Hello and welcome to Eye on AI. In this edition…A chaotic AI summit in India ends with some voluntary commitments and $200 billion for the host nation…Anthropic accuses Chinese rivals of using Claude’s answers to enhance their models…OpenAI launches an alliance with major consulting firms to sell its Frontier AI agent platform…$650 billion in AI infrastructure spending this year could be risky…and maybe don’t let an AI model advise you on using nuclear weapons.
First, many of the AI world’s 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.
Next, Chinese AI company DeepSeek has not even dropped its V4 model yet—it is expected any minute now—but it is already stoking plenty of controversy.
Yesterday, Anthropic alleged that it had detected what it described as “an industrial scale campaign” 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’s 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’s reasoning capabilities.)
Also yesterday, Reuters reported, citing an anonymous senior U.S. government official, that the U.S. believes DeepSeek trained V4 using Nvidia’s latest generation Blackwell AI GPUs, in likely violation of U.S. export controls that were supposed to prevent Chinese AI companies from acquiring Nvidia’s most advanced chips. The story said the U.S. believed that DeepSeek has a data center in Inner Mongolia stuffed full of Blackwells–although it said the U.S. was unsure exactly how it obtained them.
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’s 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’s ultimately not just performance that matters but price-performance ratios.) What’s more, the Chinese have been desperately trying to build domestic AI chips that are as capable as Nvidia’s. 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’s Blackwells.
Using AI to help map global supply chains
Now, turning to another big news item of the past week: the Supreme Court’s striking down U.S. President Donald Trump’s “Liberation Day” 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 “knowledge graph” 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.
Altana’s 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—bills of lading, shipping manifests, corporate registrations—and 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’s 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’s platform. Because then all their data gets added to the knowledge graph too.
Today, about 60% of the information contained in Altana’s 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’t 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. “If you think that in the 21st Century, the existence of your supplier relationships is your source of proprietary competitive advantage, good luck to you,” Smith says.
‘Complexity will almost certainly get worse’
What does all of this have to do with last week’s tariff ruling? Everything. Because one of Altana’s key products is effectively an AI-powered tariff management system. Smith described an “agentic” workflow that automates the notoriously arcane business of assigning Harmonized System (HS) codes to goods—the classification that determines what tariff rate applies to any given import—as 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’s 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.
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. “The effective rates may not actually fall much and the complexity will almost certainly get worse,” Smith says. In particular, Smith said he’s watching “tariff stacking,” 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. “As duties move toward components and sub-components, exposure sits deeper in the supply chain and most companies don’t actually know what’s in their Tier 2 and Tier 3 inputs,” he wrote.
Or, at least, they didn’t know before Altana and its AI came around.
With that, here’s more AI news.
Jeremy Kahn
jeremy.kahn@fortune.com
@jeremyakahn
This story was originally featured on Fortune.com
