Amazon CEO takes aim at Nvidia, Intel, Starlink, more in annual shareholder letter

TechCrunch AI reported: Jassy didn't spare Intel either. He points out that AWS's homegrown Graviton CPU, a competitor to the Intel x86 architecture, “is now used expansively by 98% of the top 1,000 EC2 customers,” aka some of the biggest companies in the world. Two companies even asked to “buy all of our Graviton instance capacity in 2026,” he writes (emphasis his). “We can’t agree to these requests given other customers’ needs, but it gives you an idea of the demand.” He promised that Amazon's Starlink competitor, Amazon Leo, scheduled to launch in mid-2026, is already succeeding, too. It's won contracts from Delta Airlines, AT&T, Vodafone, Australia’s National Broadband Network, and NASA, among others. Interestingly, he also said Amazon could be looking at selling robotics one day. It may turn all the data from its 1 million warehouse robots into “robotics solutions” for industrial uses and consumers, he wrote. Is there an Amazon humanoid in our future? We'll see. He talked up other Amazon businesses, too, like same-day delivery, groceries, and drones. But mostly, Jassy tried to make the case for the hundreds of billions of dollars of capital expenditures he's committed. In February, he announced plans to spend $200 billion in 2026 on capex, mostly building out AWS data centers. That's more than any of the o.

The story lands in a market where demand is already assumed. The more useful question is whether the supporting layer around cloud infrastructure is flexible enough to turn that demand into available capacity. The constraint is not just chip supply. Advanced compute depends on packaging, memory, networking, power delivery, and the ability to land systems inside facilities that can actually run them at high utilization.

The pressure point is timing. The underappreciated variable is deployment readiness across networking, power, and packaging, not just chip availability.

That matters for buyers because the useful capacity is the installed, cooled, powered cluster, not the purchase order. It also matters for suppliers because component shortages can shift bargaining power quickly across the stack.

The financial question is whether this development improves pricing power, locks in scarce capacity, or exposes execution risk that the market may still be discounting, the operating question is procurement timing, facility readiness, network design, and the likelihood that adjacent constraints will slow realized deployment, and the customer question is whether this changes build sequencing, partner dependence, or the economics of scaling regions and clusters over the next few quarters.

This is where AI infrastructure differs from ordinary software growth. Capacity has to be financed, permitted, powered, cooled, connected, staffed, and then sold into real workloads before the economics are visible.

The practical read is that infrastructure advantage is becoming more local and more operational. Two companies can chase the same AI demand and end up with very different outcomes if one has better access to power, more credible delivery dates, or a cleaner path through procurement and permitting.

The next signal to watch is the next disclosures on customer commitments, infrastructure readiness, and any evidence that power, cooling, silicon supply, or permitting becomes the real gating factor. The next test is whether delivery schedules, memory availability, and deployment readiness move together or start to diverge.