July 7, 2026
Too many bots, not enough brains
First Principles of Model Routing
Pick fewer AI bots, test them better, and yes—the comments are already fighting about it
TLDR: The post says AI works better when you use a small, clearly different set of models and test them on real tasks instead of chasing hype. Commenters immediately argued that mixing top bots can help, while others blasted the piece for dodging the hardest question: how do you tell an easy job from a hard one?
A new post about how to choose which AI helper gets your task should have been a neat little strategy guide. Instead, the crowd immediately turned it into a mini-drama about whether the advice is smart, incomplete, or just missing the real headache entirely. The author’s big message is simple: don’t juggle a giant pile of similar chatbots, keep your lineup small, make sure each one has a clearly different job, and test them on your actual work instead of trusting generic scoreboards. In plain English: use one expensive “star player,” one cheaper backup, and stop pretending five nearly identical bots are a genius system.
But the comments? Way spicier than the article. One reader basically said, “Nice tips, but this only makes sense in context,” then dropped the hot take that bouncing a problem between top AI systems can actually make answers better because each one thinks a little differently. That’s the sort of comment that starts a proper nerd food fight. Another person cut straight to the pain point everyone was thinking: how do you know a task is ‘easy’ or ‘hard’ before you send it to the cheap bot or the fancy bot? That question landed like a mic drop, because it pokes the biggest hole in the whole idea.
And then, in true internet fashion, someone ignored the AI debate entirely to complain about the site layout. Honestly? Iconic. The result is a classic tech-thread spectacle: half strategy, half nitpicking, and one part accidental comedy. You can read the original here, but the real show is the audience yelling from the balcony.
Key Points
- •The article presents four model-routing principles based on work building the role-model router and protocol.
- •It argues that routing is easier when models in the pool are clearly differentiated on speed, quality, or cost rather than being near-equivalent frontier models.
- •It recommends keeping the routing pool small, with two models as a default unless additional models have clearly defined roles.
- •It says routers should use internal, relative benchmarks tagged by capabilities and tasks instead of relying only on metadata or external benchmark sources.
- •It states that historical user-specific routing decisions should be reevaluated across the model pool to improve future routing performance.