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No Priors: Artificial Intelligence | Technology | Startups
Conviction
At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com.
Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade
Is the tech industry moving too quickly, or are founders letting fear of AI labs stunt their ambitions? Sarah and Elad explore the current landscape of artificial intelligence, venture capital, and startup dynamics. They discuss the realities of building multi-trillion-dollar companies, shifting market sizes and outcome-based pricing models, and how founders are reacting to the rise of major AI labs. They also talk about what the framework for startup exits should look like, the potential for researcher burnouts in the next eighteen months as ASI looms on the horizon, bottlenecks for compute, and the impact of regulatory capture and shifting ecosystems from California to Texas.
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Chapters:
00:00 – Cold Open Trailer
00:31 – Episode Introduction
01:44 – The Next Trillion-Dollar Company
03:12 – Tech Waves as Punctuated Equilibria
04:42 – TAM vs. Revenue Reality
07:14 – Market Size vs. Speed
10:32 – When Founders Should Sell
14:04 – Financing and Time Cost
17:57 – RSI and the Looming Promise of ASI
21:49 – Compute Power Laws
28:12 – Regulations and Disruption
33:06 – Beyond Transformers
34:26 – Tradeoffs - Safety vs. Progress
39:11 – Conclusion
When your AC fails in a heatwave, you don’t want a busy signal; you need a solution. Netic founder and CEO Melisa Tokmak joins host Elad Gil to explain how Netic’s autonomous AI platform acts as an intermediary between companies and customers, deploying agents to instantly handle essential services, from emergency home repairs to hospitality to pet care. Melisa describes the complexity of these real-world workloads, which have traditionally relied on large human support teams, and how over 70% of Netic’s customers interact first with AI. She also talks about the reasoning behind building a scalable product company rather than an AI roll-up, why she believes robotics will not catch up in these industries in the near future, why she doesn’t view large frontier labs as competitive threats, and how private equity’s playbook has shifted toward measurable ROI in the AI-era. Plus, why Melisa is optimistic about the impact AI will have on education.
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Chapters:
00:00 – Melisa Tokmak Introduction
00:32 – What Netic Builds
03:53 – Automating Workflows for Essential Services
06:26 – Building a Service vs. AI Roll-Up
10:38 – AI for the Real World Timeline
12:56 – Can Big Labs Compete?
15:35 – Modern Founder Mindset
19:09 – Screening for Agency
22:25 – Five Year Vision
23:53 – Selling to Slow Industries
27:23 – How Private Equity Approached AI
31:14 – What Excites Melisa About the Future of AI
34:27 – Conclusion
DoorDash is not just a delivery company. From its inception, co-founders Andy Fang and Stanley Tang operated it as a robotics and autonomy company. Andy and Stanley join Sarah Guo to explain how autonomous tech and AI are reshaping consumer habits, commerce, and delivery. Andy and Stanley talk about the rollout of Ask DoorDash, a natural-language interface that’s driving both restaurant discovery and larger grocery orders. They also discuss Dot, their in-house autonomous delivery robot that has operated in Phoenix for over two years, and how it highlights the operational and hardware challenges they have faced and solved in autonomous tech. Andy and Stanley also speak about the “first and last 100 feet problem” in autonomous delivery, why multimodal strategies are the key to success, scaling autonomy and operations, and why they believe that more Dashers, not fewer, are the future of DoorDash.
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Chapters:
00:00 – Andy Fang and Stanley Tang Introduction
00:34 – Agentic Commerce and Behavioral Changes
03:52 – Next Steps for Ask DoorDash
06:54 – Investing in Robotics and Autonomy
16:31 – Building Autonomous Tech in the Physical World
21:20 – Dot: DoorDash’s Autonomous Delivery Robot
22:08 – Collecting Realistic Data
25:48 – Why Work at DoorDash
28:04 – Challenges in Scaling Up Autonomy
39:30 – Productivity Benchmarks
44:56 – Future of Agentic Commerce
49:10 – Conclusion
When Glenn Fogel joined Priceline in 2000, the business was worth a few hundred million dollars. One week later, the Nasdaq peaked, eventually sending its stock down to a dollar a share. But over 25 years later, Booking Holdings has scaled over 1000x into an over $100 billion dollar global travel behemoth. Elad Gil is joined by Booking Holdings CEO Glenn Fogel to discuss his career, from law school and Wall Street to working at Priceline through the dot-com crash, and to helping grow the business into a multifaceted, dynamic travel marketplace in the AI era. Glenn explains how leveraging AI and agents such as Priceline’s ‘Penny’ makes travel planning and customer service better, while emphasizing the importance of preserving some human support for some users. He also talks about Booking’s strategy of reinvesting over $700 million into AI and other technologies while still offering stock buybacks and dividends, the durability of their scale and complexities of dealing with a large portfolio physical properties across the world, and why upskilling is so important for employees amid concerns about AI-driven job displacement.
