No Priors: Artificial Intelligence | Technology | Startups

Drug discovery and development has long relied on expensive, time-consuming empirical trial and error. By shifting the discovery process onto computers, Isomorphic Labs wants to fundamentally change medicine from a process of discovery to one of precision engineering. Elad Gil sits down with Isomorphic’s president Max Jaderberg to discuss his vision for using predictive AI models to achieve experimental accuracy. Max details how the success of AlphaFold provided the first proof of concept that a deep learning model could generalize in the field of biology, and how an AI model’s compounding of learnings allows it to achieve generalizability with scale, thus greatly increasing the probability of success as well as speed in real world clinical trials. He also talks about evolving past the need for animal testing through in-silico models, Isomorphic’s focus on developing drug design engines that are agnostic of disease area, target class, and modality, why accurate molecule engineering unlocks the potential for multidrug cocktails that span complex biological pathways, and how he believes AI molecule optimization will improve both human lifespan and healthspan.

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Chapters:

00:00 – Trailer

00:45 – Max Jaderberg Introduction

01:24 – Isomorphic Origins

05:23 – Drug Discovery Stagnation

11:49 – Hiring at Isomorphic

14:35 – Bringing Drug Discovery Onto Computers

19:31 – Isomorphic’s Datasets

20:30 – Rethinking Endpoints

22:50 – External vs. Internal Dependences

23:57 – Automating Biologic Labs

26:22 – Internal Development vs. Partnerships

27:19 – Areas of Human Health Focus

29:21 – Multidrug Approaches

32:17 – Risk Appetite Variation

34:26 – Disease Treatment Outlook

35:42 – Human Longevity and Drug Development

39:16 – Conclusion

What does it mean to build wealth as an individual investor in today’s society, especially as gains continue to get concentrated into smaller groups of VCs or institutional investors? Robinhood’s CEO and co-founder Vlad Tenev has a vision that can be summed up in one word: Ownership. In this episode, Sarah Guo chats with Vlad to get a closer look at how Robinhood has evolved from a simple app to buy stocks at zero commission into a global conglomerate that allows customers to own across a wide variety of surfaces and asset classes. Vlad explains how Robinhood is helping users extend ownership amidst the current economy and ongoing AI revolution through Trump accounts, tokenization of privates, and newer product offerings like Robinhood Chain, Agentic Trading, and Agentic Credit Card. He also talks about how his new mathematical superintelligence startup, Harmonic, is revolutionizing the field and how he hopes it will decentralize new discoveries and inventions. Vlad and Sarah also discuss how prediction markets could tie into the ownership conversation, whether financial infrastructure can be optimized to enable even higher volumes of high-frequency trading, and his advice for maintaining a level head when facing the vagaries of a changing financial market.

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Chapters:

00:46 – Intro to Vlad Tenev, His Background, and Robinhood Today

12:25 – A Revolutionary Future for Mathematics

18:49 – Robinhood’s Vision for Tokenization

26:06 – Monetizing Knowledge via Prediction Markets

28:48 – Impacts of Agentic Trading and Democratizing Trading

33:48 – Safeguarding Financial Infrastructure for a New Load of HFTs

38:30 – Ownership of Private Companies in the AI Revolution

48:34 – Robinhood and Trump Accounts

50:47 – Keeping a Steady Hand and Head as a Leader

53:48 – Conclusion

What happens when you put code-generation agents in the hands of enterprise IT teams? With Serval, you get a system where it's easy for everyone to be a builder. Sarah Guo and Elad Gil sit down with Serval co-founder and CEO Jake Stauch to analyze his company’s direct challenge to ServiceNow as the enterprise automation and service management system of record. Jake explains that by building an AI platform for enterprise IT departments with Serval, users can safely build while still safeguarding permissions by strict deterministic approvals and an audit trail. He also talks about ProServal, the company’s team that embeds automation engineers directly with customers in order to provide ongoing support and service. Jake also discusses how AI was the big unlock to taking on ServiceNow, why he’s betting on coding agents getting even better, why consumer is a window into what will happen next in enterprise and why the SaaS-pocalypse might look more like a consolidation, why background agents are still a moonshot in enterprise software, and the importance of hiring product-oriented engineers.

