Python, every mention
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Latent Space 324
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every year every show Latent Space 324 the MAD Podcast 232 the a16z Podcast 67 the Startup Ideas Podcast 42 the Y Combinator Startup Podcast 41 No Priors 41 TBPN 40 Top Founders 38 
Lenny's Podcast 37 
20VC 33 
Startups For the Rest of Us 19 
My First Million 17 
the Neon Show 17 
Big Technology 14 
In Depth 13 
WTF is with Nikhil Kamath 10 
A Product Market Fit Show 10 
Capital Allocators 10 
Acquired 8 
the Official SaaStr Podcast 8 
Sourcery 8 
the Knowledge Project 7 
Cheeky Pint 6 
Catalyst 6 
Mixergy 5 
the Green Blueprint 5 
All-In 5 
BG2 Pod 3 
American Optimist 3 
Invest Like the Best 3 
How I Built This 3 
Founder's Journal 2 
Startup Acquisition Stories 2 
We Live to Build 2 
Innovators & Investors 1 
David Senra 1
Verbatim, from the transcripts: passages where Python comes up on Latent Space, the MAD Podcast, the a16z Podcast, the Startup Ideas Podcast, the Y Combinator Startup Podcast
Inside Nemotron & NVIDIA’s AI Lab | Bryan Catanzaro
- ▶ 14:30 Bryan Catanzaro Uh, first Copperhead, which was a, it was a, a Python, uh, embedded language that, um, compiled to the GPU, um, which I think foreshadowed a lot of things in TensorFlow and, and PyTorch.
The GPU Myth: State of AI Compute 2026 | Stephen Balaban
- ▶ 1:01:53 Stephen Balaban You know, it outputs C code, which gets put through a compile, or it outputs Python code, which gets put through a Python interpreter.
OpenAI Board Member Zico Kolter: Modern AI Is Just 200 Lines of Code
- ▶ 0:34 Zico Kolter That entire set of code, probably two to 300 lines of Python code.
- ▶ 1:14:01 Zico Kolter That entire set of code, probably two to 300 lines of Python code.
Everything Gets Rebuilt: The New AI Agent Stack | Harrison Chase, LangChain
- ▶ 18:24 Harrison Chase Those are the four that when we launched Deep Agents, and so the story behind launching Deep Agents was we saw, we saw Manus, we saw Cloud Code, we saw Deep Research, they all had these four things, um, and we were like, okay, that's,…
- ▶ 36:43 Harrison Chase Let's, let's put some of these, uh, common patterns into a Python package and release it.
AI That Can Prove It’s Right: Verification as the Missing Layer in AI — Carina Hong
- ▶ 9:27 Corinna Hong And so lean is like similar to Python, but it also can serve as it's like self verifying, uh, function.
Voice AI’s Big Moment: Top Researcher on Why Everything Is Changing (Neil Zeghidour, Gradium AI)
- ▶ 14:54 Neil Zeghidour And I asked if I could do it in MATLAB because I didn't know, I didn't even know Python. 2 times in the scene
Mistral AI vs. Silicon Valley: The Rise of Sovereign AI
- ▶ 12:29 Timothée LeCroix Tooling that you wouldn't expect where one thing that we've done is around code modernization, uh, where you're, you turn a bunch of Excel sheets into an actual, like, uh, Python app, uh, and if you have many, many of those sheets, then…
State of LLMs 2026: RLVR, GRPO, Inference Scaling — Sebastian Raschka
- ▶ 4:58 Sebastian Raschka It's like there was like a during training and objective to, if you have a Python code to say, okay, this, at this iteration, when I, if someone would step through the code, this variable would have that and that value.
The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)
- ▶ 41:04 Tim Dettmers You just need to inspect the output that you can understand, or learn how to execute a Python program or a bash cell, and you're there.
