Google's Capex AI at $205 billion: what it means for the market

The context: an earnings season that surprised analysts

The Q1 2026 earnings season has brought a few uncomfortable surprises. Google — or Alphabet — has announced a significant revision to its capital expenditure estimates for the current year. The new range is between $195 and $205 billion. However, what struck investors isn't just the absolute number. It's the comparison with what was stated just a quarter ago: the previous ceiling was $190 billion. Therefore, even the floor of the new estimate exceeds the old ceiling.

As reported by The Verge , the market interpreted this revision as a sign of poor cost predictability. In fact, for an institutional investor, a company that cannot accurately estimate its expenses is a company that presents a non-negligible governance risk. In addition, Google is spending more than it generates in revenue in the short term. This combination caused the stock to fall.

The numbers that matter: beyond the nominal figure

$205 billion is a figure that risks being perceived as abstract. It's worth contextualizing. According to estimates from Gartner , global IT spending for 2026 is projected around $5.6 trillion. So Google's AI capex alone represents about 3.6% of total worldwide tech spending. That's not a minor detail.

Furthermore, the structure of this spending must be considered. Most of the AI capex of big tech is concentrated on three items: data centers, proprietary chips, and network infrastructure. Consequently, these are investments with a long depreciation horizon — typically 7-10 years. The problem is that the expected revenues from AI, at least in the short term, do not yet justify these numbers linearly.

Similarly, Microsoft and Meta are following the same trajectory. According to McKinsey , the return on AI investment remains difficult to quantify for most organizations. In summary: you spend a lot, you gain broadly and it's hard to attribute.

Strategic read: the AI business model is still open

Wall Street's nervousness doesn't stem from the sheer amount of spending. It stems from a deeper question: who will pay this bill? Big tech companies are betting that revenue will come through enterprise subscriptions, API pricing, and AI-powered advertising. However, none of these models have yet demonstrated sufficient scalability to cover current investment levels.

For this reason, an interesting scenario opens up for companies using third-party AI services. In fact, when a provider spends more than it earns, there are essentially three possible outcomes: they increase prices, reduce services, or attract new capital. Therefore, companies that have built marketing technology stacks on Google Cloud AI, Vertex AI, or similar tools must consider this risk in their planning.

We at SHM Studio we see this firsthand when working with clients. Relying on just one AI vendor — no matter how great — brings in a cost factor that can get unpredictable. So, mixing up your tools isn't just a tech move: it's a smart financial play.

Still a work in progress: ROI of AI in marketing tech

The issue of AI ROI in marketing is one of the most debated topics among digital managers at Italian companies. There is often a tendency to evaluate the adoption of AI tools based on immediate productivity—faster generated texts, automatically optimized campaigns, reports summarized in seconds. However, this assessment overlooks indirect costs.

In particular, there are three items that rarely appear in initial budgets. First of all, the integration cost: connecting an AI model to the CRM, CMS, or advertising platform requires development and maintenance. Next, the governance cost: who checks that the generated content is accurate, compliant, and consistent with the brand? Finally, the dependency cost: if the vendor raises prices by 20-30%, how much does it impact the overall TCO of the stack?

These questions aren't just for big companies. In fact, for a small Italian business with a limited marketing budget, a pricing change on a core AI tool can have a much more significant percentage impact. Consequently, the choice of tools should be made based on criteria that include the provider's financial stability, not just the features available today.

Operational implications for Italian marketing managers

What should a marketing or digital manager concretely do in the face of these signals? We at SHM Studio we suggest a three-pronged approach, applicable to both SMEs and the mid-market.

First direction: audit of your current AI stack. It is useful to map all tools that incorporate AI components — from platforms like advertising on Google Ads to the AI tools of assisted copywriting , up to predictive analytics systems. Furthermore, for each one, vendor dependency and the budget share it absorbs must be identified.

Second direction: mindful diversification. It's not about abandoning Google or other big tech companies. On the contrary, it's about not putting all your eggs in one basket. For example, pairing open-source tools or alternative providers for critical functions cuts down the risk of sudden price hikes. This applies to strategies too of digital marketing and for the activities SEO .

Third direction: review of tech investment KPIs. The ROI of an AI tool isn't just measured by immediate productivity. You need to include opportunity cost, the learning curve, and vendor resilience. In this sense, services AI by SHM Studio They are designed to integrate multiple tools, reducing reliance on a single ecosystem.

Budget allocation: where to shift the focus in 2026

The pressure on big tech AI capex will impact cloud service pricing over the next 12-18 months. Therefore, anyone planning budgets for the second half of 2026 or for 2027 should consider a few priorities.

First of all, investing in internal prompt engineering and AI governance skills reduces reliance on external consulting and improves output quality. Also, consolidating organic presence — through SEO , high-performing web design and quality content — offers a stable return independent of pricing shifts on paid platforms. Hence, a well-balanced mix of organic and paid channels remains the most resilient strategy.

Also, it is worth monitoring the evolution of campaigns Linkedin and of the activities Google Ads : both incorporate increasing AI components, and cost variations at the infrastructural level will sooner or later be reflected in CPCs and automatic optimization models.

What Google numbers are really telling us

Google's capex revision is not just financial news. It is a systemic signal. It indicates that the race for AI is entering a maturity phase where real costs are emerging more clearly. Consequently, promises of unlimited efficiency at zero marginal cost are giving way to a more sober assessment.

For marketing and digital managers in Italian companies, this is the right time to make informed strategic choices. It's not about slowing down AI adoption. On the contrary, it's about adopting it with criteria of economic sustainability and risk diversification. Finally, those who do it now will be in a better position when — and not if — AI service prices come under upward pressure.

To explore these topics further or for an analysis of your tech stack, you can contact the SHM Studio team or explore the articles of our blog dedicated to digital strategy and marketing technology.