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Hello and welcome to Eye on AI. In this edition\u2026A chaotic AI summit in India ends with some voluntary commitments and $200 billion for the host nation\u2026Anthropic accuses Chinese rivals of using Claude\u2019s answers to enhance their models\u2026OpenAI launches an alliance with major consulting firms to sell its Frontier AI agent platform\u2026$650 billion in AI infrastructure spending this year could be risky\u2026and maybe don\u2019t let an AI model advise you on using nuclear weapons.<\/em><\/p>\n First, many of the AI world\u2019s most important people gathered in New Delhi, India, last week for the global AI Impact Summit. The global confab was at times chaotic, my colleague Bea Nolan, who was on the ground in Delhi, reports. But, in the end, there was some movement on voluntary commitments to ensure the benefits of AI technology are spread more equitably around the world. And India itself secured $200 billion of new AI investment. You can read more about what came out of the summit from Bea here.<\/p>\n Next, Chinese AI company DeepSeek has not even dropped its V4 model yet\u2014it is expected any minute now\u2014but it is already stoking plenty of controversy.\u00a0<\/p>\n Yesterday, Anthropic alleged that it had detected what it described as \u201can industrial scale campaign\u201d by DeepSeek and two other prominent Chinese AI labs, Moonshot AI and MiniMax, to distill its Claude models. Distillation is the term AI researchers use to describe a method of boosting the performance of smaller, usually weaker AI models by fine-tuning them on the outputs of a larger, stronger model. In this case, Anthropic claims the three Chinese AI companies created 24,000 fake accounts in order to generate 16 million exchanges with Claude that they then used to train their own models, in violation of Anthropic\u2019s terms of service. (Of these exchanges, DeepSeek was only responsible for 150,000 of them, according to Anthropic, but DeepSeek-linked accounts seemed particularly interested in distilling Claude\u2019s reasoning capabilities.)<\/p>\n Also yesterday, Reuters reported, citing an anonymous senior U.S. government official, that the U.S. believes DeepSeek trained V4 using Nvidia\u2019s latest generation Blackwell AI GPUs, in likely violation of U.S. export controls that were supposed to prevent Chinese AI companies from acquiring Nvidia\u2019s most advanced chips. The story said the U.S. believed that DeepSeek has a data center in Inner Mongolia stuffed full of Blackwells\u2013although it said the U.S. was unsure exactly how it obtained them.<\/p>\n In a way, both stories ought to be seen as good news for the U.S. AI industry. For a while, a narrative has been building that Chinese labs were rapidly catching up to the U.S. in AI tech and might soon leapfrog ahead. But if the Chinese labs are resorting to covert distillation to equal the performance of U.S. AI models, there\u2019s far less danger that the U.S. companies will lose their edge when it comes to state-of-the-art performance. (Marketshare is another matter; outside the U.S. and Europe, adoption of Chinese models has been increasing because the majority of the Chinese models are open source and much cheaper to use than their American-made rivals. It\u2019s ultimately not just performance that matters but price-performance ratios.) What\u2019s more, the Chinese have been desperately trying to build domestic AI chips that are as capable as Nvidia\u2019s. The leak to Reuters would seem to indicate that those efforts, which are centered largely around Chinese hardware maker Huawei, have yet to close the gap with Nvidia\u2019s Blackwells.<\/p>\n Now, turning to another big news item of the past week: the Supreme Court\u2019s striking down U.S. President Donald Trump\u2019s \u201cLiberation Day\u201d tariffs. That news on Friday immediately made me think about my conversation a few weeks back with Evan Smith, the CEO and cofounder of Altana, a New York-based startup that has built what it describes as an AI-powered \u201cknowledge graph\u201d of the entire global supply chain. The seven-year old company has raised around $340 million in venture capital so far and says it is on track to cross $100 million in annual revenue this year.