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Sam Altman at TED 2025: Inside the most uncomfortable — and important — AI interview of the year

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI CEO Sam Altman revealed that his company has grown to 800 million weekly active users and is experiencing “unbelievable” growth rates, during a sometimes tense interview at the TED 2025 conference in Vancouver last week. […]

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OpenAI CEO Sam Altman revealed that his company has grown to 800 million weekly active users and is experiencing “unbelievable” growth rates, during a sometimes tense interview at the TED 2025 conference in Vancouver last week.

“I have never seen growth in any company, one that I’ve been involved with or not, like this,” Altman told TED head Chris Anderson during their on-stage conversation. “The growth of ChatGPT — it is really fun. I feel deeply honored. But it is crazy to live through, and our teams are exhausted and stressed.”

The interview, which closed out the final day of TED 2025: Humanity Reimagined, showcased not just OpenAI’s skyrocketing success but also the increasing scrutiny the company faces as its technology transforms society at a pace that alarms even some of its supporters.

‘Our GPUs are melting’: OpenAI struggles to scale amid unprecedented demand

Altman painted a picture of a company struggling to keep up with its own success, noting that OpenAI’s GPUs are “melting” due to the popularity of its new image generation features. “All day long, I call people and beg them to give us their GPUs. We are so incredibly constrained,” he said.

This exponential growth comes as OpenAI is reportedly considering launching its own social network to compete with Elon Musk’s X, according to CNBC. Altman neither confirmed nor denied these reports during the TED interview.

The company recently closed a $40 billion funding round, valuing it at $300 billion — the largest private tech funding in history — and this influx of capital will likely help address some of these infrastructure challenges.

From non-profit to $300 billion giant: Altman responds to ‘Ring of Power’ accusations

Throughout the 47-minute conversation, Anderson repeatedly pressed Altman on OpenAI’s transformation from a non-profit research lab to a for-profit company with a $300 billion valuation. Anderson voiced concerns shared by critics, including Elon Musk, who has suggested Altman has been “corrupted by the Ring of Power,” referencing “The Lord of the Rings.”

Altman defended OpenAI’s path: “Our goal is to make AGI and distribute it, make it safe for the broad benefit of humanity. I think by all accounts, we have done a lot in that direction. Clearly, our tactics have shifted over time… We didn’t think we would have to build a company around this. We learned a lot about how it goes and the realities of what these systems were going to take from capital.”

When asked how he personally handles the enormous power he now wields, Altman responded: “Shockingly, the same as before. I think you can get used to anything step by step… You’re the same person. I’m sure I’m not in all sorts of ways, but I don’t feel any different.”

‘Divvying up revenue’: OpenAI plans to pay artists whose styles are used by AI

One of the most concrete policy announcements from the interview was Altman’s acknowledgment that OpenAI is working on a system to compensate artists whose styles are emulated by AI.

“I think there are incredible new business models that we and others are excited to explore,” Altman said when pressed about apparent IP theft in AI-generated images. “If you say, ‘I want to generate art in the style of these seven people, all of whom have consented to that,’ how do you divvy up how much money goes to each one?”

Currently, OpenAI’s image generator refuses requests to mimic the style of living artists without consent, but will generate art in the style of movements, genres, or studios. Altman suggested a revenue-sharing model could be forthcoming, though details remain scarce.

Autonomous AI agents: The ‘most consequential safety challenge’ OpenAI has faced

The conversation grew particularly tense when discussing “agentic AI” — autonomous systems that can take actions on the internet on a user’s behalf. OpenAI’s new “Operator” tool allows AI to perform tasks like booking restaurants, raising concerns about safety and accountability.

Anderson challenged Altman: “A single person could let that agent out there, and the agent could decide, ‘Well, in order to execute on that function, I got to copy myself everywhere.’ Are there red lines that you have clearly drawn internally, where you know what the danger moments are?”

Altman referenced OpenAI’s “preparedness framework” but provided few specifics about how the company would prevent misuse of autonomous agents.

“AI that you give access to your systems, your information, the ability to click around on your computer… when they make a mistake, it’s much higher stakes,” Altman acknowledged. “You will not use our agents if you do not trust that they’re not going to empty your bank account or delete your data.”

