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You Can’t Time the AI Bubble, But You Can Position for It

November 25, 2025

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Identifying new profit pools in tech investing isn't about avoiding volatility but about building resilience when the pullback comes.

Nvidia delivered another set of blowout results last week, but equity markets sold off anyway. Management's message was clear: keep the faith in AI spending. Yet the details raised fresh concerns. Another multibillion-dollar investment in Anthropic, rising receivables suggesting customer payment stress, and questions over GPU useful lifespan all fed the growing unease. 

Historically, more investors have lost money trying to call the top of a bubble than have made money from it. The circular funding (OpenAI backed by its own vendors, data centres raising capital for empty shells) suggests, in our view, we're in bubble territory (even if NVIDIA’s Jensen Huang disagrees.) But trying to time it would be a fool's errand.

What's more fruitful, in our opinion, is understanding where in the tech sector profits are growing and where they're being destroyed and then positioning accordingly. This isn't about avoiding volatility but about building resilience when a pullback comes.

Figure 1. Hyperscaler capex growth outpaces cash flow generation

Note: Hyperscalers include the following companies: Amazon, Google, Meta, Microsoft, Oracle. Source: BofA Global Research, Bloomberg, Visible Alpha, as at 10 November 2025.

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Where are profits heading?

AI represents the first technology developed by the tech sector that will disrupt itself in such a material way. Of the US$4 trillion global tech industry, roughly US$2.4 trillion sits in the addressable market for AI disruption. That creates clear winners and losers.

IT services and call centres for example are seeing real, measurable shrinkage and here the promise of AI automation isn't theoretical anymore. These industries face existential pressure as AI proves it can handle tasks that previously required human intervention.

On the winning side are infrastructure software providers. AI models need direct access to data, making infrastructure software providers more essential, not less. Equally, mission-critical enterprise software, like enterprise resource planning (ERP), that sits at the heart of businesses won't be ripped out quickly, regardless of how fast AI develops. Even the AI companies themselves depend on this infrastructure layer and such mission critical reliance.

The key to wider AI adoption is making it cheaper to run

In AI's early stages, companies needed flexible, general-purpose chips that could handle any type of model. But as the technology matures, the focus is shifting to specialised chips designed for specific tasks. These specialised chips could deliver the same performance while using far less power and costing significantly less to operate.

The smarter investment strategy, in our view, is to back companies that are driving down AI costs through these specialised chips, whilst avoiding – or even shorting – those relying on power-hungry generic alternatives. This approach still captures the growth potential of AI, but with much better downside protection if AI spending slows or becomes more rational.

The China factor

AI competitiveness will ultimately be measured by a simple equation: tokens generated per unit of power per dollar spent. China has closed what was once a decades-long technology gap to roughly one to two years in AI capabilities. While semiconductor technology still lags by several years, Chinese hyperscalers compensate with advantages elsewhere: more data, significantly more power, and cheaper costs. China adds the equivalent of the entire US power grid every two years. American data centres, by contrast, face two to three year waits for new capacity.

With necessity being the mother of invention, Chinese semiconductor companies, driven by self-sufficiency requirements, represent an expanding opportunity as they close the technology gap at speed. Meanwhile, Chinese hyperscalers are balancing semiconductor shortcomings with software and model innovations.

For Western companies, staying competitive means securing power wherever it's available. US hyperscalers are already seeking capacity in Korea, Japan and the UAE, with those countries demanding AI compute in return. This power hunt is creating investment opportunities beyond traditional tech hubs.

Adoption will be slower than hype will make you believe

None of this means the current spending levels are risk-free or that a pullback won't happen. The tech sector has a consistent pattern: it gets excited about transformative technology, the promise seems immense, spending surges, and then reality forces a pause. We've seen it with cloud computing, with 4G and 5G rollouts, and we'll likely see it here.

The reality is that behavioural change moves far more slowly than technological capability. Technology advances rapidly, but enterprises move excruciatingly slowly. Right now, AI is being used for NDAs and managing minor administrative tasks, but not for core functions like balancing accounts because it isn't deterministic enough yet. Enterprise adoption is just scratching the surface. There's still a long way to go before AI delivers even a fraction of its promise in actual workflow transformation.

This gap between capability and adoption creates the digestion period. Capex will pull back as reality tempers enthusiasm. But if AI ultimately automates even 10% of what it's capable of, current spending levels may become justifiable over time. The US$2.4 trillion addressable market within tech alone suggests the return on investment may materialise, even if the path there includes significant volatility. The question is whether all investors will see adequate returns, and when.

 

All data sourced from Bloomberg unless otherwise stated.

Author: Sumant Wahi, a Portfolio Manager covering technology at Man Group.

 

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