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Inside China’s electric-vehicle-to-humanoid-robot pivot

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. While DOGE’s efforts to shutter federal agencies dominate news from Washington, the Trump administration is also making more global moves. Many of these center on China. Tariffs on goods from the country went into effect last week. There’s also been a minor foreign relations furor since DeepSeek’s big debut a few weeks ago. China has already displayed its dominance in electric vehicles, robotaxis, and drones, and the launch of the new model seems to add AI to the list. This caused the US president as well as some lawmakers to push for new export controls on powerful chips, and three states have now banned the use of DeepSeek on government devices.  Now our intrepid China reporter, Caiwei Chen, has identified a new trend unfolding within China’s tech scene: Companies that were dominant in electric vehicles are betting big on translating that success into developing humanoid robots. I spoke with her about what she found out and what it might mean for Trump’s policies and the rest of the globe.  James: Before we talk about robots, let’s talk about DeepSeek. The frenzy for the AI model peaked a couple of weeks ago. What are you hearing from other Chinese AI companies? How are they reacting? Caiwei: I think other Chinese AI companies are scrambling to figure out why they haven’t built a model as strong as DeepSeek’s, despite having access to as much funding and resources. DeepSeek’s success has sparked self-reflection on management styles and renewed confidence in China’s engineering talent. There’s also strong enthusiasm for building various applications on top of DeepSeek’s models. Your story looks at electric-vehicle makers in China that are starting to work on humanoid robots, but I want to ask about a crazy stat. In China, 53% of vehicles sold are either electric or hybrid, compared with 8% in the US. What explains that?  Price is a huge factor—there are countless EV brands competing at different price points, making them both affordable and high-quality. Government incentives also play a big role. In Beijing, for example, trading in an old car for an EV gets you 10,000 RMB (about $1,500), and that subsidy was recently doubled. Plus, finding public charging and battery-swapping infrastructure is much less of a hassle than in the US. You open your story noting that China’s recent New Year Gala, watched by billions of people, featured a cast of humanoid robots, dancing and twirling handkerchiefs. We’ve covered how sometimes humanoid videos can be misleading. What did you think?  I would say I was relatively impressed—the robots showed good agility and synchronization with the music, though their movements were simpler than human dancers’. The one trick that is supposed to impress the most is the part where they twirl the handkerchief with one finger, toss it into the air, and then catch it perfectly. This is the signature of the Yangko dance, and having performed it once as a child, I can attest to how difficult the trick is even for a human! There was some skepticism on the Chinese internet about how this was achieved and whether they used additional reinforcement like a magnet or a string to secure the handkerchief, and after watching the clip too many times, I tend to agree. President Trump has already imposed tariffs on China and is planning even more. What could the implications be for China’s humanoid sector?   Unitree’s H1 and G1 models are already available for purchase and were showcased at CES this year. Large-scale US deployment isn’t happening yet, but China’s lower production costs make these robots highly competitive. Given that 65% of the humanoid supply chain is in China, I wouldn’t be surprised if robotics becomes the next target in the US-China tech war. In the US, humanoid robots are getting lots of investment, but there are plenty of skeptics who say they’re too clunky, finicky, and expensive to serve much use in factory settings. Are attitudes different in China? Skepticism exists in China too, but I think there’s more confidence in deployment, especially in factories. With an aging population and a labor shortage on the horizon, there’s also growing interest in medical and caregiving applications for humanoid robots. DeepSeek revived the conversation about chips and the way the US seeks to control where the best chips end up. How do the chip wars affect humanoid-robot development in China? Training humanoid robots currently doesn’t demand as much computing power as training large language models, since there isn’t enough physical movement data to feed into models at scale. But as robots improve, they’ll need high-performance chips, and US sanctions will be a limiting factor. Chinese chipmakers are trying to catch up, but it’s a challenge. For more, read Caiwei’s story on this humanoid pivot, as well as her look at the Chinese startups worth watching beyond DeepSeek.  Deeper Learning Motor neuron diseases took their voices. AI is bringing them back. In motor neuron diseases, the neurons responsible for sending signals to the body’s muscles, including those used for speaking, are progressively destroyed. It robs people of their voices. But some, including a man in Miami named Jules Rodriguez, are now getting them back: An AI model learned to clone Rodriguez’s voice from recordings. Why it matters: ElevenLabs, the company that created the voice clone, can do a lot with just 30 minutes of recordings. That’s a huge improvement over AI voice clones from just a few years ago, and it can really boost the day-to-day lives of the people who’ve used the technology. “This is genuinely AI for good,” says Richard Cave, a speech and language therapist at the Motor Neuron Disease Association in the UK. Read more from Jessica Hamzelou. Bits and Bytes A “true crime” documentary series has millions of views, but the murders are all AI-generated A look inside the strange mind of someone who created a series of fake true-crime docs using AI, and the reactions of the many people who thought they were real. (404 Media) The AI relationship revolution is already here People are having all sorts of relationships with AI models, and these relationships run the gamut: weird, therapeutic, unhealthy, sexual, comforting, dangerous, useful. We’re living through the complexities of this in real time. Hear from some of the many people who are happy in their varied AI relationships and learn what sucked them in. (MIT Technology Review) Robots are bringing new life to extinct species A creature called Orobates pabsti waddled the planet 280 million years ago, but as with many prehistoric animals, scientists have not been able to use fossils to figure out exactly how it moved. So they’ve started building robots to help. (MIT Technology Review) Lessons from the AI Action Summit in Paris Last week, politicians and AI leaders from around the globe went to Paris for an AI Action Summit. While concerns about AI safety have dominated the event in years past, this year was more about deregulation and energy, a trend we’ve seen elsewhere. (The Guardian)   OpenAI ditches its diversity commitment and adds a statement about “intellectual freedom” Following the lead of other tech companies since the beginning of President Trump’s administration, OpenAI has removed a statement on diversity from its website. It has also updated its model spec—the document outlining the standards of its models—to say that “OpenAI believes in intellectual freedom, which includes the freedom to have, hear, and discuss ideas.” (Insider and Tech Crunch) The Musk-OpenAI battle has been heating up Part of OpenAI is structured as a nonprofit, a legacy of its early commitments to make sure its technologies benefit all. Its recent attempts to restructure that nonprofit have triggered a lawsuit from Elon Musk, who alleges that the move would violate the legal and ethical principles of its nonprofit origins. Last week, Musk offered to buy OpenAI for $97.4 billion, in a bid that few people took seriously. Sam Altman dismissed it out of hand. Musk now says he will retract that bid if OpenAI stops its conversion of the nonprofit portion of the company. (Wall Street Journal)

