AI ROI Series | EP25 | Meta's Muse is out! What does this mean? AI Tokenomics.

This week Meta shipped Muse, an AI agent that does things for you instead of just answering, and the market added tens of billions to Meta's value in a single day, dragging the whole Magnificent Seven up with it. In EP25 of the AI ROI Series I ask the question I always ask: why did one consumer app move that much money, and what does it actually mean for the people who run enterprise AI budgets? I break down why Muse won on distribution rather than raw intelligence (95 percent of its users were already on Facebook), I bring in Andrew Ng's sharp take on the AI-fear PR campaign of the last two weeks (full disclosure, Andrew's AI Fund backs Olakai), and then I connect it to the number that matters on this show: the real cost of an agent that acts. A chatbot answers in one model call. An agent runs twenty, thirty, forty calls for a single task, a browser, retries, background work. Meta can subsidize that and monetize the behavior through ads and transaction revenue-share. Your enterprise cannot. The token has to pay for itself on the work it returns. And most companies still measure AI like it is 2024, on seats and licenses and invoices, none of which survives the move to agents that act. That is why you need a system of record for enterprise AI: to see what each agent run cost, whether it finished the task, and what the task was worth. Cost per completed task, by agent, by team. Directional as always. The data is public. Check my math, and tell me if you see it differently. Chapters 00:00 A thank you, and today we talk about Meta 01:15 Muse added billions in a day, and lifted the Magnificent Seven 02:05 Why did one app move that much money 03:10 The symbolic switch: from a chatbot that answers to an agent that acts 05:00 Why it worked: distribution, not raw intelligence 06:00 Andrew Ng on the AI-fear PR campaign, and the 1,200 agents vs 1,300 laptop processes 08:00 Connecting Meta's app to your enterprise: the real cost of "doing" 08:25 A chatbot is one model call, an agent task is twenty to forty 10:00 The token has to pay for itself, and most companies still measure like it's 2024 11:15 The system of record for enterprise AI, and cost per completed task 12:10 The questions the agentic era will ask you The AI ROI Series is a Tuesday deep dive on the economics of enterprise AI, from Paul Brzozowski, co-founder of Olakai. Your AI is an investment. Let's measure it like one. #AIROI #EnterpriseAI #Meta #Muse #Tokenomics #AIAgents #FinOps #AndrewNg