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agent_notebook_market_research_2026-05-14

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Agent and AI Notebook Market Research - 2026-05-14

US workplace AI use is mainstream, but true agent use is still earlier: Gallup reports 50% of US employees use AI at work at least a few times per year, 28% use it weekly or more, and 13% daily as of February 2026. Gartner reports 75% of surveyed orgs are piloting/deploying some form of AI agent, but only 15% are considering/piloting/deploying fully autonomous agents.
Developer coding agents are much more mature than general workplace agents. Stack Overflow 2025 reports 31% of developers currently use AI agents, 17% plan to, and 69% of agent users report productivity gains. JetBrains January 2026 reports 74% of developers worldwide use specialized AI developer tools, with US/Canada Claude Code work adoption at 24%.
Codex appears to be the fastest-growing named coding-agent product with official user counts: OpenAI reported 3M weekly active Codex users in early April 2026 and more than 4M two weeks later. Earlier public milestones were 1M+ desktop app downloads in the first week and 1.6M WAU after GPT-5.3 Codex, then 3M WAU in early April.
AI meeting/notebook apps are growing quickly but remain smaller than traditional workflow platforms by users/customers. Plaud reported 2M global cumulative users by April 2026. Granola disclosed 10% weekly user growth in May 2025 but not absolute users. Read AI reported 5M MAU and 50K daily signups in February 2026. By contrast, Notion reports 100M users and monday.com reports 250K+ customers.

The market signal supports Memova's notes -> memory -> actions thesis: broad AI use is already present, but workflows have not transformed for most users.
Coding agents show the most advanced adoption pattern: users accept agents when they operate inside an existing workflow, can take action, and remain inspectable.
Meeting-note tools validate capture and summary demand, but most public growth is still around transcription and notes. Memova should differentiate on actionable memory, people/project context, and confirmed next actions rather than "better notes" alone.

Founded in 2021 in Seattle by David Shim, Elliott Waldron, and Rob Williams. The founding team came from Placed/Foursquare/Snap; Shim had been founder/CEO of Placed and later CEO/president at Foursquare.
Initial wedge: meeting analytics and AI summaries inside Zoom/Google Meet/Microsoft Teams. The product used a meeting bot and free utility to enter existing meeting workflows.
Development path: meeting summaries -> cross-meeting search and coaching -> "connected intelligence" across email/messages/meetings -> agent-like assistants such as Ada for scheduling, answers, and action handling.
Funding: about $10M seed in 2021, $21M Series A in 2024, $50M Series B in October 2024. Total disclosed funding is roughly $81M.
Growth markers: by late 2024 Read AI claimed 100K new accounts weekly and 75% Fortune 500 penetration. By February 2026 it reported 5M monthly active users, 50K daily signups, and an internal goal of 10M MAU.
Memova lesson: Read AI entered through a high-frequency, low-friction capture surface, then expanded horizontally into workflow context. Memova should consider a similarly narrow entry point, but differentiate by owning personal memory and confirmed follow-up actions rather than only meeting capture.