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A hydrogen ‘do-over’ for California

Melanie Davidson is a hydrogen policy and markets expert. Most recently she led clean fuels strategy at San Diego Gas & Electric. She is former board member of the California Hydrogen Business Council and was a founding staff member of the Green Hydrogen Coalition. Recently, over $2 billion of federal funding for the U.S. Department of […]

Melanie Davidson is a hydrogen policy and markets expert. Most recently she led clean fuels strategy at San Diego Gas & Electric. She is former board member of the California Hydrogen Business Council and was a founding staff member of the Green Hydrogen Coalition.

Recently, over $2 billion of federal funding for the U.S. Department of Energy California and Pacific Northwest Hydrogen Hubs was terminated. These Hubs were premised on the use of “renewable, electrolytic hydrogen” — meaning hydrogen generated by using renewable electricity to power water-splitting electrolyzers. The resultant hydrogen would have replaced fossil fuels for heavy duty transportation, port operations, and power generation. 

The idea of a fully renewable, water-based, hydrogen economy for the West was an exciting one — both in its altruism and the premise, backed by the DOE’s 2021 “Hydrogen Shot.” The idea was for cheap, abundant solar and rapidly declining electrolyzer cost curves to generate hydrogen from water with zero emissions — for $1/kg by 2030, no less. 

However, cuts in Hub funding, together with a 2027 sunset date for projects to qualify for the hydrogen production tax credit, are just two more blows to the many pre-existing economic challenges facing a renewable hydrogen future. At least in California, those challenges include rising (not falling) capital costs for electrolyzers and electrical equipment, high interest rates, a scarcity of water rights, and high costs of grid electricity, qualifying renewable energy credits, and land. 

The renewable Hubs were anchored on the idea that by leveraging otherwise curtailed solar (terrawatt-hours worth annually), we could generate cheap, abundant, seasonally stored renewable electrolytic hydrogen at distributed locations, then convert the hydrogen back to the grid via fuel cells as needed. It’s an elegant idea, but it doesn’t pencil. The capital costs of those electrolyzers, compressors, liquefiers, hydrogen storage vessels and fuel cells are simply too high when paired with low and unpredictable capacity factors. 

On the flip side, electrolytic projects with predictable and high capacity factors must rely on grid electricity. The economics of these projects work in markets where grid electricity costs are low — say $0.04/kWh. But in California, average industrial rates of over $0.22/kWh can lead to electrolytic hydrogen production costs exceeding $20/kg — a non-starter for any reasonable offtaker. The inconvenient truth is that without massive grid cross-subsidies borne by residential and commercial ratepayers, there is no simply no way for California to generate renewable electrolytic hydrogen at the price and scale needed to affordably decarbonize the state’s heavy duty transportation, industrial thermal energy, and power generation. 

So what are we to do? The answer is not to abandon hydrogen altogether — clean fuels will be a critical part of a reliable electrified future. The answer is to broaden the type of hydrogen we can live with. I urge all of us in the industry to use this moment as a hydrogen “do-over” for California and beyond.

It is time to put aside the theological zeal for renewable electrolytic hydrogen at all costs, and instead act with logical dispassion to deploy “clean hydrogen.” As defined under the Biden bipartisan infrastructure legislation, clean hydrogen meets a carbon intensity score of ≤4 kgCO2/kgH2. That’s it. The definition is technology and feedstock-neutral, and allows for many pathways to generate low cost, low carbon molecules. Making hydrogen cost-competitive with diesel at scale, even without subsidies, is the only way hydrogen can meaningfully help decarbonize our economy — and we certainly need its help. 

One such technology that can meaningfully usher in the clean hydrogen future is methane pyrolysis. Methane pyrolysis is a process that uses high temperatures in the absence of oxygen to break down methane into two components: hydrogen gas, and solid carbon that is permanently sequestered from atmosphere. Both products can be sold, making the investment financially attractive. The solid carbon can replace bitumen and other fossil-based products as a valuable input to industries ranging from rubber making to asphalt. In 2024, Bill Gates filled a pothole with it. 