<\/p>\n Altana\u2019s core product is essentially a map of the world economy: which companies make what, where, for whom, using inputs from where. The company aggregates publicly available trade data\u2014bills of lading, shipping manifests, corporate registrations\u2014and stitches it together into a continuously updated picture of the connections between hundreds of millions of businesses and facilities worldwide. But the real value of Altana\u2019s platform, according to Smith, comes from what happens when its customers, such as shipping giant Maersk or General Motors or the U.S. Customs and Border Protection connects to Altana\u2019s platform. Because then all their data gets added to the knowledge graph too.<\/p>\n Today, about 60% of the information contained in Altana\u2019s map of the global supply chain comes from the first party data it gets through its customers, Smith says. And while Altana has sometimes gotten pushback from potential customers who don\u2019t like the idea of sharing supply chain information with rivals, Smith says most companies come to see that being able to optimize supply chains, plan for supply chain resilience, and being able to simulate various supply chain shocks far outweighs the cost of rivals knowing who their suppliers are. \u201cIf you think that in the 21st Century, the existence of your supplier relationships is your source of proprietary competitive advantage, good luck to you,\u201d Smith says.<\/p>\n What does all of this have to do with last week\u2019s tariff ruling? Everything. Because one of Altana\u2019s key products is effectively an AI-powered tariff management system. Smith described an \u201cagentic\u201d workflow that automates the notoriously arcane business of assigning Harmonized System (HS) codes to goods\u2014the classification that determines what tariff rate applies to any given import\u2014as well as calculating country of origin under trade rules, something that has become phenomenally complicated in the era of transhipment and tariff evasion. Add to that a tariff scenario planner that allows companies to model the impact of changing trade rules across their entire extended supplier network. Use of Altana\u2019s tariff calculator has spiked 213% in the past week, the company reports. About 50% of those calculations concerned articles containing metals, while 32% were for products whose country of origin was China.<\/p>\n In an email, Smith said he thinks that following the Supreme Court ruling the Trump Administration will simply find new legal authorities under which to impose tariffs. \u201cThe effective rates may not actually fall much and the complexity will almost certainly get worse,\u201d Smith says. In particular, Smith said he\u2019s watching \u201ctariff stacking,\u201d the application of multiple, separate tariffs on a single product when it lands at the border based on the disparate origins of its various components. \u201cAs duties move toward components and sub-components, exposure sits deeper in the supply chain and most companies don\u2019t actually know what\u2019s in their Tier 2 and Tier 3 inputs,\u201d he wrote.<\/p>\n Or, at least, they didn\u2019t know before Altana and its AI came around.<\/p>\n With that, here\u2019s more AI news.<\/p>\n Jeremy Kahn<\/strong> OpenAI partners with McKinsey, BCG, Accenture, and Capgemini to push its Frontier AI agent platform<\/span>\u2014by Jeremy Kahn<\/span><\/p>\n OpenAI changed its mission statement 6 times in 9 years. It finally removed the word \u201csafely\u201d as a core value when it restructured into a for-profit<\/span>\u2014by Catherina Gioino<\/span><\/p>\n AI agents that do your work while you sleep sound great. The reality is far messier\u2014\u2018it\u2019s like a toddler that needs to be overseen\u2019<\/span>\u2014by Sharon Goldman<\/span><\/p>\n Exclusive: Anthropic rolls out AI tool that can hunt software bugs on its own\u2014including the most dangerous ones humans miss<\/span>\u2014by Sharon Goldman<\/span> Meta strikes $100 billion deal with AMD. <\/b>The social media giant has made a deal with chipmaker AMD to purchase up to 6 gigawatts of AI computing power using AMD\u2019s MI450 chips over five years. As part of the deal, Meta is receiving warrants that could give it a 10% stake in AMD if certain performance metrics are met. Read more from the <\/span>Wall Street Journal<\/span><\/i> here<\/span>.<\/span><\/p>\n AI infrastructure spending to hit $650 billion in 2026, entering \u2018a more dangerous phase.