AI spending in focus as S&P 500 earnings growth slows

S&P 500 companies are expected to post 8.8% year-on-year earnings growth for Q3 2025, down from over 13% in the first two quarters, according to LSEG.

Sales are projected to rise 5.7%, slightly below last quarter’s 6.4%.

Investors are watching whether heavy AI spending will pay off as the S&P 500 trades at about 23x forward earnings, above its 10-year average.

AI-linked megacaps have driven much of the market’s gains, but some strategists are questioning the payoff of massive AI investments.

Goldman Sachs notes US tariffs likely posed a larger profit headwind in Q3, with customs duties up 33% to US$93 billion. Still, Oliver Pursche says tariffs and uncertainty haven’t hurt earnings yet.

🔗 Source: Reuters

🧠 Food for thought

Implications, context, and why it matters.

A few AI leaders drive most earnings growth

The Magnificent 7 tech companies delivered 26.6% year over year earnings growth in Q2 2025, compared to 4% for the rest of the S&P 500 1. They are Apple; Nvidia; Microsoft; Amazon; Alphabet; Meta; Tesla.
Four of these companies ranked among the top six contributors to S&P 500 earnings growth in Q2 2025 2.
Technology sector earnings may rise 21% in Q3 2025 3. Revisions are up about 550 basis points (5.5 percentage points) since cycle lows, driven by AI demand 3.
This concentration leaves the market tied to a few firms. The index faces risk if these leaders falter or if doubts about AI investment build.

Tech leaders need clear AI ROI metrics

An MIT report said 95% of early corporate AI efforts had not delivered a return 4. Research from BetterUp Labs and the Stanford Social Media Lab said 40% of workers received “workslop” (substandard AI assisted output needing human correction) in the past month 4.
Bain forecasts annual AI capital expenditure (capex) could reach $500 billion by 2030 4. To break even, revenue would need to hit $2 trillion a year, about $800 billion above potential savings from AI efficiency gains 4.
Technology leaders outside the megacap cohort can set clear benchmarks for AI return on investment (ROI). Useful metrics include cost per inference (cost per model prediction), graphics processing unit (GPU) utilization, and measurable revenue uplift.
Investors now press for payback on AI spend. That pressure is lifting demand for third party AI ROI and Financial Operations (FinOps) benchmarking tools, which give transparent efficiency checks to guide capital allocation.