Weekly: Nvidia's PC play
12.5 min read.
Highlights
Nvidia’s PC play. Two articles, one each in the WSJ and the Economist, exploring Nvidia’s recent announcement to foray into the PC market. The two pieces both note that Nvidia’s PC play isn’t new (Nvidia was once more known for its graphics processors and gaming chips than anything else), but that success isn’t guaranteed (the CPU market is highly contested and market leaders like Intel enjoy a lot of sticky incumbency advantages). But Nvidia, with its technical strength and piles of cash, has a more than fair shot.
US-China tech war. Two articles, one each in the FT and the WSJ, on the US-China tech war. Chris Miller, of Chip War fame, writes in the FT that any apparent truce from the recent Xi-Trump summit is just buying time. Beijing, Miller writes, believes it can win the tech race with industrial output, and Washington is betting on AGI. In the WSJ, Michael Sobolik, an expert at the Hudson Institute think tank, argues for US chip export controls.
Space data centre scepticism. SemiAnalysis explores space data centres with their trademark depth. “While we think that it is possible that space datacenters could scale one day, deploying orbital compute using today’s technology currently costs several times more than deploying terrestrial compute.” Even SpaceX admits that it is not currently commercially or technologically feasible in their recent IPO filings.
Thanks for reading.
Table of Contents
Dan Gallagher and Asa Fitch, “Why ‘Nvidia Inside’ Can Work in the PC Market,” WSJ, 06/02/2026.
The Economist, “Nvidia wants to supercharge your laptop,” The Economist, 06/02/2026.
Chris Miller, “The China-US tech truce is fragile,” FT, 06/02/2026.
Michael Sobolik, “We Should Starve Adversaries of AI Compute,” WSJ, 06/02/2026.
Daniel Nishball, Pranav Myana, Ellie Holbrook, et al., “To Boldly Go: The Case for Space Datacenters,” SemiAnalysis, 06/03/2026.
Konark Bhandari, “India’s Semiconductor Ecosystem Is Maturing—and ASML Is Taking Notice,” Carnegie, 06/02/2026.
1.
Dan Gallagher and Asa Fitch, “Why ‘Nvidia Inside’ Can Work in the PC Market,” WSJ, 06/02/2026.
The “Intel Inside” marketing campaign made Intel a household name and a ubiquitous personal-computer chip supplier in the 1990s. These days, “Nvidia Inside” has a lot more selling power.
That is what Nvidia is betting with its new line of PC chips, set to be in Windows-based computers launching later this year. With its artificial-intelligence cachet, it is very likely that Nvidia will succeed, potentially upending an order in the PC world that has prevailed for pretty much the past five decades.
Investors are optimistic. Nvidia’s shares jumped more than 6% on Monday, while Windows-maker Microsoft rose more than 2%. Shares of PC makers Dell Technologies and HP both surged more than 8%. Arm Holdings, which licenses the basic blueprints that Nvidia uses in its PC chips, jumped more than 15%.
The direct impact of Nvidia’s PC play on its finances will likely be limited, given the enormous size of the company’s business selling AI chips for data centers. But the move does put Nvidia in a position to supercharge the market for AI-enabled computers and disrupt incumbents in the process.
Nvidia isn’t exactly a new entrant to the market. It has been a big player in PCs for decades through its graphics chips, which produce sharper and smoother images on computer monitors—a capability videogamers prize. But before detailing its latest chips at a trade show in Taiwan, it hadn’t made the central-processing units at the computational hearts of PCs. Intel INTC -1.65%decrease; red down pointing triangle and Advanced Micro Devices AMD 0.83%increase; green up pointing triangle dominate that market.
For Nvidia, PCs are a small part of its business now. But the company boasts a strong appeal in the segment, which bodes well for its latest effort. Nvidia’s PC-related revenue jumped 41% in the fiscal year ended January to a little over $16 billion, thanks in part to the introduction of new videogaming chips under the company’s popular Blackwell brand. Total PC unit sales grew only 8% during the calendar year, according to IDC data.
Nvidia’s rise won’t come without challenges—the largest of which may be the software stickiness that has built up around Intel’s and AMD’s processors. They use a basic chip architecture called x86. But software that works on x86 processors needs to be adapted to work well on Nvidia’s or Apple’s Arm-based chips.
2.
The Economist, “Nvidia wants to supercharge your laptop,” The Economist, 06/02/2026.
For the past few years, whenever Jensen Huang, boss of Nvidia, has taken the stage, he has followed a familiar script, unveiling ever more powerful semiconductors, software and systems for running artificial intelligence in data centres. No wonder: the AI boom has sparked a spending spree on giant server farms, and much of that money has flowed straight to the giant chipmaker. In the past four years, annual revenue at Nvidia’s data-centre division has grown from $11bn to $194bn, pushing the firm’s market value past $5trn, more than any other company ever.