Jeremy Howard on Building 5,000 AI Products with 14 People (Answer AI Deep-Dive)
- ▶ 46:37 Jeremy Howard And so then we built a system on top of that, which also takes advantage of some kind of core capabilities built into the Python programming language. 8 times in the scene
Rewriting Success: What InfluxDB 3.0 Teaches About Scaling—and Scrapping—Your Core Tech
- ▶ 25:34 Evan Kaplan Capacitor was our, um, let's call it a task engine built around the database, but the version three of the database has an embedded Python VM with a set of triggers that allows you to do a variety of stuff at the database, um, to, you…
Box’s Big AI Leap: Aaron Levie on Agents & the Future of Work
- ▶ 1:11:46 Aaron Levie You know, I like the idea of, of being able to, you know, deploy an agent to say, you know, just make this SDK work in, you know, a new language, or there's a new Python update. 2 times in the scene
Snowflake CEO on Winning the AI Arms Race
- ▶ 48:43 Sridhar Ramaswamy There's always been pain associated with getting data out to business users, especially new data, different kinds of insights very quickly, because they live in, in tables, and people have to write this language called SQL or bits of…
Chasing Real AGI: Inside ARC Prize 2025 with Chollet & Knoop
- ▶ 25:16 Francois Chollet Maybe you could also just use a, a generic programming language like Python. 2 times in the scene
Why This Ex-Meta Leader is Rethinking AI Infrastructure | Lin Qiao, CEO, Fireworks AI
Understanding Data Engineering in 2025 | Ben Rogojan, Seattle Data Guy
- ▶ 15:59 Ben Rogojan (Seattle Data Guy) I think that that's just the, the way it is, uh, in terms of skill sets, uh, it's usually things like SQL, Python, or some programming language. 5 times in the scene
Can AI Infrastructure Work Like Magic? Erik Bernhardsson, CEO, Modal
- ▶ 2:00 Erik Bernhardsson So, so we basically, you can think of as like, we take, you, you, you write a little bit of Python code, and we take that code, we stick it in a container, we execute that in the cloud in a way we don't have to think about infrastructure.
- ▶ 11:38 Erik Bernhardsson And I realized what I want is I just want something that like, lets me define application code in Python.
The Death of Big Data and Why It’s Time To Think Small | Jordan Tigani, CEO, MotherDuck
- ▶ 16:03 Jordan Tigani You know, if you're running in Python, for example, um, 4 times in the scene
- ▶ 37:27 Jordan Tigani There's a DuckDB running in your web browser, as well as on the server, um, or if you're running in a Python process, or you're running, you know, you're writing some code that, you know, connects to, that connects to Mother Duck, there's…
AGI, The Future of AI Agents And The Next Wave of Opportunities in AI | Richard Socher, CEO, You.com
- ▶ 7:25 Richard Socher and eventually Java and now Python.
Making AI Work: Fine-Tuning, Inference, Memory | Sharon Zhou, CEO, Lamini
- ▶ 22:54 Sharon Zhou Let's see, um, uh, we're a stack on top that people are able to install on top of Docker, Kubernetes, um, or even bare metal compute, uh, and developers are able to interface with it through familiar REST APIs or, you know, optional Python…
From Business to Warfare: How AI Affects the Modern World | Azeem Azhar
- ▶ 27:29 Azeem Azhar Can we automate this particular task where, which is an edge edge case that we haven't spent the time putting in the nested if, if they're now statements in Python, but maybe an LLN can kind of deal, deal with it for us.
AI at Roblox: Revolutionizing Game Creation | Morgan McGuire, Chief Scientist of Roblox
- ▶ 25:21 Morgan McGuire And so it was able to learn from looking at Python or Java code, how to write Lua code for Roblox.
2024 will be the year of ENTERPRISE AI | Florian Douetteau, CEO of Dataiku
- ▶ 16:06 Florian Douetteau People that don't love doing Python. 2 times in the scene
The Race to Build the Ultimate AI Programmer | Poolside CTO Eiso Kant
Moonhub’s Nancy Xu Unveils the AI Recruiter That’s Beating LinkedIn
- ▶ 3:48 Nancy Xu I need someone in the Bay Area, and they need to have some experience with Python, and I want someone who, you know, worked at a Series A company before. 2 times in the scene
- ▶ 12:56 Nancy Xu Um, so this allows you as the end user to look for things like, hey, show me people who currently work at an early stage startup in the Bay Area who's familiar with Python. 2 times in the scene
Reinventing Search with AI: Richard Socher on Building You.com & the Future of Google
- ▶ 23:53 Richard Socher You know, if you look for, uh, some programming thing, like you want to use Python to implement, implement the Fibonacci direct computation function, you can go to Google,
Building LlamaIndex: Jerry Liu on Scaling Retrieval-Augmented AI
The Single Platform for Everyday AI - Fireside Chat with Florian Douetteau (Dataiku) & Matt Turck
- ▶ 11:37 Florian Douetteau But of course, if you are more like kind of like a coding person, if you want to take things into control, if you want to use or integrate your own specific algorithm or piece of code, I'll do some, uh, whatever, a sequel for…
Entering the Data-Centric Era of Foundation Models with Alex Ratner, Co-Founder & CEO of Snorkel AI
- ▶ 17:26 Alex Ratner And this can be done via Python.
