The Tokenomics Of Agentic Commerce: How To Deploy Agents With Positive ROI
Tokenomics is what got me started with local/on-premises and open-source AI in the first place. While researching Tokenomics for my courses, I could not find a comprehensive explainer of the economics of agents with real-world use cases and pragmatic solutions. So let’s get into the weeds on building agents that actually make money for a use case that is here today, not looming on the horizon.
This article also ties into my series on agentic platform architecture. I cover why layers 1, 2, and 4 are architected the way I explain them with this real-world example. AI and agentic architecture are connected at the hip with Tokenomics. We build this way because it is the only path that is profitable.
The Early Victims Of AI’s Tokenomics
The premise of Tokenomics is straightforward, but nothing else about it is. Every outcome an agentic system delivers has a different cost structure and monetization paradigm than digital and cloud systems do. For businesses to succeed with agents, the average cost of delivering an outcome must be lower than the amount they can charge. Early adopters Microsoft and OpenAI showed signs that they are starting to figure out this balancing act.
OpenAI closed down Sora, which is a massive cost center for the startup. It had impressive adoption, but the amount of compute it consumed did not turn into a revenue windfall. The company had no path to positive Tokenomics, so it was a good call.
It is part of a larger cost-cutting initiative that included shifting OpenAI’s investment focus away from building data centers. OpenAI has realized that not all AI workloads are economically viable, as Fidji Simo (CEO of Applications) explained in a recent all-hands meeting. The early adopters are sending a clear message.
You could argue that one reason Anthropic has pulled ahead is that it has fewer free users creating drag on its infrastructure. That allows it to allocate more compute resources to paid users, decreasing response time and increasing inference quality. Fewer free users tip the Tokenomics in Anthropic’s favor. Even though it is not profitable yet, the startup is charting a clear path to profitability on paid consumer and enterprise users.
Microsoft is massively scaling back the free Copilot features in its 365 enterprise platform. It is a rejection of the freemium model we have seen power software and app revenue growth for the last 20 years. Again, the unit economics of prior technology cycles fail in the AI paradigm, and Microsoft is walking away from a massive cost center.
What It Means For Enterprises
Machine learning and deep learning models used to be small, so even though training costs were often high, the recurring cost of serving inference was near 0. That made businesses complacent about the Tokenomics of larger AI models. Everyone was focused on the upfront training costs, and few saw the recurring costs of inference as much of an issue.
As models scaled in size, inference costs scaled in silence. Now they have become the single largest cost of serving a customer and supporting an internal workflow. Businesses across industries are finding out the hard way that not all AI workloads are economically viable. This is yet another root cause for the high AI initiative failure rate.
Tokenomics is a framework that bridges agentic architecture, usage patterns, and unit economics to deliver agents that scale across three dimensions:
Reliability
Utility
Profitability
In this article, I will use agentic commerce to introduce Tokenomics and explain how to address the challenges it presents. This is a critical aspect of AI strategy and platform monetization strategy. I get deeper into Tokenomics in many of my courses, especially my upcoming instructor-led AI Strategist Certification. These are the critical skills powering careers that no one else teaches. It is a massive gap that I am working to fill.
The Agentic Commerce Paradigm: Customer Workflow Impacts
Agentic commerce is a structural change to how demand gets created, managed, and fulfilled. Every step between a customer’s intent and a completed purchase is being reorchestrated by agents, and the businesses that do not understand how that changes their cost model will lose money scaling AI they think is working.
The opportunity is massive. Macy’s shared that online shoppers who use its ‘Ask Macy’s AI shopping assistant spend 4.75 more than those who do not. My retail clients are seeing a range of 3X to 6X increases in sales from AI shopping assistants, but not from agents that are little more than chatbots or discovery assistants. The opportunity is massive, but the Tokenomics are complex.
As always, we start with the workflow, then design the agents and platform.