Claim OwnershipNo Priors: Artificial Intelligence | Technology | Startups
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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 incubating and investing at venture firm Greylock Partners.
Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.
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1In this episode of No Priors, Sarah and Elad explore how AI is transforming consumer apps and entertainment, with a focus on potential integrations in gaming and dating that could shift traditional societal incentives. They reflect on AI researchers winning Nobel Prizes in Science and Chemistry for the first time, discussing what this trend means for scientific discovery. The episode also covers recent AI releases, including their thoughts on OpenAI’s O1 model and Google’s NotebookLM, and examines which companies and job functions are most at risk—or resilient—in the face of AI advancements.
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Show Notes:
(0:00) Introduction
(0:47) Google releases NotebookLM
(5:20) Integrating AI into consumer apps and gaming
(9:11) Future of AI companionship and procreation
(14:45) OpenAI o1 model improves on iterative reasoning
(18:06) Sarah and Elad reflect on Nobel Prizes going to AI researchers
(21:23) Jobs and businesses at risk of disruption
(27:18) AI-durable companies
What would it take to challenge Nvidia? SemiAnalysis Founder and CEO Dylan Patel joins Sarah Guo to answer this and other topical questions around the current state of AI infrastructure. Together, they explore why Dylan loves Android products, predictions around OpenAI’s open source model, and what the landscape of neoclouds looks like. They also discuss Dylan’s thoughts on bottlenecks for expanding AI infrastructure and exporting American AI technologies. Plus, we find out what question Dylan would ask Mark Zuckerberg.
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Chapters:
00:00 – Dylan Patel Introduction
00:31 – Dylan’s Love for Android Products
02:10 – Predictions About OpenAI’s Open Source Model
06:50 – Implications of an American Open Source Model for the Application Ecosystem
10:48 – Evolution of Neoclouds
17:26 – What It Would Take to Challenge Nvidia
27:43 – What Would an Nvidia Challenger Look Like?
28:18 – Understanding Operational and Power Constraints for Data Centers
34:48 – Dylan’s View on the American Stack
43:01 – What Dylan Would Ask Mark Zuckerberg
44:22 – Poker and AI Entrepreneurship
46:51 – Conclusion
Sriram Krishnan was never interested in policy. But after seeing a gap in AI knowledge at senior levels of government, he decided to lend his expertise to the tech-friendly Trump administration. Senior White House Policy Advisor on AI Sriram Krishnan joins Elad Gil and Sarah Guo to talk about America’s AI Action Plan, a recent executive order that outlines how America can win the AI race and maintain its AI supremacy. Sriram discusses why winning the AI race is important and what that looks like, as well as the core goals of the Action Plan that he helped to author. Together, they explore how AI is the latest iteration of American cultural exportation and soft power, the bottlenecks in upgrading America’s energy infrastructure, and the importance of America owning the “full stack” from GPUs and models to agents and software.
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Chapters:
00:00 – Sriram Krishnan Introduction
01:00 – Sriram’s Role in Government
03:43 – Impetus for the America AI Action Plan
06:14 – What Winning the AI Race Looks Like
10:36 – Algorithms and Cultural Bias
12:26 – Main Tenets of the America AI Action Plan
19:13 – Infrastructure and Energy Needs for AI
22:56 – Manufacturing, Supply Chains, and AI
24:52 – Ensuring American Dominance in Robotics
26:30 – Translating Policy to Industry and the Economy
29:30 – Should the US Be a Technocracy?
32:33 – Understanding the Argument Against Open Source Models
36:07 – Conclusion
In the generative AI revolution, quality data is a valuable commodity. But not all data is created equally. Sarah Guo and Elad Gil sit down with SurgeAI founder and CEO Edwin Chen to discuss the meaning and importance of quality human data. Edwin talks about why he bootstrapped Surge instead of raising venture funds, the importance of scalable oversight in producing quality data, and the work Surge is doing to standardize human evals. Plus, we get Edwin’s take on what Meta’s investment into Scale AI means for Surge, as well as whether or not he thinks an underdog can catch up with OpenAI, Anthropic, and other dominant industry players.
