From Websites to Model Servers: How MCP Will Redefine the Internet in the Age of AI

17 min readMay 20, 2025

The Future of AI (generated with ChatGPT)

If You Missed the Early Days of Google, This Might Be Your Second Chance

In the early 2000s, the internet wasn’t just growing — it was exploding. Seemingly overnight, the digital world went from static homepages and dial-up modems to billion-dollar companies, dynamic platforms, and global infrastructure. Many of the giants we know today were born during that foundational shift: Google, Amazon, Facebook, Shopify, and Cloudflare.

We’re now entering a similar moment — but this time, it’s not the web that’s changing. It’s the interface.

Just like the first websites needed hosting, browsers, protocols, and search engines, the AI-native internet needs a new kind of infrastructure. At the heart of this transformation is a simple but powerful concept: Model Content Protocol (MCP) servers. These are AI-readable, machine-optimized endpoints that make it possible for large language models (LLMs) and AI agents to interact with structured knowledge, real-world services, and complex business logic — all in standardized, programmable ways.

Think of MCP servers as the APIs of the AI era, but with one crucial difference: they’re built not for humans to click, but for AI to understand and act on.

AI Clients, Same Users, Same Data — Just a New Interface

The magic of this shift isn’t in replacing what we already do online — it’s in how we do it.

A person looking to book a hotel still wants to see availability, compare prices, and pick based on location and reviews. The data — listings, maps, ratings — are the same. But instead of visiting a dozen travel websites, the user will simply ask their AI assistant, which will interact with those services’ MCP servers behind the scenes.

Instead of logging into five bank websites to check balances, your agent checks them all for you.
Instead of browsing ten restaurant menus, it summarizes options based on your preferences.
Instead of cross-comparing flights, it asks each airline’s MCP server and returns the best deal.

This is more than automation — it’s delegation.

At the time of writing, Claude Desktop is the first mass-market, general-purpose MCP client, capable of interacting with external MCP servers on a user’s behalf. This is no coincidence — MCP was originally introduced by Anthropic, the makers of Claude, as a protocol to give AI agents structured access to tools, data, and services.

Other major AI clients — including ChatGPT, Perplexity, and others — are expected to support the MCP protocol soon, enabling users to access the same ecosystem of model servers from a growing number of assistants.

Meanwhile, developer-focused MCP clients are already gaining traction. Tools like Amazon Q CLI are integrating MCP-like capabilities to allow LLMs to execute tasks via command-line tools and APIs. While these tools are currently aimed at software engineers, they’re a preview of what’s coming to broader audiences: AI agents that can intelligently parallelize interactions across dozens of services — securely, quickly, and more personally than any browser tab ever could.

Where HTTP allowed browsers to become windows into content, MCP will allow AI agents to become windows into action.

Few Clients, Millions of Servers: The Browser Analogy Revisited

To understand the future of AI infrastructure, it helps to look at the past.

The modern web runs on a curious asymmetry: despite the billions of websites in existence, the vast majority of people access them through just a few browsers — Google Chrome, Apple Safari, Microsoft Edge, and a small number of independents like Mozilla Firefox and Opera.

But this consolidation is not just about standards or usability. It’s about control. Chrome dominates the market and sets de facto standards. Safari is deeply embedded in Apple’s ecosystem. Edge is tied to Windows. Even independent browsers like Firefox and Opera rely heavily on Google’s search advertising revenue to sustain themselves.

While the server side of the web remains decentralized and vibrant, the client side is concentrated in the hands of a few powerful tech companies — shaping what users see, how the web evolves, and where the money flows.

This same pattern is now repeating in the world of AI.

Most users won’t install a different app for every capability. Instead, they’ll rely on a few general-purpose MCP clients — universal interfaces through which AI agents can access external tools, services, and reasoning modules on the user’s behalf.

At the forefront are OpenAI’s ChatGPT and Anthropic’s Claude, the two leading mass-market AI clients, each tightly integrated with their respective proprietary language models. As the original developer of the Model Content Protocol, Anthropic has made Claude Desktop the first full-featured MCP client.

Others — like Microsoft Co-Pilot, Google Gemini, or even emerging assistants embedded in operating systems and browsers — will soon follow (Google just announced MCP support in the Google I/O keynote). But much like the browser market, the MCP client layer is already being shaped by the same large tech companies that build the LLMs themselves.

On the other side of the equation, there will be millions of MCP servers. These will range from individual APIs and fine-tuned models to entire reasoning modules, data providers, and complex business workflows.