A Conversation with Chris Wiggins - Author of "How Data Happened"
- ▶ 17:51 Chris Wiggins Um, marketing, so when we market on other advertising platforms, that is done not using guessing and pointing and clicking, but using Python and optimization.
- ▶ 20:55 Chris Wiggins That said, there is also a lot of work being done in AWS, and plenty of developer work happening on Amazon's Cloud, um, so the data stack is, in my team, the data stack is SQL and scikit, and occasionally Go, so it's scikit-learn is, is a…
Generative AI for Speech Recognition | AssemblyAI Founder & CEO, Dylan Fox
Not Just Another Cloud Database | SurrealDB Co-Founders Jaime & Tobie Morgan Hitchcock
- ▶ 3:17 Jaime & Tobie Morgan Hitchcock As an embedded database, so being able to run that within Python, within Rust, within C, and query your data locally, but also importantly, as a cloud database,
Fundamentals of Data Engineering | Joe Reis and Matt Housley
- ▶ 14:41 Matt Housley Or, yeah, yeah, data engineers really need to know Spark, or they need to know Python, or they need to know Pandas, or Snowflake, or whatever technology it is.
Modern Data Orchestration | Astronomer Co-Founders Pete DeJoy & Viraj Parekh
- ▶ 4:14 Viraj Parekh To where we are now, where we have folks like Steve State Startups, all the way through larger retailers that encode their business logic in Python with these Airflow DAGs.
- ▶ 4:25 Viraj Parekh And, you know, Airflow was designed for this kind of Python-based job scheduling, but if we think about the personas we went through before, that's really only the first step, and orchestration's a little more than that, or a lot more than… 2 times in the scene
- ▶ 7:26 Pete DeJoy And, you know, there, there definitely is a theme there in that, you know, Airflow kind of emerged as this really incredible, we think the best in the world tool for Python
- ▶ 18:12 Pete DeJoy So I can see both my tasks, i.e. those SQL queries and Python functions I just ran, predict user trends, and I can introspect the tables that they're actually producing in my snowflake instance under the hood.
- ▶ 22:52 Pete DeJoy So, I mean, we have a Python, or excuse me, like a CLI client that allows you to just run those deployments.
A Novel Approach to Data Quality for the Modern Data Stack | Datafold’s Gleb Mezhanskiy
- ▶ 15:58 Gleb Mezhanskiy We can also run it in Python.
The Next Layer of the Modern Data Stack | dbt's Tristan Handy
- ▶ 21:49 Tristan Handy It's executing on Snowflake, or this is a Python-based data transformation.
Fireside Chat: Abe Gong (Founder & CEO, Superconductive) with Matt Turck (Partner, FirstMark)
- ▶ 14:21 Abe Gong It's JSONs, Python APIs,
- ▶ 17:05 Abe Gong Uh, the primary backends that Great Expectations runs on are Python pandas.
- ▶ 22:22 Abe Gong And there are a bunch of things that, uh, you can do once you have all the power of, you know, SAS at your disposal, and it's not just a Python library, uh, that, that will lend themselves to much better data collaboration.
Fireside Chat: Nick Schrock (Founder & CEO, Elementl) with Matt Turck (Partner, FirstMark)
- ▶ 12:08 Nick Schrock So Dexter works is an open source Python project.
Fireside Chat: Florian Douetteau (Founder & CEO, Dataiku) with Matt Turck (Partner, FirstMark)
- ▶ 11:33 Florian Douetteau Meaning people doing Python or R on an everyday basis.
Fireside Chat: Savin Goyal (ML Infra team (Metaflow), Netflix) with Matt Turck (Partner, FirstMark)
- ▶ 9:04 Savin Goyal So, you know, Metaflow ultimately at the end of the day, it's a Python package, uh, as well as an R package, because, uh, the users that we have internally, uh, they use either Python or R to get their work done. 2 times in the scene
- ▶ 24:18 Savin Goyal Uh, we have essentially focused on Python and R.
Introducing Kedro
- ▶ 8:06 Kedro Product Manager Um, we've got a Python virtual environment set up. 3 times in the scene
- ▶ 14:13 Kedro Product Manager So a node in Kedra world is just a Python function.
Fireside Chat: Wes McKinney (Founder & CEO, Ursa Computing) with Matt Turck (Partner, FirstMark)
- ▶ 0:22 Wes McKinney So pandas serve sort of as a, as a Swiss army knife for, for data access, data, data manipulation, analytics, and data visualization in Python. 4 times in the scene
- ▶ 2:50 Wes McKinney So it, people use it for, uh, not only loading the data into Python, but also the data cleaning, the, you know, what we call data preparation.
