AI's Race to the Bottom

The Treadmill of Model Releases

The pace of large language model development has moved from a steady run to a relentless series of hard sprints. Every few weeks, a new release arrives boasting better performance, faster output, or cheaper usage.

OpenAI launches GPT-4o mini. Soon after, GPT-4.1 nano arrives with a million-token context window. Anthropic responds with Claude 3.5. Google counters with Gemini 2.5 Flash — tuned for speed and priced aggressively. The cycle is familiar: short bursts of advantage followed by rapid leapfrogging.

Commoditization Creeps In

But underneath the model one-upmanship is a more consequential shift: general-purpose LLMs seem to be converging. Capabilities are leveling out. Context lengths are expanding across the board. Performance gaps are narrowing. And prices are falling—fast. GPT-4o mini dropped at $0.15 per million tokens. Gemini Flash undercut that at $0.10. It’s hard to maintain pricing power when everyone can do roughly the same thing.

This Isn’t New

This is a pattern the tech industry has seen before. In the PC era, hardware quickly became a commodity while Microsoft captured the real value by owning the operating system. The internet made bandwidth cheap, and platforms like Google and Facebook captured distribution. Smartphones followed suit: the real power went not to whoever had the best camera specs, but to those who controlled the ecosystem.

Models Are Just the Starting Layer

LLMs are on the same path. Model quality is becoming the baseline, and the long-term defensibility lies in turning that model into a platform.

The big players know this. OpenAI is pushing hard on plugins, function calling, and integration via Azure. Google is embedding Gemini anywhere it can across Google properties. Microsoft is turning GPT-4 into the connective tissue of Windows, Office, and GitHub. These are no longer standalone models. They’re infrastructure layers—embedded, extensible, and increasingly difficult to displace.

Startups Face a Shrinking Surface Area

For startups, the dynamic is mixed. On one hand, access to cheap, powerful models lowers a startup’s barrier to entry. On the other, any feature built too close to the model’s core functionality runs the risk of vanishing like a small comet caught in a black hole’s gravity well. As the foundation models improve, the surface area for defensible differentiation shrinks. Even OpenAI’s CEO has acknowledged this dynamic, warning founders not to build around temporary limitations.

Some startups try to avoid that trap by training their own models or fine-tuning open-source ones. That approach offers more control and the potential for domain-specific IP, but it comes with tradeoffs. Training is expensive, inference infrastructure is nontrivial, and open models tend to trail the frontier models in performance. For most use cases, the flexibility of fine-tuning isn’t enough to outweigh the speed of progress in general-purpose models. The risk remains: you spend months customizing a model only to see OpenAI or Google ship something better, and cheaper, before you’re out of beta.

The Smarter Bet: Build on the Curve, Not Against It

The more sustainable approach might be to build in ways that benefit from, rather than compete with, model improvements. Deep workflow integration. Proprietary data. Industry-specific use cases. Tools and interfaces that solve enduring problems regardless of which model sits underneath.

Platform Lock-In Is Coming for the Enterprise

Enterprises are facing their own version of this decision. Right now, many companies—including Drift—are experimenting across providers, mixing OpenAI, Google, Anthropic, or even local deployments of open-source models. But that optionality won’t last forever. As the models converge, integration and ecosystem lock-in will take over. The AI platform that plays nicest with your internal data, security stack, and compliance framework will win. Switching costs will climb.

The Real Race Is for the Foundation, Not the Feature

So yes, the leapfrogging will continue. Each new context window will be longer. Each release will be slightly faster or cheaper. But those edges are becoming harder to monetize and easier to replicate. The strategic game has shifted.

The real race isn’t for who has the “best” model on an increasingly irrelevant benchmark this month. It’s for who becomes the AI layer others build on—the one that developers, enterprises, and entire workflows start to depend on. The value will follow whoever becomes that layer.