In under a year, writing software became the biggest thing people do with large language models. The revenue followed the tokens: Cursor reached about $4B in annualized revenue in under four years and recently agreed to a $60B all-stock acquisition by SpaceX, and Claude Code crossed a $1B run-rate within six months of launch. Every developer we talk to has a "that demo blew my mind" story.

We think the first wave is basically settled. The model can write the code. The interesting question for the next few years isn't whether agents can generate code, it's everything that happens around the model: how we run agents at scale, how we serve them when compute is scarce, and how we trust what they ship. That is where we are spending our time at Gradient, and where we think the next set of category-defining companies gets built.

As coding adoption deepens, the bottleneck stops being model capability and becomes two things in short supply: human attention and compute. A coder's focus drifts in the minutes an agent takes to finish, and no human can review the volume of code a fleet of agents produces. Compute is the other wall, and coding hits it first because it is the most token-hungry workload on the network.