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We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li
In this monolog, Akshat (the returned prodigal) discusses the comparative immunology of jawless fish and jawed vertebrates and the fascinating ways in which the seemingly primeval immune system of the hagfish shows sophistication equal to the immunoglobulin superfamily-based adaptive immune response of humans and other jawed vertebrates.
The consistency gap in enterprise AI represents a critical failure point where unpredictable system behavior outside of controlled demos threatens to derail executive sponsorship and regulatory compliance. In this episode, Amar Akshat, SVP & Chief Architect at Paysafe, examines how leaders can move beyond experimental shadow AI by embedding determinism and high-threshold guardrails directly into the production pipeline. The discussion outlines a rigorous evaluation framework centered on treating prompts as versioned intellectual property, implementing Know Your Agent (KYA) policy envelopes, and ensuring every agentic decision remains holistically auditable. Learn how brands work with Emerj and other Emerj Media options at https://go.emerj.com/partner
Architectural decision‑making in large enterprises can break down when system knowledge is fragmented, slowing delivery and creating inconsistent outcomes across teams. In this episode, Amar Akshat, Senior Vice President of Architecture at PaySafe, examines how codified organizational memory and deterministic guardrails enable intelligent systems to accelerate development without sacrificing control. He highlights the shift toward machine‑readable decision records, intent‑driven interfaces, and standardized design patterns that help enterprises reduce drift, strengthen compliance, and move faster with greater confidence. Learn how brands work with Emerj and other Emerj Media options at http://go.emerj.com/partner
Cat & Cloud Podcast Cat & Cloud Coffee www.catandcloud.com/ Coffee Futures Fund: Community and Support for Emerging Coffee Shop Owners - Ep# 443 Summary In this episode, the crew sits down with Akshat Khandelwal, founder of Coffee Futures Fund, to unpack the reality of coffee shop ownership—and why so many founders struggle in silence. Drawing from conversations with hundreds of operators, Akshat shares how the industry has focused heavily on product quality while overlooking the people behind the bar. The conversation explores loneliness in ownership, the myth of “figuring it out alone,” and how mentorship, community, and shared experience can dramatically shift outcomes for emerging shop owners. From funding models to values-driven leadership, this episode highlights a growing movement to support independent coffee businesses in a more human, connected way. Chapters 00:00 Akshat's Origin Story and Why Coffee Pulled Him In 08:00 Why So Many Coffee Shop Owners Said “Don't Do It” 16:00 From Romantic Dream to Coffee Futures Fund 24:00 Building a Village: Mentorship, Community, and Capital 32:00 Why Coffee Shop Owners Feel Isolated 40:00 Values, Clarity, and What Makes a Cafe Actually Matter 48:00 How the Cohort Works: Goals, Mentors, and Growth 56:00 Funding Without Equity: A Different Way to Support Shops 1:04:00 Letters to Young Coffee Shop Owners and the Future of Coffee Futures Fund Cat & Cloud: Instagram www.instagram.com/catcloudcoffee/ Webstore www.catandcloud.com/ Roasters Choice Subscription www.catandcloud.com/collections/subscriptions Wholesale Partners! Interested in serving our coffee at your business? Learn more about our Partner Program https://catandcloud.com/wholesale Links – The Truth! Colombia Truji y Angelita Thermal Shock Natural https://catandcloud.com/products/colombia-truji-y-angelita-thermal-shock-natural Coffee Futures Fund: Jared Truby Interview https://youtu.be/KaTFcHxjumk?si=wlTAbJ7ZCvZleM7B Jared Truby Zine https://www.flipsnack.com/965BBFD6AED/letter-to-young-coffee-owners-issue-with-jared-truby Roundtable Event https://coffeefutures.fund/Roundtable.html World of Coffee/SCA https://usa.worldofcoffee.org/ Cafe Imports https://www.eventbrite.com/e/source-where-do-we-go-from-here-tickets-1983895046489 Cat & Cloud Coffee was founded in 2016 by three friends who believe experiences and connections shape our lives. Former barista champions and lifelong coffee professionals, they envisioned a better way to do business and set out to create a values-driven organization that put culture first. Our mission is to inspire connection by creating memorable experiences. Whether it's with guests in our 4 retail locations in Santa Cruz, our team members, or our wholesale partners across the country, we strive to leave everyone better than we found them. The Cat & Cloud Podcast is a space for us to share our experiences and adventures in coffee and business in hopes of inspiring more people to create culture and values-driven organizations. Hosted by Chris Baca and Jared Truby Produced by Casey Ryan March 2026
As geopolitical tensions once again send shockwaves through global energy markets, the pressure to accelerate the energy transition has rarely been greater. In this episode of The smarter E Podcast, we explore how digital technologies and electrification are reshaping the energy system—and why smarter, more connected grids are becoming essential in navigating today's crisis. Our first guest, Simon Evans, Global Digital Energy and Digital Twin Leader at Arup, explains what digitalization really means for electricity networks. As renewables, rooftop solar, EVs, and distributed assets multiply across the grid, traditional operating models are becoming increasingly complex. Digital technologies, including improved data sharing to digital twins and machine learning, can help system operators manage this growing complexity. But as Evans argues, the biggest hurdles are not technical. Regulatory frameworks, trust between stakeholders, and new approaches to secure data exchange will ultimately determine how quickly digitalization can scale. We also explore how smarter networks could unlock flexibility across the system like coordinating EV charging and distributed storage and reducing renewable curtailment. In some cases, digital tools could even help direct excess renewable generation toward households facing energy affordability challenges. In the second half of the episode, Bloomberg climate reporter Akshat Rathi joins the podcast to unpack the geopolitical context behind today's volatile energy markets. As disruptions to oil and gas supply chains push prices higher, governments are once again confronting energy security concerns. But unlike past crises, there is now a third option: accelerating electrification and renewable energy deployment. From digital grids to geopolitics, this episode looks at how today's energy shocks could reshape the power system and why smarter infrastructure may be key to building a more resilient, electrified energy future. ✉️ Have comments or questions? Get in touch at podcast@thesmartere.com
Major economies around the world are grappling with electricity grids under stress from equipment bottlenecks and workforce shortages. What can be done to solve it? This week on Zero, Akshat Rathi talks with Manoj Sinha, CEO of Husk Power Systems, about distributed energy resources and their potential to bring electricity to where it is needed most — from energy-poor regions in the Global South, to energy-hungry data centres in rich countries. Husk Power Systems Electricity Is Now Holding Back Growth Across the Global Economy Renewables Are Cheap. Why Aren’t People Seeing Their Bills Fall? Q&A: Got a question for Akshat and the Bloomberg Green team that you'd like to hear answered on Zero? Email us at zeropod@bloomberg.net Zero is a production of Bloomberg Green. Our producer is Oscar Boyd. Special thanks to Marilen Martin Somer Saadi, Mohsis Andam, Laura Millan and Sharon Chen. Thoughts or suggestions? Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit https://www.bloomberg.com/green. See omnystudio.com/listener for privacy information.
Shauna Smith (Savory Fund) sits down with founders Nicole Robison (Swig) and Akshat Sethi (Thai Chili 2 Go) for a conversation about building enduring restaurant brands. Together, they share their journeys from opening a single shop to scaling across states, unpacking both the highs of breakthrough moments and the challenges of personal and professional trials.The panel dives into the realities of entrepreneurship, from breaking Guinness World Records to protecting trademarks, refreshing brands, and safeguarding culture during rapid growth. With themes of resilience, kindness, and staying true to your original vision, this conversation offers both inspiration and practical lessons for anyone navigating the restaurant/F&B industry. Hosted on Acast. See acast.com/privacy for more information.
Akshat Vaidya – Managing Partner & Co-Founder, MaelstromAkshat Vaidya is the Managing Partner and Co-Founder of Maelstrom, the family office founded by BitMEX co-founder Arthur Hayes. In this role, Akshat leads Maelstrom's investment strategy across its multiple verticals, including venture investments, liquid markets, and private equity deals. He oversees the recently launched Maelstrom Equity Fund I (a US$250 million fund) which focuses on acquiring mid-sized, profitable companies in the crypto-infrastructure space.Akshat's journey into digital assets began over a decade ago. He first started buying Bitcoin in 2013 while still working in traditional finance. Before co-founding Maelstrom, he served as Head of Corporate Development and M&A at BitMEX, where he led the exchange's expansion through acquisitions and strategic partnerships. Earlier in his career, Akshat was an investor at Granite Creek Capital Partners, executing leveraged buyouts and growth investments in U.S. middle-market companies. He holds a Bachelor's in Economics from the Wharton School of the University of Pennsylvania, combining a classic finance education with a forward-looking tech perspective.Under Akshat's leadership, Maelstrom has grown from a two-person family office into a multi-strategy investment platform. The firm now operates across venture capital, liquidity provisioning, and private equity initiatives. Akshat remains a champion of long-term value creation in a market often driven by hype and short cycles. He strives to bridge traditional financial rigor with digital-asset innovation, ensuring Maelstrom's portfolio is built on strong fundamentals and sustainable growth. LinkedIn: https://www.linkedin.com/in/akshatvaidya/Adam Schlegel – Partner & Head of Private Equity, MaelstromAdam Schlegel is a Partner at Maelstrom, where he leads the firm's private equity strategy and heads its debut buyout vehicle, Maelstrom Equity Fund I. With over a decade of experience in traditional private equity and cross-border transactions, Adam brings institutional discipline to the evolving crypto-infrastructure space. He joined Maelstrom to expand its reach into buyouts of established crypto-industry companies, applying proven investment frameworks to this emerging sector.In his previous roles, Adam built a strong foundation in global finance. He was an Investor at Haveli Investments – the Austin-based private equity firm founded by Brian Sheth (co-founder of Vista Equity Partners) – focusing on enterprise SaaS buyouts and growth-equity deals. Prior to Haveli, he served as an Associate at Baring Private Equity Asia, executing large cross-border transactions across the Asia-Pacific region. Adam began his career as an Investment Banking Analyst in Morgan Stanley's Global Energy Group, where he gained experience in capital markets and corporate finance. He holds a Master of Arts in International Finance from Johns Hopkins University's School of Advanced International Studies (SAIS) and a Bachelor of Arts in Political Science (with a minor in Chinese) from Swarthmore College.At Maelstrom, Adam is driving the firm's expansion into private equity by identifying and acquiring crypto-infrastructure and data analytics companies that meet specific criteria. Adam's approach blends traditional buyout expertise with a deep understanding of how digital assets, stablecoins, and decentralized finance are transforming financial infrastructure worldwide. By applying classic private equity frameworks (e.g. rigorous due diligence, operational improvements, and long-term value creation plans) to crypto-enabled businesses, he aims to bridge the gap between conventional finance and the crypto economy. LinkedIn: https://www.linkedin.com/in/adam-schlegel-2b5247b/
In the climate space, every idea sits somewhere along the hype continuum. Some command outsize attention. Others fly under the radar despite big potential. And a rare few hit the sweet spot, earning exactly the buzz they deserve. But how do you tell which is which? In this episode, Shayle teams up with Akshat Rathi, senior reporter for climate at Bloomberg News and host of the Zero podcast, to sort it out. Akshat and Shayle run through a list of hot topics and place each one on the hype continuum. They cover topics like: Using DERs to meet load growth Co-locating generation with data centers Infrastructure bottlenecks like generation, transmission, and transformers The roles of venture capital and the Paris Agreement in shaping markets A grab-bag of other topics like sodium-ion, advanced geothermal, and advanced nuclear Resources: Catalyst: The new wave of DERs Catalyst: When to colocate data centers with generation Zero: The Device Throttling Our Electrified Future Zero: The Gas Turbine Shortage Might Be a Climate Problem Credits: Hosted by Shayle Kann. Produced and edited by Daniel Woldorff. Original music and engineering by Sean Marquand. Stephen Lacey is our executive editor. Catalyst is brought to you by EnergyHub. EnergyHub helps utilities build next-generation virtual power plants that unlock reliable flexibility at every level of the grid. See how EnergyHub helps unlock the power of flexibility at scale, and deliver more value through cross-DER dispatch with their leading Edge DERMS platform, by visiting energyhub.com. Catalyst is brought to you by Bloom Energy. AI data centers can't wait years for grid power—and with Bloom Energy's fuel cells, they don't have to. Bloom Energy delivers affordable, always-on, ultra-reliable onsite power, built for chipmakers, hyperscalers, and data center leaders looking to power their operations at AI speed. Learn more by visiting BloomEnergy.com.
