Podcasts about 25b

Bitcoin's OG investors may have just had their most profitable cycle ever, while the market now waits for the next wave of institutional capital. We break down Goldman Sachs' $2.25B acquisition of NEOS and the growing demand for Bitcoin income products, Wintermute's $1B push into AI and traditional markets, and Kalshi's move toward Wall Street-grade trading infrastructure. We also look at Japan's potential rate hike and what it could mean for global liquidity and crypto. Learn more about your ad choices. Visit megaphone.fm/adchoices

OpenAI took Apple's trade-secrets lawsuit to the court of public opinion, receipts and all. Bending Spoons bought Airtable, Anthropic inked a $10B Norway compute deal, the US eyed polysilicon tariffs, and Microsoft told engineers tokenmaxxing was over. Links OpenAI drags Apple's lawsuit into the court of public opinion (The Verge) Bending Spoons agrees to acquire Airtable in an all-cash deal valuing the US software company at $1.285B, set to close by 2026's end, its first post-IPO deal (Reuters) Bending Spoons buys Airtable at an implied equity value of ~$2.25B, five years after a $735M round valued the no-code company at $11.7B; Airtable's ARR is now ~$480M (Silicon Republic) Sources: Anthropic agreed to a $10B deal for computing capacity in Norway from Nvidia-backed AI cloud startup Volta Infra, which says the deal is for six years (Bloomberg) Sources: the US is preparing to set a price floor and impose tariffs on polysilicon, a material critical to chips, later this month, in a bid to counter China (Reuters) Sources: Beijing is growing concerned about the cyber capabilities of Mythos and other US-developed frontier AI models and their potential as offensive weapons (Bloomberg) White House to host AI companies Tuesday to review new model-testing framework (CNBC) Internal email: Microsoft introduces token budget limits for employees' AI use, saying "tokenmaxxing is not what we are optimizing for" (404 Media) Subscribe to the ad-free feed.

Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu

BUSINESS: ​New US tariff to affect $6.25B in exports – DTI | July 27, 2026Subscribe to The Manila Times Channel - https://tmt.ph/YTSubscribe Visit our website at https://www.manilatimes.net Follow us: Facebook - https://tmt.ph/facebook Instagram - https://tmt.ph/instagram Twitter - https://tmt.ph/twitter DailyMotion - https://tmt.ph/dailymotion Subscribe to our Digital Edition - https://tmt.ph/digital Check out our Podcasts: Spotify - https://tmt.ph/spotify Apple Podcasts - https://tmt.ph/applepodcasts Amazon Music - https://tmt.ph/amazonmusic Deezer: https://tmt.ph/deezer Stitcher: https://tmt.ph/stitcherTune In: https://tmt.ph/tunein#TheManilaTimes#KeepUpWithTheTimes Hosted on Acast. See acast.com/privacy for more information.

