AI Disruptors: How the Next Generation of Business is Being Built
Getting your hands on a capable AI model is the easy part now. Every team can reach the same frontier models through an API, so a strong model is not what sets a product apart.
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Getting your hands on a capable AI model is the easy part now. Every team can reach the same frontier models through an API, so a strong model is not what sets a product apart.
Coding agents today have a massive spending problem. Every request, whether you’re designing system architecture or writing a single-line docstring, often gets routed to the same expensive frontier model.
At Deploy 2026, we introduced the DigitalOcean AI-Native Cloud, built for the inference era. Batch Inference on the DigitalOcean Inference Engine enables high-volume asynchronous workloads.
Traffic doesn’t spike on a schedule. A product launch, a viral moment, or a flash sale can send request volume through the roof in seconds, long before your CPU metrics catch up. That gap is where performance suffers.
Most teams building on LLMs today make a single model decision and apply it uniformly across every request. They reach for a frontier model not because every task demands it, but because building the infrastructure to do anything smarter...
Everyone calling an LLM API has access to the same models. So what actually sets technical teams apart? It’s everything around the model like the routing logic, the live data pipelines, and the ability to scale from prototype to production...
I’ve spent the last fifteen years building cloud services: early days of AWS building S3 and EBS, helping launch Oracle Cloud Infrastructure from inception, and now building the agentic cloud at DigitalOcean for AI-natives.