No Priors: Artificial Intelligence | Technology | Startups

In this episode of No Priors, hosts Sarah and Elad are joined by Oriol Vinyals, VP of Research, Deep Learning Team Lead, at Google DeepMind and Technical Co-lead of the Gemini project. Oriol shares insights from his career in machine learning, including leading the AlphaStar team and building competitive StarCraft agents. We talk about Google DeepMind, forming the Gemini project, and integrating AI technology throughout Google products. Oriol also discusses the advancements and challenges in long context LLMs, reasoning capabilities of models, and the future direction of AI research and applications. The episode concludes with a reflection on AGI timelines, the importance of specialized research, and advice for future generations in navigating the evolving landscape of AI.

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Show Notes: 

(00:00) Introduction to Oriol Vinyals

(00:55) The Gemini Project and Its Impact

(02:04) AI in Google Search and Chat Models

(08:29) Infinite Context Length and Its Applications

(14:42) Scaling AI and Reward Functions

(31:55) The Future of General Models and Specialization

(38:14) Reflections on AGI and Personal Insights

(43:09) Will the Next Generation Study Computer Science?

(45:37) Closing thoughts

This week on No Priors, Sarah Guo and Elad Gil are joined by Howie Liu, the co-founder and CEO of Airtable. Howie discusses their Cobuilder launch, the evolution of Airtable from a simple productivity tool to an enterprise app platform with integrated AI capabilities. They talk about why the conventional wisdom of “app not platform” can be wrong,  why there’s a future for low-code in the age of AI and code generation, and where enterprises need help adopting AI.

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Show Notes: 

(00:00) Introduction

(00:29) The Origin and Evolution of Airtable

(02:31) Challenges and Successes in Building Airtable

(06:09) Airtable's Transition to Enterprise Solutions

(09:44) Insights on Product Management

(16:23) Integrating AI into Airtable

(21:55) The Future of No Code and AI

(30:30) Workshops and Training for AI Adoption

(36:28) The Role of Code Generation in No Code Platforms

This week on No Priors, we have a host-only episode. Sarah and Elad catch up to discuss how tech history may be repeating itself. Much like in the early days of the internet, every company is clamoring to incorporate AI into their products or operations while some legacy players are skeptical that investment in AI will pay off. They also get into new opportunities and capabilities that AI is opening up, whether or not incubators are actually effective, and what companies are poised to stand the test of time in the changing tech landscape.

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Show Notes: 

(0:00) Introduction

(0:16) Old school operators AI misunderstandings

(5:10) Tech history is repeating itself with slow AI adoption

(6:09) New AI Markets

(8:48) AI-backed buyouts

(13:03) AI incubation

(17:18) Exciting incubating applications

(18:26) AI and the public markets

(22:20) Staffing AI companies 

(25:14) Competition and shrinking head count

Believe or not, we’re almost halfway through 2024. Sarah and Elad have spent the first of this year talking with some of the most innovative minds in the AI industry, so we’re taking a look at some of our favorite No Priors conversations so far featuring Dylan Field (Figma); Emily Glassberg-Sands (Stripe); Brett Adcock (Figure AI); Aditya Ramesh, Tim Brooks and Bill Peebles (OpenAI’s Sora Team); Scott Wu (Cognition); and Alexandr Wang (Scale).

Watch or listen to the full episodes here:

• Build AI products at on-AI companies with Emily Glassberg Sands from Stripe
• Designing the Future: Dylan Field on AI, Collaboration, and Independence
• The argument for humanoid robots with Brett Adcock from Figure
• OpenAI’s Sora team thinks we’ve only seen the "GPT-1 of video models"
• Cognition’s Scott Wu on how Devin, the AI software engineer, will work for you
• The Data Foundry for AI with Alexandr Wang from Scale

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Show Notes: 

(0:00) Introduction

(0:46) Emily Glassberg Sands on the Future of AI and Fintech

(4:23 Dylan Field on AI and Human Creative Potential

(9:03) Brett Adcock on Running Figure AI’s Hardware and Software Processes

(12:43) OpenAI’s Sora Team on Artists’ Creative Experiences with their Model

(17:43) Scott Wu Gives Advice for Human Engineers Co-Working with AI

(21:06) Alexandr Wang on How Quality Data Builds Confidence in AI Systems

This week on No Priors, Sarah Guo and Elad Gil sit down with Karan Goel and Albert Gu from Cartesia. Karan and Albert first met as Stanford AI Lab PhDs, where their lab invented Space Models or SSMs, a fundamental new primitive for training large-scale foundation models. In 2023, they Founded Cartesia to build real-time intelligence for every device. One year later, Cartesia released Sonic which generates high quality and lifelike speech with a model latency of 135ms—the fastest for a model of this class.

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Show Notes: 

(0:00) Introduction

(0:28) Use Cases for Cartesia and Sonic 

(1:32) Karan Goel & Albert Gu’s professional backgrounds

(5:06) State Space Models (SSMs) versus Transformer Based Architectures 

(11:51) Domain Applications for Hybrid Approaches 

(13:10) Text to Speech and Voice

(17:29) Data, Size of Models and Efficiency 

(20:34) Recent Launch of Text to Speech Product

(25:01) Multimodality & Building Blocks

(25:54) What’s Next at Cartesia? 

(28:28) Latency in Text to Speech

(29:30) Choosing Research Problems Based on Aesthetic 

(31:23) Product Demo

(32:48) Cartesia Team & Hiring

 AI video generation models still have a long way to go when it comes to making compelling and complex videos but the HeyGen team are well on their way to streamlining the video creation process by using a combination of language, video, and voice models to create videos featuring personalized avatars, b-roll, and dialogue. This week on No Priors, Joshua Xu the co-founder and CEO of HeyGen,  joins Sarah and Elad to discuss how the HeyGen team broke down the elements of a video and built or found models to use for each one, the commercial applications for these AI videos, and how they’re safeguarding against deep fakes. 

Links from episode:

• HeyGen
• McDonald’s commercial

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Show Notes: 

(0:00) Introduction

(3:08) Applications of AI content creation

(5:49) Best use cases for Hey Gen

(7:34) Building for quality in AI video generation

(11:17) The models powering HeyGen

(14:49) Research approach

(16:39) Safeguarding against deep fakes

(18:31) How AI video generation will change video creation

(24:02) Challenges in building the model

(26:29) HeyGen team and company

The first step in achieving AGI is nailing down a concise definition and Mike Knoop, the co-founder and Head of AI at Zapier, believes François Chollet got it right when he defined general intelligence as a system that can efficiently acquire new skills. This week on No Priors, Miked joins Elad to discuss ARC Prize which is a multi-million dollar non-profit public challenge that is looking for someone to beat the Abstraction and Reasoning Corpus (ARC) evaluation.

In this episode, they also get into why Mike thinks LLMs will not get us to AGI, how Zapier is incorporating AI into their products and the power of agents, and why it’s dangerous to regulate AGI before discovering its full potential. 

Show Links:

• About the Abstraction and Reasoning Corpus
• Zapier Central
• ARC Prize

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Show Notes: 

(0:00) Introduction

(1:10) Redefining AGI

(2:16) Introducing ARC Prize

(3:08) Definition of AGI

(5:14) LLMs and AGI

(8:20) Promising techniques to developing AGI

(11:0) Sentience and intelligence

(13:51) Prize model vs investing

(16:28) Zapier AI innovations

(19:08) Economic value of agents

(21:48) Open source to achieve AGI

(24:20) Regulating AI and AGI

After Tengyu Ma spent years at Stanford researching AI optimization, embedding models, and transformers, he took a break from academia to start Voyage AI which allows enterprise customers to have the most accurate retrieval possible through the most useful foundational data. Tengyu joins Sarah on this week’s episode of No priors to discuss why RAG systems are winning as the dominant architecture in enterprise and the evolution of foundational data that has allowed RAG to flourish. And while fine-tuning is still in the conversation, Tengyu argues that RAG will continue to evolve as the cheapest, quickest, and most accurate system for data retrieval. 

They also discuss methods for growing context windows and managing latency budgets, how Tengyu’s research has informed his work at Voyage, and the role academia should play as AI grows as an industry. 

Show Links:

• Voyage AI
• Stanford Assistant Professor of Computer Science
• Tengyu Ma Key Research Papers:
• Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
• Non-convex optimization for machine learning: design, analysis, and understanding
• Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
• Larger language models do in-context learning differently, 2023
• Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
• On the Optimization Landscape of Tensor Decompositions

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Show Notes: 

(0:00) Introduction

(1:59) Key points of Tengyu’s research

(4:28) Academia compared to industry

(6:46) Voyage AI overview

(9:44) Enterprise RAG use cases

(15:23) LLM long-term memory and token limitations

(18:03) Agent chaining and data management

(22:01) Improving enterprise RAG 

(25:44) Latency budgets

(27:48) Advice for building RAG systems

(31:06) Learnings as an AI founder

(32:55) The role of academia in AI

Garry Tan is a notorious founder-turned-investor who is now running one of the most prestigious accelerators in the world, Y Combinator. As the president and CEO of YC, Garry has been credited with reinvigorating the program. On this week’s episode of No Priors, Sarah, Elad, and Garry discuss the shifting demographics of YC founders and how AI is encouraging younger founders to launch companies, predicting which early stage startups will have longevity, and making YC a beacon for innovation in AI companies. They also discussed the importance of building companies in person and if San Francisco is, in fact, back. 

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Show Notes: 

(0:00) Introduction

(0:53) Transitioning from founder to investing

(5:10) Early social media startups

(7:50) Trend predicting at YC

(10:03) Selecting YC founders

(12:06) AI trends emerging in YC batch

(18:34) Motivating culture at YC

(20:39) Choosing the startups with longevity

(24:01) Shifting YC found demographics

(29:24) Building in San Francisco 

(31:01) Making YC a beacon for creators

(33:17) Garry Tan is bringing San Francisco back

Alexandr Wang was 19 when he realized that gathering data will be crucial as AI becomes more prevalent, so he dropped out of MIT and started Scale AI. This week on No Priors, Alexandr joins Sarah and Elad to discuss how Scale is providing infrastructure and building a robust data foundry that is crucial to the future of AI. While the company started working with autonomous vehicles, they’ve expanded by partnering with research labs and even the U.S. government.  

In this episode, they get into the importance of data quality in building trust in AI systems and a possible future where we can build better self-improvement loops, AI in the enterprise, and where human and AI intelligence will work together to produce better outcomes. 

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(0:00) Introduction

(3:01) Data infrastructure for autonomous vehicles

(5:51) Data abundance and organization

(12:06)  Data quality and collection

(15:34) The role of human expertise

(20:18) Building trust in AI systems

(23:28) Evaluating AI models

(29:59) AI and government contracts

(32:21) Multi-modality and scaling challenges

Mikey Shulman, the CEO and co-founder of Suno, can see a future where the Venn diagram of music creators and consumers becomes one big circle. The AI music generation tool trying to democratize music has been making waves in the AI community ever since they came out of stealth mode last year. Suno users can make a song complete with lyrics, just by entering a text prompt, for example, “koto boom bap lofi intricate beats.” You can hear it in action as Mikey, Sarah, and Elad create a song live in this episode. 

