One thing I rarely see discussed is that AI cost is not just dollars per token.
There’s also latency, dependency on external infrastructure, privacy and compliance concerns, energy usage, and just the general predictability of the system itself.
My guess is that this will gradually push a lot of companies toward more hybrid architectures over time. Small or local models are probably good enough for things like filtering, routing or repetitive high volume tasks, while frontier models get reserved for the places where the quality jump actually justifies the added cost and complexity.
As useful as frontier models are, using them for absolutely everything sometimes reminds me of using a distributed system for problems that could have been solved locally with something much simpler.
I wouldn’t be surprised if, in many real world cases, a fast specialized system plus a smaller model ends up being the more practical and economical setup overall.
It’s never too late to learn something new, not even to start in software. Age can bring a different way of looking at problems, shaped by past experiences in different situations and environments.
The path can be tough at the beginning, but if you truly enjoy it, you’re not wasting your time, you’re investing in a new stage of your life.
And this is just my personal opinion: I believe a team should be as diverse as possible. The more different perspectives, the better. A 22-year-old junior doesn’t see things the same way as a 42-year-old junior, and both are valuable and perfectly compatible.