Podcasts about tokenomics

The Last Trade: Jackson, Michael, and Brian make the case that Bitcoin is carving out a structural bottom as old-coin distribution collapses and the worst of ETF and treasury-company selling exhausts itself. They break down Japan reclassifying crypto as a financial product and slashing its effective tax rate from 55% toward 20%. They dig into why the Clarity Act, not the administration, is the real green light for Wall Street. They close on Morgan Stanley's $4 to $8 trillion AI capex wave and the launch of Onramp's Back to the Basics campaign.---

Control what you're billed on. Tokens are the currency of AI, and how you design your app determines how many you spend. Compress conversation history instead of resending it raw, cap output tokens with matching prompt instructions, and cache static context so each reuse costs a fraction of the first request. Route each prompt to the right model by complexity, or set Model Router in Microsoft Foundry to handle that automatically — balanced, quality, or cost mode. Then optimize the whole stack. Run Agent Optimizer to test your prompt, model, and tool configurations together and surface better setups. Use Toolbox to dynamically select only the tools each request needs and cut input token overhead by 90%.  April Gittens, Microsoft Principal Cloud Advocate, joins Jeremy Chapman, Microsoft 365 Director, to share how to seize control of AI token spend through smarter app design.  ► QUICK LINKS:  00:00 - Tokenomics foundation 01:06 - Token cost basics 02:11 - Context Window creep 03:46 - Reduce unnecessary tokens 04:29 - Trim context costs 05:21 - Cap output tokens 06:27 - Cache for savings 07:54 - Model cost tradeoffs 09:15 - Model Router 09:42 - Toolbox in Microsoft Foundry Toolbox 11:18 - Agent Optimizer 12:44 - Other cost drivers 13:57 - Wrap up ► Link References Check out the tools in Microsoft Foundry at https://ai.azure.com For more about managing AI costs go to https://aka.ms/FoundryTokenomics ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

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

6/7  Futures in verde, attesa per minute Fed mercoledì. Trimestrali al via, per le banche bisognerà aspettare 14 luglio. Cosa aspettarsi. Tokenomics: cosa non torna in Anthropic e OpenAi. Hedge fund tagliano esposizione equity Usa. Rotazione e DRAMaggeddon: cosa dicono i gestori. Tutti i Big Tech, AI e media alla Allen&Company in Sun Valley. NATO, il vertice della discordia ad Ankara. Brent in calo su aumento produzione Opec. Risale dollaro, su anche oro, argento e Bitcoin su finestra Clarity Act. Trump inquina anche il mondiale di calcio. Questo episodio è offerto da ⁠Scalable Capital. Apri un conto con Scalable Capital e inizia a ricevere il 2,5% di interessi* sui tuoi risparmi:  https://partner.scalable-capital.de/go.cgi?pid=983&wmid=301&cpid=4&prid=13&subid=WILLHOST&target=Broker-Online *** Messaggio pubblicitario. Tasso lordo annuo variabile sulla liquidità depositata nel conto deposito non vincolato, composto da tasso base collegato al Tasso di Deposito BCE e tasso bonus discrezionale. Liquidità allocata presso banche partner e fondi monetari riconosciuti. Foglio informativo e condizioni su scalable.capital. Investire comporta dei rischi. *** Asia in rosso, continua repricing tecnologiia. Yen, Katayama: pronti a intervenire, in contatto con Usa. Giovedì PPI e CPI in Cina. SK Hynix verso Ipo venerdì al Nasdaq con valutazione oltre 29 miliardi dollari. Europa in rosso, oggi PPI e vendite dettaglio Eurozona. Attesa per minute Bce, Eurogruppo e Ecofin.Easyjet: accordo in linea di principio con Castlelake, valutazione oltre 6 mld euro. Unicredit, attesa risultato finale OPS Commerzbank verso 58%. Pirelli, magnate ceco Strnad vuole il 14% da Sinochem.  Learn more about your ad choices. Visit megaphone.fm/adchoices

If your organisation has moved its AI licenses onto a per-use plan, every single chat thread your team sends is now adding to a bill. Some companies are already burning through their entire IT budget for AI within the first couple of months of the year, and most staff have no idea their usage is costing anything at all. It doesn't have to be a mystery. There are simple habits, from which model you default to, to what time of day you send your first message, that change how far your usage actually stretches. In this How I AI episode, Neo and I unpack tokenomics, the economics of how you actually use your AI. We get into how usage limits work across the major platforms, why the timing of your first chat each day matters more than you'd think, and what changes once you move from a personal plan to an enterprise one. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: What tokenomics actually means and why it's suddenly everywhere How Claude's usage window works differently to ChatGPT and Copilot The simple morning habit that changes how far your usage window stretches Why enterprise billing is shifting, and what that means for your team's budget What managers should be doing before token usage gets out of hand Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out on July 7, 2026. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

Denny Lee is PM Director, Startups & Ecosystem at Databricks, a longtime Apache Spark, MLflow, and Delta Lake contributor — and one of the people behind Omnigent, the open-source meta-harness Databricks just released under Apache 2.0. He joins Demetrios to explain why the industry is moving from models to harnesses to meta-harnesses, why token spend is replaying the CapEx-to-OpEx shift all over again, and why he's using debating AI agents to plan a matcha farm in Taiwan.In this episode:

Ted and Calum are teaming up again to make sense of a quiet but highly interesting week for crypto. While Pav is busy making his big live TV debut on Sky News, the boys take a look under the hood to see if market history is repeating itself and where the smart money is actually positioning itself. In this episode, they dive into the numbers behind Bitcoin closing three straight red quarters for only the fourth time ever, exploring whether a 50% drawdown points to a final accumulation phase later in the year or a potential July relief bounce. The boys also unpack the heavy internet chatter after Ripple CEO Brad Garlinghouse publicly criticized Michael Saylor's financial products, right as Strategy announced a massive new capital framework to safely adjust cash reserves. Plus, they explore Kaspa's major internal network clock hard fork to explain why fixed tokenomics matter for long-term growth. Finally, they cover a surprising new local survey showing that high-powered business leaders are personally holding crypto at massive rates, and break down why political pushback on the Clarity Act is shaking up prediction markets. You'll hear:  03:15 Why a three-quarter red streak has historically set up a major accumulation phase, and what July's market behavior usually tells us. 07:14 Brad Garlinghouse clashing with Michael Saylor over structured Bitcoin products and why the Ripple boss thinks it's time to be greedy. 09:20 Calum explains the unique tokenomics of Kaspa's new hard fork 13:16 The real psychological impact behind the headlines of Strategy balancing their cash reserves and how it affects everyday market sentiment. 16:32 An awesome look into a new poll showing 54% of local managing directors and CEOs are personally holding Bitcoin for the long haul. 21:05 Why the upcoming political recess is shifting odds on prediction markets and what the delay means for building a long-term bottom. … and much more! You can read more about the Swyftx survey results here. Want to see what we're looking at every episode? Watch the YouTube version of the podcast here. Ready to start? Get $10 of FREE Bitcoin on Swyftx when you sign up and verify:  https://trade.swyftx.com.au/register/?promoRef=tappingintocrypto10btc  To get the latest updates, hit subscribe and follow us over on the gram @tappingintocrypto or X @tappingintocrypto If you can't wait to learn more, check out these blogs from our friends over at Swyftx. This podcast provides general market commentary and is for educational and entertainment purposes only. It is NOT financial advice. We are NOT licensed financial advisors. Investing in cryptocurrency carries risk. You should always conduct your own research and seek independent financial advice before making any investment decisions. Please read Swyftx's Terms and Conditions and Risk Disclosure statement before investing.

In this season finale of CDW Tech Talks, co-hosts Brian Matthews and Ivo Wiens take a step back to reflect on the biggest themes from the past season and look ahead to what's next in technology. The conversation highlights how rapidly AI has evolved and how consistently it has shaped discussions across modern workspace, cloud and cybersecurity. Drawing on experiences from recent episodes and customer engagements, they explore what organizations are seeing in real time. To learn more, visit cdw.ca Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Bentornati e bentornate su Azure Italia Podcast, il podcast in italiano su Microsoft Azure!Per non perderti nessun nuovo episodio clicca sul tasto FOLLOW del tuo player

Jetzt bei Kraken anmelden und 30 EUR Bonus erhalten: https://bit.ly/kraken-bonusDer Venice AI Token (VVV) gehört zu den stärksten KI Token am Markt. Ich erkläre das Dual-Token-System: Wie DIEM als tokenisiertes Compute funktioniert, warum die Token-Burns VVV deflationär machen, und wie sich Venice als privates KI-Gateway positioniert. Themen & Timestamps:00:00 Venice AI und seine beiden Token01:15 VVV und DiEM: So spielen die Token zusammen01:58 Dauerhaftes KI-Guthaben mit DiEM03:57 Nachfrage nach DiEM und VVV04:58 Emissionen und deflationäre Tokenomics05:42 Burn-Mechanismus und echte Nutzung07:34 OpenClaw-Hype und Venice-Wachstum09:09 Nutzerzahlen als VVV-Wette

The shifts that the agentic revolution is driving are felt in many areas of technology and Dell Technologies spans some of those that are seeing the greatest upheaval. The 451 Research team was in attendance at the annual Dell Technologies World conference and Brian Partridge, William Fellows, Henry Baltazar and Greg Macatee joined host Eric Hanselman to talk about their perspectives on the conference, agentic advancement and the technology market. Dell has positioned itself as a purveyor of not only the compute infrastructure needed to build the foundation for AI, but also as the custodian of AI's most critical raw material – data. The rapid evolution of agentic applications has created a need for new capabilities that is being complicated by both technology infrastructure demands and geopolitical events. Supply chains are being challenged by increasing demand for storage at a time when silicon pipelines were already under tremendous pressure. All of this is happening as the costs of AI are starting to have a material impact on businesses. Tokenomics, the impact of the cost of producing AI tokens, has taken center stage. More S&P Global Content: Compute sovereignty: The strategic importance of digital infrastructure AI won't solve its own energy problem – and that might be fine AI in action: unleashing agentic potential AI infrastructure results in 2025 top expectations, forecast upgraded   For S&P Global subscribers: Dell Technologies' unified private cloud strategy: IT environments reimagined AI Infrastructure Market Monitor & Forecast Quantum Computing Market Monitor & Forecast Service providers race to meet surging enterprise demand for AI infrastructure Credits: Host/Author: Eric Hanselman Guest: Brian Partridge, William Fellows, Henry Baltazar, Gregg Macatee Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith, Dylan Scheible

NEAR keeps showing up in strange places: cross-chain wallets, privacy apps, AI infrastructure, and now the emerging agent economy. Sal Ternullo, CEO of SVRN, joins us to explain why he thinks this is not another NEAR pivot, but the original thesis finally coming into focus. They dig into NEAR Intents, AI money, tokenomics, privacy, fee capture, agentic commerce, and why SVRN is trying to commercialize the NEAR ecosystem rather than simply hold the asset. ---

Are we prepared for the massive socio-economic divide of the looming quantum computing era?In this deep-dive episode of The Edge of Show, sponsored by Datavault AI, we welcomed Nathaniel Bradley, CEO and co-founder of Datavault AI. A prolific inventor holding over 70 patents , Bradley unpacks the shift from binary computing to quantum light computing, and what it means for human talent, data sovereignty, and security.Discover how Datavault AI is building the ultimate "toll booth" for digital assets. And how they outline their agnostic blockchain framework, which allows corporations to manage, evaluate, and monetize data using NASDAQ-backed systems. Also discover a groundbreaking perspective on robotics: introducing high-definition audio and wireless interoperability to give robots a universal communication layer.If you want to know how blockchain, AI, and quantum keys are turning data from a cost center into a massive revenue generator, this episode is a must-watch.Support us through our Sponsors! ☕ Want to make content like ours? Sign up with Castmagic to make your creative process easy: https://bit.ly/CastmagicReferral Work smarter, grow faster. Automate your SEO, get AI insights, and manage all your clients in one place with Helm. Start today 50% off your first month at helmseo.com