Erik Bernhardsson is the Co-founder and CEO of Modal, building high-performance AI infrastructure.We talk about building what is essentially a new cloud provider, created from the ground up, optimized for AI. We also talk about what actually happened with the great GPU shortage, how Modal fixes the inference problem in AI, and why he thinks AI will lead to 10x more developers.He also shares lessons on culture from joining Spotify as the 40th employee, treating hiring like a prediction problem, what most people get wrong working with early customers, why more people should start companies in their 30's and 40's, and reflections on fundraising in a hot market.Thank you to Tim Chen at Essence Venture Capital and Erik's Co-founder Akshat for their help brainstorming topics for this conversation.Thank you to Meow and Hanover Park for supporting this episode.Meow: Get free bookkeeping for your startup at https://www.meow.comHanover Park: Modern, AI-native fund admin at https://www.hanoverpark.com/TurnerTimestamps:(4:26) Modal: AI-native infrastructure(9:02) Why its so hard to get GPU's(15:00) Hitting PMF with AI generated media(20:37) Competing in IOI competitions(23:09) 40th employee at Spotify(27:17) Lessons from Spotify(31:17) Starting Better[dot]com(34:05) Treating hiring like a prediction problem(36:12) Erik's favorite interview question(39:07) Sales + common design partner mistakes(42:02) Startups should solve hard problems(44:05) Evolution of Modal's product over time(50:15) Rise in importance of inference in AI(52:07) AI development post-GPU scarcity(58:51) Building a brand in dev tools(1:04:31) Fundraising from Seed to Series B(1:07:42) More 30+ year old's should start companies(1:10:00) Reducing developer tax, increasing productivity(1:20:37) Why Erik's bullish and bearish on AI(1:26:17) Bubbles, downsides to inappropriate valuations(1:34:58) High CO2 levels make you dumb(1:37:38) Difference between US and European startupsReferencedModal: https://modal.comCareers at Modal: https://jobs.ashbyhq.com/modalSuno: https://suno.comPlanet Scale: https://planetscale.comHow to hire smarter than the market: https://erikbern.com/2020/01/13/how-to-hire-smarter-than-the-market-a-toy-modelInterviewing is a noisy prediction problem: https://erikbern.com/2018/05/02/interviewing-is-a-noisy-prediction-problemCloud in 2030: https://erikbern.com/2021/11/30/storm-in-the-stratosphere-how-the-cloud-will-be-reshuffledFollow ErikTwitter: https://x.com/bernhardssonLinkedIn: https://www.linkedin.com/in/erikbernFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/
Web3 Academy: Exploring Utility In NFTs, DAOs, Crypto & The Metaverse
In today's episode, we sit down with a veteran investor who's survived every Bitcoin crash, altcoin mania, and DeFi bubble since 2013, and lived to profit from them all. We unpack why Bitcoin still dominates long-term portfolios, how altcoin cycles are designed to lure and wreck retail investors, and why smart money treats hype like exit liquidity. ~~~~~
EP 387 - We're going back to the future this week with the excellent Akshat Rathi and his optimistic take on climate change.Akshat is a Bloomberg Senior Climate journalist, and the author of Climate Capitalism: Winning The Global Race To Zero Emissions.We spoke to him about his book, and:Why climate solutions investment needs to be made in the developing world and emerging markets to help them move away from coalWhy he felt it was so important to tell positive stories to help accelerate the fight against climate changeAndWhy he abandoned the idea of being a scientist (after getting a PhD) to become a storyteller in order to make a bigger positive impact on the worldAs you'd expect from someone who writes for Bloomberg, he's very balanced and very astute.He's happy to concede that climate change isn't the only crisis facing humanity, but if we don't fix it, it'll make the others a whole lot worse. You can't help feeling more positive after spending time in his company, so give it a go. *For Apple Podcast chapters, access them from the menu in the bottom right corner of your player*Spotify Video Chapters:00:00 BWB with Akshat Rathi00:49 Andy's Intro to Akshat01:53 The Importance of Climate Solutions02:23 Challenges in Climate Journalism07:23 Success Stories in Climate Action11:16 The Role of Governments and Big Businesses12:43 Akshat's Journey and Passion for Climate Journalism18:36 Global Investment in Climate Solutions28:19 Practical Steps for SMEs29:43 Economic Realities in India30:01 Air Conditioning Around the World30:41 Career Reflections and Journalism34:06 Work Culture and Balance38:40 Social Media and Its Impact42:32 Greenwashing and Corporate Responsibility45:10 Quickfire - Get To Know Akshatbusinesswithoutbullshit.meWatch and subscribe to us on YouTubeFollow us:InstagramTikTokLinkedinTwitterFacebookIf you'd like to be on the show, get in contact - mail@businesswithoutbullshit.meBWB is powered by Oury Clark
CAMB.AI is one of the most exciting companies operating at the intersection of AI, media, and sports, known for its cutting-edge voice and translation tech that brings content to life in over 140 languages. But what does it take to scale a global language-tech company, and how is this innovation reshaping the future of fan engagement?In this episode, we sat down with Avneesh Prakash, Co-Founder and CEO, and Akshat Prakash, Co-Founder and CTO of CAMB.AI, to explore their entrepreneurial journeys and vision for the future of AI in content localisation. From engineering roots and startup grit to joining the 2025 Comcast SportsTech cohort, Avneesh and Akshat share a behind-the-scenes look at what powers CAMB.AI's rapid rise.Tune in to learn more about:How CAMB.AI's AI-powered dubbing is different from traditional translation toolsWhy sports became a strategic sector for their businessThe company's plans for global expansion and new marketsWhat it means to build responsible and inclusive AILessons from launching and scaling a language-tech startupWe hope you enjoy this episode!If you had a good time listening, please support us by:Subscribing – it just takes a click!Giving us a 5-star rating on your listening platform to help us spread the wordFinally, if you want to learn more about what we do at LaSource, check out our website and LinkedIn page.LeCorner is a podcast dedicated to sports and digital. Every two weeks, we host a prominent guest in the sports industry to discuss digital innovation and strategic development.Hosted by Ausha. See ausha.co/privacy-policy for more information.
In this episode of WP Builds, I talk with Akshat Choudhary, founder of Block Vault, about his new product, Airlift. Airlift is a WordPress optimisation tool that promises to speed up websites with the click of a button by automatically implementing performance enhancements like caching, CDN, and image optimisation. Akshat discusses the challenges and importance of making websites faster for user engagement and conversions. Although building Airlift took longer than anticipated, with three-and-a-half years of development, the goal is to make fast websites accessible to everyone, emphasising the impact on user experience and engagement. Go listen.
In today's episode, we take a dive through the founders journey and what it's like to run a massive crypto fund.Clearly, crypto cycles follow a pattern.Skepticism transforms into celebration, then a cooldown period, until something innovative emerges to change the landscape. Each of these cycle presents distinct opportunities.That is what we discuss in today's episode with Akshat Vaidya from Maelstorm. We cover navigating from early Bitcoin through the DeFi expansion into staking infrastructure and how one's approach tends to evolve with each market phase. Akshat has spent years in onchain investing—identifying overlooked value, recognizing cycle timing, and determining which innovations demonstrate staying power.We look at potential future directions for crypto. Liquid staking, onchain real-world assets, the increasing convergence between traditional venture capital and crypto markets.We touch on crypto M&A, selling businesses and psychedelics as well.Let's explore.The RollupJoin The Rollup Edge: https://members.therollup.coWebsite: https://therollup.co/Spotify: https://open.spotify.com/show/1P6ZeYd..Podcast: https://therollup.co/category/podcastFollow us on X: https://www.x.com/therollupcoFollow Rob on X: https://www.x.com/robbie_rollupFollow Andy on X: https://www.x.com/ayyyeandyJoin our TG group: https://t.me/+8ARkR_YZixE5YjBhThe Rollup Disclosures: https://therollup.co/the-rollup-discl
Angad and Akshat join the pod for the second time to talk evolution of Dippy.ai and how they are using multiple subnets and integration within Bittensor's network to further the reach and capabilities of their viral roleplaying app. With a successful subnet (S11) and second subnet (S58) launched to add voice to their offering, this team is becoming a major force both in and outside of Bittensor. https://x.com/dippy_ai https://www.dippy.ai/ https://taostats.io/validators/bittensor-guru-podcast/ https://bittensor.guru
In episode 132, Premal is joined by former podcast host and current friend Akshat Singhal. Their main topic of discussion is the 12-team College Football Playoff field. Did the committee get it right? What should they change according to Premal and Akshat? Akshat has a bone to pick with the Bengals. And you don't want to miss the duo of Buckeye fans issuing a ‘You Played Yourself' to Ryan Day after Ohio State's abysmal performance against their rival. Plus, they are picking the winners of the opening round CFP games. And don't miss weighing in on the non-sports ‘You Played Yourself' and ‘Am I Hatin'?' Submit yours to be featured on our next episode, #1 podcast listeners! PREM BRULEE | prembruleepodcast@gmail.com | Twitter: @prem_brulee | Instagram: @premalthegreat Akshat Singhal | Twitter: @ASinghal31 | Instagram: @asinghal31
A few months ago I was lucky to get a chance to sit down with Akshat in Dubai Akshat is one of the people behind Arrayah Hacker House Hear more about his journey, and the vision behind starting Arrayah Hacker House Connect with Akshat here: X: https://x.com/lifeoftheshat LinkedIn: https://www.linkedin.com/in/akshat418 Connect with the host Roohi here: X:https://x.com/roohi_kr LinkedIn:https://www.linkedin.com/in/roohi-kazi-53174113b/
Akshat Rathi is a London-based senior reporter for Bloomberg News and author of the new book Climate Capitalism, which is the subject of his 2024 TED Talk. He also hosts Bloomberg Green's weekly Zero podcast and writes a weekly Zero newsletter, focused on climate change. Previously, Akshat was a senior reporter at Quartz and a science editor at The Conversation. His work has been cited widely, including in New York Times, Washington Post, Wall Street Journal, Financial Times and The Guardian. Skip ahead to the following section(s): (0:00) Introduction & National Updates (10:40) Interview w/ Akshat Rathi (24:16) Q&A Discussion (40:05) October Actions October Action Sheet: https://cclusa.org/action-sheet Take Action Make A Voting Plan: https://cclusa.org/vote Pre-Call Video: https://vimeo.com/1018718136 More About Akshat: https://akshatrathi.com/
We can blame capitalism for worsening the climate crisis, says journalist Akshat Rathi, but we can also use it to create the solutions we need for the mess we're in. He details how “climate capitalism” — the strategic use of market forces and government policies to make polluting the planet cost more than advancing climate solutions — can flip the script and actually make sustainability profitable.
We can blame capitalism for worsening the climate crisis, says journalist Akshat Rathi, but we can also use it to create the solutions we need for the mess we're in. He details how “climate capitalism” — the strategic use of market forces and government policies to make polluting the planet cost more than advancing climate solutions — can flip the script and actually make sustainability profitable.
We can blame capitalism for worsening the climate crisis, says journalist Akshat Rathi, but we can also use it to create the solutions we need for the mess we're in. He details how “climate capitalism” — the strategic use of market forces and government policies to make polluting the planet cost more than advancing climate solutions — can flip the script and actually make sustainability profitable.
A bit of a different vibe from our typical technical content as we dive into the perspective of one of the most well known crypto funds, built by Arthur Hayes, ex-CEO of BitMEX. If you've been in the space for a while, you know that market swings are extremely frequent and can be quite volatile. The key to thriving in this environment isn't about trying to predict the next big move but about understanding how to manage risk, maintain discipline, and stay informed...which is easier said than done. None of this content is to be perceived as financial advice, but we did talk more about markets than we usually do. In our conversation, we explore the transition from BitMEX's meteoric rise to its current focus, as competition from new market entrants like Binance and FTX reshaped the landscape. Akshat reminisces about the unique, egalitarian culture at BitMEX, where humility and inclusivity were the foundations of success. We'll also talk about philosophy and strategy of Maelstorm. Akshat explains how they're building a 100-year portfolio, targeting early-stage crypto projects that align with their pillars of entropy, trustless decentralization, and new models for internet monetization. The time horizon on these investments was really interesting and shifted our perspective away from typical 3-5 year cyclical journey. Enjoy this deep dive into the thought process of a successful fund manager in the volatile crypto space. Website: https://therollup.co/ Spotify: https://open.spotify.com/show/1P6ZeYd.. Podcast: https://therollup.co/category/podcast Follow us on X: https://www.x.com/therollupco Follow Rob on X: https://www.x.com/robbie_rollup Follow Andy on X: https://www.x.com/ayyyeandy Join our TG group: https://t.me/+8ARkR_YZixE5YjBh The Rollup Disclosures: https://therollup.co/the-rollup-discl
In this monolog, Akshat ponders about the spatiotemporal underpinnings of cytokine concentrations in tissue niches...and how it all appears to function like a gossip network.
