We keep asking whether AI is ready for the enterprise. That’s the wrong question. The real question is whether the enterprise is ready for AI. Why? Because capability is outpacing control. This mini-series examines the rise of the parallel agent economy: what’s changing, what’s breaking, and what executives need to build before someone else builds it for them.
We keep asking whether AI is ready for the enterprise. That’s the wrong question. The real question is whether the enterprise is ready for AI. Why? Because capability is outpacing control. Something shifted in the last six months. AI agents can now complete tasks that would take human experts twelve hours, up from one hour … Read more
Most executives treat AI as a cost line. That’s the wrong mental model, and it’s already creating the wrong decisions. A venture capitalist recently described burning 250 million tokens in a single day, twenty times the amount he had used six weeks earlier. He was running five parallel agent flows. One pulling code history. Another … Read more
Institutions are splitting into two camps with AI: those encoding discipline directly into the agent layer, and those blindly buying capability. The most durable AI transformations are coming from the first camp. And they got there by slowing down. Linear, a project management platform used by companies like OpenAI and Coinbase to manage their own … Read more
Enterprises are making agentic tooling decisions right now without enough information. That’s not a criticism. The information doesn’t exist yet. Today’s pricing tells you almost nothing about tomorrow’s. Cursor reportedly runs negative gross margins on its power users. The cost spread between coding agents is an order of magnitude wide, and the customers getting the … Read more
When an AI agent sends an email before you’ve reviewed it, people blame the model. They’re blaming the wrong thing. The model did its job. The management system failed. A human invoice clerk spots a weird date format on a vendor PDF and quietly corrects it. An agent hits the same edge case and retries … Read more
Every accountability chain in your organization rests on one load-bearing assumption: somewhere in it, a human made the decision. So that’s who the RACI points at. And that’s who signs off. Run an agent on what would take a human team twelve hours, and that check quietly disappears. The work happens in the gap between … Read more