A product discussed on Latent Space.

⚡️ The best engineers don't write the most code. They delete the most code. — Stay Sassy
Apr 13, 2026 · 57:30
Stay Sassy PM and EM join Shawn Wang to discuss how AI coding tools are forcing managers to grapple with per-person token budgets that could reach $2.5M annually as consumption-based pricing replaces subsidies, making code review more critical, not less. They argue that managing these budgets is a new bottleneck, requiring companies to decide how much to spend on individual employees—a scale unprecedented outside department-level budgeting. On build vs buy, they caution that many products are more complex than they appear, and that the old frameworks of feature analysis, administration burden, and vendor lock-in still apply. They highlight code review fatigue from running multiple agents as a risk, citing Amazon's six-hour downtime from AI-generated code, and urge teams to maintain a culture where no single person can take down prod. Finally, they suggest automating executives' standard decisions first—since much leadership work is routine—rather than targeting only junior tasks, and predict that the hardest problems in 2026 will remain human.

AI Video Is Eating The World — Olivia and Justine Moore, a16z
Jul 9, 2025 · 49:28
Alessio and Shawn (hosts of Latent Space) talk with a16z partners Justine and Olivia Moore about the explosion of AI-generated video on TikTok, Instagram, and YouTube. The Moores trace how trends moved from Reddit to consumer platforms, with examples like Italian Brainrot, Kim the Gorilla, and fruit-slicing ASMR. They detail their own hands-on creation: Olivia used Veo 3 and MiniMax to make viral clips (gold bar squishing, Tide Pod consumption), spending up to 8 generations per usable video and hitting Veo 3’s $125/month plan limits. They explain monetization paths—creator fund payouts (~$20 per million views), merch (Breadclump sweatshirts), consulting, and licensing to Netflix—and recommend tools like Overlap (clips 30–140 seconds) and ComfyUI for control. The episode closes with prompt theory: AI characters questioning their existence, and a bet that Gen Alpha’s brainrot consumption will reshape long-form content.

How NotebookLM Was Made
Oct 25, 2024 · 1:13:57
Raiza Martin (NotebookLM lead PM) and Usama Bin Shafqat (AI engineer) explain how Google's NotebookLM built the viral 'Deep Dive' audio overview feature. They reveal the product evolved from Project Tailwind, using Gemini 1.5's long context and DeepMind speech to create a two-persona dialogue format that transforms documents into engaging podcasts. The team learned from 65,000 Discord members, leaned on best-selling author Steven Johnson for a 'tool for thought' workflow, and prioritized a single format over exposed controls to preserve unpredictability and delight. Humor and tension are not explicitly prompted but emerge from giving personas different angles. Evaulation relied on internal taste ('potatoes for chefs') before formal raters, with a Likert scale on dimensions like entertainment and groundedness. Future plans include multilingual support, API access, real-time chat, and codebase podcasting, while managing non-determinism by accepting occasional bad rolls.
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