In this episode, Akshat outlines the salient features of CAR or TCR-engineered T cells, and deigns to ponder: is one modality just objectively better than the other?
In this episode, we talk to Akshat Rathi, award-winning senior climate reporter for Bloomberg News. Rathi is the host of Bloomberg's podcast Zero that explores the policies, tactics and clean technologies pushing for a zero emissions future. He is also the author of the book Climate Capitalism, which tracks the unlikely heroes driving the fight against climate change.Rathi argues that for years the aviation industry has sheltered behind the label of being “hard to decarbonise”, which is not only a misconception, but has also stalled the industry's progress towards net zero emissions. He advocates for correctly pricing flights to account for their true environmental cost, a move that could bring an end to “ridiculously” cheap flight tickets.Rathi also discusses the potential of sustainable aviation fuels (SAF) in decarbonising aviation and the need to overcome cost barriers through policy support and corporate commitment. Further, he delves into the role of electric aviation in transforming short-haul travel and regional aviation.In Rathi's view, the aviation industry stands at a crossroads, and the choices made now will determine not just the future of flying, but our ability to meet global climate targets. As he puts it, “We have to start to think about those technologies, because we do need decarbonised solutions.”If you LOVED this episode you'll also love the conversation we had with Dan Rutherford, Senior Director of Research at the International Council on Clean Transportation (ICCT), who shares the latest developments, partnerships, and challenges in reducing aviation emissions and achieving net zero by 2050. Check it out here.Learn more about the innovators who are navigating the industry's challenges to make sustainable aviation a reality, in our new book ‘Sustainability in the Air'. Click here to learn more.Feel free to reach out via email to podcast@simpliflying.com. For more content on sustainable aviation, visit our website green.simpliflying.com and join the movement. It's about time.Links & More:Zero - BloombergThe Airline Industry's Biggest Climate Challenge: A Lack of Clean Fuel - Bloomberg ‘Magical thinking': hopes for sustainable jet fuel not realistic, report finds - The Guardian How to rethink tourism and aviation for a greener future - SimpliFlying
Akshat Gupta is a national bestselling author, a TEDx speaker and an excelling screenwriter and dialogue writer in the Indian film industry. The Hidden Hindu series, authored by him, has sold over 1 lakh copies, with each book a national bestseller. Akshat is well known in the publishing industry, as well as in the Indian film industry, with a number of films and web-series signed on his name.
In episode 153 of PG Radio, we discuss spirituality, history, and mythology with our special guest, Akshat Gupta. Join us as we uncover the layers of ancient Indian wisdom, intriguing legends, and fascinating stories that continue to shape the cultural fabric of India. Akshat Gupta, a prolific storyteller and cultural historian, guides us through a captivating journey that spans sacred symbols, reincarnation tales, the origins of Vedas and much more. This is what we talked about: 00:00 - Why are "peepal" and Banyan trees sacred? 13:03 - Akshat tells us a scary story 16:50 - Reincarnation story of Akshat 25:15 - Origin of Vedas 28:05 - Lost Technologies of Ancient India 41:00 - Connection between Ramayana and Mahabharata 50:06 - Naga Sadhus and their war stories 1:04:30 - Akshat tells an interesting story about a big business family
Microsoft's recent push to capitalize on artificial intelligence has made it the world's most valuable company. But according to new figures, that ambition is coming at the expense of its climate goals. In 2020, the company pledged to be carbon-negative by the end of the decade. Instead, its emissions rose 30% between 2020 and 2023. Microsoft President Brad Smith says the company isn't giving up on its green goals — and that the good AI can do for the world will outweigh its environmental impact. Akshat tells Zero producer Mythili Rao about his conversation with Smith, and how other tech giants will be making similar calculations. Explore further: Past episode with BNEF's Jenny Chase on how to triple renewable energy by 2030 Past episode with Notre Dame professor Emily Grubert about the possibility of carbon capture Past episode with Electra CEO Sandeep Nijhawan on making zero emissions steel Zero is a production of Bloomberg Green. Our producer is Mythili Rao. Special thanks this week to Kira Bindrim, Dina Bass, and Alicia Clanton. Thoughts or suggestions? Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit https://www.bloomberg.com/green.See omnystudio.com/listener for privacy information.
In this episode of the Manufacturing Culture Podcast, host Jim Mayer interviews Akshat Thirani, CEO and founder of Amper Technologies, and Katrina Keys, a visionary in transforming organizational culture. They discuss the role of Amper in revolutionizing how manufacturers track and improve their operations and how it helps transform the culture within their customers. The culture at Amper is characterized by values such as embracing reality, obsessing over customer success, and being lean. They share stories of customers who have implemented Amper and experienced a shift in their culture, such as valuing operators as key contributors and improving resource allocation. The conversation covered various manufacturing, culture, and employee engagement topics. The guests discussed the importance of trust and teamwork in manufacturing, as well as the role of technology in customers' learning journey. They also discussed the future of manufacturing, emphasizing the need for efficiency, automation, and connectivity. The guests highlighted the significance of employee engagement and company culture, as well as how Amper's platform facilitates feedback and communication between the shop floor and management. They concluded by encouraging listeners to focus on improving systems and creating a winning culture in manufacturing.TakeawaysAmper Technologies helps manufacturers track and improve their operations by providing real-time data and enabling teams to make better decisions.Implementing Amper can lead to a shift in culture within manufacturing companies, such as valuing operators as key contributors and improving resource allocation.The culture at Amper is characterized by values such as embracing reality, obsessing over customer success, and being lean.Amper's dispersed workforce focuses on autonomy, collaboration, and communication to maintain a healthy culture.Customers have experienced aha moments when implementing Amper, such as realizing the importance of operators and gaining visibility into machine utilization. Trust and teamwork are crucial in manufacturing, where efficiency and safety are paramount.Technology plays a significant role in the learning journey of manufacturing customers.The future of manufacturing will involve increased efficiency, automation, and connectivity.Employee engagement and company culture are intertwined concepts that contribute to a successful manufacturing environment.Amper's platform facilitates feedback and communication between the shop floor and management.Improving systems is essential for creating a winning culture in manufacturing.Connect with Akshat on LinkedinConnect with Katrina on LinkedinVisit the Amper on their websitePartnersNeed to make your manufacturing business stand out? Discover Marketing Metal, the specialized marketing agency for the manufacturing industry. Whether you operate a machine shop, fab shop, or a custom manufacturing firm, Marketing Metal has the expertise to build your brand and craft marketing strategies that cut through the noise. Ready to elevate your business? Visit themfgconnector.com today and learn how Marketing Metal can help you succeed.Are your tools organized and protected? Discover Kaiser Manufacturing, the ultimate solution in tool management. Their Kaizen Shadow Foam® allows you to create custom foam inserts tailored exactly for your needs, and their Tool Caddy offers a safe, compact storage option for all your tool holders. Improve your efficiency and protect your investments with Kaiser Manufacturing. Visit themfgconnector.com today and start organizing like a pro!
Angad and Akshat join the pod from the team at Impel which recently launched on Bittensor's Subnet 11 to incentivize decentralized creation of roleplay models for their app Dippy.ai. Get to know the team, the killer pedigree and their bold objective of becoming the open source leaders in roleplaying LLMs. Video link below. https://x.com/KeithSingery/status/1790128752522858577 https://www.tryimpel.com/ https://twitter.com/impel_ai https://twitter.com/dippy_ai https://www.dippy.ai/ https://taostats.io/validators/bittensor-guru-podcast/ https://bittensor.guru
In this episode, Gil Jenkins sits down with Akshat Rathi, a senior climate reporter at Bloomberg News and the host of Bloomberg Green's Zero podcast, to discuss his new book, "Climate Capitalism: Winning the Global Race to Zero Emissions and Solving the Crisis of our Age," which was released on March 12 in the U.S. "Climate Capitalism" takes readers across five continents, tracking the unlikely heroes driving the fight against climate change. The stories within the book reveal how people, policy, and technology are converging to create a green economy that is not only possible but profitable. Akshat and Gil explore key chapters from the book, touching on stories like that of Wan Gang, a Chinese bureaucrat who played a pivotal role in the rapid expansion of electric vehicles in China. They also discuss India's significant progress toward solar power since 2015, the transformative influence of the International Energy Agency, and the UK's legally binding decarbonization commitments, among other topics.Links: About the BookAkshat on XAkshat on LinkedInZero PodcastEpisode recorded March 8, 2024 Email your feedback to Chad, Gil, and Hilary at climatepositive@hasi.com or tweet them to @ClimatePosiPod.
For more than a century, extractive industry and capitalism have dominated the developed world's economies. Some of the biggest companies in the world produce and sell oil and gas, and those commodities have made countries and people very wealthy. But they're also a major source of pollution and contributor to the climate crisis. In response, many of these companies have started investing in renewable energy, others have completely shifted their focus to clean solutions. Akshat Rathi's new book Climate Capitalism delves into this shift and argues that saving the earth is economically more advantageous than destroying it. So, what is climate capitalism? How can this new approach facilitate climate innovation and economic growth? And what will it take to move away from traditional capitalism? This week host Bill Loveless talks with Akshat about his new book and how reforming the current economic system can address climate change and be profitable. Akshat is a senior climate reporter for Bloomberg News. Prior to Bloomberg, he was a senior reporter at Quartz and a science editor at The Conversation. His new book, Climate Capitalism: Winning the Race to Zero Emissions and Solving the Crisis of our Age has been named one of the best books of the year by the The London Times and The Economic Times.
It is now cheaper to save the world than destroy it. But is capitalism up to the challenge of preventing the climate crisis? In his new book Climate Capitalism, Zero host Akshat Rathi introduces a dozen people who are already steering capitalism to solve the climate crisis: from the engineer who shaped China's electric car policies and the politician who helped make net-zero a UK law to the CEO who fought off a takeover attempt so he could stick with a sustainability strategy. Akshat argues that not only is capitalism capable of taking on the climate crisis, but harnessing it is the only way to solve the climate crisis in the time we have available. And yet while some improvements have been made over the past few years, the world is off track to meet its 2050 climate targets. So today on Zero, Bloomberg's Greener Living editor Kira Bindrim sits down with Akshat to discuss his new book, and asks him: If climate capitalism is so doable, why does it seem so difficult? Read more: Order Akshat's new book, Climate Capitalism Listen to the interview with Fatih Birol that Akshat mentions Hear Akshat and Kira talk about the reality of carbon footprints Read a transcript of this episode Zero is a production of Bloomberg Green. Our producer is Oscar Boyd and our senior producer is Christine Driscoll. Special thanks to Anna Mazarakis, Gilda di Carli and Kira Bindrim. Thoughts or suggestions? Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit bloomberg.com/green. See omnystudio.com/listener for privacy information.
