Fathom

Survive the AI Knife Fight: Building Products That Win — Brian Balfour, Reforge

Jul 14, 2025 · 14:10

Brian Balfour, CEO of Reforge, argues that winning in today's AI knife-fight requires answering 'What do I build and why will it win?' by focusing on proprietary data, unique functionality, and unmet customer needs rather than building custom AI. He illustrates with Granola, which entered a crowded AI note-taker market by understanding users wanted help taking better notes, not full automation, and assembled off-the-shelf AI (DeepGram, Anthropic, OpenAI) with unique data (user notes plus transcription) and functionality (Mac app, calendar integration) to create a competitive edge. Balfour warns competitive advantages now last only 2-3 weeks, so teams must sequence smaller moats continuously, each buying time to execute faster. The talk emphasizes treating AI as Lego blocks—assembling pre-trained models, data, and product superpowers into a system that spins a data flywheel.

Real ROI: Lessons from Enterprises that have already succeeded with LLMs at Scale: Raza Habib

Dec 31, 2024 · 20:01

Raza Habib, CEO of Humanloop, shares lessons from enterprises like Duolingo, Filevine, and Ironclad that have achieved real ROI with LLMs at scale. He argues that success hinges on centering domain experts (e.g., Duolingo's linguists do all prompt engineering), needing less ML expertise than expected, and breaking down evaluation into small, testable components rather than chasing a single metric. Habib emphasizes collecting end-user feedback (acceptance rates, edits, thumbs up/down) and building tooling for logging, regression testing, and team collaboration. Filevine doubled revenue by launching six LLM-powered products; Ironclad's open-source Rivet logging tool enabled agents to auto-negotiate 50% of contracts. The talk covers how to optimize the four-component chain (base model, prompt, data selection, function calling) and why simpler systems will win as models improve.