By Aaron Levie
AI models are inevitably going to keep getting better and better at all coding, tool and computer use, math, and other coding-related tasks. As a result, agents that can use software to do their work, or generate code from scratch, will improve at a faster rate than all other domains of knowledge work.
On one hand that could mean that only a small subset of work can get automated. But probably what happens is that more and more work just becomes coding-shaped to adapt to what agents are good at.
You can see it in the most recent examples of model releases in the past couple of months, where agents can generate code that becomes animated videos, games, 3D models for products, and so on. We also know that tool use gives agents the ability to work with much of the same software that people already work with. This helps in cyber, life sciences, finance, and a number of other domains.
Our mental model traditionally has been that the models have to be able to know everything about anything to generate value, but in reality they *really* have to know how to use or generate software effectively combined with deep domain expertise to make the right judgment calls inside of workflows.
When you have that powerful combination, almost everything else gets unlocked. This is why the acceleration of agents in knowledge work is only going to increase from here.
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