Nigel Brown, CTO of Microserve Not every voice at Dell Technologies World last week belonged to a vendor. For a partner perspective on the week’s biggest themes, In The Channel sat down with Nigel Brown, CTO of Microserve – a Burnaby, BC-based solution provider, Dell Titanium partner, and Dell’s Client Solutions Partner of the Year in Canada in consecutive years. Brown walked away from DTW with deskside agentic AI as his headline takeaway, particularly after hands-on time in a Dell lab showcasing NemoClaw – NVIDIA‘s enterprise-governance take on the OpenClaw open-source agent framework. “They’ve set it up closed by default – it can’t leave the box,” Brown says. “That’s a safety net that really opens the conversation.” That said, he’s clear-eyed about where most of his public sector and enterprise clients actually are. “Broad scope, it’s ahead. The hardware is going to follow it.” The tokenomics reality landed hard too. Brown shared a personal story about spending a hundred dollars testing Claude on a single flight – a relatable example he’s started using to frame the real cost implications of unmanaged AI usage, well before any on-premises or local inference conversation begins. On cyber resilience, Brown says he’s had to evolve his approach: “I got to be more of a jerk. I was being too nice.” His firm’s managed backup practice has seen firsthand the damage when clients – and even other MSPs – treat backup as a checkbox. When you show up after a ransomware event to find the backup server was on the same domain and hit just as hard, the conversation changes. And on Canadian data sovereignty, Brown goes beyond the standard data-residency talking points. FISA Section 702 and the CLOUD Act, he argues, represent far more serious legal exposure than most clients realize – even those who believe a Canadian cloud region is sufficient protection. The conversation also covers the AI PC refresh cycle colliding with supply chain pressure, the end-user adoption gap that’s undermining Copilot investments, and what Dell’s revised partner incentive structure signals about where the growth opportunities are. Read Full Transcript Robert Dutt: Hello and welcome to In the Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last 16 years. I’m Robert Dutt, editor of ChannelBuzz.ca and your host for the show. Last week, I was at Dell Technologies World in Las Vegas, Dell’s big annual customer and partner event. Over the course of the week, I had a number of conversations that I’ll be bringing here on In the Channel. Last week, we featured three Dell executives. This week, we’re bringing you some partners. Today, we start on that partner perspective, specifically from one of Canada’s top Dell partners. Nigel Brown is CTO of Microserve, a Burnaby, BC-based solution provider that has earned Titanium status with Dell and taken home Dell’s Client Solutions Partner of the Year in Canada in consecutive years. Microserve serves an enterprise and public sector-heavy client base, which means Nigel’s job is regularly about taking what gets announced on a stage in Las Vegas and translating it into something that makes sense for organizations that don’t necessarily move at conference speed. I caught up with Nigel on site at DTW last week. We covered a lot of ground – deskside agentic AI and what it’s actually going to take to make that real for customers, the very real cost of token economics, why he’s had to be, as he put it, more of a jerk about cyber resilience, and why the Canadian data sovereignty conversation is more urgent than most people realize. Let’s get right to it. My chat with Nigel Brown. Nigel, thanks for taking the time. Appreciate it. Nigel Brown: Happy to be here. Thanks for having me. Robert Dutt: So you guys are here, obviously, as a Titanium-level Dell partner, consecutive years as the Client Solutions Partner of the Year in Canada. What’s your overall read on this week? What made your ears stand up? What caught your attention? What are you taking back to both your team and to your customers when you go back to Burnaby? Nigel Brown: That’s a really good question. It’s also a big one. There’s been a lot of announcements, a lot of dialogue over the last couple of days. I’m trying to process that a little bit, assuming you were going to ask me that. I think the biggest takeaway I had – everybody’s heard of OpenClaw, everybody’s heard all the IT people are terrified of it, so it’s more, how do we get rid of it in our environments? Seeing this whole push around deskside agentic AI, especially given our market where we play a lot with clients – I actually had the opportunity, I did the lab today because I couldn’t resist seeing what it’s like. The governance and security wrapper on it totally makes sense and it’s opened my eyes. I think that’s probably the biggest. Beyond that, I would say the Dell hardware being able to run frontier models, seeing Gemini running local for sovereignty conversations – I think that’s a really good thing to see as well. Robert Dutt: Along those lines, obviously you touched on one of the big stories this week, which is deskside AI – the idea of physical infrastructure that’s at or near the customer’s desk, either in the data center or right there in the PC, that’s processing the models locally. It sounds like something that you’re interested in. I’m curious where it lands for your customers. Is it something that’s a conversation point, or is it ahead of where they are in the AI discussion at this point? Nigel Brown: I would say broad scope – I don’t want to lump all my customers into one bucket – but broad scope, it’s ahead. I don’t think you’re seeing a lot of organizations ready for it. We also deal heavily with public sector enterprise accounts, for example. We’re doing more and more in the commercial market where you’re going to see a little bit more playing and adoption within tech teams. But in ours, yeah, I’d say we’re definitely ahead right now. So it gives you a chance to get in there and pitch the idea as something new and plant those seeds. Once I get it past my IT and security folks, then that’s where it’s all going to start. If I can’t get it through mine in a good conversation, then I’m never going to be able to with our clients. Robert Dutt: But it sounds like there’s at least that – from your comments on OpenClaw, it sounds like there’s that door, that area of interest. Nigel Brown: Seeing it today under the NemoClaw and Viya umbrella – yeah, I think there’s definitely something there. They’ve set it up closed by default. It can’t leave the box. That’s what I saw in the lab today. So until you set up essentially like a firewall rule to allow it to do something, it’s a safety net that I think really opens the conversation and allows the idea of end users actually playing. Those are really early adopters anyway. And how could I integrate agentic AI into organizations? Robert Dutt: Man, how often does it come back down to governance with AI? Nigel Brown: Oh, absolutely. That’s pretty much the name of the game everywhere. And so we’re doing it well, and many are still scrambling. Robert Dutt: You touch on you guys having a lot of public sector, healthcare, education, all those kinds of verticals – not always the fastest to move on new tech. Along the lines of the previous questions, but sort of taken out a notch – how much of what the AI announcements we’ve heard this week translate directly to where your customers are at, versus how much needs to be, shall we say, adapted for the reality of your accounts? Nigel Brown: Well, you go to any of these events and it’s, “We’re behind if we’re not doing agentic AI everywhere.” Reality is, it’s just not true. I think it’s very forward-thinking – or very optimistic – to think we’re all moving that fast. It’s headed in that direction quicker and quicker. Executive tables are always the ones sitting there going, “We want it, we need it in our organizations, we’re going to get left behind.” So it’s very top of mind. But some organizations have very niche deployments – they’re figuring out the right solutions. Healthcare – I’ve seen it, they’ve done some phenomenal things in radiology and other areas. So it’s picking up. We’re dealing with one client right now that’s looking at online pharmacy and they’re looking at a huge Dell compute cluster to run AI on. So you see it, but it’s not commonplace. It’s not every organization. Certainly as you get into municipalities and things like that, it’s Copilot at best – that’s really where they’re trying to play – and their user base just isn’t adopting, not even close. Robert Dutt: So it sounds like there are at least a couple of steps that need to happen to get to the point of, A, using what’s already in place and, B, potentially looking at building out something internally – and the stuff that’s been talked about here a lot, the idea of running those AI workloads internally on the data center side. Nigel Brown: Yeah. I think it’s going to get there for sure. Right now the conversation has to be outcomes – not “I want AI.” And right now it’s so heavily, “Well, I know I need it, I don’t know what for yet.” I’ve seen it even in some peer groups – the dialogue is, “Well, we’re going to do AI, we’re going to build agents.” So, what for? And then there’s a long pause. Driving outcomes conversations is where it’s going to start, in my opinion. The hardware is going to follow it. And that really ties into, well, where are you going to run it? Do you understand token economics – or tokenomics, whatever the buzzword is right now – and that’s a really big deal. For me, getting that message out really loud and clear around the cost of tokens – I’ve done it, I’ve gotten burned. I spent a hundred bucks on a plane because I wanted to see Claude do something cool. And you’re going, wow, if I can do that in 10 minutes, think of what larger organizations will spend if they don’t find a smarter way to run it. Robert Dutt: That’s a good point – it’s not something you necessarily understand, but it’s something you can sure feel if you start to have adventures with the stuff. Nigel Brown: Well, exactly. And all it’s going to take – like I said, a lot of organizations started with Copilot under the Microsoft umbrella, because it was like an easy button. It was there for them, it was already set up. I am worried about some of those days changing, where that subscription turns into usage-based models. And we’ll see where that goes. You’re seeing it with Anthropic, you’re seeing it with Perplexity. I bounce off my limits all the time. Most of what I’m doing I can wait till tomorrow – but it’s easy to get out of control. Robert Dutt: And user computing is pretty core to what you guys do. There are a few things going on there – Windows 11 end-of-life support coming in October, the AI PC push coming from every direction at the same time. I’m curious if those two things are coming together in customer conversations as one refresh decision, or are they still separate tracks – the need to modernize for the Windows upgrade versus the need to modernize to get the most out of AI workloads? Nigel Brown: I think the end-of-support conversation and hardware refresh, honestly, is the biggest driver of the conversation that I’ve seen. And then that leads into, well, do I need an AI PC, and why, and what’s going to run on it? Everybody’s exploring and curious about it. There’s more skepticism about whether you need it now. Robert Dutt: How is that hitting along with the current fun situation with hardware constraints and prices spiking? And we’re hearing pretty directly from Jeff Clarke that, you know, telling customers, let us know what you want as early in the process as you can. I think the natural addendum to that is, make decisions knowing you might have this machine for a little bit longer than you previously expected. Nigel Brown: Totally right. So it’s very much my dialogue with our clients – it’s future-proofing. You better do it now. You don’t want to be stuck with a machine that can’t run an NPU for the next five years. So even if right now there’s skepticism about how much is going to run on it today, I think it is an important conversation to have and make sure that we’re ready for the moments where we’re really seeing workloads and inferencing running on device. You have to have that conversation now and pre-plan for it. But yeah, it’s been – especially in public sector – a hard conversation to have right now. Supply chain – we’re like a broken record. It still surprises me how many clients we talk to that haven’t seen this coming, that don’t know it’s real, or you get the ones going, “Well, I think it’s going to clear up in September, I’ll just wait till then.” Oh man. Brace for it. We’ve got to be ready. It just feels like a conversation on repeat these days – and it’s more than worth it, making sure we’re doing model selection with the future in mind. Robert Dutt: I find it’s a fun time to be a partner in that particular space. Nigel Brown: Well, you know, quote volume has quadrupled, because that same customer deal might take four different passes before they’ve made it through, especially in government. Pricing validity is a real challenge. It’s a moving target – no decision ever gets made fast. Robert Dutt: I want to talk a little about cyber resilience – another big topic here at the event. You guys run a managed backup practice, I understand, and you’re doing a lot of what vendors are asking MSPs to evolve towards. When you get into a customer environment today, what’s the most common gap between what they think their backup situation looks like and the reality of the situation? Nigel Brown: That’s an interesting