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Decoding OpenAI’s Super Bowl ad and Sam Altman’s grandiose blog post

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More If you were in one of the nearly 40 million U.S. households that tuned into the NFL Super Bowl LIX this year, in addition to watching the Philadelphia Eagles trounce the Kansas City Chiefs, you may […]

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If you were in one of the nearly 40 million U.S. households that tuned into the NFL Super Bowl LIX this year, in addition to watching the Philadelphia Eagles trounce the Kansas City Chiefs, you may have caught an advertisement for OpenAI.

This is the company’s first Super Bowl ad, and it cost a reported $14 million — in keeping with the astronomical sums commanded by ads during the big game, which some come to see instead of the football. As you’ll see in a copy embedded below, the OpenAI ad depicts various advancements throughout human history, leading up to ChatGPT today, what OpenAI calls the “Intelligence Age.

While reaction to the ad was mixed — I’ve seen more praise and defense for it than criticism in my feeds — it clearly indicates that OpenAI has arrived as a major force in American culture, and quite obviously seeks to connect to a long lineage of invention, discovery and technological progress that’s taken place here.

On it’s own, the OpenAI Super Bowl ad seems to me to be a totally inoffensive and simple message designed to appeal to the widest possible audience — perfect for the Super Bowl and its large audience across demographics. In a way, it’s even so smooth and uncontroversial that it is forgettable.

But coupled with a blog post OpenAI CEO Sam Altman published on his personal website earlier on Sunday, entitled “Three Observations,” and suddenly OpenAI’s assessment of the current moment and the future becomes much more dramatic and stark.

Altman begins the blog post with a pronouncement about artificial general intelligence (AGI), the raison d’etre of OpenAI’s founding and its ongoing efforts to release more and more powerful AI models such as the latest o3 series. This pronouncement, like OpenAI’s Super Bowl ad, also seeks to connect OpenAI’s work building these models and approaching this goal of AGI with the history of human innovation more broadly.

Systems that start to point to AGI* are coming into view, and so we think it’s important to understand the moment we are in. AGI is a weakly defined term, but generally speaking we mean it to be a system that can tackle increasingly complex problems, at human level, in many fields.

People are tool-builders with an inherent drive to understand and create, which leads to the world getting better for all of us. Each new generation builds upon the discoveries of the generations before to create even more capable tools—electricity, the transistor, the computer, the internet, and soon AGI.

A few paragraphs later, he even seems to concede that AI — as many developers and users of the tech agree — is simply another new tool. Yet he immediately flips to suggest this may be a much different tool than anyone in the world has ever experienced to date. As he writes:

In some sense, AGI is just another tool in this ever-taller scaffolding of human progress we are building together. In another sense, it is the beginning of something for which it’s hard not to say “this time it’s different”; the economic growth in front of us looks astonishing, and we can now imagine a world where we cure all diseases, have much more time to enjoy with our families, and can fully realize our creative potential.

The idea of “curing all diseases,” while certainly appealing — mirrors something rival tech boss Mark Zuckerberg of Meta also sought out to do with his Chan-Zuckerberg Initiative medical research nonprofit co-founded with his wife, Prisicilla Chan. As of two years ago, the timeline proposed for the Chan-Zuckerberg’s initiative to reach this goal was by 2100. Yet now thanks to the progress of AI, Altman seems to believe it’s attainable even sooner, writing: “In a decade, perhaps everyone on earth will be capable of accomplishing more than the most impactful person can today.”

Altman and Zuck are hardly the one two high-profile tech billionaires interested in medicine and longevity science in particular. Google’s co-founders, especially Sergey Brin, have put money towards analogous efforts, and in fact, there were (or are) at one point so many leaders in the tech industry interested in prolonging human life and ending disease that back in 2017, The New Yorker magazine ran a feature article entitled: “Silicon Valley’s Quest to Live Forever.”

This utopian notion of ending disease and ultimately death seems patently hubristic to me on the face of it — how many folklore stories and fairy tales are there about the perils of trying to cheat death? — but it aligns neatly with the larger techno-utopian beliefs of some in the industry, which have been helpfully grouped by AGI critics and researchers Timnit Gebru and Émile P. Torres under the umbrella term TESCREAL, an acronym for “transhumanism, Extropianism, singularitarianism, (modern) cosmism, Rationalism, Effective Altruism, and longtermism,” in their 2023 paper.

