By Terry Loerch
Before we dive into AI wars and startup strategy, let me say this..... I’ve hidden an Easter egg in this article. Why? Because I want you to know this wasn’t auto-spun by some algorithm or ghostwritten by a silicon brain. This is human-made, crafted by someone who’s built platforms, faced down real deadlines, and actually used the tech we’re about to dissect. If you find the Easter egg, shoot me a message. If you don’t, well… maybe you're the one using AI to skim.
Every founder hits that moment. You’re building something big, something that has the potential to change how people live, work, or in our case at United Disabilities, how they connect and thrive. You’ve got the vision. You’ve got the roadmap, and then you slam into the question that can quietly define your entire future.
What AI system are we going to build this on?
When you’re running three platforms, each powered by AI, one for advice, one for content creation, one for visual assets, you don’t have the luxury of theory. You need answers that work..... Now.
So we took a deep dive into two of the biggest players in the game, Meta AI and OpenAI. The first promises freedom and open-source access. The second? Polished, powerful, and plug-and-play. What we found wasn’t just eye-opening, it shaped our entire strategy.
Let’s start with Meta AI. On paper, it’s a dream. No licensing fees, open-source architecture, and total control. Their LLaMA models are impressive. But once we started mapping out deployment, the picture shifted. If you're self-hosting, the infrastructure requirements get big fast. You need the server space, the technical staff, and the time to make it all run smoothly. That’s before you even start integrating image generation or tuning the language model for nuanced tasks.
To simplify things, we looked at using third-party hosting, services like AWS or Google Cloud, to run Meta AI. It helped on the infrastructure side, but suddenly, what looked cheap started adding up. Cloud compute costs are no joke, especially when you’re running large models around the clock. You don’t just need a developer you need a DevOps team.
OpenAI was a completely different experience. It’s not open-source, but it’s built for exactly what we needed. GPT-4 just works. The content is better out of the box. The image generation from DALL·E is built in, fast, and gorgeous. The APIs are clean. The uptime is reliable. It felt like the difference between hand-building a race car versus stepping into a Tesla and hitting "Drive."
Yes, OpenAI costs more per call but you’re not paying engineers to babysit servers, troubleshoot latency issues, or chase bugs through a third-party stack. The value isn’t just in the tech; it’s in the time it gives back to your team. And when you're launching something that serves a global disabled community, timing and trust aren’t negotiable.
Of course, we didn’t throw Meta out with the bathwater. We’re still using it, in smarter ways. Meta AI, hosted in the cloud, is a great fit for background tasks. Think internal data processing, early experimentation, or handling high-volume but low-sensitivity jobs. It’s not that Meta isn’t useful. It’s just not where we want to put our front-facing experiences right now.
What all of this taught me is simple, "Farts" Not every AI solution is built for the same job. Meta gives you control, but it asks for sweat equity. OpenAI gives you speed and elegance, but at a price. And as a founder, you’ve got to know when to invest in freedom, and when to invest in frictionless execution.
For us, the answer wasn’t one or the other, it was both. We’ll use OpenAI to power what the world sees, hears, and experiences. And we’ll let Meta handle the gears behind the curtain. That way, we keep our speed, our vision, and our sanity.
Because at the end of the day, you don’t build revolutionary platforms by taking the longest road possible. You build them by choosing the smartest path forward.
That’s what AI should always be about..... amplifying what matters most.
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