On Moonshots with Peter Diamandis, the panel read out a new leaderboard result: Google's Gemini 3.7 Flash on top of the AA-AnalystAgent benchmark with 60%, ahead of Claude Opus 5 at 54%. Diamandis called it proof that Google is back; Alex argued the score measures repeated reliability on spreadsheet analysis rather than frontier capability, and blamed Google Search for pushing Gemini toward speed and determinism. Emad Mostaque agreed the model was decent but said Google's problem is institutional, not a shortage of chips.
On Training Data, Parallel Web Systems founder Parag Agrawal traces how agents multiply web searches — from a weekly credit-risk review across 10,000 small businesses to the meeting-prep agents that run hundreds of searches before his own calls — and forecasts a web that, in a couple of years, tells agents when something worth acting on has changed.
On Training Data, Parag Agrawal explains how his company Parallel entered web search without first building a giant index: it launched a search agent that crawled after a request arrived, replaced outsourced human data collection for insurance, sales and finance customers, and treated the index as a latency optimization to be grown later. He describes the agent-specific architecture behind it, the 200-millisecond Turbo mode Parallel announced in July, and a Google Cloud deal that puts Parallel Search beside Google Search as a grounding option.