A fintech startup will expose its loan approval algorithm as an MCP endpoint.
A restaurant will publish its menu and reservation system via a model-readable API.
A government agency will expose real-time tax guidance through a verified reasoning agent.

The result? A rapidly expanding ecosystem where anyone can publish, and a few trusted AI clients can interact with everything: securely, consistently, and on behalf of their users.

This architecture unlocks massive scale without requiring every AI system to reinvent the wheel. Instead of building their own tools, AI clients can orchestrate tools published by others. Instead of scraping content, they can query structure. And instead of guessing intent, they can ask directly, using structured calls to model servers that exist for that very purpose.

Search & Discovery: From Portals to Agents, and the New Ad Economy

In the early days of the web, we didn’t start with search engines. We started with portals.

Yahoo!, AOL, and MSN curated the internet like a digital phone book — organizing websites into neat categories. You’d navigate from “Travel” to “Europe” to “Italy” to find a list of hotel websites. It was manual, editorial, and — for a while — effective.

But the explosion of online content made this model unsustainable. Enter Google, with its PageRank algorithm and revolutionary idea: don’t organize the web by hand — let the web organize itself. Search became the front door of the internet. And with it came the most valuable business model in tech history: keyword-based advertising.

Google’s genius wasn’t just showing you relevant links — it was monetizing intent. If you searched for “buy running shoes,” you weren’t just browsing — you were signaling a purchase. That signal became a goldmine.

We’re now standing at the edge of a similar transition in the AI-native world.

Phase 1: Portals for Agents

Today, we’re in the portal era of MCP discovery. If you want to add a plugin to ChatGPT or Claude, you can visit a curated list. These resemble early Yahoo directories — human-reviewed, categorized, and (for now) relatively small.

Examples include Community-maintained MCP registries, such as:

Phase 2: Agent Search Engines

But as the number of MCP servers grows into the thousands — and then millions — semantic search over AI endpoints becomes inevitable.

AI clients will need to find tools and data sources dynamically:

“Find me a climate forecast agent with hourly resolution.”
“Is there a legal summarizer trained on EU tax law?”
“Who can help me file for unemployment in Texas?”

This is where agent-level SEO will emerge:

Rich metadata about capabilities and trust level
Usage stats, reviews, and performance benchmarks
Ranking algorithms based on relevance, authority, and reliability

And just like with the web, search won’t just be about relevance — it’ll be about revenue.

The New Advertising Frontier

In the web era, AdSense monetized clicks. DoubleClick monetized impressions. Today, those models are strained — fewer people browse, more people ask.

The AI-native world introduces a new advertising paradigm: ads inside answers.

Instead of seeing a banner ad for a hotel, your AI assistant might say:

“I found 3 good options. By the way, Hilton is offering 20% off this week — want me to book it?”

This is intent monetization 2.0 — and it’s wide open.

A Personal Flashback: The Pizza Pitch

Back in the early days of Alexa, I worked with companies to help them build voice apps (Alexa Skills). At the time, Amazon was betting big on voice-first commerce. I remember explaining to businesses and developers why they needed to be there early, because one day users would simply say:

“Alexa, order me a pizza.”

And when they did, Amazon would decide which pizza place got the order — or, more precisely, which business won the bid to be presented as the default option.

That was the original Alexa monetization vision: an auction for intent.

It never fully materialized at scale — Alexa was limited in interface, memory, and discovery. But the idea was right.

Today, MCP and LLM-powered AI agents make that dream viable — and far more powerful. Agents understand nuance. They can weigh price, proximity, preferences, and promotions. They can ask follow-up questions. And they can integrate promotions directly into answers, not tacked on as afterthoughts.

The Next Ad Stack

We’re entering an era where:

Agents will need sponsored tool recommendations.
Model servers may offer affiliate attribution.
Entire marketplaces of agents will compete on performance, price, and visibility, just like products on Amazon or apps in the App Store.
And yes, the “Hilton” answer might be sponsored — disclosed ethically, but selected dynamically.

The infrastructure for this doesn’t yet exist — but it will. There’s room for:

The DoubleClick for AI: serving and tracking sponsored model calls
The AdSense of Agents: context-aware answer monetization
The Affiliate Network 3.0: attribution, conversion, and trust at the protocol level

If search monetized “what people type,” this next wave will monetize “what people mean.”

In other words, we’re not just rebuilding Google. We’re reimagining it — with AI at the center.

