AI: Why I Do Not Rush for the Hype

The next LLM's "star" feature and experienced software engineers: why we would not rush for it.

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The AI publicity landscape is full of either bought or excited adopters' promotion, with a very small part of experienced engineers feedback.

As very fairly noted on LinkedIn, the opinion "temperature" (pun intended) depends on a writer's experience in engineering. The less engineering experience, the higher the excitement. Hence the flood of posts.

We have seen the "parade" of "reasoning models", "coding assistants", "agents" etc. for the past 2+ years. Still AGI is not even visible, code bases are flooded with sloppy AI code, "stars" keep replacing each other, managers rush to engage these in an attempt to get the competitive advantage but the outcome is questionable.

Watching all this, I question myself, on experienced engineer side, would it bring the practical value to rush adopting each newly proclaimed LLM "star"?

My strategy here is simple: use LLM for software engineering where the adoption costs is near zero.

Meanwhile the non-viable (e.g. "reasoning models") will silently pass, the viable ones either already are commoditized or will be very soon. So no competitive advantage for early adopters as no justification for higher adoption costs (think organization scale and opportunity cost of not doing the "clean code" thing first). Especially provided software engineering domain is so vast, LLM can impact probably only 5-10% of it.

A substantial observation from your experience? I am glad to discuss. Cheers.