Latest Interviews
Showing 31–45 of 3,487 transcripts.
Clear all filters- All-In Podcast1h 4m
Elon Musk & Gwynne Shotwell on AI Risks and Peer Review, Starship, Terafab, SpaceX/Tesla Merger
Elon Musk, Gwynne Shotwell, Chamath, Jason, Friedberg
SpaceX aims to land humans on Mars within the next decade while transitioning to a fully reusable Starship system by 2027, supported by a "fail fast" engineering culture and a management model that prioritizes hands-on technical leadership. Parallel to its space ambitions, the company is expanding into artificial intelligence and orbital compute infrastructure to ensure supply chain independence through a new TerraFab chip facility and to address existential safety risks via mandatory competitor peer reviews. Under the stewardship of long-tenured executives like Gwen Shotwell and Elon Musk, the organization continues to integrate its diverse ventures, including upcoming hardware reveals and Starlink global expansion, while maintaining a strict focus on hard engineering over bureaucratic processes.
- All-In Podcast47 min
Jensen Huang: The Doomer Hoax, Superintelligence is Here, and The Future of AI (ft. President Trump)
Jensen Huang, President Trump, Dario, Elon, Chamath, Jason, Friedberg, David
Jensen Huang highlighted NVIDIA's 97% year-over-year revenue growth while positioning the company as the exclusive infrastructure for all major frontier AI models. He rejected extinction-level risk narratives in favor of historical data, arguing that open-source models drive mass innovation despite concerns over supply chain bottlenecks and geopolitical competition from China. The event culminated in a private Oval Office discussion with President Trump, where both parties agreed that AI represents a critical economic engine for re-industrialization and energy infrastructure expansion.
- a16z50 min
Greg Brockman Says AGI Has Arrived
Greg Brockman, Ben Horowitz, Erik Torenberg
OpenAI's Greg Brockman declares the current era as the "AGI era," highlighting the breakthrough model Astra's ability to autonomously execute complex tasks over 24-hour periods via direct computer interface usage. Facing severe compute shortages and safety bottlenecks, the organization has pivoted to "pacing the frontier" by redirecting 25% of its engineering staff toward automated cybersecurity and launching a $1 billion initiative to support critical infrastructure defenders with discounted access. While consolidating product lines to prioritize operational safety, OpenAI aims to transform AI deployment from experimental R&D into a secure, high-volume infrastructure that enhances scientific discovery and lowers barriers for entrepreneurship.
How LPs Allocate to Venture in 2026: What They Want, What They Don’t | Baylor University CIO
David Morehead, Harry Stebbings
Baylor University's $2.6 billion endowment office, led by Moorhead, is restructuring its 55% private and 45% public portfolio to prioritize capital velocity and upside capture amidst declining enrollment and rising tuition pressures. The team distinguishes itself through a mechanistic deployment strategy, conservative valuation discipline, and direct GP negotiations that favor annualized growth equity returns of 30% over traditional fund duration models. While maintaining a neutral stance on private credit, the fund actively bets on the resilience of legacy SaaS, permitted data center infrastructure, and biotech, aiming to navigate its expansion into the single-digit billion scale by maximizing the absolute dollar impact of its $2.5 million core positions.
Bending Spoons Founders on Buying Airtable, AOL, Vimeo & Miro
Luca Ferrari, Francesco Patarnello, Matteo Danieli, Valentina Jerusalmi, Molly O'Shea
Bending Spoons executes a high-volume acquisition strategy centered on buying small, high-caliber teams for near-total autonomy, evidenced by recent multi-billion dollar deals such as AOL and Airtable. The company differentiates its approach through a "forever ownership" model that avoids private equity exit timelines, supported by decentralized operations in Milan and an internal culture prioritizing rapid learning, extreme ownership, and aggressive meritocracy where leadership roles are periodically contested. This operational philosophy, combined with unconventional financing and an open-desk work structure, has enabled the firm to maintain under 1% employee churn while targeting five to ten strategic acquisitions annually to maximize platform synergies.
Labour, Engineering, Social Media, GrokBots, Cybercabs: 7 Predictions for How AI Changes the World
Matteo Franceschetti, Harry Stebbings
Eight Sleep Co-founder Matteo Franceschetti details how the company replaces traditional engineering and marketing teams with AI agents to operate as a 160-person entity generating revenue per employee exceeding Apple's. The presentation outlines the firm's aggressive global expansion into markets like China via super-apps and its strategic pivot from a hardware manufacturer to a medical platform while navigating intense talent retention wars and rising AI infrastructure costs. Franceschetti concludes with predictions on the future of work, suggesting that Universal Basic Income will eventually become necessary as AI-driven efficiency drastically reduces the demand for human labor.
- Jane Street1h 6m
Trustworthy Human-AI Collaboration in a Live Type-Theoretic Computational Commons
Hazel addresses the "gap problem" in traditional programming by implementing a live execution environment that uses visual "holes" for error recovery and instant probes to provide immediate feedback on code changes. The system enforces a pure functional design with incremental memoization to enable real-time data flow analysis, while integrating advanced security measures like object capabilities and formal verification to safely coordinate human-AI collaboration. Future iterations aim to solve scalability challenges in multi-agent development through content-addressed versioning and semantic models, ultimately supporting a computational commons where code and data evolve together in a shared, verifiable state.
- a16z48 min
How AI Is Rewriting the Power Law of Venture Capital
Jen Kha, David George, Aram Verdiyan
The summary details how an extreme power law in venture capital now demands that top-tier funds secure category-defining assets like SpaceX or OpenAI to generate returns, as traditional mid-stage investing has collapsed and late-stage exits increasingly favor early-stage franchise holders. Despite valuation distortions and structural misalignments between general partners and limited partners, capital is shifting aggressively toward AI-native growth and infrastructure bottlenecks like energy and data centers, which are viewed as the primary constraints on a projected $10 trillion market opportunity. This reallocation favors long-horizon investors who can tolerate early-stage loss rates up to 60% in exchange for the liquidity potential of future trillion-dollar companies emerging from robotics, autonomy, and deep healthcare innovation.