In this episode, Elad, Sarah, And Mikey talk about how the Suno team took their experience making at transcription tool and applied it to music generation, how the Suno team evaluates aesthetics and taste because there is no standardized test you can give an AI model for music, and why Mikey doesn’t think AI-generated music will affect people’s consumption of human made music. 

Listen to the full songs played and created in this episode:

• Whispers of Sakura
• Stone 
• Statistical Paradise
• Statistical Paradise 2

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Show Notes: 

(0:00) Mikey’s background

(3:48) Bark and music generation

(5:33) Architecture for music generation AI

(6:57) Assessing music quality

(8:20) Mikey’s music background as an asset

(10:02) Challenges in generative music AI

(11:30) Business model

(14:38) Surprising use cases of Suno

(18:43) Creating a song on Suno live

(21:44) Ratio of creators to consumers

(25:00) The digitization of music

(27:20) Mikey’s favorite song on Suno

(29:35) Suno is hiring

This week on No Priors hosts, Sarah and Elad are catching up on the latest AI news. They discuss the recent developments in AI like Meta’s new AI assistant and the latest in music generation, and if you’re interested in generative AI music, stay tuned for next week’s interview! Sarah and Elad also get into device-resident models, AI hardware, and ask just how smart smaller models can really get. These hardware constraints were compared to the hurdles AI platforms are continuing to face including computing constraints, energy consumption, context windows, and how to best integrate these products in apps that users are familiar with.

Have a question for our next host-only episode or feedback for our team? Reach out to [email protected]

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Show Notes: 

(0:00) Intro

(1:25) Music AI generation

(4:02) Apple’s LLM

(11:39) The role of AI-specific hardware

(15:25) AI platform updates

(18:01) Forward thinking in investing in AI

(20:33) Unlimited context

(23:03) Energy constraints

Scott Wu loves code. He grew up competing in the International Olympiad in Informatics (IOI) and is a world class coder, and now he's building an AI agent designed to create more, not fewer, human engineers. This week on No Priors, Sarah and Elad talk to Scott, the co-founder and CEO of Cognition, an AI lab focusing on reasoning. Recently, the Cognition team released a demo of Devin, an AI software engineer that can increasingly handle entire tasks end to end.

In this episode, they talk about why the team built Devin with a UI that mimics looking over another engineer’s shoulder as they work and how this transparency makes for a better result. Scott discusses why he thinks Devin will make it possible for there to be more human engineers in the world, and what will be important for software engineers to focus on as these roles evolve. They also get into how Scott thinks about building the Cognition team and that they’re just getting started. 

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Show Notes: 

(0:00) Introduction

(1:12) IOI training and community

(6:39) Cognition’s founding team

(8:20) Meet Devin

(9:17) The discourse around Devin

(12:14) Building Devin’s UI

(14:28) Devin’s strengths and weakness 

(18:44) The evolution of coding agents

(22:43) Tips for human engineers

(26:48) Hiring at Cognition

AI-generated videos are not just leveled-up image generators. But rather, they could be a big step forward on the path to AGI. This week on No Priors, the team from Sora is here to discuss OpenAI’s recently announced generative video model, which can take a text prompt and create realistic, visually coherent, high-definition clips that are up to a minute long.

Sora team leads, Aditya Ramesh, Tim Brooks, and Bill Peebles join Elad and Sarah to talk about developing Sora. The generative video model isn’t yet available for public use but the examples of its work are very impressive. However, they believe we’re still in the GPT-1 era of AI video models and are focused on a slow rollout to ensure the model is in the best place possible to offer value to the user and more importantly they’ve applied all the safety measures possible to avoid deep fakes and misinformation. They also discuss what they’re learning from implementing diffusion transformers, why they believe video generation is taking us one step closer to AGI, and why entertainment may not be the main use case for this tool in the future. 

Show Links:

• Bling Zoo video
• Man eating a burger video
• Tokyo Walk video

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Show Notes: 

(0:00) Sora team Introduction

(1:05) Simulating the world with Sora

(2:25) Building the most valuable consumer product

(5:50) Alternative use cases and simulation capabilities

(8:41) Diffusion transformers explanation

(10:15) Scaling laws for video

(13:08) Applying end-to-end deep learning to video

(15:30) Tuning the visual aesthetic of Sora

(17:08) The road to “desktop Pixar” for everyone

(20:12) Safety for visual models

(22:34) Limitations of Sora

(25:04) Learning from how Sora is learning

(29:32) The biggest misconceptions about video models

Multimodal models are making it possible to create AI art and augment creativity across artistic mediums. This week on No Priors, Sarah and Elad talk with Suhail Doshi, the founder of Playground AI, an image generator and editor. Playground AI has been open-sourcing foundation diffusion models, most recently releasing Playground V2.5. 

In this episode, Suhail talks with Sarah and Elad about how the integration of language and vision models enhances the multimodal capabilities, how the Playground team thought about creating a user-friendly interface to make AI-generated content more accessible, and the future of AI-powered image generation and editing.

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Show Notes: 

(0:00) Introduction

(0:52) Focusing on image generation

(3:01) Differentiating from other AI creative tools

(5:58) Training a Stable Diffusion model

(8:31) Long term vision for Playground AI

(15:00) Evolution of AI architecture

(17:21) Capabilities of multimodal models

(22:30) Parallels between audio AI tools and image-generation

This week on a host-only episode of No Priors, Sarah and Elad discuss the AI wave as compared to the internet wave, the current state of AI investing, the foundation model landscape, voice and video AI, advances in agentic systems, prosumer applications, and the Microsoft/Inflection deal.

Have a question for our next host-only episode or feedback for our team? Reach out to [email protected]

Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil 

Show Notes: 

(0:00) Intro

(0:32) How to think about scaling in 2024

(3:21) Microsoft/Inflection deal

(5:28) Voice cloning

(7:02) Investing climate

(12:50) Whitespace in AI

(16:36) AI video landscape

(19:54) Agentic user experiences

(22:21) Prosumer as the first wave of application AI

Humans are always doing work that is dull or dangerous. Brett Adcock, the founder and CEO of Figure AI, wants to build a fleet of robots that can do everything from work in a factory or warehouse to folding your laundry in the home. Today on No Priors, Sarah got the chance to talk with Brett about how a company that is only 21 months old has already built humanoid robots that not only walk the walk by performing tasks like item retrieval and making a cup of coffee but they also talk the talk through speech to speech reasoning. 

In this episode, Brett and Sarah discuss why right now is the correct time to build a fleet of AI robots and how implementation in industrial settings will be a stepping stone into AI robots coming into the home. They also get into how Brett built a team of world class engineers, commercial partnerships with BMW and OpenAI that are accelerating their growth, and the plan to achieve social acceptance for AI robots. 

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Show Notes: 

(0:00) Brett’s background

(3:09) Figure AI Thesis

(5:51) The argument for humanoid robots

(7:36) Figure AI public demos

(12:38) Mitigating risk factors

(15:20) Designing the org chart and finding the team

(16:38) Deployment timeline

(20:41) Build vs buy and vertical integration

(23:04) Product management at Figure

(28:37) Corporate partnerships

(31:58) Humans at home

(33:38) Social acceptance 

(35:41) AGI vs the robots

Companies are employing AI agents and co-pilots to help their teams increase efficiency and accuracy, but developing apps that are trained properly can require a skill set many enterprise teams don’t have. This week on No Priors, Sarah and Elad are joined by Harrison Chase, the CEO and co-founder of LangChain, an open-source framework and developer toolkit that helps developers build LLM applications. In this conversation they talk about the gaps in open source app development, what it will take to keep up with private companies, the importance of creating prompts that can be compatible with many API models, and why memory is so undeveloped in this space. 

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Show Notes: 

(0:00) Introduction to LangChain

(1:45) Managing an open source environment

(4:30) Developing useful AI agents

(10:03) Sophistication and limitations of AI app development

(14:17) Switching between model APIs

(17:10) Context windows, fine-tuning and functionality

(21:37) Evolution of AI open source environment

(23:53) The next big breakthroughs

At a time when users are being asked to wait unthinkable seconds for AI products to generate art and answers, speed is what will win the battle heating up in AI computing. At least according to today’s guest, Tuhin Srivastava, the CEO and co-founder of Baseten which gives customers scalable AI infrastructures starting with interference. In this episode of No Priors, Sarah, Elad, and Tuhin discuss why efficient code solutions are more desirable than no code, the most surprising use cases for Baseten, and why all of their jobs are very defensible from AI. 

Show Links:

• Baseten
• Benchmarking fast Mistral 7B inference

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Show Notes: 

(0:00) Introduction

(1:19) Capabilities of efficient code enabled development

(4:11) Difference in training inference workloads

(6:12) AI product acceleration

(8:48) Leading on inference benchmarks at Baseten

(12:08) Optimizations for different types of models

(16:11) Internal vs open source models

(19:01) timeline for enterprise scale

(21:53) Rethinking investment in compute spend

(27:50) Defensibility in AI industries

(31:30) Hardware and the chip shortage

(35:47) Speed is the way to win in this industry

(38:26) Wrap

Figma has had a banner year and the formidable team isn’t slowing down—even after regulatory issues blocked the merger with Adobe. Today on No Priors, Sarah and Elad are joined by Dylan Field the CEO and founder of Figma, the design collaboration tool that is closing the gap between imagination and reality. They discuss what’s next for an independent Figma, how AI can augment design and speed up the iteration loop, and how Figma is expanding beyond design with products that help the entire product team’s workflow.

Show Links:

• https://www.figma.com/
• Figma and Adobe are abandoning our proposed merger

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Show Notes: 

(0:00) Introduction

(2:01) No more Adobe acquisition 

(4:20) What’s next for Figma

(7:16) FigJam, digital collaboration, and expanding beyond design

(10:50) Figma DevMode

(13:06) Incorporating AI at Figma

(15:03) How AI will change design

(19:19) Creativity augmentation and the iterative loop

(22:44) Automating repetitive design tasks

(25:35) The future of AI UI

(29:44) Investing philosophy

(31:28) Leadership evolution

Host-only episode discussing NVIDIA, Meta and Google earnings, Gemini and Mistral model launches, the open-vs-closed source debate, domain specific foundation models, if we’ll see real competition in chips, and the state of AI ROI and adoption.

Don’t miss our episodes with:

• Mistral
• NVIDIA
• AMD

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 Show Notes: 

(0:00) Introduction

(0:27) Model news and product launches

(5:01) Google enters the competitive space with Gemini 1.5

(8:23) Biology and robotics using LLMs

(10:22) Agent-centric companies

(14:22) NVIDIA earnings

(17:29) ROI in AI

(20:43) Impact from AI

(25:45) Building effective AI tools in house

(29:09) What would it take to compete with NVIDIA

(33:23) The architectural approach to compute

(35:42) the roadblocks to chip production in the US

(38:30) The virtuous tech cycles in AI

Compute is the fuel for the AI revolution, and customers want more chip vendors. AMD CTO Mark Papermaster joins Sarah and Elad on No Priors to discuss AMD’s strategy, their newest GPUs, where inference workloads will live, the chip software stack, how they are thinking about supply chain issues, and what we can expect from AMD in 2024. 