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

While DOGE’s efforts to shutter federal agencies dominate news from Washington, the Trump administration is also making more global moves. Many of these center on China. Tariffs on goods from the country went into effect last week. There’s also been a minor foreign relations furor since DeepSeek’s big debut a few weeks ago. China has already displayed its dominance in electric vehicles, robotaxis, and drones, and the launch of the new model seems to add AI to the list. This caused the US president as well as some lawmakers to push for new export controls on powerful chips, and three states have now banned the use of DeepSeek on government devices. 

Now our intrepid China reporter, Caiwei Chen, has identified a new trend unfolding within China’s tech scene: Companies that were dominant in electric vehicles are betting big on translating that success into developing humanoid robots. I spoke with her about what she found out and what it might mean for Trump’s policies and the rest of the globe. 

James: Before we talk about robots, let’s talk about DeepSeek. The frenzy for the AI model peaked a couple of weeks ago. What are you hearing from other Chinese AI companies? How are they reacting?

Caiwei: I think other Chinese AI companies are scrambling to figure out why they haven’t built a model as strong as DeepSeek’s, despite having access to as much funding and resources. DeepSeek’s success has sparked self-reflection on management styles and renewed confidence in China’s engineering talent. There’s also strong enthusiasm for building various applications on top of DeepSeek’s models.

Your story looks at electric-vehicle makers in China that are starting to work on humanoid robots, but I want to ask about a crazy stat. In China, 53% of vehicles sold are either electric or hybrid, compared with 8% in the US. What explains that? 

