Such moves are prompted, in part, by unmistakable signs that Nvidia's core customers are drifting away. The hyperscalers are no longer content only to buy Nvidia’s chips and are spending billions on designing their own. Google has for years rented access to tensor processing units (tpus), specialised ai chips, through its cloud. Now it is selling tpu systems to other firms. Amazon puts the annualised revenue of its custom-chip business, mostly tied to ai, at $25bn. Andy Jassy, the company’s boss, reckons that makes it one of the world’s three biggest data-centre chip businesses. Microsoft and Meta have also developed chips. Anthropic and Openai, two big ai labs, plan to do the same. Hyperscalers have good reasons to design their own silicon. Chips account for much of the cost of an ai data centre. Bernstein, a broker, estimates that in a server rack running Nvidia’s h100 chips, priced at $25,000 apiece, spending on chips makes up three-quarters of the total cost. Custom silicon is a fifth to a third as expensive, though less powerful. The cloud giants argue that they still get more computing power per dollar. Custom chips are also better suited to particular jobs: Google’s tpus for calculations underpinning its ai models, for example, and Meta’s processors for recommendation algorithms. Some hyperscalers think custom silicon will become a big business in its own right. In May Google teamed up with Blackstone, a private-equity giant, to establish an ai cloud firm that will rent out computing power built on Google’s chips. Amazon plans a similar venture. Anthropic and Openai intend to use Amazon’s Trainium processors. Such moves could turn custom silicon into a formidable competitor to Nvidia’s chips. Bloomberg Intelligence, a research firm, estimates that ai-chip shipments worldwide will grow from around 15m units this year to 28m by 2030. Custom chips will make up 49% of the total, with Nvidia responsible for 40%. Nvidia will probably remain dominant by revenue—but cheaper custom silicon will put pressure on its fat margins. Nvidia sees things differently. Jensen Huang, its boss, argues that custom silicon’s greatest strength—specialisation—is also its weakness: it is good for known workloads, not new ones. Nvidia’s gpus, by contrast, can handle almost any ai task. As ai spreads beyond large language models into
robotics, autonomous vehicles and industrial applications, that versatility could matter more. Keeping up with Nvidia may also prove expensive and difficult. It now releases breakthrough chips every year, up from every two. Designing a frontier ai chip typically takes other firms two to three years and costs $1bn- 3bn. Nvidia spent over $6bn on r&d in the last quarter alone. Few firms have the capital and engineering talent to sustain such an effort. Nvidia also benefits from making its own chips. Firms that turn others’ designs into the finished product, such as tsmc, the Taiwanese chipmaker, have limited capacity. Cloud companies, says Vivek Arya of Bank of America, have to decide whether to use that “precious allocation” for their own needs or those of customers. As well as defending its position against hyperscalers, Nvidia hopes to encourage other business. It wants to sell to governments trying to build domestic ai infrastructure, firms building their own data centres and “neocloud” firms that rent out ai computing power. The new partnership with Wall Street is part of a broader strategy to help customers finance ai investments. In July Nvidia launched a programme to rent unused computing capacity from neoclouds in exchange for a share of future revenues to make it easier for them to borrow and expand. It is also said to be discussing a $350bn scheme to help Openai lease a data centre in Ohio and buy gpus. Nvidia is sensitive to allegations that its deals amount to "circular financing". It says the latest structure, by including outside investors, aims to address those concerns. The line between financier and supplier, and that between customer and competitor, have been blurred. But everyone is buying computing power wherever they can find it. Nvidia still supplies the bulk. ■ This article was downloaded by zlibrary from https://www.economist.com/business/2026/08/11/nvidias-great-silicon-showdown
AI agents lie, cheat and steal. That is putting off users It is time to impose law and order on the frontier Aug 13th 2026 BARBED WIRE held a strange interest for the late Alan Greenspan, a lifelong urbanite. Sure, the former Federal Reserve chairman admired the railways, gold diggers and “lonesome cowboys” that helped open the frontier at the dawn of American capitalism. But he also had a soft spot in his economist’s heart for barbed wire, which, he argued, boosted productivity by allowing the settlers who came in their wake to safeguard their properties. Like the Wild West, artificial intelligence has its frontiersmen. In America they are a dwindling bunch. Anthropic and OpenAI remain steadfastly committed to pushing the boundaries of AI. Other tech giants, including
