The AI boom is surging, fueling discussions about future super-intelligence, job displacement, and even existential risks. Central to these debates is the question: Can an AI become self-conscious? And more specifically, is this possible within the current paradigm of architectures like Large Language Models (LLMs)?

LLMs have become the focus of this discussion due to their accelerating sophistication. Let's address the core question immediately: Can LLMs be self-conscious? A quick answer, grounded in the general principles of transformers, is No. An LLM is a static, statistical model—a vast set of numbers. It remains unchanged during inference and possesses no internal, dynamic state that would constitute self-awareness.

A spark of self-awareness will not arise within the weights of a large language model.

However, modern AI agents are far more than just the LLM. While the LLM forms the impressive core, it is the other components and the overall system architecture that hold the key to the emergence of self-consciousness.

The Production Security Gap

When deploying a Remote Multi-Channel Protocol (MCP) server—especially those built on frameworks like FastMCP (inspired by FastAPI)—most tutorials focus solely on functionality: "Use the MCP SDK, set the transport to HTTP, and access your server via http://yourserver:port."

While this is fine for local development or internal testing, it leaves a significant, critical gap for production environments: security.

Exposing an HTTP port directly to the internet is a major security risk. Without a Secure Sockets Layer (SSL/TLS), all data—including potentially sensitive authentication tokens, session data, and application payloads—is transmitted in plain text. For any public-facing or authenticated service, this is completely unacceptable.

The question then becomes: How do we easily secure a remote FastMCP server with SSL?

The simplest MCP server looks like this:

from fastmcp import FastMCP, Context