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OpenAI names Instacart leader Fidji Simo as new CEO of Applications

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Last night, OpenAI published a blog post on its official website authored by CEO and co-founder Sam Altman announcing a major new hire: Fidji Simo, currently CEO and Chair at grocery delivery company Instacart, will join […]

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Last night, OpenAI published a blog post on its official website authored by CEO and co-founder Sam Altman announcing a major new hire: Fidji Simo, currently CEO and Chair at grocery delivery company Instacart, will join OpenAI as CEO of Applications, a newly created executive position.

Simo will step into her new role at OpenAI later in 2025 following a transition period. She will report directly to Altman and lead the Applications division, which includes teams responsible for translating OpenAI’s research into products used by consumers and businesses worldwide.

Simo also shared a message with her team at Instacart, which she later posted publicly on her LinkedIn profile, stating: “This was an incredibly hard decision because I love this company… At the same time, you all know my passion for AI generally and in particular for the potential it has to cure diseases — the ability to lead such an important part of our collective future was a hard opportunity to pass up.”

She further wrote: “Joining OpenAI at this critical moment is an incredible privilege and responsibility. This organization has the potential of accelerating human potential at a pace never seen before and I am deeply committed to shaping these applications toward the public good.”

Altman was clear to state he will remain the CEO of the entire ChatGPT company, but that the organization’s growing scope across AI research, product delivery, and infrastructure meant it was time for the creation of a new division with its own executive leader.

Altman said he plans to devote increased focus to Research, Compute, and Safety Systems, which will continue reporting directly to him. He reiterated that all functions will remain integrated under a single OpenAI structure, including its nonprofit arm.

Simo will remain Instacart’s CEO for the coming months and help onboard her successor, who is expected to come from the company’s existing management team. She will continue serving as Chair of the Instacart Board after stepping down as CEO.

OpenAI’s evolving structure and AI stack

Altman emphasized the strategic importance of the new role, noting that OpenAI has evolved beyond its original identity as a research lab.

Over the past two and a half years, the company has grown into a global product provider and, more recently, an infrastructure builder delivering AI tools at large scale.

“We’re in a privileged position to be scaling at a pace that lets us do them all simultaneously, and bringing on exceptional leaders is a key part of doing that well,” Altman wrote in his blog post.

He described Simo as uniquely qualified to lead the Applications group, citing her leadership experience, operational expertise, and alignment with OpenAI’s mission.

Separately, in prepared testimony before the U.S. Congress today, Altman also included a chart showing how OpenAI views the “AI stack” it offers customers, and how the entire industry is divided into three core buckets or “layers”:

Also in that testimony, Altman revealed more examples of what OpenAI considers to be part of the AI applications layer, among them, chatbots such as its hit ChatGPT and Microsoft’s Copilot (also powered in part by AI models):

“Ultimately, both the infrastructure and platform layers support the applications layer. These are devices and software applications that use AI to deliver better services to people. ChatGPT and 4 Microsoft’s Copilot are both examples of AI applications. One of the amazing things about the applications layer is it’s not just companies – large or small or established or startup – that are creating AI applications. It’s everybody. It’s researchers using new AI-infused applications to change drug discovery. It’s non-profits changing the way they deliver services. It’s teachers using AI as a tool to improve the way they prepare material for a classroom. It’s governments making everything from the filing of a tax return to the renewal of a driver’s license easier and more efficient.

To build a new AI economy, it’s critical to get all three of these layers working and to get a flywheel turning across the ecosystem. It’s essential to build the infrastructure layer so people can develop and deploy the models at the platform layer. It’s essential to use the AI models so that people will build the applications on top of them. And it’s essential for customers to adopt the applications, so the market can grow, and drive increased investment to expand the infrastructure further. The process repeats itself. This is how a new economy is born.”

More OpenAI team members shared praise for Simo on social media, mainly X, where Chief Marketing Officer (CMO) Kate Rouch wrote of the new hire: “She’s all signal. No noise. Highest integrity leader I know. Very good news: for OpenAI, for all of us.”

Caitlin Kalinowski, who is a member of the technical staff at OpenAI and leads its hardware and robotics vision, also took to X to post, in part: “We couldn’t be luckier to have her deep experience and warm leadership style. Welcome, Fidji!!!”

