The Law of Utility Convergence

In the late 1990s, the internet was magic. Or so it seemed.

New startups appeared daily, promising to “revolutionize” commerce, communication, and everything in between. Dot com domains with no real business model raised millions. IPOs soared. The mere mention of “internet” could inflate a company’s valuation overnight. Then the crash came.

In hindsight, the diagnosis is simple: perceived value outpaced real utility. The world believed the internet could do everything. It just hadn’t yet. And the gap between belief and reality grew too wide to sustain. But here’s the twist: the internet didn’t fail.

It just hadn’t become useful yet. When utility caught up, when real workflows, business models, and cultural integration converged, everything changed. Amazon. Google. The modern web. The dot com bust wasn't the end. It was the pause before utility took the lead.

Welcome to the New Bubble

Fast forward to now. Generative AI is having its dot com moment. The landscape is familiar:

• Too Many Tools - We are awash in AI wrappers, Chrome extensions, chatbots, copilots, content blenders. The ecosystem feels flooded. Startup slop is real. New demos drop every week. Most are shiny but shallow.
• Too Much Change - Models evolve monthly. Capabilities are outpacing comprehension. Just as users get their footing with GPT-4, here comes Claude, then Gemini, then Sora. It's hard to keep up, let alone integrate.
• Too Much Money - Over $50 billion poured into AI startups in the last 24 months. Investors are hunting the next foundational breakthrough, or the next Figma for AI. The checks are big. The expectations are bigger.

So what’s the problem?

Even with all this motion, progress in actual utility remains elusive.

• Users are frustrated
• Businesses struggle to measure meaningful ROI
• Most people still don’t use LLMs day-to-day
• And under the surface, a bubble is forming

We’re seeing a familiar divergence: perception is running faster than usefulness. Value is being projected onto a technology that hasn’t yet proven its capacity to transform the daily lives of real people.

The Law of Utility Convergence

We call it the Law of Utility Convergence:

The value of any technology is capped by its realized utility.
When perceived value outpaces actual usefulness, a bubble forms.
When utility catches up, a revolution begins.

This isn’t just economic theory. It’s a call to action. A challenge to builders, funders, and users: stop chasing potential, and start building for utility. Because according to Yann LeCun, Chief AI Scientist at Meta, “We’re missing something big.”

What’s Missing

If you believe in the future of AI, and we do, then it’s time to get serious about the gap. Because what’s missing is not horsepower. It’s infrastructure. It’s design. It’s human alignment. Two fundamental shortcomings stand out:

1. The Interface Was Never Built for This

The prompt box is not a platform. “Ask me anything” is not a workflow. Most users aren’t prompt engineers, they’re writers, operators, parents, small business owners. They need tools that work with them, not blank slates demanding perfect inputs.

2. Context Management is a Full-Time Job

LLMs are brilliant, until you ask for consistency. Staying on-task, remembering goals, applying preferences, these are not trivial. And they require users to constantly re-orient the system. It’s exhausting, and it breaks the promise of 10x productivity.

The Cultural Context

To design for utility, we have to be honest about who we’re building for and what they’re up against. Because most users aren’t living in an R&D lab. They’re living in the real world. And that world looks like this:

• Short on Time - Juggling multiple jobs, income streams, side hustles, caregiving responsibilities.
• Low on Bandwidth - Managing too many priorities across too many roles. Executive. Parent. Creator. Student.
• Limited Tech Literacy - They don’t want to tinker. They don’t care about tokens or inference costs. And they’re not waiting for AGI, they want help now.

What Comes Next

We don’t need more wrappers. We need better foundations. The next wave of AI breakthroughs won’t come from larger models. They’ll come from more human systems from builders who understand that power without usability is just potential energy.

At Dapple, we’re building for the moment utility converges with perception. That’s where the real value is.