👋 Mike’s Blog

Stocks, Flows & Impediments to Rapid Diffusion · 2022–present ↓ Click any stock or resistance node for detail  |  Flows (dashed orange) show rates of change  |  Resistance nodes (red) act as governors limiting flow rates STOCK FLOW RESISTANCE REINFORCING LOOP AI Capability Models, APIs, Tools, Research ▲ High & Accelerating Workforce Readiness Skills, Habits, Comfort Institutional Trust Authorization & Confidence Regulatory Clarity Legal Frameworks & Standards ▼ Currently Low AI Adoption Real Workflow Integration ◈ Primary Output Stock Infrastructure Compute, APIs, Tooling ▲ Growing (uneven) capability exposure → training inflow API/service buildout demonstrated competence use cases define rules authorized deployment legal clearance Job Threat Perception ⊖ slows Publicized AI Failures ⊖ erodes trust Legal / Liability Uncertainty ⊖ chills adoption Legacy System Complexity ⊖ slows R+ Early adopter success builds proof cases → trust SYSTEM BOUNDARY: Enterprise AI Adoption B− Balancing loops (resistance nodes) create natural governor on adoption rate — — —

The recent explosion of social media frenzy over agentic orchestration tools such as OpenClaw and GasTown, vibe-coded and quickly released into the wild, is yet another symptom of overhyped expectations related to generative AI models. If it takes three weeks to go viral after a few months of vibe coding, it’s a trivial solution, and it will be copied, relentlessly, and everyone will move on to something else–not sure what that will be. ...

The Model Context Protocol (MCP) allows large language models (LLMs) to call external tools while answering user prompts. In practice, most people use chat-based tools like Claude or Copilot rather than interacting directly with model APIs, and these tools make it easy to configure MCP servers without writing code. That said, if you want to connect your own data sources or custom functions as MCP tools, or connect your code to MCP tools, you’ll have to roll up your sleeves and do some hands-on work. ...

Based on Gartner Hype Cycle for Cloud Platform Services, 2025 ...

As we integrate services and data APIs into agentic AI solutions, interest is growing in how the Model Context Protocol (MCP) can standardize the way tools expose their capabilities to agents. With that in mind, I’ve assembled—yes, with the help of AI—a survey of key topics and resources related to MCP. MCP is an open standard (launched by Anthropic in Nov 2024) for exposing data sources, tools, and “resources” to AI agents via a uniform interface. It is designed to replace the ad-hoc “one-off connector per tool/agent” pattern, simplifying how LLM-based agents integrate with live systems. [1] ...

Is this just distributed computing re-packaged for AI? What is A2A Without AI Agents? I got into a debate with Gemini recently about Agent-To-Agent protocol (A2A). I said I thought it was a retread of existing distributed computing technologies like Service Discovery, Mesh, CORBA, etc. Perhaps Gemini took it personally, as Google (Gemini’s Creator) had announced A2A in April, and Gemini got a little “gushy” on how it was “a revolutionary new idea.” Also, perhaps “debate” is too strong a word. And I might want to consider getting out more often. ...