[Editor’s Note: Agents of Transformation is an independent GeekWire series and 2026 event, underwritten by Accenture, exploring the people, companies, and ideas behind the rise of AI agents.]

What separates the dot-com bubble from today’s AI boom? For serial entrepreneur David Shim, it’s two things the early internet never had at scale: real business models and customers willing to pay.

People used the early internet because it was free and subsidized by incentives like gift certificates and free shipping. Today, he said, companies and consumers are paying real money and finding actual value in AI tools that are scaling to tens of millions in revenue within months.

But the Read AI co-founder and CEO, who has built and led companies through multiple tech cycles over the past 25 years, doesn’t dismiss the notion of an AI bubble entirely. Shim pointed to the speculative “edges” of the industry, where some companies are securing massive valuations despite having no product and no revenue — a phenomenon he described as “100% bubbly.”

He also cited AMD’s deal with OpenAI — in which the chipmaker offered stock incentives tied to a large chip purchase — as another example of froth at the margins. The arrangement had “a little bit” of a 2000-era feel of trading, bartering and unusual financial engineering that briefly boosted AMD’s stock.

But even that, in his view, is more of an outlier than a systemic warning sign.

“I think it’s a bubble, but I don’t think it’s going to burst anytime soon,” Shim said. “And so I think it’s going to be more of a slow release at the end of the day.”

Shim, who was named CEO of the Year at this year’s GeekWire Awards, previously led Foursquare and sold the startup Placed to Snap. He now leads Read AI, which has raised more than $80 million and landed major enterprise customers for its cross-platform AI meeting assistant and productivity tools.

He made the comments during a wide-ranging interview with GeekWire co-founder John Cook. They spoke about AI, productivity, and the future of work at a recent dinner event hosted in partnership with Accenture, in conjunction with GeekWire’s new “Agents of Transformation” editorial series.

We’re featuring the discussion on this episode of the GeekWire Podcast. Listen above, and subscribe to GeekWire in Apple Podcasts, Spotify, or wherever you listen. Continue reading for more takeaways.

Successful AI agents solve specific problems: The most effective AI implementations will be invisible infrastructure focused on particular tasks, not broad all-purpose assistants. The term “agents” itself will fade into the background as the technology matures and becomes more integrated.

Human psychology is shaping AI deployment: Internally, ReadAI is testing an AI assistant named “Ada” that schedules meetings by learning users’ communication patterns and priorities. It works so quickly, he said, that Read AI is building delays into its responses, after finding that quick replies “freak people out,” making them think their messages didn’t get a careful read.

Global adoption is happening without traditional localization: Read AI captured 1% of Colombia’s population without local staff or employees, demonstrating AI’s ability to scale internationally in ways previous technologies couldn’t.

“Multiplayer AI” will unlock more value: Shim says an AI’s value is limited when it only knows one person’s data. He believes one key is connecting AI across entire teams, to answer questions by pulling information from a colleague’s work, including meetings you didn’t attend and files you’ve never seen.

“Digital Twins” are the next, controversial frontier: Shim predicts a future in which a departed employee can be “resurrected” from their work data, allowing companies to query that person’s institutional knowledge. The idea sounds controversial and “a little bit scary,” he said, but it could be invaluable for answering questions that only the former employee would have known.

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From empty offices in 2020 to AI colleagues in 2025, the way we work has been completely rewired over the past five years. Our guest on this week’s GeekWire Podcast studies these changes closely along with her colleagues at Microsoft.

Colette Stallbaumer is the co-founder of Microsoft WorkLab, general manager of Microsoft 365 Copilot, and the author of the new book, WorkLab: Five years that shook the business world and sparked an AI-first future, from Microsoft’s 8080 Books.

As Stallbaumer explains in the book, the five-year period starting with the pandemic and continuing to the current era of AI represents one continuous transformation in the way we work, and it’s not over yet.

“Change is the only constant—shifting norms that once took decades to unfold now materialize in months or weeks,” she writes. “As we look to the next five years, it’s nearly impossible to imagine how much more work will change.”

Listen below for our conversation, recorded on Microsoft’s Redmond campus. Subscribe on Apple or Spotify, and continue reading for key insights from the conversation.

The ‘Hollywood model’ of teams: “What we’re seeing is this movement in teams, where we’ll stand up a small squad of people who bring their own domain expertise, but also have AI added into the mix. They come together just like you would to produce a film. A group of people comes together to produce a blockbuster, and then you disperse and go back to your day job.”

The concept of the ‘frontier firm’: “They’re not adding AI as an ingredient. AI is the business model. It’s the core. And these frontier firms can have a small number of people using AI in this way, generating a pretty high run rate. So it’s a whole new way to think about shipping, creating, and innovating.”

The fallacy of ‘AI strategy’: “The idea that you just need to have an ‘AI strategy’ is a bit of a fallacy. Really, you kind of want to start with the business problem and then apply AI. … Where are you spending the most and where do you have the biggest challenges? Those are great areas to actually think about putting AI to work for you.”

Adapting to AI: “You have to build the habit and build the muscle to work in this new way and have that moment of, ‘Oh, wait, I don’t actually need to do this.’ “

The biggest risk related to AI: “The biggest risk is not AI in and of itself. It’s that people won’t evolve fast enough with AI. It’s the human risk and ability to actually start to really use these new tools and build the habit.”

Human creativity and AI: “It still takes that spark and that seed of creativity. And then when you combine it with these new tools, that’s where I have a lot of hope and optimism for what people are going to be able to do and invent in the future.”

Audio editing by Curt Milton.

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