Google bets big on AI: what it means for big tech and Everyday AI
Google has rolled out its new feature “AI mode” recently in its search engine. It is currently covering the USA region and the dates for a worldwide launch have not yet been decided. Google’s recent rollout of AI Mode in its U.S. search engine is not just an enhancement; it’s a signal. A signal that Google is shifting gears from “AI features” to a fully agentic AI future where AI acts not only as a tool but as a partner.
This isn’t just about smart suggestions or faster search results. It’s a strategic move toward everyday AI solutions becoming embedded in everything you use.
But here’s the twist: Google isn’t just innovating; it’s consolidating.
What does “all-in” really mean?
When Google says “all-in”, it means that:
- It builds its own chips (called TPUs).
- It uses its own cloud infrastructure to power AI tools.
- It does in-house research to build models like Gemini and others.
- And it collects massive amounts of user data from platforms like Search, Maps, and Gmail to train these systems.
The technical aspect informs us that most modern AI systems operate across a multi-layered architecture. Here’s what each layer includes and how Google has vertically integrated them:
1. Hardware Layer (AI chips)
AI models need high-performance hardware, not traditional CPUs, to function. Google’s Tensor Processing Units (TPUs) are purpose-built for the kinds of matrix-heavy calculations required by deep learning, especially transformer-based models like Gemini.
These chips accelerate both:
- Model training: enabling faster convergence on huge datasets
- Inference: reducing latency when AI responds in real-time (like in Search or Gmail)
The vertical edge here? By controlling this foundational hardware layer, Google fine-tunes everyday AI solutions for maximum efficiency; from a user’s query to real-time personalization.
2. Infrastructure Layer (Cloud + Networking + Storage)
Google Cloud Platform (GCP) powers nearly all of Google’s internal and external AI tools. It includes advanced cloud-native services such as:
- Kubernetes for orchestration
- TPU and GPU clusters for compute-intensive AI
- Cloud Spanner and BigQuery for data processing and analytics
- Secure networking and scalable storage
Because GCP is optimized for AI, it supports Google’s vision of agentic AI that responds across contexts; whether on your phone, in your browser or in enterprise apps. This control over infrastructure allows Google to move faster, scale globally and reduce third-party dependencies.
3. Model Development Layer (AI R&D and Algorithms)
At the heart of Google’s stack are models developed by DeepMind, Google Brain and Google Research; like:
- Gemini: a multimodal LLM rivaling GPT-4
- PaLM: pre-trained language models for enterprise-scale NLP
- AlphaFold: solving protein structures with AI
- Flamingo: merging vision and language for multi-input AI
These models use cutting-edge techniques such as:
- Transformer variants and sparse attention
- Self-supervised learning
- Reinforcement learning from human feedback (RLHF)
- Mixture-of-Experts (MoE) architecture
This layer is where true agentic AI trends emerge; AI that doesn’t just respond but makes informed decisions. However, these models are not open-source, which limits academic and public-sector participation in validating or improving the systems.
4. Data Layer (User Interaction Data)
Data is the fuel for AI. Google’s ecosystem Search, Maps, Android, Gmail, YouTube collects petabytes of behavioral and contextual data daily. This is used to continuously:
- Train AI systems with real-world user behavior
- Fine-tune results based on intent and outcomes
- Build feedback loops via RLHF and federated learning
Techniques like federated learning allow models to be trained across decentralized devices, preserving privacy while still learning from patterns. This real-time refinement creates a unique form of everyday AI solution that adapts faster than competitors’ models; essentially giving Google “feedback dominance.”
Why does this vertically integrated model matter more than ever?
Google’s control over the full AI stack isn’t just a technical advantage; it’s a structural advantage. Here’s why it raises concerns:
Systemic fragility
If a vulnerability exists at any level (chip, model, or data), it can ripple through Gmail, Ads, Maps and Search simultaneously impacting billions of users.
Reduced transparency
Without open access to model parameters or training data, external auditors, researchers and regulators can’t verify how AI is behaving, making it harder to spot:
- Bias in AI outputs
- Hallucinations in AI summaries
- Security or privacy violations
Knowledge silos
By keeping data, talent and AI architectures in-house, Google forms a knowledge monopoly. This slows innovation across the ecosystem and makes collaboration with academic or open-source communities difficult.
Barriers to entry for new players
Smaller companies and startups can’t afford the compute, storage or data scale Google uses. This cements the dominance of Big Tech and limits diversity in agentic AI innovation.
Theoretical frameworks that back this concern
Several frameworks highlight the risk of AI monopolies:
- Platform Capitalism: Big Tech owns not just the tools but the entire AI value chain
- Arrow’s Information Paradox: Knowledge becomes more valuable when shared, but monopolized in this case
- Black Box Models: Proprietary AI resists outside understanding, raising ethical red flags
In short, Google’s vertically integrated AI model optimizes scale and performance—but sacrifices transparency, competition, and user autonomy in the process.
Where do we go from here?
We’re rapidly entering an era where agentic AI intermediates nearly every digital interaction; recommendations, emails, navigation, research. The benefits are real, but so are the trade-offs. We must ask:
- Who sets the ethical boundaries?
- Who gets to train the worldview of an AI?
- And how do we ensure that everyday AI solutions are fair, open, and unbiased?
Build open, scalable AI: your way, with Algoworks
At Algoworks, we believe AI should be empowering, transparent and accessible. We help enterprises build agentic AI and everyday AI services that are robust, ethical and future-ready without locking you into proprietary platforms.
Want to make smarter AI decisions for your enterprise? Let’s co-create AI systems that align with your goals, values and scale. Talk to our AI experts.
Bob Taylor
Latest posts by Bob Taylor (see all)
