In Brief
- The 1Q26 earnings season is the strongest in a while. 85% of companies have beaten expectations, the most since 2Q21 and well above the long-term average of 73%.
- The AI buildout is supporting earnings growth across sectors. Banks are benefitting from AI-driven capital markets activity, and demand for electrical equipment is boosting industrials.
- Semiconductors cannot continue outgrowing the hyperscalers forever. Ultimately, the success of the AI ecosystem depends on monetizing consumer and enterprise adoption.
1Q26 U.S. earnings are the strongest in a while
U.S. equity markets are back to the future. News about peace talks was enough to propel the S&P 500 out of a 9% drawdown and right back to all-time highs. Markets are once again all about artificial intelligence (AI), with the tech sectors driving 45% of the gains. The rally has been further supported by the strongest earnings season in a while. 85% of companies have beaten expectations, the most since 2Q21 and well above the long-term average of 73%, as seen in Exhibit 1. Technology has of course been the highlight, but results in every sector have come in meaningfully above expectations:
- Financials are on track to grow earnings by 21% year-over-year (y/y), thanks to elevated capital markets activity. Despite the geopolitical shock, the value of global mergers & acquisitions (M&A) announcements in 1Q26 was the second highest ever, trailing only 4Q25, boosting investment banking revenues. Large-scale, strategic M&A held up the best as CEOs look through the conflict to position their businesses for AI.
- Consumer companies are not seeing materially weaker demand. It is still early, but according to Chase credit card data, consumer spending accelerated in 1Q26 and remained strong through March, even in discretionary categories like retail, entertainment, and travel. The commodity shock will increase costs, but hedging and longer-term contracts with suppliers should prevent an acute shock.
- Industrials earnings are growing by 19.3% y/y, with strength across several industries. Commercial loan growth is picking up at banks, suggesting the conflict has not yet stopped companies from investing in their businesses. The datacenter buildout is driving earnings growth for electrical component makers, and airlines have continued ordering new jets, supporting the aerospace & defense industry.
Hyperscalers are hanging in
Markets have moved on from the Mag 7. They are still all about AI, but understanding of the beneficiaries is evolving. First, it was the Mag 7, then the hyperscalers and now leadership has followed capex down the AI supply chain.1 Semiconductors are up 24% year-to-date (YTD), outperforming the hyperscalers by 19%. Earnings growth too is much stronger, with semi earnings-per-share (EPS) rising 97% y/y vs. only 8% for the hyperscalers, and the dispersion is expected to continue.2
However, semiconductor success is entirely dependent on the hyperscalers themselves, who account for an estimated 22% of semi sales. Luckily, it does not seem like they will run out of money anytime soon. This quarter, hyperscalers raked in a combined USD 430billion in revenues, over 2x that of the semiconductor industry. Cloud sales grew an average of 44%, agentic AI subscriptions are in the millions, and ad prices are up double digits thanks to AI recommendation algorithms.
All that growth comes at a price. After spending a combined USD 715billion on capex from 2023 through 2025, the hyperscalers are on track to spend USD 629billion in 2026 alone, already up 35% from the January 1st estimate. Capex is not just increasing because the hyperscalers are building more compute; the AI demand frenzy is also driving up prices down the supply chain.
Remarkably, profit margins are holding steady as the hyperscalers remain hyper focused on efficiency. But this will become increasingly difficult as capex starts to accumulate in depreciation.
AI is a lot more than GPUs
All this capex is driving picks & shovels investment opportunities. About half the cost of a data center is from AI chips, but the other half is driving earnings growth across industries. Graphics processing units (GPUs) get all the attention, but central processing units (CPUs) are the brains behind the operation, assigning tasks, reading and writing to memory, and sending data around the server. And both GPUs and CPUs need memory. CPUs store information in NAND, or long-term memory, and use dynamic random access memory (DRAM), or working memory, to perform tasks. For example, HBM, or high bandwidth memory, is a special kind of DRAM used to feed GPUs data fast enough to keep them busy.
It does not stop there. AI, CPU, and memory chips are packaged together into AI servers, and thousands of these servers, along with networking, electrical and cooling equipment, make up a data center. The compute buildout is driving demand for these older tech components and creating new ones altogether. As GPUs get more powerful, so too must CPUs, memory, and everything else in the system.
AI models need GPUs, but AI products need CPUs. The training process is computationally heavy but highly repetitive, so the ideal CPU-to-GPU ratio is around 1:8. But inference, especially agentic inference, is messier. Requests need to be processed, broken into steps, distributed among chips, and paired with the right data, retrieved from the right memory. All of this is handled by the CPU. Current AI applications have a CPU to GPU ratio of 1:3 or 1:4, but the ideal ratio for agentic AI workloads could be as high as 7:1.3
AI demand is driving an exponential increase in memory prices, with DRAM and NAND up around 300% and 200% y/y.4 It takes years to add manufacturing capacity, and HBM requires more than traditional DRAM. EPS growth is in the triple digits at each of the three major memory makers.
AI is not the only thing that needs memory. It is everywhere in everyday electronics like smartphones, computers, tablets, gaming consoles, and smart televisions. These traditional electronics manufacturers are paying higher memory prices too. Some of it is coming out of margins, but personal computer (PC) prices are already up around 20%, and analysts expect the average selling price of a smartphone to increase by 10% in 2026. These higher prices will also mute demand, particularly at the cheaper end of the market, with analysts lowering 2026 forecasts for PC shipments by 9% and smartphones by 11%.5,6
Investment implications
Over the past two years in the U.S., semiconductor earnings have been growing at the expense of hyperscalers. AI capex is putting pressure on their margins, but it is driving earnings growth and investment opportunities down the supply chain.
Hyperscalers are designing custom AI chips to reduce prices, creating opportunities for design partners, fabs, and semicap equipment suppliers.
AI is driving up demand for memory and CPU chips, creating higher margin products, and inflating the cost of traditional electronics like laptops and cell phones.
Meaningful parts of the semiconductor supply chain are located outside of the U.S. These picks and shovels plays trade at a discount to their U.S. peers and can provide much needed geographical diversification.
Higher prices are increasing capex, but hyperscalers are also getting more efficient, which should bring down the cost of compute over time.
Strong earnings growth in the semiconductor industry can only continue if hyperscalers profit from their investments. Semi companies have been out-earning and out-performing, but hyperscalers need to capture more value in the long-run, or else their investments, and consequently semiconductor revenues, will drop.
The entire ecosystem ultimately rests on monetizing demand from both consumers and corporations.