Latest Interviews

Waymo Co-CEO Dmitri Dolgov: The Demo Is Only 1% Of The Work

Dmitri Dolgov

Waymo has advanced its fully autonomous fleet to operate 500 weekly trips across 15 U.S. cities, achieving a safety record 17 times better than human drivers through a multimodal sensor architecture and a foundation model utilizing both fast geometric reactions and slow semantic reasoning. The company addresses the unique challenges of physical AI by integrating real-world data into a generative simulation ecosystem that creates rare edge cases for training, while its "Safety and Readiness Framework" rigorously validates performance to ensure public trust and regulatory compliance. Looking ahead, this structural augmentation and flywheel of agent, simulator, and critic data positions Waymo to expand its technology beyond personal vehicles into trucking and broader physical applications.

World Models, JEPA And The Path To Sample-Efficient RL

Ankit, Francois

This event analyzes the critical bottleneck of sample efficiency in artificial intelligence, contrasting current deep learning models' massive data requirements with the human brain's ability to learn from minimal experience through superior world modeling. The discussion details how advancements in non-differentiable control theories, video diffusion architectures, and Joint Embedding Predictive Architectures are shifting strategies from model-free behavior cloning to synthetic, simulation-based planning for complex robotic and autonomous driving tasks. By addressing scaling challenges in high-dimensional action spaces and architectural limitations like the Transformer's inefficiency in time-domain compression, the presentation outlines a roadmap toward general-purpose robotics and AGI by 2026 through the integration of "awake sleep" mechanisms and physics-informed predictive systems.

Dot Plots: How to Actually See What Your Users Are Doing

David Lieb, Dave

Founders and enterprise product teams can uncover hidden usage patterns and early churn signals by utilizing dot plots, a visualization method originally derived from PayPal's fraud detection systems. This technique replaces opaque aggregate metrics with granular grids that map individual user activity against time, allowing stakeholders to distinguish between active cohorts and vanity behaviors that traditional dashboards mask. By combining these visual insights with cohort retention curves, organizations ranging from early-stage startups to massive platforms like Google Photos can identify specific feature correlations and usage gaps that drive product iteration and contract renewals.

Transformers Explained: The Discovery That Changed AI Forever

Ankit Gupta

This event traces the evolution of AI from early neural networks plagued by vanishing gradients to the 2017 introduction of the transformer architecture, which replaced sequential processing with parallel self-attention. Key milestones include the LSTM's ability to model long-range dependencies, Google Translate's adoption of attention-based sequence-to-sequence models, and the subsequent bifurcation of transformers into encoder-focused BERT and decoder-focused GPT series. These developments enabled the shift from single-task specialists to general-purpose large language models, establishing the foundation for current state-of-the-art systems like ChatGPT and Claude.

What Everyone Is Getting Wrong About AI And Jobs

Garry Tan

This analysis synthesizes historical precedents like containerization and cloud computing to refute extreme predictions of mass unemployment, demonstrating instead that AI efficiency triggers Jevons' Paradox by lowering costs and exploding demand for services. Prominent figures such as Andrej Karpathy and Aaron Levy argue that while AI automates rote tasks, it predominantly refills labor markets by elevating human roles to supervisory positions and addressing pent-up demand in sectors like healthcare and law. Consequently, founders and investors are urged to actively build solutions that leverage this latent demand rather than waiting for policy interventions or succumbing to fatalistic views on economic transformation.

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

Ankit Gupta

OpenAI, Alibaba Cloud, and DeepSeek have each launched significant open-weight language models featuring distinct Mixture of Experts architectures and advanced long-context capabilities. While OpenAI's GPT-OSS prioritizes inference efficiency on consumer hardware, Alibaba's Qwen 3 introduces flexible dense and sparse variants with dual reasoning modes, and DeepSeek's V3.1 achieves superior memory efficiency through Multi-Head Latent Attention. Despite differing engineering strategies for scaling and alignment, all three families demonstrate comparable performance benchmarks derived from trillions of tokens and complex post-training pipelines.

The Sales Playbook For Founders | Startup School

Tom Blomfield

Early-stage B2B founders often stall in non-revenue design partnerships, so this guide outlines a four-stage framework to accelerate sales from initial discovery to signed Annual Recurring Revenue contracts. The strategy replaces vague, months-long pilots with narrow, paid trials that enforce financial commitment and specific ROI metrics, culminating in recurring contracts featuring automatic renewal clauses. By integrating operational tactics like immediate security certification and high-touch onboarding, founders can bypass traditional procurement bottlenecks and establish scalable revenue streams.