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Chapters:
00:00 – Cold Open
00:05 – Glenn Fogel Introduction
00:41 – Glenn’s Early Career
06:49 – Lessons from the Early Internet
09:24 – Deciding Factors for Exiting
10:56 – Travel Through the Lens of AI
13:30 – Agentic Travel Planning
18:59 – Agents, Token Economics, and ROI
22:46 – Booking’s Capital Investment Philosophy
25:23 – Scale as Durable Asset
29:40 – Purpose and Choosing Wisely
33:18 – AI’s Impact on Jobs
36:38 – Upskilling in the AI Era
38:36 – Public Perception of AI
40:24 – Conclusion
While the rest of the nuclear industry still relies on simulations and paper designs, Valar Atomics is busy splitting atoms. In fact, they just powered an NVIDIA Blackwell chip directly with a live nuclear reactor in order to power the world’s first nuclear powered website. Sarah Guo joins Valar Atomics founder and CEO Isaiah Taylor on-site at their reactor site in Utah to talk about how Valar is shifting nuclear energy from the theoretical to the practical by building and perfecting reactors via hardware iteration. Isaiah discusses why the US stopped building nuclear reactors in the 1970s, and how Valar utilized a little-known pathway via the Department of Energy, revived by a Trump administration executive order, to successfully develop and run their advanced reactor. He also shares Valar’s strategy for vertical integration, their venture-backed approach to financing, their giga-site plans, and why he believes cheap, abundant atomic energy has the power to vastly improve the quality of human life.
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Chapters:
00:00 – Cold Open
00:57 – Isaiah Taylor Introduction
01:30 - Valar’s Mission and Origin
04:24 - Why Nuclear Development Stalled
07:18 - Reviving Nuclear through DoE and Executive Order
10:59 - Control Room Tour
16:17 - Misunderstandings About Nuclear
20:07 - Issues with Reliability
22:14 - Nuclear is a Hardware Execution Problem
24:32 - Timeline to Scale Production
26:32 - Introducing Ward 250
30:42 - Speed Through Simplicity
33:33 - AI Drives Nuclear Demand
35:02 - Running a Reactor with NVIDIA Blackwell
36:27 - Valar’s Nuclear Conviction
40:16 - Verticalization as Path to Scale
43:58 - Valar’s Control Skid
48:00 - Venture-Backed Nuclear
50:51 - Gigasite Strategy
53:11 - CEO Tick Rate
55:37 - Abundant Energy and Hyper-Techno Industrialism
1:01:27 – Conclusion
When a new AI model drops, it’s judged based on a static benchmark grid that doesn’t account for how long the model is allowed to think. How then should we measure a model’s true capability? OpenAI research scientist Noam Brown returns to talk with Sarah Guo about his latest essay on why the AI industry’s traditional benchmark grids are broken, and how large-scale test-time compute is fundamentally changing how models are evaluated. Noam explains how, if properly scaffolded, today’s models can reason for weeks or even months on complex tasks. He also discusses real-world implications of test-time compute, from building poker solver bots to disproving legendary math conjectures. Together, they also unpack the large gaps in current AI safety frameworks, explore the bottlenecks for recursive self-improvement, and look ahead at the future of multi-agent collaboration and global knowledge sharing.
Read more: Implications of Large-Scale Test-Time Compute
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Chapters:
00:00 – Cold Open
00:43 – Noam Brown Introduction
01:23 – Why Benchmarks Are Broken
04:19 – Compute Budgets and Projections
05:34 – How Long Should Models Think?
06:47 – Benchmark-Maxxing
08:34 – Using Poker Bots as Evals
11:26 – Safety Evals When Model Capability Scales With Budget
14:41 – Release Cycle vs. Agent Runtime
17:06 – Latent Model Capability
20:59 – Limits on Recursive Self-Improvement
27:09 – Large-Scale Multi-Agent Coordination
29:11 – Competition at the Frontier
31:51 – Breaking the Benchmark Grid Equilibrium
33:29 – Why Benchmarks Should be Evaluated by Cost
36:18 – Conclusion
At 66 years old, instead of heading towards retirement, former Cadence CEO and legendary investor Lip-Bu Tan decided to take on the hardest job in tech: turning Intel around. Elad Gil and Sarah Guo sit down with Intel CEO Lip-Bu Tan to talk about why he took the job and what “saving” Intel actually looks like. Tan explains how his experience in startup culture informed his decisions to drive Intel’s culture towards faster decisions, focus on customer satisfaction, and engineer accountability. He also discusses his strategy to strengthen Intel’s balance sheet by welcoming investments from Jensen Huang’s Nvidia, Softbank, and the US government. Tan also shares his product roadmap that centers the CPU for agentic AI and inference, the collaboration with Elon Musk on Terafab, his investing framework for semiconductors, and his views on how AI is reshaping design and operations at, as he puts it, a ‘legacy spreadsheet’ tech company.
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Chapters:
00:00 – Cold Open
01:01 – Lip-Bu Tan Introduction
01:24 – Why Lip-Bu Took the Reins at Intel
03:00 – Fixing Culture
04:08 – Intel’s 10-Year Vision
07:57 – Working with Elon Musk on Terafab
09:59 – Shifting Supply Chain for Semiconductors
15:34 – Limits to Scaling and Packaging
18:30 – Physical Limits to Engineering and Design
20:33 – Challenges in Semiconductor Investing
26:29 – Lessons from Cadence
28:02 – Scaling and Investment Decisions
32:03 – Rethinking Teams in AI Era
34:31 – Industrial Policy and Funding
37:25 – What Investors Misunderstand About Intel
41:10 – Where Compute Will Live
44:59 – Conclusion
Biohub started with an ambitious goal of curing, preventing, and managing all disease by the end of the century. A decade later, thanks to the convergence of frontier AI and biological data, that goal may have been too conservative. In this episode, Elad Gil and Sarah Guo sit down with Biohub co-founders Mark Zuckerberg and Priscilla Chan, alongside Biohub Head of Science Alex Rives. Together, they discuss Biohub’s $500 million virtual biology initiative, which integrates frontier AI with wet-lab work to build predictive world models of cells, proteins, and systems. They also talk about their newly announced open-source engine for digital protein and antibody design, ESMFold2; why Biohub is a nonprofit rather than a venture-backed startup; and how hierarchical simulations will soon allow doctors to treat patients at an individual, mechanistic level.
