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The spending behind the chatbot
When a person asks an AI assistant a question, the answer seems to appear from nowhere. In reality, it comes from a vast physical system: data centres full of specialised chips, high-speed optical networks connecting them, and enormous amounts of electricity to run it all.
A series of recent announcements shows how much money is flowing into that system — sums large enough to reshape the technology industry, energy markets and government policy.
A $115 billion deal for the connections between computers
One of the biggest announcements came on November 2, when chip company Marvell said it had agreed to acquire Lumentum, a maker of optical networking components, in a deal valued at approximately $115 billion.
Under the proposed all-stock agreement, Lumentum investors would receive 4.5 Marvell shares for each of their shares, and Marvell shareholders would own about 67 percent of the combined company. The transaction has been announced but not completed, and it remains subject to approval by regulators and shareholders.
The deal matters because of what Lumentum makes. Modern AI systems run on thousands of chips working together, exchanging enormous amounts of data at very high speed. Optical components carry that data as light through fibre, and they have become one of the most important parts of the AI supply chain.
Analysts say the deal shows the race in artificial intelligence is no longer only about who builds the smartest model. It is also about who controls the physical infrastructure behind those models.
Hundreds of billions in computing commitments
The scale of the spending goes far beyond one deal. Anthropic, the company behind the Claude AI assistant, disclosed in a regulatory filing on November 2 that it had approximately $895.4 billion in multi-year purchase and lease commitments, mostly related to computing power and infrastructure.
The figure covers agreements stretching over several years, reflecting the cost of the computing capacity needed to train and run large AI models. Observers say leading AI companies now plan infrastructure years in advance, much like airlines plan aircraft orders.
Big tech results show the same pattern
The latest financial results from the largest technology companies tell a similar story. Apple reported quarterly revenue of $114.5 billion, up about 12 percent from a year earlier, with a gross margin of 47.9 percent. At the same time, the company warned that rising memory prices and the costs of developing its AI assistant, Siri, would put pressure on margins in the December quarter, projected at 45 to 46 percent.
Memory chips are in short supply partly because AI data centres use so much of them. When demand from data centres rises, prices rise for everyone, including makers of phones and computers.
Meanwhile, Palantir, a software company that sells AI tools to businesses and governments, reported third-quarter revenue of about $2.313 billion, up 96 percent from a year earlier. Its revenue from commercial customers in the United States rose 148 percent to about $984 million, passing its government revenue for the quarter.
Governments are also getting involved. Abu Dhabi’s Media Office announced on November 2 that OpenAI had signed four agreements covering government services, energy, investment analysis and broader adoption of AI, showing how the technology is moving from private companies into public services.
Research is moving fast too
The infrastructure boom is matched by rapid research. At MIT’s Computer Science and Artificial Intelligence Laboratory, researchers reported that a frontier AI model was able to edit the internal settings, known as weights, of a smaller model directly. According to the researchers, changing only 0.016 percent of the smaller model’s weights improved its score on a mathematics test by as much as sixfold, without any additional training run. The technique is still early research, and independent reviewers have questioned the size of the effect.
What does it mean for ordinary users?
For most people, the infrastructure boom will show up in everyday ways. AI features are appearing in phones, search engines, office software and customer service. Apple, for example, confirmed that its first AI features for users in China would begin developer testing from November 4, with a wider rollout in December alongside a software update.
At the same time, the boom raises questions policymakers are still working through. Data centres use large amounts of electricity and water, computing costs are rising, and the largest companies can afford the biggest spending, which could make it harder for smaller firms to compete.
A long race, not a single moment
The announcements of the past few days are best understood as part of a longer trend. The AI industry is moving from building impressive demonstrations to building the physical and commercial systems that can support the technology at a global scale.
That shift involves deals worth tens of billions of dollars, multi-year computing commitments, and new agreements between technology companies and governments.
For readers, the key point is simple: the AI tools that are becoming part of daily life rest on an enormous and expensive foundation, and the companies and countries that control that foundation will shape how the technology develops in the years ahead.
