NextFin News - Artificial intelligence is emerging as one of the clearest profit drivers of the US earnings season, and Goldman Sachs strategist Ben Snider says the effect should show up not only in companies selling AI infrastructure but also in the broader market leadership built around the theme. Goldman said US companies are set for another strong earnings season, with analysts expecting S&P 500 earnings to surge 22% in the second quarter, a hurdle it believes companies should be able to clear. With this year’s rally in US stocks driven by blockbuster profits, the upcoming reporting season is now a critical catalyst for the market.
The setup matters because the market has already assigned a high level of importance to AI-related growth. Investors have spent much of the year rewarding firms with direct exposure to chips, data-center equipment, cloud capacity and software tied to automation, while pressuring companies that have been slower to turn AI demand into earnings power. That has left the index more dependent on profit growth than on narrative alone. In other words, the quarter is not just about whether individual companies beat estimates. It is about whether AI can keep translating into results fast enough to justify valuations that already assume a great deal of success.
Snider’s broader view has been consistent: AI investment and adoption are one of the main forces shaping US equity returns in 2026, alongside the health of the US economy and the path of the Federal Reserve. Goldman’s January outlook said it expected 12% earnings growth for the full year and argued that earnings should continue to drive the market even as valuations remain elevated. The newer earnings-season call does not replace that framework; it sharpens it. If the second quarter confirms broad profit strength, then AI looks less like a story stock phenomenon and more like a genuine earnings cycle.
That distinction is important because the market has reached the point where AI exposure alone is no longer enough. The strongest response will likely go to companies that can show real revenue, margin or cash-flow benefits from the buildout. Chipmakers, memory suppliers, network vendors and cloud platforms have already provided the clearest evidence that AI spending is still moving from experimentation into deployment. The next step is proof that that spending is creating durable earnings power beyond one-time capex cycles.
The current reporting season is therefore a test of breadth as much as a test of demand. If AI is truly driving a strong earnings season, the gains should not be confined to a small handful of megacaps. They should start to show up in more parts of the market through higher margins, stronger guidance, and better revision trends. If they do not, then investors may have to confront a narrower and more fragile AI trade than the price action suggests.
Why AI Matters More In This Earnings Season
The strongest version of Goldman’s argument is not that every company tied to AI will beat estimates, but that AI is moving from the expense line to the income statement. That distinction matters. During the early phase of the buildout, the market rewarded companies for announcing capital spending, data-center capacity and product launches. In the next phase, investors want evidence that those investments are generating revenue, improving utilization or raising productivity fast enough to change earnings trajectories.
Snider’s January outlook framed AI as a core earnings variable, not a side story. Goldman said then that the most important dynamics for US equities in 2026 would be the health of the US economy, the path of the Federal Reserve and what happens with AI investment and adoption. The firm said it expected 12% earnings growth and said that earnings should continue to drive the market even with valuations elevated relative to history. That is a coherent framework: if earnings do the heavy lifting, price/earnings multiples can remain under control; if earnings falter, the same valuations become much more dangerous.
Goldman also stressed the risk embedded in rich multiples. Its January transcript said that when valuations are high, even a small surprise can have a really large negative consequence. That warning is especially relevant now. Investors have already been willing to pay up for the AI trade, which means the upcoming season has a high bar. A company can post a respectable quarter and still disappoint if guidance does not reinforce the AI thesis or if management signals that spending is outpacing monetization.
“The most important dynamics in the US equity market this year are going to be the health of the US economy, the path of the Federal Reserve, and what happens with AI investment and adoption.”
That framing helps explain why AI has become more than a technology story. It is now part of a broader macro and earnings equation. The market is not betting on AI alone. It is betting that AI can compound with a resilient economy, a still-supportive policy backdrop and enough corporate discipline to avoid margin erosion. If those pieces line up, earnings can broaden beyond a narrow group of winners. If they do not, the market’s dependence on AI-heavy leadership becomes much more visible.
Near-term evidence already points to a season where firms closest to the AI buildout should outperform on the numbers. But the deeper question is whether the AI cycle is diffusing into the rest of corporate America. A data-center chip supplier can benefit from one wave of capital spending. A broader earnings cycle requires software monetization, workflow automation and enterprise adoption to show up in recurring revenue. That is the difference between a hot trade and a durable earnings regime.
