William Collins

Here’s a question I got asked recently: If a skill can already call a REST API using Bash, why bother with MCP? The surface-level answer is “MCP is cleaner.” That’s not wrong, but it undersells what’s actually different - and I think it’s a genuinely useful distinction to understand if you’re serious about building reliable agent workflows. Also, common-sense needs a resurgence given the massive amount of all old things are DEAD when new thing comes out clickbait that is proliferating on LinkedIn.

Let me paint you a picture. It’s 2025. You’ve discovered that you can describe a feature in plain English and an LLM will just… build it. The dopamine hit rivals or even eclipses social media. You feel as if you’re shipping things in an afternoon that used to take a week. You’re not reading diffs. You’re not understanding the internals. You’re just vibing - and it feels amazing.

If you’ve spent any time building with MCP servers, you know the drill. You start with one server, maybe two. Then suddenly you’re juggling a dozen configurations across three different LLM clients, and your claude_desktop_config.json looks like it was written during a caffeine-fueled fever dream. That’s roughly where I was when I started building gridctl.

As AI dominates tech headlines and corporate strategies in 2024, an important distinction is being blurred - the difference between developing AI versus consuming AI services. This mischaracterization risks confusing the market and overselling capabilities, but that isn’t anything new right? Take zero-trust, cloud computing, or even take a look back at the early-2000s with the web revolution. Urs Baumann tossed out a great questions in the Network Automation Forum - Slack recently, and I thought it would make for a good blog (or maybe venting session depending on how you look at it). Remember, these are opinions.

The foundation of high-performance computing that powers artificial intelligence lies not just in powerful GPUs, but in the intricate web of connections between them. As models grow exponentially in size and complexity, the networking infrastructure that facilitates their training has become a critical bottleneck. This has sparked a fascinating race to develop networking solutions tailored for AI-centric workloads, with tech giants like NVIDIA who are on an infrastructure trailblazing marathon.

In my previous blog on Dilution of Ownership, I explored how startup funding rounds impact equity. Now, let’s dive into a hot-button, highly-debated, and dramatically misunderstood matter of policy - taxing unrealized capital gains (or the wealth tax). Could this policy drastically alter the startup landscape? Would there be a tangible impact on founders, investors, and the innovation ecosystem as a whole?