Simo boasts an impressive track record focused on turning tech into viable businesses

Simo joined OpenAI’s board in March 2024 and brings extensive leadership experience in technology, consumer platforms, and healthcare innovation.

At Instacart, she oversaw the company’s public debut and a series of business milestones, including scaling its advertising business and launching new offerings.

Before that, she spent a decade at Facebook, where she led the core Facebook app and was responsible for key product areas including Video, Marketplace, Groups, and Ads.

She is also the co-founder of the Metrodora Institute, a clinic and research center focused on neuroimmune disorders, where she serves as President of the Metrodora Foundation. In addition to her board seat at OpenAI, Simo sits on the boards of Shopify and previously Cirque du Soleil.

With this leadership appointment, OpenAI is signaling a deeper focus on scaling its consumer and enterprise-facing AI offerings. By establishing a dedicated CEO role for its Applications division, the company aims to build stronger operational infrastructure while aligning with its broader mission to develop AI that benefits the public.

Implications for enterprise technical decision makers

For enterprise stakeholders building on OpenAI’s models, this leadership move could signal important changes in how OpenAI develops, packages, and delivers its software interfaces and tools. The creation of a dedicated Applications division reflects a deeper commitment to the operational scale and reliability that large organizations increasingly demand from AI vendors. As the company matures from a research-centric entity into a provider of business-critical infrastructure and applications, users may see improvements in product stability, integration support, and roadmap clarity—areas where traditional software vendors have long invested, but newer AI entrants are still catching up.

Simo’s appointment may also hint at a broader productization push within OpenAI. Her background at Facebook and Instacart underscores a focus on monetizing complex platforms and converting experimental technologies into widely adopted consumer and enterprise solutions. Enterprise leaders watching for cues on OpenAI’s future direction might reasonably expect an expansion in verticalized offerings, developer tooling, and support for enterprise-grade deployments.

More broadly, the move acknowledges a shift happening across the AI industry: successful adoption increasingly depends not just on model quality or research novelty, but on how well these systems integrate into the operational realities of businesses. As OpenAI grows its executive bench to reflect that need, it’s aligning more closely with the enterprise audience it now serves—an audience that values not only performance and innovation but reliability, control, and product maturity.

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IBM proposes unified architecture for hybrid quantum-classical computing

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Brent retreats from highs after Trump signals Iran war nearing end

@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; } Oil futures eased from recent highs Tuesday as markets reacted to comments from US President Donald Trump suggesting the war with Iran may be nearing its conclusion, easing concerns about prolonged disruptions to Middle East crude supplies. Brent crude had climbed above $100/bbl amid escalating tensions in the region and fears that the war could prolong disruptions to shipments through the Strait of Hormuz—one of the world’s most critical energy chokepoints and a transit route for roughly one-fifth of global oil supply. Prices pulled back after Pres. Trump said the war was “almost done,” prompting traders to reassess the risk premium that had built into crude markets during the latest escalation. The earlier gains were driven by the fact that the war had disrupted tanker traffic in the Strait of Hormuz, raising concerns about wider supply disruptions from major Gulf oil producers. While the latest remarks helped calm markets, analysts note that geopolitical risks remain elevated and price volatility is likely to persist as traders monitor developments in the region. Any renewed escalation could quickly send crude prices higher again.

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Southwest Arkansas lithium project moves toward FID with 10-year offtake deal

Smackover Lithium, a joint venture between Standard Lithium Ltd. and Equinor, through subsidiaries of Equinor ASA, signed the first commercial offtake agreement for the South West Arkansas Project (SWA Project) with commodities group Trafigura Trading LLC. Under the terms of a binding take-or-pay offtake agreement, the JV will supply Trafigura with 8,000 metric tonnes/year (tpy) of battery-quality lithium carbonate (Li2CO3) over a 10-year period, beginning at the start of commercial production. Smackover Lithium is expected to achieve final investment decision (FID) for the project, which aims to use direct lithium extraction technology to produce lithium from brine resources in the Smackover formation in southern Arkansas, in 2026, with first production anticipated in 2028. The project encompasses about 30,000 acres of brine leases in the region, with the initial phase of project development focused on production from the 20,854-acre Reynolds Brine Unit.   Front-end engineering design was completed in support of a definitive feasibility study with a principal recommendation that the project is ready to progress to FID.  While pricing terms of the Trafigura deal were kept confidential, Standard Lithium said they are “structured to support the anticipated financing for the project.” The JV is seeking to finalize customer offtake agreements for roughly 80% of the 22,500 tonnes of annual nameplate lithium carbonate capacity for the initial phase of the project. This agreement represents over 40% of the targeted offtake commitments. Formed in 2024, Smackover Lithium is developing multiple DLE projects in Southwest Arkansas and East Texas. Standard Lithium is operator of the projecs with 55% interest. Equinor holds the remaining 45% interest.