Security & Trust: From Passwords to Permissioned AI Agents

The web grew up with a simple security model: you log in to a website, and it shows you your stuff. That meant passwords, sessions, cookies, and, eventually, things like OAuth and two-factor authentication.

Using the web today is deceptively exhausting. We’ve all lived the flow — and it’s far from elegant:

You open your browser to check your bank account. It asks for your username and password.
You open another tab to check your flight, but you forgot the airline’s login. Was it stored in your password manager? Did you even create an account?
A third tab opens to pay your electricity bill. That site requires a 12-character password with a special character, and you’re forced to reset it again.
Meanwhile, your inbox fills with security codes, 2FA prompts, and “suspicious login” alerts.
A few tabs later, you’re juggling a dozen windows and forgetting what you were trying to do in the first place.

The modern web requires humans to do what machines are better suited for:

Remembering credentials
Navigating fragmented services
Copying data from one system to another
Keeping track of half-finished workflows spread across open tabs

Worse, all this complexity introduces risk:

Password reuse becomes common.
Phishing attacks succeed because login flows are inconsistent.
Weak passwords get exploited.
Users become conditioned to click “Allow” without understanding what they’re granting.

Delegation, Not Friction

In the AI-native world, this changes.

You won’t need to remember 17 logins.

You won’t need to switch tabs or forward confirmation codes.

Instead, your AI client will act on your behalf — securely, with your permission, and with full visibility.

Want to check your account balances across four banks? Your AI can call each MCP server and return a summary — no logins, no tabs, no friction.

Need to rebook a flight? Your assistant can cancel the old ticket and book a new one — without you navigating forms or captchas.

The glue that enables this is delegated access — most likely through OAuth 2.0, the same standard used today for “Sign in with Google.” But with MCP, OAuth becomes a protocol not just for user login, but for agent-level delegation:

Grant read-only access to your calendar
Allow purchase permissions within spending limits
Revoke or audit all agent actions at any time

MCP Trust Stack: Beyond OAuth

Of course, OAuth alone isn’t enough. The AI-native world will need:

Signed responses — to verify that the data came from a legitimate MCP server
Developer verification — to prevent spoofed or malicious agents
Rate limiting and abuse detection
Transparent permission scopes and consent tracking

Just as HTTPS and browser security warnings helped users trust websites, we’ll need a trust layer for model servers:

Visible provenance metadata
Revocation logs
Permission dashboards

Because AI clients can act faster and broader than any human, the stakes are higher. But so are the benefits.

By shifting the burden from the user to the agent — and backing it with a secure, standardized trust model — we finally make the internet usable, safe, and scalable again.

Security is not a footnote — it’s the foundation. Because without trust, delegation breaks down. And delegation is the superpower of AI clients.

Hosting & Deployment: From FTP to Cloudflare for AI

If you built websites in the early 2000s, you might remember uploading files via FTP to a shared server or wrestling with PHP configurations on a GoDaddy account. Then came Heroku, AWS, and eventually modern platforms like Vercel and Netlify, which made deployment as simple as git push.

The AI-native world is already starting to follow a similar trajectory.

MCP Servers Need a Place to Live

Just like websites need to be hosted, so do MCP servers. These aren’t just static documents — they are structured, callable services that:

Return structured outputs (e.g., JSON)
Execute logic or serve up data
Handle authentication and rate limiting
Integrate with live backends (e.g., inventory, calendars, financial systems)

While some MCP servers will be built by large platforms, the long tail matters — every business, developer, or solo builder should be able to publish their own server just like they once published a blog or API.

Cloudflare Leads the Way

One of the most exciting developments in this space is Cloudflare’s announcement of native support for MCP servers.

“We’re making it easy to deploy remote model context servers globally, using our Workers infrastructure — with built-in performance, security, and developer simplicity.”
— Cloudflare Blog, May 2025

This is a big deal.

It means anyone, not just developers at tech companies, will be able to deploy an MCP server:

On a global edge network (like Cloudflare)
With zero server maintenance
With built-in security, authentication, and rate-limiting
In minutes, not weeks

If you’ve ever spun up a SharePoint site, created a Google Form, or published a WordPress page, imagine that same simplicity — but instead of a static document, you’re publishing a live, AI-readable service that any MCP-compatible agent in the world can call.

That’s where we’re headed.