\u2019 <\/b>That total is up sharply from the $410 billion spent on AI infrastructure last year, according to a letter to investors from hedge fund Bridgewater Associates that made headlines in the past few days. Bridgewater\u2019s co-CIO Greg Jensen said that the infrastructure boom was entering \u201ca more dangerous phase\u201d because the hyperscalers building giant AI data centers were increasingly depending on outside capital. He warned that while demand for AI computing capacity is currently outstripping supply, financial markets could be battered if that dynamic shifts suddenly. He also warned that prominent AI companies such as OpenAI and Anthropic may struggle to raise more funds and justify their current valuations unless they achieve fundamental breakthroughs that make AI agents more reliable and easier to use. You can read more from Reuters <\/span>here<\/span>. <\/span> AI models are potentially dangerous national security advisors. <\/b>Kenneth Payne, a researcher at Kings College London, ran an extensive set of virtual war games in which he pitted a number of advanced AI models (Anthropic\u2019s Claude Sonnet 4, Google\u2019s Gemini 3 Flash, and OpenAI\u2019s GPT-5.2) against one another and against versions of the same model. It turned out that the models were sophisticated players, but they exhibited some tendencies that differed from human players in ways that could prove dangerous if they were advising real governments in national security crises.\u00a0<\/span><\/p>\n For instance, Payne found that the models were often willing to resort to the use of tactical nuclear weapons, and in some cases were willing to launch an all-out nuclear war rather than back down. He also found that the model behavior differed from that of human players in some key ways. \u201cThreats more often provoke counter-escalation than compliance,\u201d he wrote. \u201cHigh mutual credibility accelerated rather than deterred conflict\u201d and \u201cno model ever chose accommodation or withdrawal even when under acute pressure, only reduced levels of violence.\u201d<\/span> Feb. 24-26: <\/strong>International Association for Safe & Ethical AI (IASEAI), UNESCO, Paris, France.<\/p>\n March 2-5:\u00a0<\/strong>Mobile World Congress, Barcelona, Spain.<\/p>\n March 12-18:\u00a0<\/strong>South by Southwest, Austin, Texas.<\/p>\n March 16-19:\u00a0<\/strong>Nvidia GTC, San Jose, Calif.<\/p>\n April 6-9:\u00a0<\/strong>HumanX 2026, San Francisco.\u00a0<\/p>\n Is an era of ‘Ghost GDP’ looming on the horizon? <\/strong>A <\/span>blog post<\/span> penned by Citirini Research, a Wall Street equity research and macro analysis house that has a big social media following, went viral this past week. The post is, as Citrini warns, a scenario, a work of speculative fiction, not a forecast. The intention, the firm says, is to prepare readers \u201cfor potential left tail risks as AI makes the economy increasingly weird.\u201d Set in June 2028, it depicts the economic havoc AI could wreak if it enjoys \u201ccatastrophic success\u201d over the next two years. The scenario imagines unemployment well above 10% even as labor productivity booms to levels not seen since the early 1950s. It talks about \u201cGhost GDP,\u201d where U.S. national accounts swell, even as businesses dependent on consumer spending (which is 70% of U.S. GDP at present) whither. (Consumers are either unemployed or worried about becoming so imminently.) It talks about how the pressure on legacy software-as-a-service companies, which are starting to see now, accelerates and spills into other areas of the economy, creating a kind of downward spiral of job losses and decreases in discretionary spending and consumption, with no natural break.<\/span> #company #helping #businesses #navigate #tariff #chaos #Supreme #Court #ruling<\/p>\n","protected":false},"excerpt":{"rendered":" Hello and welcome to Eye on AI… <\/p>\n","protected":false},"author":1,"featured_media":24829,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[704,2397,1935,173,596,504,779,2936,1025,172,357,5233,1251,503,489,497,490],"_links":{"self":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts\/24828"}],"collection":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=24828"}],"version-history":[{"count":0,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts\/24828\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/media\/24829"}],"wp:attachment":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24828"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=24828"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=24828"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\nUsing AI to help map global supply chains<\/h2>\n
\u2018Complexity will almost certainly get worse\u2019<\/h2>\n
jeremy.kahn@fortune.com
@jeremyakahn<\/p>\nFORTUNE ON AI<\/h3>\n
<\/span><\/p>\nAI IN THE NEWS<\/h3>\n
