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Rystad: China’s aggressive tariff retaliation dims prospects for quick US trade deal

Tensions in the US-China trade war have escalated sharply, with both sides stepping up economic confrontation through new tariffs. In response to Washington’s latest measures, Beijing announced a 34% tariff on all US goods, effective Apr. 10, and pledged to retaliate against any further US tariff increases after US President Trump threatened an additional 50% levy on Chinese imports. The escalating conflict has already rippled through global markets. Oil prices have fallen by about $6/bbl, reflecting mounting fears that the intensifying trade dispute could trigger a broader economic downturn. According to Rystad Energy, China’s aggressive retaliation significantly reduces the likelihood of a swift resolution between the world’s two largest economies, raising the specter of a global recession. “The current hawkish stance of the Chinese government is based on its long preparation after the first trade war, including diversifying its economy and trading partners,” said Lin Ye, vice-president of Oil Commodity Markets at Rystad Energy. “We believe China’s 50,000 b/d to 100,000 b/d of oil demand growth is at risk if the trade war continues for longer, however, a stronger stimulus to boost domestic consumption could mitigate the losses. “The resilience of the Chinese economy is likely to survive the trade war. At the same time, China has been reacting to the US sanctions in a cautiously proactive manner, exploring workarounds to circumvent sanctions while trying to stay away from potential repercussions.” In March’s Two Sessions political meetings, Beijing emphasized the critical role of domestic consumption in sustaining growth. With downside risks to China’s total goods exports estimated at 7-9%, policymakers are widely expected to introduce additional stimulus measures at an upcoming conference in April to bolster consumer spending and help achieve the government’s 5% GDP growth target for 2025. If stimulus efforts fall short, however, China’s rapidly expanding petrochemical demand

Ring Energy aims to be “consolidation leader” in Central Basin Platform

Ring Energy Inc., The Woodlands, is looking to grow its production in the Central Basin Platform (CBP) both by acquisition and organically as it faces greater competition in the region sandwiched between the headline-grabbing Delaware and Midland basins. Paul McKinney, chairman and chief executive officer of Ring, and his team 6 weeks ago completed the $100 million acquisition of oil-weighted CBP assets of Lime Rock Resources IV LP (OGJ Online, Feb. 26, 2025). That deal comprised about 17,700 net acres that last fall produced 2,300 boe/d (>80% oil) and have grown Ring’s total production by 12%. McKinney is interested in other purchases like that of Lime Rock, which made Ring the fourth-largest producer in the CBP. “I’d like for Ring to be the consolidation leader of the Central Basin Platform in the southern part of the shelf,” McKinney said Apr. 1 in a conversation with Jeff Robertson, a managing director at Water Tower Research. “We know these areas very, very well. I am convinced that many of the stacked plays that are […] not being pursued, they demonstrate real promise.” McKinney told Robertson Ring has been able to take the exploration and development technologies refined by large industry players in the Delaware and Midland and adapt them to the CBP’s conventional zones. That, he said, helped the company grow its overall reserves by 3% in 2024—while net production grew to nearly 20,000 boe/d (66% oil) in the fourth quarter—and helped offset the lack of an acquisition last year. Ring missed out on several deals, McKinney said, because its leaders weren’t willing to pay the high prices being demanded and proposed. He added that he thinks last summer’s prices were “a high-water mark” for CBP transactions and is eyeing operators that aren’t pursuing newer fracturing techniques to add to Ring’s portfolio—even

