Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More President Donald Trump unveiled an ambitious plan to reshape America’s artificial intelligence landscape this week, coupling a massive $500 billion private-sector initiative with sweeping executive actions that strip away regulatory barriers — while simultaneously sparking controversy over both funding claims and environmental concerns. The centerpiece of Trump’s AI strategy, dubbed “Project Stargate,” brings together an unlikely alliance of tech giants: Sam Altman’s OpenAI, Larry Ellison’s Oracle, and SoftBank under Masayoshi Son. The initiative aims to construct up to 20 massive AI data centers across the United States, with the first facility already under construction in Abilene, Texas. “This is a resounding declaration of confidence in America’s potential,” Trump declared at the White House announcement. However, the bold initiative immediately faced skepticism from an unexpected quarter: Trump’s own adviser and tech billionaire Elon Musk. Elon Musk questions Stargate’s $500 billion funding as OpenAI rivalry intensifies “They don’t actually have the money,” Musk wrote on X.com (formerly Twitter), claiming SoftBank had secured “well under $10B.” This public clash between Musk and Altman, former collaborators turned rivals, highlights the complex dynamics within Trump’s tech coalition. Altman swiftly countered Musk’s claim, inviting him to visit the Abilene site while pointedly noting that “what is great for the country isn’t always what’s optimal for your companies” — a reference to Musk’s competing AI ventures. They don’t actually have the money — Elon Musk (@elonmusk) January 22, 2025 Industry analysts note that the funding structure remains opaque. While the initial commitment is $100 billion, the path to $500 billion appears to rely heavily on future fundraising and market conditions. Microsoft CEO Satya Nadella, whose company is notably absent from the main announcement despite its OpenAI partnership, offered measured support: “All I know is, I’m good for my $80 billion,” he told CNBC at Davos. Emergency powers and deregulation: Trump’s strategy to fast-track AI infrastructure The initiative arrives alongside an executive order that fundamentally reshapes the federal government’s approach to AI development. The order explicitly prioritizes speed over regulation, with Trump declaring he will use emergency powers to fast-track power plant construction for the energy-hungry data centers. “I’m going to get the approval under emergency declaration. I can get the approvals done myself without having to go through years of waiting,” Trump told the World Economic Forum. This approach marks a sharp departure from the Biden Administration’s emphasis on AI safety guidelines. Environmental concerns loom large. While the Abilene facility plans to use renewable energy, Trump’s order allows the data centers to “use whatever fuel they want,” including coal for backup power. This has alarmed climate activists, who warn about the massive energy requirements of AI infrastructure. Corporate DEI programs clash with White House policy as tech giants navigate Trump Era The initiative also faces potential contradictions with Trump’s other policy priorities. Many of the participating companies maintain diversity, equity and inclusion (DEI) programs that clash with Trump’s day-one executive order ending such initiatives in federal agencies. The initiative represents a striking paradigm shift in how the U.S. approaches technological development. While previous administrations carefully balanced innovation with oversight, Trump’s approach essentially throws out the regulatory playbook in favor of a move-fast-and-fix-later strategy. This creates an unprecedented experiment in AI development: Can Silicon Valley’s biggest players, freed from regulatory constraints but bound by new social restrictions, deliver on the promise of U.S. AI dominance? The contradictions are difficult to ignore. Trump is simultaneously declaring AI development a national emergency while constraining the very companies building it through restrictions on their internal practices. Tech giants like OpenAI and Oracle must now thread an increasingly narrow needle — racing to build massive AI infrastructure while potentially dismantling their DEI initiatives that have become deeply embedded in their corporate cultures and hiring practices. More concerning for AI researchers is the absence of safety guidelines in this new framework. By prioritizing speed and scale over careful development, the administration risks repeating the mistakes of previous technological revolutions, where unforeseen consequences emerged only after systems became too entrenched to easily modify. The stakes with AI are arguably much higher. America’s AI gamble: A race against China with uncertain odds For now, the tech industry appears willing to navigate these contradictions in exchange for unprecedented support for AI infrastructure development. Whether this gamble pays off may determine not just the future of American AI, but also the shape of the global tech