AI is moving fast, but after my conversation with Alex Bouzari, Co-Founder and CEO at DDN, at Google Cloud Next '26, one thing became clear.

The bottleneck is no longer the model.

It is the infrastructure behind it. Alex broke it down in a very real way. Today’s AI systems are powerful, but the way data moves through them is still inefficient. You train these large models, but when it comes to actually running them at scale, things slow down. Latency increases, costs go up, and performance becomes unpredictable.

That is what is broken.

He shared how this shows up in real scenarios. When enterprises deploy AI, especially with large models, they struggle with speed and consistency. It is not that the model cannot perform, it is that the infrastructure cannot keep up with the demand.

At Next, DDN focused on solving exactly this. Building what Alex called a new foundation for AI, designed for high-performance workloads where data access and speed matter just as much as the model itself.

One concept that stood out was KV cache.

It sounds technical, but the idea is simple. Instead of recomputing everything every time a model runs, you reuse key pieces of information. That reduces latency and makes systems faster and more efficient. In large-scale AI systems, that becomes a big deal.

The bigger shift here is clear.

We are moving from experimenting with AI to operationalizing it at scale. And that means infrastructure is becoming the deciding factor.

What makes DDN different is their focus on this layer. Not just enabling AI, but making sure it actually performs in real-world environments.

My takeaway. The future of AI will not just be defined by better models. It will be defined by better infrastructure.

#data #ai #ddn #infrastructure #googlecloudnext #api #google #theravitshow