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. 

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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.

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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.

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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