
No Priors Ep. 96 | With Modal CEO and Founder Erik Bernhardsson
Elad Gil (host), Erik Bernhardsson (guest), Sarah Guo (host)
In this episode of No Priors, featuring Elad Gil and Erik Bernhardsson, No Priors Ep. 96 | With Modal CEO and Founder Erik Bernhardsson explores modal’s Erik Bernhardsson Rethinks Cloud Infrastructure For Modern AI Workloads Erik Bernhardsson, founder and CEO of Modal, explains how his experience building ML infrastructure at Spotify and Better.com led him to create a serverless, cloud-native compute platform optimized for AI and data applications.
Modal’s Erik Bernhardsson Rethinks Cloud Infrastructure For Modern AI Workloads
Erik Bernhardsson, founder and CEO of Modal, explains how his experience building ML infrastructure at Spotify and Better.com led him to create a serverless, cloud-native compute platform optimized for AI and data applications.
Modal offers a multi-tenant pool of CPUs and GPUs with a Python-first serverless interface, aiming to make cloud development feel as fast and simple as local development while abstracting away Docker, Kubernetes, and capacity planning.
The discussion covers inefficiencies in current GPU usage, the shift from training to inference-heavy workloads, and the importance of flexible, usage-based GPU access—especially for bursty workloads like generative AI and experimental training.
They also explore broader AI infrastructure topics: vector databases and AI-native storage, when to train your own models, physics and biology simulations, and the long-term impact of AI on software engineering demand.
Key Takeaways
Flexible, usage-based GPU access is critical for modern AI workloads.
Traditional long-term GPU reservations misalign with volatile inference and experimental training needs; Modal instead charges only for actual container runtime and pools GPUs across customers to handle bursty demand.
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Abstracting away Kubernetes and Docker can dramatically boost ML developer productivity.
By turning plain Python functions into serverless cloud functions and managing containerization, scheduling, and file systems internally, Modal aims to make cloud development feel as responsive as local coding.
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A multi-tenant, cloud-native design enables better capacity utilization and scale.
Running all customers on a shared compute pool lets Modal offer near-instant access to large numbers of GPUs, offloading capacity planning and idle-resource risk from individual teams.
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Inference is the current ‘killer app,’ but end-to-end ML support is the real goal.
While most Modal usage today is inference—especially for generative audio, video, image, and music—many customers already use it for preprocessing, and the company plans to support more training workflows over time.
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Owning your model is often key to defensibility in AI-heavy products.
For companies where model quality is core to the product, relying solely on generic models weakens the moat; training specialized models (especially in audio, video, and imaging) can become a major differentiator.
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AI-native storage may look very different from today’s vector databases.
Bernhardsson suggests that future systems might accept raw modalities (text, images, video) and embed internally, moving beyond the current pattern of manually generating and querying vectors via traditional database interfaces.
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AI will likely increase, not decrease, demand for software engineers.
Historically, every major productivity leap (compilers, higher-level languages, cloud) unlocked more latent demand for software; Erik expects AI-assisted coding to follow the same pattern rather than making engineers obsolete.
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Notable Quotes
“I always wanted to build basically a better infrastructure for data, AI, and machine learning.”
— Erik Bernhardsson
“Working with the cloud is arguably kind of annoying… my idea was: what if cloud development feels almost as good as local development?”
— Erik Bernhardsson
“For GPUs, the main way to get access has been to sign long-term contracts, and fundamentally that’s just not how startups should do it.”
— Erik Bernhardsson
“Our goal has always been to build a platform and cover the end-to-end use case… the entire machine learning life cycle end-to-end.”
— Erik Bernhardsson
“Every decade, engineers get 10 times more productive, and it turns out that just unlocks more latent demand for software engineers.”
— Erik Bernhardsson
Questions Answered in This Episode
How does Modal technically achieve fast cold-start times and safe multi-tenant execution without relying on standard Docker/Kubernetes stacks?
Erik Bernhardsson, founder and CEO of Modal, explains how his experience building ML infrastructure at Spotify and Better. ...
Get the full analysis with uListen AI
What are the trade-offs for an enterprise choosing Modal’s multi-tenant GPU pool versus locking in reserved capacity with a hyperscaler?
Modal offers a multi-tenant pool of CPUs and GPUs with a Python-first serverless interface, aiming to make cloud development feel as fast and simple as local development while abstracting away Docker, Kubernetes, and capacity planning.
Get the full analysis with uListen AI
In practice, what signals should a company watch to decide it’s time to move from off-the-shelf models to training its own?
The discussion covers inefficiencies in current GPU usage, the shift from training to inference-heavy workloads, and the importance of flexible, usage-based GPU access—especially for bursty workloads like generative AI and experimental training.
Get the full analysis with uListen AI
What might an AI-native storage system look like in five to ten years, and how would developers interact with it compared to today’s databases?
They also explore broader AI infrastructure topics: vector databases and AI-native storage, when to train your own models, physics and biology simulations, and the long-term impact of AI on software engineering demand.
Get the full analysis with uListen AI
How could bandwidth-efficient training techniques change who can realistically train large models and where that training happens geographically?
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Transcript Preview
(electronic music) Today, I'm chatting with Erik Brynjarsen, founder and CEO of Modal. Modal developed a serverless cloud platform tailored for AI and machine learning in data applications. And before that, Erik worked at Better.com and Spotify, where he led Spotify's machine learning efforts and built the recommender system. Well, Erik, thanks so much for joining me today on No Priors.
Yeah. Thanks. It's great to be here.
So, um, if I remember correctly, you worked at Spotify and helped build out their ML team and recommender system, and then were also at Better.com. What inspired you to start Modal, and what problem were you hoping to solve?
Yeah. Uh, so I started at Spotify a long time ago, 2008, and I spent seven years there. And yeah, I, I built a music recommendation system. And back then, there was, like, nothing really, uh, in terms of data infrastructure. Hadoop was, like, the most modern thing. And so I, I spent a lot of time building a lot of infrastructure. Uh, in particular, I built a workflow scheduler called Luigi that basically no one uses today. I built a vector database that... called Innoai that, you know, for a brief period, people used, but no one really uses today. Uh, so I spent a lot of time building a lot of that stuff. Uh, and then later at Better, I was the CTO and thinking a lot about, like, developer productivity and stuff. And then during the pandemic, I, I took some time off and started hacking on stuff, and I realized I always wanted to build, uh, basically a better infrastructure for, for these types of things, like data AI and machine learning. So, so pretty quickly realized, like, this is what I wanted to do, and that, that was sort of the genesis of Modal.
Mm-hmm. That's cool. How did that approach evolve, or what are the main areas that the company focuses on today?
So I started looking into, first of all, just, like, what are the challenges with data AI and machine learning infrastructure? And, and I started thinking about from, like, a developer productivity point of view. What's a tool I want to have? And, and I realized, like, a big, a big sort of challenge is, like, working with the cloud is arguably kind of annoying (laughs) and, like, as much as, like, I love the cloud for the power that it gives me, and I've used the cloud since, you know, way back, 2009 or so, uh, it's actually pretty frustrating to work with. And so I, I... in my head, I had this idea of, like, what if you make cloud development feel almost, like, as good as local development, right? Like, it has its, like, fast feedback loops. And so I started thinking about, like, how do we build that? And realized pretty quickly, like, well, actually, we can't really use Docker and Kubernetes, so we're gonna have to throw that out and, and, and probably gonna have to build our own file system, which we did pretty early, and build our own scheduler and build our own container runtime. And so that was, like, basically the two first years of Modal, is just, like, laying all that, like, foundational infrastructure layer in place.
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