Stay Ahead, Stay ONMINE

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 […]

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 design to combine human expertise and contextual intelligence on one side with AI-based techniques on the other.

“When automated red teaming is complemented by targeted human insight, the resulting defense strategy becomes significantly more resilient,” writes OpenAI in the first paper (Ahmad et al., 2024).

The company’s premise is that using external testers to identify the most high-impact real-world scenarios, while also evaluating AI outputs, leads to continuous model improvements. OpenAI contends that combining these methods delivers a multi-layered defense for their models that identify potential vulnerabilities quickly. Capturing and improving models with the human contextual intelligence made possible by a human-in-the-middle design is proving essential for red-teaming AI models.

Why red teaming is the strategic backbone of AI security

Red teaming has emerged as the preferred method for iteratively testing AI models. This kind of testing simulates a variety of lethal and unpredictable attacks and aims to identify their most potent and weakest points. Generative AI (gen AI) models are difficult to test through automated means alone, as they mimic human-generated content at scale. The practices described in OpenAI’s two papers seek to close the gaps automated testing alone leaves, by measuring and verifying a model’s claims of safety and security.

In the first paper (“OpenAI’s Approach to External Red Teaming”) OpenAI explains that red teaming is “a structured testing effort to find flaws and vulnerabilities in an AI system, often in a controlled environment and collaboration with developers” (Ahmad et al., 2024). Committed to leading the industry in red teaming, the company had over 100 external red teamers assigned to work across a broad base of adversarial scenarios during the pre-launch vetting of GPT-4 prior to launch.

Research firm Gartner reinforces the value of red teaming in its forecast, predicting that IT spending on gen AI will soar from $5 billion in 2024 to $39 billion by 2028. Gartner notes that the rapid adoption of gen AI and the proliferation of LLMs is significantly expanding these models’ attack surfaces, making red teaming essential in any release cycle.

Practical insights for security leaders

Even though security leaders have been quick to see the value of red teaming, few are following through by making a commitment to get it done. A recent Gartner survey finds that while 73% of organizations recognize the importance of dedicated red teams, only 28% actually maintain them. To close this gap, a simplified framework is needed that can be applied at scale to any new model, app, or platform’s red teaming needs.

In its paper on external red teaming OpenAI defines four key steps for using a human-in-the-middle design to make the most of human insights:

  • Defining testing scope and teams: Drawing on subject matter experts and specialists across key areas of cybersecurity, regional politics, and natural sciences, OpenAI targets risks that include voice mimicry and bias. The ability to recruit cross-functional experts is, therefore, crucial. (To gain an appreciation for how committed OpenAI is to this methodology and its implications for stopping deepfakes, please see our article “GPT-4: OpenAI’s shield against $40B deepfake threat to enterprises.”)
  • Selecting model versions for testing, then iterating them across diverse teams: Both of OpenAI’s papers emphasize that cycling red teams and models using an iterative approach delivers the most insightful results. Allowing each red team to cycle through all models is conducive to greater team learning of what is and isn’t working.
  • Clear documentation and guidance: Consistency in testing requires well-documented APIs, standardized report formats, and explicit feedback loops. These are essential elements for successful red teaming.
  • Making sure insights translate into practical and long-lasting mitigations: Once red teams log vulnerabilities, they drive targeted updates to models, policies and operational plans — ensuring security strategies evolve in lockstep with emerging threats.

Scaling adversarial testing with GPT-4T: The next frontier in red teaming

AI companies’ red teaming methodologies are demonstrating that while human expertise is resource-intensive, it remains crucial for in-depth testing of AI models.

In OpenAI’s second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning” (Beutel et al., 2024), OpenAI addresses the challenge of scaling adversarial testing using an automated, multi-pronged approach that combines human insights with AI-generated attack strategies.

The core of this methodology is GPT-4T, a specialized variant of the GPT-4 model engineered to produce a wide range of adversarial scenarios.