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Show Notes: 

(0:00) Introduction and Mark’s background

(2:35) AMD background and current markets

(4:40) AMD shifting to AI space

(8:54) AI applications coming out of AMD

(10:57) Software investment

(15:15) The benefits of open-source stacks

(16:58) Evolving GPU market

(20:21) Constraints on GPU production

(24:11) Innovations in chip technology

(27:57) Chip supply chain

(30:18) Future of innovative hardware products

(35:42) What’s next for AMD

Accurate, customizable search is one of the most immediate AI use cases for companies and general users. Today on No Priors, Elad and Sarah are joined by Pinecone CEO, Edo Liberty, to talk about how RAG architecture is improving syntax search and making LLMs more available. By using a RAG model Pinecone makes it possible for companies to vectorize their data and query it for the most accurate responses. 

In this episode, they talk about how Pinecone’s Canopy product is making search more accurate by using larger data sets in a way that is more efficient and cost effective—which was almost impossible before there were serverless options. They also get into how RAG architecture uniformly increases accuracy across the board, how these models can increase “operational sanity” in the dataset  for their customers, and hybrid search models that are using keywords and embeds. 

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Show Notes: 

(0:00) Introduction to Edo and Pinecone

(2:01) Use cases for Pinecone and RAG models

(6:02) Corporate internal uses for syntax search

(10:13) Removing the limits of RAG with Canopy

(14:02) Hybrid search

(16:51) Why keep Pinecone closed source

(22:29) Infinite context

(23:11) Embeddings and data leakage

(25:35) Fine tuning the data set

(27:33) What’s next for Pinecone 

(28:58) Separating reasoning and knowledge in AI

Notion is a productivity app that has invested heavily in AI to create products that enable workers to access information instantly without having to search through their own countless notes. Today on No Priors, Sarah and Elad are joined by Ivan Zhao, the co-founder and CEO of Notion, to talk about Notions Q&A interface and calendar applications. They also get into how using RAG models means better retrieval, longer memory, and the user can be less organized and how Notion is leading the charge in this era of SaaS bundling products.

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Show Notes: 

(0:00) Introduction

(2:09) AI and Computing literacy

(5:39) Building the Notion AI team

(8:43) Notion as an application company

(12:09) Prioritizing AI investment

(14:53) The rapid evolution cycle of AI development

(17:46) Notion Q&A

(20:00) Workflow and AI for calendars

(22:43) Moving past the need for organization

(24:36) History of SaaS doesn’t repeat, it rhymes

(30:14) Design at Notion

(34:26) Notion office design

(36:52) How RAG will change the future

(38:30) Building our the software in the Notionscape

Many companies that are building AI products for their users are not primarily AI companies. Today on No Priors, Sarah and Elad are joined by Emily Glassberg Sands who is the Head of Information at Stripe. They talk about how Stripe prioritizes AI projects and builds these tools from the inside out. Stripe was an early adopter of utilizing LLMs to help their end user. Emily talks about how they decided it was time to meaningfully invest in AI given the trajectory of the industry and the wealth of information Stripe has access to. The company’s goal with utilizing AI is to empower non-technical users to code using natural language and for technical users to be able to work much quicker and in this episode she talks about how their Radar Assistant and Sigma Assistant achieve those goals. 

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Show Notes: 

(0:00) Background

(0:38) Emily’s role at Stripe

(2:31) Adopting early gen AI models

(4:44) Promoting internal usage of AI

(8:17) Applied ML accelerator teams

(10:36) Radar fraud assistant

(13:30) Sigma assistant

(14:32) How will AI affect Stripe in 3 years

(17:00) Knowing when it’s time to invest more fully in AI

(18:28) Deciding how to proliferate models

(22:04) Whitespace for fintechs employing AI

(25:41) Leveraging payments data for customers

(27:51) Labor economics and data

(30:10) Macro economic trends for strategic decisions

(32:54) How will AI impact education

(35:36) Unique needs of AI startups

Building an ecommerce business is hard – it requires merchants to have a wealth of skills: technical, logistics, marketing, pricing, vendor management, finance and analytics. That’s why Shopify is releasing new AI features that help merchants tackle things like product descriptions, marketing suggestions and search.

Today on No Priors, Glen Coates, the VP of core product at Shopify (and former founder of b2b wholesale platform Handshake), joins Sarah and Elad. They talk about the releases from Shopify Editions, why they are deploying “copilot” rather than “autopilot,” AI innovation-at-scale, how to change the basement of a house while people are living in it, and building a leadership team of entrepreneurs.

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Shopify Editions | AI Section of Shopify Editions

Show Notes: 

(0:00) Background

(2:22) Calling a “Code Red” at Shopify

(4:04) Integrating acquisitions, entrepreneurial leaders

(12:15) AI adoption

(15:51) Deciding when to ship AI products, evaluations

(17:33) Shopify’s risk orientation

(18:50) Changing the core Shopify data model, enabling AI features

(26:05) What’s missing from LLMs for merchants

(28:47) Most interesting AI developments in the industry

(33:22) What users want from LLMs and search

(38:20) No Priors social

Building adaptive AI models that can learn and complete tasks in the physical world requires precision but these AI robots could completely change manufacturing and logistics processes. Peter Chen, the co-founder and CEO of Covariant, leads the team that is building robots that will increase manufacturing efficiency, safety, and create warehouses of the future. 

Today on No Priors, Peter joins Sarah to talk about how the Covariant team is developing multimodal models that have precise grounding and understanding so they can adapt to solve problems in the physical world. They also discuss how they plan their roadmap at Covariant, what could be next for the company, and what use case will bring us to the Chat-GPT moment for AI robots.

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Show Notes: 

(0:00) Peter Chen Background

(0:58) How robotics AI will drive AI forward

(3:00) Moving from research to a commercial company

(5:46) The argument for building incrementally 

(8:13) Manufacturing robotics today

(12:21) Put wall use case

(15:45) What’s next for Covariant Brain

(18:42) Covariant’s customers

(19:50) Grounding concepts in Ai

(25:47) How scaling laws apply to Covariant

(29:21) Covariant’s driving thesis

(32:54) the Chat-GPT moment for robotics

(35:12) Manufacturing center of the future

(37:02) Safety in AI robotics

Coding in collaboration with AI can reduce human toil in the software development process and lead to more accurate and less tedious work for coding teams. This week on No Priors, Sarah talked with Beyang Liu, the cofounder and CTO of Sourcegraph, which builds tools that help developers innovate faster. Their most recent launch was an AI coding assistant called Cody. Beyang has spent his entire career thinking about how humans can work in conjunction with AI to write better code.

Sarah and Beyang talk about how Sourcegraph is thinking about augmenting the coding process in a way that ensures accuracy and efficiency starting with robust and high-quality context. They also think about what the future of software development could look like in a world where AI can generate high-quality code on its own and where that leaves humans in the coding process. 

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Show Notes: 

(0:00) Beyang Liu’s experience

(0:52) Sourcegraph premise

(2:20) AI and finding flow

(4:18) Developing LLMs in code

(6:46) Cody explanation

(7:56) Unlocking AI code generation

(11:00) search architecture in LLMs

(16:02) Quality-assurance in data set

(18:03) Future of Cody

(22:48) Constraints in AI code generation

(30:28) Lessons from Beyang’s research days

(33:17) Benefits of small models

(35:49) Future of software development

(42:14) What skills will be valued down the line

We’re looking back on 2023 and sharing a handful of our favorite conversations. Last year was full of insightful conversations that shaped the way we think about the most innovative movements in the AI space. Want to hear more? Check out the full episodes here:

• What is Digital Life? with OpenAI Co-Founder & Chief Scientist Ilya Sutskever 
• How AI can help small businesses with Former Square CEO Alyssa Henry
• Will Everyone Have a Personal AI? With Mustafa Suleyman, Founder of DeepMind and Inflection
• How will AI bring us the future of medicine? With Daphne Koller from Insitro
• The case for AI optimism with Reid Hoffman from Inflection AI
• Your AI Friends Have Awoken, With Noam Shazeer
• Mistral 7B and the Open Source Revolution With Arthur Mensch, CEO Mistral AI
• The Computing Platform Underlying AI with Jensen Huang, Founder and CEO NVIDIA

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Show Notes: 

(0:00) Introduction

(0:27) Ilya Sutskever on the governance structure of OpenAI

(3:11) Alyssa Henry on how AI can small business owners

(5:25) Mustafa Suleyman on defining intelligence

(8:53) Reid Hoffman’s advice for co-working with AI

(11:47) Daphne Koller on probabilistic graphical models

(13:15) Noam Shazeer on the possibilities of LLMs

(14:27) Arthur Mensch on keeping AI open

(17:19) Jensen Huang on how Nvidia decides what to work on

AI doomerism and calls to regulate the emerging technology is at a fever pitch but today’s guest, Reid Hoffman is a vocal AI optimist who views slowing down innovation as anti-humanistic. Reid needs no introduction, he’s the co-founder of PayPal, Linkedin, and most recently Inflection AI which is building empathetic AI companions. He is also a board member at Microsoft and former board member at OpenAI. On this week’s episode, Reid joins Sarah and Elad to talk about the historical case for an optimistic outlook on emerging technology like AI, advice for workers who fear AI may replace them, and why it’s impossible to regulate before you innovate. Plus, some predictions.

Aside from his storied experience in technology, Reid is an author, podcaster, and political activist. Most recently, he co-authors a book with GPT 4 called Impromptu: Amplifying Our Humanity Through AI.

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Show Notes: 

(0:00) Reid Hoffman’s birdseye view on the state of AI

(3:37) AI and human collaboration in workflows

(5:23) What’s causing AI doomerism

(12:28) Advice for whitecollar workers

(16:45) Why Reid isn’t retiring

(18:25) How Inflection started

(22:06) Surprising ways people are using Inflection

(25:34) Western bias and AI ethics

(30:58) Structural challenges in governing AI

(33:15) Most exciting whitespace in AI

(35:00) GPT 5 and Innovations coming in the next two years

(44:00) What future should we be building?

AI tools are helping small business owners manage their businesses, so they can stay focused on the aspects of their business they love to do. This week on No Priors, Sarah and Elad are joined by Alyssa Henry, an executive at some of the most impactful companies from Microsoft to Amazon. Most recently she was the CEO of Square. She led Square’s team as they were very early adopters of a consumer-facing product that used GPT-2 and have continued to incorporate AI into their offerings. On today’s episode, they talk about the whitespace within e-commerce for AI and lessons from the prior generation of infrastructure.

Alyssa recently retired from being longtime CEO of Square, within Block. Before that she was a vice president of AWS running, amongst other things, the storage products, or the digital storage bucket for the world. And before AWS, she ran order management software at Amazon Retail and started her tech career at Microsoft. She remains on the boards of Intel, Confluent and was previously on the board of Unity. 