Price is a huge factor—there are countless EV brands competing at different price points, making them both affordable and high-quality. Government incentives also play a big role. In Beijing, for example, trading in an old car for an EV gets you 10,000 RMB (about $1,500), and that subsidy was recently doubled. Plus, finding public charging and battery-swapping infrastructure is much less of a hassle than in the US.

You open your story noting that China’s recent New Year Gala, watched by billions of people, featured a cast of humanoid robots, dancing and twirling handkerchiefs. We’ve covered how sometimes humanoid videos can be misleading. What did you think? 

I would say I was relatively impressed—the robots showed good agility and synchronization with the music, though their movements were simpler than human dancers’. The one trick that is supposed to impress the most is the part where they twirl the handkerchief with one finger, toss it into the air, and then catch it perfectly. This is the signature of the Yangko dance, and having performed it once as a child, I can attest to how difficult the trick is even for a human! There was some skepticism on the Chinese internet about how this was achieved and whether they used additional reinforcement like a magnet or a string to secure the handkerchief, and after watching the clip too many times, I tend to agree.

President Trump has already imposed tariffs on China and is planning even more. What could the implications be for China’s humanoid sector?  

Unitree’s H1 and G1 models are already available for purchase and were showcased at CES this year. Large-scale US deployment isn’t happening yet, but China’s lower production costs make these robots highly competitive. Given that 65% of the humanoid supply chain is in China, I wouldn’t be surprised if robotics becomes the next target in the US-China tech war.

In the US, humanoid robots are getting lots of investment, but there are plenty of skeptics who say they’re too clunky, finicky, and expensive to serve much use in factory settings. Are attitudes different in China?

Skepticism exists in China too, but I think there’s more confidence in deployment, especially in factories. With an aging population and a labor shortage on the horizon, there’s also growing interest in medical and caregiving applications for humanoid robots.

DeepSeek revived the conversation about chips and the way the US seeks to control where the best chips end up. How do the chip wars affect humanoid-robot development in China?

Training humanoid robots currently doesn’t demand as much computing power as training large language models, since there isn’t enough physical movement data to feed into models at scale. But as robots improve, they’ll need high-performance chips, and US sanctions will be a limiting factor. Chinese chipmakers are trying to catch up, but it’s a challenge.

For more, read Caiwei’s story on this humanoid pivot, as well as her look at the Chinese startups worth watching beyond DeepSeek. 

Deeper Learning

Motor neuron diseases took their voices. AI is bringing them back.

In motor neuron diseases, the neurons responsible for sending signals to the body’s muscles, including those used for speaking, are progressively destroyed. It robs people of their voices. But some, including a man in Miami named Jules Rodriguez, are now getting them back: An AI model learned to clone Rodriguez’s voice from recordings.

Why it matters: ElevenLabs, the company that created the voice clone, can do a lot with just 30 minutes of recordings. That’s a huge improvement over AI voice clones from just a few years ago, and it can really boost the day-to-day lives of the people who’ve used the technology. “This is genuinely AI for good,” says Richard Cave, a speech and language therapist at the Motor Neuron Disease Association in the UK. Read more from Jessica Hamzelou.

Bits and Bytes

A “true crime” documentary series has millions of views, but the murders are all AI-generated

A look inside the strange mind of someone who created a series of fake true-crime docs using AI, and the reactions of the many people who thought they were real. (404 Media)

The AI relationship revolution is already here

People are having all sorts of relationships with AI models, and these relationships run the gamut: weird, therapeutic, unhealthy, sexual, comforting, dangerous, useful. We’re living through the complexities of this in real time. Hear from some of the many people who are happy in their varied AI relationships and learn what sucked them in. (MIT Technology Review)

Robots are bringing new life to extinct species

A creature called Orobates pabsti waddled the planet 280 million years ago, but as with many prehistoric animals, scientists have not been able to use fossils to figure out exactly how it moved. So they’ve started building robots to help. (MIT Technology Review)

