The next AI winner might not be the company with the biggest model. It could be the one that figures out how to get more useful AI for every dollar it spends. Nvidia Corp. (NVDA) is helping shape that shift by powering more efficient, task-specific AI systems that aim to squeeze more performance out of every GPU cycle.
DigitalOcean Holdings Inc. (DOCN) CEO Paddy Srinivasan told MarketDash in an exclusive email interview that AI builders are increasingly mixing different models for different jobs rather than relying exclusively on expensive frontier systems from companies such as OpenAI and Anthropic.
"We believe in: right model, right cost, for every task," Srinivasan said.
He pointed to healthcare AI company Hippocratic AI as an example, saying AI builders like Hippocratic "get better intelligence per dollar" as they optimize across models.
Hippocratic's connection to Nvidia makes that strategy particularly interesting. Nvidia says Hippocratic's Polaris architecture runs more than 25 task-specific AI models on Nvidia H200 GPUs, while its TensorRT-LLM software makes those models faster, smaller and more efficient, lowering costs and allowing more conversations to run on the same hardware.
The AI Model Doesn't Have to Be the Most Expensive
Srinivasan said frontier models are typically needed for only about 25% of the job, mainly the hardest reasoning or specialized use cases. The remaining 75% can often be handled by open-weight models, which can offer lower-cost alternatives for less demanding tasks.
That creates a different optimization problem for AI companies.
Instead of asking which model is the smartest, they can ask which model is smart enough for a particular task at the right price.
"Most AI Native companies today are already multi-model," Srinivasan said. "They all have a mixture of models and route specific prompts to the right model."
DigitalOcean's Inference Engine is designed to route workloads based on factors including performance, latency, cost and customer preference.
Hippocratic Shows Why Nvidia's Hardware Matters
Hippocratic is a useful example because its healthcare AI requires real-time responses while handling safety-sensitive conversations.
The company's Polaris system runs on Nvidia H200 GPUs and uses more than a trillion parameters across its model constellation. The goal isn't simply to use the most powerful hardware or model available. It is to make the entire system more efficient so Hippocratic can handle more interactions without proportionally increasing its computing costs.
That is the strategy behind Srinivasan's "intelligence per dollar" argument.
'Intelligence Per Dollar' Could Become the New AI Metric
Nvidia itself has increasingly emphasized the economics of AI, often focusing on concepts like performance per dollar and the cost efficiency of AI compute to describe the value businesses can get from their computing investments.
That could change how investors view the AI race.
The industry's first phase was dominated by model size, training costs and the race to build increasingly powerful systems. As AI moves into everyday business applications, however, the economics of actually running those models become harder to ignore.
If companies can use a mix of models and optimize the infrastructure underneath them, the winners may not necessarily be the companies with the biggest AI models.
They could be the companies that figure out how to get the most useful intelligence for every dollar of compute.