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Chapters:
00:00 – Cold Open
01:02 - Mark Zuckerberg, Priscilla Chan, and Alex Rives Introduction
01:26 – Why Biohub and Their Mission
08:27 – Integrating Frontier AI and Frontier Biology
09:45 – Micro to Macro Biological Modeling
14:22 – Mechanistic Interpretiability
16:58 – Why Biohub is a Non-Profit
21:41 – Understanding How Biology Works
24:23 – Timeline for Curing All Diseases
26:25 – Translating Research to Patient Impact
28:04 – Launch of ESMFold2
32:13 – Tackling Off-Target Effects and Edge Cases
38:39 – Putting the Tech in Individual Hands
41:06 – Talent at Biohub
44:25 – What’s Next After ESMFold2
46:10 – Connecting ESMFold2 to Agentic Systems
46:51 – The Virtual Cell
49:33 – Defining Success for Biohub
51:52 – Biohub Strategy Update
56:20 – Conclusion
What does it mean for a business to truly operate at the AI frontier? In a special crossover episode at Microsoft Build, Sarah Guo and Elad Gil team up with Latent Space host “swyx” to talk with Microsoft Chairman and CEO Satya Nadella about the future of AI platforms, software development, and the tech ecosystem. Satya reflects on the latest breakthroughs from Microsoft Build, the strategic shift toward multi-model harnesses, and why private evaluations (evals) are now a company’s most important intellectual property. They also discuss how autonomous AI agents are reshaping the role of software engineers, the durability of SaaS business models, and why showing communities the ROI on data centers is so critical. Plus, Satya shares his thoughts on the economic and societal impacts of the token economy, as well as the future of AI-driven education startups.
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Chapters:
00:00 – Satya Nadella Introduction
01:48 – Reflections from Microsoft Build
03:12 – Microsoft’s AI Training Strategy
05:48 – Complexity of Real-World Deployment of AI
07:33 – Augmenting Human Capital
09:37 – Harnesses for Enterprise
11:49 – Developer Value
15:09 – Can Everybody Operate at the Frontier with Their Frontier Intelligence?
15:51 – Modern Definition of IP
17:38 – Future of Vendor vs. Enterprise Agents
21:48 – Near-Term Predictions on Model Pricing
24:02 – Durability of SaaS
25:58 – What Satya’s Building
28:18 – Future of Engineering Roles
30:54 – How Microsoft Can Be More Ambitious
34:36 – Data Centers and Community Impact
38:01 – AI’s Impact on Society
39:52 - AI and Education
42:28 – Conclusion
What does it mean for a business to truly operate at the AI frontier? In a special crossover episode at Microsoft Build, Sarah Guo and Elad Gil team up with Latent Space host “swyx” to talk with Microsoft Chairman and CEO Satya Nadella about the future of AI platforms, software development, and the tech ecosystem. Satya reflects on the latest breakthroughs from Microsoft Build, the strategic shift toward multi-model harnesses, and why private evaluations (evals) are now a company’s most important intellectual property. They also discuss how autonomous AI agents are reshaping the role of software engineers, the durability of SaaS business models, and why showing communities the ROI on data centers is so critical. Plus, Satya shares his thoughts on the economic and societal impacts of the token economy, as well as the future of AI-driven education startups.
Sign up for new podcasts every week. Email feedback to show@no-priors.com
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @satyanadella | @Microsoft | @latentspacepod | @swyx
Chapters:
00:00 – Satya Nadella Introduction
01:48 – Reflections from Microsoft Build
03:12 – Microsoft’s AI Training Strategy
05:48 – Complexity of Real-World Deployment of AI
07:33 – Augmenting Human Capital
09:37 – Harnesses for Enterprise
11:49 – Developer Value
15:09 – Can Everybody Operate at the Frontier with Their Frontier Intelligence?
15:51 – Modern Definition of IP
17:38 – Future of Vendor vs. Enterprise Agents
21:48 – Near-Term Predictions on Model Pricing
24:02 – Durability of SaaS
25:58 – What Satya’s Building
28:18 – Future of Engineering Roles
30:54 – How Microsoft Can Be More Ambitious
34:36 – Data Centers and Community Impact
38:01 – AI’s Impact on Society
39:52 - AI and Education
42:28 – Conclusion
We are now closer than ever before to living in a world where AI agents are smart enough to run our power grids and manage water supplies. How do we keep them from going rogue? Sarah Guo sits down with Maxim Bar Kogan, founder and CEO of Onyx Securities, to explore the complexities of supervising and securing autonomous agents at the enterprise level. Maxim explains Onyx’s product as an AI control plane, which oversees the permissions and flexible contexts of agents while balancing latency, cost, and reliability. He also discusses how current controls have insufficient context to monitor agent intent, tradeoffs for gradual model rollout, the need for vendor-independent oversight, and Israel’s growing AI and security talent ecosystem. Plus, why Maxim is all-in on AGI.
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Chapters:
00:00 – Cold Open
00:45 – Maxim Bar Kogan Introduction
01:10 – AutoGPT and Betting on Agent Actions
05:17 – What Onyx Product Does
07:47 – State of Deployment in Large Enterprises
09:58 – Securing Agents
12:45 – Why Proxies Don’t Work
14:11 – Why Onyx Trains Its Own Models
18:38 – Onyx’s Talent Culture
21:24 – Mechanistic Interpretability
23:35 – How Onyx Builds Customer Trust
25:10 – Mitigating Risk at the Foundational Level
27:45 – Phased Rollout of Glasswing and Daybreak
29:11 – Large Enterprise Holdouts
30:46 – Onyx and the Larger AI Security Space
32:36 – Should Labs Address Model Trust and Governance?