landscape for decades to come. The stakes couldn’t be higher. As China continues its own aggressive AI development, Project Stargate represents America’s biggest bet yet on maintaining its technological edge. The question remains: Will this moonshot approach create the “golden age” Trump promises, or will regulatory rollbacks and internal conflicts undermine its ambitious goals? source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More As of a few days ago, only the nerdiest of nerds (I say this as one) had ever heard of DeepSeek, a Chinese AI subsidiary of the equally evocatively named High-Flyer Capital Management, a quantitative analysis (or quant) firm that initially launched in 2015. Yet within the last few days, it’s been arguably the most discussed company in Silicon Valley. That’s largely thanks to the release of DeepSeek-R1, a new large language model (LLM) that performs “reasoning” similar to OpenAI’s current best-available model o1 — taking multiple seconds or minutes to answer hard questions and solve complex problems as it reflects on its own analysis in a step-by-step, or “chain of thought” fashion. Not only that, but DeepSeek-R1 scored as high as or higher than OpenAI’s o1 on a variety of third-party benchmarks (tests to measure AI performance at answering questions on various subjects), and was reportedly trained at a fraction of the cost (reportedly around $5 million), with far fewer graphics processing units (GPU) that are under a strict embargo imposed by the U.S., OpenAI’s home turf. But unlike o1, which is available only to paying ChatGPT subscribers of the Plus tier ($20 per month) and more expensive tiers (such as Pro at $200 per month), DeepSeek-R1 was released as a fully open-source model, which also explains why it has quickly rocketed up the charts of AI code sharing community Hugging Face’s most downloaded and active models. Also, thanks to the fact that it is fully open-source, people have already fine-tuned and trained many variations of the model for different task-specific purposes, such as making it small enough to run on a mobile device, or combining it with other open-source models. Even if you want to use it for development purposes, DeepSeek’s API costs are more than 90% lower than the equivalent o1 model from OpenAI. Most impressively of all, you don’t even need to be a software engineer to use it: DeepSeek has a free website and mobile app even for U.S. users with an R1-powered chatbot interface very similar to OpenAI’s ChatGPT. Except, once again, DeepSeek undercut or “mogged” OpenAI by connecting this powerful reasoning model to web search — something OpenAI hasn’t yet done (web search is only available on the less powerful GPT family of models at present). An open-and-shut irony There’s a pretty delicious, or maybe disconcerting irony to this, given OpenAI’s founding goals to democratize AI for the masses. As Nvidia senior research manager Jim Fan put it on X: “We are living in a timeline where a non-US company is keeping the original mission of OpenAI alive — truly open, frontier research that empowers all. It makes no sense. The most entertaining outcome is the most likely.” Or as X user @SuspendedRobot put it, referencing reports that DeepSeek appears to have been trained on question-answer outputs and other data generated by ChatGPT: “OpenAI stole from the whole internet to make itself richer, DeepSeek stole from them and give it back to the masses for free I think there is a certain british folktale about this” But Fan isn’t the only one to sit up and take note of DeepSeek’s success. The open-source availability of DeepSeek-R1, its high performance, and the fact that it seemingly “came out of nowhere” to challenge the former leader of generative AI, has sent shockwaves throughout Silicon Valley and far beyond, based on my conversations with and readings of various engineers, thinkers and leaders. If not “everyone” is freaking out about it as my hyperbolic headline suggests, it’s certainly the talk of the town in tech and business circles. A message posted to Blind, the app for sharing anonymous gossip in Silicon Valley, has been making the rounds suggesting Meta is in crisis over the success of DeepSeek because of how quickly it surpassed Meta’s own efforts to be the king of open-source AI with its Llama models. ‘This changes the whole game’ X user @tphuang wrote compellingly: “DeepSeek has commoditized AI outside of very top-end. Lightbulb moment for me in 1st photo. R1 is so much cheaper than US labor cost that many jobs will get automated away over next 5 yrs,” later noting why DeepSeek’s R1 is more enticing to users than even OpenAI’s o1: “3 huge issues w/ o1:1) too slow2) too expensive3) lack of control for end user/reliance on OpenAIR1 solves all of them. A company can buy their own Nvidia GPUs, run these models. Don’t have to worry about additional costs or slow/unresponsive OpenAI servers” @tphaung also posed a compelling analogy as a question: “Will DeepSeek be to LLM what Android became to OS world?” Web entrepreneur Arnaud Bertrand didn’t mince words about the startling implications of