Follow the Money: Capital Expenditures
The most direct signal of a company's long-term priorities is its capital expenditure, or CapEx. This is the money spent on physical assets like data centers and servers. For 2026, Alphabet has projected a staggering CapEx of up to $190 billion, a figure
that has nearly doubled in just a year. A significant portion of this spending is earmarked for the infrastructure needed to power its AI ambitions, from its custom Tensor Processing Units (TPUs) to the latest NVIDIA GPUs. For a strategist, this massive outlay sends a clear message: the cost of competing at the highest level of AI is immense and growing. It signals that companies without their own foundational infrastructure will become increasingly reliant on cloud providers like Google Cloud. If your strategy depends on cutting-edge models, you must factor in the rising tide of compute costs or find a niche where you can win without matching Google's scale.
Read Between the Lines: The Earnings Call
Financials tell one story, but the language executives use on the earnings call tells another. On recent calls, CEO Sundar Pichai has consistently framed AI not as a cost center but as a growth driver "across the board." He highlights how AI-powered features like "AI Overviews" in Search are increasing user engagement and expanding the types of queries Google can monetize. Pay close attention to the specific products mentioned. For example, strong growth in paid seats for Gemini Enterprise and a massive backlog for Google Cloud—which nearly doubled to $460 billion in a single quarter—show exactly where customers are placing their bets. This qualitative data reveals which AI applications are finding a commercial foothold, offering a roadmap for where to direct your own product development.
Dissect the Segments: Where AI Creates Value
An earnings report isn't monolithic; it's broken into segments that reveal which parts of the business are firing on all cylinders. For Alphabet, the standout performer has been Google Cloud, which saw its revenue surge 63% in the first quarter of 2026, driven primarily by demand for enterprise AI solutions. In fact, for the first time, enterprise AI became the main growth driver for the Cloud division. Meanwhile, in Search, AI is being used to lower the cost of responses while improving ad targeting for complex queries. This segmentation allows you to see how AI is being applied differently across business models—as a high-margin enterprise service in the cloud and as an efficiency and enhancement tool in the ad-supported search business. This should prompt you to ask: in our business, is AI a new product to sell, or is it a tool to make our core product better, faster, and cheaper?
Track the New Metrics: Redefining Success
When a company starts highlighting new metrics, it's a sure sign that its definition of success is evolving. Beyond revenue and profit, Google is now pointing to AI-specific indicators. Pichai has noted the number of paid Gemini Enterprise users, the total count of consumer subscriptions driven by AI features, and the volume of data processed by its models for customers. One report highlighted that over 330 Google Cloud customers each processed over a trillion tokens in the past year, a metric of deep engagement. These new key performance indicators (KPIs) signal what the industry will likely adopt as the standard for measuring AI success. For your own strategy, this is a prompt to move beyond vague goals like "implementing AI" and toward concrete, measurable outcomes like user adoption of specific AI features, token consumption for API-based products, or the reduction in cost-per-query.
