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DeepSeek unleashes ‘Janus Pro 7B’ vision model amidst AI stock bloodbath, igniting fresh fears of Chinese tech dominance

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More DeepSeek, the fast-growing Chinese AI company, is shaking up global technology yet again. Just as the rapid rise of the company’s frontier AI models triggered a sell-off of U.S. artificial intelligence stocks, the company launched a […]

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DeepSeek, the fast-growing Chinese AI company, is shaking up global technology yet again. Just as the rapid rise of the company’s frontier AI models triggered a sell-off of U.S. artificial intelligence stocks, the company launched a brand new product: Janus Pro 7B, an open-source vision-based AI model. (You can try a demo right here.)

This unexpected release from DeepSeek intensifies investor worries about China’s growing power in AI and further pressures American tech companies. The company curiously released Janus Pro 7B today as U.S. AI stocks plunged, a timing that appears to be deliberate and designed to highlight the Beijing-based firm’s challenge to Silicon Valley.

DeepSeek’s latest launch follows its release last week of the frontier R1 large language model. Industry experts were largely impressed by R1’s efficient and strong performance. The R1 model immediately raised concerns that China is quickly advancing in AI and could disrupt the current leaders in the field. Markets reacted quickly. Nvidia, a key maker of AI chips, saw its stock price fall sharply. Other major AI companies also experienced stock drops as investors reassessed the competitive landscape with DeepSeek emerging as a strong new player.

Efficiency is the new king: Why Janus Pro 7B changes everything

DeepSeek is now extending its reach beyond language processing into the critical domain of computer vision with Janus Pro 7B. According to the technical paper released alongside the model, Janus Pro 7B is engineered for efficiency and versatility, excelling in a range of visual tasks from generating photorealistic images to performing complex visual reasoning.

“Janus [Pro] is a series of efficient vision models,” the DeepSeek research team states in their paper, “aiming to achieve a balance between performance and computational cost. We present Janus-Pro-7B, a 7 billion parameter vision model…achieving state-of-the-art performance on a wide range of vision tasks.”

This emphasis on efficiency is a crucial differentiator for enterprise customers. Unlike some of the largest and most resource-intensive AI models, Janus Pro 7B, with its 7 billion parameters, is designed to deliver high-level performance without demanding vast computational resources.

This efficiency could significantly lower the barrier to entry for businesses looking to integrate advanced vision AI into their operations. For companies ranging from startups to multinational corporations, the prospect of deploying sophisticated visual intelligence without incurring exorbitant infrastructure costs is increasingly attractive.

The research paper further details the breadth of the model’s capabilities, stating, “Janus-Pro-7B demonstrates strong performance in various vision tasks, including image generation, visual question answering, and image captioning.”

This multi-faceted functionality is particularly appealing for businesses seeking to leverage AI across diverse applications. Imagine a global retailer utilizing Janus Pro 7B to automate the creation of marketing visuals, respond to customer inquiries about product appearance, and generate detailed and visually rich descriptions for online product listings — all powered by a single, streamlined AI model. The potential for streamlining workflows, enhancing customer engagement, and improving operational efficiency is substantial.

 Charts released by DeepSeek show performance metrics for its new Janus Pro 7B vision AI. (Left) Janus Pro 7B achieves high average performance with fewer parameters than many other multimodal models. (Right) The model also scores top accuracy on text-to-image generation benchmarks, outperforming competitors. (Credit: DeepSeek)

DeepSeek’s one-two punch: R1 language model followed by vision AI intensifies market anxiety and competitive pressure

The timing of the Janus Pro 7B launch amplifies its impact. Coming on the heels of the R1 model and the ensuing market turbulence, it reinforces the narrative of DeepSeek as an innovator capable of disrupting the established order in AI.

Last week’s initial market jitters, triggered by R1’s release on a holiday Monday, escalated into full-blown panic over the weekend as leaked benchmarks and online demonstrations highlighted the model’s impressive capabilities. And today, as the tech stock sell-off intensified, DeepSeek introduced Janus Pro 7B, further amplifying the sense of urgency and competitive pressure felt by U.S. AI companies.

Markets are reacting viscerally to DeepSeek, not just to another AI competitor. They sense a rule change. For too long, AI’s story was relentless scaling: bigger models, more parameters, higher costs. This favored giants, mostly in the West. DeepSeek, with Janus Pro 7B and R1, breaks this mold. They show nimble, efficient models can overperform. It’s an architectural shift. AI advantage may shift from server farm size to smart innovation and broad distribution.

Janus Pro 7B’s open-source nature amplifies this disruption. Like open-source movements before, this increases access to advanced AI, unlike closed proprietary models. Enterprises outside Big Tech gain: cutting-edge AI without vendor lock-in or high fees. For AI powerhouses, DeepSeek poses a direct threat. Can their proprietary, premium models survive free, high-quality alternatives? The market sell-off suggests investors doubt it.

