Collaboration does three things solo work cannot match. It accelerates thinking, sharpening it in the friction between people, between perspectives, and increasingly between AI models. It distributes thinking, so discernment is not trapped in a single expert's head. And it lets an institution retain that thinking, holding onto what it learns instead of letting it slip away.
ROI of Collaboration includes not losing all of an individual's contributions when you lose an individual.
The answer is that generation was never the point; it was how you learned to discern. So here is what we are for: from day one, everyone needs what used to require a promotion: the discernment to define the problem, interrogate the output, and validate the result. Hold that against the two responses most institutions reach for, and both fall short:Reject AI, and train students and juniors to generate without it. This serves a fantasy. Many will use it anyway, only without guidance, standards, or accountability. Faculty turn to detectors that cannot reliably tell honest work from misconduct; companies block the apps and drive the behavior underground, where people paste sensitive data into consumer tools because no approved option exists.Embrace AI as a generator, and train juniors to produce faster with it, at the risk of mistaking fluency with prompts for fluency with thought8. They finish quicker, but never learn to judge whether the answer is sound or the problem well framed9, because they never build the internal mastery that judgment requires.Both fail for the same reason: each still casts the next generation as generators, the one role AI has largely taken. The work has inverted; how we develop people has to invert with it.Teaching that is what education was always for. In the spirit of a line often attributed to Plutarch, the mind is not a vessel to be filled but a fire to be lit. That fire is what we call AI Readiness:Domain Expertise: the ability to know what to ask, what context matters, and how to interrogate the answer.AI Enablement: knowing when to use AI, when not to, and how to wield it.Human Excellence: critical thinking, creativity, communication, and collaboration. Being human is the advantage, not the consolation prize.
We used to use Generation as the scaffolding to the destination of Discernment. Entry-level roles did Generation in order to learn Discernment. The application of Discernment was the reward that came with promotion. But now, Discernment is required from the start.
Edu responds either with the fantasy of rejecting AI, or the folly of embracing the generation of AI without Discernment. The former is a rapid path to irrelevance, the latter a slighly delayed path to obsolescense.
The models are finally ready. Costs of inference are getting optimized with open models, and even on-device models.
大多数人认为AI领域仍然处于早期阶段，模型成本高且实用性有限，但作者认为模型已经'准备就绪'，推理成本正在优化，这一观点暗示AI应用可能比大多数人预期的更快进入实用阶段，挑战了行业对AI成熟度的普遍认知。
[[Stefaan Verhulst p]] about AI readiness for data
If Nvidia has cracked the code on bringing AI agents easily, safely, and usefully to the masses, it could — and should — be big.
大多数人认为AI代理技术仍处于早期阶段，难以在消费级设备上有效运行，但作者暗示Nvidia已经解决了这一技术难题。这一乐观观点挑战了当前AI代理技术仍不成熟的行业共识，暗示市场可能即将迎来AI代理的大规模普及。