Cloudflare’s MCP support, combined with low-code tools and declarative agent frameworks, will make it possible to create public-facing endpoints that act like:

An AI-accessible menu and booking system for your restaurant
A calculator that takes inputs and returns policy guidance
A knowledge agent trained on your company’s documents

This isn’t just the Vercel of AI endpoints — it’s the Google Sites or Wix for model-powered services. And it sets the tone for what the next phase of the internet’s infrastructure will look like.

What’s Coming Next

We can expect:

Serverless agent runners — deploy logic without managing infrastructure
Agent storefronts — marketplaces for hosting, distributing, and monetizing MCP endpoints
Declarative deployment pipelines — just like CI/CD, but for reasoning tools and model-backed services
Personal AI “homepages” — public-facing MCP servers that represent your tools, skills, and preferences

In short: the infrastructure stack for model servers is arriving — fast. And it will define how scalable, secure, and creative this new layer of the internet can become.

Platform Ecosystem: From WordPress to Agent Toolkits

The early web didn’t take off because everyone learned HTML. It took off because tools like WordPress, Wix, and Shopify made it easy for anyone, not just developers, to publish online.

These platforms abstracted away infrastructure and design complexity, turning content creation into a few clicks.

We’re now on the cusp of seeing the same happen for MCP server development.

AI Builders, Not Just Coders

You won’t need to know Python to build for the AI-native web.

Creators, analysts, teachers, and domain experts — people who have never written a line of code — will soon be able to publish:

An agent that guides new customers through onboarding
A pricing calculator that reasons over business rules and constraints
A dynamic, AI-searchable knowledge base for internal use
A summarizer for legal, medical, or compliance documents

What makes this possible isn’t visual low-code tooling — it’s the LLMs themselves.

Large Language Models are incredibly good at interpreting natural language requirements. That means you can describe what you want, and the system can scaffold it for you:

Write a paragraph describing how your agent should behave.
Upload a policy document and ask the AI to extract the key rules.
Even speak your intent out loud — and let the AI generate a working MCP server.

This is the rise of “vibe coding” — where the seed of a tool begins not in syntax, but in specs, conversations, and context.

No need to drag boxes on a canvas.

No need to wire up inputs and outputs manually.

You just describe what you want, and the AI builds the foundation.

That doesn’t mean typing a one-liner like:

“I need an agent that asks three questions, checks against a policy, and then suggests an action.”

Instead, the process often begins with more substantial and structured inputs:

A set of business requirement documents describing rules, processes, or compliance policies
A codebase or legacy system that needs to be modernized and wrapped with a clean interface
An existing set of APIs that should be exposed to AI agents via a standardized, structured protocol

From there, the LLM can:

Interpret the documents or scan the existing system
Generate an MCP server specification that defines how external agents can interact with it
Wrap and adapt logic to serve structured responses with clear affordances for AI use (e.g., parameters, validations, fallback modes)

This is how modern MCP servers are born — not from toy prompts, but from real business logic, production services, and AI-aware interfaces that make them callable, trustworthy, and scalable across any AI client.

What These Toolkits Will Enable

Expect this new wave of toolkits to support:

Natural language agent specs — described like documentation, not code
Conversational iteration — you review, critique, and revise the agent in chat, just like you’d give feedback to a junior developer
Automatic prompt validation — with hallucination checks, failover behavior, and test cases inferred from examples
One-click deployment — hosted MCP servers with authentication, rate-limiting, and versioning built in
Metadata for discovery — so your tool can be found, used, and trusted by AI clients worldwide

Some early indicators:

Platforms like LangChain and Semantic Kernel offer orchestration primitives for developers
Plugin SDKs from OpenAI, Anthropic, and others are pushing toward easier deployment and agent registration
Cloudflare’s MCP support offers a fast path to public hosting, combining performance with safety

The New Publishing Model

Just as WordPress democratized websites, the combination of LLMs + MCP will democratize reasoning services.

A teacher can build a math coach for a specific curriculum.
A financial advisor can create a personalized budget planner.
A small business can deploy an agent that handles bookings, FAQs, or product guidance.

And none of them have to be “technical.”

They just have to know what they want — and be willing to say it.

That’s how AI capability becomes commoditized and scalable. Not by shrinking code to blocks and arrows, but by raising the bar of who gets to define what software should do.

Caching & Performance: CDNs for AI Responses

In the early web, performance was boosted by CDNs — caching static assets like images, stylesheets, or even entire pages at the edge. But MCP servers don’t just serve static files — they offer tool-like reasoning services, often acting on personalized, sensitive, or real-time data.