question. It’s a real mixed bag. I always start with, “How confident are you in your ability to recover?” And most leaders – business leaders, outside of IT – there’s like a long pause. “Well, I don’t know.” Okay. Have you ever tested your recovery capability? No. Well, that’s where we’re going to start. And in other dialogues, they think they’ve got the backups running, but nobody’s been looking at them – they’re coming from doing it themselves, or maybe a mom-and-pop IT person taking care of it. They’re not watching, they’re not looking at tools, they’re not getting alert notifications on whether it’s keeping up and whether they’re protected. So that’s very foundational. Warning new clients – it’s just, let’s take them on that journey, do an assessment of the whole environment, make sure we’re protected. And a lot of conversations are, “Do you know that you’re not protected? Like, if you got ransomware tomorrow, there’s nothing I could do to help you, even though I’m your MSP.” That’s a scary reality. I’ve seen that have to go back to boards and make some tough decisions, find budget and solve it. They usually do – they react fast – but you’ve got to make the risk abundantly clear. Robert Dutt: That makes sense. In talking to Rob Emsley, who’s on the marketing team for the cyber resilience side at Dell, he was saying that 97% of cyber attacks now are specifically targeting backup infrastructure – because it turns out that’s where all the stuff is. Does that match what you’re seeing, and has that shift changed what you’re recommending to customers about what being protected really means for them? Nigel Brown: I wouldn’t say it’s really changed our messaging. I’d like to think we were maybe ahead of the curve in talking about storage and immutability – some of these key elements of, well, you just need it. That’s how we run our hosted service for clients that use it. And if we’re building out an architecture for another client, it’s just fundamental these days. You can’t even consider a solution that doesn’t include immutability protection, being able to spot bad things happening. But I’ve seen it – we’ve come into a disaster client where, “Hey, we got ransomware, can you help us recover?” And you go to the backup server to find out it was ransomwared too. “Do you have any tapes floating around?” It’s a tough chat to have. You see that less these days, but you definitely see the attempts – people trying to do it. And even other MSPs – I hate to say it – they’re not mature enough in how they’re protecting. They took the backup server, joined it to the domain – it’s just another device on the network. And sure enough, that’s exactly what gets hit because they didn’t plan it out. So it’s all planning and doing it right in the first place. Robert Dutt: It’s a checkbox as opposed to something that’s more firmly thought through. Given that, how do you approach it with customers? Do you come at it as, “This is something you should do, these are the reasons why, this is the potential downside” – or is it a thou-shalt kind of conversation? Nigel Brown: You know, a pile of years ago, after seeing an incident hit a new customer, I kind of resolved – I’ve got to be more of a jerk. I hate to say it. I got to be a lot tougher in my stance. I was being too nice. So yeah, in all things on this, my position is to generally take a pretty firm line. It’s all about risk, though. And to business leaders especially, that’s a term they understand. I’m not telling them, “Okay, you need this type of backup solution and it’s going to do these things.” It’s all about, how do we address the risk that you have right now? Leave it to us to figure out the details as we design the solution. Rarely do we get into the weeds of it unless it’s a larger client where we’re dealing with a large IT team that has opinions. But usually in those larger environments, there are groups that are already aligned – they know what they should be doing, maybe just haven’t done it themselves yet. The new architecture is absolutely going to include all those steps. So it’s an easier conversation to have. In some ways, it’s giving them permission if you’re coming in as a new supplier – it’s the stuff they’ve wanted to do, but haven’t really had the air cover to make the case. Robert Dutt: Yeah, you come in as that outside opinion to say, this is how it needs to be. Nigel Brown: And our job is often more of just a translator for those IT teams to their leadership – to help support the business case. Robert Dutt: I want to talk about the Modern Partner Platform and some of the partner program changes that have rolled out this week. One of the big things is obviously the revised incentive structure, with cyber resilience particularly called out as a premium rebate area. From your seat as a Titanium partner, what does the new structure tell you about where Dell sees the biggest growth opportunities for partners? Nigel Brown: Well, I think it does exactly that – it says where the growth opportunities are. And largely there was no surprise. In my opinion, when you look at it, it aligns to how we want to lead deals, it aligns with the conversations we’re already going to have. Now it’s just helping incentivize that dialogue. Nothing surprising there – I just see better alignment. Robert Dutt: Let’s play a little bit of “anything can happen here.” Vendors like Dell are starting to build agentic AI into their programs, their portals, their tools – all the stuff you guys work with every day. Where do you see the most genuine value for an organization like your own in vendors – agentifying, for want of a better word – their partner programs and tools? And the flip side: are there any potholes you’re watching out for as that rolls out? Nigel Brown: You know, the more the merrier – more tools you can bring in is great. We’re always excited to see what they come up with. But to me, the bottom line is back to outcomes. It’s about reducing friction in the sales process. What do we want our sellers to do? We want them out selling. Living in a partner portal trying to find what they need, deal registration, all of those things that can be painful – sometimes it’s just admin work taking you away from conversations with clients. Reduce friction – that’s the name of the game. Do I want to see more AI-generated marketing content? No. We can do that ourselves – one prompt, feed something in, done. To me, the more you can expose what matters to us and reduce friction, the better. It keeps us doing what we should be doing and not sitting there doing admin work. Robert Dutt: It sounds like based on that comment, what Dell and a lot of its peers are doing is already on track – because I’m sure they’re asking these exact same questions of partners around the world right now. Nigel Brown: Oh, they’ve got way smarter people than me working in these massive organizations. They know the outcomes we want to achieve. And I’m excited that we’re at a point in time where we can see some of this come to fruition. Ten years ago, this was never a reality. Robert Dutt: What’s the biggest misconception you think your customers have about what it means to be AI ready right now? Nigel Brown: I think it depends on who the conversation is centered around. If it’s C-suite leadership, it’s back to, “We want AI, I don’t know what for, I don’t know what it is, but I know I need it.” There are tough conversations to be had. AI readiness is really, is your data ready? We heard that on stage this morning. Most organizations we walk into – it turns out they’ve got no data governance. So, let’s define some of this, let’s build some process, look at the right tools. In the Microsoft lens, we do a lot around Microsoft 365 and modern workplace. Well, then it’s a Purview conversation. And they get confused – “Why are you talking about DLP and Purview? I thought we were talking about AI readiness.” That’s exactly what it’s all about. The other big one I think they’re not taking seriously enough is the end-user adoption side. I’ve seen organizations – you go into their portals and have a look with them – their adoption of Copilot, where they’ve spent a whole pile of money, is abysmal. So then the dialogue is, “What you actually need to do is get your users excited. Train them, show them the cool things.” I think we’ve been really successful doing that inside our own organization, and now that’s something we deliver to our clients as well – we need to get your teams ready and thinking differently. At a C-suite level, they’re usually surprised at the path it takes, or in some cases how long it might take to get there. “Your data is in such rough shape – you’re two years away. You need to build a foundation before you can really consume it.” Now, some of the announcements this morning – okay, that starts changing the equation. We could get there faster if we have the right infrastructure in place. Robert Dutt: For a variety of reasons, the Canadian data sovereignty question feels like it’s getting louder. And I have to imagine, especially in your public sector footprint, how are you helping customers think through AI infrastructure decisions when data residency and compliance are an increasing part of the equation? Nigel Brown: It’s a non-negotiable for most of our enterprise and public sector clients. It’s going to run on-prem. They cannot afford to run on cloud. Yes, they want the latest models, the frontier models, the cool bells and whistles as we all do. But really – I presented at a conference last year on exactly this topic, why it’s important to bring it back on-prem. Never mind the tokenomics conversation – now there’s just more ammunition. I chatted with one IT leader, a commercial client, not public sector, who was all proud of how he’d migrated everything to cloud. We were in a session where they talked through the tokenomics challenge and another reason why sovereignty matters. And you watch the look on his face go, “Wow, I’m going to have to start building a data center again. I thought I got out of that.” And he was sitting there with his CEO in the room for that conversation. Kind of a wake-up call. So my dialogue is, let’s talk through what does the Patriot Act mean? What does FISA Section 702 mean? It’s a little bit scary, and people are shocked – “I thought running in Google Cloud or AWS, running it in a Canadian location was good enough.” No. That provider has access to your data. Have you heard of the CLOUD Act? That’s nothing compared to FISA 702 – they don’t even need to ask. They can just go and get it. And that’s pretty scary. So yeah, a lot of our job now is just sharing and communicating the right things to our clients and making sure they’re aware. Robert Dutt: Aside from your efforts to bring that education – do you find that the level of general awareness is on the rise? Are we getting to more of a discussion about how to solve for this, rather than still defining the scope of the problem? Nigel Brown: I would love to say it’s more mature. The reality is no – it’s still early-stage conversations. You get anomalies. We were with some clients who are way ahead and have just deployed Azure Local on Dell infrastructure. They’re doing amazing things, moving fast. So now it’s more, “How can I partner with you to go share this message? Why you went there, why you built it this way, what are you doing about it?” But no, it’s going to be a continued push – much like the supply chain story here – these dialogues just repeat as you walk into client after client. Robert Dutt: Last one for me – along the same lines as the first question, but a slightly different lens. What’s one thing from this week that you think will genuinely change what Microserve brings to customers in the next 12 months? Nigel Brown: I come back to where we started – the whole side of agentic AI. That was not on my radar, not in a serious way. “Let’s play around with this, let’s lab it out, see where it’s getting explored.” When you see a name like Dell behind what we’re doing, that got me more excited than I would have thought. I want to pilot inside our org. And if we can start building something that works here, then absolutely – taking that to clients and saying, “Okay, look at the GB10s, look at the GB300s, let’s move up the ladder.” There’s a tangible path that gives them more value than trying to build massive solutions right out of the gate. There are quick wins there, and that’s what excites me – showing a customer how there could be a quick win if we did this right. And it ties into the last thread we were pulling on – “Okay, you’re telling me I shouldn’t have all this stuff running on public cloud, so where’s it going to run?” And you’re not talking megawatts and massive data centers here. All I want to do is automate tasks and do some of this lower-level stuff. I think that’s going to be an interesting entry point for a lot of clients – making it more accessible. Everybody’s used ChatGPT, Claude, whatever their tool of choice is, so they’re into prompting. Nobody’s really understanding Copilot or understanding agentic – it’s a big buzzword. That’s our job. We can show them a slice of the possible, mock up these use cases, and those are quick wins. Then it is something deployable at scale – you just move it from the little box to a bigger box. The more people take advantage of it and keep moving up the scale, you don’t need to go spend millions upfront to play around with something like that. It’s going to open more doors. Robert Dutt: No shortage of interesting opportunities. Good luck getting out there and chasing those, and thanks again for making the time this week. Nigel Brown: You bet. Thanks for having me.

