Every tech conference I attend these days feels like a broken record: 「AI agents will change everything!」 But when I ask the obvious follow-up—「How do we actually make money from them?」—the room gets uncomfortably quiet. We’re building incredible technology that can book flights, negotiate contracts, and manage complex workflows, yet the monetization models remain stuck in the 1990s.
Let’s start with the fundamental problem: most AI agent startups are trying to sell solutions to problems users don’t know they have. Remember the Segway? Brilliant technology, but it never found its market. The same fate awaits AI agents if we don’t apply proper product thinking. As outlined in The Qgenius Golden Rules of Product Development, we must start with user pain points, not technological capabilities.
Look at what’s actually working right now. GitHub Copilot demonstrated the power of the productivity-as-a-service model—developers happily pay $10/month because it solves a specific, painful problem immediately. Jasper.ai built a successful business around content creation, though they’re now struggling with differentiation. The pattern? Start with a niche where users have strong, identifiable pain points.
The subscription model everyone defaults to might be the wrong approach for many AI agent applications. Amazon’s Alexa taught us this lesson—despite massive adoption, they’ve struggled to build sustainable revenue streams around it. Sometimes the real value isn’t in the agent itself, but in the transactions it facilitates. Think about travel agents: they don’t charge for advice; they earn commissions on bookings.
Here’s what keeps me up at night: we’re building increasingly sophisticated agents while ignoring the cognitive load we’re imposing on users. The most successful AI applications today—like ChatGPT or Midjourney—have incredibly simple interfaces. They follow the Qgenius principle that 「only products that reduce user cognitive load can achieve viral adoption.」 Yet I see teams building agent platforms that require PhD-level understanding to operate effectively.
The enterprise market presents different challenges. While companies will pay for automation, they’re rightly skeptical of black-box solutions that might expose them to regulatory or security risks. Salesforce succeeded because they understood that enterprise software isn’t just about features—it’s about trust, compliance, and integration. AI agents need similar enterprise-grade thinking.
We’re also missing the bigger picture on value creation. The most interesting monetization opportunities might come from entirely new business models. What if AI agents enabled micro-transactions for services we currently can’t monetize? Or created new marketplaces for digital labor? The innovation isn’t just in the technology—it’s in reimagining how value flows through our economy.
Here’s my controversial take: the companies that ultimately dominate AI agent monetization won’t be the ones with the best technology. They’ll be the ones that understand human psychology and business models. They’ll recognize that successful products create unequal value exchanges—where users feel they’re getting far more than they’re paying for.
So where does this leave us? We need to stop building agents because we can, and start building them because they solve real problems for specific users. We need to experiment with business models beyond subscriptions. And most importantly, we need to remember that technology serves business objectives, not the other way around.
The AI agent revolution is coming, but the monetization winners will be those who apply timeless business principles to this new technology. Are we building agents that people will actually pay for, or are we just creating expensive toys?