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Equinor makes oil and gas discoveries in the North Sea

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IEA launches record strategic oil release as Middle East war disrupts supply

The International Energy Agency (IEA) on Mar. 11 approved the largest emergency oil stock release in its history, making 400 million bbl available from member-country reserves in response to market disruptions tied to the war in the Middle East. The coordinated action, agreed unanimously by the IEA’s 32 member countries, is intended to ease supply pressure and temper price volatility as crude markets react to disrupted flows through the Strait of Hormuz. “The conflict in the Middle East is having significant impacts on global oil and gas markets, with major implications for energy security, energy affordability and the global economy for oil,” IEA executive director Fatih Birol said. The release more than doubles the previous IEA record set in 2022, when member countries collectively made 182.7 million bbl available following Russia’s invasion of Ukraine. Under the IEA system, member countries are required to maintain emergency oil stocks equal to at least 90 days of net imports, giving the agency a mechanism to respond when severe disruptions threaten global supply. The move comes after crude prices surged amid concerns that the US-Iran war could lead to prolonged disruption of exports from the Gulf. Despite the planned stock release, traders remain uncertain about whether reserve barrels alone will be enough to offset losses if the disruption persists. IEA said the emergency barrels will be supplied to the market from government-controlled and obligated industry stocks held across member countries. The action marks the sixth coordinated stock release in the agency’s history and underscores the seriousness of the current supply shock. Earlier the day, Japanese Prime Minister Sanae Takaichi said that Japan might start using its strategic oil reserves as early as next week, citing Japan’s unusually high dependence on Middle Eastern crude oil.

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Infographic: Strait of Hormuz energy trade 2025

@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; } Coordinated attacks Feb. 28 by the US and Israel on Iran and the since-escalated conflict have nearly halted shipping traffic through the Strait of Hormuz, which typically carries about 20% of the world’s crude oil and natural gas. OGJ Statistics Editor Laura Bell-Hammer compiled data to showcase 2025 energy trade through the critical transit chokepoint.   <!–> –> <!–> ]–> <!–> ]–>

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BOEM: US OCS holds 65.8 billion bbl of technically recoverable reserves

The US Outer Continental Shelf (OCS) holds mean undiscovered technically recoverable resources (UTRR) of 65.8 billion bbl of oil and 218.43 tcf of natural gas, the US Bureau of Ocean Energy Management (BOEM) said Mar. 9. Based on current production trends, these undiscovered resources represent the potential for 100 or more years of energy production from the US Outer Continental Shelf (OCS), BOEM said. A large portion of undiscovered OSC resources is located offshore the Gulf of Mexico and Alaska, according to the report. The offshore Gulf holds 26.9 million bbl of oil and 45.59 tcf of gas, while offshore Alaska holds an estimated mean 24.1 million bbl of oil and 122.29 tcf of gas. Offshore Pacific holds a mean UTRR of 10.3 million barrels of oil and 16.2 trillion cubic feet of gas, the report said. Offshore Atlantic holds a mean UTRR of 10.3 billion barrels of oil and 16.2 trillion cubic feet of gas. The assessment also evaluates the impact of prices on hydrocarbon recovery. Alaska is particularly price-sensitive, with mean undiscovered economically recoverable resources (UERR) negligible until prices average $100/bbl and $17.79/Mcf. At those levels, the mean UERR stands at 6.25 billion bbl and 13.25 tcf. At $160/bbl and $28.47/Mcf, recoverable resources jump to 14.67 billion bbl and 58.78 tcf. In the Gulf of Mexico, the mean UERR is 17.51 billion bbl of oil and 13.71 tcf at average prices of $60/bbl and $3.20/Mcf, increasing to 20.51 billion bbl and 17.49 tcf at average prices of $100/bbl and $5.34/Mcf, respectively. BOEM conducts a national resource assessment every 4 years to understand the “distribution of undiscovered oil and gas resources on the OCS” and identify opportunities for additional oil and gas exploration and development. “The Outer Continental Shelf holds tremendous resource potential,” said BOEM Acting Director Matt Giacona. “This