It's been a busy week for the enterprise tech world in Las Vegas as Dell Technologies customers, partners, and channel partners poured into the Venetian Conference Center to hear about the company's latest strategies, products, and predictions for the future of IT.In this episode, Bobby speaks to Jane about what she's learnt during her week at the conference, what some of the big announcements were, and whether her pre-conference predictions were correct.

Today’s headline news for Canadian IT solution providers: Dell PowerStore Elite and the reimagined data center: Yesterday at Dell Technologies World, Dell Technologiesintroduced Dell PowerStore Elite, a new enterprise storage platform delivering up to 3x performance over the prior generation and an industry-best 6:1 data reduction guarantee. The platform packs 5.8 petabytes into a single 3U chassis using standards-based E3 NVMe flash, and introduces Dell Cyber Detect, which identifies ransomware with 99.99% accuracy and pinpoints the last known clean copy for recovery. PowerStore Elite ships in July 2026; Cyber Detect for PowerStore follows in Q3. The broader Day 2 announcement also included 11 new PowerEdge servers, expanded Dell Private Cloud support for Broadcom, Microsoft, and Nutanix stacks, Dell PowerProtect One for simplified cyber resilience, and two new automation products: the Dell Automation Platform and Dell Automation Studio. Jeff Clarke’s tokenomics keynote: In Tuesday’s Day 2 keynote at DTW, Dell COO Jeff Clarke presented a set of ten fundamental shifts from the past year whose through-line is what he called tokenomics. The math: model prices fell 80% per token; token consumption is up 10x; GenAI software spend tripled. Net effect – AI is getting more expensive for most organizations, not less. Clarke illustrated the stakes with a concrete example: one developer running a single agentic use case on the public cloud can burn approximately $3,400 per day in token costs; the same workload runs at zero incremental cost on on-premises infrastructure. Clarke confirmed Dell moved its own operations to on-prem after internal token costs became untenable, and described work underway on what he called “token routing” – an orchestration layer that would automatically direct tasks to either a deskside AI workstation or data center hardware based on workload. He closed with three imperatives: know your token consumption, find your super users, and lead the operating model change or be disrupted by it. Intezer launches Amplify Partner Program: Intezer has officially launched its Intezer Amplify Partner Program, naming channel veteran Mark Daggett as vice president of global channels and alliances. The program formalizes Intezer’s channel investment as demand for AI-driven security operations grows and the talent gap in security operations continues to widen. According to Intezer, the program is designed to help MSSPs and solution providers step in where internal security teams lack the capacity to operationalize AI-powered alert triage and threat investigation, translating the company’s platform capabilities into managed and co-managed service offerings. Check Point agentic network security orchestration: Check Point announced an agentic network security orchestration platform on Monday designed to replace decades of rule-based complexity, reducing network policy management from months of manual effort to minutes of verified, automated action. The announcement is part of a broader Check Point push into agentic security capabilities across its Infinity platform. Zendesk unveils Autonomous Service Workforce: At its annual Relate conference, Zendesk announced the Autonomous Service Workforce, a product vision built around specialized AI agents priced per resolution rather than per seat. Key launches include a no-code Agent Builder, omnichannel coverage with shared context, and a real-time Quality Score applied to every interaction – human or AI. Riverbed extends Aternity AIOps: Riverbed has released new Aternity digital experience (DEX) capabilities positioning AIOps as proactive disruption prevention rather than reactive monitoring, giving IT teams predictive intelligence before end-user experience degrades. WinMagic brings zero trust to legacy OT: WinMagic has introduced Continuous Identity Assurance, a hardware-bound approach to endpoint identity that extends zero trust controls to air-gapped systems and legacy operational technology environments traditionally outside the reach of modern identity platforms. Read Full Transcript Welcome to The Buzz from ChannelBuzz.ca, I’m Robert Dutt, today is Wednesday, May 20, 2026, and here’s what’s happening in the channel today. Continuing coverage from Dell Technologies World in Las Vegas, where yesterday’s Day 2 product announcements shifted the spotlight from the partner program to the infrastructure portfolio. The headline item was Dell PowerStore Elite, which Dell is positioning as a new class of enterprise storage platform built for what it calls an AI-era data center. According to the company, PowerStore Elite delivers up to three times the performance of the previous generation through software-driven improvements, and backs it all with what Dell describes as an industry-best 6:1 data reduction guarantee – up from 5:1 – a number it says carries real weight in today’s supply-constrained flash market. The platform packs up to 5.8 petabytes of effective capacity into a single 3U chassis using industry-standard E3 NVMe flash rather than proprietary drives, giving partners and their customers more flexibility on cost and sourcing. The cyber resilience angle is where it gets interesting for MSPs. Dell is introducing Dell Cyber Detect for PowerStore, which inspects data at the byte level and is positioned as being able to identify ransomware with 99.99% accuracy – surfacing the last known clean copy so organizations can recover fast. That capability will be available in Q3 2026. PowerStore Elite itself is set for global availability in July. The broader data center announcement also included 11 new PowerEdge servers spanning both air-cooled and liquid-cooled environments, expanded Dell Private Cloud support for Broadcom, Microsoft, and Nutanix software stacks, and two new automation products: the Dell Automation Platform, which pairs AI agents with a conversational interface for infrastructure deployment and management, and Dell Automation Studio for building custom, full-stack orchestration workflows. Nearly 20,000 customers already run PowerStore globally, and Dell is emphasizing that existing deployments can cluster with PowerStore Elite without disruption – a meaningful selling point for partners managing live customer environments. The second big story out of Las Vegas yesterday is one that deserves some unpacking. During his keynote, Dell’s chief operating officer Jeff Clarke laid out what he called ten fundamental changes in the past twelve months – and the thread running through the whole list is a single concept: tokenomics. The numbers Clarke presented tell a story that’s easy to miss if you only hear the headline. Model prices have fallen roughly 80% per token in the last year – sounds like great news. Except token consumption is simultaneously up ten times. And GenAI software spend has tripled in twelve months. The net effect is that AI is actually getting more expensive for most organizations, not less. Clarke made it concrete with a single example: one developer, one agentic use case, building a software tool. On the public cloud, that use case can run up roughly $3,400 a day in token costs. Running the equivalent workload on on-premises infrastructure with local models? Zero incremental dollars. Clarke went further and confirmed that Dell itself made the shift to on-premises AI after its own token costs became untenable – which is a different kind of endorsement than anything you hear from a keynote stage. He also flagged something worth watching: Dell is working on what he called token routing, an orchestration layer that would automatically determine whether a given task is better handled by a deskside AI workstation or by data center infrastructure. He was clear it’s still in development, but it signals where Dell sees the intersection of its PC and server businesses heading. Clarke closed his keynote with three actionable imperatives: know your token consumption, find your super users, and lead the operating model change or be disrupted by it. That first one is the real challenge for most organizations – and the one an MSP or trusted advisor can walk into and own. Away from Las Vegas now, and Intezer has officially launched its Intezer Amplify Partner Program, naming industry veteran Mark Daggett as vice president of global channels and alliances to lead the effort. The program formalizes the company’s channel investment at a moment when demand for AI-driven security operations is accelerating. Intezer’s pitch to the channel is essentially a gap-filling argument: internal security teams are drowning in alert volume while the talent required to triage and investigate those alerts remains in short supply. The Amplify program is designed to equip partners to step into that gap, delivering Intezer’s automated alert triage and threat investigation capabilities as a managed or co-managed offering. The appointment of a dedicated channel VP is the clearest signal yet that Intezer is treating the channel as a primary route to market, not a secondary one. Partners building out managed security or MSSP practices looking to differentiate around AI-augmented SOC capabilities have another option worth a closer look. In Brief –  Check Point launches an agentic network security orchestration platform it says collapses months of manual policy work into minutes of verified action.  Zendesk unveils its Autonomous Service Workforce at the Relate conference, introducing per-resolution AI agent pricing and a no-code Agent Builder.  Riverbed announces new Aternity digital experience capabilities designed to shift AIOps from reactive visibility to proactive disruption prevention.  WinMagic introduces Continuous Identity Assurance, anchoring identity verification in hardware to extend zero trust protocols to air-gapped and legacy OT environments.  Full details and links in the show notes or the blog post. Later today on In The Channel, still from the show floor at Dell Technologies World, I sit down with Rob Emsley, director of cyber resilience marketing at Dell Technologies, on why 97% of cyber attacks now specifically target the backup infrastructure – and what it actually means to build a resilience strategy around the concept of the minimum viable company. And if you haven’t heard yesterday’s episode yet, check out my conversation with Alan Ashby, Dell’s senior director of Americas data center presales and specialty sales, on the practical infrastructure realities of the AI boom – from a deskside AI workstation for an SMB to consolidating 13 legacy servers into one. That’s how we’re seeing the headlines today. I’m Robert Dutt for ChannelBuzz.ca, thanks for listening. Have a great day.

In this episode, Lex chats with Evan Malanga — Chief Revenue Officer of Yuma, a subsidiary of Digital Currency Group focused on growing the Bittensor ecosystem. They discuss how Bittensor's $6 billion protocol incentivises AI builders worldwide through token emissions across 128 competing subnets, and why the network has produced real commercial outputs — including a 72 billion parameter model trained on-chain and a coding agent rivalling Claude at a fraction of the cost. Evan explains Yuma's role as the institutional gateway to Bittensor through its validator, accelerator, and asset management products, and they explore why the concentration of AI in OpenAI and Anthropic is a systemic risk, and whether Bittensor's future extends beyond AI into a broader coordination engine for decentralised work. NOTABLE DISCUSSION POINTS: Bittensor has crossed from experimentation into shipping benchmark-competitive work at a fraction of centralized cost. Three recent proof points: Templar (subnet 3) completed the largest decentralized pre-training run of a 72B parameter model using only the network's token incentives. Ridges, an AI agent platform, is hitting 88–90% on software engineering benchmarks, on par with Claude-class agents at ~5x cheaper, built by a 3-to-5-person team under $10M of token emissions. Score (subnet 44) is doing computer vision 200x faster than centralized counterparts. Small distributed teams are producing outputs competitive with frontier labs without raising venture capital or hiring staff. Dynamic TAO restructured emissions from validator-curated to market-curated, making each subnet its own tradeable asset. Previously, dominant validators assigned weights that determined how the 7,200 daily TAO emission flowed across subnets. Under Dynamic TAO, each of the 128 subnets has its own token denominated in TAO, and any holder can buy or sell into specific subnets, pricing them like a market rather than a committee vote. Subnet owners, miners, and validators earn fees in the respective subnet token. Distribution has settled into a power law: the top ten subnets hold ~80% of market cap. This is the move that turned Bittensor from “decentralized AI protocol” into a financial hyperstructure with hundreds of tokenized work markets layered on top. The economics for subnet owners are genuinely unusual — hundreds of millions in annual incentives, fully subsidized labor, no fundraising. A subnet owner gets access to up to ~256 miners globally competing to satisfy their problem statement, with miner compensation paid by protocol emissions rather than the subnet owner. At current TAO prices, annual incentives across the network run into hundreds of millions; at higher prices, this approaches $1B/year up for grabs. No hiring, no benefits, no recruiting, the network runs as a continuous adversarial competition where validators rank miner outputs. This is the mechanical answer to “why would an AI researcher choose Bittensor over Silicon Valley”, and explains why researchers at Meta and Google reportedly mine Bittensor on nights and weekends, with top miners on subnets like Ridges earning ~$30,000/day. TOPICS Yuma, Bittensor, Digital Currency Group, DCG, OpenAI, Anthropic, Foundry, Templar, Ridges, Bitcoin, Meta, Google, BlackRock, JPMorgan, Decentralized AI, Crypto, Blockchain, AI, Tokenomics, Decentralized Science, DeSci, AI Agents, Computer Vision, Proof of Work, Tokenization, Real World Assets, RWA, Machine Economy   ABOUT THE FINTECH BLUEPRINT

This is my second conversation with Dylan Patel. Dylan is the founder and CEO of SemiAnalysis, where he tracks the semiconductor supply chain and AI infrastructure buildout. This conversation is about the supply and demand of tokens. On demand, Dylan describes something completely explosive. He explains why the frontier model is the only model anyone wants, and willingness to pay for it is nearly unbounded. His own firm has gone from tens of thousands of dollars in AI spend last year to seven million this year. On supply, we walk through the bottlenecks across memory, logic, and fab equipment that will determine how fast any of this can scale. We also cover Claude Mythos and what the leading labs need to do to fix their growing public perception problem. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit ⁠vanta.com/invest⁠.  ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Intro: Dylan Patel (00:03:09) Semi Analysis AI Spend: Zero to $7M (00:05:16) Real-World Examples of Claude Code (00:11:41) Token Demand: “Completely Explosive” (00:14:48) Why Everyone Wants the Frontier Model (00:15:36) Mythos: Biggest Model Capability Jump in Two Years (00:20:54) Fear of Rapid Model Progress (00:23:45) Robotics as the Next Demand Wave (00:26:03) Scaling Laws & Compute Efficiency (00:27:24) OpenAI vs. Anthropic (00:31:33) Supply Side: Bottlenecks Across the Stack (00:33:26) TSMC CapEx Could Cause a Shortage (00:36:45) CPUs, ASICs, and FPGAs (00:40:12) Tokenomics (00:42:20) Protests & AI Backlash

Dans cet épisode, nous recevons ⁠Victor VL⁠ pour explorer Bittensor, un projet à la croisée de la crypto et de l'intelligence artificielle. Nous revenons sur son fonctionnement, le rôle des subnets, l'utilité du token TAO et les enjeux de gouvernance au sein de l'écosystème. Un échange pour comprendre son potentiel, ses limites et les perspectives de développement de l'IA décentralisée.Cette interview a été enregistrée avant l'affaire Covenant / Templar.Vous avez quelque chose à partager et souhaiteriez le faire pendant une interview ?Envoyez-nous un mail à ⁠⁠contact@cryptoast.fr⁠⁠Nos podcasts sont aussi sur :

Spezielle Podcast Folge!