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Show Notes: 

(0:00) Alyssa’s experience and career trajectory

(2:30) Transition from engineer to manager

(4:09) AI implementation at Square

(7:46) Small business AI applications 

(12:14) Latent demand for content generation

(15:04) The origin story of Square’s GPT-2 products

(16:54) Consolidating ecommerce workflows

(18:46) How will AI change cloud services

(23:07) Hyperscaler foundation models and the AI land grab

(25:16) Enterprise demand for open source models

(28:08) Startups in the AI semiconductor space

(31:02) Scale up architectures vs scaling out

(34:32) What’s next for Alyssa

(36:08) What Elad and Sarah are excited about in 2024

AI is the new UI for enterprise customers, according to Clara Shih, the CEO of Salesforce AI. Salesforce released Einstein, now called Einstein GPT, in 2016, making it an early example of how beneficial AI can be when embedded in enterprise software. This week on No Priors, Sarah and Elad talked with Clara about what the evolution of AI in enterprise looks like, how Salesforce is adoption AI across the organization, and the onboarding process for companies looking to integrate AI into their workflow, plus the challenges of pricing for AI services.

Clara Shih is the Chief Executive Officer of Salesforce AI where she leads the AI efforts across Salesforce including AI co-pilot and agent platform, model development, go-to-market growth, adoption, partnerships, ecosystems, and secure responsible AI. Before that was the CEO of Salesforce Service Cloud She is also the co-founder and previous CEO of Hearsay Systems. She is also on the Board of Directors at Starbucks. 

Show Links: 

• Clara’s Linkedin
• Ask more of AI podcast
• Salesforce AI

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Show Notes: 

(0:00) Clara’s Background

(0:50) From cloud services to AI

(3:25) Internal Model Development vs Open Source

(5:20) The Co-Pilot Approach

(8:50) Enterprise AI Adoption

(10:54) The future of Enterprise AI

(13:23) Cross-team collaboration

(14:40) AI is the new UI

(19:11) Structuring the Dataset

(21:25) What’s next for generative AI in Enterprise

(23:18) Pricing challenges in AI

(26:30) Startups and AI

(28:22) Collaboration in AI Industry

OpenAI’s leadership has taken us all on a rollercoaster so it’s great timing for another host-only episode. This week Sarah and Elad get into what has been going on at OpenAI and what the turbulent leadership changes tell us about the importance of good intent and good incentives when building these influential companies. They also talk about innovative products coming out of Pika Labs, why people are moving away from diffusion models to LLMs, and how, in AI investing, the ASP is the opportunity. 

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Show Notes: 

(0:00) Recapping the OpenAI saga

(9:56) AI video products

(16:14) Moving from Diffusion Models to LLMs

(19:47) The beneficial margins of AI investing

The future of tech is 25-person companies powered by AI agents that help us accomplish our larger goals. Imbue is working on building AI agents that reason, code and generally make our lives easier. Sarah Guo and Elad Gil sit down with co-founders Kanjun Qiu (CEO) and Josh Albrecht (CTO) to discuss how they define reasoning, the spectrum of specialized and generalized agents, and the path to improved agent performance. Plus, what’s behind their $200M Series B fundraise. 

Kanjun Qiu is the CEO and co-founder of Imbue. Kanjun is also a partner at angel fund Outset Capital, where she invests in promising pre-seed companies. Previously, Kanjun was the co-founder and CEO of Sourceress, a machine learning recruiting startup backed by YC and DFJ. She was previously Chief of Staff to Drew Houston at Dropbox, where she helped scale the company from 300 employees to 1200.

Josh Albrecht is the CTO and co-founder of Imbue. He also invests in other founders via his fund, Outset Capital. He has published machine learning papers as an academic researcher; founded an AI recruiting company that went through YC and a 3D injection molding software company that was acquired; helped build Addepar as an early engineer; and served as a Thiel Fellow mentor. He started programming as a kid and began working professionally as a software engineer in high school. 

Show Links: 

• Kanjun’s LinkedIn | Website | Google Scholar
• Josh’s LinkedIn | Website | Google Scholar
• Imbue raises $200M to build AI systems that can reason and code

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Show Notes: 

(00:00) - Introduction to Imbue

(04:55) - The Spectrum of Agent Tasks

(08:43) - Specialization and Generalization With Agents

(13:03) - Code and Language in AI Agents

Open Source fuels the engine of innovation, according to Arthur Mensch, CEO and co-founder of Mistral AI. Mistral is a French AI company which recently made a splash with releasing Mistral 7B, the most powerful language model for its size to date, and outperforming much larger models. Sarah Guo and Elad Gil sit down with Arthur to discuss why open source could win the AI wars, their $100M+ seed financing, the true nature of scaling laws, why he started his company in France, and what Mistral is building next.

Arthur Mensch is Chief Executive Officer and co-founder of Mistral AI. A graduate of École Polytechnique, Télécom Paris and holder of the Master Mathématiques Vision Apprentissage at Paris Saclay, he completed his thesis in machine learning for functional brain imaging at Inria (Parietal team). He spent two years as a post-doctoral fellow in the Applied Mathematics department at ENS Ulm, where he carried out work in mathematics for optimization and machine learning. In 2020, he joined DeepMind as a researcher, working on large language models, before leaving in 2023 to co-found Mistral AI with Guillaume Lample and Timothee Lacroix.

Show Links: 

• Arthur’s Linkedin
• Mistral
• Mistral 7b
• Retro: Improving language models by retrieving from trillions of tokens
• Chinchilla: Training Compute-Optimal Large Language Models

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Show Notes: 

(0:00) - Why he co-founded Mistral

(4:22) - Chinchilla and Proportionality 

(6:16) - Mistral 7b

(9:17) - Data and Annotations

(10:33) - Open Source Ecosystem 

(17:36) - Proposed Compute and Scale Limits

(19:58) - Threat of Bioweapons 

(23:08) - Guardrails and Safety 

(29:46) - Mistral Platform

(31:31) - French and European AI Startups

Each iteration of ChatGPT has demonstrated remarkable step function capabilities. But what’s next? Ilya Sutskever, Co-Founder & Chief Scientist at OpenAI, joins Sarah Guo and Elad Gil to discuss the origins of OpenAI as a capped profit company, early emergent behaviors of GPT models, the token scarcity issue, next frontiers of AI research, his argument for working on AI safety now, and the premise of Superalignment. Plus, how do we define digital life?

Ilya Sutskever is Co-founder and Chief Scientist of OpenAI. He leads research at OpenAI and is one of the architects behind the GPT models. He co-leads OpenAI's new "Superalignment" project, which tries to solve the alignment of superintelligences in 4 years. Prior to OpenAI, Ilya was co-inventor of AlexNet and Sequence to Sequence Learning. He earned his Ph.D in Computer Science from the University of Toronto.

Show Links:

• Ilya Sutskever | LinkedIn

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Show Notes:

(00:00) - Early Days of AI Research

(06:51) - Origins of Open Ai & CapProfit Structure

(13:46) - Emergent Behaviors of GPT Models

(17:55) - Model Scale Over Time & Reliability

(22:23) - Roles & Boundaries of Open-Source in the AI Ecosystem (28:22) - Comparing AI Systems to Biological & Human Intelligence (30:52) - Definition of Digital Life

(32:59) - Super Alignment & Creating Pro Human AI

(39:01) - Accelerating & Decelerating Forces 

Cyber Security is going to change significantly in the era of AI, according to Ryan Noon, cofounder of Material Security, a security company that makes cloud-based Google and Microsoft email a safe place for sensitive data. Elad Gil and Ryan talk about how Material Security started to use LLMs, potential security threats from AI hacks, and the role of the government in securing the Internet. Ryan also shares his advice for founders.

Ryan co-founded Material Security in 2017 after seeing high profile email hacks in the 2016 Presidential election. Previously, he led various engineering teams at Dropbox after it acquired his first company, Parastructure. Prior to Parastructure, he led engineering at a data analysis company spun out of Stanford by DARPA. He holds both an MS in Computer Networks and Security and a BS in Computer Science from Stanford.

Show Links: 

• Ryan Noon LinkedIn
• Material Security Website 
• The Market for Silver Bullets by Ian Grigg

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Show Notes: 

(00:00) - How 2016 Election Hacking Inspired Ryan to Start Material Security

(05:00) - Generative AI Use Cases in Cyber Security & Fine Tuning

(11:36) - Predictions on Effective Threat Levels from AI Hacks

(14:45) - Democracy, the Department of Defence, DARPA and Cyber Security

(20:14) - Is there room for startups in the Cyber Security industry?

(26:40) - New Challenges On Horizon After 7 Years as Cofounder

(32:30) - Advice to Founders

As the Lead for Generative AI in the Office of the CTO for Google Cloud, Kawal Gandhi has a unique vantage point on enterprise AI rollout. Sarah Guo and Elad Gil sit down with Gandhi this week to discuss his insights on how enterprises can effectively invest in AI development, the importance of TPUs, and Google’s internal AI applications. Plus, when will email get more intelligent? 

Kawal Gandhi has worked at Google for nearly a decade in search and ad roles before focusing on the development and marketing of AI tools.

Show Links: 

• Kawal Gandhi | LinkedIn
• Google Cloud  



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Show Notes: 

(00:00) - Generative AI in Google Cloud

(09:05) - AI Adoption in the Enterprise

(13:31) - Multi-Modal AI Models

(16:19) - AI Adoption, return-on-investment, anti-patterns

(24:43) - Google's TPU and NVIDIA GPU shortage

(31:00) - Data Marketplace and Model Training

Startups aren't the only companies racing to build the new world of AI. This week, Sarah Guo talks with Brian Halligan, the co-founder, longtime CEO and now executive chairperson of HubSpot, the fastest growing CRM. He talks about category creation, coining the term ‘inbound marketing,’ lessons in scaling from an app to a suite to a platform, staying innovative at scale, and how they're navigating the AI disruption. Brian also describes the life-threatening moment he decided to step back from the CEO role. Plus, what he’s up to at Propeller Ventures and why he’s banking on the ocean to save us from climate change.

Brian coined the term "inbound marketing" and together with Dharmesh Shah built a movement around the concept, which included organizing the industry-leading INBOUND event and co-authoring the book Inbound Marketing. Now, as the founder of Propeller Ventures, Brian directs a $100 million climate tech venture fund, specializing in ocean investments. He also serves on the boards of Navier and Aquatic Labs. Brian developed MIT’s popular Scaling Entrepreneurial Ventures class, which he’s taught for over a decade.

Show Links: 

• Brian Halligan | LinkedIn 
• Propeller VC
• WHOI Partnership
• HubSpot Culture Code
• Read his books: Inbound Marketing and Marketing Lessons From the Grateful Dead

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Show Notes: 

(0:00:00) - HubSpot's Journey from Unlikely Startup to Industry Incumbent

(0:05:32) - The End of Cold Calling (and the Birth of Inbound)

(0:16:40) - Building a Multi-Product Company

(0:22:07) - How to Stay Innovative and Hungry after Going Public

(0:29:12) - AI Workflows in CRM and the Incumbent Data Advantage

(0:36:09) - Creating a Culture Code for HubSpot

(0:40:24) - Propeller Venture Fund, Ours Oceans and Climate Investing

What Does it Take to Improve by 10x or 100x? This week is another host-only episode. Sarah and Elad talk about the path to better model quality, the potential for fine tuning to different use cases, retrieval systems (RAG), feedback systems (RLHF, RLAIF) and Meta’s sponsorship of the open source model ecosystem. Plus Sarah and Elad ask if we’re finally at the beginning of a new set of consumer applications and social networks.