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<\/span>OpenAI struggled to get its $500 billion Stargate joint venture with Softbank and Oracle off the ground, forcing it to pivot multiple times. <\/b>That\u2019s according to <\/span>a story<\/span> in <\/span>The Information<\/span><\/i> which cited unnamed sources familiar with the project. The tech publication said OpenAI has scrambled to secure computing capacity after the initial Stargate concept stalled amid leadership gaps and disagreements among the three partners. Rather than building and owning its own facilities, OpenAI has shifted toward partnering with cloud providers and structuring deals that give it design control without heavy capital commitments. But the publication said OpenAI is still falling short of its original capacity targets.<\/span>
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<\/span>Deal with AI chip startup SambaNova raises conflict-of-interest concerns for Intel CEO. <\/b>Intel is investing in a new $350 million funding round for AI chip startup SambaNova Systems and also entering a multiyear technical partnership with the company. The exact amount of Intel\u2019s investment was not disclosed. The deal has drawn scrutiny because Intel CEO Lip-Bu Tan is an early investor and chairman of SambaNova, though Intel said he recused himself from negotiations. Intel had reportedly been in talks previously to buy SambaNova. Together, the companies aim to integrate Intel Xeon processors into SambaNova\u2019s AI systems and work on building new \u201chetereogenous\u201d data centers that include multiple kinds of chips to handle various different AI and non-AI workloads. Read more from the <\/span>New York Times<\/span><\/i> here<\/span>.<\/span>
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<\/b>IBM shares hammered after Anthropic says Claude Code can modernize COBOL programs. <\/b>Big Blue\u2019s shares suffered their steepest drop in more than 25 years after Anthropic said its Claude Code tool can automate the modernization of Cobol systems that run heavily on IBM mainframes, sparking fears of AI-driven disruption. The stock fell 13% in a single day and is down sharply for the month as investors worry that AI coding tools could reduce reliance on legacy software and services tied to mainframe computing. IBM pushed back, arguing its mainframe value lies in reliability and security regardless of programming language and noting it already offers its own AI tools to help customers modernize. IBM also partnered with Anthropic last year to help bring Anthropic\u2019s models to its own customers for specific tasks, including modernizing COBOL codebases. See more from Bloomberg <\/span>here<\/span>. <\/span>
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<\/b>U.S. announces launch of \u201cTech Corps\u201d to promote U.S. AI abroad. <\/b>The White House has launched a \u201cTech Corps\u201d within the U.S. Peace Corps to deploy American volunteers with technical skills abroad, aiming to promote U.S. artificial intelligence and counter China\u2019s growing influence in developing markets, CNBC <\/span>reports<\/span>. The program will send engineers and STEM graduates to countries participating in the U.S. AI Exports Program to help implement American AI systems in sectors such as agriculture, education, health, and economic development, with deployments expected to begin in fall 2026.<\/span><\/p>\nEYE ON AI RESEARCH<\/h3>\n
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<\/span>The research has big implications for militaries and governments that are actively considering whether AI should be used as an advisor to policymakers and military commanders. But it also has potential implications in business settings where people are starting to turn to AI for advice on negotiation tactics and strategy and where boardrooms may be consulting AI for strategic advice too. In many of these settings, pursuing the most aggressive course does not always yield the best outcomes and humans will need to be wary of AI\u2019s tendency towards escalation over conciliation. You can read the research paper on the non-peer reviewed research repository arxiv.org <\/span>here<\/span>.\u00a0<\/span>
<\/span><\/p>\nAI CALENDAR<\/h3>\n
BRAIN FOOD<\/h3>\n
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<\/span>The blog is bleak reading. Fortunately, I am not sure it is correct. In fact, it is almost certainly wrong in speculating that all the effects it depicts could play out in just over two years. (One thing it depicts which I think is somewhat unlikely is that AI agents will seek to drive down transaction costs and so will turn to stable coins rather than traditional payment methods.) But it is worth reading and thinking about. And for an analysis of where Citrini is likely wrong, check out <\/span>this post<\/span> by Zvi Moshkowitz.\u00a0<\/span><\/p>\n<\/div>\n