Matt Kilgroe — President & CEO, Cyndeo Wealth Partners Matt Kilgroe shares how Cyndeo Wealth Partners grew from a newly launched $1.2B RIA to a $3.5B enterprise, and why the next challenge isn't independence, but building a firm capable of reaching $25B.  In Summary Five years after launching Cyndeo Wealth Partners from UBS, Matt Kilgroe returns to the podcast to discuss what happens after independence. Rather than focusing on the transition itself, Louis and Matt explore the next phase of growth: scaling an advisory business, attracting talent, developing niche expertise, taking on outside capital, and building an enterprise designed to last. Along the way, Matt shares how Cyndeo expanded from $1.2B to $3.5B, why serving professional athletes required a different business model, and what led the firm to partner with Rise Growth Partners as it looks toward a $25B future.  The Storyline For many advisors, independence is viewed as the finish line. For Matt Kilgroe, it became the starting point. When Cyndeo Wealth Partners launched in 2020, the goal wasn't simply to leave the wirehouse behind. It was to build a business with the flexibility to grow in ways that simply weren't possible before. Five years later, that vision has evolved into something much larger. Cyndeo has nearly tripled in size, expanded its niche serving professional athletes and entertainers, recruited advisors, added specialized operational talent, and recently welcomed Rise Growth Partners as a minority investor to help accelerate its next phase of growth. The conversation explores what changes when firm leaders stop thinking like advisors managing successful practices and begin thinking like CEOs building enduring enterprises. The discussion spans succession planning, capital strategy, recruiting, organizational design, and the mindset required to scale from billions to tens of billions—all while remaining focused on clients and culture.  Topics Covered Building an enterprise beyond independence Scaling from $1.2B to $3.5B in assets Organic growth versus recruiting Serving professional athletes and entertainers Why fiduciary independence matters for niche client segments Building operational infrastructure for growth Partnering with Dynasty Financial Partners Minority capital and Rise Growth Partners Succession planning and employee ownership Thinking from $3.5B to $25B > Download a transcript of this episode… Listen and Learn Highlights for Advisors What did Matt learn after transitioning nearly 98% of his clients? (06:20) Why client relationships—not firm logos—proved to be the firm's greatest asset during one of the most challenging transitions imaginable. How did Cyndeo nearly triple in size in five years? (16:10) Matt discusses the combination of niche specialization, disciplined organic growth, recruiting, and operational investment that fueled the firm's expansion. Why has Cyndeo become a destination for professional athletes? (17:15) The conversation explores how deep industry expertise, fiduciary flexibility, and specialized service created a business that would have been difficult to build inside a wirehouse. Why bring on a minority capital partner when the business was already thriving? (24:15) Matt explains why succession planning, future recruiting, and long-term enterprise growth made outside capital the right decision. How should advisors think about ownership versus compensation? (35:40) A candid discussion about enterprise value, equity, and why many advisors underestimate the long-term economics of ownership. What does it actually take to scale toward $25B? (42:20) From hiring executive talent to expanding geographically, Matt shares how he's thinking about the next chapter of Cyndeo's evolution. Key Takeaways Independence creates opportunities that extend well beyond higher payouts, including enterprise value, recruiting flexibility, and ownership. Scaling a business requires investing in operational leadership, not just adding advisors. Specialized client niches demand expertise that goes well beyond investment management. Outside capital can accelerate growth when it's aligned with long-term strategy rather than an exit. Building an enduring enterprise requires thinking differently about succession, talent, governance, and equity. https://youtu.be/WRYJd9Lkt7o Quotable Moments “Don't rent your practice. Own it.” “You can't work in those niches and not be a fiduciary.” “We're not done.” “The road from $3B to $25B is going to really compound on your equity.”  FAQs Why did Cyndeo decide to take on a minority capital partner? To support its next phase of growth, strengthen succession planning, recruit additional talent, and benefit from the experience of leaders who have successfully scaled wealth management businesses before. How did Cyndeo grow from $1.2B to $3.5B? Through a combination of consistent organic growth, specialized client niches, advisor recruiting, and investments in operational infrastructure. Why is serving professional athletes or other niche client segments different from serving traditional wealth clients? Niche client segments often face unique financial decisions involving private investments, business opportunities, and career transitions that require specialized knowledge and a fiduciary framework. What advantages did independence create that weren't available inside a wirehouse? Matt points to greater flexibility around private investments, the ability to build specialized client experiences, reward employees with equity, and create an enterprise with lasting value. How should advisors think about building versus joining an independent firm? The discussion highlights the tradeoffs between creating your own firm and joining an established independent enterprise, emphasizing that ownership and long-term equity often matter more than headline payouts. What does Matt believe is required to build a $25B firm? A willingness to invest beyond advisors alone, adding executive leadership, expanding geographically, recruiting strategically, and maintaining a long-term enterprise mindset. To support its next phase of growth, strengthen succession planning, recruit additional talent, and benefit from the experience of leaders who have successfully scaled wealth management businesses before. Through a combination of consistent organic growth, specialized client niches, advisor recruiting, and investments in operational infrastructure. Niche client segments often face unique financial decisions involving private investments, business opportunities, and career transitions that require specialized knowledge and a fiduciary framework. Matt points to greater flexibility around private investments, the ability to build specialized client experiences, reward employees with equity, and create an enterprise with lasting value. The discussion highlights the tradeoffs between creating your own firm and joining an established independent enterprise, emphasizing that ownership and long-term equity often matter more than headline payouts. A willingness to invest beyond advisors alone, adding executive leadership, expanding geographically, recruiting strategically, and maintaining a long-term enterprise mindset. Related Resources Article: Your Practice Isn't Worth What You ThinkMost advisors misjudge their business's value, not because of the number, but because of the framework. Learn what really drives enterprise value. Rise and Reinvent: Joe Duran on Building and Rebuilding World-Class FirmsHe's built and rebuilt some of the industry's most successful firms and now he's helping others do the same. In this episode, Joe Duran, the founder of Rise Growth Partners, shares lessons from building, selling, and starting again, and how staying curious and adaptable fuels lasting success. Matt KilgroePresident/CEO Prior to launching Cyndeo Wealth Partners in 2020, Matt ran advisory teams at Merrill Lynch and UBS Financial for 29 years. Providing guidance, counsel, and strategy for families the firm serves is Matt's passion. In addition to his role as an advisor, Matt works in a leadership capacity for Cyndeo while also helping with business development. Matt has been recognized by Barron's as a Top 1000 or Top 1200 Advisor consistently since 2009. In 2020 Forbes named him to their “Best-In-State Wealth Advisor” list. A graduate of Eckerd College, Matt has served on the Board of Trustees at his alma mater since 2012. His three children are his pride and joy. Daughter Carrington owns Sunstate Yoga studio in St. Petersburg, son Kent is a financial advisor with Cyndeo, and daughter Jillian recently graduated Florida State University. An athlete in college, Matt continues to enjoy staying in shape, playing basketball, and bike riding. NOTE: The views and opinions expressed by the guests on this podcast are their own and do not necessarily reflect the views and opinions of Diamond Consultants. Neither Diamond Consultants nor the guests on this podcast are compensated in any way for their participation. View the transcript of this episode… True Alignment: Advising Business Owners on Wealth, Significance, and Value A conversation with Jason Diamond, Nick Hubert and Taylor Gentry – Founding Partners at Panoramic Capital Partners. Jason Diamond: Welcome to the latest episode of our podcast series for financial advisors. Today’s episode is True Alignment: Advising Business Owners on Wealth, Significance, and Value. It’s a conversation with Nick Hubert and Taylor Gentry, Founding Partners, Panoramic Capital Partners. I’m Jason Diamond and this is the Diamond Podcast for Financial Advisors. Mindy Diamond: At Diamond Consultants, we help elite advisors identify the right environment for their businesses to thrive, whether that’s at a wirehouse, boutique, or independent firm. With nearly three decades of experience, we’ve guided thousands of advisors and represented more than a quarter of a trillion dollars in assets transitioned. And each year, one in four advisors managing a billion dollars or more who change firms are our clients. Our process is education-driven and based on building relationships, starting as your strategic partner well before you’re even thinking of a move. To schedule a confidential conversation, call us at 908-879-1002. Wondering why advisors change firms and where they’re headed? Are transition deals going up or down? Those very questions and more inspired us to create our annual advisor transition report. It’s the award-winning, data-driven resource designed for advisors that connects the dots between the motivations around movement and the firm’s appetite for top talent. Arm yourself with the knowledge you need to make smart decisions. Download your copy at diamond-consultants.com/transitionreport. Jason Diamond: Advisory firms that work with business owner clients typically operate through a fairly traditional wealth management lens. The business may be the source of the wealth, but the advice itself often centers around investments, planning, and asset allocation, yet Panoramic Capital Partners approaches that equation differently. Nick Hubert and Taylor Gentry are the founding partners of the roughly $450 million RIA, serving about 150 families with a seven-person team. And while they come from very different professional backgrounds, Nick with more of a relationship and storytelling orientation, Taylor from the analytical and private equity side, they’ve built the firm around a shared philosophy tied to what they call personal significance, personal wealth, and personal value. A big part of that philosophy, or the north star as they put it, is applying some of the same accountability and long-term thinking frameworks commonly seen in private equity to the advisory relationship itself, not in a transactional sense, but in helping clients think more intentionally about decision-making, alignment, and outcomes over long periods of time. As a result, our conversation delves deeply into the private equity world, reframing how clients and advisors should consider this important tool as both a growth mechanism and a strategic part of their client’s plans. We talk about how that perspective also shapes not only how they think about serving business owners specifically, but also the role private equity should play in wealth management. Then we take a view of their long runway and how they and other younger advisors might see things differently about building firms today and why clarity of vision may matter more than sheer scale in the years ahead, and much, much more. It’s a narrative that is refreshing and informative, so let’s get to it. Taylor, Nick, thank you so much for joining. Walk us through your background. What brought you to the world of wealth management? Nick, let’s start with you. Nick Hubert: Sure. I think I got my first taste of the industry actually in a sophomore year of college internship, or I interned at Morgan Stanley here in Oregon. I studied finance and accounting at University of Oregon, and so I had this affinity for finance and markets and had that privilege of having that internship. So I had it early on in my career. Ultimately ended up setting my sights on doing investment banking and going that route and did that for a short period of time. Ended up not going very long due to a medical reason, so you don’t have to be that sorry for me. And ultimately started my career in business consulting before pretty quickly realizing that I want to get back to finance, back to investing these things that just felt like core competencies and that thing that you keep coming back to when you’re alone in the middle of the night thinking about stuff, it was always that. Just had this desire to work with smaller units than large corporations, which is great for wealth where you get to work with families and small businesses. And so it was just a natural alignment that took me back full-time to the space in 2016. Jason Diamond: I like the framing it through the size of the unit you’re working with and having more of an impact on the family. Taylor, what about you? Taylor Gentry: I’m a little more circuitous, if you will. Spent a couple of years in investment banking, so you can be sorry for me. Nick and I met in undergrad at the University of Oregon, had the opportunity to work in this investment group together where we were investing a portion of the university’s endowment. And like Nick, interned in wealth management and kind of walked away from it going, “Boy, that’s boring. I don’t really like that.” And so moved to New York, cut my teeth in banking for a couple years and we were working… So an investment bank for context, helping companies raise debt, raise equity, and with mergers and acquisitions, we’re working with huge companies. So the Mattels of the world, the largest toy company in the world. Like Nick, realized, “Hey, I’m going to work with smaller companies that we can get our arms around a little bit better and be more helpful with and have a bigger impact on.” So spent about 10 years with a private equity firm in the western half of the US and we invested in companies in what’s referred to as the lower middle market. So companies doing 50 to 300 million of revenue. And we would invest in those companies, grow those businesses and then look to sell them. Awesome experience, learned a ton, got a bunch of experience around how to invest in companies, how to grow businesses. Then had the opportunity to step into the CFO seat of a couple of different operating companies during that time. It was just a great learning ground, but also to see a whole bunch of different situations. Nick and I have always invested in things together. We’ve worked on things together and we’ve always wanted to work together full time. And a few years ago, the stars really just aligned to say, “Hey, what would it look like to create a differentiated offering in the wealth space where we can blend my background on companies, transactions, how to draw on scale and all those pieces and really marry that with the wealth management piece?” And Nick will get into that further, but it’s just a really unique way to partner with families and companies that are smaller which can have a really high impact experience with those families and really move them through their life journey, if you will. Jason Diamond: Yeah, there’s a lot to unpack there and we’ll get to some of the elements of how you run the business today. First of all, you can’t fool me by using a toy company as your example to make investment banking more interesting. I’m just kidding. Actually, my real takeaway there is you have a skillset that is incredibly relevant in the current wealth management ecosystem, especially in the model you’re currently in. So let’s talk about that a little. Tell us about your current chapter, which is Panoramic Capital Partners. Who do you serve? What types of clients? Give me some perspective on size as well. Nick Hubert: I'm going to take this first. Taylor can do the PE background side and give you a bunch of numbers. I’ll give you the story and see if we can piece it together that way. Jason Diamond: I get the impression you guys use that line a lot. Nick Hubert: Oh, no, that’s the first time. How’d it land? Jason, I spent eight years at our prior firm with our third founding partner, Andrew, and he was at that firm for 30 years. And so we’ve got this core DNA that we’ve always carried of serving high net worth families in a very holistic and deep planning-based capacity, which I think a lot of modern firms say that. And so that’s not necessarily that different, but it is a DNA that carries through. When we got struck with this vision of launching Panoramic and what inspired us to build the firm, it was as, Taylor outlined, around this idea of how do we partner with entrepreneurs and business owners more holistically across their entire entrepreneurial journey, not just around the exit as is so often where the gravity of the conversation sits. And so our firm vision and inspiration was all around that. And since launching in May of 2024, it has been about how do we bring that vision to life with a different business model. And to your point, there’s a bunch to unpack there, but that is ultimately the founding vision of what we are trying to build here overall and what inspires us every day to say, how do we, as Taylor mentioned, bring the combination of skillsets to bear in a way that allows us to be a better partner along the entirety of the journey as opposed to just towards the end when assets traditionally show up, so to speak? So that’s a story from a vision perspective. Taylor, I don’t know what you want to add to that. Taylor Gentry: As Nick outlined, it’s the ability to work with folks throughout the lifecycle. So in private equity, you invest in a company, you work with that management team for three to seven years and then you sell the business and move on to the next project or deal. And really, it’s the deal mechanic that is the value creation. Whereas, with what we are building here, we have the opportunity to really step along the journey with folks when they are in the early phases building what we talk about as the middle phase of allocating, and we’ll talk about this further, and then really the third phase of stewarding capital along the way. And it’s a life cycle or entrepreneurial journey that we’re able to be hand in hand with folks over decades opposed to measured in three to five year spans. Jason Diamond: So it sounds, and you’ve both kind of touched on this now, your different backgrounds, you view as very much a positive because it gives you, Taylor, the more in the weeds analytical perspective. Nick, you’re probably more the storyteller. Do you find that to be a benefit when you’re running your firm every day? And are there instances when it’s a negative? Is there ever a time when you say, Taylor, just maybe more for you, not coming from this world, you don’t speak the same language? Nick Hubert: Do you want me to drop off the call so Taylor can be honest and he can give you the scoop and then he can jump off and I’ll give you the scoop? Taylor Gentry: Jason, we talk about that a lot, honestly. I think it is atypical for someone with my background to step into the wealth space maybe more so. And we leverage that because we have the ability to work with folks on how do you drive value in the company, how do you set the business up for a potential sale exit or transition internally? But this business, historically, we’ve talked about it as almost like two tracks. You have Taylor on the quote unquote business consulting or the business work track and you have Nick on a wealth management track. It’s really not the case. And really, the power is the ability for these two pieces to come together and there isn’t a conversation we have with clients where those two perspectives and backgrounds or contexts aren’t married into one to create really truly holistic advice. And so Nick will probably tell you otherwise, but I haven’t seen an area yet where our two backgrounds has been a negative. It’s actually been immensely positive. And then on top of it, in terms of kind of building out the firm, Nick is more of a traction visionary and I’m more of the traction implementer. What’s amazing about it from our perspective is the partnership we have allows us to, A, recognize that, B, name it, and then C, leverage it in terms of being able to dole out duties and maximize our success together. Jason Diamond: Nick, anything you’d add? Nick Hubert: I think that’s all right. I mean, Jason, your question was from an operational perspective. I think a lot of Taylor’s view is from a client perspective, which is spot on that the overlap of that is really helpful for clients and I think what allows it to be a different experience for them. Internally, operationally, I think that where you could see friction there amongst partners with differences, and I think you do see that, and at the same time, Google was the one who did team research 15 years ago where they put out what you really want, is similarity and vision and differences in skillset when building a team. And so I think we’ve been intentional about that and it’s been really helpful for… Taylor and I functionally met in a quasi-professional setting back in 2011 and developed a friendship quickly, so we’ve got that deep level of friendship that underpins all of it. And same with Andrew and our time working together. So part of it is there’s just such a strength of relationship amongst us that we give space for each other’s differences and look for those as assets as opposed to negatives, but in some sense, beauty in the eye of the beholder as is the case with anything. Jason Diamond: Yep. I appreciate you adding that context. I’ll be honest that when I first encountered your firm, my reaction was your core value prop of serving business owners is not all that differentiated. And then I learned more about the way in which you serve business owners. Can you talk about that? Because a lot of advisors in general, but then I think more specifically, a lot of RIAs would say, “We service primarily business owners.” Tell me how do you do it in a way that’s different and meaningful? Nick Hubert: I’ll take a first stab at that and then Taylor can maybe add on with specific stories. The wealth space is an awesome business and it’s a place where it’s very difficult to differentiate. And so we think a lot about that through the lens of how do we grow this business well for the long period of time to create opportunities for clients and employees. And so we spent a lot of time thinking about that, not only for the sake of differentiation, but also how do we actually just continue to add value to clients? Because if we add value in a different way, growth will take care of itself. I’d say one way of cutting that is we revisit the mission is through this idea of, okay, if I want to be a partner along the journey, it’s about more than a single transaction, more than a single exit, whatever that might be, or a series of transactions as wealth is often created over a series of transactions. It’s this idea of how do we focus on wealth creation and driving business value as the engine of wealth creation for entrepreneurs and what we call personal significance, which is the life of the entrepreneur. And so there’s a next click down framing of our framework that we work through that lens. I think the most important piece for us has been how do we build a business model that actually brings that to life and that’s the trick because we can say that, and if we basically still just operate out of an AUM-based or an asset advisory fee-based business, the reality is my incentive is still towards getting assets out of the entrepreneurial environment, so to speak, into a place that I can manage them, which may or may not be the best thing for the entrepreneur based on where they are at. And so our current work continues to be around how do we build that business model. So layering in different ways of engaging, whether it’s a retainer fee or some other way of engaging so we can start earlier when assets aren’t there and actually encourage the entrepreneur, “No, keep reinvesting in your business. It’s your highest rate of return right now and it’s where the investment needs to go.” I don’t want to have a conflict in giving that advice. And so I think step two here has been building that business model from an actual engagement perspective to enable us to enact the vision. And then I think the third piece is how do we then build tools that are different than just evaluating pre-exit planning, and as is so often, the toolkit, but actually saying, okay, what are the value drivers of a business? And this is probably where Taylor has a lot more to add because it’s 101 of the PE model, but how do we take the mission and vision of an entrepreneur, what we call north stars, translate those into value drivers, ensure those tie to strategic initiatives in the business, ensure it ties to reporting, and ultimately, how capital is allocated between the business and other investments? So then that’s our toolkit that we continue to build out to deploy the mission through our business model with tools that back it up. So that’s how we frame it right now. Taylor, we can share stories about how that’s come to fruition to create different outcomes. Jason Diamond: Taylor, I’d love to hear that. Let me just add maybe my understanding, because this is what helped me, I think, to really understand how you defer, and Nick and Taylor, correct me if I’m wrong, it sounds like the typical advisor thinks about an entrepreneur, a business owner relationship as the next liquidity event in most cases. And you take the viewpoint that it’s a journey, in some instances, 30 years in the making. It’s not even about liquidity event might come that’s beside the point. Is that a fair summary? Taylor Gentry: Yeah. We talk about it as a growing business is a healthy business, a business that is creating incremental value and adding to the multiple in terms of how the business is valued in the marketplace is a healthy business. And so whether you are going to sell that business or retain that business into perpetuity, let’s make a really valuable business and grow a very healthy business. And that’s what we do with clients. Nick laid out the north star framework. And so how do we actually go about engaging with folks on a practical level? It does start with the north star framework. It’s got five steps to it as Nick outlined in terms of defining the north star, where we’re going, what we’re trying to do and that’s across those three pillars, personal significance, personal wealth and business value. And that personal significance has to be held at that same level. Otherwise, we find folks that are mid 50s, their business is crazy valuable, they’ve got a lot of dollars, but their family life isn’t where they want it to be because they didn’t take care of that along the way. So we lay out a place map that says, “Hey, these are the north stars that we are aligning on and coming back to every month when we work with these owners.” We then push that into, okay, what are we trying to do on the business side of the equation? Let’s lay out what is going to drive the value of the business from a multiple and enterprise value perspective. We push that into a set of strategic initiatives that is tactical, who owns what, when’s it getting done, and are we red, yellow or green on it? We then build out the performance reporting package with folks. And so that is a monthly reporting package that says what happened last month and what operational data are we looking at to be able to improve the business month over month and get a good feedback loop going into the company. And then the last piece is around capital allocation that Nick mentioned where if the business generates a million dollars, where’s that capital going? I think there’s a lot in there and it’s really deep, but if you zoom all the way back out, it’s take a private equity style playbook where private equity firms come and invest in a company. And what do they do after close? They put in place good financial reporting, good operational reporting, and then hold the team accountable to that reporting and those results on a monthly, quarterly, and annual basis. And so this is not rocket science or something that’s never been seen before. It’s just most business owners that have never experienced this private equity world don’t have access to it and don’t know how to go about doing it. It’s a relatively long process to get that installed with companies and with teams to really dig in and understand it, but it’s building out those packages to be able to say, “Okay, what happened last month? What changes do we need to make and what are we doing from a initiative perspective to drive the business forward?” So to Nick’s point, it was previously, this was all about liquidity planning or from a wealth management perspective, it’s about the exit. This is about how do we make a more valuable business along the way, and that’s going to be good for the entrepreneur as they move through the journey. Nick Hubert: When we were around the dinner table, the proverbial dinner table creating the vision of this firm, it was around this idea of the silver tsunami and everything that everybody reads in the headlines of this massive wave of transition, this generational transition of business ownership that we could help facilitate. So we launched with that thesis in some sense. In addition to this broader journey perspective, we have gotten to this place by following the market and listening to what entrepreneurs actually want through the big unlock was honestly in a deal process with one of our clients where we realized, “This is a great deal. This person’s going to put a ton of money in their pockets, secure their future,” and it’s completely the wrong outcome for the entrepreneur because it’s thinking all about the deal, not thinking about what this person didn’t want was an exit. They wanted a different relationship with their business, and that required, what do you actually