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Chapters:
00:00 – Edwin Chen Introduction
00:41 – Overview of SurgeAI
02:28 – Why SurgeAI Bootstrapped Instead of Raising Funds
07:59 – Explaining SurgeAI’s Product
09:39 – Differentiating SurgeAI from Competitors
11:27 – Measuring the Quality of SurgeAI’s Output
12:25 – Role of Scalable Oversight at SurgeAI
14:02 – Challenges of Building Rich RL Environments
16:39 – Predicting Future Needs for Training AI Models
17:29 – Role of Humans in Data Generation
21:27 – Importance of Human Evaluation for Quality Data
22:51 – SurgeAI’s Work Toward Standardization of Human Evals
23:37 – What the Meta/ScaleAI Deal Means for SurgeAI
24:35 – Edwin’s Underdog Pick to Catch Up to Big AI Companies
24:50 – The Future Frontier Model Landscape
26:25 – Future Directions for SurgeAI
29:29 – What Does High Quality Data Mean?
32:26 – Conclusion
Superintelligence, at least in an academic sense, has already been achieved. But Misha Laskin thinks that the next step towards artificial superintelligence, or ASI, should look both more user and problem-focused. ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo to introduce Asimov, their new code comprehension agent built on reinforcement learning (RL). Misha talks about creating tools and designing AI agents based on customer needs, and how that influences eval development and the scope of the agent’s memory. The two also discuss the challenges in solving scaling for RL, the future of ASI, and the implications for Google’s “non-acquisition” of Windsurf.
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Chapters:
00:00 – Misha Laskin Introduction
00:44 – Superintelligence vs. Super Intelligent Autonomous Systems
03:26 – Misha’s Journey from Physics to AI
07:48 – Asimov Product Release
11:52 – What Differentiates Asimov from Other Agents
16:15 – Asimov’s Eval Philosophy
21:52 – The Types of Queries Where Asimov Shines
24:35 – Designing a Team-Wide Memory for Asimov
28:38 – Leveraging Pre-Trained Models
32:47 – The Challenges of Solving Scaling in RL
37:21 – Training Agents in Copycat Software Environments
38:25 – When Will We See ASI?
44:27 – Thoughts on Windsurf’s Non-Acquisition
48:10 – Exploring Non-RL Datasets
55:12 – Tackling Problems Beyond Engineering and Coding
57:54 – Where We’re At in Deploying ASI in Different Fields
01:02:30 – Conclusion
Much of the scientific process involves searching. But rather than continue to rely on the luck of discovery, Google DeepMind has engineered a more efficient AI agent that mines complex spaces to facilitate scientific breakthroughs. Sarah Guo speaks with Pushmeet Kohli, VP of Science and Strategic Initiatives, and research scientist Matej Balog at Google DeepMind about AlphaEvolve, an autonomous coding agent they developed that finds new algorithms through evolutionary search. Pushmeet and Matej talk about how AlphaEvolve tackles the problem of matrix multiplication efficiency, scaling and iteration in problem solving, and whether or not this means we are at self-improving AI. Together, they also explore the implications AlphaEvolve has to other sciences beyond mathematics and computer science.
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Chapters:
00:00 Pushmeet Kohli and Matej Balog Introduction
0:48 Origin of AlphaEvolve
02:31 AlphaEvolve’s Progression from AlphaGo and AlphaTensor
08:02 The Open Problem of Matrix Multiplication Efficiency
11:18 How AlphaEvolve Evolves Code
14:43 Scaling and Predicting Iterations
16:52 Implications for Coding Agents
19:42 Overcoming Limits of Automated Evaluators
25:21 Are We At Self-Improving AI?
28:10 Effects on Scientific Discovery and Mathematics
31:50 Role of Human Scientists with AlphaEvolve
38:30 Making AlphaEvolve Broadly Accessible
40:18 Applying AlphaEvolve Within Google
41:39 Conclusion
When will fully autonomous vehicles see widespread adoption? According to Applied Intuition, that future is closer than you may think. Applied Intuition’s CEO, Qasar Younis, and CTO, Peter Ludwig, talk with Elad Gil about how now is the best time to both work on self-driving vehicle technology and monetize it. Qasar and Peter discuss the advantages of developing their own OS in-house for their autonomous applications, self-driving technology’s potential to drive re-shoring of vehicle manufacturing to the United States, and how best to gauge the bar for safety in autonomous systems. Plus, they explore how self-driving technology may reshape the designs of not only vehicles, but cities themselves.
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Chapters:
00:00 Qasar Younis and Peter Ludwig Introduction
01:28 A Primer on Applied Intuition
11:08 Applied Intuition’s Customers
12:04 Impact of Chinese Vehicles Manufacturers
15:44 EV Policies in the European Market
20:49 Can Robotics and Automation Re-Shore Vehicle Manufacturing?