Google (a former front-runner that is shedding frontier-AI scientists at a rapid pace), are wavering. The hyperscalers appear to find it more lucrative to sell AI infrastructure to their cloud customers than splurge valuable compute on advancing their own models. What AI doesn’t have is enough settlers. That is, the individuals and businesses who make full use of products such as AI agents, helping to justify the reams of investment pouring into frontier AI. They are put off partly because, like in the Wild West, life on the frontier is reckless. As recent “loss-of-control” episodes by the most advanced models of Anthropic and OpenAI attest, agents, which are supposed to work on people’s behalf in “alignment” with their values, lie, cheat and steal if necessary. They break free from captivity and form harmful posses to do harm to people. They’d drink whisky and brawl if they could. Such unpredictability is too much for many firms to handle. “All you have to do is get snake-bitten once and you’d never go back,” says Jared Sine of GoDaddy, an internet firm trying to help bring order to the chaos. The need for law and order is giving rise to a new cohort of AI-infrastructure firms. They are not selling chips or compute—the typical picks and shovels of the AI gold rush. They provide protection against cyber-threats, fixes for untrustworthy and inscrutable agents, and controls if they go rogue. In other words, their business is barbed wire. The most immediate area of danger—and opportunity—is cyber-security. Recent tests of the hacking capabilities of models from Anthropic and OpenAI revealed agents going rogue, stealing credentials, creating fake identities, setting up secret chatrooms and covering their tracks—all to the shock and horror of their human evaluators. The state-of-the-art security models, Anthropic’s Claude Mythos 5 and OpenAI’s GPT 5.6-Cyber, will no doubt help defenders, too. But, according to Dawn Song, an expert on ai and cyber-security at the University of California, Berkeley, for now the balance of power rests firmly with the attackers. There is a further snag. If organisations adopt autonomous agents, they increase the potential “attack surface” for hackers, she says. The rising risks help explain why the valuations of cyber-security firms with AI capabilities are on a tear. Share prices of Palo Alto Networks and
CrowdStrike, the two biggest, have roughly doubled so far this year. M&A has soared. Led by Alphabet’s $32bn acquisition of Wiz, another cyber- security firm, more than $70bn of cyber-security-related megadeals have closed in the past year. Pitchbook, a data gatherer, says AI-related cyber- security is one of the hottest areas of venture-capital (VC) investment as well. The opportunities go beyond cyber-security. At a recent conference on agentic AI organised by Ms Song, your guest columnist heard a litany of complaints not just about the hacking potential posed by large language models (LLMs) at the peak of their powers, but about the blunders that they can make at their most clueless. One expert showed an agent-generated chart measuring the income of Europe’s top tennis players. It mistakenly omitted Spain’s Carlos Alcaraz, the world number two. “This is tennis, who cares?” he quipped. But if it were the financial analysis of a company, it would have mattered. Another drew a contrast between the amount of knowledge LLMs have about quantum physics, and their inability to order a burrito. The frailties have given rise to another group within the cohort attracting VC interest: those promising to strengthen the “trust layer” of agentic AI. One is Cyera, whose valuation has quadrupled to $12bn in 18 months. It says a lack of trust has “stalled” AI adoption recently, and provides services to prevent data leaks and unauthorised tool use. Another is Scaled Cognition, co- founded by Dan Klein, a Berkeley professor, that recently raised $100m in VC backing. It promises to reduce what Mr Klein calls the “invisible errors” produced by AI—those that are plausible enough to be missed and compound as agents perform longer tasks—by incorporating guaranteed reliability into the training of its models. Other startups specialise in creating evaluations to measure agents’ effectiveness, agent identities to determine where liability lies if they go rogue and control systems with a kill switch. AI has other problems on the frontier. Like the Wild West, it is cut-throat. There is competition from cheaper Chinese open-weight models. On August 10th Mark Zuckerberg’s Meta threw its hat into the ring, releasing an open- weight version of its most powerful model, called Muse Glimmer. The frontier is lawless, too. So strong are the concerns about rogue agents that even executives of the leading AI labs are calling for the Trump
administration to take a sheriff’s role—though weirdly the government is keeping its most recent framework on AI safety under wraps. What is clear, though, is that more checks and balances are needed before organisations adopt agents with anything close to full confidence. One of this year’s AI buzzwords is “harness”—the system that surrounds an LLM to keep agents on the straight and narrow. It might just as well be barbed wire. ■ This article was downloaded by zlibrary from https://www.economist.com/business/2026/08/12/ai-agents-lie-cheat-and-steal-that- is-putting-off-users
China is now the world’s great oil power Is China’s debt-bomb squad about to blow up? Can Singapore ever build a proper stock market? Hooray for index funds—just don’t call them passive Kevin Warsh is struggling to escape his predecessor’s problems When Japan buys yen, it unwinds a dangerous trade