DeepSeek’s success either, writing on X: “There’s no overstating how profoundly this changes the whole game. And not only with regards to AI, it’s also a massive indictment of the US’s misguided attempt to stop China’s technological development, without which Deepseek may not have been possible (as the saying goes, necessity is the mother of inventions).” The censorship issue However, others have sounded cautionary notes on DeepSeek’s rapid rise, arguing that as a startup operated out of China, it is necessarily subject to that country’s laws and content censorship requirements. Indeed, in my own usage of DeepSeek on the iOS app here in the U.S. I found it would not answer questions about Tiananmen Square, the site of the 1989 pro-democracy student protests and uprising, and subsequent violent crackdown by the Chinese military, which resulted in at least 200, possibly thousands of deaths, earning it the nickname “Tiananmen Square Massacre” in Western media outlets. Ben Hylak, a former Apple human interface designer and cofounder of AI product analytics platform Dawn, posted on X how asking about this subject caused DeepSeek-R1 to enter a circuitous loop. As a member of the press itself, I of course take

AI is set to transform business operations, with the complex area of procurement ripe for digital innovation that will drive tangible impact. Procurement plays a crucial role in organizational success because it directly impacts operational efficiency, cost-effectiveness, and customer satisfaction. However, supply chains are becoming more complex, and teams often lack real-time visibility into the movement of goods, leading to delays and difficulties in managing inventory. By investing in digital transformation — and purpose-built AI solutions designed for the supply chain in particular — procurement teams can overcome these challenges, keeping costs in check while ensuring the right products are delivered to the right people on time. Procurement challenges According to Gartner, 90% of procurement leaders expect supply chains to become even more complex over the next two years. [1] “This complexity comes from system data and processes — all becoming more intricate as procurement takes on broader responsibilities,” GEP Director of Product Marketing Alex Zhong said in a recent webinar. “Procurement is no longer just focusing on cost savings; it now has to manage risk and sustainability.” Procurement teams are also held back by operational efficiencies stemming from inconsistent workflows, leading to delays that hinder operational agility. Manual repetitive tasks further exacerbate these issues, creating scalability challenges that limit the ability to respond quickly to market demands. Making matters worse, many organizations struggle to integrate procurement systems with other enterprise tools, which creates data silos that hurt collaboration. The absence of real-time visibility into supply chain operations only compounds these problems, preventing teams from making data-driven decisions quickly. On top of this, evolving customer demands are forcing teams to build faster, more efficient procurement processes to stay competitive. Organizations must therefore adopt AI to build a procurement function that can meet the demands of a dynamic, and increasingly digitized, economy. How AI can help AI is transforming procurement with real-time tracking and visibility capabilities that provide end-to-end supply chain transparency. In fact, one manufacturer reported a 95% increase in real-time supply chain visibility after implementing GEP’s AI solutions. This improved insight enables teams to make faster, more intelligent decisions to optimize procurement. What’s more, AI reduces lead times by streamlining processes, improving supplier relationships, and enhancing inventory management. With smarter tools, procurement teams can ensure timely deliveries and maintain ideal stock levels, avoiding costly overstock and shortages. At the same time, AI can automate repetitive tasks, transforming workflows; the same manufacturer increased automation levels from 20% to 80% with AI, giving their team more time to focus on strategic initiatives. AI is also reshaping the way the manufacturer manages contracts. “We expect that in the nine months, almost 75% of their contracts will be authored, amended, and managed using AI — what we call total orchestration of contracts,” said Santosh Nair, GEP’s Chief Product Officer. Additionally, AI ensures compliance and helps teams hit key milestones by monitoring processes and flagging potential risks. Taken together, all of these benefits can help teams set the standard for what procurement excellence looks like in 2025 and beyond. To learn more about how AI can optimize your procurement function, watch the full discussion. [1] Gartner, Radically Rethinking Supply Chain Reorganization, October 2024 source

By Hailey Konnath ( January 21, 2025, 11:58 PM EST) — A Delaware Court of Chancery judge on Tuesday sanctioned Meta Platforms Inc.’s former Chief Operating Officer Sheryl Sandberg in consolidated litigation over the Facebook Cambridge Analytica data scandal, finding that she likely selectively deleted emails that related to the litigation…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