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Data mining? Old servers could become new source of rare earths

For decades, he said, “the retirement of data center equipment was treated almost entirely as a compliance and disposal issue. Enterprises focused on secure decommissioning, certified recycling, and documented destruction of sensitive hardware. Once equipment left production environments, its economic life was assumed to be largely finished.” That assumption, he pointed out, “is beginning to change, because the hardware inside modern data centres contains a wide range of strategically important materials. Servers, storage systems, networking equipment, and power components contain copper, aluminum, silver, gold, and increasingly small but significant quantities of rare earth elements and other critical minerals.” These materials play a vital role in the manufacturing of semiconductors, energy systems, defense electronics, and advanced computing infrastructure, he explained, noting, “as global demand for digital infrastructure continues to expand, the volume of retired hardware entering disposal channels is rising quickly.” Electronic waste has already become one of the fastest growing waste streams in the world. “Global volumes now exceed 60 million tonnes annually and are projected to move toward eighty million tonnes by the end of the decade if current trends continue,” he said. “Data center infrastructure represents only a portion of that total, but it is a particularly important portion because it is concentrated, professionally managed, and replaced in structured cycles.” For a metals producer, he said, data center infrastructure represents a highly attractive feedstock, because unlike consumer electronics, enterprise hardware is replaced in large batches and flows through professional asset management channels. That predictability, said Gogia, “allows recyclers to design specialized processes that target specific components and materials. Over time, this creates the foundation for an industrial scale circular supply chain in which retired electronics feed back into the production of new materials.”

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Meta is developing more AI chips for itself

With demand for AI chips rising and supplies tightening, Meta is taking its AI computing needs into its own hands and developing more of its own chips: It will produce four new generations of chips over the next two years. Cloud computing giants including Meta, AWS, and Google have been keen to develop their own chips to improve the performance of their own data centers. Meta started its own chip program in 2023, when it implemented the Meta Training and Inference Accelerator (MTIA), a family of custom-built silicon chips to power its AI workloads efficiently. The MTIA 300, which Meta will use for ranking and recommendations training, is already  in production, Meta said. It will use the other planned chips, the MTIA 400, 450, and 500, mainly for generative AI inference production, it said.

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Arista targets AI data centers with new liquid cooled pluggable optic module

To prove their point, the authors imagined a 400 MW AI datacenter with 1024 GPU racks of 128 GPUs each for a total of 128,000 GPUs. “Assume 12.8T scale-up and 1.6T scale-out bandwidth per GPU. With OSFP switch racks that have a density of 1.6 Pbps per rack, this would require more than 1,400 switch racks for scale-up and scale-out fabrics. With XPO, this would require 75% fewer racks, saving over 1,050 racks or 44 % of the floor space,” Bechtolsheim and Vusirikala stated in the blog.  “Eliminating 75% of switch racks translates to massive reductions in construction and infrastructure costs, including power distribution, plumbing and installation costs, while accelerating deployment timelines,” Bechtolsheim and Vusirikala stated. Arista said the water-cooling capability of XPO is also an important feature. “All large AI data centers will be liquid cooled and the switches that go into these data centers also need to be liquid cooled,” Bechtolsheim and Vusirikala stated.  “While one can add liquid cooled cold plates on flat-top OSFP modules, this does not substantially improve thermal performance.” XPO solves this problem by integrating a liquid cold plate inside the module, with two 32-channel paddle cards sharing the common cold plate which can cool both low power as well as high-power optics such as 8x1600G-ZR/ZR+ with up to 400W of power, Bechtolsheim and Vusirikala stated. XPO modules are much simpler than OSPF modules which improves reliability as well. “Each 32-channel paddle card has only one microcontroller and one set of voltage converters, a 75% reduction in common components versus 4 OSFPs,” Bechtolsheim and Vusirikala wrote. 