Here’s how each component of the methodology contributes to a stronger adversarial testing framework:

  • Goal diversification. OpenAI describes how it is using GPT-4T to create a broad spectrum of scenarios, starting with initially benign-seeming prompts and progressing to more sophisticated phishing campaigns. Goal diversification focuses on anticipating and exploring the widest possible range of potential exploits. By using GPT-4T’s capacity for diverse language generation, OpenAI contends that red teams avoid tunnel vision and stay focused on probing for vulnerabilities that manual-only methods miss.
  • Reinforcement learning (RL). A multi-step RL framework rewards the discovery of new and previously unseen vulnerabilities. The purpose is to train the automated red team by improving each iteration. This enables security leaders to refocus on genuine risks rather than sifting through volumes of low-impact alerts. It aligns with Gartner’s projection of a 30% drop in false positives attributable to gen AI in application security testing by 2027. OpenAI writes, “Our multi-step RL approach systematically rewards the discovery of newly identified vulnerabilities, driving continuous improvement in adversarial testing.”
  • Auto-generated rewards: OpenAI defines this as a system that tracks and updates scores for partial successes by red teams, assigning incremental rewards for identifying each unprotected weak area of a model.

Securing the future of AI: Key takeaways for security leaders

OpenAI’s recent papers show why a structured, iterative process that combines internal and external testing delivers the insights needed to keep improving models’ accuracy, safety, security and quality.

Security leaders’ key takeaways from these papers should include: 

Go all-in and adopt a multi-pronged approach to red teaming. The papers emphasize the value of combining external, human-led teams with real-time simulations of AI attacks generated randomly, as they reflect how chaotic intrusion attempts can be. OpenAI contends that while humans excel at spotting context-specific gaps, including biases, automated systems identify weaknesses that emerge only under stress testing and repeated sophisticated attacks.

Test early and continuously throughout model dev cycles. The white papers make a compelling argument against waiting for production-ready models and instead beginning testing with early-stage versions. The goal is to find emerging risks and retest later to make sure the gaps in models were closed before launch.

Whenever possible, streamline documentation and feedback with real-time feedback loops. Standardized reporting and well-documented APIs, along with explicit feedback loops, help convert red team findings into actionable, trackable mitigations. OpenAI emphasizes the need to get this process in place before beginning red teaming, to accelerate fixes and remediation of problem areas.

Using real-time reinforcement learning is critically important, as is the future of AI red teaming. OpenAI makes the case for automating frameworks that reward discoveries of new attack vectors as a core part of the real-time feedback loops. The goal of RL is to create a continuous loop of improvement. 

Don’t settle for anything less than actionable insights from the red team process. It’s essential to treat every red team discovery or finding as a catalyst for updating security strategies, improving incident response plans, and revamping guidelines as required.

Budget for the added expense of enlisting external expertise for red teams. A central premise of OpenAI’s approach to red teaming is to actively recruit outside specialists who have informed perspectives and knowledge of advanced threats. Areas of expertise valuable to AI-model red teams include deepfake technology, social engineering, identity theft, synthetic identity creation, and voice-based fraud. “Involving external specialists often surfaces hidden attack paths, including sophisticated social engineering and deepfake threats.” (Ahmad et al., 2024)

Papers:

Beutel, A., Xiao, K., Heidecke, J., & Weng, L. (2024). “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning.” OpenAI.

Ahmad, L., Agarwal, S., Lampe, M., & Mishkin, P. (2024). “OpenAI’s Approach to External Red Teaming for AI Models and Systems.” OpenAI.

Shape
Shape
Stay Ahead

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy,  bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

Shape

18 essential commands for new Linux users

[jdoe@fedora ~]$ ls -ld /home/jdoedrwx——. 1 jdoe jdoe 106 Apr 3 14:39 /home/jdoe As you may have suspected, “r” stands for read, “w” means write and “x” is for execute. Note that no permissions are available for other group members and anyone else on the system. Each user will be

Read More »

What are GPUs? Inside the processing power behind AI

AI and generative AI Today’s increasingly sophisticated AI technologies — notably large language models (LLMs) and generative AI — require lots of speed, lots of data and lots of compute. Because they can perform simultaneous calculations and handle vast amounts of data, GPUs have become the powerhouse behind AI (e.g.,

