Goldman Sachs Calls Strong Q2 Earnings Season Fueled by AI Infrastructure Spending

What Goldman Sachs is projecting for Q2 earnings

Goldman Sachs analysts issued a forecast that Q2 2026 will mark a strong earnings season for S&P 500 companies, with AI infrastructure spending cited as the primary driver. The bank's research points to record capital expenditure by the largest cloud providers on AI compute, including GPU clusters, data-center expansion, and networking upgrades, translating into above-consensus revenue for the companies supplying those components.

The call comes ahead of results from technology companies that have been at the center of the AI build-out. Goldman's view is that the spending cycle, which began with large language model training in 2023, has matured into a broad commercial-deployment phase, creating a more durable revenue stream for chip and cloud companies than a single-quarter capex event.

Why AI infrastructure spending drives chip and cloud earnings directly

The link between AI and data-center capex and chip company revenue is a single step. Data centers buy graphic processors for training and inference workloads. Cloud platforms bill customers per compute-hour on those GPUs. When enterprise AI adoption accelerates, both the hardware and software layers see higher revenue in the same reporting period.

That spending compounds across the supply chain. Each GPU cluster requires high-bandwidth memory, advanced networking chips, wafer-fabrication equipment to manufacture future chips, and software subscriptions for the AI models running on the hardware. A quarter in which hyperscalers raise their AI capex guidance tends to lift results across that supply chain simultaneously.

Which stocks are in focus, and the economic channel

Goldman Sachs is named directly as the institution behind this forecast. Goldman's own revenue benefits when equity markets are healthy, deal pipelines are full, and companies are beating earnings expectations. A strong earnings environment supports more initial public offerings, more mergers-and-acquisitions advisory mandates, and higher prime-brokerage revenues.

Nvidia is the most direct indirect beneficiary. Goldman's AI-boom thesis centers on GPU compute demand, and Nvidia holds the dominant market position in AI training and inference hardware. A quarter defined by AI capex growth is a quarter in which Nvidia's data-center revenue is more likely to grow.

Advanced Micro Devices also has a concrete stake in the outcome. AMD's Instinct MI300X GPU has gained share in AI workloads, particularly from customers seeking to diversify beyond a single chip supplier. A rising total market for AI compute expands the addressable revenue for both companies.

What to watch

The strongest confirmation of Goldman's thesis will come from the hyperscalers' own Q2 reporting calls, specifically any increase to second-half capital expenditure plans. If Microsoft, Amazon, and Alphabet each raise their AI infrastructure budgets, that spending flows to chip and equipment makers in subsequent quarters. Watch also for GPU lead times and order backlog commentary: lengthening lead times signal demand is outpacing supply, which is a forward revenue signal for Nvidia and AMD.