21:53 Training Models for Autonomous Vehicles
26:41 Gauging the Bar for Autonomous Vehicles Safety
32:03 Timeline for Large-Scale Autonomous Vehicle Adoption
36:28 Rethinking Urban Design for Autonomous Vehicles
38:47 How Applied Intuition Uses AI for Tooling and OS
42:09 Designing for User Experience
43:31 Applied Intuition’s Hiring Strategy
45:01 Conclusion
What happens when you give AI researchers unlimited compute and tell them to compete for the highest usage rates? Ben Mann, Co-Founder, from Anthropic sits down with Sarah Guo and Elad Gil to explain how Claude 4 went from "reward hacking" to efficiently completing tasks and how they're racing to solve AI safety before deploying computer-controlling agents. Ben talks about economic Turing tests, the future of general versus specialized AI models, Reinforcement Learning From AI Feedback (RLAIF), and Anthropic’s Model Context Protocol (MCP). Plus, Ben shares his thoughts on if we will have Superintelligence by 2028.
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Links:
ai-2027.com/
Chapters:
00:00 Ben Mann Introduction
00:33 Releasing Claude 4
02:05 Claude 4 Highlights and Improvements
03:42 Advanced Use Cases and Capabilities
06:42 Specialization and Future of AI Models
09:35 Anthropic's Approach to Model Development
18:08 Human Feedback and AI Self-Improvement
19:15 Principles and Correctness in Model Training
20:58 Challenges in Measuring Correctness
21:42 Human Feedback and Preference Models
23:38 Empiricism and Real-World Applications
27:02 AI Safety and Ethical Considerations
28:13 AI Alignment and High-Risk Research
30:01 Responsible Scaling and Safety Policies
35:08 Future of AI and Emerging Behaviors
38:35 Model Context Protocol (MCP) and Industry Standards
41:00 Conclusion
In this episode of No Priors, Sarah and Elad are joined by Dr. Fei-Fei Li, AI pioneer, co-director of Stanford’s Human-Centered AI Institute, and founder of World Labs. Fei-Fei shares why she’s building at the intersection of embodiment and intelligence, and what today’s AI systems are still missing. From the early days of ImageNet to her vision for the next generation of robotics, she unpacks the human and technical motivations behind World Labs. They also discuss the challenges of 3D world modeling, her approach to building exceptional teams, and the special qualities that have led her students like Andrej Karpathy to make major breakthroughs.
Show Notes:
0:00 Why and what Dr. Fei-Fei Li is building
3:00 World models at World Labs
6:44 Missing gaps in the AI future
9:16 Robotics and physical intelligence
16:15 Greatest challenges of 3D
19:08 Fei-Fei’s work in PhD in ImageNet
23:05 Special moments in Dr. Li's career
29:33 Building teams
32:05 Human-centered AI
In this episode of No Priors, Sarah and Elad unpack the current state of the AI market - whether it’s consolidating, what’s enabling or blocking key mergers, and where the most promising untapped opportunities lie, particularly in biotech. They also explore the rise of world models and how AI’s novel methods for understanding complex systems may ultimately reshape how humans approach discovery and problem-solving.
Show Notes:
0:00 Is the AI market consolidating into clear winners? + the physics of the current landscape
7:01 Why more companies don’t merge (even when it makes sense)
10:09 Exploring biotech’s biggest commercial opportunities and the challenges founders face
17:14 Building world models
21:34 How AI is expanding the way humans reason, design, and evolve systems
This week on No Priors, Elad and Sarah sit down with Eric Mitchell and Brandon McKinzie, two of the minds behind OpenAI’s O3 model. They discuss what makes O3 unique, including its focus on reasoning, the role of reinforcement learning, and how tool use enables more powerful interactions. The conversation explores the unification of model capabilities, what the next generation of human-AI interfaces could look like, and how models will continue to advance in the years ahead.
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Show Notes:
0:00 What is o3?
3:21 Reinforcement learning in o3
4:44 Unification of models
8:56 Why tool use helps test time scaling
11:10 Deep research
16:00 Future ways to interact with models
22:03 General purpose vs specialized models
25:30 Simulating AI interacting with the world
29:36 How will models advance?
This week on No Priors, Sarah and Elad are joined by Josh Goldman, cofounder and president of KoBold Metals. KoBold is using AI to transform how we discover critical minerals like lithium and cobalt, making the exploration process faster, more precise, and more scalable than traditional methods. In this episode, Josh explains how KoBold is rethinking the fundamentals of mineral exploration by combining unique datasets, scientific modeling, and predictive algorithms. They dive into the company’s driving philosophy and technical approach, how they validate underground hypotheses, and why regulatory knowledge and a localized approach are crucial. Josh also discusses what success looks like in exploration today and the scarcity of world-class deposits.