Lessons from the AI Action Summit in Paris

Last week, politicians and AI leaders from around the globe went to Paris for an AI Action Summit. While concerns about AI safety have dominated the event in years past, this year was more about deregulation and energy, a trend we’ve seen elsewhere. (The Guardian)  

OpenAI ditches its diversity commitment and adds a statement about “intellectual freedom”

Following the lead of other tech companies since the beginning of President Trump’s administration, OpenAI has removed a statement on diversity from its website. It has also updated its model spec—the document outlining the standards of its models—to say that “OpenAI believes in intellectual freedom, which includes the freedom to have, hear, and discuss ideas.” (Insider and Tech Crunch)

The Musk-OpenAI battle has been heating up

Part of OpenAI is structured as a nonprofit, a legacy of its early commitments to make sure its technologies benefit all. Its recent attempts to restructure that nonprofit have triggered a lawsuit from Elon Musk, who alleges that the move would violate the legal and ethical principles of its nonprofit origins. Last week, Musk offered to buy OpenAI for $97.4 billion, in a bid that few people took seriously. Sam Altman dismissed it out of hand. Musk now says he will retract that bid if OpenAI stops its conversion of the nonprofit portion of the company. (Wall Street Journal)

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Intel bets on Terafab to help it reassert itself in the AI chip race

Tesla, SpaceX, and xAI say that Terafab will be the largest chip manufacturing facility ever, outputting 1TW a year of compute power and “combining logic, memory and advanced packaging under one roof.” Intel’s ability to “design, fabricate, and package ultra-high-performance chips at scale” will help accelerate those 1TW/year ambitions, the

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Cisco joins Anthropic’s multivendor effort to secure AI software

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Cloud-first vs. sovereign-first: Navigating the trade-off

Encryption is often suggested as a way to address data sovereignty because the customer holds the key to protect data in motion, in use, and at rest. However, Buest noted, most regulators have not explicitly approved the use of encryption or other security measures or deemed them sufficient for compliance.

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United States and Australia meet for Mining, Minerals and Metals Investment Ministerial

We, the Australian Minister for Resources and Northern Australia, the Hon Madeleine King MP, and Secretaries and senior representatives from the United States, including Secretary of Interior Doug Burgum, Administrator of the U.S. Environmental Protection Agency Lee Zeldin, Chairman of the U.S. Export Import Bank John Jovanovic, and Assistant Secretary of Energy Audrey Robertson,  held our inaugural Mining, Minerals, and Metals Investment Ministerial in Tokyo on 14 March 2026, to advance cooperation under the landmark bilateral agreement, the United States–Australia Framework for Securing Supply in the Mining and Processing of Critical Minerals and Rare Earths (the Framework). Under the Framework, Australia and the United States are delivering concrete outcomes to strengthen, secure, and diversify critical minerals and rare earth supply chains. Within six months of agreement of the Framework, we have each taken measures to provide at least USD $1 billion in financing to key critical minerals projects. By mobilising government and private sector capital, these investments support the development of our shared industrial base and strengthen longer term supply for defence, manufacturing, and energy supply chains. To build resilience, enhance stability, and bolster economic security in support of our shared critical minerals interests, Australia and the United States today announce the establishment of the Critical Minerals Supply Security Response Group and commit to deeper cooperation between our key agencies. In line with the Framework, the Critical Minerals Supply Security Response Group, led by senior representatives from the United States Department of Energy and the Australian Department of Industry, Science and Resources, will cooperate on priority minerals and supply chain vulnerabilities and coordinate efforts to accelerate the delivery of processed minerals under the Framework. Australia and the United States also commit to leveraging shared policy and interagency regulatory tools and, where appropriate, investments to secure critical minerals supply, including through cooperation between Australia’s

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Energy Security for Indo-Pacific Endurance, a Global Growth Center of the 21st Century