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Chapters:

00:47 – Jake Stauch Introduction and Serval’s Background

03:00 – Expanding Coding Agents Beyond Engineers

04:40 – Limitations with Personal Agents in Enterprise

06:29 – Deploying Serval Effectively in Large Orgs

07:55 – Day 1 Ambition and Attacking Enterprise Systems of Record

09:51 – Taking on ServiceNow with Workflow Automation Platform

11:01 – Betting on the Evolution of Coding Agents and Building Guardrails in Enterprise

13:48 – Big Risks, Big Rewards, and Big Predictions for Serval and Coding Agents

16:40 – Adopting Different Sets of Building Principles at an Enterprise Level

20:49 – Token Maxing, Long-Term Budget Impacts, and ROI

22:39 – The SaaS-pocalypse and SaaS vs. AI

24:39 – The Mental Model for Delivering Platform-Level Power to the IT User at Enterprise

26:43 – Building Automations with Catalyst and Raising Technical Competence

28:55 – The Next Wave: Background Agents and Autonomy in Enterprise

32:23 – Hiring Velocity and Unlimited Ambition at Serval

34:17 – Keeping Pace with Customers and Fundamental Shifts in Product Function

37:05 – Conclusion

In the transition to the agentic web, security has become an afterthought. Now, organizations are racing to figure out what AI security means, and how to keep their data safe. Live from Cloudflare Connect, Sarah Guo sits down with Cloudflare CTO Dane Knecht to discuss the changing attacker landscape, and how his company is allocating its engineering resources to meet this emerging security challenge. Dane explains how enterprises that have moved away from castle-and-moat infrastructure to more of a zero trust infrastructure for security are in better positions than those who haven’t. He also discusses the challenges of patching 70 years of software and why the software development lifecycle will need to change for security for the agentic web, why he sees token spend in the next 18 months primarily going towards security rather than engineering, how the current web is in a ‘messy middle’ state where humans and agents are riding the same ‘rails’ on the internet, Cloudflare’s conference monetization gateway release as well as its Signals project with OpenAI, the rationale behind the release of Cloudflare OS, and the thinking behind a recent company restructuring. Plus, why Cloudflare hired one thousand interns, and why curiosity about AI is required to be successful at Cloudflare.

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Chapters:

00:00 – Trailer

00:55 – Cloudflare CTO Top Priorities

05:03 – Today’s Attacker Landscape

06:16 – State of Internet Infrastructure

07:31 – Shift Towards Security Spend

08:39 – Road to Better Software and Internet Security

09:50 – Building Secure Production Software

11:41 – Reducing Surface Area

12:54 – Near Future of the Web

17:27 – Monetization and Signals

19:13 – Success for Content Creators

20:07 – Workplace OS and Use Cases

24:07 – Restructuring for AI-First

27:11 – Token Budgeting

29:32 – Cloudflare’s Intern Strategy

33:39 – Importance of Curiosity

35:29 – Attacking the Legacy Software Security Problem

37:08 – Engineering Team Prioritization

38:25 – Open vs. Closed Models

39:43 – Democratizing Security for Everyone

41:29 – Conclusion

Adversaries are already running AI agents, and the fastest attacks now play out in seconds. Sarah Guo and Elad Gil sit down with CrowdStrike president and CEO George Kurtz on what AI-equipped attackers mean for enterprise security. George explains how agents are flattening the adversary pyramid by putting advanced tradecraft in the hands of low-skill attackers, and why CrowdStrike and Nvidia are building a frontier-style lab of their own to train security models. They also get into how open-weight and closed models compare on security, where CrowdStrike is directing its platform investment, lessons from the recent Hugging Face incident, and why George sees AI, like every platform shift before it, as a net creator of opportunity.

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Chapters:

00:00 – Trailer

00:59 – George Kurtz Introduction

01:24 – Agentic Landscape

02:45 – AI and Adversaries

04:51 – Open Weight Models and Adversarial Attacks

06:37 – Adversary Geography

08:34 – Attacker Capability in Context

11:24 – Hugging Face Takeaways

13:59 – CrowdStrike Frontier Security Models

16:32 – CrowdStrike Key Offerings

19:51 – What Differentiates CrowdStrike’s Models

23:57 – Near Term State of Security World

25:46 – State of Model Distillation

27:53 – Open Weight Model Importance and Cost

29:59 – Assessing Real Agent Threat

32:40 – Areas to Watch in AI

35:02 – Case for Defense Dominance

39:15 – Executive Advice

43:16 – Shifts Create Opportunity

45:27 – Conclusion