OPEC+ accelerates oil supply hike amid tariff tensions

The market reaction was intensified by broader macroeconomic concerns, including new US trade measures. On Apr. 2, US President Donald Trump unveiled sweeping new tariffs targeting key US trading partners. The administration imposed a 10% minimum tariff on all imported goods, coupled with country-specific reciprocal tariffs. Trump revealed a chart announcing that the US would impose a 34% reciprocal tariff on imports from China. This new tariff is in addition to the existing duties on specific Chinese goods, such as automobiles, and the 20% tariff already set during his first administration. Moreover, a similar 20% tariff will also apply to products imported from the EU and other significant trading partners. However, energy products—including oil and natural gas—were notably excluded from the new trade penalties. The White House confirmed that the existing tariff framework, introduced in March, would remain in place for Canada and Mexico. Under that system, USMCA (United States-Mexico-Canada Agreement)-compliant goods continue to receive 0% tariffs, while non-compliant goods face tariffs of up to 25%, depending on the category. Non-USMCA compliant energy and potash will see a 10% tariff. “We welcome President Trump’s decision to exclude oil and natural gas from new tariffs, underscoring the complexity of integrated global energy markets and the importance of America’s role as a net energy exporter,” said Mike Sommers, American Petroleum Institute (API) president and chief executive officer, in a statement. “We will continue working with the Trump administration on trade policies that support American energy dominance.” While energy may be spared for now, the ripple effects of rising trade barriers are likely to shape both global economic performance and the energy markets in the months ahead. Analysts warn that the escalating trade tensions could depress global economic growth and energy demand. In a recent note, S&P Global Market Intelligence projected that an

US court ruling puts 299 federal offshore Gulf leases sold in 2023 in jeopardy

A federal court judge put 299 federal leases sold during a congressionally mandated March 2023 sale in limbo Mar. 24, ruling that the US Interior Department’s environmental assessment failed to consider the environmental impacts of potential development. Specifically, Judge Amit Mehta of the US District Court’s DC Circuit found that the Bureau of Ocean Energy Management (BOEM) violated the National Environmental Policy Act (NEPA) by improperly evaluating Lease Sale 259’s effects on the endangered Rice’s whale and greenhouse gas emissions from new drilling. The sale, encompassing about 1.6 million acres in federal waters off the US Gulf Coast, raised about $263.8 million and was expected to yield about 1.1 billion bbl of oil and over 4 tcf of natural gas over 50 years, according to BOEM, which administered the sale and conducted the environmental assessment. Chevron was a major player, with winning bids of $108 million, including on popular deepwater tracts in the Green Canyon and Keathley Canyon areas (OGJ Online, Mar. 29, 2023). Congress ordered the sale—the last Gulf sale held during the Biden administration—as part of a compromised reached to pass the Inflation Reduction Act (IRA) of 2022. Earthjustice, which argued the case on behalf Healthy Gulf and other environmental groups, urged the judge to invalidate the leases. The American Petroleum Institute (API), an intervenor in the case, insisted the court could not throw out the leases, given the IRA’s mandate to hold the sale. “That argument misses the mark,” Mehta wrote. He ordered that the parties submit additional briefs to the court on possible remedies that could include sending the environmental analysis back to the agency for supplemental environmental review or canceling the leases. A federal judge last July overturned another mandated lease sales, in Alaska’s Cook Inlet, on similar grounds (OGJ Online, July 17, 2024). API

DeepSeek unveils new technique for smarter, scalable AI reward models

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More DeepSeek AI, a Chinese research lab gaining recognition for its powerful open-source language models such as DeepSeek-R1, has introduced a significant advancement in reward modeling for large language models (LLMs). Their new technique, Self-Principled Critique Tuning (SPCT), aims to create generalist and scalable reward models (RMs). This could potentially lead to more capable AI applications for open-ended tasks and domains where current models can’t capture the nuances and complexities of their environment and users. The crucial role and current limits of reward models Reinforcement learning (RL) has become a cornerstone in developing state-of-the-art LLMs. In RL, models are fine-tuned based on feedback signals that indicate the quality of their responses. Reward models are the critical component that provides these signals. Essentially, an RM acts as a judge, evaluating LLM outputs and assigning a score or “reward” that guides the RL process and teaches the LLM to produce more useful responses. However, current RMs often face limitations. They typically excel in narrow domains with clear-cut rules or easily verifiable answers. For example, current state-of-the-art reasoning models such as DeepSeek-R1 underwent an RL phase, in which they were trained on math and coding problems where the ground truth is clearly defined. However, creating a reward model for complex, open-ended, or subjective queries in general domains remains a major hurdle. In the paper explaining their new technique, researchers at DeepSeek AI write, “Generalist RM requires to generate high-quality rewards beyond specific domains, where the criteria for rewards are more diverse and complex, and there are often no explicit reference or ground truth.” They highlight four key challenges in creating generalist RMs capable of handling broader tasks: Input flexibility: The RM must handle various input