Leicester On BlockDAG Allegedly Showing 100 Billion More Coins After Opening Deposits; Inflation Triggered #Crypto #Cryptocurrency #podcast #BasicCryptonomics #Bitcoin #Webot $BDAG Website: ⁠⁠⁠⁠https://CryptoTalk.FM Facebook: ⁠⁠⁠⁠@ThisIsCTR⁠⁠⁠⁠ Discord:⁠⁠⁠⁠ @CryptoTalkRadio⁠⁠⁠⁠ Chapters (00:00:00) - Riding Your Rugged Horse(00:00:17) - CryptoTalk FM: Despite my Every Effort, It Failed(00:02:52) - Tokenomics page has been changed(00:07:43) - Dissent on Ethereum and the listing process(00:13:07) - BlackBerry Doesn't Get Anything For Their Stock

Terinspirasi dari presentasinya Jensen Huang (Nvidia) di acara GTC 2026. "Token" akan menjadi komoditas di era AI. Dia akan menjadi "mata uang" baru seperti halnya "kripto" (cryptocurrency).#AI #token #tokenomics

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with David Lachmish, co-founder of Ika, to explore the cutting-edge world of decentralized cryptography and its real-world applications. They cover the foundational problem of zero-trust custody and interoperability in crypto, breaking down why most people end up relying on centralized custodians despite crypto's original promise of removing third-party trust, and how Ika's novel 2PC-MPC cryptographic protocol addresses this with decentralized wallets (d-wallets) that require both the user and the Ika network to generate a signature. The conversation also touches on AI agents and the critical need for access control guardrails when agents handle real financial transactions, the philosophical parallels between crypto's growing pains and the early internet, decentralized governance and its potential to reshape how societies make decisions, and a surprising look at how decentralized certificate authorities could dramatically improve everyday internet security. David also gives a first public mention of an upcoming privacy-focused project called Encrypt.Links mentioned:- Ika website: https://ika.xyz- Ika on X: https://x.com/iкаdotxyz- David Lachmish on X: https://x.com/d3h3d_- Encrypt (upcoming project): https://encrypt.xyzTimestamps00:00 - David Lachmish introduces Ika and DWallet Labs, explaining their cybersecurity and cryptography background led them to solve zero trust custody and interoperability.05:00 - The d wallet concept is revealed as a decentralized signing mechanism controlled jointly by user and network, requiring new cryptography breakthroughs.10:00 - Crypto's philosophical parallels to early Internet are drawn, framing scams and misuse as inevitable growing pains of transformative infrastructure.15:00 - Wallet abstraction and agent constraints are explored, comparing future seamless crypto interaction to modern WiFi versus early modem connections.20:00 - Public key cryptography's binary ownership problem is explained, leading into MPC secret shares and Fireblocks' centralized access control tradeoffs.25:00 - 2PC MPC protocol is introduced as Ika's breakthrough, enabling decentralized policy enforcement without trusting any single entity.30:00 - Decentralized governance via token staking and code as law is discussed, contrasting corporate representative governance with crypto's direct decision-making.35:00 - Futarchy prediction markets and decision trees are connected to knowledge graphs, tracing humanity's accelerating governance transition.40:00 - Automation's historical parallels are examined, arguing AI's displacement of lawyers and developers mirrors every prior technological revolution.45:00 - Bitcoin and Ethereum's uncertain futures are assessed alongside Ika's positioning in custody and interoperability infrastructure.50:00 - Zero trust interoperability is explained, revealing how bridges create dangerous honeypots that Ika eliminates through native cryptographic control.55:00 - MetaMask's limitations for agents are detailed, contrasting stored private keys against Ika's policy-enforced guardrails for agentic transactions.60:00 - HumanTech's Wallet as a Protocol is presented as a practical way to give agents spending policies while maintaining user cryptographic control.65:00 - Decentralized certificate authorities emerge as Ika's broader cybersecurity vision, eliminating single points of failure across the entire Internet.Key Insights1. Zero Trust Custody and Interoperability: David and his cofounders at DWallet Labs identified that most cryptocurrency is held by centralized custodians, which contradicts crypto's core purpose of removing third-party trust. They set out to create "zero trust custody and zero trust interoperability" — systems where users maintain cryptographic control without sacrificing usability or relying on any single entity.2. The D-Wallet Primitive: Ika is built around a new cryptographic concept called a "d-wallet" — a decentralized wallet controlled jointly by the user and a decentralized network. A signature cannot be generated without the user's participation, meaning even if all network operators are compromised, they cannot act unilaterally. This required inventing new cryptography called 2PC-MPC.3. Access Control as the Missing Layer: Traditional crypto wallets operate on binary ownership — you either have full control or none. The d-wallet model introduces programmable access control policies enforced by a decentralized network, enabling features like spending limits and whitelisted addresses without trusting a centralized company like Fireblocks.4. Bridges Are Crypto's Biggest Security Vulnerability: Interoperability across blockchains typically requires trusting a bridge, which creates a honeypot for hackers. Ika eliminates this by allowing users to natively control assets on multiple chains simultaneously, maintaining cryptographic guarantees without a trusted intermediary.5. AI Agents Need Cryptographic Guardrails: Giving AI agents control over crypto wallets like MetaMask is dangerous due to hallucination and prompt injection risks. Ika enables agents to operate within strict, code-enforced policies — they can transact autonomously but cannot exceed boundaries set by the user, combining automation with genuine security.6. Decentralized Governance as a Structural Advantage: Ika operates as a permissionless network where two-thirds of token-staking operators control the protocol's direction. Even the founding team cannot unilaterally change the network, making governance transparent and resistant to capture — a meaningful contrast to closed, corporate-controlled systems.7. Decentralized Certificate Authorities as a Future Application: Beyond crypto, David envisions d-wallets solving broader cybersecurity problems. Today's internet relies on a handful of certificate authorities whose compromise would break global web security. A decentralized certificate authority built on Ika's infrastructure would require attacking hundreds of operators simultaneously, representing a fundamental upgrade to how trust is managed across the internet.

The CLARITY Act is on the verge of passing, and clear rules for tokenized stocks and revenue-sharing tokens are about to hit crypto. Instead of waiting for slow, top-down Wall Street adoption, Solana and Jupiter's JUP ecosystem are building from the bottom up: empowering retail, communities, and real users first, so that when compliant tokenized assets and revenue flows arrive, they plug directly into live, high-throughput rails that already work at scale.~This episode is sponsored by Tangem~Tangem ➜ https://bit.ly/TangemPBNUse Code: "PBN" for Additional Discounts!Guest: Kash Dhanda -COO ( and Cat Herder) at JupiterFollow Kash on X ➜  https://x.com/kashdhandaJupiter Exchange ➜ https://bit.ly/JUPonSolana00:00 Intro00:20 Sponsor: Tangem01:15 CLARITY: Does Solana most benefit?02:15 CLARITY affect on Jupiter ecosystem?03:30 JUP's current lineup of products05:20 New product updates06:30 Tokenized Stocks vs TradFi09:30 When will investors trust tokenized stocks?11:00 Are institutions ready for CLARITY?12:15 Will banks try to ban JUP USD vaults?13:20 JUP Vault benefits14:50 HYPE vs JUP16:40 Revenue vs TVL17:30 Vote net zero emissions20:50 $JUP tokenomics22:45 LIGHTING ROUND29:40 CLARITY marks crypto bottom?#Crypto #Solana #Ethereum~Clear CLARITY Winner = $JUP on Solana?

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Lars van der Zande, founder and CEO/technical architect of Inkwell Finance, for what Lars describes as his first-ever podcast appearance. The conversation covers a wide range of blockchain infrastructure topics, including Lars's work with Sui and Solana blockchains, the innovative capabilities of Ika's programmatic wallets and blockchain of signatures, and how Inkwell Finance is building revenue-based financing solutions for on-chain entities—from AI agents to protocols. They explore the evolving landscape of crypto regulation, the merging of traditional finance with blockchain technology, the future of decentralized legal systems, and how the user experience barrier is being lowered through technologies that eliminate constant transaction signing. Lars also discusses Inkwell's embedded financing approach and their pre-seed fundraising round.Links mentioned:- Inkwell's website: inkwell.finance- Inkwell on Twitter: @__inkwell- Lars on Twitter: @LMVDZandeTimestamps00:00 Introduction to Inkwell Finance and Technical Architecture02:06 Understanding Sui and Solana: Blockchain Dynamics05:55 The Role of Ika in Inkwell Finance11:51 Leviathan: Revenue Generation and Financing in Crypto17:38 The Future of AI Agents and Programmatic Wallets23:23 Smart Contracts: Legal Implications and Future Directions25:06 The Future of Inqvil Finance25:42 Decentralization and Its Evolution27:32 The Merging of Traditional and Crypto Systems29:33 Global Financial Dynamics and Market Reactions31:48 The Collapse of Traditional Financial Systems32:46 Jurisdictional Shifts in the Crypto World33:59 Legal Systems and Blockchain Integration35:57 On-Chain Credit and Financial Opportunities39:29 The Role of AI in Finance41:30 Learning from Peer-to-Peer Lending History43:14 Disruption in Insurance and Risk Management44:54 On-Chain vs Off-Chain Data46:54 The Evolution of the Internet and Blockchain49:12 Future Subscription Models in BlockchainKey Insights1. Ika's Revolutionary Blockchain Signature Technology: Lars discovered Ika, a blockchain of signatures built on Sui that enables any blockchain transaction to be signed without revealing the underlying message. Using patented 2PC MPC technology, Ika splits key shares across validators and encrypts them in transit, performing complex cryptographic operations that allow smart contracts on Sui to generate signatures for transactions on any other blockchain. This eliminates the need to build separate smart contracts on each blockchain, fundamentally changing how cross-chain interactions work and opening possibilities for truly interoperable decentralized applications.2. Programmatic Wallets vs Traditional Wallets: Traditional wallets like MetaMask require manual user approval for every transaction through a front-end interface, but Ika's D-wallet introduces programmatic wallets with policy-based controls embedded in smart contracts. These wallets can execute transactions based on predetermined conditions checked against on-chain data like Oracle prices, without requiring individual user signatures. For example, a Bitcoin D-wallet can hold native Bitcoin without wrapping or bridging to a custodian, and smart contract policies determine when and how that Bitcoin can be transferred, creating unprecedented security and automation possibilities for decentralized finance.3. Inkwell's Revenue-Based Financing Model: Inkwell Finance is building Leviathan, a revenue-based financing platform for on-chain entities including protocols, AI agents, and individual traders with verifiable track records. Borrowers receive capital based on their on-chain performance metrics like sharp ratio and drawdown, with loan repayment automatically deducted from their revenue stream. The profit split structure allocates approximately 60% to borrowers, 30% to lenders, and 10% split between Inkwell and integrating platforms. This creates a sustainable lending model where flight risk is minimized through D-wallet policy controls that restrict how borrowed capital can be used.4. Wallet-as-a-Protocol and the Future of User Experience: The crypto industry is moving toward embedded wallet solutions that eliminate the friction of traditional wallet management, with Wallet-as-a-Protocol representing the next evolution beyond services like Privy and Dynamic. Unlike current embedded wallets that lock users into specific applications, Wallet-as-a-Protocol enables single sign-on across multiple applications while users maintain control of their keys. Combined with app-sponsored gas fees, this approach allows non-crypto-native users to interact with blockchain applications without knowing they're using crypto, removing the biggest barrier to mainstream adoption and creating web2-like user experiences on web3 infrastructure.5. AI Agents as Financial Entities: AI agents are emerging as revenue-generating entities with on-chain transaction histories that create verifiable track records for creditworthiness assessment. Inkwell Finance is specifically targeting this market, recognizing that AI agents will need wallets and capital to operate effectively. The programmatic nature of D-wallets pairs perfectly with AI agents, as policy controls can restrict agent behavior to specific smart contract interactions, preventing unauthorized fund transfers while allowing automated trading or revenue generation. This creates a new category of borrower that operates 24/7 with completely transparent performance metrics, fundamentally different from traditional loan recipients.6. Cross-Chain Liquidity Without Asset Transfer: Ika's technology enables users to take loans against revenue generated on one blockchain and deploy that capital on entirely different blockchains without moving their original liquidity positions. For instance, someone earning yield on Sui's Fusol protocol could borrow against that revenue stream and deploy capital on Solana opportunities, effectively creating multiple on-chain businesses that generate their own credit scores and revenue to service debt. This ability to read state across different blockchains from within smart contracts opens possibilities for multi-chain strategies that don't require withdrawing capital from productive positions, maximizing capital efficiency across the entire crypto ecosystem.7. The Convergence of Traditional Finance and Crypto Infrastructure: The regulatory landscape is rapidly evolving with initiatives like the Genius Act and Clarity Act creating frameworks where traditional financial systems merge with crypto infrastructure through mechanisms like stablecoins backed by US treasuries. Companies are increasingly establishing entities in the United States to access capital networks and Delaware's established legal framework while issuing tokens through jurisdictions like Switzerland. This hybrid approach, combined with emerging concepts like Gabriel Shapiro's "cybernetic agreements" that make smart contract parameters legally enforceable in traditional courts, suggests the future isn't pure decentralization but rather a sophisticated integration of on-chain and off-chain legal and financial systems.