But on June 1st at Computex, an annual tech-industry jamboree in Taiwan, Mr Huang did more than refresh Nvidia’s data-centre wares. He unveiled RTX Spark, a chip to be launched later this year for personal computers (PCs), built in collaboration with Microsoft, whose software they will run. Nvidia is taking on Intel and AMD, the chipmakers that dominate the segment—and is betting that the next phase of AI will play out not just in data centres, but on devices at the edge.
Nvidia argues that this will require a different sort of machine. PCs rely on central processing units (CPUs), general-purpose chips that handle everything from word processing to web browsing. CPUs may co-ordinate the work of AI agents, but the models those agents rely on need another type of chip: graphics-processing units, or GPUs, the market for which Nvidia dominates. With RTX Spark, Nvidia is combining the two types into a “superchip”. The upshot, according to Mr Huang, is that the PC is being reinvented for the first time in 40 years, replacing the old model, in which humans did most of the clicking and typing, with one in which AI agents do much of the work.
Success is far from assured. Nvidia is not a stranger to PCs; before the AI boom, it made a big chunk of its money selling GPUs for gaming machines. But in CPUs for PCs, it is a newcomer. Intel and AMD together supply more than four-fifths of all CPUs sold for PCs. As for software, Nvidia says it has worked with Microsoft for over two and a half years on the new chip. Even so, some analysts remain sceptical that developers and users will quickly embrace a new class of AI-first PC.
Yet Nvidia has plenty of advantages. For a start, it has piles of money to invest; analysts expect the company to generate roughly $200bn in free cashflow this year. It also has the clout to draw in partners and a brand that may help catch the attention of consumers. A number of the biggest PC-makers, including HP, Lenovo and Acer, have already signed up to offer the new chip in their devices.
Nvidia’s move is another sign of how fiercely contested the chip business has become, with firms venturing ever further from their original turf. In March Arm, a British designer that licenses chip blueprints to others, announced its own CPU for AI data centres; RTX Spark uses Arm’s designs for its CPU. A day after Mr Huang spoke at Computex, Lip-Bu Tan, Intel’s boss, presented his own plans. Among them was an AI chip due later this year, aimed at running models in data centres. Intel, Mr Tan said, was “going to do really well”. Mr Huang is equally confident.
3.
Chris Miller, “The China-US tech truce is fragile,” FT, 06/02/2026.
Donald Trump and Xi Jinping both smiled for the cameras at their summit last month but neither side has mistaken the tech truce for peace. Beijing has continued shipping some rare-earth magnets to the US, while Washington will postpone long-delayed restrictions on Chinese chipmakers. Behind the scenes, however, each side is sharpening its knives for a new wave of supply chain conflict.
Right before the summit, Beijing announced a sweeping set of regulations to penalise foreign companies for complying with third-party sanctions. This will be a powerful tool to squeeze multinationals to take China’s side amid future supply chain escalation.
Meanwhile, in Washington, Congress has pushed the White House towards a tougher line. The Republican-controlled House Foreign Affairs Committee has advanced a large package of export control bills. The Match Act would close loopholes that allow US allies more scope to sell chipmaking equipment to China than US companies have. Both the House and Senate have also proposed legislation to limit the ability of American companies to sell AI chips to China.
If both Beijing and Washington foresee the tech war continuing, what’s the logic of a truce? The answer is time.
China believes that it is winning the race for advanced manufacturing — and in key sectors such as batteries and autos, it is. The US Chamber of Commerce recently released a report finding that China has a “dominant position in global value chains”. Nvidia, which has lobbied hard to sell its chips there, is an exception. Most US companies see China more as a rising competitor than a growing market.
What is Washington’s theory of the truce? Silicon Valley types talk of being “AGI pilled” — a reference to the film The Matrix — when referring to the belief that AI will become increasingly capable and economically transformative. The US government seems to be betting that AI will dramatically enhance US power, too.
Instead of being AGI-pilled, Xi and other Chinese leaders are industrial output-pilled. The next year will test which of these approaches is more valuable.
4.
Michael Sobolik, “We Should Starve Adversaries of AI Compute,” WSJ, 06/02/2026.
Neil Chilson’s op-ed (“AI Overwatch Act Would Help China,” May 27) makes two arguments in opposition to restricting high-end chip exports to China. First, these restrictions will make American companies less competitive globally, and second, the U.S. could lose in the artificial-intelligence race with China. Both arguments rest on a faulty premise that no meaningful trade-offs exist between national security and economic engagement with adversaries.
Huawei has no meaningful share of the AI chip market outside China—primarily because of American export controls. That’s a good thing for American companies and national security. If anything, selling H200 chips to China would harm American customers, whose orders could be delayed to service the fabrication of China-bound chips.