Gunvor Snapped Up North Sea Oil During Futures-Market Tumble

Gunvor Group, one of the world’s biggest independent oil traders, piled into the North Sea crude market during a wider rout in futures markets. The North Sea crude market, home to the world’s most important physical oil-price benchmark Dated Brent, often sees individual traders taking relatively large positions, meaning Gunvor’s actions aren’t definitive proof of bullishness. Still, the Geneva-based firm — which took some hits on oil trading last year, including in the North Sea — snapped up three cargoes of benchmark-setting crude in a pricing window run by S&P Global Commodity Insights on Monday. It was also a significant buyer of Contracts for Difference that further help to define Dated Brent. While it’s not uncommon for individual firms to make bold North Sea trades, the timing of Gunvor’s move is eye-catching. Brent futures collapsed 14% in the three sessions through Monday as the market evaluated the demand impact of sweeping US tariffs on all the nation’s main trading partners. A Gunvor spokesperson declined to comment. Buying activity from other participants in the North Sea market has been limited, as has trading of both Mediterranean and West African barrels. Several physical oil traders said they were waiting to see what the impact of the tariffs and countermeasures will be, while some others were looking to sell. Dated Brent helps to set prices for more than two-thirds of the world’s traded crude oil, as well as influencing a web of associated derivatives. In an interview with Bloomberg last month, the trader’s co-founder and CEO Torbjörn Törnqvist acknowledged that the firm had lost money on some oil trades. Gunvor, which he said was renewing its leadership after the losses, posted a 42% slump in net profits in 2024 as energy market chaos eased. An overhaul in its top leadership team has seen the departures of several

Rystad: China’s aggressive tariff retaliation dims prospects for quick US trade deal

Ring Energy aims to be “consolidation leader” in Central Basin Platform

OPEC+ accelerates oil supply hike amid tariff tensions

US court ruling puts 299 federal offshore Gulf leases sold in 2023 in jeopardy

DeepSeek unveils new technique for smarter, scalable AI reward models

Gunvor Snapped Up North Sea Oil During Futures-Market Tumble

US court ruling puts 299 federal offshore Gulf leases sold in 2023 in jeopardy

OPEC+ accelerates oil supply hike amid tariff tensions

Ring Energy aims to be “consolidation leader” in Central Basin Platform

Rystad: China’s aggressive tariff retaliation dims prospects for quick US trade deal

Gunvor Snapped Up North Sea Oil During Futures-Market Tumble

WTI Falls Below $60 Amid Trade War Escalation

Oil fell to close below $60 a barrel for the first time since 2021 after the Trump administration moved to intensify a tit-for-tat trade war with China, inflaming concerns about the trajectory of global demand growth. West Texas Intermediate futures fell for a fourth straight session to close at $59.58 a barrel before grazing a fresh four-year low after settlement. US President Donald Trump is planning to proceed with implementing tariffs that would amount to 104% on many Chinese goods just after midnight, according to a White House official. That would mark another significant escalation in the actions against China, a major crude-importing economy. Beijing had responded earlier by saying it’s prepared to “fight to the end” via retaliatory trade measures. Crude, along with equities, bonds and other commodities, has been roiled this month as the US president presses on with his aggressive trade policy. The ructions have stoked concerns about a global slowdown or recession that would jeopardize energy demand. At the same time, OPEC+ delivered a bigger-than-expected output hike, hurting the outlook for oil market balances. “We can expect a lot more flip-flopping in the near-term, for better or worse,” said Christina Qi, chief executive officer of Databento, a market data provider. “Unless there’s some kind of clear directional catalyst, like a decision from OPEC or a major central bank, the dynamic is going to stay very push-and-pull.” Banks have been rushing to cut their forecasts in recent days as a result. Societe Generale SA sees West Texas Intermediate at $57 a barrel by the end of the year, while Goldman Sachs Group Inc. warned of $40 Brent in an extreme case. A top US oil executive is already calling on the Trump administration to explain how a global trade war will help domestic producers. The turbulence also

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.