As these authors elucidate, the veneer of progress sometimes masks uglier beliefs such as in the inherent racial superiority or humanity of those with higher IQs, specific demographics, and ultimately evoking racial science and phrenology of more openly discriminatory and oppressive ages past.

There’s nothing to suggest in Altman’s note that he shares such beliefs, mind you…in fact, rather the opposite. He writes:

“Ensuring that the benefits of AGI are broadly distributed is critical. The historical impact of technological progress suggests that most of the metrics we care about (health outcomes, economic prosperity, etc.) get better on average and over the long-term, but increasing equality does not seem technologically determined and getting this right may require new ideas.”

In other words: he wants to ensure everyone’s life gets better with AGI, but is uncertain how to achieve that. It’s a laudable notion, and one that maybe AGI itself could help answer, but for one thing, OpenAI’s latest and greatest models remain closed and proprietary as opposed to competitors such as Llama’s Meta family and DeepSeek’s R1, though the latter has apparently caused Altman to re-assess OpenAI’s approach to the open source community as he mentioned on a recent separate Reddit AMA thread. Perhaps OpenAI could start by open sourcing more of its technology to ensure it spreads wider to more users, more equally?

Meanwhile, speaking of specific timelines, Altman seems to project that while the next few years may not be wholly remade by AI or AGI, he’s more confident of a visible impact by the end of the decade 2035. As he puts it:

The world will not change all at once; it never does. Life will go on mostly the same in the short run, and people in 2025 will mostly spend their time in the same way they did in 2024. We will still fall in love, create families, get in fights online, hike in nature, etc.

But the future will be coming at us in a way that is impossible to ignore, and the long-term changes to our society and economy will be huge. We will find new things to do, new ways to be useful to each other, and new ways to compete, but they may not look very much like the jobs of today.

Anyone in 2035 should be able to marshall [sic] the intellectual capacity equivalent to everyone in 2025; everyone should have access to unlimited genius to direct however they can imagine. There is a great deal of talent right now without the resources to fully express itself, and if we change that, the resulting creative output of the world will lead to tremendous benefits for us all.”

Where does this leave us? Critics of OpenAI would say it’s more empty hype designed to continue placating OpenAI’s big-pocketed investors such as Softbank and put off any pressure to have working AGI for a while longer.

But having used these tools myself, watched and reported on other users and sene what they’ve been able to accomplish — such as writing up complex software within mere minutes without much background in the field — I’m inclined to believe Altman is serious in his prognostications, and hopeful in his commitment to equal distribution.

But keeping all the best models closed up under a subscription bundle clearly is not the way to attain equal access to AGI — so my biggest question remains on what the company does under his leadership to ensure it moves in this direction he so clearly articulated and that the Super Bowl ad also celebrated.

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@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: var(–color-primary-main); } .ebm-page__main h1, .ebm-page__main h2, .ebm-page__main h3, .ebm-page__main h4, .ebm-page__main h5, .ebm-page__main h6 { font-family: Inter; } body { line-height: 150%; letter-spacing: 0.025em; font-family: Inter; } button, .ebm-button-wrapper { font-family: Inter; } .label-style { text-transform: uppercase; color: var(–color-grey); font-weight: 600; font-size: 0.75rem; } .caption-style { font-size: 0.75rem; opacity: .6; } #onetrust-pc-sdk [id*=btn-handler], #onetrust-pc-sdk [class*=btn-handler] { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-accept-btn-handler, #onetrust-banner-sdk #onetrust-reject-all-handler, #onetrust-consent-sdk #onetrust-pc-btn-handler.cookie-setting-link { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #c19a06 !important; border-color: #c19a06 !important; } In this Market Focus episode of the Oil & Gas Journal ReEnterprised podcast, Conglin Xu, managing editor, economics, takes a look into the LNG market shock caused by the effective closure of the Strait of Hormuz and the sudden loss of Qatari LNG supply as the Iran war continues. Xu speaks with Edward O’Toole, director of global gas analysis, RBAC Inc., to examine how these disruptions are intensifying global supply constraints at a time when European inventories were already under pressure following a colder-than-average winter and weaker storage levels. Drawing on RBAC’s G2M2 global gas market model, O’Toole outlines disruption scenarios analyzed in the firm’s recent report and explains how current events align with their findings. With global LNG production already operating near maximum utilization, the market response is being driven by higher prices and reduced consumption. Europe faces sharper price pressure due to storage refill needs, while Asian markets are expected to see greater demand reductions as consumers switch fuels. O’Toole underscores the importance of scenario-based modeling and supply diversification as geopolitical risk exposes structural vulnerabilities in the LNG market—offering insights for stakeholders navigating an increasingly uncertain global