In 1996, four men made Canadian history when they won the Olympic gold medal in the men’s 4×100-meter relay in a time of 37.69 seconds. This historic win was especially surprising, considering the team had struggled in the events leading up to it. They had the quickness but needed to work through the mechanics of the race and interpersonal issues before finding success. Why? Because a relay race isn’t simply about speed, — it is also about chemistry. And when you’re competing at the highest levels, the difference between winning and losing the race comes down to the team’s performance in the handoff and transition zones. It isn’t much different for B2B organizations. You can have great sales and customer success (CS) teams, but if you don’t have smooth account handoffs and transitions from one team to the next, you will be failing your customers and lose speed in the race to retention and growth. A successful transition supports delivery of a customer experience that feels like a single, seamless interaction — or a baton pass made without losing stride — from first contact to onboarding and beyond. In aligning sales and CS teams to provide a seamless transition, there are some best practices to consider that are integral to passing the baton in a way that sets up the next team, and the customer, to succeed. So where to start? Two tactics to consider: Create a transition checklist. CS teams need specific information from sales, and creating a checklist provides clarity and supports consistency. It should include information that can only come from sellers, such as the customer’s key stakeholders, org chart, promises made during the purchase, specific use cases, goals, and pain points. Ensure that the checklist lives in a shared tool such as a CRM or customer success platform, making it easily accessible to both teams. Establish a knowledge transfer process. To make sure that customers realize the value promised, sellers should share the information gathered during the buying process, breathing life into the transition checklist. This allows the customer success manager (CSM) to gain more context, especially around goals and outcomes. These insights provide CSMs with the knowledge and information needed to start building a joint customer success plan prior to the partnership kickoff call, complete with measurable, identifiable milestones. To successfully progress customers around the proverbial track toward the finish line, a robust process that prioritizes the partnership aspect and includes the insights that matter helps to make it more than a “check the box” task. Clear track lanes also provide ownership clarity for each team, optimizing their performance in the transition zones and alleviating the need for strenuous training. To learn more about successful account transitions, read Drive Retention Through Effective Sales-To-Customer Success Account Transitions, and if you’re a Forrester client looking to improve this process, reach out to your account team to book a guidance session with me today. source

By Tom Zanki ( January 22, 2025, 7:28 PM EST) — Defense and space exploration company Voyager Technologies Inc. said Wednesday it has confidentially filed plans for an initial public offering, marking the second company from the industry to join the IPO pipeline this week and potentially benefiting from increased government funding for space travel…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

“The role will likely remain in the C-suite as long as data is the differentiator,” he says. “Data is important for modern enterprises, and as AI evolves, having someone who can manage, protect, and strategically utilize that data is crucial.” However, CDOs need to demonstrate measurable value, such as operational efficiencies, new revenue from data-driven services, or improved compliance and transparency, Kearney says. “These outcomes will establish the CDO as an essential role rather than a temporary one,” he adds. Like many CDOs, Kearney sees the role as in a state of evolution, and CDOs need the vision required to translate multimodal data into valuable business insights, he says. source

It’s been some time since Forrester has written about this market, and a lot has changed. Automation is the cornerstone of speed and operational efficiency. With the increasing complexity in IT ecosystems, business applications, and data, the demand for smarter automation is greater than ever. Batch automation and workload automation are certainly not new concepts (they date back to the early days of the mainframe), but they are undergoing a renaissance as organizations optimize their processes. In our upcoming research, we will delve into why it’s time to revisit these technologies, explore the macro trends that impact this market, and how they have the potential to reshape organizations’ automation plans. We’ll help our clients understand the current state of the market, the impact of the