Sep 08, 2026

Why 95% of All AI Projects Fail - Boris Scharinger | Podcast #174

Boris Scharinger, Industrial AI Strategist at Siemens and author of "Industrial AI: From Pilot to Profit," discusses why 95% of AI proofs of concept fail in industrial settings. He highlights the 5-10x cost multiplier from pilot to production, the critical role of the "harness" around AI models, and the unique challenges of data access and legacy systems in physical environments. Scharinger advocates for a two-tiered investment strategy, separating foundational data capabilities from use-case-specific ROI, and addresses cultural resistances from engineers. He provides guidance on differentiating commodity from strategic AI applications and how to effectively scale industrial AI for profit.

Sep 06, 2026

Why companies are becoming a series of loops | Anish Acharya (a16z)

Anish Acharya, a General Partner at a16z, shares profound insights into the AI era. He debunks fears of a "permanent underclass," arguing that AI amplifies human agency and ambition. Anish introduces the concept of company building as creating cascading "loops" across all functions, with human intuition crucial for overcoming local maxima. He identifies the biggest consumer opportunity as "/loop, make me happier," focusing AI on fundamental human needs like connection and fun. He also discusses the nuances of model specialization, how moats are discovered rather than designed, the rising importance of distribution, and the potential for "expensive consumer software," offering actionable advice for product builders to embrace AI.

Aug 29, 2026

The Signal Layer: What to Build When Anything Can Be Built — Lena Hall, Akamai

Lena Hall explores the paradox of AI's abundance: while it grants immense leverage, it also makes average work valueless as competitors gain the same capabilities. She argues that AI, acting as a "convergence machine," produces identical answers from common knowledge. The true challenge shifts from building to "pointing"—deciding which problems are worth solving. Hall emphasizes that "taste" is trainable by AI, but genuine "judgment" about future events or unique customer relationships is not. She identifies critical points of signal distortion in startups and large organizations and advocates for engineering a "thin signal layer" to preserve unique intent. Ultimately, in a world of infinite alternatives, the scarcest and most valuable commodity is trust, which cannot be automated or graded.

Aug 18, 2026

The Reason Your Claude Output Still Looks Like Slop (with Priyanka Vergadia)

Priyanka Vergadia, The Cloud Girl, discusses why most companies see no ROI from AI tools, proposing a 7:1 budget split for training over tools. She explains how AI has made "taste" the new ceiling, detailing how to build effective Claude skills by breaking tasks into explicit subtasks with human-in-the-loop oversight. She introduces her 10-20-70 framework for AI budgets, emphasizing the long-term investment needed for employee skilling and community building. Priyanka also shares her transition to full-time entrepreneurship, focusing on product and career storytelling, and reveals her upcoming book on tech storytelling.

Aug 11, 2026

How Harvey Built a Research Lab on a Budget | Gabe Pereyra

Gabe Pereyra of Harvey details a playbook for application companies to compete with frontier AI labs by leveraging the ecosystem. Key strategies include building specialized benchmarks like Legal Agent Bench, using domain experts for synthetic data generation to overcome sensitive client data issues, partnering with multiple 'neo labs' for post-training, and developing robust model serving and evaluation infrastructure. He emphasizes open-sourcing data for validation and the 'Moneyball' philosophy for success, addressing challenges like talent acquisition and long-context management in the Q&A.

Aug 11, 2026

How Companies Are Building Their Own Intelligence | Sonya Huang, Sequoia Capital

Sonya Huang of Sequoia Capital discusses the strategic imperative for companies to embrace "sovereign AI" by owning their AI models and weights. She identifies cost, speed, performance, and controlling destiny as the four driving forces behind this shift. Huang argues that the competitive landscape is moving towards owning the intelligence layer, positioning application companies as the new innovation labs. She provides a practical, opinionated framework covering strategy (what to own vs. rent), team building, ensuring external legibility of research, and a technical roadmap for implementation, emphasizing how open-weight models now enable frontier-level performance through ownership and customization.