36:56 – What Needs to Happen in Security
39:14 – Why Maxim is AGI-Pilled
41:15 – Conclusion
Companies in Silicon Valley from Nvidia to AMD are racing to fuel the AI revolution with postage stamp-sized AI chips. Meanwhile, a chip the size of a dinner plate just fueled a $63 billion IPO for Cerebras. Elad Gil and Sarah Guo sit down with Cerebras founder and CEO Andrew Feldman to discuss the company’s journey to making one of the largest tech go-publics in history. Andrew details the multi-year journey of pioneering wafer-scale AI computing, including surviving a brutal period of being ahead of market demand. He also explains the engineering breakthroughs that led to delivering inference speeds at 20x that of standard GPUs. Andrew then shares how a remarkable $20 billion deal with OpenAI came together in only four weeks. Plus, Andrew’s thoughts on why architecting the future of AI requires the fortitude to be a “professional David” against the Goliaths of tech.
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Chapters:
00:00 – Cold Open
00:36 – Andrew Feldman Introduction
01:19 – Cerebras’ Evolution
02:48 – Wafer-Scale Bet Pays Off
06:38 – Challenges and Breakthroughs
08:37 – Crossing the Market Chasm
10:38 – Scaling Software and Hardware
12:03 – Relevance of AI-Generated Coding
13:31 – Leadership and Hiring Culture
17:16 – When to Quit vs. Persist
19:40 – Why Cerebras Went Public
22:57 – The OpenAI Deal
25:54 – Open Source and Post-Trained Workloads
27:37 – How Speed Opens Up New Business
30:33 – Conclusion
Securing AI dominance requires more than just semiconductors; it demands a complete overhaul of how the West manages everything that goes into them, from rare earth minerals to actuators. Enter: Pax Silica. Sarah Guo and Elad Gil sit down with US Under Secretary of State for Economic Affairs Jacob Helberg to discuss the launch and expansion of Pax Silica, a 14-country economic security coalition designed to secure the entire AI supply chain. Jacob talks about the creation of a forward-deployed industrial base in the Philippines, where 4,000 acres will be developed into an “economic security zone.” He also compares and contrasts Pax Silica with China’s Belt and Road initiative, explains how the US plans to reindustrialize through automation and robotics, and explores how the Trump administration envisions making these policies durable across future presidencies. Plus, we hear why Jacob believes America to be a “global underdog.”
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Chapters:
00:00 – Cold Open
00:41 – Jacob Helberg Introduction
01:02 – Pax Silica’s Mission
03:51 – Investing in AI Chip Supply Chains
05:43 – Comparing Pax Silica to China’s Belt and Road Initiative
12:38 – Pax Silica’s Value Proposition
14:38 – US vs. Partnered Manufacturing
19:10 – Rare Earth Mineral Pricing
22:16 – Role of Venture Capital in Pax Silica
24:50 – Near vs. Long-Term Priorities
27:09 – Making AI Policy Durable
28:09 – How Policies Impact Entrepreneurs
31:00 – Trump’s Entrepreneurial Administration
33:00 – Why America is a Global Underdog
38:00 – Conclusion
The world’s first AI-take-private just proved that AI can revolutionize the real economy. Long Lake Management co-founder and CEO Alexander Taubman joins Elad Gil to discuss his firm’s agreement to acquire the legacy platform American Express Global Business Travel (Amex GBT) in a deal valued at $6.3 billion. Alexander explains the mechanics of AI-driven roll-ups, and why Long Lake chooses to acquire and transform businesses rather than simply selling them software. He also talks about how Long Lake’s horizontal AI platform, Nexus, automates workflows across diverse verticals, and how automation through AI not only powers growth for their portfolio companies, but results in both satisfied customers and employees. Plus, they explore Alexander’s vision of Amex GBT as a multi-decade compounding machine.
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Chapters:
00:00 – Alexander Taubman Introduction
00:30 – Long Lake’s Nexus Platform
03:35 – Retention and Talent Flywheel
05:01 – Acquisition vs. Offering Software
06:57 – Building Long Lake’s Founding Team
10:37 – Taking American Express Global Business Travel Private
13:36 – Taking Berkshire Hathaway’s Approach to Management
16:37 – How AI Strategy Makes Long Lake Stand Out
19:32 – AI Makes Services Scale
22:00 – Conclusion
Baseten CEO and co-founder Tuhin Srivastava sits down with Sarah Guo and Elad Gil to discuss the rapid growth of AI inference demand, Baseten’s 30x growth, and why inference is becoming the strategic “last market.” Tuhin Srivastava argues the application layer will persist because companies with unique user signals can encode value into workflows and post-train specialized models, citing examples like Abridge and support workflows. The conversation covers GPU capacity constraints, Baseten’s multi-cloud fabric across 18 clouds and 90 clusters, long-term contracting dynamics, the importance of the software layer for stickiness, evolving workloads, multichip possibilities, and operational lessons at scale.