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Show Notes:

0:03:00 - AI Models and Open AI Advances 

0:08:59 - Addressing Hallucinations in AI Models 

0:13:22 - Open Source Models in Consumer Engagement

0:16:23 - New Trends in Social Content Creation

0:21:53 - Balancing Ambition With Realistic Customer Expectations

Ginkgo Bioworks is using DNA as code to digitize the cell programming revolution. Ginkgo is using AI and synthetic biology to keep the next pandemic at bay, and accelerate our production capabilities for medicine, food, and agriculture. Ginkgo’s co-founder and CEO Jason Kelly joins hosts Sarah Guo and Elad Gil to discuss bioengineering protein as a foundational model, specialized data learning from an evolutionary perspective, what we need to prepare for a future pandemic, and more.

Jason has served as a member of our board of directors since Ginkgo’s founding in 2008. He has also served as a director of CM Life Sciences II Inc. (Nasdaq: CMII), a special purpose acquisition company with a focus on the life sciences sector, since its initial public offering in February 2021. Jason holds a Ph.D. in Biological Engineering and a B.S. in Chemical Engineering and Biology from the Massachusetts Institute of Technology.

Show Links: 

• Jason Kelly - Co-founder & CEO of Ginkgo Bioworks | LinkedIn  
• Ginkgo Bioworks
• The Plausibility of Life: Resolving Darwin's Dilemma

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Show Notes: 

(0:00:00) - The Difference Between Software Engineering and Biological Engineering

(0:06:51) - Abstractions and Infrastructure in Synthetic Bio

(0:09:23) - The Role of AI, Foundation Models that Speak Biology

(0:13:17) - AWS for Cell Engineering

(0:17:52) - Where are the AI-discovered Drugs? And Data at Gingko

(0:19:12) - Pandemic Response and Biosecurity in the Age of AI

(0:22:47) - The Likelihood of Existential AI Risk from Lone Actors Harnessing Viruses, and The Need for Defense-in-Depth

(0:31:47) - Will Progress in AI Be Biologically Inspired? And Evolution

Replit’s develop-to-deploy platform and new AI tool, Ghostwriter, are breaking down the barriers to entry for beginner programmers. Replit’s CEO, co-founder, and head engineer Amjad Masad joins hosts Sarah Guo and Elad Gil to discuss how AI can change software engineering, the infrastructure we still need, open source foundation models, and what to expect from AI agents.

Before co-founding Replit, Amjad Masad worked at Facebook as a software engineer, where he worked on infrastructure tooling. He was a founding engineer at CodeAcademy. Throughout his career, Masad has been an advocate for open-source software.

Show Links: 

• Amjad Masad - CEO & Co-founder of Replit | LinkedIn  
• Replit

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Show Links: 

• Amjad Masad - CEO & Co-founder of Replit | LinkedIn  
• Replit

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Show Notes: 

0:03:55 - Impact of AI on Code Generation 

0:11:09 - Breaking Down Barriers to Entry in Development with Replit

0:14:35 - The Impact of Open Source Models, Meta/Llama

0:20:32 - Bounties, Agents who Make Money

0:24:26 - The Missing Data Spec-to-Code

0:32:29 - Building the Future of AI, Money as a Programmable Primitive

More than 25 million users are using NEAR-powered applications. Co-founder of NEAR protocol and Transformers author Illia Polosukhin joins hosts Sarah Guo and Elad Gil to discuss the intersections of crypto and AI technology, what we should expect from AI agents, decentralized data labeling, why AI’s alignment problem is really a human problem, and more. 

Show Links: 

• Illia Polosukhin - Co-founder of NEAR | LinkedIn  
• NEAR

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Show Notes: 

(0:00:00) - Blockchain, AI, and Web3 Intersection

(0:06:39) - How We Might Combine Blockchain and AI for Cancer Research

(0:23:35) - Inference and Decentralized Data Labeling

(0:30:13) - AI SaaS Strategic Challenges

(0:38:18) - The Future of Hardware Accelerators

The GPU supply crunch is causing desperation amongst AI teams large and small. Cerebras Systems has an answer, and it’s a chip the size of a dinner plate. Andrew Feldman, CEO and Co-founder of Cerebras and previously SeaMicro, joins Sarah Guo and Elad Gil this week on No Priors. They discuss why there might be an alternative to Nvidia, localized models and predictions for the accelerator market.

Show Links: 

• Andrew Feldman - Cerebras CEO & Co-founder | LinkedIn  
• Cerebras

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Show Notes: 

(0:00:00) - Cerebra Systems CEO Discusses AI Supercomputers

(0:07:03) - AI Advancement in Architecture and Training

(0:16:58) - Future of AI Accelerators and Chip Specialization

(0:26:38) - Scaling Open Source Models and Fine-Tuning

Everything digital is increasingly intermediated through web user experiences, and now AI development can be frontend-first, too. Just ask Guillermo Rauch, the founder and CEO of Vercel, the company behind Next.js. In this episode of No Priors, hosts Sarah Guo and Elad Gil speak to Guillermo about their AI SDK and AI templates, and why Vercel is focused on making it easy for every frontend engineer to build with AI. They also discuss what applications Guillermo's most excited about, how to prepare for the world of bots, whether the winds are changing in web architectures, and why he believes in the AI-fueled 100X engineer.

Prior to Vercel, Guillermo co-founded several startups and created the JavaScript library, Socket.io, which allows for real-time bi-directional communication between web clients and servers.

Show Links:

• Guillermo Rauch - CEO & Founder of Vercel | LinkedIn 
• Vercel
• Vercel AI

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Show Notes: 

(0:00:00) - Vercel's AI Strategy and Future Plans

(0:10:36) - AI Frameworks, Observability, and Bot Mitigation

(0:17:24) - Crawling the Web and Architecture Changes

(0:27:54) - AI's Impact on Web Personalization

"Biological Software" is the future of medicine. Jakob Uszkoreit, CEO and Co-founder of Inceptive, joins Sarah Guo and Elad Gil this week on No Priors, to discuss how deep learning is expanding the horizons of RNA and mRNA therapeutics.

Jakob co-authored the revolutionary paper Attention is All You Need while at Google, and led early Google Translate and Google Assistant teams. Now at Inceptive, he's applying these same architectures and ideas to biological design, optimizing vaccine production, and magnitude-more efficient drug discovery. We also discuss Jakob's perspective on promising research directions, and his point of view that model architectures will actually get simpler from here, and be driven by hardware.

Show Links: 

• Inceptive - CEO & Founder - Jakob Uszkoreit | LinkedIn  
• Inceptive

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Show Notes: 

(0:00:00) - Creating Biological Software

(0:06:54) - The Hardware Drivers of Large-Scale Transformers

(0:14:32) - Challenges in Optimizing Compute Allocation

(0:23:25) - Deep Learning in Biology and RNA

(0:32:49) - The Future of Drug Discovery

(0:41:41) - Collaboration and Innovation at Inceptive

The future of education is right at your children’s fingertips. Sal Khan, CEO and Founder of Khan Academy, joins Sarah Guo and Elad Gil this week on No Priors. For over a decade, Sal Khan has been trying to reform education, beginning with tutoring his cousins in math. 

He's the father of the YouTube "chalk talk" format, and has now served tens of millions of students through Khan Academy. 

He guides us through how Khan Academy is using AI to personalize a student's educational experience, transporting students into immersive learning experiences that allow them to debate historical figures, to assisting teachers with lesson plans that address the learning gaps keeping students from reaching their full potential, to a Khanmigo, a tutor for every child. 

Prior to founding Khan Academy, Sal worked as a hedge fund analyst. He holds an MS in business from Harvard University, as well as an MS in Engineering and a BS in Computer Science from MIT.

Show Links: 

• Khan Academy - CEO & Founder - Khan Academy | LinkedIn  
• Khan Academy

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Show Notes: 

[0:00:06] - Sal Khan's Journey

[0:08:41] - Mastery Learning and AI in Education

[0:19:53] - Future of AI Tutors in Education

[0:23:10] - Education's Future With Generative AI

[0:29:35] - Connecting Learning Through Tutoring and Collaboration

[0:33:22] - Implications of GPT 4 on Education

[0:40:42] - Future of Education and Job Skills

[0:46:47] - Importance of Traditional Skills in Education

This week on the podcast, Sarah Guo and Elad Gil answer listener questions on the state of technology and artificial intelligence. Sarah and Elad also talk about the 2024 tech market, what type of companies may reach their highest valuation ever and the (former) unicorns that may go bust. Plus, how do Sarah and Elad define happiness? Hint: it’s a use case for a specialized AI agent. 

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Show Links:

• Cerebras Systems signs $100 million AI supercomputer deal with UAE's G42 | Reuters 
• Our World in Data 

Show Notes: 

[0:00:37] - Impact of GPU Bottleneck in the near and long term

[0:10:30] - Timeline for existing incumbent enterprises to use AI in products 

[0:11:50] - Vertical versus broad applications for AI Agents  

[0:19:33] - 2024 tech market predictions & how founders should think about valuations 

How are ML developer tools helping to advance our capabilities? Lukas Biewald, CEO of Weights & Biases, joins Sarah Guo and Elad Gil this week on No Priors. Lukas explores the impact of ML in various industries like gaming, AgTech, and fintech through his insightful perspective. He discusses the impact of LLMs, puts them in context of the evolution of ML engineering over the past decade and a half, and tells the backstory of Weights & Biases' success. He gives advice for aspiring AI company founders, placing emphasis on customer feedback and using insecurity as a vehicle for better customer discovery.

Prior to founding Weights & Biases, Lukas attacked the problem of data collection for model training as the Founder of Figure Eight, which he sold in 2019. He holds an MS in Computer Science and a BS in Mathematics from Stanford University.

Show Links: 

• Lukas Biewald - CEO & Co-founder - Weights & Biases | LinkedIn  
• Weights & Biases

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Show Notes: 

[0:00:00] - Lucas Wald's Journey in AI

[0:08:16] - Startup Evolution and Machine Learning

[0:18:54] - Open Source Models Implications and Adoption

[0:29:54] - ML Impact in Various Industries

[0:40:27] - Advice for AI Company Founders

Can frontiers as high-stakes as next-generation, AI-enabled defense depend on something as mundane as data integration? Can "large language models" work in such mission critical applications? In this episode of No Priors, hosts Sarah Guo and Elad Gil are joined by Shyam Sankar, the Chief Technical Officer of Palantir Technologies and inventor of their famous Forward Deployed Engineering force.

Early employee and longtime leader Shyam explains the evolution of technology at Palantir, from ontology and data integration to process visualization and now AI. He describes how a company of Palantir's scale has adopted foundation models and shares customer stories. They discuss the case for open source AI models fine-tuned on private, domain-specific data, and the challenges of anchoring AI models in reality.