In Climate Capitalism: Winning the Global Race to Zero Emissions, Akshat Rathi tells the stories of people around the world who are building impactful solutions to tackle climate change.Rathi is a senior reporter for Bloomberg News, focusing on climate and energy. He also hosts the weekly Zero podcast, in which he talks to the people leading the fight for a zero-emissions future. In his new book, Rathi argues that the best way to cut carbon pollution is by harnessing capitalism. Combating climate change requires a combination of smart policies, financing, technological innovations, and leadership—without killing markets or competition.Together with Martin Reeves, Chairman of the BCG Henderson Institute, Rathi discusses the essence of climate capitalism, how to scale up individual success stories, and how to navigate the challenging political context. Key topics discussed: 02:09 | Definition of climate capitalism07:19 | Success stories: Chinese EVs, Orsted11:31 | The need to combine tech, policies, and finance12:52 | How to scale case studies to big solutions16:24 | Navigating a polarized political context18:45 | Making climate solutions profitable24:06 | Where CEOs should startThis podcast uses the following third-party services for analysis: Chartable - https://chartable.com/privacy
We're back with Dr. Akshat Jain who shares highlights in bleeding disorder research from ASH 2023, plus the differences between hemophilia A vs hemophilia B gene therapies with Dr. Mark Redding. We close out with our latest Elite Athletes segments featuring bleeding disorder community legend, Perry Parker. Don't miss it! Show Notes: Subscribe: The BloodStream Podcast Presenting Sponsor: Takeda, visit bleedingdisorders.com to learn more. Connect with BloodStream Media: BloodStreamMedia.com BloodStream on Facebook BloodStream on Twitter
The climate clock is ticking faster and faster. How can we use capitalism to undo the bad stuff that capitalism did and maybe even make things better? That's today's big (loaded) question, and my returning guest is Akshat Rathi. Akshat is a London-based senior reporter, newsletter writer, and podcaster for Bloomberg News.Akshat has a PhD in organic chemistry from the University of Oxford, and a BTech in Chemical Engineering from the Institute of Chemical Technology in Mumbai. Akshat was previously a senior reporter at Quartz and a science editor at The Conversation. He is here today to talk about his first book, Climate Capitalism.This wonderful book tells the stories of people building solutions at scale to tackle one of humanity's greatest challenges. Some solutions we've already built, like solar and batteries, and some we're still working on because they take a lot of work, and money, and politics.In a world where journalism is going bye-bye, and the climate clock is ticking, but we've made so much progress, and we can make so much more, Akshat's reporting in this book couldn't be more timely, as we seek to answer the question, where are we on this timeline?-----------Have feedback or questions? Tweet us, or send a message to questions@importantnotimportant.comNew here? Get started with our fan favorite episodes at podcast.importantnotimportant.com.-----------INI Book Club:The Long View by Richard FisherFind all of our guest recommendations at the INI Book Club: https://bookshop.org/lists/important-not-important-book-clubLinks:Order Climate Capitalism in the US/Canada (out March 12) Order Climate Capitalism in the UK (out now)Order Climate Capitalism in India (out now)Order Climate Capitalism in the rest of the worldListen to Akshat on his podcast Zero, and subscribe to his newsletterRead about the Biden Administration's regulations on the social costs of climate change here and hereFollow us:Subscribe to our newsletter at
We're writing this one day after the monster release of OpenAI's Sora and Gemini 1.5. We covered this on ‘s ThursdAI space, so head over there for our takes.IRL: We're ONE WEEK away from Latent Space: Final Frontiers, the second edition and anniversary of our first ever Latent Space event! Also: join us on June 25-27 for the biggest AI Engineer conference of the year!Online: All three Discord clubs are thriving. Join us every Wednesday/Friday!Almost 12 years ago, while working at Spotify, Erik Bernhardsson built one of the first open source vector databases, Annoy, based on ANN search. He also built Luigi, one of the predecessors to Airflow, which helps data teams orchestrate and execute data-intensive and long-running jobs. Surprisingly, he didn't start yet another vector database company, but instead in 2021 founded Modal, the “high-performance cloud for developers”. In 2022 they opened doors to developers after their seed round, and in 2023 announced their GA with a $16m Series A.More importantly, they have won fans among both household names like Ramp, Scale AI, Substack, and Cohere, and newer startups like (upcoming guest!) Suno.ai and individual hackers (Modal was the top tool of choice in the Vercel AI Accelerator):We've covered the nuances of GPU workloads, and how we need new developer tooling and runtimes for them (see our episodes with Chris Lattner of Modular and George Hotz of tiny to start). In this episode, we run through the major limitations of the actual infrastructure behind the clouds that run these models, and how Erik envisions the “postmodern data stack”. In his 2021 blog post “Software infrastructure 2.0: a wishlist”, Erik had “Truly serverless” as one of his points:* The word cluster is an anachronism to an end-user in the cloud! I'm already running things in the cloud where there's elastic resources available at any time. Why do I have to think about the underlying pool of resources? Just maintain it for me.* I don't ever want to provision anything in advance of load.* I don't want to pay for idle resources. Just let me pay for whatever resources I'm actually using.* Serverless doesn't mean it's a burstable VM that saves its instance state to disk during periods of idle.Swyx called this Self Provisioning Runtimes back in the day. Modal doesn't put you in YAML hell, preferring to colocate infra provisioning right next to the code that utilizes it, so you can just add GPU (and disk, and retries…):After 3 years, we finally have a big market push for this: running inference on generative models is going to be the killer app for serverless, for a few reasons:* AI models are stateless: even in conversational interfaces, each message generation is a fully-contained request to the LLM. There's no knowledge that is stored in the model itself between messages, which means that tear down / spin up of resources doesn't create any headaches with maintaining state.* Token-based pricing is better aligned with serverless infrastructure than fixed monthly costs of traditional software.* GPU scarcity makes it really expensive to have reserved instances that are available to you 24/7. It's much more convenient to build with a serverless-like infrastructure.In the episode we covered a lot more topics like maximizing GPU utilization, why Oracle Cloud rocks, and how Erik has never owned a TV in his life. Enjoy!Show Notes* Modal* ErikBot* Erik's Blog* Software Infra 2.0 Wishlist* Luigi* Annoy* Hetzner* CoreWeave* Cloudflare FaaS* Poolside AI* Modular Inference EngineChapters* [00:00:00] Introductions* [00:02:00] Erik's OSS work at Spotify: Annoy and Luigi* [00:06:22] Starting Modal* [00:07:54] Vision for a "postmodern data stack"* [00:10:43] Solving container cold start problems* [00:12:57] Designing Modal's Python SDK* [00:15:18] Self-Revisioning Runtime* [00:19:14] Truly Serverless Infrastructure* [00:20:52] Beyond model inference* [00:22:09] Tricks to maximize GPU utilization* [00:26:27] Differences in AI and data science workloads* [00:28:08] Modal vs Replicate vs Modular and lessons from Heroku's "graduation problem"* [00:34:12] Creating Erik's clone "ErikBot"* [00:37:43] Enabling massive parallelism across thousands of GPUs* [00:39:45] The Modal Sandbox for agents* [00:43:51] Thoughts on the AI Inference War* [00:49:18] Erik's best tweets* [00:51:57] Why buying hardware is a waste of money* [00:54:18] Erik's competitive programming backgrounds* [00:59:02] Why does Sweden have the best Counter Strike players?* [00:59:53] Never owning a car or TV* [01:00:21] Advice for infrastructure startupsTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: Hey, and today we have in the studio Erik Bernhardsson from Modal. Welcome.Erik [00:00:19]: Hi. It's awesome being here.Swyx [00:00:20]: Yeah. Awesome seeing you in person. I've seen you online for a number of years as you were building on Modal and I think you're just making a San Francisco trip just to see people here, right? I've been to like two Modal events in San Francisco here.Erik [00:00:34]: Yeah, that's right. We're based in New York, so I figured sometimes I have to come out to capital of AI and make a presence.Swyx [00:00:40]: What do you think is the pros and cons of building in New York?Erik [00:00:45]: I mean, I never built anything elsewhere. I lived in New York the last 12 years. I love the city. Obviously, there's a lot more stuff going on here and there's a lot more customers and that's why I'm out here. I do feel like for me, where I am in life, I'm a very boring person. I kind of work hard and then I go home and hang out with my kids. I don't have time to go to events and meetups and stuff anyway. In that sense, New York is kind of nice. I walk to work every morning. It's like five minutes away from my apartment. It's very time efficient in that sense. Yeah.Swyx [00:01:10]: Yeah. It's also a good life. So we'll do a brief bio and then we'll talk about anything else that people should know about you. Actually, I was surprised to find out you're from Sweden. You went to college in KTH and your master's was in implementing a scalable music recommender system. Yeah.Erik [00:01:27]: I had no idea. Yeah. So I actually studied physics, but I grew up coding and I did a lot of programming competition and then as I was thinking about graduating, I got in touch with an obscure music streaming startup called Spotify, which was then like 30 people. And for some reason, I convinced them, why don't I just come and write a master's thesis with you and I'll do some cool collaborative filtering, despite not knowing anything about collaborative filtering really. But no one knew anything back then. So I spent six months at Spotify basically building a prototype of a music recommendation system and then turned that into a master's thesis. And then later when I graduated, I joined Spotify full time.Swyx [00:02:00]: So that was the start of your data career. You also wrote a couple of popular open source tooling while you were there. Is that correct?Erik [00:02:09]: No, that's right. I mean, I was at Spotify for seven years, so this is a long stint. And Spotify was a wild place early on and I mean, data space is also a wild place. I mean, it was like Hadoop cluster in the like foosball room on the floor. It was a lot of crude, like very basic infrastructure and I didn't know anything about it. And like I was hired to kind of figure out data stuff. And I started hacking on a recommendation system and then, you know, got sidetracked in a bunch of other stuff. I fixed a bunch of reporting things and set up A-B testing and started doing like business analytics and later got back to music recommendation system. And a lot of the infrastructure didn't really exist. Like there was like Hadoop back then, which is kind of bad and I don't miss it. But I spent a lot of time with that. As a part of that, I ended up building a workflow engine called Luigi, which is like briefly like somewhat like widely ended up being used by a bunch of companies. Sort of like, you know, kind of like Airflow, but like before Airflow. I think it did some things better, some things worse. I also built a vector database called Annoy, which is like for a while, it was actually quite widely used. In 2012, so it was like way before like all this like vector database stuff ended up happening. And funny enough, I was actually obsessed with like vectors back then. Like I was like, this is going to be huge. Like just give it like a few years. I didn't know it was going to take like nine years and then there's going to suddenly be like 20 startups doing vector databases in one year. So it did happen. In that sense, I was right. I'm glad I didn't start a startup in the vector database space. I would have started way too early. But yeah, that was, yeah, it was a fun seven years as part of it. It was a great culture, a great company.Swyx [00:03:32]: Yeah. Just to take a quick tangent on this vector database thing, because we probably won't revisit it but like, has anything architecturally changed in the last nine years?Erik [00:03:41]: I'm actually not following it like super closely. I think, you know, some of the best algorithms are still the same as like hierarchical navigable small world.Swyx [00:03:51]: Yeah. HNSW.Erik [00:03:52]: Exactly. I think now there's like product quantization, there's like some other stuff that I haven't really followed super closely. I mean, obviously, like back then it was like, you know, it's always like very simple. It's like a C++ library with Python bindings and you could mmap big files and into memory and like they had some lookups. I used like this kind of recursive, like hyperspace splitting strategy, which is not that good, but it sort of was good enough at that time. But I think a lot of like HNSW is still like what people generally use. Now of course, like databases are much better in the sense like to support like inserts and updates and stuff like that. I know I never supported that. Yeah, it's sort of exciting to finally see like vector databases becoming a thing.Swyx [00:04:30]: Yeah. Yeah. And then maybe one takeaway on most interesting lesson from Daniel Ek?Erik [00:04:36]: I mean, I think Daniel Ek, you know, he started Spotify very young. Like he was like 25, something like that. And that was like a good lesson. But like he, in a way, like I think he was a very good leader. Like there was never anything like, no scandals or like no, he wasn't very eccentric at all. It was just kind of like very like level headed, like just like ran the company very well, like never made any like obvious mistakes or I think it was like a few bets that maybe like in hindsight were like a little, you know, like took us, you know, too far in one direction or another. But overall, I mean, I think he was a great CEO, like definitely, you know, up there, like generational CEO, at least for like Swedish startups.Swyx [00:05:09]: Yeah, yeah, for sure. Okay, we should probably move to make our way towards Modal. So then you spent six years as CTO of Better. You were an early engineer and then you scaled up to like 300 engineers.Erik [00:05:21]: I joined as a CTO when there was like no tech team. And yeah, that was a wild chapter in my life. Like the company did very well for a while. And then like during the pandemic, yeah, it was kind of a weird story, but yeah, it kind of collapsed.Swyx [00:05:32]: Yeah, laid off people poorly.Erik [00:05:34]: Yeah, yeah. It was like a bunch of stories. Yeah. I mean, the company like grew from like 10 people when I joined at 10,000, now it's back to a thousand. But yeah, they actually went public a few months ago, kind of crazy. They're still around, like, you know, they're still, you know, doing stuff. So yeah, very kind of interesting six years of my life for non-technical reasons, like I managed like three, four hundred, but yeah, like learning a lot of that, like recruiting. I spent all my time recruiting and stuff like that. And so managing at scale, it's like nice, like now in a way, like when I'm building my own startup. It's actually something I like, don't feel nervous about at all. Like I've managed a scale, like I feel like I can do it again. It's like very different things that I'm nervous about as a startup