Embeddings, MCP, and the Future of AI Product Infrastructure
Every AI product relies on more than just models. Behind every intelligent feature—search, personalization, summarization—there’s a hidden layer of infrastructure quietly making it all possible. Two of the most transformative pieces of that layer are Embeddings and the Model Context Protocol (MCP).
They might sound technical, but understanding them is becoming essential for AI product managers who want to build scalable, flexible, and truly intelligent systems.
What Embeddings Really Are
Embeddings turn data—like words, sentences, or even images—into numerical representations called vectors. These vectors capture meaning, context, and similarity in a way machines can understand.
Think of it as translating human understanding into math:
“car” and “truck” end up close together in vector space,
while “car” and “cucumber” are far apart.
For AI products, this unlocks powerful capabilities:
Semantic search: finding results by meaning, not keywords.
Personalization: matching users with content that feels relevant.
Recommendation: connecting similar items or users based on hidden relationships.
Every modern product that “understands” users—Spotify, Notion, YouTube, or ChatGPT plugins—relies on embeddings somewhere in the stack.
The Role of MCP (Model Context Protocol)
As AI systems grow, they increasingly need access to live, external data sources. The Model Context Protocol (MCP) is a new standard that allows AI models to securely interact with other systems—APIs, databases, or internal tools—without retraining.
It gives models “awareness” of context they wouldn’t otherwise have.
A customer support chatbot can query a CRM for the latest ticket status.
A finance assistant can fetch real-time exchange rates.
A legal copilot can retrieve current regulations directly from a database.
Instead of giving AI static knowledge, MCP lets it connect to the right data at the right time.
Why This Matters for PMs
Embeddings and MCP aren’t just technical building blocks—they shape the future of AI product design.
Scalability: products powered by embeddings and standardized protocols are easier to extend and update.
Security and control: MCP allows PMs to define exactly which data sources AI can access, reducing risk.
Speed: these systems shorten time to market by decoupling models from constantly retraining on new data.
Customization: embedding-based systems can deliver deeply personalized results without writing new models for each use case.
In other words, they turn AI from a monolithic black box into a modular, flexible ecosystem.
PM’s Role in Shaping This Infrastructure
You don’t need to architect embeddings or implement MCP—but you do need to understand their implications for your product strategy.
Where can embeddings improve discoverability, personalization, or search?
Which user experiences require secure real-time data access through MCP?
How will you monitor and govern what data the model can see or act on?
Product managers who understand these systems can plan roadmaps that scale intelligently instead of rebuilding from scratch every time.
Real-World Example
OpenAI’s ecosystem uses embeddings for semantic retrieval and MCP to connect models with third-party tools safely. Similarly, enterprise AI platforms like Pinecone, Weaviate, and LangChain rely on vector databases and context protocols to make products faster and more context-aware.
These systems form the connective tissue of modern AI—quietly powering everything from search bars to copilots.
Final Thought
The future of AI product infrastructure is modular, contextual, and secure.
Embeddings give AI the power to understand relationships. MCP gives it the ability to act on that understanding safely.
For PMs, this is more than infrastructure—it’s a new way of thinking about product architecture. The products that win won’t just have the smartest models. They’ll have the smartest systems around them.