The quickest way for the U.S to lose the AI race would be to sell advanced chips that Chinese companies are currently incapable of fabricating. China has already leveraged American chips for military training—missile defense ambush, counter-strikes, drone training and psychological warfare—all with an eye on defeating America on the battlefield. Policymakers should starve adversaries of AI compute, not supply it.
This is the policy logic underneath the AI Overwatch Act, a legislation that Mr. Chilson dismisses. The bill focuses on chips designed or marketed for data centers.
Mr. Chilson’s most curious objection, however, is his insistence that policymakers should avoid controlling chip exports to the Chinese in case “alliances evolve.” Does anyone expect that U.S.-China relations will evolve into a friendship, let alone an alliance?
5.
Daniel Nishball, Pranav Myana, Ellie Holbrook, et al., “To Boldly Go: The Case for Space Datacenters,” SemiAnalysis, 06/03/2026.
Everyone has been talking about datacenters in space. Furthering space-based compute was also one of the stated motivations behind the merger of xAI into SpaceX (as a ‘reorganization of entities under common control’), and is a key part of SpaceX’s plans to go public, as stated in their S-1 filing on 20 May 2026.
A few casual arguments made in favor of space datacenters include the following:
· Space can provide free solar energy 24 hours a day
· Cooling is “free”. Some erroneously point to space being cold as a key positive
· Communications latency in space is low as you’re just sending light through a vacuum
· There is no need for permitting in space… so far…
Many of these points sound like they hold merit on the surface, but a deeper analysis of each apparent advantage reveals a far more complex story.
While we think that it is possible that space datacenters could scale one day, deploying orbital compute using today’s technology currently costs several times more than deploying terrestrial compute. Achieving Space-Earth cost parity will require significant engineering work, material science breakthroughs and cost scaling progresses and will still take years to achieve. There are also important reliability and servicing obstacles to overcome - for instance - how GPU servers will recover from faults that require human intervention, effectively shielding accelerators from radiation, among many others.
When we deploy compute in space, it won’t be because of the four superficial reasons we have cherry-picked above. Rather, Space-based datacenters make sense in the world where AI demand well exceeds all of the four layers of terrestrial datacenter supply that we will introduce below. For Space datacenters to step up to this call - it is a necessary condition that major space datacenter cost items like radiators, solar arrays and launch costs decline considerably, and that a number of key operational obstacles are overcome.
The four layers of incremental power supply for terrestrial datacenters include:
· Grid-connected supply,
· Converted bitcoin miners and powered land,
· Behind the meter generation, and finally,
· Industrial capacity and manpower to build further power infrastructure.
A necessary condition for AI related IT equipment demand to reach levels exceeding terrestrial datacenter supply is for there to be enough chip fabrication capacity to fulfill this demand in the first place, before we even discuss datacenters! We wrote about this in great detail in our recent article on the Great AI Silicon Shortage, where we concluded that the industry has moved from a power-constrained to an accelerator-constrained regime. Available datacenter capacity and power now exceed AI compute demand, but TSMC’s N3 wafer capacity and HBM supply cannot keep pace with the pace of accelerator deployments. This means that today, and for the next few years, chip manufacturing will be the global constraint before we even worry about supply for these four layers.
The chip constraint forms a separate fifth layer of supply - Semiconductor Production, and it is a “universal” constraint on all chip deployment, whether deployed on Earth or in Space. Users of our AI Space Datacenter TCO Model can see how this constraint applies well into the future, and under what scenarios regarding chip manufacturing capacity addition that Semiconductor Production may not be the constraint.
Elon Musk is clearly well aware of this constraint, and it is the impetus behind his Terafab Initiative. The AI Space Datacenter TCO Model also includes knobs and sliders for users to tune to test out various Terafab scenarios.
6.
Konark Bhandari, “India’s Semiconductor Ecosystem Is Maturing—and ASML Is Taking Notice,” Carnegie, 06/02/2026.
ASML, the Netherlands-based manufacturer of semiconductor lithography machines, recently signed a Memorandum of Understanding (MoU) with Tata Electronics—a partnership stated as critical to accelerating the setting up of the latter’s semiconductor fab in Dholera.
As first glance, this looks like a straightforward commitment to bolster semiconductor supply chains. Press releases from the two firms do little to suggest otherwise and Tata has recently signed MoUs with other key industry players as well, including Tokyo Electron, Merck Electronics, ROHM, and Intel. Individually, each MoU may not seem like much. But taken collectively, these developments point towards a deliberate effort to build a whole ecosystem in India. What this also indicates, is that since the announcement of the Dholera fab project in February 2024, the ecosystem has grown large enough to acquire the critical mass needed to attract major players like ASML.
This MoU comes on the back of a formal “Partnership on Semiconductors and Related Emerging Technology” between the governments of the Netherlands and India. Accordingly, a government-to-government push to work together on semiconductors is also likely. However, it is important to note that this semiconductor ecosystem has been years in the making and represents real commercial logic.
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