founder. But yeah, I started Modal three years ago after sort of, after leaving Better, I took a little bit of time off during the pandemic and, but yeah, pretty quickly I was like, I got to build something. I just want to, you know. Yeah. And then yeah, Modal took form in my head, took shape.Swyx [00:06:22]: And as far as I understand, and maybe we can sort of trade off questions. So the quick history is started Modal in 2021, got your seed with Sarah from Amplify in 2022. You just announced your Series A with Redpoint. That's right. And that brings us up to mostly today. Yeah. Most people, I think, were expecting you to build for the data space.Erik: But it is the data space.Swyx:: When I think of data space, I come from like, you know, Snowflake, BigQuery, you know, Fivetran, Nearby, that kind of stuff. And what Modal became is more general purpose than that. Yeah.Erik [00:06:53]: Yeah. I don't know. It was like fun. I actually ran into like Edo Liberty, the CEO of Pinecone, like a few weeks ago. And he was like, I was so afraid you were building a vector database. No, I started Modal because, you know, like in a way, like I work with data, like throughout my most of my career, like every different part of the stack, right? Like I thought everything like business analytics to like deep learning, you know, like building, you know, training neural networks, the scale, like everything in between. And so one of the thoughts, like, and one of the observations I had when I started Modal or like why I started was like, I just wanted to make, build better tools for data teams. And like very, like sort of abstract thing, but like, I find that the data stack is, you know, full of like point solutions that don't integrate well. And still, when you look at like data teams today, you know, like every startup ends up building their own internal Kubernetes wrapper or whatever. And you know, all the different data engineers and machine learning engineers end up kind of struggling with the same things. So I started thinking about like, how do I build a new data stack, which is kind of a megalomaniac project, like, because you kind of want to like throw out everything and start over.Swyx [00:07:54]: It's almost a modern data stack.Erik [00:07:55]: Yeah, like a postmodern data stack. And so I started thinking about that. And a lot of it came from like, like more focused on like the human side of like, how do I make data teams more productive? And like, what is the technology tools that they need? And like, you know, drew out a lot of charts of like, how the data stack looks, you know, what are different components. And it shows actually very interesting, like workflow scheduling, because it kind of sits in like a nice sort of, you know, it's like a hub in the graph of like data products. But it was kind of hard to like, kind of do that in a vacuum, and also to monetize it to some extent. I got very interested in like the layers below at some point. And like, at the end of the day, like most people have code to have to run somewhere. So I think about like, okay, well, how do you make that nice? Like how do you make that? And in particular, like the thing I always like thought about, like developer productivity is like, I think the best way to measure developer productivity is like in terms of the feedback loops, like how quickly when you iterate, like when you write code, like how quickly can you get feedback. And at the innermost loop, it's like writing code and then running it. And like, as soon as you start working with the cloud, like it's like takes minutes suddenly, because you have to build a Docker container and push it to the cloud and like run it, you know. So that was like the initial focus for me was like, I just want to solve that problem. Like I want to, you know, build something less, you run things in the cloud and like retain the sort of, you know, the joy of productivity as when you're running things locally. And in particular, I was quite focused on data teams, because I think they had a couple unique needs that wasn't well served by the infrastructure at that time, or like still is in like, in particular, like Kubernetes, I feel like it's like kind of worked okay for back end teams, but not so well for data teams. And very quickly, I got sucked into like a very deep like rabbit hole of like...Swyx [00:09:24]: Not well for data teams because of burstiness. Yeah, for sure.Erik [00:09:26]: So like burstiness is like one thing, right? Like, you know, like you often have this like fan out, you want to like apply some function over very large data sets. Another thing tends to be like hardware requirements, like you need like GPUs and like, I've seen this in many companies, like you go, you know, data scientists go to a platform team and they're like, can we add GPUs to the Kubernetes? And they're like, no, like, that's, you know, complex, and we're not gonna, so like just getting GPU access. And then like, I mean, I also like data code, like frankly, or like machine learning code like tends to be like, super annoying in terms of like environments, like you end up having like a lot of like custom, like containers and like environment conflicts. And like, it's very hard to set up like a unified container that like can serve like a data scientist, because like, there's always like packages that break. And so I think there's a lot of different reasons why the technology wasn't well suited for back end. And I think the attitude at that time is often like, you know, like you had friction between the data team and the platform team, like, well, it works for the back end stuff, you know, why don't you just like, you know, make it work. But like, I actually felt like data teams, you know, or at this point now, like there's so much, so many people working with data, and like they, to some extent, like deserve their own tools and their own tool chains, and like optimizing for that is not something people have done. So that's, that's sort of like very abstract philosophical reason why I started Model. And then, and then I got sucked into this like rabbit hole of like container cold start and, you know, like whatever, Linux, page cache, you know, file system optimizations.Swyx [00:10:43]: Yeah, tell people, I think the first time I met you, I think you told me some numbers, but I don't remember, like, what are the main achievements that you were unhappy with the status quo? And then you built your own container stack?Erik [00:10:52]: Yeah, I mean, like, in particular, it was like, in order to have that loop, right? You want to be able to start, like take code on your laptop, whatever, and like run in the cloud very quickly, and like running in custom containers, and maybe like spin up like 100 containers, 1000, you know, things like that. And so container cold start was the initial like, from like a developer productivity point of view, it was like, really, what I was focusing on is, I want to take code, I want to stick it in container, I want to execute in the cloud, and like, you know, make it feel like fast. And when you look at like, how Docker works, for instance, like Docker, you have this like, fairly convoluted, like very resource inefficient way, they, you know, you build a container, you upload the whole container, and then you download it, and you run it. And Kubernetes is also like, not very fast at like starting containers. So like, I started kind of like, you know, going a layer deeper, like Docker is actually like, you know, there's like a couple of different primitives, but like a lower level primitive is run C, which is like a container runner. And I was like, what if I just take the container runner, like run C, and I point it to like my own root file system, and then I built like my own virtual file system that exposes files over a network instead. And that was like the sort of very crude version of model, it's like now I can actually start containers very quickly, because it turns out like when you start a Docker container, like, first of all, like most Docker images are like several gigabytes, and like 99% of that is never going to be consumed, like there's a bunch of like, you know, like timezone information for like Uzbekistan, like no one's going to read it. And then there's a very high overlap between the files are going to be read, there's going to be like lib torch or whatever, like it's going to be read. So you can also cache it very well. So that was like the first sort of stuff we started working on was like, let's build this like container file system. And you know, coupled with like, you know, just using run C directly. And that actually enabled us to like, get to this point of like, you write code, and then you can launch it in the cloud within like a second or two, like something like that. And you know, there's been many optimizations since then, but that was sort of starting point.Alessio [00:12:33]: Can we talk about the developer experience as well, I think one of the magic things about Modal is at the very basic layers, like a Python function decorator, it's just like stub and whatnot. But then you also have a way to define a full container, what were kind of the design decisions that went into it? Where did you start? How easy did you want it to be? And then maybe how much complexity did you then add on to make sure that every use case fit?Erik [00:12:57]: I mean, Modal, I almost feel like it's like almost like two products kind of glued together. Like there's like the low level like container runtime, like file system, all that stuff like in Rust. And then there's like the Python SDK, right? Like how do you express applications? And I think, I mean, Swix, like I think your blog was like the self-provisioning runtime was like, to me, always like to sort of, for me, like an eye-opening thing. It's like, so I didn't think about like...Swyx [00:13:15]: You wrote your post four months before me. Yeah? The software 2.0, Infra 2.0. Yeah.Erik [00:13:19]: Well, I don't know, like convergence of minds. I guess we were like both thinking. Maybe you put, I think, better words than like, you know, maybe something I was like thinking about for a long time. Yeah.Swyx [00:13:29]: And I can tell you how I was thinking about it on my end, but I want to hear you say it.Erik [00:13:32]: Yeah, yeah, I would love to. So to me, like what I always wanted to build was like, I don't know, like, I don't know if you use like Pulumi. Like Pulumi is like nice, like in the sense, like it's like Pulumi is like you describe infrastructure in code, right? And to me, that was like so nice. Like finally I can like, you know, put a for loop that creates S3 buckets or whatever. And I think like Modal sort of goes one step further in the sense that like, what if you also put the app code inside the infrastructure code and like glue it all together and then like you only have one single place that defines everything and it's all programmable. You don't have any config files. Like Modal has like zero config. There's no config. It's all code. And so that was like the goal that I wanted, like part of that. And then the other part was like, I often find that so much of like my time was spent on like the plumbing between containers. And so my thing was like, well, if I just build this like Python SDK and make it possible to like bridge like different containers, just like a function call, like, and I can say, oh, this function runs in this container and this other function runs in this container and I can just call it just like a normal function, then, you know, I can build these applications that may span a lot of different environments. Maybe they fan out, start other containers, but it's all just like inside Python. You just like have this beautiful kind of nice like DSL almost for like, you know, how to control infrastructure in the cloud. So that was sort of like how we ended up with the Python SDK as it is, which is still evolving all the time, by the way. We keep changing syntax quite a lot because I think it's still somewhat exploratory, but we're starting to converge on something that feels like reasonably good now.Swyx [00:14:54]: Yeah. And along the way you, with this expressiveness, you enabled the ability to, for example, attach a GPU to a function. Totally.Erik [00:15:02]: Yeah. It's like you just like say, you know, on the function decorator, you're like GPU equals, you know, A100 and then or like GPU equals, you know, A10 or T4 or something like that. And then you get that GPU and like, you know, you just run the code and it runs like you don't have to, you know, go through hoops to, you know, start an EC2 instance or whatever.Swyx [00:15:18]: Yeah. So it's all code. Yeah. So one of the reasons I wrote Self-Revisioning Runtimes was I was working at AWS and we had AWS CDK, which is kind of like, you know, the Amazon basics blew me. Yeah, totally. And then, and then like it creates, it compiles the cloud formation. Yeah. And then on the other side, you have to like get all the config stuff and then put it into your application code and make sure that they line up. So then you're writing code to define your infrastructure, then you're writing code to define your application. And I was just like, this is like obvious that it's going to converge, right? Yeah, totally.Erik [00:15:48]: But isn't there like, it might be wrong, but like, was it like SAM or Chalice or one of those? Like, isn't that like an AWS thing that where actually they kind of did that? I feel like there's like one.Swyx [00:15:57]: SAM. Yeah. Still very clunky. It's not, not as elegant as modal.Erik [00:16:03]: I love AWS for like the stuff it's built, you know, like historically in order for me to like, you know, what it enables me to build, but like AWS is always like struggle with developer experience.Swyx [00:16:11]: I mean, they have to not break things.Erik [00:16:15]: Yeah. Yeah. And totally. And they have to build products for a very wide range of use cases. And I think that's hard.Swyx [00:16:21]: Yeah. Yeah. So it's, it's easier to design for. Yeah. So anyway, I was, I was pretty convinced that this, this would happen. I wrote, wrote that thing. And then, you know, I imagine my surprise that you guys had it on your landing page at some point. I think, I think Akshad was just like, just throw that in there.Erik [00:16:34]: Did you trademark it?Swyx [00:16:35]: No, I didn't. But I definitely got sent a few pitch decks with my post on there and it was like really interesting. This is my first time like kind of putting a name to a phenomenon. And I think this is a useful skill for people to just communicate what they're trying to do.Erik [00:16:48]: Yeah. No, I think it's a beautiful concept.Swyx [00:16:50]: Yeah. Yeah. Yeah. But I mean, obviously you implemented it. What became more clear in your explanation today is that actually you're not that tied to Python.Erik [00:16:57]: No. I mean, I, I think that all the like lower level stuff is, you know, just running containers and like scheduling things and, you know, serving container data and stuff. So like one of the benefits of data teams is obviously like they're all like using Python, right? And so that made it a lot easier. I think, you know, if we had focused on other workloads, like, you know, for various reasons, we've like been kind of like half thinking about like CI or like things like that. But like, in a way that's like harder because like you also, then you have to be like, you know, multiple SDKs, whereas, you know, focusing on data teams, you can only, you know, Python like covers like 95% of all teams. That made it a lot easier. But like, I mean, like definitely like in the future, we're going to have others support, like supporting other languages. JavaScript for sure is the obvious next language. But you know, who knows, like, you know, Rust, Go, R, whatever, PHP, Haskell, I don't know.Swyx [00:17:42]: You know, I think for me, I actually am a person who like kind of liked the