Jenn Tejada has seen her fair share of economic cycles.

Based in San Francisco, the Executive Chair and former CEO of PagerDuty, a platform that helps companies detect, prioritize and respond to technology outages, took PagerDuty public in 2019 and led the company through a decade of growth. She recently transitioned into her new role to focus on opportunities emerging in the AI boom while continuing to serve on the Board of The Estée Lauder Companies.

I had a chance to talk to her about the effect the AI IPOs are going to have on San Francisco where she’s based and how the technology is changing the industry.

“I was in tech in the late 90s when the Internet was becoming a force and a new way of building businesses and monetizing technology and I was here for the cloud revolution. It’s exciting to be here now as AI starts to shift from the experimentation phase to production.”

She explained how she’s seeing a ripple effect across the country with massive infrastructure investment starting to pour into communities and provide jobs, like with Meta’s massive data center buidout and its America’s Workforce Academy to train the next generation of workers. With a record high $725 billion estimate of hyperscaler capex spending expected for 2026, a near doubling in the past year, according to BNP Paribas , the opportunities for the entire ecosystem have never been greater.

“Nvidia is a customer, and I was spending time with the head of engineering introducing our new CEO John DiLullo to him. When I walked in I noticed a class of interns starting, and it reminded me that you don’t need to be experienced to be part of this amazing transformation that we’re in.”

She said with AI we’re going to see the emergence of one and two pizza teams. An Amazon term used to describe a team small enough that they would only need one or two pizzas. And with AI, we’re also going to see systems with greater complexity, and with that, the risk of something breaking, like what happened when AWS crashed last October and took down much of the Internet with it .

It doesn’t really matter if the issue was caused by a human or an AI agent, or solved by a human or agent, she said, the platform is designed to stop problems before they happen.

With agentic systems, there are things like model drift which has different early signs than when software fails which just stops working.

“When AI drifts, it’s actually harder to see, and you don’t see until it’s executed that drift in a number of ways, and now it’s evolved into multiple failures,” she said. That makes early detection essential, as AIOps platforms increasingly monitor AI models alongside traditional digital infrastructure.

“Over time humans are going to want something watching their agents for them and giving them an unbiased opinion of how their agents are working and enabling them to disrupt or interrupt their agents’ work if they see something that they don't like.”

“The upside to all this complexity are amazing advances, from healthcare to transportation. And yes, some failure, but failure is also the path to innovation and resilience," she said. "The goal is to avoid the major failure and learn from the minor failure."

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