How Distribution Is Putting Google Ahead of OpenAI and Apple

The year has just begun, and the momentum appears to be firmly on Google’s side. On January 7, Alphabet, the search giant’s parent, overtook Apple to become the world’s second most valuable publicly traded company, behind NVIDIA. 

Alphabet’s market capitalisation closed at approximately $3.89 trillion, edging past Apple’s valuation of about $3.85 trillion following a recent surge in its share price. It is the first time since 2019 that Alphabet has surpassed Apple in market value.

The reshuffle is striking, given Apple’s near-final integration of Google’s Gemini models into Apple Intelligence. In late 2025, multiple reports said Apple would pay Google roughly $1 billion annually to run a custom version of Gemini as part of its next-generation Siri stack. In effect, Apple is buying intelligence from the very company that just overtook it.

That shift reflects a broader realignment underway in AI.

During a recent podcast with entrepreneur Nikhil Kamath, Elon Musk pointed to Google and NVIDIA as the companies most likely to accumulate long-term value from AI and robotics. Google, he said, had “laid the groundwork for an immense amount of value creation from an AI standpoint.”

Market data suggests that groundwork is beginning to pay off.

According to Similarweb, ChatGPT’s share of global visits to generative AI chatbot websites fell from about 86.7% in early 2025 to roughly 64.5% in January 2026. Over the same period, Gemini’s share climbed from 5.7% to around 21.5%, signalling a rapid narrowing of the gap.

The pressure has not gone unnoticed inside OpenAI. Last year, chief executive Sam Altman announced an internal ‘code red’, triggered by a rival development seen as a potential threat to OpenAI’s position.

Much of Google’s recent momentum is tied to Gemini 3, its latest flagship model, which has drawn a strong user response. The company’s newer image-generation system, Nano Banana Pro, has also gained attention for handling text reliably and producing detailed infographics, areas where earlier models often struggled. 

Distribution, Not Just Models

Gemini’s biggest edge, however, is not technical benchmarks, but distribution. Shay Boloor, chief market strategist at Futurum Equities, wrote on X that Google can ship new AI capabilities instantly across Search, YouTube, Android, and Workspace, reaching billions of users without asking them to change behaviour.

According to him, the company that can successfully distribute intelligence widely across devices, applications, and business processes will dominate the new AI economy, and Google is clearly establishing this system across its whole ecosystem.

More than 3 billion active Android devices worldwide can surf Gemini features directly through Google Assistant, while Google Search, which holds close to a 90% of the global market, is already rolling out AI-powered results through AI Overviews. Gmail, Maps and YouTube extend Gemini’s reach further, embedding AI into products people already use daily.

Samsung Electronics recently said it plans to expand Galaxy AI features across its devices, much of it powered by Gemini. The rollout is expected to grow from about 400 million devices last year to roughly 800 million smartphones and tablets by 2026.

Google is also pushing Gemini deeper into productivity. The company has introduced what it calls the “Gemini era” of Gmail, bringing AI-generated summaries, natural-language question answering and contextual writing tools directly into the inbox. Instead of searching keywords, users receive synthesised answers across email threads by default.

Owning the AI Stack: Models, Chips, and Cloud

Google’s advantage runs deeper than consumer products. Unlike most AI players, it controls the entire technical stack.

At the infrastructure layer, Google designs its own Tensor Processing Units (TPUs), custom chips optimised for training and running large language models at scale. 

Gemini runs on this infrastructure by default, as do the AI systems behind Search, Maps, Photos, and Ads. 

That strategy is beginning to attract external validation. Anthropic has said it will use Google’s TPUs, while Meta has reportedly explored them for parts of its training workloads. Gemini 3 Pro, widely regarded as Google’s most powerful frontier model, was trained entirely on TPUs.

OpenAI, by contrast, depends heavily on Microsoft’s cloud and NVIDIA hardware, exposing it to cost pressure and supply constraints. Google’s vertical integration allows faster iteration and lower marginal deployment costs.

Beyond Screens

Perhaps the clearest signal of Google’s long-term AI ambitions lies beyond chat interfaces.

In early 2026, Google DeepMind announced a partnership with Boston Dynamics to integrate Gemini-based foundation models into next-generation robots, including humanoid systems.

The goal is to bring large multimodal AI models into the physical world, allowing robots to perceive, reason, and act with greater autonomy. This work builds on years of Google research in reinforcement learning, robotics simulation, and embodied AI.

Neither OpenAI nor Apple currently operates a comparable robotics program at scale. Google is betting that the next phase of AI will move off screens and into the physical world.

Two years ago, Google was criticised for moving too slowly in generative AI. Today, the picture looks very different.

Gemini is gaining users. Distribution is unmatched. The company owns its chips, its cloud and its delivery channels. Its models are spreading across consumer products, enterprise software and robotics. Even competitors are becoming customers.

ChatGPT may have sparked the AI wave, but Google now seems focused on shaping what happens next.

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