want out of life, that personal significance piece? And it required, “Hey, if we can actually create a layer of team members and reporting that allows you to manage this like a board chair would do as opposed to a highly engaged CEO. That’s actually what you want. You don’t want out of this business. You want to still have this be a huge rock in your life.” And so we’ve ran through that door, said no to the deal with them and have been building the infrastructure around this, and that was the unlock and aha moment for us. There’s something bigger here and that’s what then inspired, in some sense, the broader build out of the toolkit, but I think puts more meat on the bone of actually saying no to a deal, which is not the classic wealth manager outcome to get to a way better outcome for the client and is ultimately still an awesome client for us as a firm and somebody that we can go build with for the next 20 years. I think just telling it through the lens of a story that’s different than what’s normal, so to speak, is a way to frame that up. Jason Diamond: It’s such a hyper focus on a fairly long-term and honestly nebulous potential outcome. You don’t have certainty. That, I think, is why most advisors would prefer the near-term liquidity. I mean, it’s not a secret, right? You can bill on assets, firms are incentivizing it and it’s a pretty direct recipe to net new asset growth, but it’s certainly a refreshing point of view. It resonates with me. I’m wondering if it’s resonated with clients and prospects. I guess what I’m asking is, do they feel that this is something different than the typical wealth management experience for this type of client? Nick Hubert: Yeah, Taylor, tell that story of the guy who said, “I’ve had this, but I felt alone.” I think that story of partnership, you tell pretty well. Taylor Gentry: Yeah. Jason, it was actually that same client, he had a investment banker, a wealth manager, attorney, and a CPA. CPA said, “The deal’s terrible, you shouldn’t do the deal.” Investment bankers obviously incentivized to do the deal. And so he’s saying, “You should do the deal.” That’s how he gets paid. He had a wealth manager who was silent and he had an attorney who just pushing paperwork. Jason Diamond: It’s like the start of a bad joke. Taylor Gentry: Yeah. No, seriously, it’s pretty remarkable. It’s like this guy did what he was supposed to do. He put the team of resources around himself. He got professionals in the seat. It’s that no one could connect the dots of all four of those people because they have the seat of those four people. And so it’s really resonated because there’s an ability to see a bigger picture and connect these dots and say, “Okay, this investment banker is saying X because of A, B and C.” And the CPA is saying it’s a bad deal and that it’s not a market deal. It’s 100% a market deal. This deal is right down the fairway in terms of what the market should value your company at and they just don’t understand how the transaction mechanics should work. And so it’s worked really well from that perspective of being able to be the quarterback or centralized point or personal CFO for folks in understanding where interests lie and also being able to think about what they are pursuing in a bit of a different lens. I think the second piece on that is where does it resonate for folks? I think that there is a gap in the marketplace that we are still working to close, and that gap is that business owners do not know what this monthly reporting package looks like. They do not know what really good reporting on their business looks like in terms of they have always run their… You’ve got a business owner. They’ve run their business for 10 or 20 years. They have a pulse on the business from their gut feel. That does not mean that the business has been optimized, is ready to go to the next level or is ready for a transaction and go through a transaction because they have not done the work on the backend to understand the moving pieces of the business at a granular level. This recording package, we oftentimes get this confusion around, well, I’ve got a temporary CFO or a controller or X, Y, Z. That is very different than what we’re talking about. Well, that is all accounting, close the books, have clean numbers. What we’re talking about is how do I marry operational data in the business, number of units ships, number of jobs completed, time on job, operational data to the financials in the business so I can then go make adjustments operationally on how to improve the business and continue taking steps forward. Jason Diamond: It’s very clear. Nick, anything you’d want to add to that? Nick Hubert: I’d say it’s easy to still cut that from a deal lens and say, look, when an investment partner comes to evaluate a business to sit in their seat for a moment, they’re going to look at the replicability of what that leader has done without that leader still in the seat. And if so many businesses are still reliant on that person and this gets talked about as processes, reporting systems, that ultimately results in a discount to the value of the business because although it can be viewed… For the leader, it’s like, it’s that control thing that entrepreneurs deal with. It’s what made them good. It’s what got you there. And so that transition is really hard. And that’s important from a deal lens because that does a direct impact to value. And to widen out the scope beyond the deal and to think about the entrepreneur’s life, this goes back to the dynamic that a lot of times entrepreneurs look for the exits because they’ve built something that it’s now owning them and what they’ve built is not resulting in the life that they want. And so how can we use this system to actually change that relationship, as I mentioned earlier, with the business so that they can run it more like an executive might and get out of the knife fight, so to speak, that often is how this can feel for a lot of folks, even for pretty large businesses. It can just feel like you’re a firefighter, you’re in a knife fight, whatever you want to use for that terminology. I think it’s as much about creating a different life outcome and different relationship and owning and leading a business as it is in driving deal value. Jason Diamond: Taylor, maybe I’ll ask this of you. Forgive the question, but private equity, I think in our space, has a little bit of a negative stigma at the moment. I don’t think that’s true across the board. I think people appreciate generally the need for capital and there are certainly benefits of private equity. But I’ll say as a whole, advisors are, let’s say, suspicious of private equity. You ever get that pushback? Does anybody ever view your experience or the way you position the story as a negative? Taylor Gentry: I think most people that we talk to don’t know what private equity is. They may have seen it in the headlines. They may have some sort of connotation around it. They won’t come out and say that they don’t like it. They don’t know why they don’t like it. The average American business owner, they don’t know what it is or what it means. So yes, you do have to fight that because of the headline piece around private equity, bad actor ABC, and that’s what gets the headlines. I think what private equity is really good at is taking a business that is not optimized or not running on systems and processes that it can run on. Again, it's not rocket science is not crazy hard. It’s just the private equity world has created ways to install systems and process that improve the value of the business by way of providing visibility to financials and operations in a way that the owner previously didn’t have. And so for us, we view it not by any means as the end all be all or the answer. There are clients we’ve worked with that have taken private equity capital and grown successfully, executed on some acquisitions and then exited again. There are clients that have evaluated those transactions and said, “Hey, not for me.” We are actually fairly agnostic to it. What we really spend a lot of our time on is what are we solving for? What’s the end game? How do we use this private equity transaction to get to where we’re trying to go and is it what we want at the end of the day? Because the reality is, if you’re going to stay on and run that business with private equity investment in, there’s a higher expectation on what you need to do Monday morning than when you owned it yourself and it was a little bit of your personal piggy bank too. Jason Diamond: I love it because you bring it back to the north star concept. Taylor Gentry: Yes, that’s exactly right. It’s what are we solving for and what game are we playing to be able to get to where we ultimately want to go? And for, as Nick mentioned that client that turned down the deal, it was a private equity investment. We got very clear with that, “Hey, here are going to be the expectations. You will have a monthly financial reporting call. You’re going to have quarterly board meetings.” These are things that need to happen in this business to be able to upgrade the management and cadence in this company. You don’t have to do it all tomorrow, but that is how you make a more valuable company, is installing some of these systems, process and cadence. And so we’re working with him now on doing that, just in a private context instead of in the private equity backed environment. Nick Hubert: I think there are three things embedded in this. I’d say number one, to Taylor’s point, this is a massive black box, in some ways by design. Wall Street’s had not a great reputation for a very long time of putting things behind the paywall, so to speak. And so we think a lot about our job as empowerment and education. Jason Diamond: Education, yep. Nick Hubert: Yeah. And so part of it is just, number one, how do we just demystify this thing and name things and take away the go to or bad? Because it can be that, but it should not be that from a core basis. That’s number one. Number two, a lot of entrepreneurs feel like they cannot get access to this ability to professionalize or level up or whatever these things are without bringing on that investment partner. And so part of our motivation is how do we actually bring this skillset in without needing to bring on an investment partner because oftentimes, that investment partner comes when you’re done, and so you don’t actually get to experience it. That’s number two. Number three is, Jason, part of your point earlier was like there’s still a trap here of potentially being able to get motivated primarily by the exit. And so again, that gets back to our business model, making sure our price Racing is right, all that good stuff. And it’s also the reality that a lot of businesses, if you just look at a very broad scope of American businesses, a lot of them don’t have value in the marketplace in a massively material way and/or won’t exit in a traditional way. And so the wealth creation journey then becomes much more of a conversation of, how do we manage the balance between investing in the company and distributing out of the company to invest elsewhere because we should actually be creating investment assets along the way because when you get to the exit, there’s no better power position at the moment of exit than already having financial security to some degree and giving you choice in the right deal, not the highest and best deal because you need to fill the piggy bank for retirement. Jason Diamond: I just want to be sure to ask because you did mention a couple times your pricing structure. How have you set it up so that you can be more agnostic about this as opposed to the typical… You want to talk about it for a minute? Nick Hubert: As it’s structured now, it starts with a retainer earlier on where we are working… As Taylor mentioned, we are going deep in the operational build of the business. We will do that on a monthly retainer. We’re engaging consistently. As assets get built up and if assets get built up, we start to chew that retainer down as assets go up. I think what we are ideally trying to figure out, and still honestly have not figured out yet, is how do we get to parity so that we don’t create an… I want to be able to work agnostically with a client to say- Jason Diamond: Yeah, I love it. Nick Hubert: … regardless of how I’m engaging with you, that’s the goal. So I’d say we haven’t cracked the code on exactly what that is yet, but mechanically, we’ve got the levers to pull to say how we price and move that retainer down is basically allowing to keep it at par, so to speak, for the client and allowing us to say, “I’m here to engage in making the best wealth creation outcome for you along the way, whether that’s investing in the business or investing outside the business.” Jason Diamond: I think that’s the right recipe. I agree. The levers can be fine-tuned, but to me, that’s the model you want to create where you can credibly look your prospects and clients in the eyes and tell them, “Our job is to serve you in the best way… We’re sitting on the same side of the table as you.” I want to turn this inward for a second. The home cooking concept. M&A, within the RIA independent space, is obviously a hot topic. Have you thought about it? Do you think it’s a critical part of a potential growth trajectory of a healthy, independent firm? I’m curious your perspective. I feel you, Taylor in particular, probably have a unique lens on this coming from the world you came from. Taylor Gentry: Yeah, Jason, I think if Nick and I wanted to put as much money as we possibly could in our pockets as fast as humanly possible. It’s a pretty easy recipe. It’s go get some private equity capital backer, roll up a few RIAs, get to a few billion of AUM and then sell it to the next private equity firm or roll it to the next private equity firm, do that a few times. We’d all make plenty of money and go on our way. We’ve been really intentional on this front, and again, I talk about this is what we want to do for the next 30 plus years. And really being intentional around building a business that has that enduring nature to it, decided to take private equity capital on, you are on a shot clock to some degree. Yes, you’re trying to build a best business, all of those pieces. You get cadence. You get capital. There’s a ton of value there, but you are on a shot clock that is not a shot clock we’re trying to get on at this stage. I’d say we opportunistically are looking at acquisitions. So we think about it, and Nick and I talk about it all the time, how much of our time should we be spending on acquisitions? And we think of it as 80/20 or even 90/10, 80% or 90% organic growth-focused, 10 to 20% acquisitions-focused. And so we’re actively evaluating those consistently and see deals on a monthly basis that we look at and evaluate, but it’s less of the focus today than it could be down the road. Jason Diamond: And Nick, do you think of that when you guys talk? Do you guys call that your true north? Do you think the same way you coach your clients and prospects to say, “For right now, it wouldn’t be the right move for us to take private equity capital and to do this acquisition rollup strategy because A, B and C are more important for us”? Nick Hubert: Yes. I think if we take our life north star for Taylor. I’m speaking for Taylor, but we’re close and so we share this of… To Taylor’s point, the life outcome of scaling that quickly with that type of capital backing is likely to create a life that I don’t actually want that’s not good for me, not good for my family, and honestly, not good for our clients at this point. And so that overrides in this case, even though the wealth, north star might say, “Hey, absolutely do that.” At some point something has to win. And so that is true. At the business side, as the north star is motivated by this mission of the entire entrepreneur journey, the worst thing I could do is shortcut my ability to be on that journey for a long period of time. One of our friends in this space says, “The best thing I can do for my clients is still be in the seat 30 years from now because I’ve lived a good life that enables that.” And I think that’s spot on for us, is everything, it’s so easy in today’s world to be consumed by short-termism and we are intentional in ensuring that we don’t succumb to that. While still recognizing to your point, I mean, you’re in this all day, Jason, right? There’s a massive opportunity in front of us to be thoughtful about how acquisitions fit into this. And I think we want to be open to that in a way that ensures we just don’t lose the core of the goodness of what we’re trying to build. Jason Diamond: I think that’s the right answer. The only wrong answer in my mind is we’re not open to this or we’re closed to it. To not at least be opportunistically aware of the dynamics in the market, I think is naive. But also, I’ll be honest, Nick, when I think about the concept of the north star, I have a hard time imagining, because we use a similar concept when we counsel advisors. What is your true north or your north star and your best business life, whatever you want to call it? To me, it does include absolutely the personal piece. I think it’s hard to define it only on the economic verticals because, I mean, I think about this for a transitioning advisor. Almost never is the conversation about crunch the spreadsheet and get us the biggest check possible. It’s, yeah, sure, transition capital is important, but it’s let’s also, we want a better work life and we want freedom to market and blah, blah, blah. To me, I think it’s a completely fair way. You two are looking at it at least for now and I assume you reserve the right to revise that opinion down the line. Nick Hubert: I think acquiring for size and scale is as often the headline is, yeah, we’re not into that at this point because I think… And yet, hey, if the right acquisition with the right people came along in that, we’d be extremely excited and would move very quickly to execute on that. So it’s a little bit of a both hand. Taylor Gentry: Yeah. Jason, I think it goes without saying, but my background on having done a bunch of transactions of businesses like this, it’s a natural fit for us to have this as a lever. And so we are looking at deals. We just haven’t prioritized it as the top priority. Jason Diamond: I think also where you are, 2024 was the launch of the business. It’s pretty common to see, all right, let’s nail this, let’s get our feet under us, client service model and then we’ll start to think about that down the line. A couple other things I want to ask you about running an independent firm. This is a pretty glowingly positive review, I think, of your ability to service clients, your ability to grow and to build and run the business that you want. Has there been anything negative that you haven’t enjoyed about running and operating this business, other than working with each other, of course? Nick Hubert: No, I was going to say, I’m like, can we get Taylor off the call again? Taylor Gentry: Jason, maybe I’ll take a first cut at it. I think for both Nick and I, it’s just the administrative components of running an independent business that we don’t enjoy candidly. I don’t think many people would. That said, you come full circle and it is a pretty glowingly positive review of running an independent business because we get to run it in the way that we see fit. And oh, by the way, we use the same things that we use with our clients. So the value drivers we’ve talked about, we have a value drivers worksheet. We refresh it every six months. Nick, Andrew, and I get together every six months and we’re 18 months into this thing and we’ve already got this cadence and system to it, if you will. So I personally really enjoy the running the business piece of it from a macro perspective. Yeah, I’m responsible for running our fee billing and running the math on all that and getting that done, for example. Jason Diamond: I think that’s actually a very thoughtful answer. And I appreciate you saying I enjoy running… I feel the same way, by the way. There’s some elements of running a business that I think are immensely fun. I think it gets painted with this brush of, “Ugh, running the business is the hassle and I want to work in the business.” Agreed, nobody likes invoicing and accounts receivable for the most part, but Nick, what are your thoughts on this? Nick Hubert: Yeah, I think mine is different a little bit coming from a different background where it’s easier for me to sit with the rose-colored glasses of the joy of the freedom that we have in this model. At the same time, when I’m counseling folks who are talking with folks or mentoring folks, younger people who are thinking about, “Okay, I want to go start my own thing,” I’m like, “Hey, it’s like I’m the same way. I want to look in the mirror and think I’m the boss or I’m one of the bosses and we get to go build this.” Then the reality is, at the end of the day, if there was something that you didn’t want to do that had to get done and you didn’t do it, you got to look in the mirror and be like, “Well, you’re the boss, you didn’t do it.” It’s the both sides of the coin that I think a positive, negative cut is one way to look at that because it can feel that way sometimes. And the reality is every job has 20 to 30% of it that you just don’t enjoy doing, and that’s totally true. Jason Diamond: It’s why they call it work. That’s why they pay you. Nick Hubert: They’d be pretty quick to point out that I’m the one of the partnership group that they’re going to have to chase for a smaller administrative item because, yeah, I honestly, just similarly speaking, don’t enjoy that. I want to go talk to clients. I want to go focus on building what we’re building. In finance speaks, it is a higher beta to just the all encompassing realities of running a business that is really hard to underscore without being in the seat. And yeah, there’s definitely 20 to 30% of that I would love to wave a magic wand and say, I don’t have to do anymore. Jason Diamond: Yeah, I appreciate that. Nick Hubert: You can’t have one without the other. It’s both sides. Jason Diamond: I think it’s getting easier and I think it’s getting more offloadable and some of it probably gets more… In some ways, more offloadable as you scale, but then you get a new set of problems, probably two, because you’re dealing with bigger… It’s a never ending. I think most business owners would agree with that. And you said it well, you take the good with the bad and overwhelmingly, most people we speak with in the independent space feel as you do, which is, are there things I would prefer to offload or that I would prefer not to do? Of course, but that’s almost just the price you pay for the freedom and for doing all the things you want to do. Two more questions that I want to be sure to ask about where this has been a great episode. One is AI. Need to know your thoughts. Is this coming for our jobs? Do you think your firm is positioned to capture either asset flows or also just to leverage this technology and use it to serve clients better? Just give me your thoughts. Nick Hubert: I think, in some sense, it would be irresponsible as people this early in our entrepreneurial journey and thinking about how do we optimize what we do for clients to not be engaging with AI in some way, shape or form, at least in an evaluative posture. So we are actively, in a bunch of different ways, whether it’s buy it off the shelf or build it, continuing to find ways to think about, not only how do we drive efficiency, because there’s an obvious surface level dynamic of if I can save time and spend more time with clients, that is a go to thing objectively. And there’s this deeper dynamic of if it can amplify what… Actually, back to your prior question, if it can amplify what I’m best at and enjoy and reduce what I don’t enjoy, that’s a massive win. And I think we’re on the surface of seeing that. That’s the opportunity we are motivated by that and pursuing that. And at the same time, I would say an operational principle that really is important to us, and you can almost call it a north star within the business is client security can never be put at risk for the sake of our own growth, our own efficiency, or anything else. There’s, I think, still a question mark as to how we think about trusting this. And so we are very cautious as we think about we will never try to move so quickly on any technology, whether it’s AI or otherwise that we risk our clients in some way, shape or form, because the reality is we are also in a context where AI is, when pulled, one of the least popular things happening in the world today for the average American. And so there’s no kudos here for being a leader. Jason Diamond: I totally agree. The first mover advantage here is slim to none. Nick Hubert: Yeah, you don’t want to be the one sticking your neck out on this in our industry. And yet there still objectively has a potential to be better for the clients. Navigating that I think is messy. Taylor Gentry: I think the only thing I’d add, which is pretty short, is the use of these tools has the ability to create a better deliverable for clients on a more consistent basis. And marrying that with exactly what Nick just outlined around the risk is really the magic piece here. And so I think, to the extent we can get it implemented effectively with the security, but also with, this is going to result in a lot better outcome for clients across the board, that’s a pretty attractive objective to go after and it’s pretty exciting to be in the industry with that now on the forefront in terms of ability to improve that experience over time. Jason Diamond: Yeah. No, that’s a good color to add. I want to end here with a potential HR violation, but you’ll forgive me. I’m not going to ask about age, but you are clearly both relatively young advisors. And this is a hot button issue in our industry, the idea that there are not a lot of talented, young next gen advisors at a time when a lot of gen one or older advisors are retiring out of the business. So what would you say… I think one of you made the comment earlier, it’s not necessarily the coolest industry to go into at 23 years old right out of school. I think more commonly people go into sales and trading, investment banking or some of the other finance verticals. What would you say to younger folks interested in wealth? And maybe I’d ask also, do you have any thoughts on how we solve this next gen talent crisis? And if you’re both secretly 90 years old, you can just do it. Taylor Gentry: You talking my internal age or my actual age? Jason Diamond: Why don’t you go first? Nick Hubert: Yeah, go ahead, Taylor. Taylor Gentry: I think there’s two threads here. The first is it’s not a sexy industry to go into and not as sexy as an investment banking, private equity shtick, if you will. I think from my perspective, it’s really important what you’re working on. The ability to be in a firm like what we are building with the diversity of work that is available is a little bit like the world’s your oyster and we’re designing it with that in mind. For Nick and I, the ability to work on many different situations throughout the day and throughout the week is actually why this business is so attractive and interesting and why we want to do it for 30 years. And so we’re building with that context. And so, in some ways, it’s almost like a plug for younger advisors, the ability to work in a firm like what we’re building where you’ve got this diversity of work that is not just trading stocks and bonds or just spreadsheeting or just financial planning. This is a much broader expression and experience than what I would call “traditional” wealth management. So I think that’s the key on that front. Then, on the talent development side of the equation, if you will, this AI thing is going to be a big question mark. And what I mean by that is there is significant training that will be required in, call it traditional wealth management or the firm we’re building with regard to folks’ ability to actually learn when you can plug it into AI and get an answer that you don’t have to critically question or think through. And so there’s going to be a significant learning curve for folks that we’re going to have to continue to train and educate on in order to produce talent that can be long-term sustainable and beneficial for clients more writ large. Jason Diamond: Nick. Nick Hubert: Well, first and foremost, we haven’t given our third partner enough here of time. I think we have a tremendous benefit of having a multi-generational team at the partnership level where he’s in his mid to late 50s and can bring that additional experience to bear and as is necessary, and as is important because investing is an experienced business and a lot of clients want that. And so the power of that matters. I think that actually speaks to firms being willing to think of partnership at that level that partnership is not reserved for just once you’ve been there for a long time. So I think it’s getting at like, how do you share ownership earlier, do it in a way that is actually giving people a stake in the outcome and allowing that elevation to happen. I think that’s number one. Number two, honestly, the existence of people like you and your team and that your family has built over the years, Jason, is awesome. And because of the ability for you to help people navigate and see how easy it is to actually run this business and build this business in some sense… And that’s in the broader spectrum of having seen. We work with so many different types of companies. We sometimes say our business is so much easier to run and it has come so far with technology and with people like you who are providers to us to allow it to be easier for us so to speak. That’s a big deal. I think that should be talked about more that there is a massive… What that allows is more time to, as Taylor mentioned, build what you actually want because you can outsource the compliance piece in a major way that allows you to not spend as much time on that as you used to. So I don’t think that gets talked about enough. And I think if you just zoom out and view this in the perspective of post-2020, there was this massive movement of entrepreneurship through acquisitions and people looking at this idea of how do I get the life I want by way of not having to be on a two-year clock to go to the next job to the next job. Have something that I can have a long-term impact on where I get to build something and have employees. This is the perfect space for that because it’s such an awesome business where you get to work so intimately with people and clients and their life outcomes. They’re, again, relatively speaking, easier businesses to run relative to what’s out there. I’m just baffled by the fact that it is not seen a larger wave of younger people coming out of these more “traditional” paths and seeing this as an awesome place when they’re willing to go buy an HVAC company. This is so much easier than that. So honestly, I think