- ▶ 4:25 Wes McKinney Um, so it's, it's, you know, tends to be interactive, uh, very flexible, uh, and enables you to, to work with, uh, you know, datasets from a general purpose programming language like Python. 2 times in the scene
- ▶ 5:08 Matt Turck And all of this is, is in the context of the, of the Python world. 7 times in the scene
- ▶ 9:10 Wes McKinney So, uh, you know, I was interested in this problem and I'd experienced it from the perspective of pandas and the Python ecosystem, like wanting to build bridges from Python into all of these other, all of these other systems. 2 times in the scene
- ▶ 10:43 Matt Turck And that's Python. 2 times in the scene
- ▶ 13:58 Wes McKinney So traditionally data comes into your, uh, into your Python interpreter, into your, into your R interpreter, your process, and you immediately have to convert it into, um, into some other, into some other format.
- ▶ 16:46 Wes McKinney They have one thing to think about and we can, on the Python side, we can deal with like, okay, you know, I know how to optimize getting data into pandas so we can maintain that for them and their developers don't have to solve that…
- ▶ 18:12 Wes McKinney They said, you know, make Python work better with, with all of this big data.
- ▶ 21:50 Wes McKinney Spark, uh, Spark supports Arrow as a, as an interchange format, and it's used heavily in the interface with Python and R, for example. 2 times in the scene
Fireside Chat: Alok Gupta (Head of Data Science & ML, DoorDash) with Matt Turck (Partner, FirstMark)
- ▶ 7:24 Alok Gupta We use Python and Databricks to, uh, and Spark to pull data in, build models. 2 times in the scene
Fireside Chat: Jeremiah Lowin (Prefect), Tristan Handy (dbt) with Matt Turck (Partner, FirstMark)
- ▶ 18:56 Tristan Handy All the technologies that are required to do a good job of answering those questions, whether that's, you know, sometimes Excel, sometimes SQL, sometimes Python or R, um, but they don't self-identify as technologists. 3 times in the scene
- ▶ 50:28 Jeremiah Lowin Uh, standpoint and it, it made us realize we've kind of pushed this like batch DAG static workflow, probably as far as it can go, like from the old, from YAML and then into Python.
Fireside Chat: Ashley Kramer (CMO & CPO, Sisense) with Matt Turck (Partner, FirstMark)
- ▶ 7:51 Ashley Kramer They said, there are hundreds of thousands of people with coding skills like SQL R and Python.
- ▶ 25:40 Ashley Kramer And so coming soon, we will also integrate the rest of the notebook like experience, being able to put Python and R within the experience and instantly get your insights.
Fireside Chat: Nate Stewart (CPO & BOD at Cockroach Labs) with Matt Turck (Partner, FirstMark)
- ▶ 26:26 Nate Stewart So right now we have a getting started with a CockroachDB course, and we're in the process of building our, um, you know, how to build a CockroachDB app using Python course.
Designing an AI Supercomputer // Michael James, Cerebras (FirstMark's Data Driven NYC)
- ▶ 13:36 Michael James Here we're just taking these structural graph models that come out of some Python framework.
How to Resurrect Innovation in the Enterprise // Amr Awadallah, Google (FirstMark's Data Driven NYC)
- ▶ 32:19 Amr Awadallah Uh, Python.
Data Science Is A Literacy, Not A Job // Peter Wang, Anaconda (FirstMark's Data Driven NYC)
- ▶ 3:22 Peter Wang You know, because Python is too, too hard. 5 times in the scene
- ▶ 7:19 Peter Wang That's really no better than spending an afternoon learning Python, right? 9 times in the scene
- ▶ 16:02 Peter Wang And, um, and so the software, uh, there's this great quote about platforms, I forget who it comes from, but they said that ultimately successful platforms seek to commoditize their complement, and so what we have is we sort of stumbled… 6 times in the scene
Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
- ▶ 8:37 Solmaz Shahalizadeh So, um, we basically moved from, uh, using Vertica, uh, to building a ETL tool in-house using, uh, Spark, uh, and, uh, Python. 3 times in the scene
- ▶ 24:34 Solmaz Shahalizadeh Like most of them come with knowing Python or R and we do a very good, uh, onboarding or we put a lot of effort in our onboarding.
- ▶ 32:11 Solmaz Shahalizadeh There's, uh, like, um, packages in Python being written.
