Tim Urbanowicz at Innovator maps AI’s “big wave” investors can actually trade

If you are an investor trying to ride the AI wave, Tim Urbanowicz is basically asking the question that quietly destroys portfolios: where does the next surge come from after the first rush of excitement? Urbanowicz, chief investment strategist at Innovator from Goldman Sachs Asset Management, is tackling the AI boom, and the core promise of his take is about identifying the next “big wave” for the AI trade.

That matters because “AI” is not a single trade. It is a whole stack of opportunities, from compute and infrastructure to software and distribution, and each layer tends to hit its own cycle. Early winners can become late-cycle laggards, not because the tech stops working, but because expectations move faster than fundamentals. The moment the market starts asking “what’s next,” investors who only bought the first narrative can get stranded. Urbanowicz’s role is to help investors avoid that specific trap: he is not just celebrating AI, he is focusing on where the next tradable momentum could emerge.

To understand why a strategist at an ETF and indexing focused shop like Innovator would frame it this way, it helps to remember what “AI trades” usually look like in practice. Many investors do not pick a single stock they believe in for the next decade. Instead, they look for thematic exposure that can diversify risk and still track a fast-moving story. But themes are slippery. When everyone piles into the same visible pieces, the market can become crowded. Crowding does not mean the theme is wrong. It means your entry point, your time horizon, and your “what now” thesis are doing heavy lifting.

This is the second-order risk that boards and investment committees should keep in mind: AI investments are rarely judged on whether AI exists. They are judged on whether the trade timing worked. Early-stage enthusiasm is one thing. Later-stage allocation is another. Once a strategy becomes widely owned, marginal buyers get harder to find, and returns can become more dependent on catalysts like earnings revisions, capex signals, regulatory clarity, or product adoption. So when a strategist points investors toward a next wave, the underlying question is not “is AI real.” It is “where is the incremental demand likely to show up next, and how could that show up in market pricing?”

Regulation is part of the backdrop, even when a given interview is not a policy deep dive. AI can attract oversight because it touches data, security, labor outcomes, and consumer risk. That means the market does not just price innovation, it prices uncertainty. For example, when regulators or lawmakers signal tighter rules, the burden of compliance can shift costs across the stack. In other periods, regulatory action can reduce ambiguity, which can help companies that are positioned to meet requirements more quickly. Even for executives who do not directly control an AI product roadmap, regulatory uncertainty can change investor appetite, which then affects capital formation, partnership behavior, and hiring.

There is also the incentive problem that shows up in every boom. Management teams want to be associated with “AI growth.” Investors want exposure that fits their mandates. And product teams want funding tied to measurable milestones. That is why “big wave” language is useful: it implies a shift from broad excitement to concentrated traction. A big wave is usually where demand becomes more specific. It is where spending moves from experimental to operational, or where software adoption creates a durable revenue signal rather than a demo-driven story.

For decision-makers reading this, the strategic stakes are simple. If you are a CFO or board member overseeing capital allocation, you need to understand not only what AI companies do, but how the market might rotate between beneficiaries. If you are an investor or allocator, you need to know what kind of exposure you are holding: is it a bet on the next narrative stage, or a bet on an already crowded trade? Urbanowicz, as chief investment strategist at Innovator from Goldman Sachs Asset Management, is addressing that exact issue by turning the AI boom into a question of where investors may find the next “big wave” for the AI trade.

In other words, the real value is not that AI is hot. It is that AI is tradable across phases, and the next phase does not always line up with the loudest headlines. The executives who win in this environment are the ones who can translate a theme into timing, and timing into risk control.