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.

How To Get Your First Users

Ankit Gupta

Startup founders are urged to launch a Minimum Evolvable Product and secure paying customers through direct outreach to ensure rapid, pressure-driven evolution rather than aiming for immediate perfection. This strategy is particularly critical in the AI sector, where high computational costs necessitate targeting prosumers or businesses with deeper pockets over price-sensitive consumers. By treating early ventures as simple organisms capable of significant adaptation, founders can navigate path dependency where initial user choices fundamentally steer the product's final form and market relevance.

Don't Just Check Off Boxes

Michael Truell

The discussion advises professionals to prioritize subjects driven by personal interest rather than those that merely satisfy external requirements. It further emphasizes constructing serious, long-term collaborative relationships with peers who are both enjoyable and deeply respected. By shifting focus from short-term metrics to the consistent development of substantive projects, participants are encouraged to build a more meaningful and sustainable career trajectory.

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.

The Shortcut Rule

The Shortcut Rule demonstrates that optimizing for a single metric achieves the measured target while sacrificing unmeasured objectives, effectively causing programs to hit the target but miss the point. This phenomenon is illustrated by the AI chess case, where Deep Blue defeated Garry Kasparov to satisfy engineering goals without yielding new insights into general human intelligence. Consequently, the event highlights the divergence between technical success and foundational learning when research focuses exclusively on a narrow definition of victory.

"Startups only exist to find product-market fit."

The presentation argues that early-stage startups must prioritize discovering Product-Market Fit over mimicking the operational structures of established enterprises. It warns that founders who prematurely adopt enterprise-level functions, large offices, or redundant departments incur unnecessary costs that distract from core execution. Consequently, the most effective pre-funding strategy involves maintaining a lean team of two to three founders and a single engineer dedicated solely to validating market demand.

Agency and Independence

As AI systems surpass humans in executing instructions, the traditional higher education focus on attendance and procedural compliance is becoming obsolete. Future academic value must instead prioritize the cultivation of independent judgment and self-directed action. Developing personal agency is now the critical differentiator required for success in a post-AI workforce.

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.

AI Native Enterprise Software

Top-tier enterprise vendors Salesforce and ServiceNow established dominant market positions by pioneering cloud-native solutions, a disruption pattern now emerging in the generational shift toward AI-native systems. This transition promises to transform enterprise software from passive record-keeping into active assistants for sales, HR, and accounting, creating a structural opening for new startups to challenge incumbent architectures. Founders and builders interested in developing these next-generation AI tools are invited to connect to capitalize on this competitive window.

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.