Latest Interviews

AI Native Hedge Funds

A former quantitative researcher identifies the current inflection point where AI-native strategies are displacing legacy quantitative trading models, noting that established hedge funds are already lagging due to slow adoption. The initiative targets the development of autonomous "swarms of cloud agents" capable of synthesizing financial data and executing trades without human intervention to secure a decisive competitive advantage. This speaker is currently recruiting through Y Combinator to build the first fully automated financial firm that mirrors the paradigm shifts seen in the 1980s.

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.

AI in 2026: 3 Predictions For What’s To Come (a16z Big Ideas)

Oliver Hsu, Bryan Kim, David Haber

Speakers Oliver Hsu, Brian Kim, and David Haber analyze distinct trajectories for AI adoption, ranging from autonomous scientific discovery in life sciences to consumer platforms shifting focus from productivity to digital connectivity. Hsu identifies the maturation of robot learning and simulation as prerequisites for closing the loop on self-driving labs, while Kim argues that future consumer value hinges on AI agents facilitating human relationships through deep emotional engagement. Concurrently, Haber demonstrates that the most robust business models integrate AI to directly reinforce revenue generation and proprietary data loops, as seen in contingency law firms and loan servicing platforms that scale success rather than merely cutting costs.

How AI Agents Will Transform in 2026 (a16z Big Ideas)

Marc Andrusko, Stephanie Zhang, Olivia Moore, Steph Ng

The event details a fundamental shift from reactive prompt-based interfaces to proactive AI agents that autonomously diagnose issues and execute actions, expanding the addressable market from software spending to the broader labor economy. Speakers discuss how design priorities are evolving toward machine legibility to enable high-autonomy applications in sales, security, and content discovery, while voice AI agents achieve scalable enterprise deployment in healthcare, finance, and customer service by outperforming humans in compliance and multilingual accuracy. This transition is redefining the human-in-the-loop dynamic, where critical decisions retain oversight while routine tasks are increasingly automated to drive efficiency across nearly every industry vertical.

Making GPUs Actually Fast: A Deep Dive into Training Performance

Corwin, Savant Diaz, Sylvain De Wecker

Jane Street engineers optimize deep learning infrastructure by eliminating CPU-GPU synchronization bottlenecks and fusing PyTorch operations via `torch.compile` and Triton to maximize throughput. When automated compilation fails on complex Python logic, they deploy custom CUDA kernels that leverage shared memory and warp-level reductions to achieve nearly 1,000x speedups in specialized tensor operations. This hierarchical approach, ranging from standard PyTorch to hand-optimized C++, ensures efficient utilization of the H100's 132 Streaming Multiprocessors and strict memory bandwidth constraints.

Marc Andreessen and Ben Horowitz on the State of AI

Marc Andreessen, Ben Horowitz, Erik Torenberg

Industry leaders assess that AI product evolution will likely follow historical cycles of radical interface shifts while facing immediate constraints from a severe talent shortage and a looming hardware transition from chip scarcity to power availability. Geopolitical analysis warns that although the U.S. currently leads in software innovation, a six-month gap combined with American deindustrialization risks allowing China to overtake the nation in the critical embodied robotics phase. Despite speculation about a market bubble, the consensus remains that demand currently exceeds supply, with long-term success ultimately determined by whether customers validate the technology and by how effectively legacy incumbents and new entrants execute superior product strategies.

From Idea to $650M Exit: Lessons in Building AI Startups

Jake Heller

Case Text, led by a founder who pivoted the company to artificial intelligence in summer 2022, was acquired by Thomson Reuters for $650 million following the successful launch of its Co-Counsel legal assistant. The speaker outlines a methodology for building reliable AI applications by combining domain expertise with granular workflow decomposition, rigorous evaluation metrics, and iterative prompt engineering to achieve near-human accuracy. Key strategic insights for scaling include adopting value-based pricing, prioritizing side-by-side human-AI pilots to build trust, and viewing proprietary data integration and evaluation frameworks as the primary sources of long-term defensibility.

Bryan Johnson’s Best Health Hack: Lower Your RHR Before Sleep = Sleep Better and Live Longer

Bryan Johnson

Brian Johnson presents an ambitious framework positioning the indefinite extension of human life as a new global ideology, utilizing biological age testing to illustrate that organs age independently and require targeted management. He identifies resting heart rate as the primary health metric and details a specific protocol involving strict sleep hygiene, diet timing, and screen discipline to lower this rate by approximately 10%. This approach aims to reverse aging trends while simultaneously treating sleep as the fundamental intervention for anxiety and depression, ultimately preparing humanity for a future defined by superintelligence.