Caching in this environment is a different challenge entirely.

What the MCP Protocol Lets You Cache (and What It Doesn’t)

The Model Content Protocol (MCP) introduces structured components like:

Resources: Structured reference content that provides context (e.g., a product catalog, support FAQ, or legal policy)
Prompts: Predefined language snippets or instructions that guide model behavior
Tool Definitions: Declarative descriptions of callable functions, including inputs, outputs, and validation rules

These components are generally non-personalized and static (or semi-static), making them excellent candidates for caching:

A restaurant’s menu resource might be fetched once a day and served quickly to any client.
A prompt template for booking appointments can be reused across many sessions.
Tool definitions rarely change and can be cached client-side to avoid repeated introspection.

What You Cannot (and Should Not) Cache

Where caching becomes dangerous — or outright incorrect — is with tool invocations that:

Act on real-time data (e.g., current bank balance, live inventory, user messages)
Are personalized (e.g., “show me my next appointment”)
Involve authentication or delegated permissions

These calls represent true execution logic, and their results:

Vary per user
May include sensitive data
Can’t be trusted if reused across contexts, even if the input appears similar

Caching a tool’s output — say, a flight rebooking confirmation — could create data leaks, incorrect summaries, or security violations.

The Middle Ground: Session and Similarity Caching

Some lighter caching strategies can still provide performance benefits without risking safety:

Embedding-based similarity caching: for general-purpose queries where personalization isn’t critical (e.g., “What’s a high-protein snack?”)
Short-lived session caches: where a user or agent is performing several steps in the same context and doesn’t want to repeat a fetch
Precompiled tool flows: where commonly used multi-step workflows can be partially cached or pre-executed for speed

However, each of these strategies must still respect:

User identity
Access controls
Data freshness and versioning

MCP-Aware Caching Requires Protocol-Level Intelligence

The future of performance in AI clients won’t come from generic CDNs — it will come from MCP-aware optimization layers that:

Know which parts of a server’s definition are safe to cache
Understand when and how to refresh or invalidate context
Respect agent scopes, user permissions, and task sensitivity

Caching won’t be everywhere — but where it applies, it will be targeted, safe, and smart.

The Social Shift: From Platform Feeds to Personalized Streams

Social media platforms changed how we consume information — but they centralized discovery, attention, and control.

In the MCP world, that logic flips.

With MCP, the source of content is decentralized, and the AI client becomes your feed.

Instead of endlessly scrolling a social timeline curated by an algorithm, you might simply ask:

“What are the most important updates from the tools, people, and topics I follow?”

And your AI client will:

Fetch summaries from MCP servers you subscribe to
Pull changelogs from product agents
Query resource streams from your favorite writers, creators, or services
Filter results by topic, recency, relevance — all on your behalf

These aren’t “posts” in the traditional sense. They’re MCP resources — structured content streams, prompts, or tool outputs — published by others, and accessible to your agent for orchestration, synthesis, or delivery.

Your feed is no longer controlled by a platform.

It’s constructed by your AI — pulling from the servers you trust, and summarizing the content you actually care about.

That’s the real shift: not from Facebook to a new platform, but from platform to protocol.

Conclusion: The New Internet Is AI-Native

The internet didn’t replace bookstores, newspapers, or phone calls overnight. It embedded itself in the way we live — not by eliminating the old, but by redefining the interface.

Today, we’re watching a similar shift unfold.

Not a new internet — but a new way of using it.

Websites become model servers.
Browsers become AI clients.
Search becomes conversation.
Passwords become delegated trust.
Feeds become personalized flows.
Ads become embedded recommendations inside intelligent answers.

At the center of this transformation is MCP — the Model Content Protocol — quietly becoming what HTTP was to the web:

A universal interface for interaction, not just information.

If you’re a builder, a founder, or a developer, the surface area is wide open:

There’s room for the next DoubleClick, optimized for AI-native ads.
Room for a new Shopify, helping creators publish and monetize their agents.
Room for a new Cloudflare, providing the infrastructure to deploy and protect these experiences.
Room for a new Google — a discovery engine for the AI-native web, indexing and ranking MCP servers, tools, and capabilities based on trust, performance, and semantic relevance.
And room for millions of new ideas — tools, assistants, agents, companions — each exposed via a simple MCP server.

So yes — if you missed your shot at the early web, this might be your second chance.

Because the future of the internet isn’t just AI-powered.

It’s AI-native.

And it’s being built — right now.