US perspectives: are we in an AI bubble?

As with any investment, your capital is at risk.

The situation is nuanced and complex. In our view, today’s US market reflects real, compounding progress in digital infrastructure and software, while also carrying significant areas of potential overheating that demand attention. Now, more than ever, being a long-term US growth investor requires selectivity, discipline in valuation, and a willingness to back effective and founder-led cultures that continue to execute on their opportunities amidst market noise.

Is the US market expensive?

The US market appears expensive by historical standards, but the mix matters: a handful of global platform companies now control the compute, data, and distribution rails upon which AI depends (outside of China). Their scale advantages resemble an oligopoly more than a gold rush. That concentration makes their spending, and by extension, some of their valuations, more defensible than historical late-cycle manias. For example, NVIDIA's fundamentals have grown significantly, resulting in a Price-to-Earnings (P/E) multiple that is only slightly above its 2022 lows and materially below its five-year average. Compare this with Cisco Systems in the dot-com bubble, which became increasingly less profitable while its share price went parabolic. Its valuation turned more expensive on every measure before crashing. 

In this situation, a key risk to watch is capital intensity: if investment outpaces returns or if the oligopoly broadens materially, the economics could compress, echoing what followed the telecom build-out after the dot-com era. For now, though, we do not see evidence of a broad US bubble ready to burst so much as a concentrated market whose premium is anchored in a unique corporate mix and superior profitability.

We are also seeing a “two‑speed” economy. AI investment is materially boosting GDP and equity indices, but the benefits are concentrated among higher-income cohorts and in sectors directly impacted by AI. That asymmetry is a typical feature of technological diffusion, rather than an indictment of the technology itself: early adopters and directly exposed sectors capture outsized gains first, while productivity and wage effects broaden only over time. At least, that's the historical pattern in previous technological revolutions. Another key question is whether this pattern will be different this time – if it is different, it poses various risks to society.

Either way, the situation argues for stock‑picking to be paramount over making calls about when we will see a "style rotation" in the US in the coming quarters, which many in the market are focusing on. Until growth stops being so concentrated in a relatively small number of companies, a wholesale rotation to “value” feels unlikely in the US, which is different to what we've seen in Europe and elsewhere this year.

Of note, our US Growth portfolio's valuation premium versus the market is low by historical standards and yet forecast to grow materially faster over the coming years.

Valuations and revisions: separating heat from light

Yet, there is undoubtedly heat in the US market. Where, then, is the US market looking bubbly?

Let's examine how market valuations have evolved in the last few years to see. Starting from May 2023 (six months after the trough of the post-Covid sell-off, where market valuations had normalised somewhat) through to November 2025, the S&P 500’s valuation on a forward price‑to‑sales basis has re-rated upwards by about 43 per cent in aggregate.

Of the 100 largest re‑ratings in the index since May 2023, 31 are companies directly tied to the AI datacentre build‑out, such as power, storage, cooling and construction. The price-to-sales ratios of those companies have increased by about 130 per cent on average, compared to about 18 per cent for the rest of the index beyond these 31. Companies like Western Digital (P/S [price-to-sales] +432 per cent) and Seagate Technology (P/S +230 per cent) in data storage, as well as Vistra (P/S +331 per cent) and Constellation Energy (P/S +261 per cent) in electricity generation, are among the largest reratings. While fundamentals have improved at these companies, it's arguable that most of the rerating is due to the anticipated significant future demand for datacentres and AI-associated energy. This appears to be the most overheated part of the market currently in valuation terms. It's an area that could potentially decline significantly if AI enthusiasm wanes. We have no direct holdings in this area, so while we have AI exposure through the likes of Amazon, Cloudflare and NVIDIA, we’re less exposed to the “datacentre build out” stocks than the benchmark.

For comparison, our US Growth portfolio over the same time horizon has been rerated by only slightly more than the weighted market average, up approximately 48 per cent (when we remove AppLovin, which is a significant outlier). As high-growth, long-duration managers, we'd expect to be more expensive than the broad market, but the devil is in the details. The median rerating in our portfolio is +10 per cent over that time, compared to +12 per cent in the S&P 500.

Further, 43 per cent of companies in the S&P 500 have re-rated by more than 50 per cent since May 2023. This is only 20 per cent for our portfolio. Yet our portfolio has materially outperformed the S&P 500 over this time. Our portfolio has derated year-to-date, while the S&P has continued to be rerated upwards. Our portfolio fundamentals are strong and forecast to continue improving, and at a higher rate than the market.

It seems that the broader market has hot spots. It’s not just high-growth stocks that are optically expensive.

Being Active Matters

Thus, there is a non‑zero chance that we’re in an exuberant phase of AI infrastructure build‑out, much like railroads, PCs, or the early internet. In such times, overinvestment and exceptional businesses can coexist; the critical variables for long-term wealth creation are not the narrative, but execution: generating cash and reinvesting it against large, durable opportunities, all while maintaining portfolio discipline around valuation. This is where we keep our focus.

