No Priors: Artificial Intelligence | Technology | Startups

In this episode of No Priors, hosts Sarah and Elad are joined by Ramp co-founders Eric Glyman and Karim Atiyeh of Ramp. The pair has been working to build one of the fastest growing fintechs since they were teenagers. This conversation focuses on how Ramp engineers have been building new systems to help every team from sales and marketing to product. They’re building best-in-class SaaS solutions just for internal use to make sure their company remains competitive. They also get into how AI will augment marketing and creative fields, the challenges of selling productivity, and how they’re using LLMs to create internal podcasts using sales calls to share what customers are saying with the whole team. 

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Show Notes: 

(0:00) Introduction to Ramp

(3:17) Working with startups

(8:13) Ramp’s implementation of AI

(14:10) Resourcing and staffing

(17:20) Deciding when to build vs buy

(21:20) Selling productivity

(25:01) Risk mitigation when using AI

(28:48) What the AI stack is missing

(30:50) Marketing with AI

(37:26) Designing a modern marketing team

(40:00) Giving creative freedom to marketing teams

(42:12) Augmenting bookkeeping

(47:00) AI-generated podcasts 

Hunting down receipts and manually filling out invoices kills productivity. This week on No Priors, Sarah Guo and Elad Gil sit down with Pedro Franceschi, co-founder and CEO of Brex. Pedro discusses how Brex is harnessing AI to optimize spend management and automate tedious accounting and compliance tasks for teams. The conversation covers the reliability challenges in AI today, Pedro’s insights on the future of fintech in an AI-driven world, and the major transitions Brex has navigated in recent years.

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Show Notes: 

(0:00) Introduction

(0:32) Brex’s business and transitioning to solo CEO

(3:04) Building AI into Brex 

(7:09) Solving for risk and reliability in AI-enabled financial products

(11:41) Allocating resources toward AI investment

(14:00) Innovating data use in marketing 

(20:00) Building durable businesses in the face of AI

(25:36) AI’s impact on finance

(29:15) Brex’s decision to focus on startups and enterprises

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:

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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:

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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:

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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:

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

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