Read AI, which made its mark analyzing online meetings and messages, is expanding its focus beyond the video call and the email inbox to the physical world, in a sign of the growing industry trend of applying artificial intelligence to offline and spontaneous work data.

The Seattle-based startup on Wednesday introduced a new system called Operator that captures and analyzes interactions throughout the workday, including impromptu hallway conversations and in-person meetings in addition to virtual calls and emails, working across a wide range of popular apps and platforms. 

With the launch, Read AI is releasing new desktop clients for Windows and macOS, and a new Android app to join its existing iOS app and browser-based features.

For offline conversations — like a coffee chat or a conference room huddle — users can open the Read AI app and manually hit record. The system then transcribes that audio and incorporates it into the company’s AI system for broader insights into each user’s meetings and workday.

It comes as more companies bring workers back to the office for at least part of the week. According to new Read AI research, 53% of meetings now happen in-person or without a calendar invite — up from 47% in 2023 — while a large number of workday interactions occur outside of meetings entirely.

In a break from others in the industry, Operator works via smartphone in these situations and does not require a pendant or clip-on recording device. 

“I don’t think we’d ever build a device, because I think the phones themselves are good enough,” said Read AI CEO David Shim in a recent interview, as featured on this week’s GeekWire Podcast.

This differs from hardware-first competitors like Limitless and Plaud, which require users to purchase and wear dedicated devices to capture “real-world” audio throughout the day.

While these companies argue that a wearable provides a frictionless, “always-on” experience without draining your phone’s battery, Read AI is betting that the friction of charging and wearing a separate gadget is a bigger hurdle than simply using the device you already have.

To address the privacy concerns of recording in-person chats, Read AI relies on user compliance rather than an automated audible warning. When a user hits record on the desktop or mobile app, a pop-up prompts them to declare that the conversation is being captured, via voice or text. On mobile, a persistent reminder remains visible on the screen for the duration of the recording.

Founded in 2021 by David Shim, Robert Williams, and Elliott Waldron, Read AI has raised more than $80 million and landed major enterprise customers for its cross-platform AI meeting assistant and productivity tools. It now reports 5 million monthly active users, with 24 million connected calendars to date.

Operator is included in all of Read AI’s existing plans at no additional cost.

[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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