Selectivity is paramount. At the AI base layer, the likely winners are already clear, Amazon Web Services - AWS, Microsoft Azure, and Google Cloud Platform on the cloud side, with NVIDIA in accelerated compute. Higher up the stack, the edge goes to application players with distribution, proprietary data, and fast iteration cycles. We find these traits most common in founder‑led, product‑centric cultures. This logic underpins our exposure across names where we are already seeing tangible AI‑driven improvements in growth and operating leverage (such as Samsara, Snowflake and Datadog). Equally, it is why we’re sceptical when “agentic” rhetoric outpaces delivered customer value – it seems every SaaS (software-as-a-service) company is talking about how AI agents will improve their products, but few are delivering on this today. We hear that scepticism echoed in conversations with leaders at private frontier model and data companies in which we invest.

Our portfolio positioning reflects this stance. We have been tempering some of our consumer-sensitive exposure (for example, selectively trimming positions in Affirm, Roblox, and DoorDash) and redeploying into uncorrelated, underappreciated areas in healthcare and industrials. Businesses like Ensign, Penumbra, and Knife River that compound through execution and local scale advantages rather than trying to ride a hype cycle. We’re continuing to balance the enablers of AI with its application beneficiaries, and we're working with our Investment Risk Team to test sensitivity to AI adoption, AI capital expenditure cycles, and a potential consumer slowdown.

Guardrails maintaining portfolio shape during this period

Our portfolio construction guardrails are doing what they were designed to do – maintaining portfolio shape and balance. On a trailing three‑year basis, roughly 59 per cent of the portfolio sits in the most profitable categories of our financial maturity measures (from positive EBITDA [earnings before interest, taxes, depreciation and amortisation] to >10 per cent ROE [return on equity]). The portfolio is net cash. On a trailing one‑year basis, nearly 90 per cent of holdings are self‑funding and still reinvesting in Research and Development (R&D) at roughly three times the rate of the S&P 500. This is a resilient yet future-focused portfolio.

The portfolio skews toward transformational and enduring high-growth businesses, with minimal exposure to early-stage investments. Relative volatility levels have returned to pre-pandemic levels, despite increased index volatility.

This is not to say that we won’t be more volatile than the broad market. We likely will be. But there is no doubt that US benchmarks are increasingly risky due to their concentration at levels not seen in decades. That raises the stakes for passive holders and increases the opportunity for active investors to ignore index weight, look beyond headline multiples, and focus on fundamentals and long-term opportunities.

Some "expensive" companies could actually be very cheap

Indeed, there is also the chance that the efficiency gains from AI are underappreciated, especially in areas of the market that are already highly profitable, such as software, interactive media, and technology. Companies in these industries are already at the leading edge of AI adoption, are at the high end of the market in terms of FCF (free cash flow) yield and yet have high labour-cost-to-sales ratios. Historical margins may not be a reliable indicator of how profitable these companies could be in the next 12-36 months. Tobi Lütke at Shopify has informed us that he expects to continue growing fundamental at the current rate (about 20-30 per cent per annum) for several years without needing to increase headcount.

This is why selectivity is key. It ensures the portfolio is as resilient as possible, with adaptability baked in through effective and often founder-led cultures. The passive benchmark does not rebalance or adjust for change.

What we’re watching from here

1. Return on AI capital expenditure. Do leaders sustain high incremental returns on compute and model spend, or does competition force uneconomic arms races?
2. Diffusion to the application layer. As AI transitions from infrastructure to everyday workflows, distribution and proprietary data will become increasingly important. We favour companies that have adaptable cultures that embrace new technologies and business models.
3. Consumer behaviours. Parts of US demand are soft, while other areas are showing strength. We will continue to monitor and shape the portfolio accordingly.

Bottom line

We don’t need to declare a bubble to invest prudently in a period of AI exuberance. We will invest our clients’ capital in companies that convert technology into durable economic benefits, rather than in companies that benefit from narratives instead of fundamentals. Our approach in US Growth is built for exactly that: concentrated ownership of well‑led, financially robust businesses; continuous testing of risk; and the willingness to trim where valuations appear stretched and upside asymmetry narrows, while adding where it widens. In a market defined by AI exuberance, we will keep doing this unglamorous but critical work while remaining future focused and exerting a level of patience few can match.

US Equity Growth

Annual past performance to 30 September each year (%)

Annualised returns to 30 September 2025 (%)

Source: Revolution, S&P. US dollars. Returns have been calculated by reducing the gross return by the highest annual management fee for the composite. 1 year figures are not annualised. 

Past performance is not a guide to future returns.

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Glossary

Price-to-Earnings (P/E) ratio:

The price-to-earnings (P/E) ratio measures a company's share price relative to its earnings per share (EPS). Often called the price or earnings multiple, the P/E ratio helps assess the relative value of a company's stock. It's handy for comparing a company's valuation against its historical performance, against other firms within its industry, or the overall market.

Price-to-Sales (P/S) ratio:

The price-to-sales ratio (P/S) is a valuation metric that compares a company's stock price to its revenue per share. It is calculated by dividing the stock's price by its sales per share, or by dividing the company's market capitalisation by its total sales.

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