Show Links:

• Shyam Sankar - Chief Technical Officer - Palantir Technologies | LinkedIn  
• Palantir

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Show Notes: 

[0:00:00] - Palantir's CTO Discusses Company's Background

[0:10:17] - Apollo and AIP

[0:20:25] - Future of UI and Application Integration

[0:28:29] - Investment in Co-Pilot Models and Education

[0:31:22] - Exploring AI Implementation in Various Industries

[0:38:19] - Operational and Analytical Workflows in Context

Video dominates modern media consumption, but video creation is still expensive and difficult. AI-generated and edited video is a holy grail of democratized creative expression. This week on No Priors, Sarah Guo and Elad Gil sit down with Devi Parikh. She is a Research Director in Generative AI at Meta and an Associate Professor in the School of Interactive Computing at Georgia Tech. Her work focuses on multimodality and AI for images, audio and video. Recently, she worked on Make a Video 3D, also called MAV3D, which creates animations from text prompts. She is also a talented AI-generated and analog artist herself.

Elad, Sarah and Devi talk about what’s exciting in computer vision, what’s blocking researchers from fully immersive Generative 4-D, and AI controllability.

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Show Links:

• Devi Parikh - Google Scholar 
• Text-To-4D Dynamic Scene Generation named MAV3D (Make-A-Video3D)
• Full Research Paper
• Website with examples of image to 4 D generation
• Devi’s Substack

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Show Notes:

(0:00:06) - Democratizing Creative Expression With AI-Generated Video

(0:08:31) - Challenges in Video Generation Research

(0:15:57) - Challenges and Implications of Video Processing

(0:20:43) - Control and Multi-Modal Inputs in Video

(0:25:50) - Audio's Role in Visual Content

(0:39:00) - Don't Self-Select & Devi’s tips for young researchers

Frank Slootman, CEO of Snowflake Computing, joins Sarah Guo and Elad Gil this week on No Priors. Before scaling Snowflake to its blockbuster IPO and beyond, Frank was also the CEO from early to scale for landmark enterprise companies ServiceNow and Data Domain. Frank grew up in the Netherlands and is also the author of three books: Amp It Up, Rise of the Data Cloud, and Tape Sucks.

In this episode, our hosts talk with Frank about the opportunity for generative AI in the enterprise, why Snowflake isn't really a data warehousing company, their acquisitions of Neeva and Streamlit, apps within Snowflake, and how AI relates to traditional analytics and BI. He also talks about his personal journey, why it's always a good time to do performance management, and why most leaders struggle to raise the bar for performance.

** No Priors is taking a summer break! The podcast will be back with new episodes in three weeks. Join us on July 20th for a conversation with Devi Parikh, Research Director in Generative AI at Meta. **

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Show Links:

• Forbes: How CEO-For-Hire Frank Slootman Turned Snowflake Into Software’s Biggest-Ever IPO
• Amp It Up: Leading for Hypergrowth by Raising Expectations, Increasing Urgency, and Elevating Intensity
• Rise of the Data Cloud (Audible Audio Edition): Frank Slootman, Steve Hamm, Zach Hoffman, Snowflake: Books
• TAPE SUCKS: Inside Data Domain, A Silicon Valley Growth Story eBook : Slootman, Frank: Kindle Store
• Frank Slootman’s LinkedIn

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Show Notes:

[00:06] - Frank’s Insights on Career Success as a three-time CEO

[12:42] - The message of his book Amp It Up

[25:01] - Future of Natural Language and Data

[36:29] - Data Management and Industry Transformation Future

[45:13] - Managing Resources in Changing Economic Environment

[50:09] - Amping Up Energy and Intensity Amid Economic Headwinds

Fidji Simo, the CEO of Instacart and co-founder of Metrodora Institute, a medical center and research institute for neuroimmune axis disorders, shares her personal journey from growing up in France, to leading the Facebook app, to becoming a wartime CEO. Fidji talks about the future of Instacart, their AI strategy, how the current era of AI is different from prior ML waves, and the impact of LLMs in commerce, robotics and healthcare. She also shares how she earns followership from her teams.

** No Priors is taking a summer break! The podcast will be back with new episodes in three weeks. Join us on July 20th for a conversation with Devi Parikh, Research Director in Generative AI at Meta. **

No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.

Show Links:

• May 31, 2023: Instacart users can now plan meals using AI - Fast Company Profile
• May 13, 2023: Instacart CEO Fidji Simo makes groceries personal. Now she’s doing the same for women’s health
• December 2, 2021: Rapid Response: Re-founding Instacart, w/ first-time CEO Fidji Simo | Podcast: Masters of Scale with Reid Hoffman
• Metrodora Institute
• Fidji Simo’s LinkedIn

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Show Notes:

[00:01] - Leading With Impact and Authenticity

[11:48] - Implementing AI

[17:28] - Future of Grocery Shopping With AI

[25:38] - AI in Advertising and Commerce

[32:54] - Metrodora: AI in Biotech and Healthcare

[34:18] - The positive impact of AI, mitigating harm & role of regulations

Olivier Pomel, co-founder and CEO of Datadog, the leading observability company, discusses the company’s founding story, early product sequencing, platform strategy, and acquisitions. Olivier also shares his thoughts on their more recent expansion into security, and why he’s bullish on the potential for AI in DevOps.

** No Priors is taking a summer break! The podcast will be back with new episodes in three weeks. Join us on July 20th for a conversation with Devi Parikh, Research Director in Generative AI at Meta. **

No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.

Show Links:

• Nov 22, 2022: Product-led Growth: Founder 1-on-1 with Datadog and Aiven - Olivier Pomel & Oskari Saarenmaa
• May 25, 2022: Datadog, Inc. (DDOG) CEO Olivier Pomel Presents at J.P. Morgan's 50th Annual Global Technology, Media and Communications Conference
• Jan 6, 2021: Datadog CEO Olivier Pomel on the cloud computing outlook
• Datadog’s Official Website
• Olivier Pomel LinkedIn

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Show Notes:

[00:10] - DevOps and AI Potential

[06:54] - Datadog and Generative AI

[20:40] - Datadog's Acquisition and Expansion Strategy

[31:46] - LLMs in Automation and Precision

[42:35] - Datadog's Customer Value and Growth

This week on No Priors, Sarah and Elad do another hangout to answer listener questions. Topics include debunking common misconceptions about AI and its implications on the world, the analogy to nuclear power and nuclear safety, the impact of larger context windows, developer productivity, incumbent announcements of AI products, and some requests for (fat) startups.

No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.

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Show Notes:

[00:21] - What Are People Getting Wrong About AI Right Now? / New Capabilities of NLP

[04:35] - Nuclear Power and Safety Concerns

[11:12] - Emerging AI Companies and Research

[15:54] - China's Hardware Sanctions and Funding Ramp

[20:34] - Innovation in Heterogeneous Compute Infrastructure

[28:08] - Enterprise Stack and Decision Making

[33:44] - Data's Impact on the World

Today on No Priors, we discuss defense technology, AI, drones, and autonomous vehicles (think giant submarine drones!) with Brian Schimpf, the co-founder and CEO of Anduril, a next-generation defense technology company. From his early days of coding at age 12 to working on self-driving cars, and finally founding Anduril, Brian's incredible journey led him to create innovative solutions for pressing defense problems.

This episode covers the impact of AI, intelligent software, and other technologies to defense. We discuss the challenges of deploying and selling technology in the government spaceBrian shared his perspective on building general-purpose defense technology, the importance of a software-first approach, and how Anduril is working to solve urgent defense problems with speed and efficiency.

As we wrapped up our conversation, we touched on the recent shift in the low cost of space launch, which has changed the way the US thinks about defense. We examined the proliferation of satellites, drones, and hypersonic missiles, and how these technologies can be applied, scaled, and built in a way that can fundamentally shift America's approach to defense. Don't miss this fascinating episode with Brian Schimpf as we uncover the cutting edge of defense technology and its implications for the future.

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Show Links:

• May 16, 2023: AI in Military Operations and How We Can Prevent It From Outsized Effects
• May 9, 2023: CNBC Disruptor 50 - Anduril Industries
• Anduril Website
• Anduril Newsroom

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Show Notes:

[0:00:01] - Exploring AI in Defense Tech

[0:05:15] - Lower Cost Defense With Intelligent Software

[0:15:10] - Building General Purpose Defense Technology

[0:20:41] - Autonomy in Defense Challenges

[0:25:05] - Machine Learning in Defense & Intelligence

[0:29:06] - Scaling a Defense Tech Company

[0:37:08] - The Future of Defense Technology

[0:46:53] - Allied Forces and Washington Engagement

[0:51:47] - Discussion on Leadership Popularity

In this episode, Sarah and Elad speak with Microsoft CTO Kevin Scott about his unlikely journey from rural Virginia to becoming the driving force behind Microsoft's AI strategy. 

Sarah and Elad discuss the partnership that Kevin helped forge between Microsoft and OpenAI and explore the vision both companies have for the future of AI. They also discuss yesterday’s announcement of “copilots” across the Microsoft product suite, Microsoft’s GPU computing budget, the potential impact of open source AI models in the tech industry, the future of AI in relation to jobs, why Kevin is bullish on creative and physical work, and predictions for progress in AI this year.

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Show Links:

• May 23, 2023: The Verge - Microsoft CTO Kevin Scott Thinks Sydney Might Make a Comeback
• May 23, 2023: Microsoft Outlines Framework For Building AI Apps and Copilots
• January 10, 2023: A Conversation with Kevin Scott: What’s Next In AI

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Show Notes:

[00:00] - Kevin Scott's Journey to Microsoft CTO

[12:44] - Microsoft and Open AI Partnership

[21:18] - The Future of Open Source AI

[32:12] - AI for Everyone

[45:29] - AI and the Future of Jobs

[51:44] - The Future of AI and Regulation

[58:10] - Taking a Global Perspective

What if AI could revolutionize healthcare with advanced language learning models? Sarah and Elad welcome Karan Singhal, Staff Software Engineer at Google Research, who specializes in medical AI and the development of MedPaLM2. On this episode, Karan emphasizes the importance of safety in medical AI applications and how language models like MedPaLM2 have the potential to augment scientific workflows and transform the standard of care.

Other topics include the best workflows for AI integration, the potential impact of AI on drug discoveries, how AI can serve as a physician's assistant, and how privacy-preserving machine learning and federated learning can protect patient data, while pushing the boundaries of medical innovation.

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Show Links:

• May 10, 2023: PaLM 2 Announcement
• April 13, 2023: A Responsible Path to Generative AI in Healthcare
• March 31, 2023: Scientific American article on Med-PaLM
• February 28, 2023: The Economist article on Med-PaLM
• KaranSinghal.com

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Show Notes:

[00:22] - Google's Medical AI Development

[08:57] - Medical Language Model and MedPaLM 2 Improvements

[18:18] - Safety, cost/benefit decisions, drug discovery, health information, AI applications, and AI as a physician's assistant.

[24:51] - Privacy Concerns - HIPAA's implications, privacy-preserving machine learning, and advances in GPT-4 and MedPOM2.

[37:43] - Large Language Models in Healthcare and short/long term use.

Mustafa Suleyman, co-founder of DeepMind and now co-founder and CEO of Inflection AI, joins Sarah and Elad to discuss how his interests in counseling, conflict resolution, and intelligence led him to start an AI lab that pioneered deep reinforcement learning, lead applied AI and policy efforts at Google, and more recently found Inflection and launch Pi.