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

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

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

Three Aberdeen oil company headquarters sell for £45m

Three Aberdeen oil company headquarters have been sold in a deal worth £45 million. The CNOOC, Apache and Taqa buildings at the Prime Four business park in Kingswells have been acquired by EEH Ventures. The trio of buildings, totalling 275,000 sq ft, were previously owned by Canadian firm BMO. The financial services powerhouse first bought the buildings in 2014 but took the decision to sell the buildings as part of a “long-standing strategy to reduce their office exposure across the UK”. The deal was the largest to take place throughout Scotland during the last quarter of 2024. Trio of buildings snapped up London headquartered EEH Ventures was founded in 2013 and owns a number of residential, offices, shopping centres and hotels throughout the UK. All three Kingswells-based buildings were pre-let, designed and constructed by Aberdeen property developer Drum in 2012 on a 15-year lease. © Supplied by CBREThe Aberdeen headquarters of Taqa. Image: CBRE The North Sea headquarters of Middle-East oil firm Taqa has previously been described as “an amazing success story in the Granite City”. Taqa announced in 2023 that it intends to cease production from all of its UK North Sea platforms by the end of 2027. Meanwhile, Apache revealed at the end of last year it is planning to exit the North Sea by the end of 2029 blaming the windfall tax. The US firm first entered the North Sea in 2003 but will wrap up all of its UK operations by 2030. Aberdeen big deals The Prime Four acquisition wasn’t the biggest Granite City commercial property sale of 2024. American private equity firm Lone Star bought Union Square shopping centre from Hammerson for £111m. © ShutterstockAberdeen city centre. Hammerson, who also built the property, had originally been seeking £150m. BP’s North Sea headquarters in Stoneywood, Aberdeen, was also sold. Manchester-based

2025 ransomware predictions, trends, and how to prepare

Zscaler ThreatLabz research team has revealed critical insights and predictions on ransomware trends for 2025. The latest Ransomware Report uncovered a surge in sophisticated tactics and extortion attacks. As ransomware remains a key concern for CISOs and CIOs, the report sheds light on actionable strategies to mitigate risks. Top Ransomware Predictions for 2025: ● AI-Powered Social Engineering: In 2025, GenAI will fuel voice phishing (vishing) attacks. With the proliferation of GenAI-based tooling, initial access broker groups will increasingly leverage AI-generated voices; which sound more and more realistic by adopting local accents and dialects to enhance credibility and success rates. ● The Trifecta of Social Engineering Attacks: Vishing, Ransomware and Data Exfiltration. Additionally, sophisticated ransomware groups, like the Dark Angels, will continue the trend of low-volume, high-impact attacks; preferring to focus on an individual company, stealing vast amounts of data without encrypting files, and evading media and law enforcement scrutiny. ● Targeted Industries Under Siege: Manufacturing, healthcare, education, energy will remain primary targets, with no slowdown in attacks expected. ● New SEC Regulations Drive Increased Transparency: 2025 will see an uptick in reported ransomware attacks and payouts due to new, tighter SEC requirements mandating that public companies report material incidents within four business days. ● Ransomware Payouts Are on the Rise: In 2025 ransom demands will most likely increase due to an evolving ecosystem of cybercrime groups, specializing in designated attack tactics, and collaboration by these groups that have entered a sophisticated profit sharing model using Ransomware-as-a-Service. To combat damaging ransomware attacks, Zscaler ThreatLabz recommends the following strategies. ● Fighting AI with AI: As threat actors use AI to identify vulnerabilities, organizations must counter with AI-powered zero trust security systems that detect and mitigate new threats. ● Advantages of adopting a Zero Trust architecture: A Zero Trust cloud security platform stops

DeepSeek unveils new technique for smarter, scalable AI reward models

New open source AI company Deep Cogito releases first models and they’re already topping the charts

