While the history of each tech era is written by the eventual winner - the narrative in the moment is often told as a battle of new vs old, of nimble vs lumbering, of innovative startup against legacy incumbent. Think Apple vs IBM. Google vs Yahoo. Adobe vs Figma. But when technology revolutions unfold, the eventual ‘winners’ tend to be those who control the infrastructure and distribution through which it reaches and impacts the world.
From that perspective, the big business of AI is less about building smarter models or a point solution, and more about power in the underlying forces that make them valuable. Strip the industry down to its fundamentals, and five structural forces determine who will capture long-term value: infrastructure, expertise, enterprise reach, consumer monetization, and financial flexibility.
Across each of these dimensions, Google’s accumulated advantages compound - creating structural dominance that’s difficult for newer entrants to replicate.
Five pillars of AI value creation - and why Google owns most of them
1. Core Infrastructure
AI is built on compute, networking, and hardware. Google has spent over a decade constructing one of the world’s most advanced distributed infrastructures, across in-house optimized silicon (Google TPUs), global scale data centers, and global network backbone. While Nvidia dominates chip supply, Google’s vertical integration gives it both cost efficiency and strategic control over its own compute destiny, whether its for in house applications and services, or supplying Google-grade infrastructure for clients.
2. AI Expertise
Few companies have deeper technical expertise in AI. Google Brain researchers pioneered the Transformer - the architecture behind almost all modern large language models, and the T in GPT. While there were some early missteps with Bard, it’s clear the full weight of Google went into deeply integrating AI across the whole organization, combining their research pedigree with production-grade deployment capabilities.
3. Enterprise Distribution
As AI shifts from proof-of-concept to productivity, distribution to enterprise becomes critical. Google Cloud has direct relationships with thousands of large organizations and proximity to proprietary enterprise data through Workspace, Gmail, and Docs, as well as GCP. Proximity to proprietary enterprise data through Workspace gives Google a natural channel to integrate AI tools where that data lives, even if not directly trained on it. This embedded position makes it easier to infuse AI into workflows rather than bolt it on as an external service - in fact, 70% of existing Google Cloud customers use Google AI products - with a subsequent significant increase in deal size.
4. Consumer Monetization
AI will reshape the consumer internet, and Google already runs the world’s most sophisticated intent-based advertising system. Its unmatched understanding of consumer intent - across Search, YouTube, and Android - gives it a clear path to monetize AI-enhanced user experiences, from smarter search results to generative ads. The introduction of Gemini to the masses through Google Search (as well as natively on Google Pixel devices) increases the surfaces for intent-based consumer attention.
5. Financial flexibility
Finally, Google’s core business gives it time, optionality, and significant financial resources. The transition to AI-first experiences to access knowledge from the traditional Google Search can be made one feature or functionality at a time, at the pace of user adoption or market forces. Where as new players like OpenAI are building first in an AI world, this is an evolution, not reinvention. What originally was considered a competitor to Search, AI-powered knowledge experiences can be seen as a transition to a “v2” - while also opening up a whole new opportunity, with strong parallels to adding cloud services and GCP. The internal systems, technologies, and processes, needed to deliver these services for their core offering can be productized and turn into a significant revenue opportunity.
Counterpoint
There are some strategic areas where Google fall behind their AI counterparts. Here are three signficant areas where Google is at a disadvantage:
Closed platform vs Open-source: The primary counterpoint to a unified platform is the ability to fully customize and optimize the technology for your needs. Open source tooling and models like Hugging Face and Llama respectively are popular choices that provide significant flexibility for developers and cost considerations when it comes to lifetime cost of ownership.
Developer Adoption: Developers wield outsized influence in shaping AI platform choices -especially during proof-of-concept and initial deployment stages. Visibility and momentum within the developer ecosystem often drive experimentation toward whichever tools are perceived as most innovative or state-of-the-art in that moment. In this context, open and modular platforms gain traction faster than tightly integrated enterprise stacks. While Google’s AI offerings are robust and production-grade, they can appear less “cutting-edge” to developers seeking flexibility and quick wins - a perception gap that can delay grassroots adoption even within enterprise environments.
Product velocity: Iteration speed compounds - every product release generates new data, user feedback, and brand momentum. Startups like OpenAI and Anthropic have set a new cadence for visible progress and experimentation. Frequent, public iteration reinforces developer enthusiasm and user trust, creating the perception of constant forward motion. By contrast, early missteps with Bard, and Google’s scale and wide range of existing product focus often slow its release cycles, leading to a gap between technical capability and public visibility.
In short
After the ‘PR-first’ approach previously seen in the short-lived days of Bard - Google has now been quietly stacking compounding wins across the board, and demonstrated by the latest published stats: 1 quadrillion tokens processed per month, and growing 20x in the past year.
The AI revolution may not crown a new king - it may simply reinforce the old one. The idea that Search is dying and would be the end of Google didn’t take into consideration the expertise and structural advantages that a lifetime in Search, building global infrastructure, growing enterprise distribution, and optimizing consumer monetization would have: positioning Google to truly own a leading role in the AI Era.
What else?
- AI Mode: How quickly does Google want to move to AI-first functionality on their core business?
- Owning the application layer too: The universal front door for consumers (AI Browsers & Assistants) and enterprise work (Gemini Enterprise) - will have the same monetization and control opportunities that created Google’s dominance in the first place.
- Google Pixel & Android: How will Google best leverage it’s significant marketshare in mobile device OS and growing marketshare of mobile hardware for the front door as well as distributed edge compute.