Mustafa offers insights on the changing structure of the web, the pressure Google faces in the age of AI personalization, predictions for model architectures, how to measure emotional intelligence in AIs, and the thinking behind Pi: the AI companion that knows you, is aligned to your interests, and provides companionship.

Sarah and Elad also discuss Mustafa’s upcoming book, The Coming Wave (release September 12, 2023), which examines the political ramifications of AI and digital biology revolutions.

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Show Links:

• Forbes - Startup From Reid Hoffman and Mustafa Suleyman Debuts ChatBot
• Inflection.ai
• Mustafa-Suleyman.ai

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Show Notes:

[00:06] - From Conflict Resolution to AI Pioneering

[10:36] - Defining Intelligence

[15:32] - DeepMind's Journey and Breakthroughs

[24:45] - The Future of Personal AI Companionship

[33:22] - AI and the Future of Personalized Content

[41:49] - The Launch of Pi

[51:12] - Mustafa’s New Book The Coming Wave

How do you personalize AI models? A popular school of thought in AI is to just dump all the data you need into pre-training or fine tuning. But that may be less efficient and less controllable than alternatives — using AI models as a reasoning engine against external data sources.

Kelvin Guu, Senior Staff Research Scientist at Google, joins Sarah and Elad this week to talk about retrieval, memory, training data attribution and model orchestration. At Google, he led some of the first efforts to leverage pre-trained LMs and neural retrievers, with >30 launches across multiple products. He has done some of the earliest work on retrieval-augmented language models (REALM) and training LLMs to follow instructions (FLAN).

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Show Links:

• Kelvin Guu Website
• Google Scholar
• FLAN: Finetuned Language Models Are Zero-Shot Learners
• Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs
• ROME: Locating and Editing Factual Associations in GPT
• Branch-Train-Merge: Scaling Expert Language Models with Unsupervised Domain Discovery
• Large Language Models Struggle to Learn Long-Tail Knowledge 

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Show Notes:

[1:44] - Kelvin’s background in math, statistics and natural language processing at Stanford

[3:24] - The questions driving the REALM Paper

[7:08] - Frameworks around retrieval augmentation & expert models

[10:16] - Why is modularity important

[11:36] - FLAN Paper and instruction following

[13:28] - Updating model weights in real time and other continuous learning methods

[15:08] - Simfluence Paper & explainability with large language models

[18:11] - ROME paper, “Model Surgery” exciting research areas

[19:51] - Personal opinions and thoughts on AI agents & research

[24:59] - How the human brain compares to AGI regarding memory and emotions

[28:08] - How models become more contextually available

[30:45] - Accessibility of models

[33:47] - Advice to future researchers

This week on No Priors, Sarah and Elad answer listener questions about tech and AI. Topics covered include the evolution of open-source models, Elon AI, regulating AI, areas of opportunity, and AI hype in the investing environment. Sarah and Elad also delve into the impact of AI on drug development and healthcare, and the balance between regulation and innovation.

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Show Notes: 

[0:00:06] - The March of Progress for Open Source Foundation Models 

[0:06:00] - Should AI Be Regulated?

[0:13:49] - Investing in AI and Exploring the AI Opportunity Landscape

[0:23:28] - The Impact of Regulation on Innovation

[0:31:55] - AI in Healthcare and Biotech

So much of the AI conversation today revolves around models and new applications. But this AI revolution would not be possible without one thing – GPUs, Nvidia GPUs.

The Nvidia A100 is the workhorse of today’s AI ecosystem. This week on No Priors, Sarah Guo and Elad Gil sit down with Jensen Huang, the founder and CEO of NVIDIA, at their Santa Clara headquarters. Jensen co-founded the company in 1993 with a goal to create chips that accelerated graphics. Over the past thirty years, NVIDIA has gone far behind gaming and become a $674B behemoth. Jensen talks about the meaning of this broader platform shift for developers, making very long term bets in areas such as climate and biopharma, their next-gen Hopper chip, why and how NVIDIA chooses problems that are unsolvable today, and the source of his iconic leather jackets.

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Show Links:

• Jensen Huang | NVIDIA
• Nvidia's A100 is the $10,000 chip powering the race for A.I. | CNBC
• Nvidia CEO Jensen Huang: A.I. is at ‘inflection point’ | Fortune

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Show Notes: 

[1:26] - The early days when Jensen Co-founded NVIDIA

[4:58] - Why NVIDIA started to expand its aperture to artificial intelligence use cases 

[10:42] - The moment in 2012 Jensen realized AI was going to be huge

[13:52] - How we’re in a broader platform shift in computer science

[17:48] - His vision for NVIDIA’s future lines of business

[18:09] - How NVIDIA has two motions: Shipping reliable chips and solving new use cases 

[25:41] - Why no one should assume they’re right for the job of CEO and why not every company needs to be architected as the US military 

[31:39] - What’s next for NVIDIA’s Hopper 

[32:57] - Durability of Transformers 

[35:08] - What Jensen is excited about in the future of AI & his advice for founders

Noam Shazeer played a key role in developing key foundations of modern AI - including co-inventing Transformers at Google, as well as pioneering AI chat pre-chatGPT. These are the foundations supporting today’s AI revolution. On this episode of No Priors, Noam discusses his work as an AI researcher, engineer, inventor, and now CEO. 

Noam Shazeer is currently the CEO and Co-founder of Character AI, a service that allows users to design and interact with their own personal bots that take on the personalities of well-known individuals or archetypes. You could have a socratic conversation with Socrates. You could pretend you’re being interviewed by Oprah. Or you could work through a life decision with a therapist bot. Character recently raised $150M from A16Z, Elad Gil, and others. Noam talks about his early AI adventures at Google, why he started Character, and what he sees on the horizon of AI development.

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Show Links:

• Noam Shazeer - Google Scholar
• Noam Shazeer - Chief Executive Officer - Character.AI | LinkedIn 
• Character.AI

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Show Notes: 

[1:50] - Noam’s early AI projects at Google

[7:13] - Noam’s focus on language models and AI applications

[11:13] - Character’s co-founder Daniel de Freitas Adiwardana work on Google’s Lambda

[13:53] - The origin story of Character.AI 

[18:47] - How AI can express emotions

[26:51] - What Noam looks for in new hires

If you have 30 dollars, a few hours, and one server, then you are ready to create a ChatGPT-like model that can do what’s known as instruction-following. Databricks’ latest launch, Dolly, foreshadows a potential move in the industry toward smaller and more accessible but extremely capable AIs. Plus, Dolly is open source, requires less computing power, and fewer data parameters than its counterparts.

Matei Zaharia, Cofounder & Chief Technologist at Databricks, joins Sarah and Elad to talk about how big data sets actually need to be, why manual annotation is becoming less necessary to train some models, and how he went from a Berkeley PhD student with a little project called Spark to the founder of a company that is now critical data infrastructure that’s increasingly moving into AI.

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Show Links:

• Hello Dolly: Democratizing the magic of ChatGPT with open models
• Dolly Source Code on Github
• Matei Zaharia - Chief Technologist & Cofounder - Databricks | LinkedIn
• Matei Zaharia - Google Scholar
• Databricks debuts ChatGPT-like Dolly, a clone any enterprise can own | VentureBeat

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Databricks | @Matei_Zaharia

Show Notes: 

[01:29] - Origin of Databricks

[4:30] - Work at Stanford Lab

[5:29] - Dolly and Role of Open Source

[12:30] - Industry focus on high parameter count, understanding reasoning at small model scale

[18:42] - Enterprise applications for Dolly & chat bots

[25:06] - Making bets as an academic turned CTO

[36:23] - The early stages of AI and future predictions

Everyone talks about the future impact of AI, but there’s already an AI product that has revolutionized a profession. Alex Graveley was the principal engineer and Chief Architect behind Github Copilot, a sort of pair-programmer that auto-completes your code as you type. It has rapidly become a product that developers won’t live without, and the most leaned-upon analogy for every new AI startup – Copilot for Finance, Sales, Marketing, Support, Writing, Decision-Making.

Alex is a longtime hacker and tinkerer, open source contributor, repeat founder, and creator of products that millions of people use, such as Dropbox Paper. He has a new project in stealth, Minion AI. In this episode, we talk about the uncertain process of shipping Copilot, how code improves chain of thought for LLMs, how they improved product, performance, how people are using it, AI agents that can do work for us, stress testing society's resilience to waves of new technology, and his new startup named Minion.

No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.

Show Links:

• Alex Graveley - San Francisco, California, United States | Professional Profile | LinkedIn
• Minion AI

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @alexgraveley | @ai_minion

Show Notes:

[1:50] - How Alex got started in technology 

[2:28] - Alex’s earlier projects with Hack Pad and Dropbox Paper

[07:32] - Why Alex always wanted to make bots that did stuff for people

[11:56] - How Alex started working at Github and Copilot

[27:11] - What is Minion AI

[30:30] - What’s possible on the horizon of AI

With advances in machine learning, the way we search for information online will never be the same.

This week on the No Priors podcast, we dive into a startup that aims to be the most trustworthy place to search for information online. Perplexity.ai is a search engine that provides answers to questions in a conversational way and hints at what the future of search might look like.

Aravind Srinivas is a Co-founder and CEO of Perplexity. He is a former research scientist at Open AI and completed his PhD in computer science at University of California Berkeley.

Denis Yarats is a Co-Founder and Perplexity’s CTO. He has a background in machine learning, having worked as a Research Scientist at Facebook AI Research and a machine learning engineer at Quora.

No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.

Show Links:

• Aravind Srinivas on Google Scholar
• Denis Yarats on Google Scholar
• Perplexity AI
• Perplexity AI Discord
• AI Chatbots Are Coming to Search Engines. Can You Trust Them? - Scientific American

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AravSrinivas | @denisyarats

Show Notes: 

[1:46] - How Perplexity AI iterates quickly and how the company has changed over time

[5:46] - Approach to hiring and building a fast-paced team

[10:43] - Why you don’t need AI pedigree to transition to work or research AI

[14:01] - Challenges when transitioning from AI research to running a company as CEO & CTO

[16:50] - Why Perplexity only shows answers it can cite

[19:33] - How Perplexity approaches reinforcement learning

[20:49] - Trustworthiness and if an answer engine needs a personality

[23:05] - Why answer engines will become their own market segment

[26:38] - Implications of “the era of fewer clicks” on publishers and advertisers

[30:20] - Monetization strategy

[33:20] - Advice for those deciding between academia or startups

For the first time in decades web search might be at risk for disruption. Bing is allied with OpenAI to integrate LLMs. Google has committed to launching new products. New startups are emerging.

Sridhar Ramaswamy co-founded the challenger AI-powered, private search platform Neeva in 2019. He is a former 16-year Google veteran who most recently led the internet’s most profitable business as SVP in charge of Google Ads, Commerce and Privacy. Sridhar, Elad and Sarah talk about the challenge of building search, how LLMs have changed the landscape, and how chatbots and "answer services" will affect web publishers.

No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.

