Episode 14: They Spent HOW Much?!

One enterprise reportedly spent $500 million on AI in 30 days. Uber burned through its annual AI budget in four months after creating an internal leaderboard that rewarded teams for using the most AI. Jonathan saw those stories and could not let them go.But Andrew's response is not simply “enterprise should have known better.” His counterargument is that the math looks very different from inside a large company. If AI turns a $50,000 professional-services project into a $1,000 token bill, then whether that same work could have been done for $250 may look like a rounding error. The savings are so large that efficiency does not feel urgent — at least not yet.That tension becomes the center of the episode. Jonathan argues that token literacy is not optional; it is a discipline that compounds, and the time to learn it is before waste is already baked into the organization. Andrew argues that enterprise is still in the adoption phase, where the bigger problem is getting people to use AI at all — and where the value created by even inefficient AI use can still dwarf the spend.They also get into how enterprise AI adoption actually unfolds: internal chatbots, API layers, individual keys, coding agents, and eventually employees applying AI to work that budgets were never designed to forecast. As Andrew puts it, the budget may have been made when AI meant “a chatbot with a cute little RAG on it.” A year and a half later, AI means something much broader.The conversation starts with blown budgets and token costs, but it ends somewhere more human: with Andrew telling Jonathan, “You don't understand how free you are.” Individuals can tune their tools, workflows, and habits almost immediately. Enterprise has budgets, silos, review boards, procurement, and thousands of people still learning what AI is for.New episodes every week — full video on YouTube. Hosted by Jonathan Spanos and Andrew Signore.