Because natural gas is cheap and abundant and already delivered to customers via existing, built-and-paid-for pipeline infrastructure, methane pyrolysis has the ability, in many cases, to literally meet customers where they are: delivering low-cost, low carbon hydrogen at the point of use. In this way, collocating hydrogen production and use can eliminate the cost and energy required for hydrogen compression, storage, and dedicated pipelines.

It is still relatively early days for methane pyrolysis, but it is showing up on the radar, including in the California Air Resources Board’s recent SB 1075 report. The report gave hydrogen from methane pyrolysis of natural gas a carbon intensity, or CI, score of 1.8 kgCO2/kgH2, and a cost range of $2-$4.50/kgH2 by 2045. My own analysis found that the average levelized cost of hydrogen from pyrolysis today, sans subsidies of any kind, falls between $5-8/kg, well below diesel at $11/kg.

The same CARB report showed some pretty startling data for electrolysis — data that should give all renewable hydrogen fans pause. Grid connected electrolysis has a CI score of over 9 today, falling to 3 by 2045- still more than pyrolysis of natural gas. Additionally, the report found that to meet California’s expected hydrogen demand in 2045 pursuing an electrolytic hydrogen-only approach, it would require a stunning ~200,000 acres of dedicated solar and a whopping 72 billion liters of water annually.

Commercial scale methane pyrolysis is still in early days, but it is starting to take off. Companies such as Modern Hydrogen, Graphitic Energy, Etch, Aurora and others have strong teams, strong investors, and a promising value proposition. Publicly funded investment in this tech would surely accelerate its development. In the meantime, non-California utilities like Northwest Natural, Puget Sound Energy, CPS and Peoples Energy are leading the way with pilots, presumably because their state policies have room for clean hydrogen of all types. California can and should make room as well.

Let’s use this moment of challenge for our existing hydrogen policies and marketplace and turn it into an opportunity for a “do-over.” California policy makers must take swift action, reverse the overly restrictive, “renewable hydrogen” only approach and embrace clean hydrogen of all types. In this way, we can meet the state’s goals of a clean, reliable, and affordable energy future for all.

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Oil Holds Steady Ahead of OPEC+ Talks

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TotalEnergies Sees Debt Pile Falling Further

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USA Energy Sec Says USA Is Ready to Sell More Oil, Gas to China

Energy Secretary Chris Wright said the US is prepared to sell more oil and natural gas to China if Beijing cuts back on purchases from Russia.  “There’s so much space for mutually beneficial deals between the US and China,” Wright said Thursday during a Bloomberg Television interview, noting that the US is the world’s largest oil and gas exporter, while China is the biggest importer.  The energy secretary plans to travel to Asia within weeks, or possibly sooner, following President Donald Trump trip to the continent this week.  During his trip, Trump said he reached deals with Chinese President Xi Jinping and South Korea President Lee Jae Myung to buy more US oil and gas. Trump also cited a “very large scale” transaction involving Alaskan oil and gas in a post on the social media site Truth Social but didn’t provide more details. “There is lots of room from the United States to grow our role in supplying natural gas, oil, and frankly nuclear technology to South Korea,” Wright said in the interview.  WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

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DTE inks first data center deal to grow electric load 25%

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AEP capital spending plan surges 33% to $72B in utility ‘super cycle’

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A hydrogen ‘do-over’ for California

Melanie Davidson is a hydrogen policy and markets expert. Most recently she led clean fuels strategy at San Diego Gas & Electric. She is former board member of the California Hydrogen Business Council and was a founding staff member of the Green Hydrogen Coalition. Recently, over $2 billion of federal funding for the U.S. Department of Energy California and Pacific Northwest Hydrogen Hubs was terminated. These Hubs were premised on the use of “renewable, electrolytic hydrogen” — meaning hydrogen generated by using renewable electricity to power water-splitting electrolyzers. The resultant hydrogen would have replaced fossil fuels for heavy duty transportation, port operations, and power generation.  The idea of a fully renewable, water-based, hydrogen economy for the West was an exciting one — both in its altruism and the premise, backed by the DOE’s 2021 “Hydrogen Shot.” The idea was for cheap, abundant solar and rapidly declining electrolyzer cost curves to generate hydrogen from water with zero emissions — for $1/kg by 2030, no less.  However, cuts in Hub funding, together with a 2027 sunset date for projects to qualify for the hydrogen production tax credit, are just two more blows to the many pre-existing economic challenges facing a renewable hydrogen future. At least in California, those challenges include rising (not falling) capital costs for electrolyzers and electrical equipment, high interest rates, a scarcity of water rights, and high costs of grid electricity, qualifying renewable energy credits, and land.  The renewable Hubs were anchored on the idea that by leveraging otherwise curtailed solar (terrawatt-hours worth annually), we could generate cheap, abundant, seasonally stored renewable electrolytic hydrogen at distributed locations, then convert the hydrogen back to the grid via fuel cells as needed. It’s an elegant idea, but it doesn’t pencil. The capital costs of those electrolyzers, compressors, liquefiers, hydrogen storage vessels and fuel

