The Frontier

Jeffrey Cannell reports Hermes Agent is now ranked number one on Open Router and recently launched a desktop app, marking rapid growth over the last three months.

Steven Johnson explains Notebook LM's foundation is a source-grounded AI experience, providing state-of-the-art citations and audio overviews, with its most significant update integrating its separate research, creation, and source-analysis agents into a single chat agent.

Russ D'Sa reveals LiveKit powers voice AI for high-profile clients including Spotify, Tesla's support and service centers, Grok Voice, Salesforce's Agent Force, and SAP's Joule.

Steven Johnson contrasts Harvard Law's mandatory use of Notebook LM for a constitutional law class with Berkeley Law's restrictive AI policy that only permits AI for finding sources.

Jeffrey Cannell argues AI agents will automate much entry-level work, creating a disconnect between college preparation and a tightening job market.

Steven Johnson advocates using AI as a world-class tutor and editor to amplify cognitive processes rather than bypass learning, a framework he believes would make AI skills valuable in any future job market.

Panelists critique Apple's new Siri AI for a persistent user experience problem where users don't know its capabilities, making it slower than using a browser, and for lacking a conversational, human-like interaction flow.

Steven Johnson is optimistic about Apple's standalone Siri app as a potential new AI application paradigm, citing Apple's history with breakthrough apps like GarageBand and HyperCard.

Jeffrey Cannell suggests Apple may have avoided training frontier models because the costs are prohibitive and a fourth player was unnecessary, instead partnering with Google and investing in open-source via their MLX platform for Apple Silicon.

Russ D'Sa predicts the ultimate winners in AI will be platforms that transcend specific devices for digital work automation and companies focused on embodied AI robots for physical chore automation, not device-centric players like Apple.

Jeffrey Cannell describes reaching 'functional AGI' where on specific tasks, AI is as good as the best humans, citing his own transition from writing code manually to using AI for all coding work.

Panelists agree Claude Opus 4.5 was the inflection point where AI coding models crossed a threshold to become better than human developers, leading to a phase of rapid, reliable agentic automation.

Jeffrey Cannell identifies corporate 'token maxing' as a failure case where employees use unlimited AI budgets inefficiently, while high-performers can be worth 10x the token spend, a value hard to assess at large scale.

Russ D'Sa notes his top engineers spend up to $10k-$15k monthly on AI tokens, which he considers a high-value investment that turns them into vastly more productive workers.

Jeffrey Cannell states current smaller local models lack the quality for coding agents compared to frontier models, and the scaling trajectory points to ever-larger models, making local high-performance compute a niche.