Would you say utilizing multiple similar AI models is worth it, and is that only if they hold different tasks?
Yes, utilizing multiple similar AI models is worth it when they hold different tasks. You want to match the model to the task rather than relying on a single model for everything.
I start with the most capable model to verify feasibility, then run evaluations to see how often it succeeds under guardrails. If a smaller, cheaper model performs almost as well (e.g., Astra 6 medium vs high), I'll use it to save tokens. I test across a set of models, averaging results over 10‑20 runs rather than 100, and pick the most efficient that meets the required accuracy.
Absolutely. AI intelligence has been decreasing in cost with virtually every new model drop; models are released almost daily and now compete on cost per task or token rather than intelligence. Soon you'll be able to run all these models locally on your computer as long as you have one, which is the entry ticket for everyone, so you'll be fine. It's worth building the skills now for when that time arrives.
AI‑generated automation templates will undercut builders who lack business skills, turning them into mere API endpoints and increasing competition while lowering the barrier to entry. My agency model remains profitable, and for someone just starting, it’s definitely worth the effort to learn and use these tools.
If you’re making $0‑$20 k per month, keep selling broad AI implementations and use AI to create the perception of a highly customized service. Once you want to go beyond that revenue level, you need to productize and template your offering; that part of the market hasn’t changed even as AI models have advanced.
ai implementationnichingbroad aispecific solutions