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Chapters:
00:31 Baseten growth
01:55 Why the app layer wins
05:57 Serving frontier customers
07:55 Open source model mix
09:21 Chinese models and geopolitics
13:07 Custom inference dominates
14:22 Post training acquisition
17:10 When to invest in custom models
18:35 Supply crunch and data centerse
22:25 Longer GPU Contracts
24:09 What Makes a Winner
26:07 Multi Chip Future
28:19 Runtime Roadmap
31:08 Scaling Edge Cases
33:48 Hiring and Leadership
36:44 Operations Pager Culture
38:19 Efficiency Drives Demand
40:41 Concierge Everything Future
42:34 Conclusion
More than fifty years ago, the modern idea of the standard enterprise software was birthed at SAP. Now, after managing companies through technological shifts from the mainframe to mobile, SAP is at the forefront of closing the AI adoption gap for their customers. SAP Chief Technology Officer Philipp Herzig joins Sarah Guo to talk about how SAP has remained a durable end-to-end “operating system” for its more than 400,000 customers from finance to supply chain. Philipp argues that the AI transition in businesses should focus on customer outcomes, UI changes, business processes, and the data layer. He also explains the challenges in enterprise AI adoption, including security, scaling, and data fragmentation, as well as the importance of evals and verifiability. They also discuss SAP’s suite of AI products, limitations of predictive tabular models, how SAP is shifting its pricing models in the AI era, and Philipp’s interest in quantum computing optimization.
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Chapters:
00:00 – Cold Open
00:42 – Philipp Herzig Introduction
01:18 – What SAP Does
02:51 – Why SAP Endures
06:53 – CTO Priorities and AI Push
12:14 – Scaling AI in Enterprise
17:06 – Verifiability and Agent Mining
20:42 – Tool Calling vs. Computer Use
22:11 – Domains Where Agents Deliver Value
24:58 – Limitations of Predictive Tabular Models
29:07 – Barriers to Enterprise Adoption
31:54 – How AI Will ‘Uplevels’ Work
34:03 – How AI Changes SAP’s Pricing Model
36:41 – What Makes a Winner in the AI Era
38:53 – Day in the Life of a CTO
40:08 – Customer Challenges
42:36 – Business Problem of Quantum Computing
46:21 – Conclusion
Few teens are business owners, but by age 16, Bill McDermott had purchased and was running a local deli. Now he runs leading global technology powerhouse ServiceNow, a company that is defining how the world’s largest organizations transform for the digital age. Sarah Guo sits down with ServiceNow CEO Bill McDermott to discuss his journey from child entrepreneur to CEO, and how he navigates his role as a leader in the age of AI. Bill argues that human connection is still a vital part of being a successful leader, and as such, AI must be used to serve people rather than substitute for ambition. He breaks down the mechanics of hyper-growth, and the art of staying customer-centric at a global scale. They also discuss the future of enterprise software, how generative AI is fundamentally reshaping the labor market, and what founders need to know about building a resilient company culture that survives economic and technological shifts.
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Chapters:
00:00 – Cold Open
00:50 – Bill McDermott Introduction
01:14 – Lesson from Buying a Deli
07:35 – Leadership in the AI Era
09:41 – How Bill Got Hired at Xerox
15:47 – Can Agency Be Taught?
18:40 – Seeing Change as Opportunity
25:18 – ServiceNow as an AI Control Tower
30:30 – Which SaaS Gets Disrupted?
32:22 – Defining a Platform Business
36:25 – Does AI Decrease Implementation Time?
39:06 – Agents Will Reshape the Workforce
40:59 – Success Signals at ServiceNow
44:07 – Enterprise Attitudes About AI
48:41 – How AI Has Changed Customer Conversations
50:48 – Bill’s Curiosity Beyond ServiceNow
52:29 – Day in the Life of a CEO
57:27 – Conclusion
AI agents can already collaborate, but they lack a trustworthy medium in which to store value and execute contracts. Enter Circle’s Arc Blockchain, an economic “operating system” designed for a world where machines drive the real economy. Circle co-founder and CEO Jeremy Allaire joins Elad Gil to dive into the future of programmable money and the agentic economy. Jeremy explains why traditional banking fails to support the needs of AI agents, and how stablecoins like USDC facilitate an internet-native economy. They also discuss the tokenization of real-world assets, the move toward full-reserve banking, and Jeremy’s predictions for double-digit GDP growth as AI and blockchain reach their “broadband moment.”
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Chapters:
00:00 – Cold Open
00:05 – Jeremy Allaire Introduction
00:21 – Origin Story of Circle
02:11 – Rethinking the Financial System
05:26 – The Role of Stablecoins
09:52 – Use Cases for USDC
11:30 – Programmable Money
12:25 – Blockchain as Operating System
14:37 – The Agentic Economy
17:45 – Arc Blockchain Use Cases
27:00 – Scaling Models and Privacy Tech
30:45 – Securitization of Other Assets Under the Blockchain
34:16 – Prediction Markets
35:09 – Incremental Revenue Through GPU Usage
37:19 – Jeremy’s 10 Year Future Vision
41:12 – AI and GDP
44:00 – Conclusion
What happens when you apply the scaling laws of large language models to the physical work of atoms? Elad Gil sits down with Liam Fedus, co-founder at Periodic Labs, which is pioneering an AI foundation lab for atoms. Liam discusses how he pivoted from dark matter physics research to the front lines of artificial intelligence, including stints at Google Brain and working on ChatGPT at OpenAI. He talks about how Periodic is connecting massive language models to the physical world to overcome data bottlenecks in material science. Liam also shares how they use language models as an orchestration layer operating alongside specialized neural nets to run closed-loop physical experiments. They also explore the future of AGI and ASI, as well as the role of robotics in lab automation.