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.

A Keynote by Thomas Sohmers, Co Founder & CTO of Positron

Thomas Sohmers

Positron AI, a startup founded in 2023, is deploying specialized hardware to address the inference bottleneck that now drives global GDP growth and limits traditional GPU efficiency for Transformer models. Their Atlas server and upcoming Titan system prioritize massive memory bandwidth over raw compute, delivering 70% higher throughput per dollar while operating at 150 watts to eliminate the need for liquid cooling. By enabling concurrent execution of multiple models up to 512 billion parameters on standard air-cooled infrastructure, the company aims to drastically reduce AI operating costs and reshape the economic landscape of software development.

Cresta: The Path to 90% Service Automation: Pitfalls, Secrets & Lessons from Enterprise Deployments

Ping Wu

Ping Wu, CEO of Cuesta, addresses the unique challenges of automating contact centers by distinguishing private, legacy-bound support data from the public datasets that drove coding automation. His platform deploys a three-part architecture of conversation intelligence, agent assistants, and native AI agents to achieve high task-level automation metrics while maintaining human oversight for complex escalations. This strategy aims to shift customer experience from defensive cost-cutting to an era of abundant, proactive engagement where AI handles 90% of sub-tasks, lowering interaction costs and enabling more frequent customer contact.

June Paik (FuriosaAI): Tensor Contraction Processor: NextGen AI Inference Chip for Data Centers

June Paik

Seoul-based Furiosa AI CEO Jun Paek unveiled the Renegade, a second-generation silicon chip engineered to solve energy bottlenecks in the shift toward frontier agentic model inference. Built on TSMC's 5-nanometer node, this Tensor Contraction Processor delivers 190% superior tokens-per-second-per-watt performance compared to NVIDIA H100 units by optimizing for 1.5 terabytes per second memory bandwidth and 180-watt power consumption. Currently in the sampling phase with global enterprise clients, the chip enables manufacturers and sovereign sectors to own their full AI stack while drastically reducing the capital and operational costs associated with scaling massive data centers.

Des Traynor, Co-Founder & CSO @ Intercom & Creator of Fin.ai: The Death of SaaS, The Dawn of Agents

Des Traynor

Industry leaders identify AI agents as an existential force converging fragmented B2B SaaS workflows, presenting a critical "adapt or die" choice where obsolescence looms for firms unable to deploy robust, job-executing systems within two years. While strategic ambition ranges from efficiency-boosting copilots to full organizational replacements, success hinges on overcoming severe operational hurdles regarding unstructured data, compounding error rates in multi-step chains, and balancing agent agency with strict reliability controls. Although pioneers like Intercom report five-year growth through this paradigm, widespread adoption requires fundamentally rewriting pricing, sales, and validation architectures to bridge the gap between experimental prototypes and production-ready performance.

Guillaume Verdon, Extropic AI: Thermodynamic Computing and the Energy Efficiency Crisis of AI

Guillaume Verdon

Xtropic is addressing the impending AI energy crisis by developing thermodynamic hardware that leverages natural electron fluctuations for probabilistic computation rather than traditional deterministic logic. Founders Trevor and colleagues have successfully miniaturized this technology to room temperature on standard silicon, currently testing chips with hundreds of degrees of freedom that consume attojoules per operation while promising a 100 million-fold efficiency gain over current GPUs. With mass production scheduled for 2025 and early customer deployments targeted for the end of summer, the company aims to disrupt the multi-billion dollar semiconductor market by making energy efficiency the primary driver for scaling future artificial intelligence.

Anish Agarwal, Traversal: Production Software Keeps Breaking, and It Will Only Get Worse

Anish Agarwal

Founded by MIT PhDs and Citadel alumni, Traversal employs causal machine learning, advanced reasoning models, and agent swarms to automate root cause analysis for large enterprises overwhelmed by AI-generated code complexity. The platform processes petabyte-scale telemetry to distinguish symptoms from causes, delivering proven results such as a 38% reduction in resolution time for DigitalOcean and sub-five-minute identification for a major financial firm. By converging on solutions through agentic MapReduce rather than traditional AIOps, the company aims to automate the full software reliability lifecycle while addressing the growing maintenance burden in modern development workflows.