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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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The AI Infrastructure Split Screen: Capital Rush Meets Community Resistance

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New York State just hit pause on the AI data center boom

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TeraWulf’s $19B Anthropic Lease Puts Its Brownfield AI Strategy to the Test

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Comparing Space-Driven Data Center Strategies: Modular Satellites vs. Integrated Rocket Nodes

In addition to developing radiation-tolerant computing, optical communications, deployable solar arrays and orbital thermal-management systems, Cowboy must successfully design, manufacture, test and license a new rocket. Its launch vehicle would require authorization from the Federal Aviation Administration in addition to the approvals needed for the satellite constellation. Cowboy nevertheless enters the race with considerably more capital than Orbital. The company announced a $275 million Series B round in May at a reported $2 billion valuation. Founded in 2024 by Robinhood co-founder Baiju Bhatt, with a focus on space-based solar power before expanding into orbital computing and launch systems. One Hundred Kilowatts Versus One Megawatt The clearest distinction between the two proposals is the capacity assigned to each node. Orbital’s production design calls for approximately 100 kilowatts of computing power per satellite. Cowboy is targeting megawatt-class spacecraft, potentially giving each Stampede node approximately 10 times the power capacity of an Orbital satellite. At their stated maximum scales, Orbital’s 100,000 satellites would provide approximately 10 gigawatts. If Cowboy ultimately achieved one megawatt across all 20,000 Stampede spacecraft, its theoretical aggregate capacity would approach 20 gigawatts. Those figures should be treated as design objectives, not capacity forecasts. Neither company has demonstrated even one operational node at its proposed production power level. Orbital’s smaller satellites may be easier to test and deploy incrementally. The company can begin with a single hosted GPU, progress to a purpose-built prototype and expand as launch economics and customer demand permit. Cowboy’s larger nodes could provide more useful computing capacity with fewer satellites and potentially fewer launches. Combining the rocket stage and data center would also reduce the amount of structural mass that does not directly support power generation or computing. The tradeoff is concentration risk. The failure of a megawatt Cowboy spacecraft would remove considerably more capacity than

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AI’s Future Must Return to the Edge: How Power Constraints and Local Politics Are Redefining AI Infrastructure

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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

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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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