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Show Notes:
0:00 Introduction
0:29 KoBold Metals
3:14 Using unique datasets
6:20 Traditional methods of lithium exploration
8:38 Regulatory vs. rarity constraints
13:40 Technical approach
16:25 Validating hypotheses
23:56 Redefining success in mineral exploration
25:44 Scarcity of good projects and deposits
32:44 Philosophy behind prediction
36:46 KoBold’s origin story
On this episode of No Priors, Sarah and Elad sit down with Brendan Foody, CEO and cofounder of Mercor, to discuss the company’s rapid growth and their vision for the future of the labor market. They dive into how AI is reshaping the workforce in real, tangible ways and what skills are worth investing in today. Brendan shares insights on evaluating talent in an AI-driven world, including how models might identify outlier or 10x candidates and even assess “taste.” The conversation also touches on the evolving role of human data, the future of hiring in fast-scaling startups, and whether AI will act as an individual contributor or a data-centric manager.
Show Notes:
0:00 Introduction
0:16 Building Mercor
3:00 Identifying outlier talent with AI
9:07 How AI is reshaping the workforce: job displacement & evolution
11:18 What skills should we invest in now?
12:18 Verifiability
13:36 Evaluating models
16:07 What should kids learn today?
17:05 Evaluating taste in talent assessments
18:45 Future of data collection
26:07 Humans’ role in the AI economy
28:53 AI as a contributor vs. a manager
33:03 Mercor’s goals
34:50 Evolution of labor markets
36:00 Hiring advice
This week on No Priors, Sarah and Elad talk with GitHub CEO Thomas Dohmke about the rise of AI-powered software development and the success of Copilot. They discuss how Copilot is reshaping the developer workflow, GitHub’s new Agent Mode, and competition in the developer tooling market. They also explore how AI-driven coding impacts software pricing, the future of open source vs. proprietary APIs, and what Copilot’s success means for Microsoft. Plus, Thomas shares insights from his journey growing up in East Berlin and navigating rapidly changing worlds.
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Show Notes:
0:00 Introduction
0:37 GitHub Copilot’s capabilities
4:12 Will agents replace developers?
6:04 Copilot’s development cycle
8:34 Winning the developer market
10:40 Agent mode
13:25 Where GitHub is headed
16:45 Building for the new challenges of AI
21:50 Dev tools market formation
29:56 Copilot’s broader impact
32:17 How AI changes software pricing
39:16 Open source vs. proprietary APIs
48:01 Growing up in East Berlin
This week on No Priors, Sarah is joined by Dan Hendrycks, director of the Center of AI Safety. Dan serves as an advisor to xAI and Scale AI. He is a longtime AI researcher, publisher of interesting AI evals such as "Humanity's Last Exam," and co-author of a new paper on National Security "Superintelligence Strategy" along with Scale founder-CEO Alex Wang and former Google CEO Eric Schmidt. They explore AI safety, geopolitical implications, the potential weaponization of AI, along with policy recommendations.
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Show Notes:
0:00 Introduction
0:36 Dan’s path to focusing on AI Safety
1:25 Safety efforts in large labs
3:12 Distinguishing alignment and safety
4:48 AI’s impact on national security
9:59 How might AI be weaponized?
14:43 Immigration policies for AI talent
17:50 Mutually assured AI malfunction
22:54 Policy suggestions for current administration
25:34 Compute security
30:37 Current state of evals
This week on No Priors, Sarah sits down with Noubar Afeyan, Co-founder and CEO of Flagship Pioneering, the biotech firm behind groundbreaking companies like Moderna. They explore how Flagship creates the conditions for scientific breakthroughs, tackles regulatory uncertainty, and pushes the boundaries of discovery. Noubar shares insights on AI’s role in healthcare, the challenges of bringing new therapies to market, and lessons learned from past pandemics. He also discusses Flagship’s platform approach to biotech innovation and introduces the idea of polyintelligence.
