The easier a technology becomes, the more we use it. The early days of digital marketing taught us a simple lesson: just because something becomes more efficient at a micro level doesn’t mean it becomes cheaper at scale. In fact, the opposite is often true.
Search got cheaper. We spent more.
Social got easier. We produced more.
Content development got faster. We created exponentially more of it.
Now we’re watching the same thing happen with AI.
A few days ago, I saw a post from Itamar Novick that stuck with me. He spent $280 in a single day using Anthropic. Not a company-wide deployment. Not a scaled system. One person, running workflows. Just think, if he spent that everyday over 30 days that $8,400 and over $100K in a year! That’s not surprising to me. It’s predictable.
Because the narrative right now is that AI is getting cheaper. Token costs are going down. Models are becoming more efficient. The assumption is that this leads to lower overall spend. But that’s not how this works in the real world.
What’s actually happening is that while the cost per unit is decreasing, the number of units required to get meaningful work done is increasing. Dramatically. Workflows are more complex. Tasks are multi-step. Systems are constantly running, retrying, chaining, and expanding.
So yes, the cost per token might be going down. But the total cost to produce real, production-grade outcomes is going up. And in some cases, it’s starting to rival the cost of human labor – and that's the part people aren’t prepared for.
Over the last year, most of the conversation in AI has been focused on intelligence. Smarter models. Better reasoning. More capabilities. The assumption is that whoever builds the best model wins.
I don’t see it that way. We’ve seen this movie before.
In digital marketing, the winners weren’t the platforms with the most features. They were the ones who understood how to connect everything. Strategy mattered more than tools. Integration mattered more than specialization. AI is heading in the same direction. The real problem isn’t intelligence. It’s execution.
Not what the model can do, but how it actually runs in the real world:
How it’s orchestrated
How it’s optimized
How it scales
How it performs under pressure
How much it costs when it’s doing real work, not just running a demo
AI doesn’t operate in a vacuum, it operates in systems. And most of those systems are wildly inefficient right now. They call the same models over and over again. They use expensive models for simple tasks. They lack routing, optimization, and control. They break, retry, and burn tokens in the process. No one is really managing execution. They’re just layering intelligence on top of inefficiency. That’s where the cost problem comes from.
To me, this is the white space. Everyone is focused on building smarter AI. Very few are focused on making AI economically viable at scale. That’s a very different problem to solve.
If AI is going to become a core part of how businesses operate, especially for professional individuals, SMBs, and the mid-market, it has to make financial sense. It can’t just be impressive. It has to be sustainable. Those companies don’t have the luxury of absorbing inefficiency. They will demand value. They will demand lower costs. And they will choose the solutions that deliver outcomes, not just intelligence. That’s the shift that’s coming.
AI Doesn’t Win on Intelligence. It Wins on Efficiency.
At Rival, we’re not competing with OpenAI or Anthropic. They’re building intelligence. We focus on how it runs, how workloads are routed, how systems are optimized, and how costs are controlled. Because if AI isn’t efficient, it isn’t viable.
Models are going to become invisible. They’ll compete on cost and capability behind the scenes - faster, smarter, cheaper - but over time, no one will care which one is being used. What they’ll care about is the outcome: how fast the work gets done, how reliable it is, and what it costs. In most real-world scenarios, it won’t be one model anyway. It will be many, each handling a specific task. Execution is what determines what actually works.
We’ve seen this before. Infrastructure gets abstracted. Platforms become interchangeable. The advantage shifts to how effectively you use them. AI is heading in the same direction, which means the winners won’t just provide intelligence, they’ll control how it’s executed.
Customers aren’t paying for tokens. They’re paying for outcomes and time.
The only thing scaling faster than AI right now is the bill. That won’t last. The companies that fix execution will change the economics, and when they do, AI won’t just be powerful. It will finally be practical.