Aaron Levie:后台智能体将带来 100X-1000X token 消耗
Moving from chatting back and forth with agents, to having agents that you spin up in the background to do useful work for you is probably on the order of 100X
Box CEO Aaron Levie 认为,从与智能体来回对话转向在后台启动智能体替你干活,token 消耗量约为当前的 100X 到 1000X。他指出知识工作大多受限于信息处理步骤,过去 3 年多的聊天机器人只是让这些任务更快,而未来可把任意数量的智能体投入一个问题,用于代码与数据防护、QA、文档风险审查和公司审计,且多为净新增工作。前提是模型持续变强、AI 成本可负担,下一道前沿是算力是否足够。
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Moving from chatting back and forth with agents, to having agents that you spin up in the background to do useful work for you is probably on the order of 100X or 1000X more token consumption than what we’re seeing today.
Most work right now is essentially rate limited by our ability to process and work with information. Moving data between systems. Reviewing a document. Looking through code for security issues. Processing log data to look for even patterns. Looking up information about a prospective client. Almost all of knowledge work is constrained by these steps, and AI has become a huge unlock to that.
So our first step was to use AI to do these individual tasks much faster for us. That was the chatbot phenomenon that we have been experiencing for the past 3+ years. But we can still then only augment the work that we remain in the loop for, and we’re highly limited by our ability to parallelize this work by our own bandwidth and context.
But where we’re going is quite different. As AI models continue to improve at incredible rates, we can hand off more and more work to the agent. First it’s letting the agent run with a harder problem. Then it’s giving a few agents multiple parts of a task at once. But we’re not far off from being able to throw an arbitrary number of agents at a problem and just say “come back when you’ve accomplished this goal”.
Now imagine that agent swarm for any problem we can think of. Protecting our codebases, data, and systems. QAing every change we make and make sure all our infrastructure is robust and does what it should. Looking through every document for potential risks. Auditing this company with vastly more data reviewed. And on and on. Most of this isn’t even replacing existing work we do, it’s all net new.
These agents will get deployed as swarms by people, by workflows, by triggers, and by other agents. And none of this would be possible if the models weren’t getting better, and if the cost of AI didn’t make it affordable. Both of those trends though are playing out. Now the next frontier is just having enough compute. But this is where it’s heading.