Andrew Ng: Building Faster with AI

Andrew Ng

AI Fund accelerates startup velocity by co-founding approximately one venture monthly through direct code writing and feature definition, leveraging agentic AI workflows to address complex tasks in sectors like healthcare and legal compliance. The organization emphasizes concrete product hypotheses and agile prototyping to shift engineering bottlenecks toward product management, thereby altering standard PM-to-engineer ratios to 0.5:1 while empowering non-engineering staff with coding literacy. Strategic guidance further stresses that rapid iteration and ethical filters outweigh speculative narratives, prioritizing application-layer revenue generation and open-source accessibility over fears of existential risk or regulatory gatekeeping.

Andrej Karpathy: Software Is Changing (Again)

Andrej Karpathy

The presentation outlines the evolution of software from human-written code to neural network weights and finally to natural language prompts, establishing Large Language Models as a new operating system layer. It details the current infrastructure challenges, cognitive limitations such as hallucination and memory loss, and the strategic shift toward "partial autonomy" systems that maintain human oversight through granular verification loops. Ultimately, the industry faces a transitional decade requiring developers to rewrite existing codebases for agent interaction while managing new risks associated with centralized intelligence infrastructure.

Fusion Energy Will Power the AI Boom

Gustaf Alströmer

Helion Energy is accelerating commercial fusion development by deploying a compact, linear machine that uses helium-3 fuel and direct energy capture to bypass traditional inefficiencies and safety concerns. The company recently secured a strategic partnership with Microsoft to power a data center, marking a pivotal shift from experimental research to real-world utility deployment. With this transition, Helion aims to establish the world's first affordable, carbon-free electricity source that cannot be weaponized, potentially transforming global energy standards within a decade.

Why OpenAI's o1 Is A Huge Deal | YC Decoded

OpenAI has released the O1 Preview and O1 Mini models, a new class of systems designed for advanced reasoning that match PhD-level performance on complex scientific and coding benchmarks through self-generated chain-of-thought processes. Unlike previous architectures requiring manual prompt engineering, these models utilize large-scale reinforcement learning to decompose problems into verifiable steps, with accuracy improving dynamically as they allocate more "thinking time" to correct errors. While early testing shows mixed preference over GPT-4o for creative tasks, Sam Altman characterizes the technology as a foundational leap comparable to the GPT-2 stage, promising rapid evolution toward GPT-4 capabilities and future multi-modal integration within months.

Starting A Company? The Key Terms You Should Know | Startup School

Dalton Caldwell

This session defines critical startup financial mechanics, including burn rate, profitability thresholds, and the structural differences between angel, seed, and Series A funding rounds. It distinguishes between equity instruments like convertible notes and SAFEs while explaining how metrics such as ARR and Total Addressable Market inform valuation and growth strategy. The discussion concludes by outlining the transition from finding product-market fit to scaling operations, ultimately leading toward an IPO or sustained bootstrapping.

How To Find A Co-Founder | Startup School

Harj Taggar

The event outlines a comprehensive strategy for startup founders to secure co-founders who can double execution capacity, enhance quality control, and provide essential emotional resilience. It emphasizes selecting partners based on stress tolerance and aligned goals rather than specific technical skills, recommending equal equity splits and a trial period to mitigate the risks of role disputes or work ethic mismatches. By leveraging networks for organic connections and maintaining regular communication, founders can avoid common breakup causes and build a durable team capable of competing with established entities.

How To Convert Customers With Cold Emails | Startup School

Aaron Epstein

This event details a data-driven outreach framework where founders must personally execute high-volume email campaigns using warm introductions or targeted cold strategies to secure B2B customers. The speaker outlines a specific funnel architecture that requires sending dozens of daily messages while applying seven principles of effective copy, such as humanized tone, deep personalization, and clear calls to action, to maximize response rates. By analyzing case studies of both failed and successful campaigns, the presentation emphasizes that manual, founder-led execution is essential for building initial traction before any automation is introduced.

Enterprise Sales | Startup School

Pete Koomen, Pete Kuhman

Optimizely co-founder Pete Kuhman outlines a rigorous enterprise sales framework for technical founders, emphasizing that selling before product-market fit requires experimentation with targeted prospecting and personalized outreach. The strategy prioritizes deep discovery during initial calls, uses product-specific narratives to demonstrate value during demos, and treats pricing as a signal of problem severity rather than a fixed metric. By actively managing implementation roadmaps to ensure customer adoption and navigating procurement hurdles with internal champions, founders can transform sales skills into a transferable superpower for fundraising and hiring.