Show Links:

• LinkedIn
• Neeva Search
• Neeva Gist
• Poe by Quora

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @RamaswmySridhar

Show Notes: 

[1:32] - Why Sridhar started a private search engine after leaving Google

[11:11] - Information Retrieval Problems, Mapping Search Queries and LLMs

[15:25] - Google and Bing’s approach to search with LLMs

[19:06] - Scale challenges when building a search engine startup

[22:26] - Distribution challenges and why they release Neeva Gist

[24:11] - Why Neeva is a privacy centric subscription service 

[28:25] - The relationship between search and publishers/content creators

[30:16] - Sridhar’s predictions on how AI will disrupt current ecosystems

When AI research is evolving at warp speed and takes significant capital and compute power, what is the role of academia? Dr. Percy Liang – Stanford computer science professor and director of the Stanford Center for Research on Foundation Models talks about training costs, distributed infrastructure, model evaluation, alignment, and societal impact.

Sarah Guo and Elad Gil join Percy at his office to discuss the evolution of research in NLP, why AI developers should aim for superhuman levels of performance, the goals of the Center for Research on Foundation Models, and Together, a decentralized cloud for artificial intelligence.

No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.

Show Links:

• See Percy’s Research on Google Scholar
• See Percy’s bio on Stanford’s website
• Percy on Stanford’s Blog: What to Expect in 2023 in AI
• Together, a decentralized cloud for artificial intelligence
• Foundation AI models GPT-3 and DALL-E need release standards - Protocol
• The Time Is Now to Develop Community Norms for the Release of Foundation Models - Stanford

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @PercyLiang

Show Notes: 

[1:44] - How Percy got into machine learning research and started the Center for Research and Foundation Models at Stanford

[7:23] - The role of academia and academia’s competitive advantages

[13:30] - Research on natural language processing and computational semantics

[27:20] - Smaller scale architectures that are competitive with transformers

[35:08] - Helm, holistic evaluation of language models, a project with the the goal is to evaluate language models

[42:13] - Together, a decentralized cloud for artificial intelligence

Life-saving therapeutics continue to grow more costly to discover. At the same time, recent advances in using machine learning for the life sciences and medicine are extraordinary. Are we on the verge of a paradigm shift in biotech?

This week on the podcast, a pioneer in AI, Daphne Koller, joins Sarah Guo and Elad Gil on the podcast to help us explore that question. Daphne is the CEO and founder of Insitro — a company that applies machine learning to pharma discovery and development, specifically by leveraging “induced pluripotent stem cells.” We explain Insitro’s approach, why they’re focused on generating their own data, why you can’t cure schizophrenia in mice, and how to design a culture that supports both research and engineering. Daphne was previously a computer science professor at Stanford, and co-founder and co-CEO of edutech company Coursera.

Show Links: 

• Insitro - About 
• Video: AWS re:Invent 2019 – Daphne Koller of insitro Talks About Using AWS to Transform Drug Development 

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @DaphneKoller

Show Notes: 

[1:49] - How Daphne combined her biology and tech interests and ran a bifurcated lab at Stanford

[4:34] - Why Daphne resigned an endowed chair at Stanford to build Coursera 

[14:14] - How insitro approaches target identification problems and training data 

[18:33] - What are pluripotent stem cells and how insitro identifies individual neurons 

[24:08 ] - How insitro operates as an engine for drug discovery and partners to create the drugs themselves

[26:48] - Role of regulations, clinical trials and disease progression in drug delivery 

[33:19] - Building a team and workplace culture that can bridge both bio and computer sciences 

[39:50] - What Daphne is paying attention to in the so-called golden age of machine learning  

[43:12] - Advice for leading a startup in edtech and healthtech

After starting as a talking emoji companion, Hugging Face is now an organizing force for the open source AI research ecosystem. Its models are used by companies such as Apple, Salesforce and Microsoft, and it's working to become the GitHub for ML.

This week on the podcast, Sarah Guo and Elad Gil talk to Clem Delangue, co-founder and CEO of Hugging Face. Clem shares how they shifted away from their original product, why every employee at Hugging Face is responsible for community-building, the modalities he's most interested in, and what role open source has in the AI race.

Show Links:

• Hugging Face website
• The $2 Billion Emoji: Hugging Face Wants To Be Launchpad For A Machine Learning Revolution - Forbes

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ClementDelangue

Show Notes: 

[01:53] - how Clem first became interested in ML, being shouted at by eBay sellers, and the foretelling of the end of barcode scanning

[3:34] - early iterations of Hugging Face, trying to make a less boring AI tamagotchi, and switching directions towards open source tools

[5:36] - advice for founders considering a change in direction, 30%+ experimentation

[7:39] - 1st users, MLTwitter, approach to community

[10:47] - enterprise ML maturity, days to production

[12:54] - open source vs. proprietary models

[15:56] - main model tasks, architectures and sizes

[19:12] - decentralized infrastructure, data opt out

[24:16] - Hugging Face’s business model, GitHub

[28:09] - What Clem is excited about in AI

This is a special bonus episode from our Founder Stories series, where entrepreneurs share the story of their startup journey.

A delivery with Zipline is the closest thing we have to teleportation. It sounds like science fiction, but Zipline delivers life saving medical supplies such as blood and vaccines to hospitals, doctors and people in need around the world with the world's largest autonomous drone network.

This week on the podcast, Sarah Guo talks to Keller Rinaudo Cliffton, the co-founder and CEO of Zipline, about building a full-stack business that involves software, hardware and operations, how a culture of ruthless engineering practicality enabled them to do unlikely things, the state of autopilot in aircraft, their AI acoustic detect-and-avoid system, and why founders should build for users beyond the "golden billion."

Show Links:

• Zipline's website
• Video: Drone Delivery Start-Up Zipline Beats Amazon, UPS And FedEx To The Punch | CNBC
• Keller Rinaudo: How we're using drones to deliver blood and save lives | TED Talk
• Meet Romotive: An Ambitious Startup That Blew Our Minds

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @KellerRinaudo

Show Notes: 

[2:07] - Keller’s earlier projects and early inspiration for Zipline and transforming logistics 

[7:40] - Why Zipline focused on healthcare logistics and Zipline’s early near death experiences as a company 

[15:32] - How Zipline iterated on the hardware while being ruthlessly practical with getting products in the customers’ hands 

[21:52] - The difference between AI and Autopilot

[25:51] - How Zipline developed AI acoustic-based detect and avoid system

[31:30] - Zipline’s partnership with Rwanda’s public health system 

[34:25] - Challenges in the business model 

AI-generated images have been everywhere over the past year, but one company has fueled an explosive developer ecosystem around large image models: Stability AI. Stability builds open AI tools with a mission to improve humanity. Stability AI is most known for Stable Diffusion, the AI model where a user puts in a natural language prompt and the AI generates images. But they're also engaged in progressing models in natural language, voice, video, and biology.

This week on the podcast, Emad Mostaque joins Sarah Guo and Elad Gil to talk about how this barely one-year-old, London-based company has changed the AI landscape, scaling laws, progress in different modalities, frameworks for AI safety and why the future of AI is open.

Show Links:

• Stability.AI
• Stable Diffusion V2 on Hugging Face 

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @EMostaque

Show Notes: 

[2:00] - Emad’s background as one of the largest investors in video games and artificial intelligence

[7:24] - Open-source efforts in AI

[13:09] - Stability.AI as the only independent multimodal AI company in the world

[15:28] - Computational biology, medical information and medical models

[23:29] - Pace of Adoption

[26:31] - AGI versus intelligence augmentation

[31:38] - Stability.AI’s business model

[37:44] - AI Safety

For a long time, AI-generated images and video felt like a fun toy. Cool, but not something that would bring value to professional content creators. But now we are at the exciting moment where machine learning tools have the power to unlock more creative ideas.

This week on the podcast, Sarah Guo and Elad Gil talk to Cristobal Valenzuela, a technologist, artist and software developer. He’s also the CEO and co-founder of Runway, a web-based tool that allows creatives to use machine learning to generate and edit video. You've probably already seen Runway's work in action on the Late Show with Stephen Colbert and in the feature film Everything Everywhere All at Once.

Show Links:

• Watch Cris Valenzuela’s 2018 thesis presentation at New York University’s ITP program.
• Read how Runway is used on the Late Show and in Everything Everywhere All at Once on the Runway Blog.

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @c_valenzuelab

Show Notes: 

[1:50] - Cris’s background and how he doesn’t see barriers between art and machine learning

[6:46] - How Runway works as a tool

[8:36] - The origins and early iterations of Runway

[12:22] - Product sequencing and roadmapping in a fast growing space

[15:43] - Runway as an applied research company

[19:10] - Common pitfalls for founders to avoid

[22:35] - How Runway structures teams for effective collaboration

[24:22] - Learnings from how Runway built Greenscreen product

[28:01] - Building a long-term and sustainable business

[32:34] - Finding Product Market Fit

[36:34] - The influence of AI tools in art as an artistic movement

AI is transforming our future, but what does that really mean? In ten years, will humans be forced to please our AGI overlords or will we have unlocked unlimited capacity for human potential?

That's why Sarah Guo and Elad Gil started this new podcast, named No Priors. In each episode, Sarah and Elad talk with the leading engineers, researchers and founders in AI, across the stack. We'll talk about the technical state of the art, how that impacts business, and get them to predict what's next.

Follow the podcast wherever you listen so you never miss an episode. We’ll see you next week with a new episode. Email feedback to [email protected]

AGI can beat top players in chess, poker, and, now, Diplomacy. In November 2022, a bot named Cicero demonstrated mastery in this game, which requires natural language negotiation and cooperation with humans. In short, Cicero can lie, scheme, build trust, pass as human, and ally with humans. So what does that mean for the future of AGI?

This week’s guest is research scientist Noam Brown. He co-created Cicero on the Meta Fundamental AI Research Team, and is considered one of the smartest engineers and researchers working in AI today.

Co-hosts Sarah Guo and Elad Gil talk to Noam about why all research should be high risk, high reward, the timeline until we have AGI agents negotiating with humans, why scaling isn’t the only path to breakthroughs in AI, and if the Turing Test is still relevant.

Show Links:

• More about Noam Brown
• Read the research article about Cicero (diplomacy) published in Science. 
• Read the research article about Liberatus  (heads-up poker) published in Science. 
• Read the research article about Pluribus (multiplayer poker) published in Science. 
• Watch the AlphaGo Documentary.
• Read “How Smart Are the Robots Getting?” by New York Times reporter Cade Metz 

Sign up for new podcasts every week. Email feedback to [email protected]

Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Polynoamial

Show Notes: 

[01:43] - What sparked Noam’s interest in researching AI that could defeat games

[6:00] - How the AlexaNET and AlphaGo changed the landscape of AI research

[8:09] - Why Noam chose Diplomacy as the next game to work on after poker

[9:51] - What Diplomacy is and why the game was so challenging for an AI bot

[14:50] - Algorithmic breakthroughs and significance of AI bots that win in No-Limit Texas Hold'em poker

[23:29] - The Nash Equilibrium and optimal play in poker

[24:53] - How Cicero interacted with humans 

[27:58] - The relevance and usefulness of the Turing Test

[31:05] - The data set used to train Cicero

[31:54] - Bottlenecks to AI researchers and challenges with scaling

[40:10] - The next frontier in researching games for AI

[42:55] - Domains that humans will still dominate and applications for AI bots in the real world

[48:13] - Reasoning challenges with AI