Michael Saylor's "never sell" era is OVER. Strategy sold 3,588 BTC (~$216M) at a 20% loss to cover $1.8B in dividend obligations — and Canadian pensions are holding close to $1B of MSTR stock. On June 29, Strategy's board adopted its "Digital Credit Capital Framework": $1B stock buybacks, a 12% STRC dividend, and a $1.25B "BTC Monetization Program" authorizing Bitcoin sales "when strategic." CEO Phong Le calls it "evolving from one-way capital issuance to active capital management." The mNAV premium that financed five years of buying has collapsed from 2.66x to around 1x — and one in three Bitcoin treasury companies now trades below the value of its coins. In this episode of the Canadian Bitcoiners Podcast:- Strategy's pivot: the mNAV death spiral, coins sold at a 20% realized loss, insider selling, and JPMorgan's $2.8B-$11.6B index-exclusion warning- The Canada connection: CPPIB, AIMCo, National Bank, RBC and HOOPP hold ~$1B of MSTR — your pension bought the wrapper trade- A quantum-proof recovery tool that works for everyone except Satoshi's 1.1M BTC- OkoBot: malware that fakes your Ledger/Trezor recovery screen- Global hashrate is shrinking — but Pakistan is up 733%- CleanSpark signs a $6.6B, 20-year AI data center lease- Canada bans crypto political donations under Bill C-25- Clown World North: Stan Cho's $16,203 hotel bill, Canada Post's $30.8M in bonuses against a $1.57B loss, and $159,800 in flight catering- The BIS confirms Canada's housing crash is the biggest on record as 56,400 Canadians leave in a year The leverage cycle is unwinding — the conviction cohort isn't. Long-term holders just hit a record 14.85M BTC. ETF outflows and treasury-company stress are paper Bitcoin changing hands; the base layer doesn't care. Hold your own keys. — Canadian Bitcoiners Podcast- Website: https://canadianbitcoiners.com- X: @CanadianBTCPod- Subscribe & turn on notifications ————————————————————————————————SPONSORS

