Kyle Tautenhan is the CEO and co-founder of Oberhahn. For nearly a decade, Kyle has built a developer infrastructure company serving startups and enterprises operating at internet scale. During that time, he spent years managing one of technology's most unpredictable expenses: infrastructure.

Kyle led a company responsible for storing petabytes of data and serving billions of requests every month. As the business grew, so did the complexity of managing cloud costs. But infrastructure spending was not viewed as a mystery. Teams measured it, forecasted it, attributed it, and optimized it because understanding cost was essential to making good business decisions.

Then AI arrived. Overnight, AI was being deployed across engineering, support, operations, and countless other internal workflows. Unlike traditional software or infrastructure spending, Kyle had little visibility into how AI was being used, who was creating costs, or whether those costs were producing meaningful business outcomes.

Kyle felt his company investing heavily in a resource they could not effectively measure. That became the foundation for Oberhahn.

Kyle believes intelligence, whether produced by humans, models, or autonomous agents, will become one of the largest operating expenses modern organizations manage. His focus is helping leaders understand the economics behind that investment so they can make informed decisions about where AI creates value, where it creates waste, and how it contributes to the long-term performance of the business.

Matt Ober is the CTO and co-founder of Oberhahn. Matt has spent his career building the systems that power modern software companies. As an infrastructure and systems engineer, he has architected platforms responsible for petabytes of data, billions of monthly requests, and globally distributed workloads operating at scale.

Throughout that work, he developed a deep appreciation for a simple reality: the most successful systems are the ones that can be measured, understood, and improved over time.

When AI began entering engineering organizations, Matt became an early adopter. He integrated AI into software development workflows, helped build AI-powered products, and worked directly with teams using models to accelerate everything from coding to operational tasks.

As his organization adopted more models, agents, and AI-powered workflows, questions began to emerge that traditional engineering tools were not designed to answer. Which models were creating value? Which teams were driving costs? How should he think about intelligence as a resource that needed to be managed?

Matt saw the same pattern he had encountered throughout his infrastructure career. Teams could not optimize what they could not measure.

At Oberhahn, Matt leads product and technology strategy. His focus is building the systems that make intelligence observable, attributable, and manageable, giving organizations the ability to understand the true cost and impact of AI across people, models, and agents.