Read More »

WTI Rebounds 2% on Supply, Geopolitical Shifts

Oil rose as the potential for the US to curtail Iranian flows added to a rebound driven by broader markets. West Texas Intermediate advanced about 2% to settle near $64 a barrel, recouping most of the previous day’s losses, which were driven by President Donald Trump’s public rebuke of the Federal Reserve. Also supporting prices, Trump said he and Israeli Prime Minister Benjamin Netanyahu spoke on Tuesday and are aligned on trade and their approach to Iran. That came after the US announced sanctions against Iranian national and liquefied petroleum gas magnate Seyed Asadoollah Emamjomeh and his corporate network. Crude has slumped this month on concern that mounting tensions between the US and its top trading partners will hurt economic growth and curtail oil consumption, adding to pre-existing expectations of a surplus. Crude eased off of intraday highs later in the session after the Financial Times reported that Russian President Vladimir Putin has offered to halt his country’s invasion of Ukraine across the current front line. At the same time, White House Press Secretary Karoline Leavitt reiterated Trump’s criticism of the Fed and said the president will travel to Saudi Arabia, Qatar and the United Arab Emirates in May. Despite the widespread expectations for an oversupply of crude, market gauges have been pointing to a stronger near-term market. The closest WTI futures contract is trading at its biggest premium to the next month since February, signaling tighter supply and demand balances. “Near-term, I would expect that the biggest wave of selling is behind us,” Martijn Rats, global oil strategist at Morgan Stanley, said in a Bloomberg Television interview. “As we go through the summer, seasonal demand does support things a little bit, but then in the second half we will probably have another period of downward pressure.” Elsewhere, Vice President

Read More »

Atlanta to launch Climate Resilience Action Plan

Atlanta plans to launch its first comprehensive plan to address climate resilience and the disproportionate impacts of climate change April 30. The Climate Resilience Action Plan sets goals that include reducing greenhouse gas emissions by 59% by 2030, adding 250 electric vehicle charging stations by 2025 and reducing the energy burden on 10% of the city’s most energy-burdened households. The Mayor’s Office of Sustainability and Resilience spent 18 months studying and gathering community input into how Atlanta’s three primary climate challenges — extreme heat, drought and flooding — affect the city’s different populations. The plan spells out how the city can respond at municipal and neighborhood levels “in ways that make our communities more climate-resilient and more equitable,” said Chandra Farley, Atlanta’s chief sustainability officer. Atlanta consistently ranks in the nation’s top five cities for energy burden, as indicated by the percentage of household income spent on energy bills, Farley said. Traditionally,  households spending 6% or more of their monthly household income on energy are considered energy burdened; Atlanta has many households that spend from 6% to 19% of their monthly income on utilities, Farley said. The Georgia city’s focus on creating energy equity has put the plan’s federal funding in the crosshairs of the Trump administration’s efforts to eliminate diversity, equity and inclusion efforts. Farley said her team received a cease-and-desist letter regarding a grant it was working on with the Southeastern Energy Efficiency Alliance to address equity and building codes, for example. That grant is on hold as agreements are updated to remove DEI language. The city’s efforts to address inequity in energy burdens will continue, Farley said. “We understand that climate resilience must also be focused on the disproportionate impact that some of our distressed neighborhoods feel more acutely than other neighborhoods,” Farley said. “It is a

Read More »

Haven Energy offers no-cost solar, batteries to income-qualified California customers