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Nvidia GTC show news you need to know round-up

In the case of Flex, it will use digital twins to unify inventory, labor, and freight operations, streamlining logistics across Flex’s worldwide network. Flex’s new 400,000 sq. ft. facility in Dallas is purpose-built for data center infrastructure, aiming to significantly shorten lead times for U.S. customers. The Flex/Nvidia partnership aims to address the country’s labor shortages and drive innovation in manufacturing, pharmaceuticals, and technology. The companies believe the partnership sets the stage for a new era of giga-scale AI factories. Nvidia and Oracle to Build DOE’s Largest AI Supercomputer Oracle continues its aggressive push into supercomputing with a deal to build the largest AI supercomputer for scientific discovery — Using Nvidia GPUs, obviously — at a Department of Energy facility. The system, dubbed Solstice, will feature an incredible 100,000 Nvidia Blackwell GPUs. A second system, dubbed Equinox, will include 10,000 Blackwell GPUs and is expected to be available in the first half of 2026. Both systems will be interconnected by Nvidia networking and deliver a combined 2,200 exaflops of AI performance. The Solstice and Equinox supercomputers will be located at Argonne National Laboratory, the home to the Aurora supercomputer, built using all Intel parts. They will enable scientists and researchers to develop and train new frontier models and AI reasoning models for open science using the Nvidia Megatron-Core library and scale them using the Nvidia TensorRT inference software stack.

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AWS opens giant data center for AI training

Just over a year after construction began, Amazon Web Services (AWS) has opened its giant data center near Lake Michigan in the US state of Indiana. The data center, which is part of AWS Project Rainier, covers 1,200 acres, or 4.86 square kilometers. This makes it one of the largest data centers in the world, CNBC reports. The construction cost amounted to 11 billion dollars, which is currently equivalent to 103 billion Swedish kronor.

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Samsung’s memory ramp-up may ease AI and cloud upgrade concerns

The company confirmed that its latest-generation HBM3E chips are now being shipped to “all related customers,” a possible sign that supply to major AI chipmakers like Nvidia may be stabilizing. With mass production of HBM4 expected next year, Samsung could eventually help relieve pressure on the broader enterprise infrastructure ecosystem, from cloud providers building new AI clusters to data center operators seeking to expand switching and storage capacity. Samsung’s Foundry division also plans to begin operating its new 2nm fab in Taylor, Texas, in 2026 and supply HBM4 base-dies, a move that could further stabilize component availability for US cloud and networking infrastructure providers. Easing the memory chokehold Easing DRAM and NAND lead times will unlock delayed infrastructure projects, particularly among hyperscalers, according to Manish Rawat, semiconductor analyst at TechInsights. “As component availability improves from months to weeks, deferred server and storage upgrades can transition to active scheduling,” Rawat said. “Hyperscalers are expected to lead these restarts, followed by large enterprises once pricing and delivery stabilize. Improved access to high-density memory will also drive faster refresh cycles and higher-performance rack designs, favoring denser server configurations. Procurement models may shift from long-term, buffer-heavy strategies to more agile, just-in-time or spot-buy approaches.” Samsung’s expanded role as a “meaningful volume supplier” of HBM3E 12-high DRAM will also be crucial for hyperscalers planning their 2026 AI infrastructure rollouts, according to Danish Faruqui, CEO of Fab Economics. “Without Samsung’s contribution, most hyperscaler ASIC programs, including Google’s TPU v7, AWS’s Trainium 3, and Microsoft’s in-house accelerators, were facing one- to two-quarter delays due to the limited HBM3E 12-high supply from SK Hynix,” Faruqui said. “These products form the backbone of next-generation AI data centers, and volume ramp-up depends directly on Samsung’s ability to deliver.”