We, the ministers and representatives of Australia, Bangladesh, Brunei, Japan, Malaysia, New Zealand, Philippines, Republic of Korea, Singapore, Timor-Leste, United States, and Vietnam met in Tokyo, Japan, on March 14–15, 2026, to hold the historic Indo-Pacific Energy Security Ministerial and Business Forum. The forum was co-hosted by the Chair and Vice Chair of the U.S. National Energy Dominance Council, Secretary of the Interior Doug Burgum, Secretary of Energy Chris Wright, and Japanese Minister of Economy, Trade and Industry Akazawa Ryosei. We affirm our shared determination to work collectively to ensure stable and secure energy supply in the Indo-Pacific region. To this end, we focused on three key themes: reliable energy for Indo-Pacific growth and security; securing energy supply chains, infrastructure and maritime routes; and enabling trade and investment. To support these goals, Ministers affirm the value of: The necessity of reliable, affordable, secure and dispatchable energy from all sources depending on each country’s situation, in meeting the region’s surging energy demand.  Promoting quality as a key procurement mechanism to mitigate risk of operational liabilities. Protecting against rising cyber threats to the security of the energy grid, critical infrastructure, vehicles, and devices. Investment in comprehensive energy infrastructure that encompasses the entire energy supply chain from upstream development facilities to downstream equipment to support an affordable, reliable, and secure energy supply including baseload electricity.  Continuing to supply affordable and reliable energy sources in the Indo-Pacific region, including through emergency response measures, to benefit both producers and consumer countries. While maintaining strong relations with current partners, expanding and diversifying energy suppliers and fuel types in order to strengthen energy security. Promoting transparent, long-term energy contracts that reduce market volatility. As the global economy expands, so too does demand for energy driven by AI and electrification, we, as countries committed to a free and

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Energy Department Issues Funding Opportunity to Strengthen American Critical Minerals and Materials Supply Chain

WASHINGTON—The U.S. Department of Energy’s (DOE) Office of Critical Minerals and Energy Innovation (CMEI) and Hydrocarbons and Geothermal Energy Office (HGEO) today announced a funding opportunity of up to $69 million for technologies or processes that advance the domestic production and refining of critical materials. Projects selected through this Notice of Funding Opportunity (NOFO) will address the greatest technical obstacles to a stronger critical materials supply chain. “This funding will help establish a more secure and affordable supply of the critical minerals and materials that are foundational to American energy dominance, national security, and industrial competitiveness,” said Assistant Secretary of Energy (EERE) Audrey Robertson. DOE is seeking projects that bridge the gap between bench-scale innovations and commercially viable technologies. Selected project teams will form industry-led partnerships and conduct research and development with support from the U.S. national laboratories. The NOFO, which is part of DOE’s Critical Minerals and Materials Accelerator Program and jointly funded by CMEI’s Advanced Materials and Manufacturing Technologies Office and HGEO’s Office of Geothermal, has three primary topic areas: Production and material efficiency for critical materials including rare earth elements Processes to refine and alloy gallium, gallium nitride, germanium, and silicon carbide Cost-competitive direct lithium extraction, separation, and processing CMEI will host an informational webinar on April 16, 2026, to discuss the NOFO and application requirements. Letters of intent are due on April 21, 2026, by 5 p.m. ET. Deadlines for full applications will be staggered based on topic area, starting in May 2026. For more details on sub-topics and deadlines, visit the NOFO landing page. The Critical Minerals and Materials Accelerator is one of several programs developed through DOE’s Critical Materials Collaborative. This NOFO is part of $1 billion in critical materials funding announced by DOE in August 2025, and follows the Manufacturing Deployment Office’s announcement

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Latin America returns to the energy security conversation at CERAWeek