Dive Brief: Haven Energy is offering a “first of its kind” program to bring solar and battery storage systems to low- and moderate-income California utility customers at no cost to them, the company said Monday. The initiative, which sees Haven owning and operating the systems, is made possible by the state’s $280 million Self-Generation Incentive Program’s Residential Solar & Storage Equity framework, SGIP RSSE, Haven said. Haven is also partnering with The Energy Coalition, a Los Angeles nonprofit group, to install home battery systems for low- and moderate-income customers in Southern California through the Bassett Avocado Heights Advanced Energy Community program, it said. Dive Insight: As the Trump administration targets utility-scale clean energy development, experts say smaller-scale distributed energy resources could pick up some of the slack. In a January update to its Pathways to Commercial Liftoff: Virtual Power Plants report, the U.S. Department of Energy said the country’s current VPP capacity of approximately 30 GW could scale to at least 80 GW by 2030 following a concerted effort by utilities, grid operators, resource aggregators and other stakeholders to encourage broader DER adoption, simplify program design and enrollment, and integrate VPPs into utility planning and wholesale markets. The announcements this week show Haven is doing its part to support the first two goals while delivering more capacity into a California wholesale market that offers multiple forms of support for DERs and VPPs. With the SGIP RSSE framework capable of supporting solar and storage installations for an estimated 8,000 to 10,000 qualifying California households, Haven anticipates deploying 10 MW of distributed capacity in what would become one of the state’s largest VPPs, the company said. Haven automatically enrolls its customers in an eligible demand response program for 10 years, it said. Haven has already installed more than 1,000 batteries under the

Read More »

Charging Forward: Eku Energy acquires Bluestone’s UK battery storage portfolio, and more

In this week’s Charging Forward, Eku Energy acquires a portfolio of seven UK battery energy storage system (BESS) projects from Bluestone Energy, while Gresham House leads industry criticism of Ofgem and NESO. This week’s headlines: UK BESS operators criticise long-duration energy storage scheme ‘bias’ Eku Energy acquires Bluestone Energy’s UK BESS portfolio Zenobe wades into UK zonal pricing debate Balance Power’s Hinksford BESS approved SAE secures £8.5m 240 MWh Welsh BESS E.ON and Superdielectrics collaborate on polymer-based energy storage International news: Gelion achieves sulphur battery breakthrough UK BESS operators criticise long-duration energy storage scheme ‘bias’ A group of UK BESS operators have written an open letter criticising UK authorities over its long-duration energy storage (LDES) revenue support plans. The firms, led by Gresham House, say there are significant flaws in the upcoming LDES cap and floor scheme which, they believe, will create “arbitrary barriers to entry” lithium-ion BESS projects. Other UK firms to sign the open letter include Zenobe, Field Energy, Eku Enegy, Harmony Energy, Adaptogen Capital and Voltwise Power. Together, the group represents 36% of the UK’s current operational BESS capacity. The group claims changes to the LDES scheme could save UK consumers as much as £2.22 billion compared to “unproven” technologies such as liquid air energy storage. © Supplied by Highview PowerA design image of a Highview Power liquid air energy storage system. . The Department for Energy Security and Net Zero (DESNZ) initially said in January 2024 that it would exclude lithium-ion batteries from the LDES support scheme. However, officials subsequently said lithium-ion BESS would be eligible if they meet specific criteria, including a minimum eight-hour duration. But the group of developers said analysis from LCP Delta shows BESS is the “most competitive” LDES technology, adding the current scheme could “risk jeopardising” the government’s 27 GW target for shorter

Read More »

Chevron, TotalEnergies Start Up Ballymore offshore Louisiana

Chevron Corp. and TotalEnergies SE have unlocked new production of up to 75,000 barrels a day of oil and 50 million cubic feet a day of gas with the startup of the deepwater Ballymore field. Located about 120 kilometers (74.56 miles) off the coast of Louisiana in around 6,600 feet of water, Ballymore holds estimated recoverable resources of 150 million barrels of oil equivalent (MMboe) according to the partners. “Ballymore, the latest in a series of Chevron projects to start up in the past year, represents another step towards the company’s goal to produce 300,000 net barrels per day of oil equivalent from the Gulf in 2026”, operator and 60 percent owner Chevron said in an online statement. Ballymore, a tieback, is Chevron’s first development in the Norphlet trend of the United States Gulf. Ballymore is in the Mississippi Canyon area. “The start-up of Ballymore will increase TotalEnergies’ production capacity in U.S. deepwater to more than 75,000 boe/d and contribute to the Company’s targeted hydrocarbon production growth of over 3 percent in 2025”, Nicolas Terraz, president for exploration and production at TotalEnergies, said separately. The French energy giant owns 40 percent of Ballymore. “The United States is a major market for the deployment of our integrated energy model, which combines low breakeven and low emissions oil and gas projects with LNG and integrated power developments”. Ballymore was discovered 2018. The partners approved the development May 2022. In another Gulf partnership, Chevron and TotalEnergies put online the Anchor field in the third quarter of 2024. Chevron said the development is the first to successfully deploy a high-pressure deepwater technology. The technology, which consists of components for drilling, completion, intervention and subsea production, can safely operate at up to 20,000 pressure per square inch in reservoirs that are 34,000 feet below sea level