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Cisco grows high-end optical support for AI clusters

Cisco has also upgraded its Network Conversion System (NCS) with a 1RU, 800GE line card offering 12.8T capacity, with 32 OSFP-based ports for 100GE, 400GE, and 800GE clients and 800ZR/ZR+ WDM trunks. The NCS 1014  doubles the density of previous-generation NCS versions and now includes MACsec encryption (IEEE 802.1AE) to secure point-to-point links with hardware-based encryption, data integrity, and authentication for Ethernet traffic, Ghioni stated. It supports enhanced capacity and performance with C&L-band support and NCS 1014 systems with the 2.4T WDM line card based on the Coherent Interconnect Module 8 and now supports 800 GE clients, which can be mapped directly to a wavelength or inverse multiplexed across two wavelengths to maximize reach, Ghioni wrote.  In the pluggable optic arena, Cisco is now offering a Quad Small Form Factor Pluggable Double Density (QSFP-DD) Pluggable Protection Switch Module that can monitor the optical link and switch traffic if it detects a fault in less than 50 milliseconds. The module occupies a quarter of the rack space compared to traditional protection devices—offering 90% rack space saving over available options, Ghioni wrote.  It is aimed at Metro and DCI network customers where sub-50 ms failure recovery is essential and data centers needing fiber protection without bulky hardware, Ghioni stated.  Cisco also added its Acacia developed Bright QSFP28 100ZR 0 dBm coherent optical pluggable in a standard QSFP28 form factor.  It is aimed at edge, access, enterprise, and campus network deployment. Cisco has been actively growing its optical portfolio  recently adding the Cisco Silicon One G300, which powers 102.4T N9000 and Cisco 8000 systems, as well as advanced 1.6T OSFP optics and 800G Linear Pluggable Optics. 

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Datalec targets rapid infrastructure deployment with new modular data centers

“We are engineering the data center with a new lens bringing pre-engineered system designs that are flexible and adaptable that enables a tailored solution for clients,” said John Lever, director of modular solutions at Datalec. The systems are flexible enough that these solutions cater for all types of data center, from standard server technology to AI and high-density compute. Datalec also provides “bolt-on” solutions, including a ‘digital wrapper’ including digital twinning and lifecycle and global support, Lever says. Another way Datalec says it differentiates from competing modular designs is a larger share of work is done offsite in a controlled manufacturing environment, which cuts onsite construction time, improves safety and limits disruption to live facilities, Lever says. The company competes with other modular data center vendors including Schneider Electric, Vertiv, Flex many others. DPI’s says its services are aimed at colocation providers, hyperscale and AI infrastructure teams, and large enterprises that need to add capacity quickly, safely and cost effectively across multiple regions.

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Study finds significant savings from direct current power for AI workloads

The result is a 50% to 80% reduction in copper usage, due to fewer conductors and less parallel cabling, and an 8% to 12% reduction in annual energy-related OpEx through lower conversion and distribution losses. By reducing conductor count, cabling, and redundant power components, 800VDC enables meaningful savings at both build-out and operational stages. AI-first facilities can see a $4 million to $8 million in CapEx savings per 10 MW build by reducing upstream AC. For a one-gigawatt data center, you’re saving a couple million pounds of copper wire, he said. Burke says an all-DC data center is best done with a whole new facility rather than retrofitting old facilities. “[DC] is going to be in a lot of greenfield data centers that are going to be built, and data centers that are going to go to higher compute power are also going to DC,” he said. He did recommend all-DC retrofits for existing data centers that are going to employ high power computing with GPUs. Enteligent’s unnamed and as yet unreleased product is a converter that takes 800 volts and partitions it to 50 volts for the computing servers. The company will provide a new power supply, power shelf that converts 800 volts DC to 50 volts DC much more efficiently than any current power supplies. Burke said the company is doing NDA level testing and pilot programs now with its product, but it will be making a formal announcement within the next few weeks. There are a number of players in the DC arena focusing on different parts of the power supply market including Vertiv, Rutherford, Siemens, Eaton and many more.

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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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