Tech Giants Battle Over AI Leadership, IBM Pushes Chip Advancements

“It’s not just an incremental step, it’s a meaningful leap forward,” said Jay Gambetta , director of IBM Research and IBM Fellow, in an advance media briefing. He described the new chip technology as “pointing to a future where computing becomes significantly more powerful without a corresponding increase in energy.”

It’s worth unpacking what the “world’s first sub-1 nanometer chip technology” means, because it is impractical to build reliably functional chips with transistors and other features smaller than 1 nanometer due to various physical limitations.

Specifically, IBM describes its new chip technology as being built at the 0.7-nanometer node, which it has named the 7 angstrom node because 1 nanometer consists of 10 angstroms.

But keep in mind that such node numbers have nothing to do with the actual physical dimensions of IBM’s chip features. Older generations of chips developed in the 1970s and 1980s had physical features with dimensions matching the number in the name of their chip technology’s node or process—such as chips made at the 180-nanometer node—but that has not been the case for decades and certainly not for the latest chip generations made with a 3-nanometer or 2-nanometer process.

To overcome the physical scaling limits facing modern chip designers, IBM’s new nanostack architecture vertically stacks transistors in a staggered layout to pack more transistors into the same chip space. The nanostack architecture builds on the company’s prior development of nanosheet transistors that paved the way for its 2-nanometer chip node introduced in 2021.

The Trump administration, apprehensive of potential security issues, has reportedly asked OpenAI to stagger the release of its next big-ticket model, GPT-5.6.

The Information reported that OpenAI CEO Sam Altman told employees Wednesday in a company Q&A that it would release GPT-5.6 in limited preview form — granting access only to a small group of enterprise customers — in compliance with a request from the federal government. During that preview period, the Trump administration itself would reportedly approve access for customers on a case-by-case basis.

Now, some of those concerns seem to be coming to pass — and in a decidedly uneven way, depending on the company.

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Consumers who pay for AI have been increasingly choosing Anthropic’s Claude, trend data from credit card transaction analysis company Indagari shows.

This is a sign that the AI lab has a wider and healthier set of customers than the niche it is generally considered to command — enterprise and startup developers using Claude Code.

Indagari analyzes billions of anonymized credit card transactions from about 28 million U.S. consumers. So while that data can’t give us absolute numbers about Anthropic’s revenue or total customers, it is a large enough sampling to spot trends.

The iPad line is also getting more expensive, between $100 and $200, depending on the model. Smaller price increases have been applied to products like the Apple TV and HomePod. The price of iPhones remains unchanged, at least for now.

The culprit? The soaring price of memory, according to an interview that Apple CEO Tim Cook gave to The Wall Street Journal earlier this month. “Unfortunately, price increases are unavoidable,” Cook told the paper. “We’re doing our best to mitigate the huge increases that are being passed to us, and we’ve been trying to shield our customers from the increases, but the situation has become unsustainable.”

As AI investments rocketed, chipmakers increasingly focused on the more profitable memory used in data centers rather than the memory intended for consumer products.

Consequently, supply shortages and high memory prices have been affecting the tech industry for months now, driving up the prices of many consumer electronics and causing others to disappear from sale. For example, in March, Apple quietly removed a memory-heavy configuration of the M3 Ultra Mac (which featured 512GB of memory) from its store.

For long-time Apple customers who remember how much Apple used to charge for RAM during the PowerPC days, these price rises might induce a little déjà vu.

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AI agents are becoming more sophisticated. They are evolving from answering questions to autonomously executing multi-step complex tasks.

But before these agents can be trusted to book trips or conduct financial analysis on behalf of users, model providers and the startups building such agents want to ensure that they perform reliably across a vast range of scenarios.

AI labs often use benchmarks to show off their model’s prowess, but a high score, even on an agent-oriented benchmark, doesn’t actually prove that an AI can accomplish various complex, real-world jobs correctly.