With geopolitical risk central to conversations about energy, and with long-cycle supply once again in focus, Latin America’s mix of hydrocarbons and export potential drew renewed attention at CERAWeek by S&P Global in Houston. Argentina, resource story to export platform Among the regional stories, Argentina stood out as Vaca Muerta was no longer discussed simply as a large unconventional resource, but whether the country could turn resource quality into sustained export capacity.  Country officials talked about scale: more operators, more services, more infrastructure, and a larger industrial base around the unconventional play. Daniel González, Vice Minister of Energy and Mining for Argentina, put it plainly: “The time has come to expand the Vaca Muerta ecosystem.” What is at stake now is not whether the basin works, but whether the country can build enough above-ground capacity and regulatory consistency to keep development moving. Horacio Marín, chairman and chief executive officer of YPF, offered an expansive version of that argument. He said Argentina’s energy exports could reach $50 billion/year by 2031, backed by roughly $130 billion in cumulative investment in oil, LNG, and transportation infrastructure. He said Argentine crude output could reach 1 million b/d by end-2026. He said Argentina wants to be seen less as a recurrent frontier story and more as a future supplier with scale. “The time to invest in Vaca Muerta is now,” Marín said. The LNG piece is starting to take shape. Eni, YPF, and XRG signed a joint development agreement in February to move Argentina LNG forward, with a first phase planned at 12 million tonnes/year. Southern Energy—backed by PAE, YPF, Pampa Energía, Harbour Energy, and Golar LNG—holds a long-term agreement with SEFE for 2 million tonnes/year over 8 years. The movement by global standards is early-stage and relatively modest, but it adds to Argentina’s export

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Market Focus: LNG supply shocks expose limited market flexibility

@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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Libya’s NOC, Chevron sign MoU for technical study for offshore Block NC146

@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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Aria Networks raises $125M and debuts its approach for AI-optimized networks

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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New v2 UALink specification aims to catch up to NVLink

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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Nvidia’s SchedMD acquisition puts open-source AI scheduling under scrutiny

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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Two New England states say no to new data centers

It’s getting harder and harder for governments to ignore the impact that data centers are having on their communities, consuming vast amounts of water and driving up electricity prices, experts say. According to a Pew Research Center analysis, data centers consumed 183 terawatt-hours of electricity in 2024, more than 4% of total U.S. electricity use. That demand is projected to more than double to 426 terawatt-hours by 2030. The impact is significant. In 2023, data centers consumed about 26% of Virginia’s electricity supply, although Virginia is notable for having an extremely dense collection of data centers. Alan Howard, senior analyst for infrastructure at Omdia, says he is not surprised at all. “The amount of national press coverage regarding what is arguably a limited number of data center ‘horror’ stories has many jurisdictions and states spooked over the potential impacts data center projects might have,” he said. It’s an evolution that’s been coming for some time whereby local legislators have embraced the idea that they don’t want to learn the hard way as others already have, he argues. “All that said, it seems unlikely that there will be broad bans on data center development that would cripple the industry. There’s lots of places to go in the U.S. and developers have warmed up to siting projects in places amenable to their needs, although not ideally convenient,” said Howard.

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

Nscale has moved quickly from startup to serious contender in the race to build infrastructure for the AI era. Founded in 2024, the company has positioned itself as a vertically integrated “neocloud” operator, combining data center development, GPU fleet ownership, and a software stack designed to deliver large-scale AI compute. That model has helped it attract backing from investors including Nvidia, and in early March 2026 the company raised another $2 billion at a reported $14.6 billion valuation. Reuters has described Nscale’s approach as owning and operating its own data centers, GPUs, and software stack to support major customers including Microsoft and OpenAI. What makes Nscale especially relevant now is that it is no longer content to operate as a cloud intermediary or capacity provider. Over the past year, the company has increasingly framed itself as an AI hyperscaler and AI factory builder, seeking to combine land, power, data center shells, GPU procurement, customer offtake, and software services into a single integrated platform. Its acquisition of American Intelligence & Power Corporation, or AIPCorp, is the clearest signal yet of that shift, bringing energy infrastructure directly into the center of Nscale’s business model. The AIPCorp transaction is significant because it gives Nscale more than additional development capacity. The company said the deal includes the Monarch Compute Campus in Mason County, West Virginia, a site of up to 2,250 acres with a state-certified AI microgrid and a power runway it says can scale beyond 8 gigawatts. Nscale also said the acquisition establishes a new division, Nscale Energy & Power, headquartered in Houston, extending its platform further into power development. That positioning reflects a broader shift in the AI infrastructure market. The central bottleneck is no longer simply access to GPUs. It is the ability to assemble power, cooling, land, permits, data center

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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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