Read More »

China Owned Supertankers Face $5.2MM in Fees Per USA Call

Made-in-China oil supertankers are set to be slapped with more hefty charges under America’s latest proposal to penalize ships manufactured in the Asian nation as the trade war between Washington and Beijing rages on.  China-made supertankers sailing under non-Chinese owners or operators can expect to be hit with a surcharge of nearly $1.9 million upon calling at a US port under the new rules, according to estimates by the research arm of Arrow Shipbroking Group. The sum balloons to $5.2 million for any China-owned or -operated ships, the firm said in an April 18 note. Under Washington’s previous proposal, market observers had estimated charges of up to $3.5 million per US port call.  The drastic spike in fees under the latest proposal by the United States Trade Representative stems from a new method of calculating levies based on the vessel’s cargo capacity, or net registered tonnage. From mid-October, the cost for any China-made vessel operated by a non-Chinese entity stands at $18 per nrt, with rates rising to $50 should the tanker be owned or operated by a Chinese company. That’s in contrast with the previous proposal that levied charges on a per-visit basis.  As a result, supertankers such as VLCCs face much costlier fees with this methodology, as compared with smaller ships such as aframaxes. Under the new rules, product tanker of different sizes stand to pay between $575,000 to $1.2 million per US visit for ships that are owned or operated by China.  All in all, the new levies are seen as less severe than before, after taking into account carve-outs and exemptions, said Arrow. However, it “still has the potential to impose heavy tolls on shippers, and Chinese owners in particular,” the company said.  Most of the tankers currently trading on water are South Korean-built, however, with the Chinese

Read More »

Copper-to-optics technology eyed for next-gen AI networking gear

Broadcom’s demonstration and a follow-up session explored the benefits of further developing CPC, such as reduced signal integrity penalties and extended reach, through channel modeling and simulations, Broadcom wrote in a blog about the DesignCon event. “Experimental results showed successful implementation of CPC, demonstrating its potential to address bandwidth and signal integrity challenges in data centers, which is crucial for AI applications,” Broadcom stated. In addition to the demo, Broadcom and Samtec also authored a white paper on CPC that stated: “Co-packaged connectivity (CPC) provides the opportunity to omit loss and reflection penalties from the [printed circuit board (PCB)] and the package. When high speed I/O is cabled from the top of the package advanced PCB materials are not necessary. Losses from package vertical paths and PCB routing can be transferred to the longer reach of cables,” the authors stated. “As highly complex systems are challenged to scale the number of I/O and their reach, co- packaged connectivity presents opportunity. As we approach 224G-PAM4 [which uses optical techniques to support 224 Gigabits per second data rates per optical lane] and above, system loss and dominating noise sources necessitate the need to re-consider that which has been restricted in the back of the system architect’s mind for years: What if we attached to the package?” At OFC, Samtec demonstrated its Si-FlyHD co-packaged cable assemblies and Samtec FlyoverOctal Small Form-factor Pluggable (OSFP) over the Samtec Eye Speed Hyper Low Skew twinax copper cable. Flyover is Samtec’s proprietary way of addressing signal integrity and reach limitations of routing high-speed signals through traditional printed circuit boards (PCBs). “This evaluation platform incorporates Broadcom’s industry-leading 200G SerDes technology and Samtec’s co-packaged Flyover technology. Si-Fly HD CPC offers the industry’s highest footprint density and robust interconnect which enables 102.4T (512 lanes at 200G) in a 95 x