Michael Saylor's "never sell" era is OVER. Strategy sold 3,588 BTC (~$216M) at a 20% loss to cover $1.8B in dividend obligations — and Canadian pensions are holding close to $1B of MSTR stock. On June 29, Strategy's board adopted its "Digital Credit Capital Framework": $1B stock buybacks, a 12% STRC dividend, and a $1.25B "BTC Monetization Program" authorizing Bitcoin sales "when strategic." CEO Phong Le calls it "evolving from one-way capital issuance to active capital management." The mNAV premium that financed five years of buying has collapsed from 2.66x to around 1x — and one in three Bitcoin treasury companies now trades below the value of its coins. In this episode of the Canadian Bitcoiners Podcast:- Strategy's pivot: the mNAV death spiral, coins sold at a 20% realized loss, insider selling, and JPMorgan's $2.8B-$11.6B index-exclusion warning- The Canada connection: CPPIB, AIMCo, National Bank, RBC and HOOPP hold ~$1B of MSTR — your pension bought the wrapper trade- A quantum-proof recovery tool that works for everyone except Satoshi's 1.1M BTC- OkoBot: malware that fakes your Ledger/Trezor recovery screen- Global hashrate is shrinking — but Pakistan is up 733%- CleanSpark signs a $6.6B, 20-year AI data center lease- Canada bans crypto political donations under Bill C-25- Clown World North: Stan Cho's $16,203 hotel bill, Canada Post's $30.8M in bonuses against a $1.57B loss, and $159,800 in flight catering- The BIS confirms Canada's housing crash is the biggest on record as 56,400 Canadians leave in a year The leverage cycle is unwinding — the conviction cohort isn't. Long-term holders just hit a record 14.85M BTC. ETF outflows and treasury-company stress are paper Bitcoin changing hands; the base layer doesn't care. Hold your own keys. — Canadian Bitcoiners Podcast- Website: https://canadianbitcoiners.com- X: @CanadianBTCPod- Subscribe & turn on notifications ————————————————————————————————SPONSORS