’14 definitions from 10 researchers’: Inside OpenAI’s struggle to define AGI

In a revealing moment, Altman admitted that even within OpenAI, there’s no consensus on what constitutes artificial general intelligence (AGI) — the company’s stated goal.

“It’s like the joke, if you’ve got 10 OpenAI researchers in a room and asked to define AGI, you’d get 14 definitions,” Altman said.

He suggested that rather than focusing on a specific moment when AGI arrives, we should recognize that “the models are just going to get smarter and more capable and smarter and more capable on this long exponential… We’re going to have to contend and get wonderful benefits from this incredible system.”

Loosening the guardrails: OpenAI’s new approach to content moderation

Altman also disclosed a significant policy change regarding content moderation, revealing that OpenAI has loosened restrictions on its image generation models.

“We’ve given the users much more freedom on what we would traditionally think about as speech harms,” he explained. “I think part of model alignment is following what the user of a model wants it to do within the very broad bounds of what society decides.”

This shift could signal a broader move toward giving users more control over AI outputs, potentially aligning with Altman’s expressed preference for letting the hundreds of millions of users — rather than “small elite summits” — determine appropriate guardrails.

“One of the cool new things about AI is our AI can talk to everybody on Earth, and we can learn the collective value preference of what everybody wants, rather than have a bunch of people who are blessed by society to sit in a room and make these decisions,” Altman said.

‘My kid will never be smarter than AI’: Altman’s vision of an AI-powered future

The interview concluded with Altman reflecting on the world his newborn son will inherit — one where AI will exceed human intelligence.

“My kid will never be smarter than AI. They will never grow up in a world where products and services are not incredibly smart, incredibly capable,” he said. “It’ll be a world of incredible material abundance… where the rate of change is incredibly fast and amazing new things are happening.”

Anderson closed with a sobering observation: “Over the next few years, you’re going to have some of the biggest opportunities, the biggest moral challenges, the biggest decisions to make of perhaps any human in history.”

The billion-user balancing act: How OpenAI navigates power, profit, and purpose

Altman’s TED appearance comes at a critical juncture for OpenAI and the broader AI industry. The company faces mounting legal challenges, including copyright lawsuits from authors and publishers, while simultaneously pushing the boundaries of what AI can do.

Recent advancements like ChatGPT’s viral image generation feature and video generation tool Sora have demonstrated capabilities that seemed impossible just months ago. At the same time, these tools have sparked debates about copyright, authenticity, and the future of creative work.

Altman’s willingness to engage with difficult questions about safety, ethics, and the societal impact of AI shows an awareness of the stakes involved. However, critics may note that concrete answers on specific safeguards and policies remained elusive throughout the conversation.

The interview also revealed the competing tensions at the heart of OpenAI’s mission: moving fast to advance AI technology while ensuring safety; balancing profit motives with societal benefit; respecting creative rights while democratizing creative tools; and navigating between elite expertise and public preference.

As Anderson noted in his final comment, the decisions Altman and his peers make in the coming years may have unprecedented impacts on humanity’s future. Whether OpenAI can live up to its stated mission of ensuring “all of humanity benefits from artificial general intelligence” remains to be seen.

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Reliance Gets USA License to Directly Buy VEN Crude

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Baker Hughes Explores $1.5B Sale of Waygate Unit

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EIA Raises 2026 WTI Forecast, Lowers 2027 Projection

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Some OPEC+ Members See Scope to Resume Hikes in April

Some OPEC+ members see scope for the alliance to resume supply increases in April, believing concerns of a glut in global oil markets to be overblown. The group led by Saudi Arabia and Russia hasn’t committed to any course of action or begun formal discussions ahead of its meeting on March 1, according to several delegates, who asked not to be identified as the process is private. Their ultimate decision may depend on whether US President Donald Trump launches military action against — or reaches a nuclear deal with — OPEC member Iran, one added.  Nonetheless, some nations in the Organization of the Petroleum Exporting Countries and its allies said they see room to resume the output increases the coalition paused during the seasonal demand slowdown of the first quarter.  Trump’s assertive stance toward OPEC members Venezuela and Iran, along with disruptions spanning from North America to Kazakhstan, drove oil prices to a strong start of the year despite warnings of a supply glut. Several top traders have said that prices are supported by tightness in key markets, as many of the surplus barrels are from producers subject to sanctions like Russia and Iran, and thus remain unavailable to a wider pool of buyers. That has made the market surprisingly resilient. Brent futures are up 11% this year, after spiking to a six-month high near $72 a barrel at the end of January over concerns a conflict might erupt in the Middle East. Oil inventories piled up last year at the fastest pace since the 2020 pandemic amid swelling output from both OPEC+ and its competitors in the Americas, according to the International Energy Agency, though the impact on prices was tempered as China scooped up barrels for its strategic reserves. Last April, the Saudis stunned crude traders by steering OPEC+ to

