模型更强之后，谁来补上部署、验证与责任：TBPN 的四场 AI 访谈

Nikesh Arora argues that vulnerability awareness must become deployment work, because a centrally managed service can push a fix while distributed software and hardware still depend on customers to test and install it.

Personal agents need stricter boundaries around irreversible actions than around minor inconveniences, because trust requires explainability, auditability, and a clear allocation of liability before a mistaken transfer becomes someone's financial loss.

Arora favors inspecting open models but rejects the idea that open weights make deployment free, since an organization must still obtain compute and evaluate the total operating cost of its chosen infrastructure.

Strong demand for compute does not determine which business models will survive, because Arora expects profit pools to shift among chips, power, land, and AI businesses as different constraints become binding.

Aidan Smith describes a systems-first lab where kernel engineers and post-training researchers work together, treating GPU execution and model design as a coupled problem rather than optimizing a fixed workload in isolation.

Smith argues that data-efficient algorithms could broaden model capabilities beyond easily verifiable tasks, while repeated optimization on familiar training traces may narrow a model's style instead of producing more varied ideas.

Akhil Iyer presents simulation as a way for planners to examine more plausible courses of action together, not as a guarantee that generating many futures will eliminate black swans or predict what actually happens.

Better frontier models do not automatically produce better planning tools, because Watchtower must connect them to an appropriate harness and simulation engine while working within government networks and presenting interpretable outputs to users.

Max Junestrand says legal AI requires more careful change management than coding tools because a plausible draft can hide consequential errors that tests alone may not reveal and that experienced practitioners must still judge.

Legora uses a library of more than five thousand real tasks to compare uneven model strengths and route work across models, but its claims of superior efficiency remain company assertions rather than independently established benchmark results.