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Chapters:
00:00 – Cold Open
00:05 – Liam Fedus Introduction
00:39 – Liam’s Background at Google Brain, OpenAI
05:14 – From ChatGPT to Materials and Atoms
06:34 – Training Data in the Physical World
09:52 – Generalization Across Domains
11:31 – Models as an Orchestration Layer
12:48 – Commercialization and Business Model
16:10 – How Periodic’s Success May Shape the Future
17:45 – Multidisciplinary Scaling
19:41 – Capital and Compute
21:12 – Hiring at Periodic
21:44 – Thoughts on AGI and ASI
23:30 – Timeline for Machine-Directed Self-Improvement
25:39 – Automation and Data Generation
27:59 – Why Liam is Excited About the Future of Robotics
29:25 – Conclusion
What happens when AI agents can design experiments, collect data, and improve — without a human in the loop? Andrej Karpathy joins Sarah Guo on the state of models, the future of engineering and education, thinking about impact on jobs, and his project AutoResearch: where agents close the loop on a piece of AI research (experimentation, training, and optimization, autonomously).
00:00 Andrej Karpathy Introduction
02:55 What Capability Limits Remain?
06:15 What Mastery of Coding Agents Looks Like
11:16 Second Order Effects of Natural Language Coding
15:51 Why AutoResearch
22:45 Relevant Skills in the AI Era
28:25 Model Speciation
32:30 Building More Collaboration Surfaces for Humans and AI
37:28 Analysis of Jobs Market Data
48:25 Open vs. Closed Source Models
53:51 Autonomous Robotics
1:00:59 MicroGPT and Agentic Education
1:05:40 Conclusion
Notion isn’t designing AI agents that just use tools. Their agents can autonomously build their own integrations, as well as write the code needed to finish a task. Sarah Guo sits down with Notion Co-Founder Simon Last to explore Notion’s rapid evolution from a simple writing assistant to a sophisticated platform for custom AI agents. Simon discusses the technical hurdles of indexing disparate data from sources like Slack and Google Drive, as well as the internal shift toward using coding agents to build Notion itself. Plus, Simon elaborates on what he sees as a fundamental transition in productivity: moving from a tool where humans do the work, to one where humans manage a swarm of agents.
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Chapters:
00:00 – Cold Open
00:05 – Simon Last Introduction
00:26 – Genesis of Notion AI
04:10 – Challenge of Semantic Indexing and Retrieval
07:16 – The Six-Month Rewrite Cycle
08:12 – Notion’s Coding Agent Era
09:44 – Impact on Team Dynamics
12:49 – Launching Custom Agents
15:39 – Notion as the ‘Switzerland’ for Models
17:33 – Designing APIs for Agent Customers
20:09 – Simon’s Personal Agentic Workflows
24:48 – Notion: Tool for Work is Now A Tool for Agents
27:28 – How Building Has Changed for Simon
29:00 – Conclusion
By the end of 2026, AI capital expenditure is projected to hit nearly $700 billion. The question isn’t who has the best model, but who has the most creative financing to build out AI infrastructure and beyond. Sarah Guo is joined by Neil Tiwari, Managing Director at Magnetar Capital, a financial innovator helping the AI industry scale from billions to trillions of dollars in CapEx. Neil explains some of the debt structures used to finance massive GPU clusters, who is taking the risk, and how the industry is maturing. Sarah and Neil also discuss how power distribution, energy storage, and physical materials like steel are the bottlenecks of the AI industry. Plus, Neil gives his take on the future of inference-optimized clouds, and why the market shift away from software and into infrastructure might be an overreaction.
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Chapters:
00:00 – Cold Open
00:05 – Neil Tiwari Introduction
00:26 – Magnetar’s Story
01:28 – Why CoreWeave Helped Magnetar Win
06:15 – Scaling CapEx Efficiently
09:02 – Debunking GPU Collateral Risk
11:42 – How Deal Structures Evolve
13:01 – What Bottlenecks Buildout
15:28 – Circular Financing Critiques
17:35 – The Shift from Training to Inference Workloads
23:10 – AI Factories
24:12 – Constraints of the Current Power Grid
28:27 – Sovereign Compute Buildouts
29:54 – Physical AI Capital Needs
32:48 – The Capital Rotation Away from SaaS
36:04 – Conclusion
In this episode of No Priors, Sarah and Elad dive into the evolving landscape of software, exploring how AI is transforming the traditional SaaS model. They discuss whether SaaS as we know it is coming to an end, what new business and sales strategies are emerging, and how AI is reshaping the way software is built, sold, and scaled. The conversation also examines whether or not these shifts are a good thing for both big and small companies, and how coders and software experts are reacting to abrupt AI transitions. They also dig into how AI is reshaping sales, automating workflows, and enabling more predictive customer strategies. Beyond individual companies, they examine how tech giants are increasingly dominating the S&P 500, and what this concentration of power means for the future of startups, innovation, and the broader entrepreneurial ecosystem.
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Chapters:
00:00 – Cold Open
00:35 – The SaaS-polcalypse discussion
4:55 – AI Change Management in Large vs. Small Companies
05:43 – “Is Software Eating the World?”
08:38 – Addressing the Unsolved Problems
14:00 – The Noise of the Last Month vs. Excitement
21:32 – What Proportion of GDP is Tech?
23:20 – Market Cap Shifts
25:02 – As a Company, When Should You Sell?
29:05 – Multi-Product Bundle Defense
30:45 – Conclusion
Autonomous vehicle technology has moved past human-coded rules and into an era of neural networks and custom computer chips. And to solve the most difficult driving scenarios, electric vehicle company Rivian abandoned its original technology platform to build a vertically integrated data stack. Sarah Guo sits down with Rivian Founder and CEO RJ Scaringe to explore the seismic shift in the automotive industry toward AI-driven, software-defined vehicles . RJ discusses the move away from function or domain-based architecture for vehicle electronic systems to software-defined architecture, which allows for dynamic, monthly updates to features in Rivian’s vehicles. RJ also talks about the upcoming launch of Rivian’s R2 model, which aims to be a distinct, affordable, mass-market alternative to the Tesla Model Y. Plus, RJ shares his vision for a future where vehicles don’t just drive us, but inspire personal freedom and exploration.