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@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: var(–color-primary-main); } .ebm-page__main h1, .ebm-page__main h2, .ebm-page__main h3, .ebm-page__main h4, .ebm-page__main h5, .ebm-page__main h6 { font-family: Inter; } body { line-height: 150%; letter-spacing: 0.025em; font-family: Inter; } button, .ebm-button-wrapper { font-family: Inter; } .label-style { text-transform: uppercase; color: var(–color-grey); font-weight: 600; font-size: 0.75rem; } .caption-style { font-size: 0.75rem; opacity: .6; } #onetrust-pc-sdk [id*=btn-handler], #onetrust-pc-sdk [class*=btn-handler] { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-accept-btn-handler, #onetrust-banner-sdk #onetrust-reject-all-handler, #onetrust-consent-sdk #onetrust-pc-btn-handler.cookie-setting-link { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #c19a06 !important; border-color: #c19a06 !important; } The National Oil Corp. of Libya (NOC) signed a memorandum of understanding (MoU) with Chevron Corp. to conduct a comprehensive technical study of offshore Block NC146. The block is an unexplored area with “encouraging geological indicator that could lead to significant discoveries, helping to strengthen national reserves,” NOC noted Chairman Masoud Suleman as saying, noting that the partnership is “a message of confidence in the Libyan investment environment and evidence of the return of major companies to work and explore promising opportunities in our country.” According to the NOC, Libya produces 1.4 million b/d of oil and aims to increase oil production in the coming 3-5 years to 2 million b/d and then to 3 million b/d following years of instability that impacted the country’s production.   Chevron is working to add to its diverse exploration and production portfolio in the Mediterranean and Africa and continues to assess potential future opportunities in the region.  The operator earlier this year entered Libya after it was designated as a winning bidder for Contract Area 106 in the Sirte basin in the 2025 Libyan Bid Round. That followed the January 2026 signing of a

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That embedded telemetry feeds adaptive tuning of Dynamic Load Balancing parameters, Data Center Quantized Congestion Notification (DCQCN) and failover logic without waiting for a threshold breach or a manual intervention. The platform architecture is layered. At the lowest levels, agents react in microseconds to link-level events such as transceiver flaps, rerouting leaf-spine traffic in milliseconds. At higher layers, agents make more strategic decisions about flow placement across the cluster. At the cloud layer, a large language model-based agent surfaces correlated insights to operators in natural language, allowing them to ask questions about specific jobs or alert conditions and receive context-aware responses. Karam argued that simply bolting an LLM onto an existing architecture does not deliver the same result. “If you ask it to do anything, it could hallucinate and bring down the network,” he said. “It doesn’t have any of the context or the data that’s required for this approach to be made safe.” Aria also exposes an MCP server, allowing external systems such as job schedulers and LLM routers to query network state directly and integrate it into their own decision-making. MFU and token efficiency as the target metrics Traditional networking is often evaluated in terms of bandwidth and latency. Aria is centering its platform around two metrics: Model FLOPS Utilization (MFU) and token efficiency. MFU is defined as the ratio of achieved FLOPS per accelerator to the theoretical peak. In practice, Karam said, MFU for training workloads typically runs between 33% and 45%, and inference often comes in below 30%. “The network has a major impact on the MFU, and therefore the token efficiency, because the network touches every aspect, every other component in your cluster,” Karam said.