What The Market Has Already Priced In
The market has already done much of the work for management teams. Equity investors have been willing to pay up for growth names linked to AI, even as benchmark valuations remained rich by historical standards. Goldman has said those valuations can magnify the impact of any disappointment, and that is especially true when earnings expectations are high entering a reporting season. The implication is straightforward: the stronger the setup looks for AI beneficiaries, the more punishing a miss can be if actual results fail to match the story.
That matters for the way investors should read the next batch of reports. A company can produce a solid quarter and still disappoint if guidance does not reinforce the AI thesis. It can also report strong demand and still see its stock struggle if the market had already discounted too much future success. In this environment, the most valuable information is not backward-looking revenue alone, but forward-looking commentary on customer demand, contract length, utilization rates and spending plans.
Goldman’s 12% full-year earnings-growth outlook for the S&P 500 also implies that the bar is not static. If the index really is on track for double-digit earnings growth, then sectors outside technology need to contribute more than they have over the past several quarters. That gives AI a strange kind of importance: the theme is concentrated in a few names, but the earnings consequences are distributed across the index. Every company that benefits from lower labor costs, faster product development or better cloud economics becomes part of the same narrative.
The risk is that investors confuse exposure with earnings power. A company can be close to AI spending without turning that spending into durable profit growth. Others may benefit only temporarily from the buildout phase, with margins pressured once the spending wave matures. That is why the current season is so important: it will help separate infrastructure suppliers with persistent demand from the broader group of companies merely riding the theme.
That separation is likely to be the main source of volatility. If earnings revisions keep rising, then AI can continue to anchor market leadership. If revisions flatten, the same concentration that helped the index climb can quickly become a vulnerability. In that sense, earnings season is not just a test of demand. It is a test of whether investors have moved too far ahead of the profit cycle.
Why This Is Bigger Than The Magnificent Few
The most interesting part of Snider’s message is that it does not stop at the largest technology firms. AI has become a broad industrial process: it requires chips, memory, network equipment, power, cooling, software, data pipelines and enterprise integration. That makes earnings season more wide-ranging than the usual megacap-versus-rest-of-market debate. A strong AI cycle can support suppliers, integrators and even parts of the industrial economy that are building the physical infrastructure behind the model boom.
That breadth is also why the earnings season may end up being more important for the second half of the year than for the quarter itself. If companies confirm that AI spending is still ramping and that the payoff is visible in margins or revenue acceleration, then analysts will have reason to raise estimates across multiple industries. If they do not, the market may have to confront a more limited story: AI is powerful, but mostly for a small number of balance sheets that already have scale.
Goldman’s January view that earnings growth should power market gains depends on that broadening. A narrow rally can persist for a while, but a durable advance usually needs earnings participation beyond one or two sectors. That is why Snider’s optimism is less about an isolated technology boom than about a profit cycle that can spread through the index.
“We’re expecting 12% earnings growth which should power a 12% return for the market.”
That is the key benchmark investors will now be using, whether they say it explicitly or not. If earnings are growing at that pace, AI looks like a genuine driver of market performance. If the next few quarters show growth concentrated in only a few companies while the rest of the index lags, then the market will have to reprice the durability of the theme.
For now, the central judgment is clear. AI is no longer just supporting valuations; it is starting to support earnings expectations. That is a stronger story, but also a more demanding one. Markets can tolerate expensive narratives for a while. They have a harder time tolerating expensive narratives that stop producing profit growth.
What To Watch Next
The next catalyst is not a philosophical debate about AI’s future. It is the next round of earnings reports, management guidance and capital-expenditure commentary. Investors will watch whether companies describe AI demand as accelerating, stabilizing or merely still early. They will also watch whether the benefits show up in gross margins, operating margins and free cash flow, rather than just in longer-term ambition.
The broader market will be listening for something else as well: whether AI can keep broadening beyond the names that have already benefited the most. If that happens, the earnings season could reinforce Goldman’s thesis that profits are doing the real work behind the rally. If it does not, then the market may discover that the AI trade still depends on a very small set of companies carrying a very large share of the burden.
Either way, this earnings season is about more than beating estimates. It is about whether artificial intelligence has become a genuine earnings engine for US equities, or whether it is still mostly a powerful explanation for why a few stocks have already moved so far.
That is why Snider’s call matters now. The story is no longer whether AI will change the market. The question is how quickly it is changing the numbers.
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