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ADNOC Drilling Delivers ‘Best Year on Record’

In a release sent to Rigzone on Thursday, ADNOC Drilling said it had delivered its “best year on record with 2025 net profit of $1.45 billion”. “ADNOC Drilling Company PJSC announced today record fourth quarter and full year 2025 results, marking a step change in scale, technology-enabled performance and excellence in execution,” the company noted in the release. “This performance represents the strongest in the company’s history, reflecting high asset utilization and continued growth across integrated drilling and oilfield services, and driven by strong operational execution across the fleet,” it added. ADNOC Drilling highlighted in the release that its 2025 net profit marked an 11 percent year on year increase. In 2025, the company reported revenue of $4.9 billion, which it pointed out was a 22 percent increase year on year, and EBITDA of $2.2 billion, which it highlighted was an increase of nine percent year on year. “2025 was a defining year for ADNOC Drilling,” ADNOC Drilling CEO Abdulla Ateya Al Messabi said in the release. “Our record breaking results were delivered by our people, whose discipline, innovation and commitment to operational excellence and safety underpin every milestone we achieve,” Messabi added. “Our resilience as a business, built on strong systems, disciplined operations and the ability to adapt at pace, continues to reinforce our competitive strength,” the CEO continued. “Through execution excellence, technology‑led efficiency and a disciplined approach to capital allocation and operations, we continue our transformation into the region’s most advanced energy services company,” Messabi said. “By expanding across the GCC, pioneering AI‑driven operations and setting new benchmarks in sustainability, we are unlocking value and helping power the UAE’s energy future. This is just the beginning of a new era of growth, innovation and impact,” Messabi went on to note. In a release posted on its site in November last year, ADNOC Drilling announced that

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From NIMBY to YIMBY: A Playbook for Data Center Community Acceptance

Across many conversations at the start of this year, at PTC and other conferences alike, the word on everyone’s lips seems to be “community.” For the data center industry, that single word now captures a turning point from just a few short years ago: we are no longer a niche, back‑of‑house utility, but a front‑page presence in local politics, school board budgets, and town hall debates. That visibility is forcing a choice in how we tell our story—either accept a permanent NIMBY-reactive framework, or actively build a YIMBY narrative that portrays the real value digital infrastructure brings to the markets and surrounding communities that host it. Speaking regularly with Ilissa Miller, CEO of iMiller Public Relations about this topic, there is work to be done across the ecosystem to build communications. Miller recently reflected: “What we’re seeing in communities isn’t a rejection of digital infrastructure, it’s a rejection of uncertainty driven by anxiety and fear. Most local leaders have never been given a framework to evaluate digital infrastructure developments the way they evaluate roads, water systems, or industrial parks. When there’s no shared planning language, ‘no’ becomes the safest answer.” A Brief History of “No” Community pushback against data centers is no longer episodic; it has become organized, media‑savvy, and politically influential in key markets. In Northern Virginia, resident groups and environmental organizations have mobilized against large‑scale campuses, pressing counties like Loudoun and Prince William to tighten zoning, question incentives, and delay or reshape projects.1 Loudoun County’s move in 2025 to end by‑right approvals for new facilities, requiring public hearings and board votes, marked a watershed moment as the world’s densest data center market signaled that communities now expect more say over where and how these campuses are built. Prince William County’s decision to sharply increase its tax rate on

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Nomads at the Frontier: PTC 2026 Signals the Digital Infrastructure Industry’s Moment of Execution

