"In 2026, the companies that win won't have the best models—they'll have the fastest pipes. The network is the product."
We're witnessing a fundamental shift in where value is created in the AI stack. For years, the assumption was simple: better models = better products. Companies poured billions into training larger and larger models, chasing ever-higher benchmark scores. But that era is ending.
Look at the landscape: GPT-4-class capabilities are now available from multiple providers. Open-source models have closed the gap dramatically. Meta releases Llama for free. Mistral competes on efficiency. The marginal difference between top-tier models is shrinking every quarter.
When the models converge, what differentiates? Infrastructure. Specifically: how fast can you deliver those models to users? How reliably? At what cost? These operational concerns—once boring—are now the competitive frontier.
Best model wins. Invest in training. Chase benchmarks.
Fastest delivery wins. Invest in network. Chase latency.
Training a competitive model requires billions of dollars and years of effort. Anyone can access GPT-4 through an API. But building infrastructure that delivers models 10x faster than the competition? That's a moat that compounds over time.
We're not building models. We're building the fastest way to use them.
Our competitive advantage isn't a proprietary model—it's a network optimized to deliver any model faster than anyone else. The pipe, not the water.
If you're building AI products, this shift is good news. You no longer need to pick the "winner" in the model wars. You need to pick infrastructure that lets you access any model, fast. The model is a commodity; the experience is the product.
The network is the product. And we're building the fastest one.