The Case for Hiring More Analysts // Benn Stancil, Mode (FirstMark's Data Driven NYC)
- ▶ 16:18 Benn Stancil So rather than being something that's like, hey, here's a Python notebook, which is great for an analyst, but terrible for a CEO. 2 times in the scene
The State of AI & What's Next // Dileep George, Vicarious AI (FirstMark's Data Driven NYC)
- ▶ 27:28 Dileep George Um, so we, you know, when we, um, broke, uh, captures our algorithms were running on, uh, you know, just using C and running using Python, et cetera.
A New Kind of Logging System // Zach Sherman & Ben Johnson, Timber (FirstMark's Data Driven NYC)
- ▶ 18:38 Zach Sherman Um, ETL and batch and they work with Python and Jupyter notebooks and a lot of things like that.
Building an Operating System for AI // Diego Oppenheimer, Algorithmia (FirstMark's Data Driven)
- ▶ 15:30 Diego Oppenheimer Maybe I start in Python and scikit-learn.
A Fresh Approach to Technical Computing // Viral Shah & Stefan Karpinski, Julia Computing
- ▶ 3:59 Stefan Karpinski This is, this is, I'll talk about how we unify this, but this dichotomy, you know, that he observed is actually, has led to a pretty standard compromise in systems, and it's actually, it's actually very pragmatic, so for convenience, you… 4 times in the scene
- ▶ 10:50 Viral Shah Yeah, 800 people, there are 1600 contributed Julia packages, but people keep coming up to us and be like, oh, but, you know, R has 6000, or, you know, JavaScript has a bazillion or something, and, um, you know, the thing is that you could…
- ▶ 19:33 unnamed speaker I'm curious to know, people, ah, are using Python a lot because the libraries are so feature rich, it, ah, lets you write with less code, so how far will Julia go to that extent? 13 times in the scene
Three Loops of Analytics Efficiency // Sean Kandel, Trifacta (FirstMark's Data Driven)
- ▶ 6:41 Sean Kandel Uh, so probably still today the most common is using kind of hand coding tools, um, so programming languages, Python, Spark,
AI, Big Data, and Data Governance // Stan Christiaens, Collibra (FirstMark's Data Driven)
- ▶ 3:23 Stan Christiaens Uh, ideally, this translates all the way through, not into the technicalities of how to make a Python script, but actually into how does data get into an MBA program, for example, right?
- ▶ 12:24 Stan Christiaens Typing in Python commands in these notebooks and then immediately seeing a classifier or a visualization.
- ▶ 14:01 Stan Christiaens Right, and if you need to write a Python script or hack into the database of your own internal company, just do it.
Big Data as a Service // Prat Moghe, Cazena (FirstMark's Data Driven)
- ▶ 4:52 Prat Moghe It's Hadoop, it's Spark, it's Python, it's R, and it's like every three months there's a new open source project around it.
- ▶ 10:18 Prat Moghe You're running Python.
The Power of GPU Analytics // Todd Mostak, MapD (FirstMark's Data Driven)
- ▶ 12:46 Todd Mostak So one of the great things is that behind the scenes, MapD is ultimately a very, very fast database, so if I wanted to, I could create a, um, Python notebook out of this, um,
- ▶ 20:51 Todd Mostak Probably first written in CUDA, but later maybe in Python, right on top of the database.
Project Jupyter // Jason Grout & Sylvain Corlay, Bloomberg (FirstMark's Data Driven)
- ▶ 5:21 Jason Grout Now, it started with Python, but the point is, if you have a language or a computer system that's 4 times in the scene
Lessons Learned from Advanced Data Science Orgs // Domino Data Lab [FirstMark's Data Driven]
- ▶ 0:14 Nick (Domino Data Lab) I'll say a little bit more of what I mean by that in a second, but, um, just to help me understand folks in the audience, show hands of people who work with statistical programming languages, Python R, kind of predictive analytics, not,…
- ▶ 9:45 Nick (Domino Data Lab) Um, so, you know, let's say we're, we're working on a project, and we're, we're developing a trading strategy, and I've got some code in R, in Python, um, and I've got my script here, so it's plain Python code, there's sort of nothing… 2 times in the scene
Making On-Demand Delivery Profitable // Jeremy Stanley, Instacart (Data Driven NYC / FirstMark)
- ▶ 17:24 Jeremy Stanley Uh, the data science teams are using either Python or R, 2 times in the scene
The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)
- ▶ 5:26 Praveen Murugesan Um, so it was originally written with, like, Python, Celery, and, like, some, like, uh, original technologies which Uber, people at Uber were very familiar with.
The Path to A.I. Augmented Human Intelligence // Christopher Nguyen, Arimo [FirstMark's Data Driven]
- ▶ 7:26 Christopher Nguyen There's also support for R, Python, and so on.
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