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Show Notes:
0:00 Introduction
0:48 Founding Flagship
5:51 Fostering environments for emergence
11:17 Expanding into new frontiers
14:26 Developing technology amid regulatory uncertainty and risk
19:12 How Flagship has evolved
22:47 AI applications in healthcare
27:30 Bottlenecks in bringing new therapies to market
32:20 Lessons for the next pandemic
34:11 Building a platform
38:10 Polyintelligence
On this week’s episode of No Priors, Sarah Guo is joined by leading members of the teams at Vevo Therapeutics and the Arc Institute – Nima Alidoust, CEO/Co-Founder at Vevo Therapeutics; Johnny Yu, CSO/Co-Founder at Vevo Therapeutics; Patrick Hsu, CEO/Co-Founder at Arc Institute; Dave Burke, CTO at Arc Institute; and Hani Goodarzi, Core Investigator at Arc Institute. Predicting protein structure (AlphaFold 3, Chai-1, Evo 2) was a big AI/biology breakthrough. The next big leap is modeling entire human cells—how they behave in disease, or how they respond to new therapeutics. The same way LLMs needed enormous text corpora to become truly powerful, Virtual Cell Models need massive, high-quality cellular datasets to train on. In this episode, the teams discuss the groundbreaking release of the Tahoe-100M single cell dataset, Arc Atlas, and how these advancements could transform drug discovery.
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Download the Tahoe Dataset
Show Notes:
0:00 Introduction
1:40 Significance of Tahoe-100M dataset
4:22 Where we are with virtual cell models and protein language models
10:26 Significance of perturbational data
17:39 Challenges and innovations in data collection
24:42 Open sourcing and community collaboration
33:51 Predictive ability and importance of virtual cell models
35:27 Drug discovery and virtual cell models
44:27 Platform vs. single hypothesis companies
46:05 Rise of Chinese biotechs
51:36 AI in drug discovery
Kyle Vogt joins Sarah and Elad on this week’s episode of No Priors. A serial entrepreneur, Kyle co-founded Twitch, transforming live streaming, and later Cruise, the autonomous vehicle company acquired by GM for $1 billion. Now he’s taking on AI-powered home robotics with The Bot Company. In this episode, Kyle shares his journey building transformative tech companies, the challenges of scaling autonomous systems, and why he believes home robots are the next frontier. They also discuss the parallels between AVs and robotics, overcoming consumer skepticism, US vs. China manufacturing, and the policies needed to foster a competitive robotics industry.
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Show Notes:
0:00 Introduction
0:29 Founding Cruise
3:12 Tesla vs. Waymo approach
4:44 Scaling autonomous vehicles
10:03 The Bot Company
16:35 Deploying robots in the home
17:56 Parallels between robots and AV markets
20:51 Personifying robots and overcoming consumer skepticism
25:00 Timeline on consumer robots
26:47 Chinese vs. US manufacturing
29:15 Fostering a competitive domestic robotics industry
34:00 Lessons from Cruise & personal philosophies
This week on No Priors, Sarah sits down with Harvey cofounder and CEO Winston Weinberg. Harvey is one of the leading application layer AI companies, building domain-specific AI for law firms, professional service providers, and the Fortune 500. They are already working with companies like Bridgewater, KKR, PWC, and O’Melveny with over $500M in funding from OpenAI, Sequoia, Kleiner, GV and Elad and Sarah. In this episode, Sarah and Winston cover AI product strategy, the future of professional services, company values, keeping up with research, and the law industry of the future.
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Show Notes:
0:00 Introduction
2:39 Harvey’s founding story
3:46 Capability improvement
6:39 Building teams around AI capabilities
9:17 End to end task completion
12:37 Beginning with large industry leaders
17:21 Working with users skeptical of automation
20:40 Being a lawyer today and in the future
26:02 Adapting product for other domains
26:58 Hiring philosophy at Harvey
30:39 Lessons and mistakes as a founder
32:53 Personal drive
40:21 Advice to other founders
44:35 Prediction for next ChatGPT moment
This week on No Priors, Sarah and Elad celebrate the 100th episode! They dive into the biggest AI stories of 2025, breaking down DeepSeek—truth vs. hype, the rapid consumer adoption, and the real cost of training the models. They debate model commoditization and the value of being a frontier model provider vs. building on existing work. Plus, they unpack OpenAI’s new Deep Research release and the latest on Stargate. Finally, they share bold predictions for 2025, covering robots, autonomous vehicles, local AI models, emerging data-generation strategies, and reasoning breakthroughs.
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Show Notes:
0:00 Introduction
0:19 DeepSeek
5:38 Are models commoditizing?
8:33 DeepSeek’s consumer adoption
9:16 OpenAI’s Deep Research release
13:30 Stargate
15:04 Elad & Sarah’s Predictions for 2025
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