This week on the BlockDrops Podcast we speak with Tony Drummond, founder of Tokenomics.net, who brings a lot of solopreneur mindset energy to the 227th episode of BlockTalks. Link on the newsletter, Substack and wherever you get your podcasts.Holler:  #blockchain #web3 #DeFi #stablecoin #blockdrops #podcast @spotifycreator @spotifypodcasts @substackincLinksSocialsLinkedIn: https://www.linkedin.com/in/tonydrummond/X: https://x.com/hyper27374Websitehttps://www.tokenomics.netFree Tokenomics Strategy Callhttps://calendly.com/tonydrummond/strategy-call. Redes sociais / comms.. https://blockdropspodcast.xyz/.. https://blockdrops.substack.com .. Instagram.com/blockdropspodcast.. Twitter.com/blockdropspod.. Blockdrops.lens .. https://warpcast.com/mauriciomagaldi.. youtube.com/@BlockDropsPodcast.. Meu conteúdo em inglês twitter.com/0xmauricio.. Newsletter do linkedin  https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7056680685142454272.. blockdropspodcast@gmail.com

Matt O'Connor is the Co-founder of Legion, a platform for compliant and merit-based public token offering that enables teams to select investors based on criteria such as onchain history, social clout, and developer contributions. He is the former lead algorithmic engineer for Bridgewater Associates; tokenomics researcher for the Stacks Foundation (SEC qualified 2019 ICO); and token economics lead for Status (2017 ICO). His open source book, Tokenomics for Builders, has been positively reviewed by founders and VCs from Monad, Placeholder, Tensor, AllianceDAO, Galaxy Digital, and more. In this conversation, we discuss:- ICOs, IDOs, launchpads, private SAFT rounds - Merit-based, compliant token offerings - Why IPO access has deteriorated for retail investors - How Legion differs from AngelList, Carta, Republic, or SeedInvest - The convergence of IPOs ICOs - Companies with equity holders and token holders - Tokenomics 101 - Common mistakes when designing tokenomics - KPI based vesting for founders LegionX: @legiondotccWebsite: legion.ccLinkedIn: Legion | Merit-based FundraisingMatt O'ConnorX: @matty_LinkedIn: Matt O'Connor---------------------------------------------------------------------------------This episode is brought to you by PrimeXBT.PrimeXBT offers a robust trading system for both beginners and professional traders that demand highly reliable market data and performance. Traders of all experience levels can easily design and customize layouts and widgets to best fit their trading style. PrimeXBT is always offering innovative products and professional trading conditions to all customers.  PrimeXBT is running an exclusive promotion for listeners of the podcast. After making your first deposit, 50% of that first deposit will be credited to your account as a bonus that can be used as additional collateral to open positions. Code: CRYPTONEWS50 This promotion is available for a month after activation. Click the link below: PrimeXBT x CRYPTONEWS50FollowApple PodcastsSpotifyAmazon MusicRSS FeedSee All

In this episode, Lex speaks with Michael Egorov - Founder of Curve Finance and YieldBasis. Kicking things off about his journey from experimental physicist to founder of Curve Finance and YieldBasis, highlighting how theoretical physics concepts influenced his creation of financial invariants in DeFi protocols.Curve pioneered fully automated concentrated liquidity for stablecoins and introduced veTokenomics, a governance model rewarding long-term commitment with voting power and protocol fees. Egorov defends veTokenomics against criticisms of unlock-driven volatility, citing that most CRV locks average over 3 years and behave like permanent commitments. YieldBasis expands Curve's approach by offering impermanent gain strategies to counter impermanent loss in volatile markets like Bitcoin, aiming to scale toward a $50B market ceiling.The discussion closes with reflections on DeFi token market structure challenges and Egorov's call for protocols to connect token value to real economic flows by activating fee-sharing mechanisms.NOTABLE DISCUSSION POINTS:veTokenomics Drives Long-Term Alignment and Token Sink EfficiencyMichael Egorov introduced veTokenomics in Curve to address short-termism in token governance by requiring users to lock CRV tokens for up to 4 years to gain voting power and protocol rewards. This mechanism has proven effective in practice, with the average CRV lock time exceeding 3 years, effectively removing tokens from circulation. Egorov notes that veTokenomics removed 3x more tokens from supply than buybacks would have, highlighting its material impact on protocol stability and investor alignment.YieldBasis Aims to Neutralize Impermanent Loss via Engineered Impermanent GainYieldBasis builds on Curve's AMM infrastructure by combining two layers: a Curve pool experiencing impermanent loss, and a complementary structure engineered to capture “impermanent gain”. This dual-layer approach statistically delivers net profit in volatile assets like Bitcoin, assuming mean-reverting price movements. Egorov estimates the market ceiling for this strategy at $50 billion, positioning YieldBasis as a scalable solution for volatility-based yield generation.DeFi's Market Structure Issues Stem from Uncertain Token-Economics LinkagesEgorov critiques much of DeFi for failing to connect protocol economics to token value. While Curve distributes fees directly to CRV lockers, most protocols (like Uniswap) have not activated fee-sharing mechanisms (”fee switches”), creating valuation uncertainty. Egorov argues that unless projects “turn the switch on” and reduce economic ambiguity, token pricing will remain volatile and fragile, hindering broader adoption and investment confidence.TOPICSCurve Finance, YieldBasis, Uniswap, MakerDAO, Convex, StakeDAO, Threshold Network, NuCypher, AladdinDAO, Athena, Yearn, DeFi, veTokenomics, AMM, Stablecoin, Tokenomics, Governance, CRV Token, Ethereum, ETH, Bitcoin, BTC ABOUT THE FINTECH BLUEPRINT

If you're enjoying the content, please like, subscribe, and comment! Mo's Links: Website: https://www.animocabrands.com/X: https://x.com/Mo_Ezz14Mohamed Ezeldin is the Head of Tokenomics at Animoca Brands, where he has been instrumental in shaping the company's approach to digital economies and governance. With a background in mathematics and education, he has been involved in the tokenomics space since 2018 and has led various projects for Animoca Brands, including the development of token frameworks and strategies for their portfolio companies. Ezeldin emphasizes the importance of sustainable digital ecosystems and has been vocal about the challenges faced by token economies, advocating for a shift away from short-term ROI chasing towards long-term value creation. His work at Animoca Brands has positioned him as a key figure in the evolution of Web3 and blockchain technology._______________________Follow us!@worldxppodcast Instagram - https://bit.ly/3eoBwyr@worldxppodcast Twitter - https://bit.ly/2Oa7BzmSpotify - http://spoti.fi/3sZAUTGYouTube - http://bit.ly/3rxDvUL#rights #crypto #cryptocurrency #digitalart #digitalcurrency #data #datarights #education #college #debt #propertyrights #web3 #capitalism #app #subscribe #explore #explorepage #podcastshow #longformpodcast #podcasts #podcaster #podcasting #worldxppodcast #viralvideo #youtubeshorts

In this episode we are joined by Omnia and we discuss Kinetiq's liquid staking strategy on Hyperliquid, including kHYPE and kmHYPE products, xLSTs, risk isolation, and token design. We also cover the upcoming Markets exchange by Kinetiq, oracle and asset plans, ecosystem maturity, security practices, and distribution strategy. Thanks for tuning in! As always, remember this podcast is for informational purposes only, and any views expressed by anyone on the show are solely their opinions, not financial advice. -- Follow Blockworks Research: https://x.com/blockworksres Follow Kinetiq: https://x.com/kinetiq_xyz Follow Markets: https://x.com/markets_xyz Follow Omnia: https://x.com/0xOmnia Follow Shaunda: https://x.com/shaundadevens Follow Danny: https://x.com/defi_kay_ Follow Boccaccio: https://x.com/salveboccaccio -- Katana directs chain revenue back to DeFi users for consistently higher yields. It starts with VaultBridge, which turns bridged assets into yield streams that back a perpetually funded real yield, boosting rewards for DeFi users. Katana is pioneering Productive TVL, assets actually being used in DeFi and reinforces this with Chain-owned Liquidity, permanent liquidity the chain controls. Stop sleeping on your bags: https://app.katana.network/?utm_source=BW-Pod -- Subscribe on YouTube: https://bit.ly/3foDS38 Subscribe on Apple: https://apple.co/3SNhUEt Subscribe on Spotify: https://spoti.fi/3NlP1hA Get top market insights and the latest in crypto news. Subscribe to Blockworks Daily Newsletter: https://blockworks.co/newsletter/ -- Timestamps: (0:00) Introduction (1:03) Kinetiq's Major Announcement (5:28) kmHYPE and xLSTs (11:49) kmHYPE's Risk Profile (16:10) Asset Listings (18:22) Oracle Providers (21:42) Trading Activity Monetization (26:07) Bringing In New HYPE Stakers (31:04) Katana Ad (31:46) Kinetiq's Tokenomics (41:20) How Has The HyperEVM Landscape Changed? (47:28) Omnia's Closing Comments (48:42) Katana Ad (49:17) Final Thoughts -- Check out Blockworks Research today! Research, data, governance, tokenomics, and models – now, all in one place Blockworks Research: https://www.blockworksresearch.com/ Free Daily Newsletter: https://blockworks.co/newsletter -- Disclaimer: Nothing said on 0xResearch is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Boccaccio, Danny, and our guests may hold positions in the companies, funds, or projects discussed.

Welcome to The Chopping Block — where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week, the crew dives into the shift from airdrops to ICOs as Monad, MegaETH, and Coinbase's new sale format spark a rethink of how tokens should be distributed. They discuss ICO Beast's hedging fiasco, why most airdrops fail to create real users, and whether fixed-price ICOs are a better path for long-term alignment. The gang also unpacks Uniswap's major “unification,” the end of Labs vs. Foundation, and UNI finally becoming the protocol's value-accrual asset. In the back half, they touch on the “low carb crusader” MEV trial, the hung jury, and the broader question of whether MEV games belong in criminal court at all. A concise, high-signal look at where tokenomics, distribution, and crypto's legal boundaries are heading next. Show highlights

Der Markt ist unschlüssig aber Uniswap gibt Gas dank neuen Tokenomics und neuem Token Reward. 00:00 Intro 00:24 Hinweis 00:29 Vorschau 00:53 Bitcoin 03:31 Ethereum 04:33 Uniswap 05:19 Charts 07:16 Verabschiedung #krypto #cryptonews #cryptotrading #swissquotebank _____