1. A Noble Desire 2. A Bold Approach 3. A Sure Faith Scripture: Ruth 3 Psalters" 108A, 66B, 91A, 25B, 72G

Michael Saylor siempre defendió una idea: comprar Bitcoin y no vender jamás. Pero Strategy, antes MicroStrategy, acaba de abrir una grieta enorme en ese relato: la compañía ya ha vendido BTC para pagar dividendos de sus valores preferentes y además tiene autorizado un programa para monetizar hasta $1.25B en Bitcoin. En este directo analizamos qué está pasando realmente con Saylor, MSTR y Bitcoin: - Por qué Strategy vendió Bitcoin. - Qué dice exactamente el filing 8-K. - Cómo funciona la maquinaria financiera de MicroStrategy. - Por qué Bitcoin no genera yield ni dividendos. - De dónde sale entonces el dinero para pagar a los inversores. - Qué papel juegan las preferentes, la deuda y la reserva en dólares. - Si esto es una genialidad financiera o una bomba de relojería. - Por qué recuerda, salvando distancias, al escándalo de las preferentes en España. No se trata de atacar Bitcoin de forma simplona. La pregunta seria es otra: ¿qué ocurre cuando construyes deuda, dividendos y promesas de rentabilidad encima de un activo que no genera caja?✅SIN CENSURA en SUBSTACK: https://substack.com/@magnatesladrillo✅¿Necesitas un PSI (Personal Shopper Inmobiliario) para acompañarte a invertir en bienes raíces en la Com.Madrid?: magnatesladrillo@gmail.com✅Si vas en serio «La Biblia del Magnate del Ladrillo» está AQUÍ✅

Saylor authorized $1.25B in Bitcoin sales. 514K full-time jobs vanished. Trump made $2.2B selling access. 250 years later, we need another revolution.• This episode is sponsored by Odoo. Sign up for free at https://www.odoo.com/r/peterThe June jobs report came in at 57,000 — half of the lowest estimate — with May revised down from 172,000 to 129,000. Full-time employment collapsed by 514,000 in a single month, bringing total full-time job losses since Trump took office to 2.24 million. The labor force participation rate dropped to 61.5% as 700,000 workers simply gave up, and manufacturing has lost 73,000 jobs despite Trump's tariff promises.Michael Saylor officially surrendered the "never sell Bitcoin" thesis, establishing a "Bitcoin monetization fund" authorizing up to $1.25 billion in Bitcoin sales to maintain dollar reserves, buy back common stock, and repurchase Stretch preferreds trading at $87.80. He raised the Stretch dividend to 12% with mandatory increases ahead. Peter Schiff argues this transforms Strategy from the market's biggest buyer into its biggest seller, removing the bid that underpinned Bitcoin's price. Trump's financial disclosure revealed $2.2 billion in income — $1.4 billion from meme coins and tokens alone — while Mar-a-Lago membership hit $1 million and the Executive Club charges $500,000 for a restaurant seat next to cabinet members. Peter closes with a 250th Independence Day reflection, arguing Americans today face more tyranny than the colonists ever did under King George, and that the revolution we need now must happen at the ballot box.Chapters:00:00 Holiday Intro and July 4th Tease00:37 Jobs Report Shock and Revisions03:43 Full-Time Jobs Slide and Tariff Reality07:29 Fed Rate Hike Theater and Gold Bounce16:28 Bitcoin Strategy Turns Seller29:57 Meme Coins as Bribes31:09 Paying for Trump Access34:26 Debt Gimmicks and Deficits35:38 Independence Day and Founding Ideals45:45 Modern Tyranny and Peaceful RevolutionFollow @peterschiffX: https://twitter.com/peterschiffInstagram: https://instagram.com/peterschiffTikTok: https://tiktok.com/@peterschiffofficialFacebook: https://facebook.com/peterschiffFree Reports & Market Updates: https://www.europac.comBook Store: https://schiffradio.com/booksSign up for Peter's most valuable insights at https://schiffsovereign.comSchiff Gold News: https://www.schiffgold.com/news#PeterSchiffShow #IndependenceDay #BitcoinCrashOur Sponsors:* Check out Chilipad and use my code GOLD for a great deal: https://sleep.me* Check out Fast Growing Trees and use my code GOLD for a great deal: https://www.fast-growing-trees.com* Check out Plaud AI and use my code GOLD for a great deal: https://plaud.ai* Check out Quince and use my code quince.com/gold for a great deal: https://www.quince.com* Check out TruDiagnostic and use my code GOLD20 for a great deal: https://www.trudiagnostic.comPrivacy & Opt-Out: https://redcircle.com/privacy

Pat Casey was the first person besides founder Fred Luddy to write code at ServiceNow back in 2005, when it was called Glide and lived above a friend's restaurant. Twenty years later, he's CTO of a company where 85% of the Fortune 500 are customers, and until recently ran all of engineering: 10,000 people, 7,000 of them writing code. Almost nobody survives the journey from first engineer to public-company CTO. Pat did. Tobi and Pat dig into how ServiceNow actually works under the hood: a metadata processing engine running 90,000 single-tenant databases and over 25 billion queries an hour, why they bought a 15-person German database company and turned it into RaptorDB, and why tearing apart a 20-year-old monolith is harder than every senior engineer thinks. Then the conversation turns to AI. Pat bought 7,000 Windsurf licenses and measured a real, but unglamorous, 15% productivity bump, with a small subset of engineers going 5–6x while most barely changed. His thesis: AI coding is like playing five chessboards at once, and it's reshuffling the deck on who the top engineers will be. On agents, ServiceNow's answer is disarmingly simple: create a user called "AI Pat," assign it cases, and make it follow the exact same rules as humans because you should not trust an LLM more than you trust a human being. Topics covered: - From Atari 400 and floppy-disk jockey at Aldus to first engineer at ServiceNow - Scaling engineering from a stuffed fish on a monitor to 10,000 people — and the productivity trough at ~100 engineers - Single-tenant architecture: 90,000 databases, 25B+ queries/hour, and the monolith-to-Kubernetes migration - Why ServiceNow bought Swarm64 and built RaptorDB on a Postgres fork - 7,000 Windsurf licenses, Claude Code, and the real numbers on AI coding productivity - "AI Pat": the anthropomorphic model for enterprise agents outcomes, not toolkits - Whether AI kills seat-based SaaS, and why incumbents may have the inside track - Pat's advice to CTOs: this is not a time for excessive caution

I end today's episode talking about my Roth IRA and how it's great I'm up almost 70% in 1 year - but Alpha Picks is up almost 90% in that 1 year. ⁠⁠⁠⁠⁠⁠The Seeking Alpha Summer sale⁠⁠⁠⁠⁠⁠ continues with HUGE savings. Don't miss it - there are only 2 per year with discounts off what they normally provide. You'll have to wait until December to get the next one. FORMULA - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alpha Picks + Seeking Alpha Premium ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠+ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Trendspider and Sidekick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - PERFECT TOGETHER! THESE SALES END SOON: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TRENDSPIDER - JULY 4TH SALE THIS WEEKEND - get my 4 hour algorithm included on any annual plan.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Seeking Alpha's SUMMER SALE ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠*BEST DEAL - SEEKING ALPHA BUNDLE - Save over $250 and get Premium and Alpha Picks together ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- EXTRA $100 OFF ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ALPHA PICKS - Want to Beat the S&P? Save $124⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ EXTRA $74 OFF⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Seeking Alpha Premium ONLY - FREE 7 DAY TRIAL ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SEEKING ALPHA PRO - SAVE $600 ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠EPISODE SUMMARY