Read More »

The Rise of AI Factories: Transforming Intelligence at Scale

AI Factories Redefine Infrastructure The architecture of AI factories reflects a paradigm shift that mirrors the evolution of the industrial age itself—from manual processes to automation, and now to autonomous intelligence. Nvidia’s framing of these systems as “factories” isn’t just branding; it’s a conceptual leap that positions AI infrastructure as the new production line. GPUs are the engines, data is the raw material, and the output isn’t a physical product, but predictive power at unprecedented scale. In this vision, compute capacity becomes a strategic asset, and the ability to iterate faster on AI models becomes a competitive differentiator, not just a technical milestone. This evolution also introduces a new calculus for data center investment. The cost-per-token of inference—how efficiently a system can produce usable AI output—emerges as a critical KPI, replacing traditional metrics like PUE or rack density as primary indicators of performance. That changes the game for developers, operators, and regulators alike. Just as cloud computing shifted the industry’s center of gravity over the past decade, the rise of AI factories is likely to redraw the map again—favoring locations with not only robust power and cooling, but with access to clean energy, proximity to data-rich ecosystems, and incentives that align with national digital strategies. The Economics of AI: Scaling Laws and Compute Demand At the heart of the AI factory model is a requirement for a deep understanding of the scaling laws that govern AI economics. Initially, the emphasis in AI revolved around pretraining large models, requiring massive amounts of compute, expert labor, and curated data. Over five years, pretraining compute needs have increased by a factor of 50 million. However, once a foundational model is trained, the downstream potential multiplies exponentially, while the compute required to utilize a fully trained model for standard inference is significantly less than

Read More »

Google’s AI-Powered Grid Revolution: How Data Centers Are Reshaping the U.S. Power Landscape

Google Unveils Groundbreaking AI Partnership with PJM and Tapestry to Reinvent the U.S. Power Grid In a move that underscores the growing intersection between digital infrastructure and energy resilience, Google has announced a major new initiative to modernize the U.S. electric grid using artificial intelligence. The company is partnering with PJM Interconnection—the largest grid operator in North America—and Tapestry, an Alphabet moonshot backed by Google Cloud and DeepMind, to develop AI tools aimed at transforming how new power sources are brought online. The initiative, detailed in a blog post by Alphabet and Google President Ruth Porat, represents one of Google’s most ambitious energy collaborations to date. It seeks to address mounting challenges facing grid operators, particularly the explosive backlog of energy generation projects that await interconnection in a power system unprepared for 21st-century demands. “This is our biggest step yet to use AI for building a stronger, more resilient electricity system,” Porat wrote. Tapping AI to Tackle an Interconnection Crisis The timing is critical. The U.S. energy grid is facing a historic inflection point. According to the Lawrence Berkeley National Laboratory, more than 2,600 gigawatts (GW) of generation and storage projects were waiting in interconnection queues at the end of 2023—more than double the total installed capacity of the entire U.S. grid. Meanwhile, the Federal Energy Regulatory Commission (FERC) has revised its five-year demand forecast, now projecting U.S. peak load to rise by 128 GW before 2030—more than triple the previous estimate. Grid operators like PJM are straining to process a surge in interconnection requests, which have skyrocketed from a few dozen to thousands annually. This wave of applications has exposed the limits of legacy systems and planning tools. Enter AI. Tapestry’s role is to develop and deploy AI models that can intelligently manage and streamline the complex process of

Read More »

Podcast: Vaire Computing Bets on Reversible Logic for ‘Near Zero Energy’ AI Data Centers