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Chapters:
00:00 – Cold Open
00:35 – RJ Scaringe Introduction
0:58 – Rivian’s Autonomy Evolution
05:19 – Why Rivian’s Tech is Vertically Integrated
10:06 – Levels of Autonomous Driving Technologies
14:00 – Importance of a Software-Defined Architecture
19:28 – Differentiating Autonomous Vehicle Models
23:20 – R2: The First Mass Market Autonomous Vehicle
25:02 – Do Americans Want EVs?
29:05 – How Our Relationship to Vehicles is Evolving
30:45 – Conclusion
From “virtual doppelgängers” to “real-time dreaming,” online gaming platform Roblox is using AI technology to build the “Holodeck” envisioned in science fiction decades ago. Sarah Guo and Elad Gil sit down with Roblox CEO Dave Baszucki at Roblox headquarters to explore the intersection of AI, physics simulation, and the future of human connection. Dave discusses the evolution of the 4D creation tool in Roblox, a high-fidelity simulation that enables thousands of people to interact in real-time with photo-realistic graphics and acoustic physics. Dave reveals how Roblox is leveraging 13 billion hours of monthly user data to train native AI models that go beyond simple LLMs, enabling NPCs that can navigate and play games with human-like intuition. He also talks about how immersive communication will change video conferencing, how Roblox searches for unlikely talent outside of traditional elite universities, and how he balances rapid weekly iterations with keeping a “long view” on Roblox’s vision.
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Chapters:
00:00 – Cold Open
00:36 – Dave Baszucki Introduction
01:16 – Realizing Robolox’s 20-Year Vision
05:29 – Using 4D Immersive Simulations in Virtual Interactions
08:22 – Physics Engine vs. Photorealism
11:50 – Storing Roblox History as Vector Data
14:00 – Training NPCs - Moving Beyond LLMs
18:05 – The Future of the Game Designer
19:54 – Video Latent World Models
23:53 – Social Simulation - AI Companions and Virtual Relationships
27:26 – Why Asset Costs Haven’t Changed the Gaming Industry
29:52 – AI Coding in Roblox Studio
31:36 – The Roblox Creator Economy
33:57 – Long-Term Conviction vs. Weekly Iteration
37:50 – Dave’s Hiring Philosophy for Roblox
43:44 – Conclusion
What if we could pause biological time to wait for a cure for a disease? Thanks to innovations and research in reversible cryopreservation, this possibility is no longer just science fiction. Sarah Guo sits down with Laura Deming, CEO and co-founder of biotech startup Until, to dive deep into the growing field of reversible cryopreservation. Laura talks about how her time as a Thiel Fellow as well as her founding of the Longevity Fund fueled her obsession with solving the “social blindspot” of aging. Laura details how her new startup, Until, seeks to build tools that allow for “pressing pause” on biological time, starting with human organs with the hopes of scaling up to full body medical hibernation. Together, they also discuss why ice is the enemy of tissue, using engineering tools to help solve biological problems, and how this technology may revolutionize organ transplantation by removing time as a variable.
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Chapters:
00:00 – Cold Open
01:08 – Laura Deming Introduction
01:53 – Why Laura Focused on Cryo Preservation and Longevity
06:20 – Bringing on Co-Founder Hunter Davis
07:55 – Until’s Goal
10:10 – Other Use Cases for Cryo Technology
12:22 – Scientific Challenges in Cryo Tech
15:36 – Using Engineering Principles to Solve Biological Problems
20:18 – Scaling Up Cryo Preservation
21:48 – Leading and Recruiting at Until
25:02 – Why Hasn’t Cryo Tech Been Worked On More?
27:14 – Making Time Not a Variable in Organ Transplants
29:06 – Changing How the Molecular World is Depicted
30:47 – Conclusion
Why are there only a handful of companies in the world with over $10 billion in pure-play software revenue? CJ Desai believes the reason is that products are replaceable, but platforms are forever. For No Priors’ very first live from MongoDB.local SF, Sarah Guo is joined by CJ Desai, CEO and President of software developer MongoDB, to discuss the shifting landscape of enterprise software. CJ discusses whether AI will erode the value of software, and what truly constitutes a “moat” in the age of generative AI. CJ also talks about why AI adoption with Fortune 500-sized companies is still lagging, the importance of customer relationships, and why the “bear thesis” on SaaS may be overblown.
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Chapters:
00:00 – Cold Open
00:58 – CJ Desai Introduction
01:38 – The AI Stack and the Future of Software
04:18 – Why Platforms, Not Products, Are Sticky
09:59 – Vibe Coding and the Threat of On-Demand Apps
12:15 – Paths to Success for Software Vendor Incumbents
14:24 – How CJ Chose MongoDB
18:55 – Debunking the SaaS Bear Thesis
22:07 – Fortune 500 Perspectives on AI Value
24:24 – Can AI Native Startups Replace Systems of Record?