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Deep Cogito, a new AI research startup based in San Francisco, officially emerged from stealth today with Cogito v1, a new line of open source large language models (LLMs) fine-tuned from Meta’s Llama 3.2 and equipped with hybrid reasoning capabilities — the ability to answer quickly and immediately, or “self-reflect” like OpenAI’s “o” series and DeepSeek R1. The company aims to push the boundaries of AI beyond current human-overseer limitations by enabling models to iteratively refine and internalize their own improved reasoning strategies. It’s ultimately on a quest toward developing superintelligence — AI smarter than all humans in all domains — yet the company says that “All models we create will be open sourced.” Deep Cogito’s CEO and co-founder Drishan Arora — a former Senior Software Engineer at Google who says he led the large language model (LLM) modeling for Google’s generative search product —also said in a post on X they are “the strongest open models at their scale – including those from LLaMA, DeepSeek, and Qwen.” The initial model lineup includes five base sizes: 3 billion, 8 billion, 14 billion, 32 billion, and 70 billion parameters, available now on AI code sharing community Hugging Face, Ollama and through application programming interfaces (API) on Fireworks and Together AI. They’re available under the Llama licensing terms which allows for commercial usage — so third-party enterprises could put them to work in paid products — up to 700 million monthly users, at which point they need to obtain a paid license from Meta. The company plans to release even larger models — up to 671 billion parameters — in the coming months. Arora describes the company’s training approach, iterated distillation and amplification

Wells Fargo’s AI assistant just crossed 245 million interactions – no human handoffs, no sensitive data exposed

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Wells Fargo has quietly accomplished what most enterprises are still dreaming about: building a large-scale, production-ready generative AI system that actually works. In 2024 alone, the bank’s AI-powered assistant, Fargo, handled 245.4 million interactions – more than doubling its original projections – and it did so without ever exposing sensitive customer data to a language model. Fargo helps customers with everyday banking needs via voice or text, handling requests such as paying bills, transferring funds, providing transaction details, and answering questions about account activity. The assistant has proven to be a sticky tool for users, averaging multiple interactions per session. The system works through a privacy-first pipeline. A customer interacts via the app, where speech is transcribed locally with a speech-to-text model. That text is then scrubbed and tokenized by Wells Fargo’s internal systems, including a small language model (SLM) for personally identifiable information (PII) detection. Only then is a call made to Google’s Flash 2.0 model to extract the user’s intent and relevant entities. No sensitive data ever reaches the model. “The orchestration layer talks to the model,” Wells Fargo CIO Chintan Mehta said in an interview with VentureBeat. “We’re the filters in front and behind.” The only thing the model does, he explained, is determine the intent and entity based on the phrase a user submits, such as identifying that a request involves a savings account. “All the computations and detokenization, everything is on our end,” Mehta said. “Our APIs… none of them pass through the LLM. All of them are just sitting orthogonal to it.” Wells Fargo’s internal stats show a dramatic ramp: from 21.3 million interactions in 2023 to more than 245 million in 2024, with over 336

Nvidia’s new Llama-3.1 Nemotron Ultra outperforms DeepSeek R1 at half the size

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Even as Meta fends off questions and criticisms of its new Llama 4 model family, graphics processing unit (GPU) master Nvidia has released a new, fully open source large language model (LLM) based on Meta’s older model Llama-3.1-405B-Instruct model and it’s claiming near top performance on a variety of third-party benchmarks — outperforming the vaunted rival DeepSeek R1 open source reasoning model. Llama-3.1-Nemotron-Ultra-253B-v1, is a dense 253-billion parameter designed to support advanced reasoning, instruction following, and AI assistant workflows. It was first mentioned back at Nvidia’s annual GPU Technology Conference (GTC) in March. The release reflects Nvidia continued focus on performance optimization through architectural innovation and targeted post-training. Announced last night, April 7, 2025, the model code is now publicly available on Hugging Face, with open weights and post-training data. It is designed to operate efficiently in both “reasoning on” and “reasoning off” modes, allowing developers to toggle between high-complexity reasoning tasks and more straightforward outputs based on system prompts. Designed for efficient inference The Llama-3.1-Nemotron-Ultra-253B builds on Nvidia’s previous work in inference-optimized LLM development. Its architecture—customized through a Neural Architecture Search (NAS) process—introduces structural variations such as skipped attention layers, fused feedforward networks (FFNs), and variable FFN compression ratios. This architectural overhaul reduces memory footprint and computational demands without severely impacting output quality, enabling deployment on a single 8x H100 GPU node. The result, according to Nvidia, is a model that offers strong performance while being more cost-effective to deploy in data center environments. Additional hardware compatibility includes support for Nvidia’s B100 and Hopper microarchitectures, with configurations validated in both BF16 and FP8 precision modes. Post-training for reasoning and alignment Nvidia enhanced the base model through a multi-phase post-training pipeline.

