For two years, spending on artificial intelligence (AI) was enough to win the market's favour. This earnings season, that has changed, and the next wave of winners may increasingly include companies using AI rather than simply building it. The market is beginning to focus less on AI investment itself and more on AI diffusion – how quickly artificial intelligence spreads across industries and translates into higher productivity, stronger competitive positions and better returns on capital.
More than halfway through the Q2 2026 earnings season, and results have so far reinforced something that, on the face of it, looks fairly straightforward. Corporate earnings have generally been good. Margins have held up better than many feared and, despite plenty of macro uncertainty, management teams have continued to execute well. What has been much more interesting, however, is the market's reaction.
We have seen some extremely sharp share price moves, particularly across technology, but those moves often have had less to do with whether companies beat consensus and much more to do with what management teams have said about the future. Two companies could deliver very similar quarterly numbers and experience completely different outcomes . For example, Texas Instruments produced excellent numbers with record industrial revenue and raised guidance, yet the shares fell nearly 4%, as expectations were already too high to leave room for upside. Amazon faced the opposite test: it raised its own spending plans even further, yet shares jumped nearly 10%, because investors saw accelerating growth at AWS, Amazon's cloud computing division, as proof the extra spending would pay off. Expect that same scrutiny to shape reactions through the rest of results season, particularly as more companies report on the back of heavy AI-related spending.
AI investment is no longer enough
Over the past two years, markets have largely rewarded companies for participating in the AI investment cycle, with spending more on AI infrastructure generally viewed positively. This quarter, the conversation changed. Investors are increasingly wanting to see evidence that AI investment is beginning to generate economic returns.
Microsoft and Amazon provided the most important evidence here, having both published results at the end of July. Both demonstrated that AI is driving a re-acceleration in cloud demand, but Amazon in particular has spent considerable time explaining the economics behind its investment programme. The message was effectively that today's weak free cash flow reflects the construction phase of very large infrastructure projects rather than poor underlying economics. Once those assets become operational, utilisation increases, revenues grow more quickly than incremental capital expenditure and returns improve materially. That has resonated because, stripped back, these are not really technology questions – at heart these are questions about returns on capital. If these investments generate returns well above their cost of capital, then management teams should continue investing aggressively. If they don't, they shouldn't. That's ultimately the debate the market is beginning to have.
Guidance mattered more than the quarter
Arguably, the biggest lesson from this results season has been that the market has increasingly looked through reported earnings and focused on what comes next. Guidance around demand, margins, cash generation and capital allocation matters much more than the quarterly beat itself. This also helps explain some of the apparently inconsistent market reactions. Valuation is beginning to matter again. Companies priced for perfection have found it difficult to outperform, even after delivering very strong quarters, whereas businesses where expectations had become too pessimistic often reacted very positively. For active investors, that's a healthy development.
Dispersion is increasing
Another feature of this results season was just how much dispersion we have seen beneath the headline numbers. Within software, infrastructure software and cybersecurity continue to benefit directly from AI investment, while the broader software as a service (SaaS) sector still needs to demonstrate that AI becomes an accelerator rather than a disruptor of growth.
Consumer businesses have also painted a much more nuanced picture than the aggregate data suggests. The lower-income US consumer continues to look relatively cautious, whereas spending among affluent consumers has remained much more resilient. Emerging markets outside China continue to surprise positively, particularly India and Latin America, while luxury remains highly company specific rather than moving as a single sector.
Industrials have arguably provided one of the more interesting read-across. Companies exposed to power infrastructure continue to perform well, but increasingly we are hearing about demand extending into automation, productivity software and factory optimisation. This looks like a global economy moving beyond simply building AI infrastructure and towards deploying it more broadly.
Capital allocation is becoming a differentiator
One thing that has stood out across multiple sectors is the increased focus on capital allocation. In defence, investors are increasingly focused not simply on growing order books, but on how companies are investing to expand production capacity and how shareholder returns are being balanced against that investment. Similarly, within energy, discussion has centred less on commodity prices themselves and more on portfolio optimisation, capital discipline and strategic decision making. This suggests the market is placing greater weight on management quality than it has for some time.
Key takeaways
The biggest takeaway from this results season has not actually been within technology. It has been seeing AI emerge as an increasingly important growth driver across such a broad range of businesses. For the past couple of years, the AI investment story has been largely about the companies building the infrastructure – namely semiconductor manufacturers, equipment suppliers, hyperscalers and data centres. That story remains very much intact.
However, increasingly the debate is beginning to broaden. The next phase is likely to be less about who is spending the most on AI and more about who is using AI most effectively. Across this earnings season we have repeatedly heard companies discussing AI in the context of engineering, customer service, software development, automation, product design, and operational efficiency. In other words, AI is beginning to move beyond being a technology investment theme and is becoming a productivity theme. History suggests that is often how major technology shifts evolve. The early winners build the infrastructure. The longer-term winners use that infrastructure to improve economics, capture market share and generate sustainably higher returns on capital.
If this interpretation is correct, the investment opportunity also begins to broaden. Yes, the infrastructure providers remain critical, but increasingly we should also be looking for companies – in industrials, healthcare, financials, consumer businesses and professional services – that can use AI to widen competitive advantage and create stronger earnings power over time. For fundamental investors, this is an encouraging development. As the market places greater emphasis on economics rather than narrative, analysing competitive advantage, returns on capital, and capital allocation should once again become increasingly important sources of investment edge.
The question is becoming less 'who is spending the most on AI?' and increasingly 'who is creating the most value from AI?'
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