28:10 – The Importance of Customer Relationships
31:46 – Managing Through Massive Technology Transitions
36:37 – Conclusion
Today’s arms race looks a little different from those of the past. Under the Trump administration, the US Department of War (DoW) is deploying generative AI to millions of employees in order to maintain a strategic edge over our global adversaries. Sarah Guo and Elad Gil sit down with Emil Michael, the Under Secretary of War for Research and Engineering of the United States, to discuss the radical technological transformation of the US military. Emil outlines the architecture and launch of GenAI.mil, a DoW internal AI platform powered by Gemini and Grok that reached over one million unique users in its first 30 days. He also highlights critical technology priorities for national security, including hypersonics, direct energy, and autonomous drone swarms. Together, they also explore the urgent need to rebuild the American defense industrial base and end dependency on foreign supply chains for critical materials, as well as how Emil is recruiting the next generation of “fixer-builder” workers to serve their country in government.
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Chapters:
00:00 – Cold Open
00:00 – Emil Michael Introduction
00:58 – Emil’s Role at the Department of War
05:22 – Innovation Priorities for the DoW
08:27 – Shift Toward Autonomous Defense Technologies
10:41 – Identifying Common Needs Across the DoW
12:02 – Architecting GenAI.mil
13:48 – Applied AI Initiatives at the DoW
15:57 – The Future of Warfare
17:55 – Recruiting for DoW
19:33 – Arsenal of Freedom Tour
22:25 – Opportunities for Entrepreneurs at DoW
25:49 – Speeding Up and Scaling DoW Initiatives
28:37 – Innovation in Defense Tech
30:00 – Change Management in Government
32:09 – Rebuilding the Defense Industrial Base
37:27 – Initiatives and Opportunities at the Office of Strategic Capital
41:41 – Lessons from Emil’s Government Experience
44:30 – Conclusion
Even if ChatGPT never existed, the tech giant NVIDIA would still be winning. The end of Moore’s Law—says NVIDIA President, Founder, and CEO Jensen Huang—makes the shift to accelerated computing inevitable, regardless of any talk of an AI “bubble.” Sarah Guo and Elad Gil are joined by Jensen Huang for a wide-ranging discussion on the state of artificial intelligence as we begin 2026. Jensen reflects on the biggest surprises of 2025, including the rapid improvements in reasoning, as well as the profitability of inference tokens. He also talks about why AI will increase productivity without necessarily taking away jobs, and how physical AI and robotics can help to solve labor shortages. Finally, Jensen shares his 2026 outlook, including why he’s optimistic about US-China relations, why open source remains essential for keeping the US competitive, and which sectors are due for their “ChatGPT moment.”
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Chapters:
00:00 – Jensen Huang Introduction
00:17 – Biggest AI Surprises of 2025
04:12 – AI and Jobs: New Infrastructure and Demand for Skilled Labor
09:03 – Task vs. Purpose Framework in Labor
12:31 – Solving Labor Shortages with Robotics
15:14 – The Layer Cake of AI Technology
18:39 – The Importance of Open Source
21:52 – The Myth of “God AI” and Monolithic Models
23:54 – Addressing the “Doomer” Narrative and Regulation
29:25 – The Plummeting Cost of Compute and Tokenomics
35:09 – The Return to Research
37:49 – Future of Coding and Software Engineering
43:20 – The Industries Due For Their “ChatGPT” Moments
46:00 – The Evolution of Self-Driving Cars and Robotics
54:06 – Energy Demand and Growth for AI
58:49 – 2026 Outlook: US-China Relations and Geopolitics
1:04:43 – Is There An AI Bubble?
1:16:20 – Conclusion
Pundits are screaming about the so-called “AI bubble.” But historically slow-to-adopt industries like medicine and law are actually embracing AI at an unprecedented speed. Sarah Guo and Elad Gil look ahead to 2026, breaking down the major trends that will define the next era of AI technologies. They explore the future of AI foundational models, predicting breakthroughs in solving complex scientific problems. They share competing views on the timeline for robotics and self-driving cars, debating whether startups have a chance for survival or if incumbents will dominate. Elad and Sarah also discuss the return of tech IPOs and M&As, forecast a new wave of AI consumer agent software, and explore why consumer product innovation has been slower than expected. Finally, the two offer bold non-AI predictions for the new year, including the acceleration of defense tech startups and the second-order underrated impacts of GLP-1 drugs on biohacking.
Plus, stick around to hear predictions on what’s next for AI in 2026 from some of tech’s biggest names and industry leaders. We hear from Jensen Huang (Founder/CEO NVIDIA), Arvind Jain (Founder/CEO, Glean), Winston Weinberg (Founder/CEO, Harvey), Scott Wu (Founder/CEO, Cognition), Raiza Martin (Founder/CEO Huxe), Zach Ziegler (Founder/CTO, Open Evidence), Aaron Levie (Founder/CEO, Box), Misha Laskin (Founder/CEO, ReflectionAI), Noam Brown (Research Scientist, OpenAI), Joshua Meier (Founder/CEO Chai Discovery), Bryan Johnson (Living Man, Don't Die), Sholto Douglas (Member of the Technical Staff, Anthropic), Ben & Asher Spector (Stanford PhDs) and Dylan Patel (Founder/CEO SemiAnalysis).
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Chapters:
00:00 – Introduction
02:43 – AI Predictions for 2026
04:40 – Adoption of AI in Professional Fields
07:17 – Robotics and Self-Driving Cars
08:25 – Robotics: Incumbents vs. Startups
13:59 – Future of IPOs and M&A in AI
16:42 – Challenges in Consumer AI Innovation
21:08 – Funding of Neo Labs, RL Research
26:28 – Predictions for 2026 Beyond AI
26:44 – The Future of Defense and Technology
28:23 – Biohacking and Peptide Therapies
30:37 – 2026 Prediction from AI Industry Leaders
40:46 – Conclusion
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