No Priors: Artificial Intelligence | Technology | Startups

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

Technology has always been the fundamental driver of improvement for human quality of life. And with the recent rollout of advanced frontier models, like OpenAI’s GPT-6, humanity can now massively accelerate scientific discovery for its benefit. Sarah Guo and Elad Gil are joined by OpenAI President and co-founder Greg Brockman to discuss the company’s rapid progress across frontier models, cybersecurity, and scientific discovery. Greg shares how nearly 400 century-old math problems have been solved using its AI agents, and how the technology has the potential to transform other fields like biology in the realm of drug discovery. He also talks about how proactive, always-on AI agents like Dots impact modern productivity, why he believes the best UI is no UI at all, how cybersecurity is in a critical window of time to use peak intelligence to find and fix vulnerabilities in systems, and why OpenAI remains committed to keeping humans in the driver’s seat even as models surpass us in capability.

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Chapters:

00:42 – Trailer

00:59 – Greg Brockman Introduction

01:11 – AI and Advancements in Mathematics

05:55 – AI Communication and Oversight

07:45 – AI for Overcoming Biology Data Bottlenecks

11:28 – Revolutionizing Drug Discovery

14:18 – Changing Regulations to Accelerate Progress

16:51 – Dots the Proactive Agent

19:19 – Unifying Work and Consumer Agents

22:02 – How UI Will Evolve

26:08 – Cybersecurity in the AI Age

32:55 – Shift in Security Investment

35:49 – OpenAI’s Human-Centric Ethos

38:28 – Conclusion

Is the future of frontier AI open or closed? ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo and Elad Gil to discuss the launch of Beam, the company’s 500 billion parameter open-weight reasoning model. Misha breaks down the pre-training and reinforcement learning required to produce Beam’s reasoning efficiency, the shift in enterprise compute from renting to owning intelligence, and how he believes that open models will capture the majority of global token demand. He also talks about the open model ecosystem in China and why competition with China’s models is a good thing, safety considerations around open models, and how frontier-level open models may accelerate the pace of scientific discovery.

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Chapters:

00:42 – Misha Laskin Introduction

00:59 – Latest with ReflectionAI

02:07 – Challenges Building an Open Model

03:56 – ReflectionAI’s Agentic Shift

07:37 – Resources for Model Training

10:09 – Scaling Efficiency

12:18 – Training Beam

16:51 – Where Model Value Comes From

19:55 – Beam’s Reasoning Efficiency

22:35 – Monetizing Open Weight Models

25:03 – Future of Open Versus Closed Tokens

31:29 – Competition with Open Models

34:38 – Chinese Open Source Model Ecosystem

38:40 – Will Chinese Models Remain Open

44:44 – Safety and Open Models

53:02 – Debating Access to Powerful Tools

57:16 – AI and Scientific Progress

01:00:36 – Data Centers and Jobs

01:02:07 – Beam and Scientific Research

01:05:37 – Research Head Count to Compute Ratio

01:10:29 – Conclusion