idea of programming language advancements being improvements in developer experience. But all I saw out of the academic sort of PLT type people is just type level improvements. And I always think like, for me, like one of the core reasons for self-provisioning runtimes and then why I like Modal is like, this is actually a productivity increase, right? Like, it's a language level thing, you know, you managed to stick it on top of an existing language, but it is your own language, a DSL on top of Python. And so language level increase on the order of like automatic memory management. You know, you could sort of make that analogy that like, maybe you lose some level of control, but most of the time you're okay with whatever Modal gives you. And like, that's fine. Yeah.Erik [00:18:26]: Yeah. Yeah. I mean, that's how I look at about it too. Like, you know, you look at developer productivity over the last number of decades, like, you know, it's come in like small increments of like, you know, dynamic typing or like is like one thing because not suddenly like for a lot of use cases, you don't need to care about type systems or better compiler technology or like, you know, the cloud or like, you know, relational databases. And, you know, I think, you know, you look at like that, you know, history, it's a steadily, you know, it's like, you know, you look at the developers have been getting like probably 10X more productive every decade for the last four decades or something that was kind of crazy. Like on an exponential scale, we're talking about 10X or is there a 10,000X like, you know, improvement in developer productivity. What we can build today, you know, is arguably like, you know, a fraction of the cost of what it took to build it in the eighties. Maybe it wasn't even possible in the eighties. So that to me, like, that's like so fascinating. I think it's going to keep going for the next few decades. Yeah.Alessio [00:19:14]: Yeah. Another big thing in the infra 2.0 wishlist was truly serverless infrastructure. The other on your landing page, you called them native cloud functions, something like that. I think the issue I've seen with serverless has always been people really wanted it to be stateful, even though stateless was much easier to do. And I think now with AI, most model inference is like stateless, you know, outside of the context. So that's kind of made it a lot easier to just put a model, like an AI model on model to run. How do you think about how that changes how people think about infrastructure too? Yeah.Erik [00:19:48]: I mean, I think model is definitely going in the direction of like doing more stateful things and working with data and like high IO use cases. I do think one like massive serendipitous thing that happened like halfway, you know, a year and a half into like the, you know, building model was like Gen AI started exploding and the IO pattern of Gen AI is like fits the serverless model like so well, because it's like, you know, you send this tiny piece of information, like a prompt, right, or something like that. And then like you have this GPU that does like trillions of flops, and then it sends back like a tiny piece of information, right. And that turns out to be something like, you know, if you can get serverless working with GPU, that just like works really well, right. So I think from that point of view, like serverless always to me felt like a little bit of like a solution looking for a problem. I don't actually like don't think like backend is like the problem that needs to serve it or like not as much. But I look at data and in particular, like things like Gen AI, like model inference, like it's like clearly a good fit. So I think that is, you know, to a large extent explains like why we saw, you know, the initial sort of like killer app for model being model inference, which actually wasn't like necessarily what we're focused on. But that's where we've seen like by far the most usage. Yeah.Swyx [00:20:52]: And this was before you started offering like fine tuning of language models, it was mostly stable diffusion. Yeah.Erik [00:20:59]: Yeah. I mean, like model, like I always built it to be a very general purpose compute platform, like something where you can run everything. And I used to call model like a better Kubernetes for data team for a long time. What we realized was like, yeah, that's like, you know, a year and a half in, like we barely had any users or any revenue. And like we were like, well, maybe we should look at like some use case, trying to think of use case. And that was around the same time stable diffusion came out. And the beauty of model is like you can run almost anything on model, right? Like model inference turned out to be like the place where we found initially, well, like clearly this has like 10x like better agronomics than anything else. But we're also like, you know, going back to my original vision, like we're thinking a lot about, you know, now, okay, now we do inference really well. Like what about training? What about fine tuning? What about, you know, end-to-end lifecycle deployment? What about data pre-processing? What about, you know, I don't know, real-time streaming? What about, you know, large data munging, like there's just data observability. I think there's so many things, like kind of going back to what I said about like redefining the data stack, like starting with the foundation of compute. Like one of the exciting things about model is like we've sort of, you know, we've been working on that for three years and it's maturing, but like this is so many things you can do like with just like a better compute primitive and also go up to stack and like do all this other stuff on top of it.Alessio [00:22:09]: How do you think about or rather like I would love to learn more about the underlying infrastructure and like how you make that happen because with fine tuning and training, it's a static memory. Like you exactly know what you're going to load in memory one and it's kind of like a set amount of compute versus inference, just like data is like very bursty. How do you make batches work with a serverless developer experience? You know, like what are like some fun technical challenge you solve to make sure you get max utilization on these GPUs? What we hear from people is like, we have GPUs, but we can really only get like, you know, 30, 40, 50% maybe utilization. What's some of the fun stuff you're working on to get a higher number there?Erik [00:22:48]: Yeah, I think on the inference side, like that's where we like, you know, like from a cost perspective, like utilization perspective, we've seen, you know, like very good numbers and in particular, like it's our ability to start containers and stop containers very quickly. And that means that we can auto scale extremely fast and scale down very quickly, which means like we can always adjust the sort of capacity, the number of GPUs running to the exact traffic volume. And so in many cases, like that actually leads to a sort of interesting thing where like we obviously run our things on like the public cloud, like AWS GCP, we run on Oracle, but in many cases, like users who do inference on those platforms or those clouds, even though we charge a slightly higher price per GPU hour, a lot of users like moving their large scale inference use cases to model, they end up saving a lot of money because we only charge for like with the time the GPU is actually running. And that's a hard problem, right? Like, you know, if you have to constantly adjust the number of machines, if you have to start containers, stop containers, like that's a very hard problem. Starting containers quickly is a very difficult thing. I mentioned we had to build our own file system for this. We also, you know, built our own container scheduler for that. We've implemented recently CPU memory checkpointing so we can take running containers and snapshot the entire CPU, like including registers and everything, and restore it from that point, which means we can restore it from an initialized state. We're looking at GPU checkpointing next, it's like a very interesting thing. So I think with inference stuff, that's where serverless really shines because you can drive, you know, you can push the frontier of latency versus utilization quite substantially, you know, which either ends up being a latency advantage or a cost advantage or both, right? On training, it's probably arguably like less of an advantage doing serverless, frankly, because you know, you can just like spin up a bunch of machines and try to satisfy, like, you know, train as much as you can on each machine. For that area, like we've seen, like, you know, arguably like less usage, like for modal, but there are always like some interesting use case. Like we do have a couple of customers, like RAM, for instance, like they do fine tuning with modal and they basically like one of the patterns they have is like very bursty type fine tuning where they fine tune 100 models in parallel. And that's like a separate thing that modal does really well, right? Like you can, we can start up 100 containers very quickly, run a fine tuning training job on each one of them for that only runs for, I don't know, 10, 20 minutes. And then, you know, you can do hyper parameter tuning in that sense, like just pick the best model and things like that. So there are like interesting training. I think when you get to like training, like very large foundational models, that's a use case we don't support super well, because that's very high IO, you know, you need to have like infinite band and all these things. And those are things we haven't supported yet and might take a while to get to that. So that's like probably like an area where like we're relatively weak in. Yeah.Alessio [00:25:12]: Have you cared at all about lower level model optimization? There's other cloud providers that do custom kernels to get better performance or are you just given that you're not just an AI compute company? Yeah.Erik [00:25:24]: I mean, I think like we want to support like a generic, like general workloads in a sense that like we want users to give us a container essentially or a code or code. And then we want to run that. So I think, you know, we benefit from those things in the sense that like we can tell our users, you know, to use those things. But I don't know if we want to like poke into users containers and like do those things automatically. That's sort of, I think a little bit tricky from the outside to do, because we want to be able to take like arbitrary code and execute it. But certainly like, you know, we can tell our users to like use those things. Yeah.Swyx [00:25:53]: I may have betrayed my own biases because I don't really think about modal as for data teams anymore. I think you started, I think you're much more for AI engineers. My favorite anecdotes, which I think, you know, but I don't know if you directly experienced it. I went to the Vercel AI Accelerator, which you supported. And in the Vercel AI Accelerator, a bunch of startups gave like free credits and like signups and talks and all that stuff. The only ones that stuck are the ones that actually appealed to engineers. And the top usage, the top tool used by far was modal.Erik [00:26:24]: That's awesome.Swyx [00:26:25]: For people building with AI apps. Yeah.Erik [00:26:27]: I mean, it might be also like a terminology question, like the AI versus data, right? Like I've, you know, maybe I'm just like old and jaded, but like, I've seen so many like different titles, like for a while it was like, you know, I was a data scientist and a machine learning engineer and then, you know, there was like analytics engineers and there was like an AI engineer, you know? So like, to me, it's like, I just like in my head, that's to me just like, just data, like, or like engineer, you know, like I don't really, so that's why I've been like, you know, just calling it data teams. But like, of course, like, you know, AI is like, you know, like such a massive fraction of our like workloads.Swyx [00:26:59]: It's a different Venn diagram of things you do, right? So the stuff that you're talking about where you need like infinite bands for like highly parallel training, that's not, that's more of the ML engineer, that's more of the research scientist and less of the AI engineer, which is more sort of trying to put, work at the application.Erik [00:27:16]: Yeah. I mean, to be fair to it, like we have a lot of users that are like doing stuff that I don't think fits neatly into like AI. Like we have a lot of people using like modal for web scraping, like it's kind of nice. You can just like, you know, fire up like a hundred or a thousand containers running Chromium and just like render a bunch of webpages and it takes, you know, whatever. Or like, you know, protein folding is that, I mean, maybe that's, I don't know, like, but like, you know, we have a bunch of users doing that or, or like, you know, in terms of, in the realm of biotech, like sequence alignment, like people using, or like a couple of people using like modal to run like large, like mixed integer programming problems, like, you know, using Gurobi or like things like that. So video processing is another thing that keeps coming up, like, you know, let's say you have like petabytes of video and you want to just like transcode it, like, or you can fire up a lot of containers and just run FFmpeg or like, so there are those things too. Like, I mean, like that being said, like AI is by far our biggest use case, but you know, like, again, like modal is kind of general purpose in that sense.Swyx [00:28:08]: Yeah. Well, maybe I'll stick to the stable diffusion thing and then we'll move on to the other use cases for AI that you want to highlight. The other big player in my mind is replicate. Yeah. In this, in this era, they're much more, I guess, custom built for that purpose, whereas you're more general purpose. How do you position yourself with them? Are they just for like different audiences or are you just heads on competing?Erik [00:28:29]: I think there's like a tiny sliver of the Venn diagram where we're competitive. And then like 99% of the area we're not competitive. I mean, I think for people who, if you look at like front-end engineers, I think that's where like really they found good fit is like, you know, people who built some cool web app and they want some sort of AI capability and they just, you know, an off the shelf model is like perfect for them. That's like, I like use replicate. That's great. I think where we shine is like custom models or custom workflows, you know, running things at very large scale. We need to care about utilization, care about costs. You know, we have much lower prices because we spend a lot more time optimizing our infrastructure, you know, and that's where we're competitive, right? Like, you know, and you look at some of the use cases, like Suno is a big user, like they're running like large scale, like AI. Oh, we're talking with Mikey.Swyx [00:29:12]: Oh, that's great. Cool.Erik [00:29:14]: In a month. Yeah. So, I mean, they're, they're using model for like production infrastructure. Like they have their own like custom model, like custom code and custom weights, you know, for AI generated music, Suno.AI, you know, that, that, those are the types of use cases that we like, you know, things that are like very custom or like, it's like, you know, and those are the things like it's very hard to run and replicate, right? And that's fine. Like I think they, they focus on a very different part of the stack in that sense.Swyx [00:29:35]: And then the other company pattern that I pattern match you to is Modular. I don't know.Erik [00:29:40]: Because of the names?Swyx [00:29:41]: No, no. Wow. No, but yeah, yes, the name is very similar. I think there's something that might be insightful there from a linguistics point of view. Oh no, they have Mojo, the sort of Python SDK. And