#Enterprise

The labs solved how to distribute AI skills to a company: connect a GitHub repo and it syncs. They stopped short of how a company manages that repo at scale. We built Skillhouse to fill the gap, and we're running on it ourselves.

Consumption pricing is suppressing the usage the frontier AI labs need most right now. From inside the enterprise, a simple fix: make the model cheap and sell the harness by the seat, at a price a company can actually predict.

Three observations that show up at the start of every Gadoci engagement: the work is mostly about people, there's pent-up innovation waiting to be released, and the new shape of knowledge work is conducting, not typing. The notes I'd give you before you kick off your own AI program.

Across multiple clients, the same question keeps surfacing: should we go all-in on one AI platform or maintain several? After working through it enough times, we've formed an opinion. Most enterprises need three tools, and they already have one.

Anthropic scaled from $1B to $14B in annualized revenue in 14 months, making it the fastest-growing enterprise software company in history. Here's what the numbers actually mean for organizations navigating AI adoption.

Most enterprises are treating AI innovation and AI governance as separate conversations. They shouldn't be. The gap between having a policy and proving it's being followed is where the real risk lives.

The data estate conversation has been happening for years. It's important work, but it was never meant to be a prerequisite for progress.