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Oracle’s cloud strategy an increasingly risky bet

However, he pointed out, “theatre is not delivery. What Oracle served was less a coronation than a carefully staged performance: a heady cocktail of ambition, backlog, and speculation. At Greyhound Research, we argue that such moments call not for applause but for scrutiny. The right instinct is not to toast, but to check the bill.” Oracle ‘betting the farm’ on AI Rob Tiffany, research director in IDC’s worldwide infrastructure research organization, had a different view, saying, “in an effort to catch up with the other hyperscaler clouds, Oracle has been aggressively building out its Oracle Cloud Infrastructure (OCI) data center regions all over the world prior to their Stargate endeavor with Crusoe, OpenAI, and SoftBank, to capitalize on the AI opportunity.” Speculation about the burst of the AI bubble aside, he said, “the strength and success of the OCI buildout thus far rests with Oracle’s dominant database and Fusion Cloud ERP, and those enterprise customers should be confident  in Oracle’s future.” Scott Bickley, advisory fellow at Info-Tech Research Group, added, “[while it is] extraordinary to see them take on this kind of debt, [Oracle] are really betting the farm on the AI revolution panning out. There are a lot of risks involved if momentum in the AI space loses its current trajectory. There could be a lot of stranded infrastructure and capital.” The ultimate risk, he said “lies in the viability of OpenAI. These guys have said they’re going to spend $1.4 trillion on AI capacity build out, and they’re sitting on a revenue base of $13 billion a year right now. If they go up in smoke, then that could leave a lot of this investment stranded. That would be the worst case kind of Black Swan scenario.” At this point, he said, “CIOs would not want that bubble

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Google wants to restart closed nuclear power plant in Iowa

The enormous amount of energy required to power a modern data center has prompted major tech companies to sign major partnership agreements with power companies. Most recently, Google signed an agreement with Next Era Energy to restart the Duane Arnold Energy Center in Iowa. The nuclear power plant in question was shut down in 2020 and it is expected to take four years to make it operational again, CNBC reports.

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Arista fills out AI networking portfolio

The 7280R4-32PE features 25.6 Tbps switching capacity and supports 32x 800 GbE ports with Octal Small Form-Factor Pluggable (OSFP) or Quad Small Form-Factor Pluggable – Double Density (QSFP-DD) optical uplinks. It’s targeted at customers that need to support AI/ML workloads and routing-intensive edge use cases, Arista stated. It supports 25% lower power per Gbps compared to the prior generation, according to Arista.  A second version, the 7280R4-64QC-10PE, is aimed at dense, deep buffer-requiring workloads in data centers with 100G/800G requirements. The box supports 64x 100 GbE and 10x 800 GbE OSFP in addition to 4x 1/10/25 GbE for management or additional low-speed interfaces, Arista started. The box promises 20% lower power requirement per Gbps over the prior generation of the box, Arista stated.  At the high end, the new 7800R4 is the vendor’s latest flagship networking box capable of supporting 36 ports of 800GbE OSFP and QSFP-DD line cards in 4, 8, 12, and 16-slot chassis configurations. The box offers a high radix capacity – meaning it can be fully loaded with line card and support 576 physical 800 Gigabit Ethernet ports or 1,152 400GbE ports, Arista stated.  In addition, the 7800R supports a new 3.2 TbpsEthernet line card called HyperPort that supports 4 800G channels to tie together widely dispersed data centers via a technique Arista calls “scale across.” It’s designed to scale across buildings in the same metropolitan region or across sites in different cities or countries. This routed Data Center Interconnect technology that can extend AI clusters over Metro or long-haul WAN links, according to Arista. “Building on the flexible Extensible Operating System (EOS) software foundation [which runs across all Arista networking gear] and deep buffering, HyperPort delivers up to 44% faster job completion time (JCT) for high-bandwidth AI flows via a single high-speed port, compared to

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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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