they have the Modular Inference Engine, which is their sort of their cloud stack, their sort of compute inference stack. I don't know if anyone's made that comparison to you before, but like I see you evolving a little bit in parallel there.Erik [00:30:01]: No, I mean, maybe. Yeah. Like it's not a company I'm like super like familiar, like, I mean, I know the basics, but like, I guess they're similar in the sense like they want to like do a lot of, you know, they have sort of big picture vision.Swyx [00:30:12]: Yes. They also want to build very general purpose. Yeah. So they're marketing themselves as like, if you want to do off the shelf stuff, go out, go somewhere else. If you want to do custom stuff, we're the best place to do it. Yeah. Yeah. There is some overlap there. There's not overlap in the sense that you are a closed source platform. People have to host their code on you. That's true. Whereas for them, they're very insistent on not running their own cloud service. They're a box software. Yeah. They're licensed software.Erik [00:30:37]: I'm sure their VCs at some point going to force them to reconsider. No, no.Swyx [00:30:40]: Chris is very, very insistent and very convincing. So anyway, I would just make that comparison, let people make the links if they want to. But it's an interesting way to see the cloud market develop from my point of view, because I came up in this field thinking cloud is one thing, and I think your vision is like something slightly different, and I see the different takes on it.Erik [00:31:00]: Yeah. And like one thing I've, you know, like I've written a bit about it in my blog too, it's like I think of us as like a second layer of cloud provider in the sense that like I think Snowflake is like kind of a good analogy. Like Snowflake, you know, is infrastructure as a service, right? But they actually run on the like major clouds, right? And I mean, like you can like analyze this very deeply, but like one of the things I always thought about is like, why does Snowflake arbitrarily like win over Redshift? And I think Snowflake, you know, to me, one, because like, I mean, in the end, like AWS makes all the money anyway, like and like Snowflake just had the ability to like focus on like developer experience or like, you know, user experience. And to me, like really proved that you can build a cloud provider, a layer up from, you know, the traditional like public clouds. And in that layer, that's also where I would put Modal, it's like, you know, we're building a cloud provider, like we're, you know, we're like a multi-tenant environment that runs the user code. But we're also building on top of the public cloud. So I think there's a lot of room in that space, I think is very sort of interesting direction.Alessio [00:31:55]: How do you think of that compared to the traditional past history, like, you know, you had AWS, then you had Heroku, then you had Render, Railway.Erik [00:32:04]: Yeah, I mean, I think those are all like great. I think the problem that they all faced was like the graduation problem, right? Like, you know, Heroku or like, I mean, like also like Heroku, there's like a counterfactual future of like, what would have happened if Salesforce didn't buy them, right? Like, that's a sort of separate thing. But like, I think what Heroku, I think always struggled with was like, eventually companies would get big enough that you couldn't really justify running in Heroku. So they would just go and like move it to, you know, whatever AWS or, you know, in particular. And you know, that's something that keeps me up at night too, like, what does that graduation risk like look like for modal? I always think like the only way to build a successful infrastructure company in the long run in the cloud today is you have to appeal to the entire spectrum, right? Or at least like the enterprise, like you have to capture the enterprise market. But the truly good companies capture the whole spectrum, right? Like I think of companies like, I don't like Datadog or Mongo or something that were like, they both captured like the hobbyists and acquire them, but also like, you know, have very large enterprise customers. I think that arguably was like where I, in my opinion, like Heroku struggle was like, how do you maintain the customers as they get more and more advanced? I don't know what the solution is, but I think there's, you know, that's something I would have thought deeply if I was at Heroku at that time.Alessio [00:33:14]: What's the AI graduation problem? Is it, I need to fine tune the model, I need better economics, any insights from customer discussions?Erik [00:33:22]: Yeah, I mean, better economics, certainly. But although like, I would say like, even for people who like, you know, needs like thousands of GPUs, just because we can drive utilization so much better, like we, there's actually like a cost advantage of staying on modal. But yeah, I mean, certainly like, you know, and like the fact that VCs like love, you know, throwing money at least used to, you know, add companies who need it to buy GPUs. I think that didn't help the problem. And in training, I think, you know, there's less software differentiation. So in training, I think there's certainly like better economics of like buying big clusters. But I mean, my hope it's going to change, right? Like I think, you know, we're still pretty early in the cycle of like building AI infrastructure. And I think a lot of these companies over in the long run, like, you know, they're, except it may be super big ones, like, you know, on Facebook and Google, they're always going to build their own ones. But like everyone else, like some extent, you know, I think they're better off like buying platforms. And, you know, someone's going to have to build those platforms.Swyx [00:34:12]: Yeah. Cool. Let's move on to language models and just specifically that workload just to flesh it out a little bit. You already said that RAMP is like fine tuning 100 models at once simultaneously on modal. Closer to home, my favorite example is ErikBot. Maybe you want to tell that story.Erik [00:34:30]: Yeah. I mean, it was a prototype thing we built for fun, but it's pretty cool. Like we basically built this thing that hooks up to Slack. It like downloads all the Slack history and, you know, fine-tunes a model based on a person. And then you can chat with that. And so you can like, you know, clone yourself and like talk to yourself on Slack. I mean, it's like nice like demo and it's just like, I think like it's like fully contained modal. Like there's a modal app that does everything, right? Like it downloads Slack, you know, integrates with the Slack API, like downloads the stuff, the data, like just runs the fine-tuning and then like creates like dynamically an inference endpoint. And it's all like self-contained and like, you know, a few hundred lines of code. So I think it's sort of a good kind of use case for, or like it kind of demonstrates a lot of the capabilities of modal.Alessio [00:35:08]: Yeah. On a more personal side, how close did you feel ErikBot was to you?Erik [00:35:13]: It definitely captured the like the language. Yeah. I mean, I don't know, like the content, I always feel this way about like AI and it's gotten better. Like when you look at like AI output of text, like, and it's like, when you glance at it, it's like, yeah, this seems really smart, you know, but then you actually like look a little bit deeper. It's like, what does this mean?Swyx [00:35:32]: What does this person say?Erik [00:35:33]: It's like kind of vacuous, right? And that's like kind of what I felt like, you know, talking to like my clone version, like it's like says like things like the grammar is correct. Like some of the sentences make a lot of sense, but like, what are you trying to say? Like there's no content here. I don't know. I mean, it's like, I got that feeling also with chat TBT in the like early versions right now it's like better, but.Alessio [00:35:51]: That's funny. So I built this thing called small podcaster to automate a lot of our back office work, so to speak. And it's great at transcript. It's great at doing chapters. And then I was like, okay, how about you come up with a short summary? And it's like, it sounds good, but it's like, it's not even the same ballpark as like, yeah, end up writing. Right. And it's hard to see how it's going to get there.Swyx [00:36:11]: Oh, I have ideas.Erik [00:36:13]: I'm certain it's going to get there, but like, I agree with you. Right. And like, I have the same thing. I don't know if you've read like AI generated books. Like they just like kind of seem funny, right? Like there's off, right? But like you glance at it and it's like, oh, it's kind of cool. Like looks correct, but then it's like very weird when you actually read them.Swyx [00:36:30]: Yeah. Well, so for what it's worth, I think anyone can join the modal slack. Is it open to the public? Yeah, totally.Erik [00:36:35]: If you go to modal.com, there's a button in the footer.Swyx [00:36:38]: Yeah. And then you can talk to Erik Bot. And then sometimes I really like picking Erik Bot and then you answer afterwards, but then you're like, yeah, mostly correct or whatever. Any other broader lessons, you know, just broadening out from like the single use case of fine tuning, like what are you seeing people do with fine tuning or just language models on modal in general? Yeah.Erik [00:36:59]: I mean, I think language models is interesting because so many people get started with APIs and that's just, you know, they're just dominating a space in particular opening AI, right? And that's not necessarily like a place where we aim to compete. I mean, maybe at some point, but like, it's just not like a core focus for us. And I think sort of separately, it's sort of a question of like, there's economics in that long term. But like, so we tend to focus on more like the areas like around it, right? Like fine tuning, like another use case we have is a bunch of people, Ramp included, is doing batch embeddings on modal. So let's say, you know, you have like a, actually we're like writing a blog post, like we take all of Wikipedia and like parallelize embeddings in 15 minutes and produce vectors for each article. So those types of use cases, I think modal suits really well for. I think also a lot of like custom inference, like yeah, I love that.Swyx [00:37:43]: Yeah. I think you should give people an idea of the order of magnitude of parallelism, because I think people don't understand how parallel. So like, I think your classic hello world with modal is like some kind of Fibonacci function, right? Yeah, we have a bunch of different ones. Some recursive function. Yeah.Erik [00:37:59]: Yeah. I mean, like, yeah, I mean, it's like pretty easy in modal, like fan out to like, you know, at least like 100 GPUs, like in a few seconds. And you know, if you give it like a couple of minutes, like we can, you know, you can fan out to like thousands of GPUs. Like we run it relatively large scale. And yeah, we've run, you know, many thousands of GPUs at certain points when we needed, you know, big backfills or some customers had very large compute needs.Swyx [00:38:21]: Yeah. Yeah. And I mean, that's super useful for a number of things. So one of my early interactions with modal as well was with a small developer, which is my sort of coding agent. The reason I chose modal was a number of things. One, I just wanted to try it out. I just had an excuse to try it. Akshay offered to onboard me personally. But the most interesting thing was that you could have that sort of local development experience as it was running on my laptop, but then it would seamlessly translate to a cloud service or like a cloud hosted environment. And then it could fan out with concurrency controls. So I could say like, because like, you know, the number of times I hit the GPT-3 API at the time was going to be subject to the rate limit. But I wanted to fan out without worrying about that kind of stuff. With modal, I can just kind of declare that in my config and that's it. Oh, like a concurrency limit?Erik [00:39:07]: Yeah. Yeah.Swyx [00:39:09]: Yeah. There's a lot of control. And that's why it's like, yeah, this is a pretty good use case for like writing this kind of LLM application code inside of this environment that just understands fan out and rate limiting natively. You don't actually have an exposed queue system, but you have it under the hood, you know, that kind of stuff. Totally.Erik [00:39:28]: It's a self-provisioning cloud.Swyx [00:39:30]: So the last part of modal I wanted to touch on, and obviously feel free, I know you're working on new features, was the sandbox that was introduced last year. And this is something that I think was inspired by Code Interpreter. You can tell me the longer history behind that.Erik [00:39:45]: Yeah. Like we originally built it for the use case, like there was a bunch of customers who looked into code generation applications and then they came to us and asked us, is there a safe way to execute code? And yeah, we spent a lot of time on like container security. We used GeoVisor, for instance, which is a Google product that provides pretty strong isolation of code. So we built a product where you can basically like run arbitrary code inside a container and monitor its output or like get it back in a safe way. I mean, over time it's like evolved into more of like, I think the long-term direction is actually I think more interesting, which is that I think modal as a platform where like I think the core like container infrastructure we offer could actually be like, you know, unbundled from like the client SDK and offer to like other, you know, like we're talking to a couple of like other companies that want to run, you know, through their packages, like run, execute jobs on modal, like kind of programmatically. So that's actually the direction like Sandbox is going. It's like turning into more like a platform for platforms is kind of what I've been thinking about it as.Swyx [00:40:45]: Oh boy. Platform. That's the old Kubernetes line.Erik [00:40:48]: Yeah. Yeah. Yeah. But it's like, you know, like having that ability to like programmatically, you know, create containers and execute them, I think, I think is really cool. And I think it opens up a lot of interesting capabilities that are sort of separate from the like core Python SDK in modal. So I'm really excited about C. It's like one of those features that we kind of released and like, you know, then we kind of look at like what users actually build with it and people are starting to build like kind of crazy things. And then, you know, we double down on some of those things because when we see like, you know, potential new product features and so Sandbox, I think in that sense, it's like kind of in that direction. We found a lot of like interesting use cases in the direction of like platformized container runner.Swyx [00:41:27]: Can you be more specific about what you're double down on after seeing users in action?Erik [00:41:32]: I mean, we're working with like some companies that, I mean, without getting into specifics like that, need the ability to take their users code and then launch containers on modal. And it's not about security necessarily, like they just want to use modal as a back end, right? Like they may already provide like Kubernetes as a back end, Lambda as a back end, and now they want to add modal as a back end, right? And so, you know, they need a way to programmatically define jobs on behalf of their users and execute them. And so, I don't know, that's kind of abstract, but does that make sense? I totally get it.Swyx [00:42:03]: It's sort of one level of recursion to sort of be the Modal for their customers.Erik [00:42:09]: Exactly.Swyx [00:42:10]: Yeah, exactly. And Cloudflare has done this, you know, Kenton Vardar from Cloudflare, who's like the tech lead on this thing, called it sort of functions as a service