latest technological advancements, and new emerging use cases. Why Are We Revisiting This Research? Increased client demand. Enterprises are increasingly demanding insights into the direction of this market and vendors’ ability to solve their operational and organizational requirements. Hybrid and multicloud environments. Firms today live and operate in a hybrid setup — a mix of on-premises and public cloud services. Applications, infrastructure, and data are spread across this setup, and workload/batch automation must likewise seamlessly integrate across it. Native capabilities in business applications. Some business applications have native capabilities to perform workload automation. We will explore how these impact standalone tools in the market. AI and AI agent enhancements. While AI is no secret in automation, we want to make clear how AI will help advance solutions. When should agents take over (if at all)? Demand for operational and cyber resiliency. With the growing threat of system failures and cybersecurity issues, all automation solutions must be designed with capabilities to address these challenges. Workload/batch automation can no longer be just a tool for the IT organization: Like all other types of automation, it must be a strategic enabler for modern businesses. By revisiting research in this space, we will explore new possibilities for scalability, efficiency, and resilience. Get Involved Over the next two months, we will be conducting interviews and taking briefings with vendors. If you would like to participate in our research, please contact Meg Bellavance ([email protected]). source

By Christopher Cole ( January 24, 2025, 8:25 PM EST) — Federal Communications Commission Chair Brendan Carr on Friday announced several staff appointments, including acting officials to lead international affairs, engineering, economics and media relations…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

WHSmith just launched a retail media network (RMN) to bring retail media to its airport stores. Like every other RMN launched since Amazon, Walmart, and Best Buy pioneered retail media over a decade ago, WHSmith promises “more exciting and engaging retail experiences for consumers” and is “tailored to the needs of … supplier brands.” Our take: WHSmith’s network is yet another addition to the long tail of Amazon Ads copycats — and Amazon Ads’ scale leads other RMNs to project unrealistic growth. In the US, Amazon Ads is larger than all other RMNs combined. It’s growing faster than others and now selling advertising technology as a service, signaling sustained dominance. For smaller RMNs, operational realities interfere with execution. Advertisers tell us that they lack media know-how, mask trade promotion as media spend, and struggle to prove performance. Here are the facts: When retail media grows, trade funding declines. More than half of retail media ad spend comes from existing trade and shopper marketing budgets. Rather than earning incremental revenue, RMNs divert dollars that would have funded temporary price reductions, featured endcaps, and in-store demos into advertising. Retailers obscure RMNs’ inability to tap into digital and national media budgets by consolidating trade and retail media when reporting revenue publicly. Cannibalizing co-op funds remains a chief concern of executives at large RMNs, especially for multicategory, multibrand retailers. RMN execution is weaker than it should be. RMNs struggle to demonstrate incrementality, power real-time results, and offer self-service platforms, making it difficult for brands and agencies to plan, buy, and optimize ads. In fact, most RMNs remain mostly manual. Furthermore, despite in-store ads earning more attention than any other format, according to Forrester’s Consumer Benchmark Survey, 2024, in-store ads remain constrained by their difficulty to buy and measure. The few retailers that have invested in smart carts and digital displays have yet to roll them out nationally due to the capital expenditure that they require and their uncertain return on ad spend. Going forward, RMNs should prioritize self-service. Retail media is run by several ex-agency staff hired by RMNs to manage campaigns. Each RMN has tons of advertisers, so when media management is manual, it creates a lot of low-level labor that could be better spent on capabilities such as analytics. Resource-intensive, white-glove service may satisfy retailers’ largest first-party sellers, but there’s a long tail of first- and third-party sellers interested in allocating performance media budgets to self-serve highly relevant, revenue-generating ads. When they’re more self-service, RMNs have bigger budgets for sales, marketing, product, and engineering to focus on maximizing onsite profitability, full-funnel measurement, and making retail media programmatic. To learn what else RMNs should prioritize, check out The State Of Retail Media, 2025, by Sucharita Kodali and myself. We clarify retail media’s potential and challenges and advise how retailers can sell more ads. As always, feel free to schedule time to discuss. source