On Episode 310 of The Six Five Pod, Patrick Moorhead and Daniel Newman unpack the biggest stories from the week, including insights from Qualcomm Investor Day 2026, OpenAI and Broadcom's Jalapeño AI chip, Anthropic's Micron partnership, SpaceX's massive Reflection AI compute deal, Sakana AI's new Fugu orchestrator, and why memory is emerging as a critical layer of AI infrastructure. Plus, Bulls & Bears covers NVIDIA's $25B bond offering, Apple's MacBook price increases, Micron's record quarter, and Cerebras' first earnings as a public company. The handpicked topics for this week are: Qualcomm Investor Day 2026 — The Data Center Debut: Pat and Dan break down Qualcomm's push into the data center after the company took the stage with Microsoft's Satya Nadella and Meta's Mark Zuckerberg as named customers. They unpack the new Dragonfly platform, including the C1000 250-core data center CPU with PCIe Gen 7 and CXL, the AI200 and AI250 inference accelerators, and a novel High Bandwidth Compute (HBC) architecture that stacks compute under LPDDR memory at dramatically lower cost than HBM. They highlight Qualcomm's ambitious growth targets: $15B data center revenue target for FY 2029, an increased total non-handset revenue goal from $22B to  $40B, and a shortened timeline for automotive revenue by two years. They also debate the identity of Qualcomm's unnamed hyperscaler customer and why its robotics opportunity may be flying under the radar. (The Decode) OpenAI and Broadcom Unveil Jalapeño, OpenAI's First Custom Chip: A photo of Sam Altman and Hock Tan holding a wafer and packaged die kicked off OpenAI's reveal of Jalapeño, a custom inference chip built with Broadcom and slated for late-2026 deployment. The chip reached tape-out in roughly nine months, which is an aggressive cycle for an ASIC of this size, and uses HBM3E memory. Pat takes a victory lap on his long-standing heterogeneous compute thesis: every hyperscaler and now every model lab is building accelerators, and the XPU efficiency argument has played out as predicted. Dan frames OpenAI's broader move as existential: they cannot serve frontier models at premium margins if compute remains constrained. He flags that OpenAI is trying to do everything from chips and fabs to social networks and browsers, and that its IPO is now delayed. (The Decode)   Anthropic and Micron Sign a Strategic Multi-Year Memory Agreement: Anthropic and Micron announced a multi-year supply agreement for HBM, DRAM, and SSDs, including co-designed next-generation memory for AI workloads, along with a strategic investment by Anthropic in Micron. The pattern mirrors Samsung and SK Hynix's pre-funding Anthropic in May, and follows OpenAI's Jalapeño as another frontier lab moving to lock in supply chain control. Dan frames it as the same circular financing playbook NVIDIA ran two to three years ago, but with the ball now in the memory triopoly's court. Pricing-floor agreements with no ceilings, customized rather than commoditized memory architecture, and demand running well past the previously assumed 2027-2028 horizon. Pat notes that the rumored 14% free cash flow margin at Anthropic makes the strategic investment math work cleanly for both sides. (The Decode)   SpaceX Signs $6.3B Compute Deal with Reflection AI: SpaceX inked a $6.3B compute lease with open-source AI lab Reflection AI, at $150M per month from July 2026 through 2029, giving Reflection access to NVIDIA GB300 chips inside the Colossus infrastructure. Combined with the $920M-per-month Google compute contract and existing xAI commitments, SpaceX now has a contracted backlog larger than most public AI startups' entire revenue base, with some calling it the largest commercial AI infrastructure provider at $80B in contracted revenue. Pat reads it as XAI failing to land with developers, consumers, or enterprises, leaving SpaceX with a pot of gold worth far more as wholesale capacity than as XAI's own training compute. Dan flags that Google owning 7% of SpaceX ahead of an IPO is not accidental, and the open question is whether this becomes a Nebius-style infrastructure trade or a full-stack Google-equivalent platform. (The Decode)   Japan's Agentic Orchestrator Sakana AI Ships Fugu Plus and Fugu Ultra: Japan's Sakana AI released Fugu Plus and Fugu Ultra, an agentic orchestrator built on a multi-agent MOE approach that routes workloads across multiple underlying models rather than training a new frontier base model. Sakana claims agentic capabilities on par with or better than top frontier models at significantly lower input/output token costs, similar to the DeepSeek and GLM cost-undercut narrative. Pat compares the architecture to OpenRouter and notes the developer-facing parallel to Perplexity Computer's model-routing approach. Both agree that models themselves are no longer moats, and suggests the real moat is the harness, tooling, connectivity, looping, agentic stack, and total compute availability. Expect more sovereign agentic plays from Japan, the Middle East, and elsewhere on the same template. (The Decode)   The Flip — Is the Era of Memory as a Commodity Over? Daniel takes the FOR side: memory has moved from commodity to strategic AI infrastructure, citing 16 multi-year agreements covering $22B in committed volume booked through 2027, 84.9% gross margins higher than NVIDIA's, the technology barriers of HBM yield/stacking/packaging that only three companies can clear, and demand drivers tied to HBM as the binding constraint on every AI accelerator rather than to elastic consumer cycles. Patrick takes the AGAINST side: long-term agreements and SCAs signal a commodity in a strong cycle, not a structural rerating; nearly every relevant memory standard — DDR5, MRDIMM, HBM3/3E/4, LPDDR5X/6, GDDR6/7, LPCAM2 — is JEDEC-standard and therefore commodity at the pin; and CXMT's China DDR5 production ramps in 2H 2026 with Lenovo already shipping and HP and Dell qualifying. Custom HBM4 and Qualcomm-style HBC are where strategic memory genuinely lives. (The Flip)   NVIDIA's $25B Investment-Grade Bond Offering: NVIDIA priced a $25B multi-tranche bond offering on June 15, its first investment-grade debt sale since 2021, with seven tranches maturing between 2028 and 2056 and $85B in orders against an initial $20B target. Dan reads it as raising when capital is cheap, and oversubscription is real. NVIDIA doesn't need the money, it has a gold balance sheet, and is establishing a credit benchmark rather than funding CapEx. Pat agrees the optics are clean, but flags the irony of NVIDIA, with negative debt, borrowing while the stock trades like dead money at a sub-20x forward P/E. Both note that NVIDIA's underperformance reflects the market's skepticism on memory-as-strategic and on NVIDIA's own capex pace relative to the buildout opportunity ahead. (Bulls & Bears)   Tim Cook Calls Apple's Memory Crunch Price Raises on MacBook and iPad "Unsustainable": Apple announced MacBook and iPad price increases of up to $300, with Tim Cook telling the WSJ the memory cost environment is unsustainable. AAPL fell ~5% on the news, the broader rally was momentarily wiped out before Micron held the gains by close. Dan frames it as a moment when the market saw who is going to pay for the AI buildout: the consumer. He notes Apple's pricing power and inelasticity test is now live. Pat traces the backstory to Apple's negative-margin pricing pressure on Micron during the 2022-2023 memory downturn. The question is whether consumer-price blowback will eventually flow back to the memory vendors. (Bulls & Bears)   Micron Blows the Doors Off Fiscal Q3 — $41.46B Revenue, 84.9% Gross Margin: The memory story continues as Micron reported its largest beat in company history with fiscal Q3 revenue of $41.46B versus a $35.69B consensus, EPS of $25.11, year-over-year growth of more than 340%, and a record 84.9% gross margin that is roughly 10 points above NVIDIA's. Q4 guidance came in at a $50B midpoint against a $43B consensus. The 16 multi-year strategic customer agreements add up to $22B in committed volume, with most contracts containing pricing floors but no ceilings on most of the volume — a structurally asymmetric setup. Pat notes 95% of the beat came from price, not units, which reinforces his commodity argument; Dan flips it as the early innings of an NVIDIA-style run that puts Micron's 2027 profit on par with Google. (Bulls & Bears)   Cerebras' First Earnings Report Since IPO — Revenue Doubles, Margins Compress: Cerebras (CBRS) reported its first earnings as a public company, doubling year-over-year revenue and beating the top line while missing EPS, but the stock sold off hard amid gross margin deterioration. Core revenue came in at $191M, up 12% sequentially, with a $194M Q2 guide that is essentially flat, core gross margins at 47% guiding to 36-38% and 38-41% for the year, and operating margins flipping from positive 2% to a guided -30% to -32%. Customer concentration is shifting from Core42 and G42 (86% of FY25 revenue) to OpenAI, which loaned Cerebras $1B and gets paid quarterly in warrants. Pat flags that Cerebras' uncontested speed claim is no longer uncontested with Groq, TPU v8i, and Tenstorrent putting up real numbers. Cathie Wood is down 52% on her position. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode Qualcomm Investor Day Lands the Data Center Pivot — Microsoft Deploying Qualcomm HBC XPUs in Azure (Per Satya Nadella) + Meta MOU on Three New Qualcomm Datacenter CPUs (Per Zuckerberg); $3.9B Modular Acquisition; Dragonfly Brand + AI200/AI250 Roadmap; HUMAIN 200MW Ramp; Qualcomm to Become Largest Automotive Silicon Company; Targets $3B Datacenter Revenue FY27, $35B by FY31 https://finance.yahoo.com/markets/stocks/articles/qualcomm-investor-day-detail-data-163247063.html  OpenAI Begins Vertical Integration — First Custom Inference Chip "Jalapeño" Unveiled With Broadcom June 24 (Hock Tan: As Good as Blackwell + TPU; ~50% Cost Savings; Late-2026 Microsoft Deployment, 10GW Multi-Gen Roadmap); Daybreak Cyber Stack (June 22) Confirms the Platform Shift https://x.com/OpenAI/status/2069770172802773292  Frontier AI Labs Are Now Financing Their Own Supply Chains — Anthropic Locks In Multi-Year Micron HBM/DRAM/SSD Supply + Micron Becomes Series H Investor; Same Pattern as Samsung + SK hynix Pre-Funded Anthropic in May; $965B Post-Money, $47B Revenue Run-Rate, October IPO Target https://investors.micron.com/news-releases/news-release-details/micron-and-anthropic-announce-strategic-agreement-scale-next  SpaceX Signs $6.3B Compute Deal With Reflection AI — $150M/Month July 2026 → End of 2029; NVIDIA GB300 + Colossus 2 Capacity; SpaceX Now Largest Commercial AI Infrastructure Provider With $80B+ Committed Compute Revenue Through 2029 https://finance.yahoo.com/technology/ai/articles/spacex-reportedly-grant-reflection-ai-162749237.html  The Sovereign AI Stack Lands — Japan's Sakana Ships Fugu + Fugu Ultra Multi-Agent System (June 22) That Beats Opus 4.8, GPT-5.5, and Gemini 3.1 Pro on 10 of 11 Benchmarks; Designed Around US Export-Control Risk; Completes the Three-Bloc Sovereign-AI Map With Mistral Compute (Europe) + DeepSeek $7.4B (China) https://www.datacamp.com/blog/sakana-fugu  The Flip Is the Era of Memory as a Commodity Over? FOR: Memory is now strategic AI infrastructure with multi-year supply lock-ins. The cycle dynamics that defined the last 30 years no longer apply. https://www.benzinga.com/markets/tech/26/06/60062500/micron-earnings-could-echo-nvidias-2023-moment-says-futurum-ceo  AGAINST: Memory is cyclical and priced for perfection. This print is either step change or top of the cycle, and the second one is more likely. https://www.cnbc.com/2026/06/25/apple-macbook-ipad-price-hike-memory.html Bulls & Bears NVIDIA (NVDA) $25B Bond Sale Anchors the AI Debt-Finance Boom — First Bond Offering Since 2021; Joins Alphabet $80B, Amazon $27.5B, Meta $30B, Oracle Stack; Dan: "Locking In Cheap Capital While It Can" https://finance.yahoo.com/technology/ai/articles/nvidia-record-us-25-billion-131039687.html  Apple (AAPL) Falls −5%+ Thursday June 25 on Confirmed MacBook + iPad Price Hikes — Tim Cook RAM "Unsustainable" Comment Lands as Real Price Action; Apple Hikes Erase Micron-Driven Tech Rally Mid-Session; Memory Beneficiaries (SanDisk, Micron) Surge; Analysts "Mostly Nonplussed" https://tickerspark.ai/market/apple-inc-aapl-drops-5-3-as-price-hikes-spook-investors-1782399950638  Micron (MU) Q3 FY26 ACTUALS — Largest Beat in Company History; Revenue $41.46B (+346% YoY) Crushes $35.69B Consensus; Non-GAAP EPS $25.11 (+1,215% YoY) Beats $20.49; Record 84.9% Gross Margin (Higher Than NVIDIA); Q4 Guide $50B Midpoint vs $43B Consensus; Stock +18-19% Overnight to $1,242 https://www.nasdaq.com/articles/nvda-who-micron-blows-doors-q3-earnings-revs  Cerebras Systems (CBRS) Q1 ACTUALS — First Earnings Post-IPO; Revenue $193.4M Nearly Doubled YoY; 2026 Guide $855-$865M Beats $824M; BUT Gross Margins Forecast 38-41% (Down From 45% Q1, Half of NVIDIA + Micron); Stock −20% AH on Margin Compression; Sets Up Inference-Tier Margin Debate https://investors.cerebras.ai/news-releases/news-release-details/cerebras-systems-announces-strong-first-quarter-2026-results  

