Balance sheets beat hype in AI race

Text transcript

Welcome to Advisor to Go, brought to you by CIBC Asset Management, a podcast bringing advisors the latest financial insights and developments from our subject-matter experts themselves. 

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Mickey Ganguly, associate portfolio manager with CIBC Asset Management 

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The factors that explain the performance divergence between Oracle and Alphabet in 2025 that goes beyond the AI narrative was their differences in balance-sheet health, regulatory outcomes, especially for Alphabet, and business-model concentration. 

In the first half of 2025, Oracle clearly outperformed Alphabet, surging approximately 90% by September of last year. However, in the fourth quarter, Alphabet saw a massive relief rally due to favourable regulatory decisions, and a positive release of their AI model, Gemini 3, which diminished fears of Alphabet falling behind rivals such as OpenAI. 

I do want to address three factors which cause a divergence in performance: one, balance sheet health; two, regulatory outcomes; and three, business model concentration. I believe the most significant differentiator between the two companies was how each company plans to fund their capital expenses. 

Alphabet maintained cash balances of over $100 billion, which has covered their capital expenses of approximately $93 billion for 2025. This was further supported by an additional $24 billion of free cash flow for the year. Alphabet also has minimal debt levels compared to Oracle. 

Oracle, on the other hand, signed on a $300-billion infrastructure contract with OpenAI in 2025, which caused the stock to surge on the news. However, as investors digested this, questions began surfacing on how Oracle would finance the project, along with elevated counterparty risks. Throughout the year, Oracle went on a borrowing spree, increasing their leverage close to four times, and turning their free cash flow negative as their capital expenses outpaced their cash flow from operations. 

On the regulatory front, there was considerable overhang throughout the first half of the year on whether Alphabet would have to divest their internet browser, Chrome, and their mobile operating system, Android. This was a worst-case scenario for Alphabet, and the uncertainty weighed on the stock. 

However, in September of last year, a federal judge ruled that Alphabet would not be forced to divest Chrome. While there were certain restrictions placed on Alphabet around their search product, the ruling avoided this worst-case scenario, causing Google stock to jump by 15% in the month of September of 2025. 

While this was unfolding, investors started questioning the relationship between Oracle and OpenAI, where Oracle’s fortunes were perceived to be tied to a single large client. The customer concentration was further aggravated by the financial health of OpenAI, who continued to commit large capital expenses without generating the profits required to meet those commitments. 

Finally, Alphabet has a diversified growth business model with exposure to the cloud segment, advertising through their search and video platforms, and a segment-leading AI model through Gemini 3. Paired with their financial health and accelerating growth in key segments of the business, this allowed Alphabet to further differentiate itself from Oracle. 

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So how does that lead into my outlook for each company for 2026? 

We continue to hold an overweight position in Alphabet and remain on the sidelines on Oracle. We believe 2026 will be a theme of capital durability, which Alphabet continues to screen well for. 

Alphabet enters 2026 with a position of strength, with continued momentum in their Gemini 3 model, and their announced partnership with Apple provides further validation of their AI capabilities. Their announced cloud growth is also encouraging, which is prompting their record capital expenditure guidance of $180 billion for 2026, which is nearly double that of 2025. This is not only supported by their robust cloud growth, but also their backlog of $240 billion driven by enterprise Gemini native migrations. 

We anticipate Oracle to have a more volatile 2026, despite a large backlog of $523 billion, which remains concentrated in OpenAI. Our view of Oracle’s extended capital requirements is also playing out this year, with them announcing plans to raise $50 billion to fund data-centre construction. This capital will be raised through a blend of debt and equity, which further impacts their debt levels and has led to negative outlooks issued by credit agencies. Equity raise further dilutes equity holders, who will have to be more patient to see returns on their investment as Oracle goes through this capital cycle. 

For 2026, we expect Google to remain the defensive big tech AI play due to their ability to fund their growth with their own balance sheet, whereas Oracle will need to tap into capital markets to do the same. However, once the backlog of $523 billion starts to convert to revenue, some of the overhang on Oracle stock should be lifted over time. 

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While 2025 can be categorized by companies trying to sign up capacity and clients for the AI buildout, I believe 2026 will see the winners and losers of AI determined by execution, energy autonomy, and balance-sheet durability. 

To broaden that out, in 2026 we will likely see a proliferation of agentic AI. I believe companies who are able to launch, automate and execute on multi-step workflows will come out on top. We’re already seeing this with multiple product launches from the likes of Anthropic and OpenAI, who are racing to automate facets of professional services. 

The losers of the AI buildout will be the ones who are not quick to evolve with their customers’ needs and their competitors. I believe if you are ‘AI washing’ — which is essentially trying to put an AI wrapper onto legacy applications, you will likely not succeed. 

Next comes the ability to source power. My belief is that companies who can source their own power or have contracted a supply of power will likely come out on top. Specifically, companies who have secured behind-the-meter power without relying on aging infrastructure will come out ahead. Also, we prefer companies who use renewable energy, mainly nuclear power, to power their data centres. We are cautious on energy usage, however, as we are seeing more focus on utility pricing, but remain confident on large hyperscaler ability to fund any additional capital requirements. 

In the power space, we are also cautious on companies who overly rely on public grids, which is facing increasing reliability concerns, along with potential curtailment risks to protect residential usage. Continued power source through data centre is critical, as data centres cannot go offline. 

Finally, capital structure will continue to play an important role in differentiating winners from losers. I prefer to own companies who can fund their own capital programs with their own balance sheet, versus tapping into capital markets. Even if some of our holdings have to tap into the debt market, we prefer to own companies who have a strong free cash flow and can pay down their debt in a relatively short period of time. 

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The biggest risk right now is the sheer cost of the AI buildout, and if there’ll be a return on this investment. This is further accentuated by demand-and-supply cycles, where capacities are being brought online due to continued demand for compute power. Any signs of demand weakening will likely cause markets to be negatively impacted. 

And there are a few ways we mitigate this risk. If a company builds the infrastructure that it can’t monetize fast enough, that will likely cause downward pressure on the share price. Another risk is if a company builds a data centre for a client whose revenue growth stalls, the builder may not see a return on their invested capital. 

We have highlighted this previously, but I believe the best way to mitigate this risk is to invest in companies that exhibit balance-sheet durability, who can internally fund their capital programs. Also look for companies with favourable credit-and-return metrics, which can highlight their ability to withstand potential shocks. 

Another risk is if we suddenly see capital spending slowdown from the hyperscalers. This will likely drag down performance across the AI ecosystem, from semiconductors to utilities. 

We could see a shift in the industry to lower cost offerings as well, which will impact top line and profitability growth expectations. But we do see a lower probability of this scenario playing out in the near future. 

In our innovation portfolio, which has concentrated positions in the AI ecosystem, I try to diversify risk across the technology stack. We hold multiple XPU providers, software companies and technology-adjacent companies, like in the utility space. I try not to have one company or theme drive the portfolio, which provides some insulation from company or thematic risks. 

We also try to find companies who don’t have outsized customer concentration. With the AI buildout, there is an increased amount of few companies, like the hyperscalers, spending the bulk of the capital. However, our exposure to these hyperscalers also diversifies our risk, as these hyperscalers have thousands of customers who use a variety of their services. 

Finally, I try to focus on companies who not only have a diverse range of customers, but multiple revenue sources. This further provides downside protection, whereas customer concentration, along with revenue concentration will be more challenging in periods of volatility. This is further highlighted by our conversation on this topic between Google and Oracle, where Google has a wide range of customers with a wide range of revenue sources, versus Oracle that has more concentrated customer risk.

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