Announcing the 2025 Product 50 Award winners!

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In an increasingly uncertain global market, product and growth leaders have a greater effect than ever on a company’s success, impacting not only business growth, but making business transformation possible. Amplitude’s Product 50 Awards were launched as a way to recognize and celebrate product and growth leaders globally whose innovation and creativity are shaking up their companies, disrupting their industries and changing the world. Selecting the Product 50 Now in its third year, the Product 50 call for nominees garnered nearly 600 nominations across ten categories, and the final list of nominees and winners represents nine countries across four continents. From each of the ten categories, leaders were selected by a panel of industry judges: Francois Ajenstat, Chief Product Officer, Amplitude Sara Rossio, Chief Product Officer, G2 Carlos Gonzalez de Villaumbrosia, Founder & CEO, Product School Adrienne Tan, Co-founder & CEO, Brainmates Timo Dechau, Founder, Deepskydata The judges based their evaluation on commitment to innovation, business impact, social impact and influence on fellow product leaders and their industry. Congratulations to all the finalists and winners! Read on to learn who they are — and learn more about the Product 50 at amplitude.com/product50. Introducing the 2025 Product 50 Award winners Best data-driven product leader Colin Hili, head of product at Jumbo Interactive Colin Hili is a strategic product leader specializing in B2B2C, with 10+ years of experience driving scalable growth, innovation and customer engagement. With data-driven strategies like cohort analysis to optimize pricing and engagement, Hili generated a substantial new recurring revenue stream for Jumbo Interactive while reinforcing customer loyalty, and is forecasted to add 8-10 million ARR within the first year of operation. His Splash for Good initiative not only raised

Move over, Alexa: Amazon launches new realtime voice model Nova Sonic for third-party enterprise development

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Amazon is best known as an e-commerce giant and then somewhere perhaps slightly further down the list of notable offerings is its Alexa AI voice assistant product, which just got a big intelligence upgrade last month thanks in part to Amazon investment Anthropic. Now Alexa will have to make space for a new Amazon voice AI sibling: today the company is introducing Amazon Nova Sonic, a new foundation model designed to allow third-party app developers to build realtime, naturalistic, conversational voice interactivity to their products using Amazon’s web platform Bedrock It’s available now via a bi-directional streaming application programming interface (API). Obvious use cases include customer support and service, guidance, information retrieval, and entertainment. A unified approach Nova Sonic addresses a key challenge in voice AI: the fragmentation of technologies. Traditionally, building voice interfaces required combining separate models for speech recognition, language processing, and speech synthesis, according to Rohit Prasad, SVP and Head Scientist for Artificial General Intelligence (AGI) at Amazon, in a video call interview with VentureBeat yesterday using Amazon’s Chime video service. This complexity often results in robotic, unnatural interactions and increased development overhead. Now, Sonic seeks to improve on this state of affairs by combining all three distinct model types into one. Prasad explained the model’s core innovation: “Nova Sonic brings together three traditionally separate models—speech-to-text, text understanding, and text-to-speech—into one unified system that can model not just the ‘what’ but also the ‘how’ of communication.” By retaining the acoustic context—such as tone, cadence, and style—Nova Sonic helps maintain the nuances of human conversation. Recognizing the intricacies and quirks of live, two-way audio conversations One of Nova Sonic’s defining capabilities is its ability to handle live, two-way conversations.

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