Latest Interviews

Scientists Are Built for Startups

Arvind Veluvali, Ayman Saleh, Dima Yanovsky

The speaker leverages their experience engineering fungus-based materials and simulating the NASA Perseverance rover to argue that scientists, particularly those from structured space agencies, possess the ideal mindset for startup entrepreneurship. Contrasting NASA's rigid, decade-long project lifecycles with the high-pressure, iterative environment of seed-stage ventures, the speaker demonstrates that technical feasibility and the willingness to conduct long-term experiments are more critical than formal business training. Ultimately, the presentation posits that researchers accustomed to managing massive-scale contractor coordination and undefined success metrics are uniquely prepared to lead new ventures despite the inherent risks.

Multiplayer AI

While current AI adoption remains limited to isolated, single-player interactions, the proposed shift toward "multiplayer agents" enables teams to observe, redirect, and hand off autonomous tasks in real-time, mirroring human collaboration. This platform transition aims to replace static transcript sharing with live agent sessions tailored for specific departments like engineering, sales, and legal to solve complex problems collectively. An open call for partnership invites organizations to build future AI systems with these multiplayer capabilities at their core.

" It is very possible that the first people to live to a thousand are alive right now."

Max Hodak

Technological accelerations in artificial intelligence and brain-computer interfaces are driving a paradigm shift where the first individuals capable of living to 1,000 years may already exist. By 2035, widespread AI accessibility and new lateral treatments will reframe the human condition despite market undervaluation and low public awareness. These advancements will fundamentally reconfigure human interaction by creating functional equivalents to direct brain-to-brain communication.

Differences Between The Leading AI Models

Madhav Jha

Current AI models operate as specialized tools where Opus functions as a general workhorse, Codex excels in back-end debugging, and Gemini leads in front-end applications. Within three to six months, the market is expected to shift toward commoditization driven by converging model behaviors and narrowing gaps between open-source and proprietary options. Strategic success will therefore depend on building a user-centric layer that leverages these unique capabilities to optimize the customer journey rather than relying on raw model differentiation.

This Is The Holy Grail Of AI

Ian Fischer

Poetic is advancing a recursively self-improving AI system that autonomously enhances its own intelligence at a fraction of the cost and time required by major competitors like Anthropic, OpenAI, and Google. Unlike industry standards that demand hundreds of millions of dollars and months of effort to train new Large Language Models from scratch, Poetic's proprietary method accelerates these cycles significantly. This speed and efficiency advantage allows Poetic to potentially outpace rivals who remain constrained by the prohibitive expenses and long timelines of traditional retraining approaches.

What Boris Cherny Learned From Building Claude Code

Boris Cherny

Quad Code's founder executed a grueling three-month sprint to launch a terminal-based product, accepting current code-generation limitations as an inevitable starting point rather than a final state. This strategy mirrors Anthropic's philosophy of building software for capabilities expected six months into the future, encouraging founders to target where models currently fail rather than optimizing for today's constraints. By prioritizing this forward-looking approach, both entities anticipate that natural model progression will resolve existing gaps without requiring premature adaptation to immature technology.

Why OpenClaw Broke The Internet

OpenClaw distinguishes itself as a local AI agent that executes autonomously on user hardware with full permissions to interface directly with IoT devices and control physical environments. By leveraging comprehensive access to a user's entire local dataset, the system successfully reconstructed detailed historical narratives from forgotten files to deliver deep, unexpected insights. Unlike cloud-based competitors, this platform grants users granular control over their digital footprint while enabling direct interaction with tangible assets such as vehicles, appliances, and smart beds.

Building The World's First Reusable Rockets

Andy Lapsa, Tom Feldman

Recognizing a critical industry flaw where single-use rockets are discarded after a single flight, founders launched a venture dedicated to developing rapidly reusable orbital vehicles to fill this technological vacuum. They proceeded with the conviction that full reusability was a necessary pursuit, accepting the venture as the "easiest hardest decision" based on a moral imperative to attempt the solution regardless of the probability of failure. This bold entry into the aerospace market was driven by the belief that the potential for a sustainable launch model justified the risks of building a new company from scratch.

The AI Economy Is Stabilizing

Jared Friedman

By 2025, the AI economy has stabilized into a three-tier ecosystem of model developers, application providers, and infrastructure firms following a volatile 2024. A standardized playbook for building AI-native companies has enabled broad profitability across all layers without any single entity achieving market dominance. This progression reflects steady, incremental improvements in model capabilities rather than revolutionary breakthroughs that would disrupt the current trajectory.

Finding Opportunity in Outdated Rules

Emerging companies frequently capitalize on legal ambiguities, such as OpenAI's copyright challenges or Lyft's initial fear of criminal liability, to launch disruptive models before regulations adapt. These ventures often succeed because legal frameworks evolve post-launch to accommodate high-value consumer innovations, creating a window where outdated statutes fail to align with modern technological realities. Consequently, identifying and exploiting these gray areas becomes a strategic pathway for market entry, as the substantial value delivered to consumers eventually forces the superseding of obsolete laws.

Founder Stories: Rishi Choudhary

Rishi Choudhary

Founded by Rishi Chaudhry, Castle AI deploys voice automation agents to serve three of the top 25 US loan servicers, specifically targeting large banks and mortgage lenders. Moving away from previous corporate constraints, Chaudhry now facilitates direct executive engagement to help enterprise clients define their five-year AI roadmaps. This strategic partnership enables major financial institutions to integrate Castle AI as a central component of their long-term technology transformation.

Founder Stories: Cathy Di

Cathy Di, kathy

Princeton senior Kathy founded Daedalus Labs just months before submitting a late application to Y Combinator's Summer Batch, transitioning from a student to the CEO of an $11 million valued AI agent platform. The company, which provides a simplified environment for building and deploying artificial intelligence agents, leveraged immediate alumni support to bridge its initial lack of customers and revenue. Kathy's rapid trajectory and success at age 21 underscore her belief that current market conditions are optimal for launching new ventures despite the risks of abandoning a traditional big tech career path.

Founder Stories: Saurav Kumar

Saurav Kumar

UIUC graduate Saurav Kumar launched Fleetline, a Y Combinator-backed venture utilizing graph algorithms to optimize truck positioning and potentially boost customer revenue by 20%. Following his recent graduation in Computer Science and Statistics, Kumar secured six-figure contracts and a $4 million seed round to drive the technology's commercial adoption. The program's environment further normalized high ambition and iterative development, enabling the company to align the founder's inherent strengths with immediate market demands.

Founder Stories: Pablo Hansen

Pablo Hansen

Y Combinator graduate Pablo Hanson has developed Keystone, a modern bug triage platform that aggregates ecosystem data to generate consolidated insights and initial fixes. Rejecting acquisition offers exceeding eight figures, the company recently secured over $5 million in funding to drive its operations amid a strategic vision of transformative technological evolution. The Y Combinator program further enabled the formation of a supportive community and established the foundational operational tone for the startup's growth.

From dorm rooms to million-dollar companies

Former Princeton AI master's student and current founder of an $11M valuation firm secured over $5M in funding after growing monthly revenue from $100 to seven figures annually through contracts with three major U.S. mortgage lenders. The company now collaborates directly with top bank executives to guide their AI implementation strategies, leveraging a network of Y Combinator alumni to achieve rapid customer acquisition. This trajectory, marked by rejected eight-figure acquisition offers, highlights the role of early-stage entrepreneurship in shaping enterprise AI infrastructure.