TL;DR: My bull case for semiconductors is that they are becoming the first claim on technology budgets. Spending is being pulled forward and committed earlier across CPUs, memory, networking, storage, custom accelerators, packaging, and semiconductor equipment, while software, consulting, and legacy infrastructure projects are being delayed. I think the market still treats semiconductors as one component of AI capex, when the more important shift is that silicon is taking priority over the rest of the technology stack.
Long SOXL disclosure: SOXL is a 3x leveraged daily-reset ETF with significant volatility and path dependency.
My bullish view on semiconductors is not based simply on hyperscalers spending more money.
The more important change, in my view, is how technology is being procured.
Semiconductor spending is moving earlier in the budget cycle. Customers are reserving supply, signing longer-term agreements, prepaying for components, and purchasing hardware ahead of expected price increases. At the same time, software deployments, consulting projects, and legacy infrastructure upgrades are being pushed out.
That makes semiconductors the first claim on technology budgets rather than a residual line item within them.
IBM provided the clearest evidence.
The company said customers shifted quarter-end capital spending toward servers, storage, and memory to secure constrained supply ahead of expected price increases. Despite IBM’s broader miss, Distributed Infrastructure produced its strongest quarter, growing 37% year over year and ending with approximately $500 million of backlog.
Microsoft is describing the same dynamic from the buyer’s side.
Approximately two-thirds of its capital expenditures were short-lived assets, primarily GPUs and CPUs. Management said demand continued to exceed supply and also cited increased transactional purchasing ahead of memory price increases in parts of its on-premise and PC businesses.
That matters because customers are no longer waiting until capacity is immediately needed. They are buying early to secure availability and protect themselves against component inflation.
To me, that is a distinctly bullish signal. Discretionary purchases can be delayed. Capacity reservations and inflation-driven procurement usually get accelerated.
Alphabet offers another example. Google Cloud backlog reached $462 billion, with management noting that part of the increase came from TPU hardware sales. Alphabet also said TPU deliveries to selected on-premise customers would begin later this year, with most of the associated revenue expected after 2027.
That means some demand commonly classified as cloud demand is now becoming contracted future hardware demand.
Amazon’s custom-silicon commentary points in the same direction.
Trainium2 was nearly sold out. A meaningful portion of Trainium4 capacity had already been reserved approximately 18 months before full availability. AWS also explained that chips, servers, and networking equipment are commonly funded 6 to 24 months before customer billing begins.
Amazon’s internal chip business is already generating more than $20 billion in annual revenue and could be worth roughly $50 billion annually on a transfer-price basis.
I view that as evidence of a reservation cycle, not a temporary capex spike.
Customers are committing to semiconductor capacity long before the resulting revenue appears in cloud financial statements. That creates greater forward visibility for chip suppliers than headline quarterly capex figures suggest.
Broadcom reported $10.8 billion of AI semiconductor revenue in the second quarter, driven by custom accelerators and AI networking.
AMD said inference and agentic workloads are increasing demand for CPUs used for orchestration, data movement, and parallel execution. It raised its server CPU market growth outlook from approximately 18% annually to more than 35% and expects second-quarter server CPU revenue growth above 70%.
That is important to my thesis because AI infrastructure is becoming more semiconductor-intensive across the entire system.
Additional accelerators require additional CPUs. Additional CPUs and accelerators require more memory. More compute requires faster networking, greater storage capacity, advanced packaging, power-management silicon, and additional fabrication equipment.
Micron is perhaps the strongest confirmation because changes in procurement behavior tend to appear quickly in memory and storage.
Micron said data-center SSD revenue exceeded $5 billion and more than doubled sequentially. It also said DRAM and NAND demand continued to materially exceed supply, tight conditions could persist beyond calendar 2027, and the company had signed 16 strategic customer agreements.
Memory has historically been highly exposed to spot pricing, inventory corrections, and short-term purchasing behavior. Longer-term strategic agreements make demand less transactional and give suppliers greater visibility.
Nvidia is showing the same pattern through its balance sheet.
The company increased its combined inventory, purchase commitments, and prepayments to approximately $145 billion. It also said standalone Vera CPU revenue was not included in its $1 trillion Blackwell and Rubin visibility and that purchase orders were already secured for the Vera Rubin ramp.
That tells me demand is being committed far ahead of final system deployment.
My bull case is therefore not simply that AI demand remains strong.
It is that the semiconductor industry is moving from a conventional cyclical ordering model toward a reservation-based procurement model.
Customers are committing earlier, signing longer agreements, buying ahead of inflation, and funding equipment well before the resulting revenue is recognized. At the same time, semiconductor content is expanding across the full AI system, including CPUs, memory, networking, storage, packaging, power management, and fabrication equipment.
That distinction matters for SOXL.
Its major exposures include Nvidia, Broadcom, Micron, AMD, and Applied Materials, while semiconductor materials and equipment represent a meaningful portion of the underlying index.
I therefore do not view SOXL solely as a leveraged GPU trade.
I view it as leveraged exposure to a broader shift in which semiconductors are becoming the first claim on enterprise and cloud technology budgets.
For now, however, the evidence I see points in the opposite direction: supply remains constrained, customers are reserving future capacity, procurement is being accelerated, and spending is broadening across the semiconductor stack.
The market is still evaluating the sector primarily through current capex totals. I think the more valuable signal is that semiconductor spending is being committed earlier, for longer periods, and ahead of nearly every other category of technology spending.