FB Pixel no scriptPlaud reaches USD 100 million ARR in two years as AI hardware gains traction
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Plaud reaches USD 100 million ARR in two years as AI hardware gains traction

Written by T. K. Lin Published on   3 mins read

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The Plaud Note. Image source: Plaud.
The company expects conversations to become the starting point for AI workflows.

Plaud has reached a milestone few artificial intelligence companies have achieved at comparable speed, but the significance extends beyond revenue growth.

The company said on June 17 that it had grown from USD 1 million to USD 100 million in annual recurring revenue, or ARR, within two years, serving more than two million users across more than 170 countries. The milestone places Plaud among a small group of AI companies that have quickly reached the USD 100 million ARR threshold, a cohort largely made up of software-first businesses.

Plaud’s trajectory, however, has been built around hardware as much as software. Its products, including the Plaud Note, Plaud Note Pro, and Plaud NotePin S, are designed to capture conversations and convert them into structured notes, summaries, and workflows powered by AI.

While many AI companies have scaled through chat interfaces, coding assistants, and enterprise software platforms, Plaud is focusing on a form of AI adoption that begins before users sit down at a keyboard.

“The conversations that actually move things forward don’t happen on a keyboard,” Nathan Xu, co-founder and CEO of Plaud, said in a statement. “We built the interface for the post-screen world.”

The argument reflects a broader shift across the AI sector. As models become more capable, attention is moving toward how AI systems collect context from the physical world and interact with users beyond traditional screens.

Several companies are exploring similar territory. Earlier this year, ByteDance-owned collaboration platform Lark and consumer electronics company Anker Innovations were reported to be developing an AI-powered recorder, highlighting growing interest in dedicated devices that can capture and process spoken information. Some smartphone makers, including Honor, have also been exploring AI features for on-device meeting assistants, memory functions, and autonomous task execution.

These efforts suggest the race to build AI-native interfaces is expanding beyond software applications and web browsers. Companies are increasingly experimenting with hardware, wearables, and ambient computing experiences that can gather information before it is manually entered into a system.

For Plaud, conversations sit at the center of that vision.

The company argues that much of the information that drives decisions is lost when it is translated into documents, summaries, or prompts after the fact. By capturing meetings, calls, and in-person discussions directly, Plaud aims to preserve what it describes as the “source-of-truth” context that can power AI-driven workflows.

That proposition has attracted attention from larger technology players. In May, Plaud was reportedly exploring deeper collaboration opportunities with Tencent, including a potential partnership involving hardware from the Chinese technology giant.

Plaud’s ambitions have also gone beyond individual note-taking.

The company said it is expanding into collaborative and enterprise-oriented workflows through Plaud Team, which brings conversation intelligence into shared work environments. It is also introducing integrations built around the Model Context Protocol (MCP) and other workflow connections designed to link conversations with broader AI agent ecosystems.

The strategy reflects a wider shift in enterprise AI. Rather than functioning only as standalone productivity tools, AI applications are increasingly being integrated into systems that can trigger actions, distribute information, and coordinate work across platforms.

Still, AI hardware remains an uncertain category. Smartphones, collaboration software, and emerging AI assistants are all competing to become the primary interface through which users interact with intelligent systems.

Yet, Plaud’s growth suggests there may be demand for products built around a different assumption: that conversations, rather than screens, could become the starting point for the next generation of AI workflows.

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