In this episode, Lex speaks with David Namdar - CEO of the BNB Network Company, kicking off with his journey from early Bitcoin adoption in 2012 to co-founding Galaxy Digital and now leading the BNB Network Company. Namdar explains the evolution of public markets' engagement with crypto, highlighting how regulatory hurdles and speculative cycles shaped market participation. He outlines the rise of Digital Asset Treasury (DAT) companies, crediting Michael Saylor's MicroStrategy for pioneering the model by converting $400 million in cash to Bitcoin - now holding over $75 billion in BTC. We examine how Binance, with 290 million users and 40% of global crypto volume, supports BNB as a deflationary asset, burning up to $2 billion per quarter. Finally, Namdar shares why BNB, not Bitcoin, is the focus of his new DAT initiative, offering U.S. investors exposure to an underrepresented but powerful asset.NOTABLE DISCUSSION POINTS:Digital Asset Treasuries Are Emerging as Crypto ETFs in Disguise: Public companies like MicroStrategy and MetaPlanet are turning their balance sheets into crypto holdings, offering indirect exposure to Bitcoin, Ethereum, and BNB. This model is attracting billions and creating a new on-ramp for investors -especially where ETFs or direct access are limited.BNB Is Massively Used Yet Underrepresented in U.S. Markets: With 290 million users and up to $2B in quarterly token burns, BNB is one of the most used tokens globally. Yet it's largely inaccessible to U.S. investors, creating a major disconnect and a potential opportunity for BNB-focused public vehicles.Crypto Booms Often Rely on Misunderstood, Unsustainable Incentives: Namdar highlights how past cycles inflated demand through staking rewards and nominal yields, not real value. A lack of economic literacy continues to fuel hype over fundamentals, risking long-term sustainability. TOPICSBNB Network Company, Binance, BNB, Galaxy Digital, SolidX Partners, MicroStrategy, Bitcoin, Bitcoin treasury, Ethereum, Digital Asset Treasury, DAT, treasury, crypto, convertible debt, tokenomics, crypto treasury, capital markets ABOUT THE FINTECH BLUEPRINT

Analyst James Check (Checkmate) joins to unpack the current state of bitcoin and gold markets, the impact of shifting liquidity conditions, the relevance of four-year cycles, the future of bitcoin treasury companies, and why understanding market structure, capital flows, and the debasement trade is key to anticipating bitcoin's next major move.Connect with Onramp // Onramp Business // Onramp Institutional⁠ // ⁠⁠⁠James Check on X⁠The Last Trade: a weekly, bitcoin-native podcast covering the intersection of bitcoin, tech, & finance on a macro scale. Hosted by Jackson Mikalic, Michael Tanguma, & Brian Cubellis. Join us as we dive into what bitcoin means for how individuals & institutions save, invest, & propagate their purchasing power through time. It's not just another asset...in the digital age, it's The Last Trade that investors will ever need to make.00:00 — Market Overview & Sentiment03:39 — Are 4-Year Cycles Dead?06:26 — Liquidity & Macro Regime09:24 — Gold vs Bitcoin: Signals & Sovereign Bid11:39 — Institutions vs Retail Demand13:44 — Onchain: Retail, OG Supply, Smart Money19:59 — Treasury Companies: MNAV, Risks, Reality32:15 — Who Buys Next? ETFs, WM, Sovereigns37:44 — Retail Reflexivity & Hardware Tell41:42 — Real Inflation & BTC vs Traditional Assets56:33 — BTC Dominance, Altcoins & Valuation01:05:40 — Liquidations, Tokenomics & Value Capture01:13:29 — Barbell: Gold + BTC, Cycles & Leverage01:24:44 — OutroPlease subscribe to Onramp Media channels and sign up for weekly Research & Analysis to get access to the best content in the ecosystem weekly.

Show Notes:00:00 Sahej's Journey into Crypto and DeFi01:41 Building Avantis: Vision and Strategy04:40 Choosing Base: Strategic Decisions07:12 Market Dynamics and User Engagement09:48 Innovative Trading Models at Avantis12:30 Liquidity Provider Innovations15:29 Tokenomics and Community Engagement23:42 Strategic Buybacks and Growth Focus26:22 Balancing Token Holder Incentives and Team Sustainability29:00 Innovative Staking Mechanisms and Risk Management31:12 Leveraging AMMs for Unique Trading Features34:12 Scaling Open Interest and Market Maker Incentives37:10 Governance Dynamics and Community Engagement38:46 Navigating Partnerships and Market Dynamics45:05 Future Roadmap and Technological Innovations X: @0xSehaj / @avantisfi Website: avantisfi.com If you like this episode, you're welcome to tip with Ethereum / Solana / Bitcoin:如果喜欢本作品，欢迎打赏ETH/SOL/BTC：ETH: 0x83Fe9765a57C9bA36700b983Af33FD3c9920Ef20SOL: AaCeeEX5xBH6QchuRaUj3CEHED8vv5bUizxUpMsr1KytBTC: 3ACPRhHVbh3cu8zqtqSPpzNnNULbZwaNqG Important Disclaimer: All opinions expressed by Mable Jiang, or other podcast guests, are solely their opinion. This podcast is for informational purposes only and should not be construed as investment advice. Mable Jiang may hold positions in some of the projects discussed on this show. 重要声明：Mable Jiang或嘉宾在播客中的观点仅代表他们的个人看法。此播客仅用于提供信息，不作为投资参考。Mable Jiang有时可能会在此节目中讨论的某项目中持有头寸。

In light of the kick-off of Points Program Stage 3 of Aster I'm publishing this episode recorded on September 29, 2025 with Leonard, CEO of Aster, on Aster's value proposition, all things related to the token transparency, Aster Chain, and future plans00:27 Leonard's Journey into Crypto03:56 Defining Aster's Current and Future Vision07:21 Aster's Unique Value Proposition09:57 Governance and Decentralization in Aster11:39 Leonard's Preference on Other DEX Products13:39 The Dark Pool Design Explained18:36 Tokenomics and Supply Dynamics23:57 Genesis Stage 2 / 3 Points Program28:16 Spot Trading and Liquidity Development33:05 Postmortem on XPL Incident35:31 Oracle and Tokenized Stocks37:17 Token Buybacks Philosophy43:04 Product Features and User Demographics46:31 Aster Chain and Future Directions51:02 Market Maker Program and Mobile App DevelopmentX: @LeonardAster/ @Aster_DEX Website: https://www.asterdex.com/ If you like this episode, you're welcome to tip with Ethereum / Solana / Bitcoin:如果喜欢本作品，欢迎打赏ETH/SOL/BTC：ETH: 0x83Fe9765a57C9bA36700b983Af33FD3c9920Ef20SOL: AaCeeEX5xBH6QchuRaUj3CEHED8vv5bUizxUpMsr1KytBTC: 3ACPRhHVbh3cu8zqtqSPpzNnNULbZwaNqG Important Disclaimer: All opinions expressed by Mable Jiang, or other podcast guests, are solely their opinion. This podcast is for informational purposes only and should not be construed as investment advice. Mable Jiang may hold positions in some of the projects discussed on this show. 重要声明：Mable Jiang或嘉宾在播客中的观点仅代表他们的个人看法。此播客仅用于提供信息，不作为投资参考。Mable Jiang有时可能会在此节目中讨论的某项目中持有头寸。

Note: there was a small piece (about 5 mins) missing from the recording (yes, sorry, Yoaquim rugged - he didn't finish uploading xd). If you want to see the full livestream, please go to: https://x.com/Mable_Jiang/status/1971575903491940630 01:34 Transition from VC to Building04:22 Discussion on Quanto and Migration from OX07:13 User Experience and Team 09:35 Handling User Funds10:17 Choosing Solana as the Main Venue12:28 Recent Market Events and Community Response15:23 Design and Functionality of Quanto18:05 Proof of Reserves and User Verification20:35 Withdrawal Processes and Risk Management22:22 Tokenomics and QTO Flywheel31:13 Future Plans and Partnerships34:02 Market Expansion and Asset Listings X: @yqboom / @quantoWebsite: https://quanto.trade/en If you like this episode, you're welcome to tip with Ethereum / Solana / Bitcoin:如果喜欢本作品，欢迎打赏ETH/SOL/BTC：ETH: 0x83Fe9765a57C9bA36700b983Af33FD3c9920Ef20SOL: AaCeeEX5xBH6QchuRaUj3CEHED8vv5bUizxUpMsr1KytBTC: 3ACPRhHVbh3cu8zqtqSPpzNnNULbZwaNqG Important Disclaimer: All opinions expressed by Mable Jiang, or other podcast guests, are solely their opinion. This podcast is for informational purposes only and should not be construed as investment advice. Mable Jiang may hold positions in some of the projects discussed on this show. 重要声明：Mable Jiang或嘉宾在播客中的观点仅代表他们的个人看法。此播客仅用于提供信息，不作为投资参考。Mable Jiang有时可能会在此节目中讨论的某项目中持有头寸。

Runwago wants to make running pay—without ponzinomics. Founder Martin explains how their SportsFi app lets everyday runners monetize realistic goals using a transparent “challenge pool” model: everyone joins a challenge, finishers get 100% of their stake back plus rewards funded by those who don't finish. No shoe NFTs, no inflationary reward token.Timestamps[00:00] Opening & why SportsFi still matters[00:03] Martin's crypto OG journey (since 2012)[00:05] The problem: 300M daily runners, almost zero monetization[00:08] The model: pooled challenges; finishers earn from non-finishers[00:11] Devices & anti-cheat: Garmin/Apple/WearOS integrations[00:16] App Store realities: compliance, custody, reviews[00:19] Tokenomics done differently (RUNWAGO ≠ reward token)[00:22] Fiat challenges, company revenue, future revenue-share to holders[00:25] GTM: Puma tie-ins, offline activations, club strategy[00:29] Community > celebrities; micro-KOLs and run clubs[00:36] What they're hiring/raising/partnering for next[00:39] How to try the app + meet at Token2049Connecthttps://www.runwago.com/https://x.com/runwagohttps://www.linkedin.com/in/martin-lepka-12b51120b/DisclaimerNothing mentioned in this podcast is investment advice and please do your own research. Finally, it would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.Be a guest on the podcast or contact us - https://www.web3pod.xyz/

Hyperliquid is less than a year old, yet it's already rivalling Ethereum and Solana in revenue. In this episode, Ryan and Michael from the DeFi Report dive deep into the rise of crypto's hottest exchange: from its fair-launch token drop to its Binance-like UX, to why whales and builders can't get enough of it. We cover the project's inception story, the ecosystem forming around HyperEVM, and the unique buyback model funnelling millions back into its token. Along the way, we unpack tough questions about valuation, decentralization, and regulatory risk. Is Hyperliquid the future of on-chain trading or just another bull market phenomenon? Tune in for a full breakdown of the fundamentals, the risks, and the potential upside. ---

Altcoin froth meets political theater. The team dissects World Liberty Financial's explosive debut: a $22B token backed by the Trump family, a disputed Aave partnership, insider buybacks, and a “gold paper” instead of a whitepaper. We break down Justin Sun's role, why critics call it crypto's “garbage moat,” and how WLFi could become the Thanksgiving dinner debate of 2025. Plus: Gavin Newsom's meme coin tease, GDP data going on-chain, and the CFTC reopening U.S. markets to global exchanges. Welcome to The Chopping Block – where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week, the crew dives into the wild debut of World Liberty Financial — Trump's $22B DeFi token that launched with a “gold paper,” insider allocations, and buybacks despite no product. We break down the Trump family's $5B paper fortune, the disputed Aave deal, and whether WLFi is a serious stablecoin project or just another garbage fire in crypto's moat. From Justin Sun's backing to Thanksgiving dinner debates, we unpack what WLFi means for politics, memes, and markets. Then we zoom out to Gavin Newsom's meme coin tease, the U.S. Commerce Department posting GDP on-chain, and fresh CFTC moves that could reshape crypto exchanges and ETFs. Show highlights

Before you invest in any crypto project, you need to understand its tokenomics — the economic design behind the token itself. Tokenomics reveals how a token is created, distributed, and used… and it can make or break a project's long-term success.In this beginner-friendly episode of New To Crypto Podcast, I'll break it all down in plain English: ✅ What tokenomics actually means (and why it's like the DNA of a project) ✅ The 3 key elements every investor must check: supply, distribution, and utility ✅ Bonus: incentives, burning mechanisms, and how they impact value ✅ Red flags that signal you should walk away from a projectIf you've ever looked at a crypto project and wondered, “Is this legit—or just hype?” this episode will give you a clear framework to evaluate tokenomics like an informed investor.