Saronic's 4 Co-Founders on Building a $9.25B Shipbuilder | Part III
Dino Mavrookas, Rob Lehman, Doug Lambert, Vibhav Altekar, Molly O'Shea
Ceronic, a hard tech manufacturer valued at $9.25 billion with $2.6 billion in funding, is addressing the critical U.S. naval shipbuilding deficit by developing autonomous surface vessels capable of surviving extreme accelerations exceeding 20 Gs. Partnering with Palantir and Path Robotics to secure aluminum supplies and master aluminum welding, the company is transitioning from low-volume prototyping to mass production models that utilize hardware-software co-design to reduce costs and eliminate personnel from harm's way. This industrial strategy aims to expand the U.S. fleet from 290 to 381 ships over three decades while creating a scalable, cost-effective platform for both defense operations and emerging commercial ocean markets.
Jensen Huang Declares AGI Has Arrived | Tesla Launches Cybercabs | Index Pulls Out of Town
Jensen Huang, Jason Lemkin, Rory O'Driscoll, Harry Stebbings
Jensen Huang, Jason Calacanis, and other industry leaders debated the arrival of AGI and the economic potential of AI in coding versus law while discussing critical safety breaches that bypassed current guardrails. The conversation also covered market dynamics where aggressive startups like Instinct challenge regulations, alongside autonomous vehicle unveilings by Elon Musk and a wave of record-breaking valuations and M&A activity. Ultimately, the panel warned of an impending "Neolab" bubble correction, emphasizing that only companies capable of rapid evolution or those with massive corporate backing will survive the current capital crunch.
- a16z1h 5m
Inside OpenAI’s Breakthroughs in Mathematical Reasoning
Lisha Li, Mehtaab Sawhney, Mark Sellke
The AI model Astra has achieved significant breakthroughs in pure mathematics by resolving open problems in sphere packing, spherical codes, and the existence of non-sofic groups through emergent reasoning capabilities like backtracking and parallel processing. These results demonstrate that large language models can synthesize complex algebraic structures and high-dimensional analysis without explicit programming, effectively closing long-standing gaps in asymptotic bounds and group theory. Consequently, the mathematical community is shifting its focus from theorem proving to the curation of problems and the interpretation of AI-generated insights, signaling a new era where human intuition complements machine persistence.
- Y Combinator1h 0m
Self-Improving Harnesses, Local Personal AI And YC's Agent For Work | YC Paper Club
Seth Karten, Jon Saad-Falcon, Josh France, Regan Bell
YC Harness Club researchers demonstrate that "self-improving harnesses" capable of modifying their own code and memory have driven Arc AGI success rates from 30% to 100%, surpassing standard prompt engineering by leveraging recursive learning architectures. Presentations by Seth and John Sad Falcone detail specific implementations like Prime Agent's multi-tier context management and OpenJarvis's local on-device stack, which collectively achieve frontier-level performance while reducing costs by 800x and enabling autonomous long-horizon research. Furthermore, the QM project establishes a centralized cloud-hosted framework with dynamic sandboxing and fine-grained permissions, addressing critical challenges in system-wide coordination and data security for scalable multi-agent deployment.
Town vs Instinct vs GrokBot | Why the AI Assistant Market Is Not a Bubble
Jean-Denis "JD" Grèze, Harry Stebbings
Town, led by founder Jean-Denis, has pivoted from a failed tax prep venture to a mainstream email and calendar AI assistant targeting a $100 billion valuation through a network effect at the agent level. With conversion rates exceeding 15% and a multi-model routing strategy to optimize costs, the company navigates competitive pressure from tech giants like Google and Apple by prioritizing enterprise workflow integration over consumer subsidies. JD anticipates that future market leaders will be defined by their ability to build emotional defensibility through autonomous agents while maintaining strict token economics to ensure sustainable revenue growth.
- Milken Institute57 min
Multi-Asset Management Across Public and Private Markets | Global Conference 2026
Romaine Bostick, Michael Brandmeyer, Shikha Gupta, Nabeel Qadir, Rohit Sipahimalani, David Steinbach
Institutional investors are fundamentally restructuring portfolios toward a total-asset approach that doubles private real-asset exposure to 50% of capital while prioritizing liquidity and uncorrelated return drivers to navigate inflation and geopolitical fragmentation. Strategic shifts include compressing private equity premiums to 100–150 basis points, redirecting AI investments into infrastructure, and expanding the secondary market to $500 billion over five years to secure resilience against macro regime changes. As the era of easy beta generation ends, managers face a "show me" imperative to demonstrate operating value creation and select firms with hard-asset capabilities rather than relying on historical correlations.
How to Build Your Own Data Center & Why Every Startup Should Do It
Cliff Weitzman, Harry Stebbings
Speechify founder Cliff Weitzman openly acknowledged a strategic error in initially avoiding the B2B API market, prompting the company to launch competitive products against rivals like 11 Labs while leveraging a proprietary hardware strategy to secure cost-efficient access to next-generation NVIDIA GPUs. By owning assets to lower training costs and prioritizing a specialized "Simba 3.2" model, Speechify aims to dominate the B2C voice space and compete directly with industry giants through a hybrid compute approach that decouples legacy inference hardware from high-performance training clusters. This infrastructure investment supports the company's pivot toward generalist AI agents and synthetic data generation, positioning Speechify to capitalize on a predicted shift from screen-based to voice-first human-computer interfaces over the next five years.