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Last time, I wrote that artificial intelligence (AI) has taken markets by storm, going from niche experiment to mainstream productivity engine. But any powerful tech boom attracts bubble talk, and AI is no exception. There are real yellow flags that deserve attention.
One concern is circularity. Large cloud platforms are investing heavily in AI startups, which then spend a significant portion of that capital back on cloud and chips from the same platforms. Revenue looks strong, but some of it is essentially capital recycling. The telecom bubble saw something similar with capacity swaps. That ended badly.
Another concern is the gap between investment and visible revenue. Estimates for U.S. AI infrastructure spend in a single year sit in the hundreds of billions of U.S. dollars, while clearly identifiable AI revenue is still a fraction of that. Surveys suggest most enterprises have yet to generate positive financial returns on AI programs. Consumer services like ChatGPT still monetize only a small portion of their user base.
There are also accounting questions. GPUs and related hardware become obsolete quickly, yet some firms are depreciating them over delayed time horizons, which flatter short-term earnings.
These are valid issues, and in smaller, more speculative names, we already see signs of bubble behaviour. However, several factors differentiate this cycle from the dot-com era.
First, the core players are some of the most profitable companies in history, not pre-revenue concepts. Microsoft, Google, Amazon, and NVIDIA are integrating AI into products that customers already pay for and are seeing real revenue uplift. Azure’s growth has re-accelerated with a meaningful contribution from AI workloads on an $80+ billion base. OpenAI is already at a multi-billion-dollar revenue run rate and is growing quickly. These are not speculative hopes; they’re sizable businesses.
Second, the capacity being built is getting used. The constraint over the past two years often wasn't demand, but access to GPUs. Many new data centres are effectively presold. That is very different from the empty fibre networks of the early 2000s.
Third, the productivity and cost-saving benefits are tangible. Companies are cutting costs, collapsing processes, and improving service levels with AI. Internal deployment of co-pilots and agents is allowing large firms to slow hiring or even reduce headcount while supporting more activity. That operating leverage is hard to ignore if you are a CFO.
Finally, the underlying technology is improving at an unparalleled pace. The cost to run a given level of AI capability has fallen roughly 1,000x in just a few years. When the marginal cost of intelligence collapses, entirely new use cases appear. That’s classic Jevons-style dynamics: As a resource like AI becomes more efficient, consumption of the resource rises. I believe this supports the view that this is a general-purpose technology with wide and durable demand, not a narrow fad.
None of this means there will not be drawdowns or disappointments. Valuations for the leaders already embed strong expectations. Some projects will fail, and cycles in CapEx and sentiment are inevitable. But as with the internet and cloud, even if parts of the trade prove bubbly, I believe the infrastructure and platforms built during the boom will power the next decade of digital growth.
Looking to 2026 and beyond, leadership is likely to concentrate in players that control multiple layers of the stack.
The cloud giants are positioning themselves as full-stack AI platforms. Microsoft’s partnership with OpenAI has turned into a strategic flywheel, with Azure providing compute, OpenAI providing models, and the Microsoft 365 suite embedding AI deeply in daily workflows. Amazon and Google are following similar playbooks, pairing proprietary silicon and models with massive customer reach and integration.
On top of that, data and software platforms that can sit in the middle of enterprise data estates and orchestrate AI are emerging as important winners. Names like Snowflake and Databricks are evolving from databases into AI application fabrics that connect models, data, and business logic. The more data and workflows they centralize, the more valuable they become.
A major shift is also underway in software monetization. As AI agents become capable of performing complex tasks, pricing tied to seats looks increasingly mismatched to value. Expect more usage-based and outcome-based pricing as vendors experiment with charging for tasks completed, insights delivered, or savings generated. Incumbents that already own systems of record and deep integration points should be able to bolt agents on and capture that value. Startups that are AI native will likely push from below.
Ultimately, scale will matter. Those with the most data, compute, distribution, and integration will be able to offer better AI at lower marginal cost, which attracts more users and data, which further improves the product. That feedback loop can produce new forms of concentration, just as we saw in search, social, and cloud.
Heading into 2026, the technology and AI outlook is optimistic, but should be approached with clear eyes. We’re in the early stages of an AI-driven productivity boom that is reshaping business models and capital allocation. A historic CapEx cycle looks to be laying down the rails, power, and silicon for an AI-centric world.
There are genuine concerns around circularity, accounting, and overenthusiasm in pockets of the market, and investors should expect volatility. In my estimate, the core of this cycle is anchored in real products, real revenue, and real efficiency gains at some of the strongest franchises in global markets.
For institutional investors, I believe in staying focused on the secular trend while being selective on exposure. Look at vertically integrated, scale-driven platforms, and the critical infrastructure and data layers that support them. Be wary of pure narrative stories that lack cash flow and competitive moats.
The tide is shifting toward an economy where intelligence is cheap, abundant, and deeply embedded in every workflow. The companies that build, distribute, and harness that intelligence best are likely to drive superior returns through the back half of this decade. Our job is to own them, size them appropriately, and hold our nerve as we climb the next tech wall of worry.
We are the first generation to collaborate with non-human intelligence. How we choose to use that partnership will define the century.
Strong Convictions. Loosely Held.
Nicholas Mersch is Associate Portfolio Manager at Purpose Investments Inc.
Visit Purpose Investments Inc. for information on Purpose Global Innovators Fund.
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