as a service.Erik [00:42:17]: Yeah, that's exactly right. FaSasS.Swyx [00:42:21]: FaSasS. Yeah, like, I mean, like that, I think any base layer, second layer cloud provider like yourself, compute provider like yourself should provide, you know, it's a mark of maturity and success that people just trust you to do that. They'd rather build on top of you than compete with you. The more interesting thing for me is like, what does it mean to serve a computer like an LLM developer, rather than a human developer, right? Like, that's what a sandbox is to me, that you have to redefine modal to serve a different non-human audience.Erik [00:42:51]: Yeah. Yeah, and I think there's some really interesting people, you know, building very cool things.Swyx [00:42:55]: Yeah. So I don't have an answer, but, you know, I imagine things like, hey, the way you give feedback is different. Maybe you have to like stream errors, log errors differently. I don't really know. Yeah. Obviously, there's like safety considerations. Maybe you have an API to like restrict access to the web. Yeah. I don't think anyone would use it, but it's there if you want it.Erik [00:43:17]: Yeah.Swyx [00:43:18]: Yeah. Any other sort of design considerations? I have no idea.Erik [00:43:21]: With sandboxes?Swyx [00:43:22]: Yeah. Yeah.Erik [00:43:24]: Open-ended question here. Yeah. I mean, no, I think, yeah, the network restrictions, I think, make a lot of sense. Yeah. I mean, I think, you know, long-term, like, I think there's a lot of interesting use cases where like the LLM, in itself, can like decide, I want to install these packages and like run this thing. And like, obviously, for a lot of those use cases, like you want to have some sort of control that it doesn't like install malicious stuff and steal your secrets and things like that. But I think that's what's exciting about the sandbox primitive, is like it lets you do that in a relatively safe way.Alessio [00:43:51]: Do you have any thoughts on the inference wars? A lot of providers are just rushing to the bottom to get the lowest price per million tokens. Some of them, you know, the Sean Randomat, they're just losing money and there's like the physics of it just don't work out for them to make any money on it. How do you think about your pricing and like how much premium you can get and you can kind of command versus using lower prices as kind of like a wedge into getting there, especially once you have model instrumented? What are the tradeoffs and any thoughts on strategies that work?Erik [00:44:23]: I mean, we focus more on like custom models and custom code. And I think in that space, there's like less competition and I think we can have a pricing markup, right? Like, you know, people will always compare our prices to like, you know, the GPU power they can get elsewhere. And so how big can that markup be? Like it never can be, you know, we can never charge like 10x more, but we can certainly charge a premium. And like, you know, for that reason, like we can have pretty good margins. The LLM space is like the opposite, like the switching cost of LLMs is zero. If all you're doing is like straight up, like at least like open source, right? Like if all you're doing is like, you know, using some, you know, inference endpoint that serves an open source model and, you know, some other provider comes along and like offers a lower price, you're just going to switch, right? So I don't know, to me that reminds me a lot of like all this like 15 minute delivery wars or like, you know, like Uber versus Lyft, you know, and like maybe going back even further, like I think a lot about like sort of, you know, flip side of this is like, it's actually a positive side, which is like, I thought a lot about like fiber optics boom of like 98, 99, like the other day, or like, you know, and also like the overinvestment in GPU today. Like, like, yeah, like, you know, I don't know, like in the end, like, I don't think VCs will have the return they expected, like, you know, in these things, but guess who's going to benefit, like, you know, is the consumers, like someone's like reaping the value of this. And that's, I think an amazing flip side is that, you know, we should be very grateful, the fact that like VCs want to subsidize these things, which is, you know, like you go back to fiber optics, like there was an extreme, like overinvestment in fiber optics network in like 98. And no one made money who did that. But consumers, you know, got tremendous benefits of all the fiber optics cables that were led, you know, throughout the country in the decades after. I feel something similar abou
This conversation with Akshat Rathi explores the concept of climate capitalism and how capitalism can be a driving force for climate change mitigation. It discusses the modification of capitalism to align with climate goals as well as the challenges of partnering with fossil fuel companies in the context of climate hardware startups, the Breakthrough Energy model, enabling factors for climate technologies, the importance of storytelling in climate tech, and Akshat's current focus as a senior reporter for Bloomberg News. TakeawaysCapitalism can be a powerful tool for addressing climate change when it is modified to align with climate goals.Understanding the limits and regulations imposed by nature is crucial in modifying capitalism for climate change.Founders should carefully consider the potential benefits and drawbacks of partnering with oil and gas companies, taking into account the availability of climate tech funding and the skills needed for their startups.Enabling factors for climate technologies include policy, finance, global diplomacy, shareholder activism, and effective storytelling.Effective storytelling is crucial for climate tech founders to communicate their ideas in a simple, compelling, and memorable way. Mentioned in the podcast:Askhat's book, Climate Capitalism: https://akshatrathi.com/book/Breakthrough Energy: https://breakthroughenergy.org/ Connect with us:Guest: https://akshatrathi.com/contact/Email us: info@climatetech360.comHost: https://www.linkedin.com/in/samiaqader
COP28 comes to a close. 200 countries came together, 100,000 people flew in, and what did they produce? A piece of text. But sometimes that piece of text can have real world consequences. In this week's episode Akshat speaks with producer Oscar Boyd about what is in the final COP28 text and the significance of agreeing to transition off of fossil fuels. Read More: COP28 Nations Reach First-Ever Deal to Move Away From Fossil Fuels Climate Fight Takes Aim at Food in First Ever Net-Zero Plan Sign up to the Green newsletter Zero is a production of Bloomberg Green. Our producer is Oscar Boyd and our senior producer is Christine Driscoll. Special thanks to Kira Bindrim. Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit https://www.bloomberg.com/green.See omnystudio.com/listener for privacy information.
Four billion people live in countries where climate change-related disasters are becoming more severe and frequent. Spending money to adapt, known as “climate adaptation finance” is a fraught topic. Who will pay for those adaptations and how, as well as a global goal on adaptation are all being discussed at COP28. To find out more, Akshat speaks with Patrick Verkooijen, head of the Global Center on Adaptation about the history of climate adaptation finance, what negotiations are taking place, and why the money promised still hasn't arrived.Read More: A quick Q&A with Patrick Verkooijen at COP28 A UN report shows that the climate adaptation gap is growing Sign up to the Green newsletter Fill out Bloomberg Green's climate anxiety survey Zero is a production of Bloomberg Green. Our producer is Oscar Boyd and our senior producer is Christine Driscoll. Special thanks to Kira Bindrim. Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit https://www.bloomberg.com/green.See omnystudio.com/listener for privacy information.
Tripling renewables is one of the goals the COP28 discussions are circling around. It sounds good, but what will meeting it actually entail? Jenny Chase of BloombergNEF joins Akshat to break down where more investments are needed and why decarbonizing energy is the easy part. Listen to our previous episode with Jenny Chase on solar's explosive growth Sign up to the Green newsletter Fill out Bloomberg Green's climate anxiety survey Zero is a production of Bloomberg Green. Our producer is Oscar Boyd and our senior producer is Christine Driscoll. Special thanks to Kira Bindrim. Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit https://www.bloomberg.com/green.See omnystudio.com/listener for privacy information.
Bloomberg's Akshat Rathi joins us to make the case that capitalism can be harnessed in service of addressing the climate crisis. Learn more about your ad choices. Visit megaphone.fm/adchoices
If reducing emissions from industry is the first step for carbon capture, then drawing down excess CO2 to reverse climate change is the next. This week Akshat speaks to Dr. Jennifer Wilcox, head of the US Department of Energy's office that is funding two gigantic carbon removal hubs and many small demonstration projects. They talk about why carbon removal is so complicated, crucial, and hitting the magic number $100. This is the second in a two part series about carbon management. Listen to the previous episode in this series: Big promise, little success: The state of carbon capture Read More: Bill Gates-Backed Startup Uses Old Wood to Remove Carbon From Air Climeworks Battles Big Oil For $1 Trillion Carbon Capture Market Send us your questions about COP via zeropod@bloomberg.net and we'll try to answer them from the conference Zero is a production of Bloomberg Green. Our producer is Oscar Boyd and our senior producer is Christine Driscoll. Special thanks to Kira Bindrim, Brian Kahn, and Michelle Ma. Thoughts or suggestions? Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit bloomberg.com/green. See omnystudio.com/listener for privacy information.
You may not know Jim Hagemann Snabe by name, but he has been called Europe's top industrialist. Snabe has held leadership positions at some of the world's biggest companies like Maersk and Siemens. He is now a chairperson of Northvolt, Europe's largest battery manufacturer with 4,000 employees, $55B worth of orders and the competitive edge of greener batteries. Akshat spoke with Jim Snabe at the Bloomberg Tech Summit in London about how industrial behemoths like Maersk and Siemens can meet climate goals, whether zero-emission shipping will ever be a reality, and whether Northvolt can ever outcompete the Chinese battery industry. Send your questions about COP to zeropod@bloomberg.net and we'll try to answer them from the conference. Zero is a production of Bloomberg Green. Our producer is Oscar Boyd and our senior producer is Christine Driscoll. Special thanks to Kira Bindrim. Thoughts or suggestions? Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit bloomberg.com/green. See omnystudio.com/listener for privacy information.
The U.S. is spending billions on carbon capture as a climate solution, but is it realistic? The method has been around for 50 years and used primarily as a way to extract more oil. To find out how and if carbon capture can work as a climate solution, Akshat speaks with Emily Grubert, a professor at Notre Dame about what tech demonstrations have actually demonstrated and where this precious resource should be deployed. This is the first in a two part series about carbon management. Read More Occidental Quietly Ditched World's Biggest Carbon Capture Plant What Carbon Capture Failures Say About Its Future Fill out Bloomberg Green's climate anxiety survey Listen More A kingdom built on oil now controls the world's climate progress Peak oil is here. Well, maybe. Vicki Hollub is selling net zero oil, do you buy it? Zero is a production of Bloomberg Green. Our producer is Oscar Boyd and our senior producer is Christine Driscoll. Special thanks to Kira Bindrim. Thoughts or suggestions? Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit bloomberg.com/green. See omnystudio.com/listener for privacy information.
Industrial Talk is onsite at Hexagon LIVE and talking to Akshat Sharma, Monitoring and Controls Manager at ITT, Inc. about sensor technology providing asset condition insights. Here are some quick points: Industrial innovation and collaboration. 0:03 Scott MacKenzie interviews Akshot Sharma from ITT at Hexagon Live, discussing innovation and collaboration in the industrial industry. Akshot Sharma discusses industrial control wireless communications and IoT experience. Vibration monitoring and data analytics in industrial settings. 3:48 Akshot discusses how advancements in sensor technology can improve asset maintenance and reduce downtime. Akshot explains how J five software digitizes operator rounds, allowing for real-time vibration data collection and automation. Vibration technicians live in a world of information overload, similar to data analytics, where they must interpret and make sense of the vibration data they collect. Vibration monitoring and alert system for industrial equipment. 7:59 Akshot explains how the iAlert system provides valuable insights into machine vibration, including the ability to detect misalignment issues and predict potential faults. The iAlert system uses AI to analyze FFT data and detect faults in real-time, providing proactive notifications to prevent equipment failure. Akshot explains how the device can detect faults on a pump and motor by linking data from both sensors, providing real-time monitoring and proactive notifications. Akshot highlights the device's security features, including encrypted data transfer and storage, to protect customer networks and data. Industrial IoT solutions for predictive maintenance. 14:18 The company's device can detect faults in motors within 5 minutes of installation, and the analytics platform can recognize issues and faults immediately. The company is exploring the use of AI to enhance the fault detection system, particularly in recognizing baselines and feature characteristics for accurate fault detection. Scott MacKenzie interviews Speaker 3 about their company, iDASH alert, and their innovative solutions for the industry. MacKenzie and Akshot discuss the importance of reaching out to iDASH alert for more information and to learn more about their products. Also, get your exclusive free access to the Industrial Academy and a series on “Why You Need To Podcast” for Greater Success in 2023. All links designed for keeping you current in this rapidly changing Industrial Market. Learn! Grow! Enjoy! AKSHAT SHARMA'S CONTACT INFORMATION: Personal LinkedIn: https://www.linkedin.com/in/akshat-sharma-3646309b/ Company LinkedIn: https://www.linkedin.com/company/itt/ Company Website: https://www.itt.com/home THE...
Peak oil is here, or is it? Depends on how you measure, but at least one person is sure crude isn't coming back. This week Akshat speaks with Bloomberg Opinion columnist David Fickling about why he thinks the world has reached peak crude oil demand, what comes next, and what it all has to do with the American soap opera Dallas. Read more David's original article: Peak Oil Has Finally Arrived. No, Really Not everyone agrees: The Harsh Truth: We're Using More Oil Than Ever Latest IEA forecasts: Global Oil Demand to Reach Its Peak This Decade, IEA Says Read or pre-order Akshat's book Climate Capitalism Listen Listen to the interview with IEA head Fatih Birol Listen to the interview about EVs and oil demand with Colin McKraccher Zero is a production of Bloomberg Green. Our producer is Oscar Boyd and our senior producer is Christine Driscoll. Special thanks to Kira Bindrim. Thoughts or suggestions? Email us at zeropod@bloomberg.net. For more coverage of climate change and solutions, visit bloomberg.com/green. See omnystudio.com/listener for privacy information.
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