Bigger Isn't Always Better
Does your AI chatbot really need to understand the complete works of William Shakespeare before it can answer a customer's billing question?
Probably not.
Yet here we are—watching companies chase bigger and bigger AI models, convinced that if the first attempt didn't work perfectly, the solution must be MORE. More data. More parameters. More everything.
But here's what nobody's saying out loud:
Your customer support bot doesn't need to debate the Renaissance. Your voice assistant doesn't need to quote Julius Caesar (unless you're literally running a Roman history museum call center).
Most businesses are being sold a solution that's wildly oversized for their actual problem.
And that oversizing? It's not just unnecessary—it's expensive. It slows down responses. It increases hallucinations. It makes systems harder to control. And it absolutely murders your compute costs.
The best AI solutions aren't the biggest. They're the most relevant.
Think about it: AI doesn't need to know everything. It just needs to know what matters to YOUR business.
When you focus on curated, domain-specific knowledge instead of chasing model size, something magic happens:
- Outputs get more accurate
- Behavior becomes predictable
- ROI goes through the roof
Right-sized beats oversized every single time.
If your AI isn't performing well, I promise the fix isn't a bigger model. It's a smarter approach—one that's actually built around the problems you're trying to solve.
Whether you're a solo founder or running a 500-person enterprise, the same principle applies: precision beats bloat.
What's your take? Have you fallen for the "bigger is better" trap with AI implementations? I'd love to hear what's worked (or hasn't) in your business.
Drop a comment, share your story, or reach out if you're wrestling with how to right-size your AI strategy. Always happy to talk through what's actually worked in real-world deployments.
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