Scale AI
Data annotation and labeling company
Episodes
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#6030: Who Do You Actually Hire to Train a Model?Research engineers and data engineers are diverging. Here's who you actually hire to train a model — and why the org chart keeps changing. -
#5946: Inside Humanity's Last Exam: 2,500 Questions AI Can't Ace2,500 questions, ~1,000 experts, and a top score that jumped from 2.7% to 54.8% in 18 months. What is HLE actually measuring? -
#5927: Where Generalists Get Priced: Inside the FDE RoleA $485K median salary for a job with no clean definition — and why the résumé can't capture it. -
#3852: The Hidden Workforce Behind AI's IntelligenceBehind every "intelligent" AI system are millions of workers in Kenya, India, and the Philippines doing repetitive tasks for poverty wages. -
#2196: The Invisible Workforce Behind AIAnnotation is the invisible foundation of AI—and a $17B industry by 2030. Here's what dataset curators actually need to know about the tools, platf... -
#2131: The CIA Is on GitHubIn-Q-Tel is on GitHub. Explore the IC's strategic investment arm and its use of open-source AI for wargaming. -
#1882: The Hidden Human Labor Behind AIAI isn't free—it costs billions for humans to label data. See why annotation is the real engine behind models like Gemini. -
#1837: The Human-in-the-Loop Price Tag: What Safety Costs in 2026From $0.50 reviews to $500 platforms, we break down the real cost of keeping humans in charge of AI agents. -
#1596: Why Your AI Agent Needs a Ticket System, Not a ChatboxStop confusing your AI agents. Learn how context compaction and ticket-driven development are ending the era of "vibe coding" for good. -
#1464: When AI Agents Author 4% of All CodeExplore how agentic harnesses transform AI from a passive chatbot into an active developer capable of full-cycle software engineering.