Aaron Levie：模型越强，应用层越需按领域专业化
The big bitter lesson question in the applied AI layer is that as models get better and better, what is the best way to diffuse that intelligence into an enterp
Box CEO Aaron Levie 认为，模型能力越强、能处理更长任务和更好工具调用，应用层按领域差异化的空间反而更大，而非缩小。他指出模型能力有限时只能做“wrapper”，而智能体调用工具、运行数小时至数天的任务、协调 agent swarm、领域专属数据与模型路由，都让系统专业化程度可能上升。他预计安全、生命科学、医疗、金融、制造等领域将出现此前难以构建的自主系统。
查看原帖the more intelligent the underlying model becomes, the less intelligence a founder needs to build into a product in some sense. if you imagine some end stage of ai, it’s simply just intelligence without any fluff around it. today building ai software is much more about adding guardrails & specifying direction more than creating capabilities. this may be…
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the more intelligent the underlying model becomes, the less intelligence a founder needs to build into a product in some sense. if you imagine some end stage of ai, it’s simply just intelligence without any fluff around it. today building ai software is much more about adding guardrails & specifying direction more than creating capabilities. this may be why there is a fuck ton of convergence with slight deviations everywhere because the guardrails are sort of universal at least for general purpose stuff. i’m not saying this is a bad thing btw but it’s certainly relevant.
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The big bitter lesson question in the applied AI layer is that as models get better and better, what is the best way to diffuse that intelligence into an enterprise.
Ironically, I’d argue that as models are getting more capable, and can handle longer running tasks and better tool use, the ability to differentiate on a domain by domain basis goes up instead of down.
When models were quite limited in what they could do, ironically all you could be literally was a “wrapper” because you were effectively delivering tokens back to the user with a custom prompt or some limited RAG.
But in a world of agents using tools, tasks that run on for hours or days, workflows that kick off agents, agent swarms that need to be coordinated against a goal, data sources that are domain specific, model routing that requires domain-specific routing, implementation that requires real change management of the workflow instead of “just type into this chat box”, it’s very very possible that specialization of these systems goes up more.
Obviously this doesn’t apply to everything - the closer you are to the labs core use-cases the tougher this play gets, but I’d argue we’re going to see autonomous systems for a wide range of job functions that weren’t practical to build out before. We’ll see this in security, life sciences, healthcare, finance, manufacturing, and many other fields. Either way going to be a fun and wild couple of years.