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But given there are no products currently available using UALink 1.0, UALink 2.0 might be viewed as a premature launch Need to play catch up David Harold, senior analyst with Jon Peddie Research, was guarded in his reaction. “While 2.0 is a significant step forward from 1.0, we need to bear in mind that even 1.0 solutions aren’t shipping yet – they aren’t due until later this year. So, Nvidia is way ahead of the open alternatives on connectivity, indeed ahead of the proprietary or Ethernet based solutions too,” he said. What this means, he added, is that non-Nvidia alternatives are currently lagging in the market. “They need to play catch up on several fronts, not just networking. … I can’t think of a single shipping product that meaningfully has advantages over a Nvidia solution,” he said. “Ultimately UALink remains desirable since it will enable heterogeneous, multi-vendor environments but it’s quite a way behind NVLink today. ” There are plenty of signs that organizations will find it hard to break free of the Nvidia dominance, however. A couple of months ago, RISC-V pioneer SiFive signed a deal with Nvidia to incorporate Nvidia NVLink Fusion into its data center products, a departure for RISC companies. According to Harold, other companies could be joining it. “Custom ASIC company MediaTek is an NVLink partner, and they told me last week that they are planning to integrate it directly into next-generation custom silicon for AI applications,” he said. “This will enable a wider range of companies to use NVLink as their high-speed interconnect.” Other options And, Harold noted, Nvidia is already looking at other options. “Nvidia is now shifting to look at the copper limit for networking speed, with an interest in using optical connectivity instead,” said Harold.

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Is the concern valid? Dr. Danish Faruqui, CEO of Fab Economics, a US-based AI hardware and datacenter advisory, said the risk was real. “The skepticism that Nvidia may prioritize its own hardware in future software updates, potentially delaying or under-optimizing support for rivals, is a feasible outcome,” he said. As the primary developer, Nvidia now controls Slurm’s official development roadmap and code review process, Faruqui said, “which could influence how quickly competing chips are integrated on new development or continuous improvement elements.” Owning the control plane alongside GPUs and networking infrastructure such as InfiniBand, he added, allows Nvidia to create a tightly vertically integrated stack that can lead to what he described as “shallow moats, where advanced features are only available or performant on Nvidia hardware.” One concrete test of that, industry observers say, will be how quickly Nvidia integrates support for AMD’s next-generation chips into Slurm’s codebase compared with how quickly it integrates its own forthcoming hardware and networking technologies, such as InfiniBand. Does the Bright Computing precedent hold? Analysts point to Nvidia’s 2022 acquisition of Bright Computing as a reference point, saying the software became optimized for Nvidia chips in ways that disadvantaged users of competing hardware. Nvidia disputed that characterization, saying Bright Computing supports “nearly any CPU or GPU-accelerated cluster.” Rawat said the comparison was instructive but imperfect. “Nvidia’s acquisition of Bright Computing highlights its preference for vertical integration, embedding Bright tightly into DGX and AI Factory stacks rather than maintaining a neutral, multi-vendor orchestration role,” he said. “This reflects a broader strategic pattern — Nvidia seeks to control the full-stack AI infrastructure experience.”

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Nscale Expands AI Factory Strategy With Power, Platform, and Scale

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Google Research touts memory-compression breakthrough for AI processing

The last time the market witnessed a shakeup like this was China’s DeepSeek, but doubts emerged quickly about its efficacy. Developers found DeepSeek’s efficiency gains required deep architectural decisions that had to be built in from the start. TurboQuant requires no retraining or fine-tuning. You just drop it straight into existing inference pipelines, at least in theory. If it works in production systems with no retrofitting, then data center operators will get tremendous performance gains on existing hardware. Data center operators won’t have to throw hardware at the performance problem. However, analysts urge caution before jumping to conclusions. “This is a research breakthrough, not a shipping product,” said Alex Cordovil, research director for physical infrastructure at The Dell’Oro Group. “There’s often a meaningful gap between a published paper and real-world inference workloads.” Also, Dell’Oro notes that efficiency gains in AI compute tend to get consumed by more demand, known as the Jevons paradox. “Any freed-up capacity would likely be absorbed by frontier models expanding their capabilities rather than reducing their hardware footprint.” Jim Handy, president of Objective Analysis, agrees on that second part. “Hyperscalers won’t cut their spending – they’ll just spend the same amount and get more bang for their buck,” he said. “Data centers aren’t looking to reach a certain performance level and subsequently stop spending on AI. They’re looking to out-spend each other to gain market dominance. This won’t change that.” Google plans to present a paper outlining TurboQuant at the ICLR conference in Rio de Janeiro running from April 23 through April 27.

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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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