The AI revolution is charging ahead—but powering it shouldn’t cost us the planet. That tension lies at the heart of Vaire Computing’s bold proposition: rethinking the very logic that underpins silicon to make chips radically more energy efficient. Speaking on the Data Center Frontier Show podcast, Vaire CEO Rodolfo Rossini laid out a compelling case for why the next era of compute won’t just be about scaling transistors—but reinventing the way they work. “Moore’s Law is coming to an end, at least for classical CMOS,” Rossini said. “There are a number of potential architectures out there—quantum and photonics are the most well known. Our bet is that the future will look a lot like existing CMOS, but the logic will look very, very, very different.” That bet is reversible computing—a largely untapped architecture that promises major gains in energy efficiency by recovering energy lost during computation. A Forgotten Frontier Unlike conventional chips that discard energy with each logic operation, reversible chips can theoretically recycle that energy. The concept, Rossini explained, isn’t new—but it’s long been overlooked. “The tech is really old. I mean really old,” Rossini said. “The seeds of this technology were actually at the very beginning of the industrial revolution.” Drawing on the work of 19th-century mechanical engineers like Sadi Carnot and later insights from John von Neumann, the theoretical underpinnings of reversible computing stretch back decades. A pivotal 1961 paper formally connected reversibility to energy efficiency in computing. But progress stalled—until now. “Nothing really happened until a team of MIT students built the first chip in the 1990s,” Rossini noted. “But they were trying to build a CPU, which is a world of pain. There’s a reason why I don’t think there’s been a startup trying to build CPUs for a very, very long time.” AI, the

Read More »

Pennsylvania’s Homer City Energy Campus: A Brownfield Transformed for Data Center Innovation

The redevelopment of the Homer City Generating Station in Pennsylvania represents an important transformation from a decommissioned coal-fired power plant to a state-of-the-art natural gas-powered data center campus, showing the creative reuse of a large brownfield site and the creation of what can be a significant location in power generation and the digital future. The redevelopment will address the growing energy demands of artificial intelligence and high-performance computing technologies, while also contributing to Pennsylvania’s digital advancement, in an area not known as a hotbed of technical prowess. Brownfield Development Established in 1969, the original generating station was a 2-gigawatt coal-fired power plant located near Homer City, Indiana County, Pennsylvania. The site was formerly the largest coal-burning power plant in the state, and known for its 1,217-foot chimney, the tallest in the United States. In April 2023, the owners announced its closure due to competition from cheaper natural gas and the rising costs of environmental compliance. The plant was officially decommissioned on July 1, 2023, and its demolition, including the iconic chimney, was completed by March 22, 2025. ​ The redevelopment project, led by Homer City Redevelopment (HCR) in partnership with Kiewit Power Constructors Co., plans to transform the 3,200-acre site into the Homer City Energy Campus, via construction of a 4.5-gigawatt natural gas-fired power plant, making it the largest of its kind in the United States. Gas Turbines This plant will utilize seven high-efficiency, hydrogen-enabled 7HA.02 gas turbines supplied by GE Vernova, with deliveries expected to begin in 2026. ​The GE Vernova gas turbine has been seeing significant interest in the power generation market as new power plants have been moving to the planning stage. The GE Vernova 7HA.02 is a high-efficiency, hydrogen-enabled gas turbine designed for advanced power generation applications. As part of GE Vernova’s HA product line, it

Read More »

Dell data center modernization gear targets AI, HPC workloads

The update starts with new PowerEdge R470, R570, R670 and R770 servers featuring Intel Xeon 6 with P-cores processors in single- and dual-socket configurations designed to handle high-performance computing, virtualization, analytics and artificial intelligence inferencing. Dell said they save up to half of the energy costs of previous server generations while supporting up to 50% more cores per processors and 67% better performance. With the R770, up to 80% of space can be saved and a 42U rack. They feature the Dell Modular Hardware System architecture, which is based on Open Compute Project standards. The controller system also received a significant update, with improvements to Dell OpenManage and Integrated Dell Remote Access Controller providing real-time monitoring, while the Dell PowerEdge RAID Controller for PCIe Gen 5 hardware reduces write latency up to 33-fold.

Read More »

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.

Read More »

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

Read More »

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

Read More »

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

Read More »

The future of AI processing

In partnership withArm Artificial Intelligence (AI) is emerging in everyday use cases, thanks to advances in foundational models, more powerful chip technology, and abundant data.

Read More »