For enterprise technology decision-makers, the message is increasingly clear: the AI landscape is undergoing a rapid transformation, and DeepSeek represents a significant new force.

Ignoring the implications of Janus Pro 7B, and DeepSeek’s broader strategic approach, would be a critical oversight. Businesses must now assess the opportunities and challenges presented by this new wave of AI innovation, even amidst ongoing market volatility and geopolitical uncertainties. The era of unchallenged U.S. AI leadership may be drawing to a close, and the global economy is entering a more dynamic and potentially disruptive phase of AI-driven competition.

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Cisco CIO on the future of IT: AI, simplicity, and employee power

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Oil Slips as Traders Weigh OPEC+ Hike

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Israeli Gas Flows to Egypt Return to Normal as Iran Truce Holds

Israeli natural gas flows to Egypt returned to normal levels after a truce with Iran allowed the Jewish state to reopen facilities shuttered by the 12-day conflict. Daily exports have climbed to 1 billion cubic feet per day, according to two people with direct knowledge of the situation. That’s up from 260 million cubic feet when Israel’s Leviathan gas field, the country’s biggest, restarted on Wednesday, they said, declining to be identified because they’re not authorized to speak to the media.  The increased flows have let Egyptian authorities resume supplies to some factories that had been halted because of the shortages. Israel temporarily closed two of its three gas fields – Chevron-operated Leviathan and Energean’s Karish – shortly after launching attacks on Iran on June 13. The facilities that provided the bulk of exports to Egypt and Jordan resumed operations last week after a US-brokered ceasefire with the Islamic Republic took hold. The ramped-up supplies are a relief for Cairo, which has swung from a net exporter to importer of natural gas in recent years. As Israel and Iran traded blows, Egypt enacted contingency plans that included seeking alternative fuel purchases, limiting gas to some industries and switching power stations to fuel oil and diesel to maintain electricity output. What do you think? We’d love to hear from you, join the conversation on the Rigzone Energy Network. The Rigzone Energy Network is a new social experience created for you and all energy professionals to Speak Up about our industry, share knowledge, connect with peers and industry insiders and engage in a professional community that will empower your career in energy.

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California Regulator Wants to Pause Newsom Refinery Profit Cap

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State utility regulators urge FERC to slash ROE transmission incentive

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Affordability a ‘formidable challenge’ as load shifts to tech, industrial customers: ICF

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Trump attacks on NRC independence pose health, safety risks

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Datacenter industry calls for investment after EU issues water consumption warning

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HPE-Juniper deal clears DOJ hurdle, but settlement requires divestitures

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Data center costs surge up to 18% as enterprises face two-year capacity drought

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Cisco backs quantum networking startup Qunnect

In partnership with Deutsche Telekom’s T-Labs, Qunnect has set up quantum networking testbeds in New York City and Berlin. “Qunnect understands that quantum networking has to work in the real world, not just in pristine lab conditions,” Vijoy Pandey, general manager and senior vice president of Outshift by Cisco, stated in a blog about the investment. “Their room-temperature approach aligns with our quantum data center vision.” Cisco recently announced it is developing a quantum entanglement chip that could ultimately become part of the gear that will populate future quantum data centers. The chip operates at room temperature, uses minimal power, and functions using existing telecom frequencies, according to Pandey.

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HPE announces GreenLake Intelligence, goes all-in with agentic AI

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MEF goes beyond metro Ethernet, rebrands as Mplify with expanded scope on NaaS and AI

While MEF is only now rebranding, Vachon said that the scope of the organization had already changed by 2005. Instead of just looking at metro Ethernet, the organization at the time had expanded into carrier Ethernet requirements.  The organization has also had a growing focus on solving the challenge of cross-provider automation, which is where the LSO framework fits in. LSO provides the foundation for an automation framework that allows providers to more efficiently deliver complex services across partner networks, essentially creating a standardized language for service integration.  NaaS leadership and industry blueprint Building on the LSO automation framework, the organization has been working on efforts to help providers with network-as-a-service (NaaS) related guidance and specifications. The organization’s evolution toward NaaS reflects member-driven demands for modern service delivery models. Vachon noted that MEF member organizations were asking for help with NaaS, looking for direction on establishing common definitions and some standard work. The organization responded by developing comprehensive industry guidance. “In 2023 we launched the first blueprint, which is like an industry North Star document. It includes what we think about NaaS and the work we’re doing around it,” Vachon said. The NaaS blueprint encompasses the complete service delivery ecosystem, with APIs including last mile, cloud, data center and security services. (Read more about its vision for NaaS, including easy provisioning and integrated security across a federated network of providers)

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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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