Google’s Best Year Since 2009 Signals AI Paying Off
We’ve reached the end of the year with an intriguing note. Alphabet finishes 2025 with momentum that’s hard to ignore. Google’s stock delivered its best annual performance since 2009, outpacing several megacap peers largely because the artificial intelligence narrative stopped being a promise and started showing up in product, margins, and cash flow. The rally wasn’t a late-year blip. Throughout 2025, analysts saw ad revenue regain traction, YouTube consolidate watch time and performance formats, and Google Cloud contribute growth with a more visible path to profitability. For investors, the turning point was translating AI advances into concrete, defensible services with a roadmap that fits the balance sheet. Taken together, that combination explains why Alphabet ended the year as one of the large-cap tech names with the strongest relative return.
Comparisons with other giants help frame the move. As the market rotated toward stories with strong cash and direct exposure to AI infrastructure, Alphabet’s share price pulled ahead of names that typically anchor institutional portfolios. Reports in late November already placed it ahead of Microsoft and Amazon year to date, a shift driven by the sense that Google had found balance between cost discipline, compute investment, and rolling AI features into search and consumer products. Closing the year with the best performance since 2009 reinforces that the strategic reset landed with investors.
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Google Cloud kept scaling with customers that need infrastructure for models and data, with a stronger emphasis on enterprise use cases moving from pilot to production. At the same time, the company accelerated the addition of generative and reasoning-assist capabilities in search and YouTube, widening the runway for higher-performing ad formats. Add to that new features in the Gemini family and developer tooling that makes it easier to build agents and multimodal experiences on Google’s services. The message to investors was clear. AI is no longer confined to conference demos; it’s reaching products used by billions and cloud contracts with recurring revenue.
Markets also reacted to signals of financial execution. As 2025 progressed, Alphabet stressed operating-expense discipline and prioritized capex tied to data centers, networks, and accelerator supply. Portfolio managers read it simply. If the company can balance aggressive infrastructure spend with efficiency gains and incremental monetization across platforms, the multiple can expand without leaning on a single catalyst. At the same time, appetite for rebalancing toward businesses with strong cash and direct participation in the AI cycle worked in Alphabet’s favor.
Competition in models and agents intensified, with Microsoft and OpenAI setting the pace on the enterprise side and Amazon defending its ground in cloud and integrations. Regulation remained a variable to watch, from antitrust cases in the United States and Europe to debates about how generative models are embedded in mass-reach platforms. Capital commitments to data centers and energy will stay elevated, and model efficiency will have to advance at the same rate to protect margins in a world where compute costs and power constraints matter more.
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For customers, the most important shift is practical. Weaving AI into search, productivity, and video opens new surfaces for monetization and forces a rethink of data, measurement, and content strategies. On the enterprise front, Google Cloud strengthens its position as a partner for AI workloads that require security, compliance, and observability, an arena where competition is defined less by lab benchmarks and more by the ability to run models day to day under demanding service agreements.
Heading into 2026, the conversation will move from demo fascination to colder comparisons of cost per query, latency, security policies, and ease of integration with existing systems. The advantage won’t be static. It will shift with how quickly providers align chips, networks, data centers, and orchestration software. In that race, Alphabet enters with the tailwind of its best year in more than a decade and the pressure to prove that adoption of its AI services can sustain growth without diluting profitability.
On the operational side, many companies planning to lean on Google’s platform for AI projects discover that the gap between a flashy proof of concept and a stable solution is bridged by teams that integrate data, security, and continuous delivery. That’s where Square Codex fits. Based in Costa Rica, we work under a nearshore staff-augmentation model that places software engineers, data specialists, and AI teams directly inside North American organizations. Our role is to turn initiatives built on Google Cloud and model ecosystems into measurable operations, with auditable pipelines, sound MLOps practices, and cost and availability metrics tied to business outcomes.
That approach becomes especially valuable as 2026 budgets demand tangible results. Square Codex plugs into clients’ boards and repos, accelerates services that combine first-party data with AI APIs, and ensures cloud dependencies don’t turn into technical lock-ins. In a market where stock performance rewards consistent execution, having teams that connect strategy, engineering, and operations helps capture the real value of the AI wave without losing control or pace.
