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Key Takeaways AI adoption is not the real conversation anymore — capability is, and the gap between people using AI at the surface and those going deep is compounding into fundamentally different levels of output within the same company.
The instinct to standardize AI enablement through committees and best practices breaks down at this pace of change — what actually works is protected, facilitated time (three hours minimum, no multitasking) where every operational group experiments in the context of their own work and shares what they learned.
AI is not rolling out in a smooth curve. It is moving in step changes, where what felt like strong performance a few months ago quietly becomes the baseline, often without any clear signal that the bar has moved. And it has moved again since I started writing this article.
The pace of this shift is catching most teams off guard. Just in the last few weeks, we’ve gone from AI tools that help with individual tasks to systems that can operate autonomously across your entire computer. Agentic platforms like OpenAI’s Operator, Perplexity’s computer use, OpenClaw and Anthropic’s Claude Cowork are now executing complex workflows from start to finish. The implications for how we think about work are immediate and hard to overstate.
Many tasks that used to take hours can now be done in minutes. A workflow that requires coordination across teams can be handled by one person. The volume of AI innovation shipped in March alone outpaced anything we’ve seen before, and Anthropic is scaling revenue at a pace no company has hit before.
Today, a lot of the conversation still focuses on adoption: who is using AI, who is not and how quickly teams are rolling it out. But that framing misses what is actually happening.
Capability is no longer evenly distributed
Some people are fully engaged with these tools. They are testing ideas, building custom GPTs, creating skills in Claude and developing real instinct for where AI actually helps. Others use it more cautiously, keeping it at the edges of their work.
There is also a third group emerging. These are the people going much deeper, using open-source tools, pushing on the edges of what is possible and running “scary” experiments — Zuckerberg building an AI version of himself, for example — sometimes faster than the organization can keep up with.
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