Declan Fox from Consensys joins Sam to discuss Linea — Ethereum's zkEVM rollup with the largest ecosystem fund in Web3. He shares his journey from private blockchains to launching Linea, the chain's unique tokenomics, native yield, and burn mechanism, and why distribution (not just tech) will define the winners in the L2 race.Key Timestamps[00:00] Declan's journey from driverless cars → private chains → Consensys & Linea.[00:04] Linea's mission: gateway to Ethereum for users, institutions & capital.[00:06] Differentiation in L2 wars: distribution > just faster/cheaper tech.[00:08] Ecosystem fund: 75% of supply for builders, LPs, open-source software.[00:10] Tokenomics: burn mechanism — 20% ETH, 80% Linea token.[00:11] Ether X: new institutional-grade DEX on Linea solving the DEX trilemma.[00:12] Next narratives: tokenized equities, institutional DeFi, payments.[00:14] Key challenge: regulation & Ethereum's global perception vs. other L1s.[00:17] Lessons learned: focus on UX, partner selection, community building.[00:21] Roadmap: native yield (staking ETH bridged to Linea) + burn live Q3.[00:25] Great consolidation of L2s — few winners, many sunset chains.[00:27] Ask: Builders, institutions, and funds to join Linea's ecosystem.Connecthttps://consensys.io/https://www.linkedin.com/company/polygonlabs/https://www.linkedin.com/in/declan-fox-b743869b/https://x.com/LineaBuildhttps://x.com/DeclanFox14DisclaimerNothing mentioned in this podcast is investment advice and please do your own research. Finally, it would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.Be a guest on the podcast or contact us - https://www.web3pod.xyz/

Follow Proof of Coverage Media: https://x.com/Proof_CoverageSantiago Santos, Jason Badeaux, Mahesh Ramakrishnan and Connor Lovely welcome Nick Carpinito from Blockworks Research to discuss Helium's path toward deflation, decentralized energy grids, and the role of crypto and private equity in solving large-scale challenges. They touch on New York's political climate and its potential impact on innovation, highlight Inversion's talent-focused approach, and explore user-generated services like Daisy that enable real-time content monetization. Nick shares insights on Helium and Geodnet's growth, tokenomics, and capital attraction, with the conversation underscoring the need for clear, aligned structures that prioritize community benefits over traditional shareholder gains.Timestamps:00:00 - Introduction01:24 - New York Politics and Social Issues02:24 - New York vs. San Francisco03:16 - The Role of DePIN in Social Cohesion03:58 - Mamdani's Stance on Crypto04:17 - Inversion's Recent Developments05:34 - Hiring Trends and Talent in Crypto06:58 - Building in the Open vs. Secrecy08:53 - Insights on Hiring and Talent Acquisition09:54 - The Intersection of Crypto and Tradfi10:04 - Helium's Growth and MVNO Metrics12:09 - DePIN's Potential in Payments19:06 - The Concept of User-Generated Services22:19 - Introduction of Nick from Blockworks Research22:45 - Helium's Revenue Growth and Deflationary Potential25:03 - Tokenomics and Equity Conversion Discussions29:00 - The Importance of Clarity in Token Structures31:27 - Corporate Structures and Alignment in Crypto34:29 - Geodnet's Growth and Market Position39:29 - Revenue Dynamics in Geodnet and Helium41:42 - Earnings Dispersion Among DeployersDisclaimer: The hosts and the firms they represent may hold stakes in the companies mentioned in this podcast. None of this is financial advice.

Linea unveils the tokenomics for its LINEA token. ETHZilla unveils its ETH treasury strategy. The SEC approves in-kind crypto ETPs. And BitMine now holds 625k ETH. Read more: https://ethdaily.io/750 Disclaimer: Content is for informational purposes only, not endorsement or investment advice. The accuracy of information is not guaranteed.

The Midnight GlacierDrop is finally here, and in this episode, I break down everything you need to know about claiming your NIGHT tokens.Midnight is a new privacy-focused blockchain built by IOG, featuring two key tokens: NIGHT (a utility and governance token) and DUST (a non-transferable shielded resource for private transactions).This episode walks through the entire claim and redemption process, eligibility requirements, and why the staggered release of NIGHT is designed to prevent token dumping. The GlacierDrop claim is open to holders of ADA, BTC, ETH, XRP, BNB, SOL, AVAX, and BAT based on a snapshot taken on June 11, 2025. ADA holders receive the largest allocation—50% of the eligible airdrop supply. You'll need to connect your wallet to the official GlacierDrop portal when it launches and claim within a 60-day window.After that, unclaimed tokens move to the Scavenger Mine phase, followed by a long-term Lost & Found period that spans four years. Redemption of NIGHT tokens follows a 360-day vesting schedule with unlocks every 90 days, starting randomly within the first 90 days post-claim.Each redemption requires a transaction on Cardano, so be ready to cover network fees.Also, Cardano SPOs can earn NIGHT by participating in Midnight's block production. Delegators to those pools will benefit too—so be sure to speak with your SPO. 

Lex chats with Yuval Rooz, CEO and co-founder of Digital Asset, about the company's transformation from its early institutional blockchain experiments to launching the Canton Network - a purpose-built, privacy-enabled smart contract platform designed for financial markets. Rooz shares insights into why Digital Asset was inspired by Bitcoin's financial principles rather than its technical assumptions, highlighting the importance of rethinking blockchain infrastructure rather than replicating flawed legacy models. He also unpacks the hard lessons from high-stakes projects like the Australian Stock Exchange overhaul, emphasizing why large-scale financial infrastructure must evolve incrementally to succeed. Finally, the conversation dives into Canton's unique tokenomics, where 70% of block rewards go to the developers and users who create economic activity on the network, challenging traditional validator-centric models and aligning incentives more fairly for long-term ecosystem growth. Notable discussion points: 1. Canton's innovative tokenomics: Unlike Ethereum, where validators capture most of the rewards, Canton allocates 70% of block rewards to developers and applications, creating sustainable alignment. 2. Lessons from ASX: Rooz reflects on the failed ASX blockchain migration, advocating for iterative upgrades rather than “big bang” infrastructure transformations. 3. True tokenization: Rooz critiques superficial on-chain IOU models, asserting that real tokenization must place the asset's books and records natively on-chain to unlock the benefits of DeFi and composability. MENTIONED IN THE CONVERSATION Topics: Digital Asset, Canton Network, DRW, ASX, Ethereum, Bitcoin, Plaid, DAML, fintech, web3, tokenization, digital assets, financial infrastructure, DeFi, onchain ABOUT THE FINTECH BLUEPRINT 

Follow Proof of Coverage Media: https://x.com/Proof_CoverageConnor, Mahesh, Santi, and Jason are joined by Amir Haleem of Helium to explore the evolving landscape of decentralized networks. They dive into Helium's impressive revenue growth - from $400K to $2.7M per month - driven by its mobile subscriber base, and discuss the complexities of blending off-chain and on-chain revenue. The conversation covers tokenized equity, sustainable business models beyond token sales, and the convergence of crypto with traditional finance. Amir shares how Helium has shifted from a crypto-first approach to prioritizing service delivery and user satisfaction, offering key lessons in product distribution, user retention, and innovative tokenomics.Timestamps:00:00 - Introduction02:25 - Microstrategy and Digital Asset Accumulation  03:42 - Market Trends: Crypto and Wall Street  05:03 - Santi's Perspective on Market Efficiency  06:19 - DePIN Projects and Public Market Strategies  06:41 - Helium's Potential for Going Public  08:50 - Cash Flow and Tokenomics in DePIN12:07 - Helium's Recent Revenue Growth 12:55 - PMF for DePIN Networks  18:03 - User Engagement and Helium's Growth  19:05 - Helium's Revenue Sources Explained  21:06 - Convergence of Off-Chain and On-Chain Revenue  24:12 - Learning from Helium's Evolution  25:03 - Focus on Distribution Over Product  27:27 - Daily Active Users and Their Interaction  31:26 - Valuable Users and Helium's Ecosystem  33:45 - Cloud Points and User Experience  36:44 - Retention Curves: Crypto vs. Traditional Users  39:59 - Aligning Token and Equity Interests  Disclaimer: The hosts and the firms they represent may hold stakes in the companies mentioned in this podcast. None of this is financial advice.

Follow Proof of Coverage Media: https://x.com/Proof_CoverageConnor speaks with Tom Trowbridge, co-founder of Fluence Labs and host of the DePIN Day & DePINEd Podcast about the evolving world of decentralized compute platforms and the broader DePIN ecosystem. Tom explains how Fluence operates as a “cloudless” service—open, resilient, and more cost-effective than traditional cloud providers—and outlines its innovative tokenomics, including fixed-dollar payments to contributors and a capped token supply to build investor trust. The discussion explores how DePINs offer unique advantages over centralized models like Uber and Airbnb, particularly the ability for early participants to benefit from token appreciation. Tom also touches on governance, buy-and-burn mechanisms, and how the convergence of hardware, open-source technology, and crypto economics is driving the future of decentralized infrastructure.Timestamps:00:00 - Introduction02:54 - The Benefits of Podcasting05:10 - Overview of Fluence and Its Mission07:54 - Tokenomics Insights and Security Considerations10:01 - DePIN as Sharing Economy 2.012:32 - Marketing Dynamics in DePIN vs. Traditional Models14:10 - Payment Structures: Stablecoins vs. Native Tokens16:55 - Staking Rewards and Their Implications18:18 - Token Supply: Capped vs. Uncapped20:49 - Token Value Accrual Methods25:09 - Best Practices in Revenue Generation and Tokenomics29:29 - Future Opportunities in the DePIN Sector31:00 - Conclusion and Final ThoughtsDisclaimer: The hosts and the firms they represent may hold stakes in the companies mentioned in this podcast. None of this is financial advice.

Summary   In this episode, Wayne Marcel hosts Bruno Sessions and Jeremy to discuss the launch of Bitcoinciaga, a unique apparel brand that integrates cryptocurrency and community engagement. The conversation explores the innovative concept behind the brand, the importance of building a community through apparel, and the role of tokens in enhancing customer experience and loyalty. They delve into the tokenomics of the brand, discussing distribution strategies and the potential for future growth, including the integration of AI technology. In this conversation, the speakers discuss the intersection of luxury branding and cryptocurrency, particularly focusing on Bitcoin and its potential as a powerful brand. They explore the unique opportunities presented by the Bitcoinciaga project, including token distribution, staking rewards, and the importance of community building. The conversation also highlights the upcoming Bitcoin conference and the excitement surrounding it, emphasizing the need for education and onboarding in the crypto space. Overall, the discussion reflects a forward-looking perspective on the future of cryptocurrency and its integration into everyday life.   Learn more at https://www.onchainthreads.com/    Takeaways   The idea for Bitcoinciaga originated from observing luxury fashion trends. The brand aims to create a community around crypto apparel. Luxury brands often do not reward their customers or community. Bitcoinciaga offers a unique value proposition through token rewards. The brand's success is tied to the growth of Bitcoin. Community engagement is essential for the brand's development. The token serves as a central element of the brand's ecosystem. Tokenomics are designed to incentivize early adopters. AI technology will play a role in community engagement and education. The brand aims to redefine the relationship between apparel and cryptocurrency.  Celebrity encounters can enhance brand recognition. Luxury is not just about price; quality matters. Bitcoin is a powerful brand statement. Using Bitcoin's brand can foster business innovation. Token distribution models can incentivize community engagement. The X-verse wallet supports various Bitcoin-related tokens. Community building is essential in the crypto space. DeFi offers endless possibilities for token utility. Education is key to onboarding new crypto users. Simplicity in purchasing can lead to greater crypto adoption.   Chapters   00:00 Introduction to Bitcoinciaga 03:00 The Concept Behind Bitcoinciaga 05:51 Building Community Through Apparel 08:59 The Role of Tokens in the Brand 11:59 Tokenomics and Distribution Strategy 14:55 The Future of Bitcoinciaga and AI Integration 21:33 Celebrity Encounters and Brand Recognition 22:49 Luxury and Quality in the Bitcoin Era 24:34 The Power of the Bitcoin Brand 26:08 Token Distribution and Staking Rewards 27:31 The X-verse Wallet and Its Capabilities 28:28 Anticipation for the Bitcoin Conference 30:50 Building Community and Partnerships 32:31 The Future of DeFi and Utility in Crypto 34:21 Education and Onboarding in the Crypto Space 40:20 Final Thoughts and Future Prospects