Each January, the Pacific Telecommunications Council conference serves as a barometer for where digital infrastructure is headed next. And according to Nomad Futurist founders Nabeel Mahmood and Phillip Koblence, the message from PTC 2026 was unmistakable: The industry has moved beyond hype. The hard work has begun. In the latest episode of The DCF Show Podcast, part of our ongoing ‘Nomads at the Frontier’ series, Mahmood and Koblence joined Data Center Frontier to unpack the tone shift emerging across the AI and data center ecosystem. Attendance continues to grow year over year. Conversations remain energetic. But the character of those conversations has changed. As Mahmood put it: “The hype that the market started to see is actually resulting a bit more into actions now, and those conversations are resulting into some good progress.” The difference from prior years? Less speculation. More execution. From Data Center Cowboys to Real Deployments Koblence offered perhaps the sharpest contrast between PTC conversations in 2024 and those in 2026. Two years ago, many projects felt speculative. Today, developers are arriving with secured power, customers, and construction underway. “If 2024’s PTC was data center cowboys — sites that in someone’s mind could be a data center — this year was: show me the money, show me the power, give me accurate timelines.” In other words, the market is no longer rewarding hypothetical capacity. It is demanding delivered capacity. Operators now speak in terms of deployments already underway, not aspirational campuses still waiting on permits and power commitments. And behind nearly every conversation sits the same gating factor. Power. Power Has Become the Industry’s Defining Constraint Whether discussions centered on AI factories, investment capital, or campus expansion, Mahmood and Koblence noted that every conversation eventually returned to energy availability. “All of those questions are power,” Koblence said.

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Cooling Consolidation Hits AI Scale: LiquidStack, Submer, and the Future of Data Center Thermal Strategy

As AI infrastructure scales toward ever-higher rack densities and gigawatt-class campuses, cooling has moved from a technical subsystem to a defining strategic issue for the data center industry. A trio of announcements in early February highlights how rapidly the cooling and AI infrastructure stack is consolidating and evolving: Trane Technologies’ acquisition of LiquidStack; Submer’s acquisition of Radian Arc, extending its reach from core data centers into telco edge environments; and Submer’s partnership with Anant Raj to accelerate sovereign AI infrastructure deployment across India. Layered atop these developments is fresh guidance from Oracle Cloud Infrastructure explaining why closed-loop, direct-to-chip cooling is becoming central to next-generation facility design, particularly in regions where water use has become a flashpoint in community discussions around data center growth. Taken together, these developments show how the industry is moving beyond point solutions toward integrated, scalable AI infrastructure ecosystems, where cooling, compute, and deployment models must work together across hyperscale campuses and distributed edge environments alike. Trane Moves to Own the Cooling Stack The most consequential development comes from Trane Technologies, which on February 10 announced it has entered into a definitive agreement to acquire LiquidStack, one of the pioneers and leading innovators in data center liquid cooling. The acquisition significantly strengthens Trane’s ambition to become a full-service thermal partner for data center operators, extending its reach from plant-level systems all the way down to the chip itself. LiquidStack, headquartered in Carrollton, Texas, built its reputation on immersion cooling and advanced direct-to-chip liquid solutions supporting high-density deployments across hyperscale, enterprise, colocation, edge, and blockchain environments. Under Trane, those technologies will now be scaled globally and integrated into a broader thermal portfolio. In practical terms, Trane is positioning itself to deliver cooling across the full thermal chain, including: • Central plant equipment and chillers.• Heat rejection and controls

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Infrastructure Maturity Defines the Next Phase of AI Deployment

