platform-based approaches to their enterprise connectivity infrastructure to overcome complexity and unlock new business value. When enhanced by AI, enterprise platforms can increase productivity, enrich end-user experiences, enhance security, and ultimately drive new opportunities for innovation.” In IDC’s Worldwide AI in Networking Special Report, 78% of survey respondents agreed or strongly agreed with the statement: “I am moving to an AI-powered platform approach for networking.” Gartner predicts that 70% of enterprises will select a broad platform for new multi-cloud networking software deployments by 2027, an increase from 10% in early 2024. The breakdown of silos between network and security operations will be driven by organizations implementing zero-trust principles as well as the adoption of AI and AIOps. “In the future, enterprise networks will be increasingly automated, AI-assisted and more tightly integrated with security across LAN, data center and WAN domains,” according to Gartner’s 2025 Strategic Roadmap for Enterprise Networking. While all of the major networking vendors have announced cloud-based platforms, it’s still relatively early days. For example, Cisco announced a general framework for Cisco

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Oracle to spend $40B on Nvidia chips for OpenAI data center in Texas

OpenAI has also expanded Stargate internationally, with plans for a UAE data center announced during Trump’s recent Gulf tour. The Abu Dhabi facility is planned as a 10-square-mile campus with 5 gigawatts of power. Gogia said OpenAI’s selection of Oracle “is not just about raw compute, but about access to geographically distributed, enterprise-grade infrastructure that complements its ambition to serve diverse regulatory environments and availability zones.” Power demands create infrastructure dilemma The facility’s power requirements raise serious questions about AI’s sustainability. Gogia noted that the 1.2-gigawatt demand — “on par with a nuclear facility” — highlights “the energy unsustainability of today’s hyperscale AI ambitions.” Shah warned that the power envelope keeps expanding. “As AI scales up and so does the necessary compute infrastructure needs exponentially, the power envelope is also consistently rising,” he said. “The key question is how much is enough? Today it’s 1.2GW, tomorrow it would need even more.” This escalating demand could burden Texas’s infrastructure, potentially requiring billions in new power grid investments that “will eventually put burden on the tax-paying residents,” Shah noted. Alternatively, projects like Stargate may need to “build their own separate scalable power plant.” What this means for enterprises The scale of these facilities explains why many organizations are shifting toward leased AI computing rather than building their own capabilities. The capital requirements and operational complexity are beyond what most enterprises can handle independently.

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New Intel Xeon 6 CPUs unveiled; one powers rival Nvidia’s DGX B300

He added that his read is that “Intel recognizes that Nvidia is far and away the leader in the market for AI GPUs and is seeking to hitch itself to that wagon.” Roberts said, “basically, Intel, which has struggled tremendously and has turned over its CEO amidst a stock slide, needs to refocus to where it thinks it can win. That’s not competing directly with Nvidia but trying to use this partnership to re-secure its foothold in the data center and squeeze out rivals like AMD for the data center x86 market. In other words, I see this announcement as confirmation that Intel is looking to regroup, and pick fights it thinks it can win. “ He also predicted, “we can expect competition to heat up in this space as Intel takes on AMD’s Epyc lineup in a push to simplify and get back to basics.” Matt Kimball, vice president and principal analyst, who focuses on datacenter compute and storage at Moor Insights & Strategy, had a much different view about the announcement. The selection of the Intel sixth generation Xeon CPU, the 6776P, to support Nvidia’s DGX B300 is, he said, “important, as it validates Intel as a strong choice for the AI market. In the big picture, this isn’t about volumes or revenue, rather it’s about validating a strategy Intel has had for the last couple of generations — delivering accelerated performance across critical workloads.”  Kimball said that, In particular, there are a “couple things that I would think helped make Xeon the chosen CPU.”

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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.

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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

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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

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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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