The State of Data Infrastructure Global Report 2025 from Hitachi Vantara arrives at a moment when the data center industry is undergoing one of the most profound structural shifts in its history. The transition from enterprise IT to AI-first infrastructure has moved from aspiration to inevitability, forcing operators, developers, and investors to confront uncomfortable truths about readiness, resilience, and risk. Although framed around “AI readiness,” the report ultimately tells an infrastructure story: one that maps directly onto how data centers are designed, operated, secured, and justified economically. Drawing on a global survey of more than 1,200 IT leaders, the report introduces a proprietary maturity model that evaluates organizations across six dimensions: scalability, reliability, security, governance, sovereignty, and sustainability. Respondents are then grouped into three categories—Emerging, Defined, and Optimized—revealing a stark conclusion: most organizations are not constrained by access to AI models or capital, but by the fragility of the infrastructure supporting their data pipelines. For the data center industry, the implications are immediate, shaping everything from availability design and automation strategies to sustainability planning and evolving customer expectations. In short, extracting value from AI now depends less on experimentation and more on the strength and resilience of the underlying infrastructure. The Focus of the Survey: Infrastructure, Not Algorithms Although the report is positioned as a study of AI readiness, its primary focus is not models, training approaches, or application development, but rather the infrastructure foundations required to operate AI reliably at scale. Drawing on responses from more than 1,200 organizations, Hitachi Vantara evaluates how enterprises are positioned to support production AI workloads across six dimensions as stated above: scalability, reliability, security, governance, sovereignty, and sustainability. These factors closely reflect the operational realities shaping modern data center design and management. The survey’s central argument is that AI success is no longer

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AI’s New Land Grab: Meta’s Indiana Megaproject and the Rise of Europe’s Neocloud Challengers

While Meta’s Indiana campus anchors hyperscale expansion in the United States, Europe recorded its own major infrastructure milestone this week as Amsterdam-based AI infrastructure provider Nebius unveiled plans for a 240-megawatt data center campus in Béthune, France, near Lille in the country’s northern industrial corridor. When completed, the campus will rank among Europe’s largest AI-focused data center facilities and positions northern France as a growing node in the continent’s expanding AI infrastructure map. The development repurposes a former Bridgestone tire manufacturing site, reflecting a broader trend across Europe in which legacy industrial properties, already equipped with heavy power access, transport links, and industrial zoning, are being converted into large-scale digital infrastructure hubs. Located within reach of connectivity and enterprise corridors linking Paris, Brussels, London, and Amsterdam, the site allows Nebius to serve major European markets while avoiding the congestion and power constraints increasingly shaping Tier 1 data center hubs. Industrial Infrastructure Becomes Digital Infrastructure Developers increasingly view former industrial sites as ideal for AI campuses because they often provide: • Existing grid interconnection capacity built for heavy industry• Transport and logistics infrastructure already in place• Industrial zoning that reduces permitting friction• Large contiguous parcels suited to phased campus expansion For regions like Hauts-de-France, redevelopment projects also offer economic transition opportunities, replacing legacy manufacturing capacity with next-generation digital infrastructure investment. Local officials have positioned the project as part of broader efforts to reposition northern France as a logistics and technology hub within Europe. The Neocloud Model Gains Ground Beyond the site itself, Nebius’ expansion illustrates the rapid emergence of neocloud infrastructure providers, companies building GPU-intensive AI capacity without operating full hyperscale cloud ecosystems. These firms increasingly occupy a strategic middle ground: supplying AI compute capacity to enterprises, startups, and even hyperscalers facing short-term infrastructure constraints. Nebius’ rise over the past year

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FTC digs deeper into Microsoft’s bundling and licensing practices

Relationship with OpenAI While much of the initial query, and subsequent ones, have focused on licensing and bundling, the FTC is also looking into the company’s relationship with OpenAI, and raising questions about Microsoft’s data centers, capacity constraints, and AI spending and research. Notably, the tech giant’s initial $1 billion investment in OpenAI has grown into a multi-billion-dollar partnership, with Microsoft rolling out ChatGPT-powered features across its product line in 2023. The FTC is examining whether the relationship is an undisclosed merger that should have been subject to antitrust review. Further, the federal agency is scrutinizing Microsoft’s alleged decision to scale back its own AI research following the OpenAI investment, potentially reducing competition. Ultimately, all of this recalls the industry-shaping 1990s US federal investigation into Microsoft’s monopoly of desktop software and web browsers. A federal judge ruled at the time that the company deliberately built the Internet Explorer (IE) browser into Windows to edge out rivals like the now-defunct Netscape. And, analysts note, it’s an indication that Microsoft hasn’t learned from those past lessons. “While technology and trends may have evolved since Microsoft’s first anti-trust case in 1998, where they were forced to unbundle IE from Windows OS, their tactics have stayed remarkably the same,” Bickley noted.

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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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