Joe's Premium Subscription: www.standardgrain.comGrain Markets and Other Stuff Links —Apple PodcastsSpotifyTikTokYouTubeFutures and options trading involves risk of loss and is not suitable for everyone.

Jon Najarian @jonnajarian and @jeffkilburg on @LarryKudlowShow Broke it down perfectly:"Yesterday's selloff was healthy profit-taking — nothing more." $SPX still just 3% off all-time highs, up 7.9% YTD and 24% over the last year. Nasdaq up 10.6% YTD, small caps even stronger. Semiconductors still +80% this year despite the dip.#VIX spiked from super-low levels — traders simply rotating ahead of the massive SpaceX IPO next week at ~$135 post-split. Smart money locking in gains in chips $SMH $NVDA to make room for the big inflow everyone's anticipating. AI compute demand is exploding: Google just signed a $900M+/month deal, Anthropic another $1.25B/month. Earnings are gangbusters (27.7% YoY growth). This is a temporary reset, not a reversal. As Dr. J always says — don't flinch. FOMO comes back fast. Stay long the future. $NDX $GOOG $AMZN $TSLA

Our 247th episode with a summary and discussion of last week's big AI news!Recorded on 06/03/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Opus 4.8 with improved benchmark scores, discussed eval-awareness findings and welfare/corrigibility themes from its system card, and introduced Dynamic Workflows for long-running multi-agent tasks.Microsoft unveiled the always-on Microsoft Scout assistant built on OpenClaw plus new in-house MAI models (including MAI Thinking 1) and “frontier tuning,” emphasizing enterprise security architecture and model-from-scratch capability.Major business moves included Anthropic's $65B Series H at a $965B valuation alongside an IPO filing, a JPMorgan analysis arguing OpenAI needs major revenue growth to justify infrastructure spend, and Cognition raising $1B at a $25B valuation.Policy and security highlights covered Trump's voluntary pre-release government testing framework for powerful AI, Meta AI support being exploited to hijack Instagram accounts, tightened US Nvidia export controls and China's travel approvals for AI experts, plus expanded Glasswing/Mythos-style cyber and biodefense initiatives.Timestamps:(00:00:10) Intro / Banter(00:04:10) Sponsors(00:07:10) News PreviewTools & Apps(00:07:54) Anthropic releases Opus 4.8 with new 'dynamic workflow' tool | TechCrunch(00:22:37) Microsoft Scout is a new AI personal assistant built on OpenClaw | The Verge(00:26:55) Microsoft launches new MAI family of AI models at Microsoft Build | Mashable(00:37:43) Robinhood now lets your AI agents trade stocks | TechCrunch(00:40:49) OpenAI launches new Codex tools for white-collar work | TechCrunch(00:43:40) ElevenLabs' new music-generation model can switch genres mid-track | TechCrunchApplications & Business(00:44:35) Anthropic Hits $965 Billion Valuation, Surpassing OpenAI - WSJ(00:45:32) Anthropic Files to Go Public, Setting Stage for Huge I.P.O. - The New York Times(00:51:15) China's ByteDance Developing New AI Chips Like Those from Nvidia Partner Groq(00:55:00) Anthropic expands Mythos to 150 additional organizations(00:55:35) OpenAI needs a 26x revenue increase to justify its buildout(00:58:46) AI coding startup Cognition raises $1B at $25B pre-money valuation | TechCrunchProjects & Open Source(01:00:50) MiniMax-M3 debuts, eclipsing GPT-5.5 and Gemini 3.1 Pro on key benchmark performance for just 5-10% of the cost | VentureBeatPolicy & Safety(01:06:08) Trump Signs Executive Order Seeking Oversight of A.I. Models - The New York Times(01:11:45) Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked(01:13:058) Chinese AI experts in private firms now required to secure approval before international travel — Beijing enforces policy to secure top-tier talent, expands measures beyond government(01:17:53) U.S. Tightens Controls on Nvidia AI Chip Exports | Let's Data Science(01:21:47) OpenAI launches Rosalind Biodefense, offers federal agencies early access to its life-sciences model(01:24:00) Using LLMs to secure source code(01:26:19) Project Glasswing: An initial update(01:29:30) White House Approves $9 Billion for Spy Agencies to Catch Up on A.I.(01:32:11) US Law Enforcement Warns of ‘Anti-Tech Extremism' as AI Hatred GrowsSynthetic Media & Art(01:35:38) YouTube will now automatically label AI videos | TechCrunchResearch & Advancements(01:36:22) Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention(01:41:26) From Simulation to Enaction: Post-trained language models recognize and react to their own generationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, Chris sits down with Josh Zegen, Co-Founder & Managing Principal of Madison Realty Capital, a $25 billion real estate private credit firm he started with his college roommate in 2004. They dig into how he built one of the largest private lenders in the country starting from a desk in his dad's law office - and why he still thinks of himself as a businessman first and a real estate guy second. Josh got into lending almost by accident. Laid off from a VC firm at 26 when the dot-com bubble burst, he took one mortgage deal nobody else would do, saw how fragmented and non-institutional the market was, and built a fund around it before "private credit" meant anything. Chris and Josh go deep on surviving '08, reinventing the business when capital dried up, and how Madison grew into a platform that now lends to other lenders. They discuss: How Josh went from a laid-off VC associate living back home to founding a $25B firm Surviving '09 - including giving up 50% of the company for a $50M anchor that collapsed at the last minute Why he built servicing, asset management, and capital raising in-house instead of outsourcing The $10B back-leverage book that makes Madison the lender to ~100 other private lenders The $720M single loan behind the largest office-to-residential conversion in NYC Where he sees real estate credit headed - and why he stays away from office, data centers, and anything "binary" Timestamps:(00:00) Intro(00:52) Rate Volatility and a Stalled CRE Investment Market(09:44) What's Getting Done Today: Construction, Conversions, and Recaps(18:49) Founding Madison: Seeing Opportunity in a Fragmented Market(25:19) The GFC: Gating Investors and Going Vertically Integrated(31:26) The $50M REIT Deal That Nearly Ended Madison—And the Door It Opened(44:28) Why Borrowers Now Prefer Private Credit Over Banks(47:17) In-House Loan Servicing as Madison's Competitive Edge(49:06) The Back Leverage Business: Lending to Private Lenders(55:35) Capital Markets Expansion and Staying True to Real Estate(1:05:55) The Pfizer Deal, Lifecycle Lending, and Madison's Road Ahead(1:15:06) Staying Relevant by Constantly Innovating and Looking for Acquisition Opportunities ----- Presented by Airshare: Trusted across the country for fractional ownership, jet cards, charter, and aircraft management, Airshare gives you a smarter way to fly private - over 25 years of experience, operating their own fleet, with the top safety ratings in the industry. Drive up to the FBO, walk on, and go. Go to flyairshare.com to learn more. ----- Sponsored by: Collateral Partners builds institutional-grade investor materials for private credit, private equity, real estate, and family office firms - the kind of marketing collateral that helps you close capital. Learn more at collateral.com/fort. Relay Human Cloud helps you build a highly skilled global team that operates as a true part of your business - not an outsourced vendor. From accounting to operations, Relay's talent works inside your systems and alongside your local team, unlocking 24-hour productivity and significant cost savings. Learn more at https://www.relayhumancloud.com/powers-podcast/ ----- Chris on Social Media: X: https://x.com/fortworthchris Instagram: https://www.instagram.com/thepowerspodcast LinkedIn: https://www.linkedin.com/in/chrispowersjr/ Visit our website: https://www.powerspod.com/Leave a review on Apple: https://bit.ly/45crFD0Leave a review on Spotify: https://bit.ly/3Krl9jO

Corruption Central has new address: 1600 Pennsylvania Avenue. From Trump's Weaponization slush fund to international negotiations including Trump Towers and sketchy stock trades and Crypto deals, this next gift should come as no surprise: Dell wins a $9.7B Pentagon deal — weeks after Trump publicly urged people to “go out and buy a Dell” and after Michael Dell's family pledged $6.25B to seed the administration's “Trump accounts.” We'll check out the contract (a five‑year Core Enterprise Technology Agreement to supply Microsoft licensing, cloud subscriptions and on‑prem software). We'll drill down into the Pentagon's claim the award followed a competitive process and will save roughly $422M a year, and we'll examine why timing — stock purchases, public praise, and Dell's ties to the Trump administration are now prompting ethics and conflict‑of‑interest scrutiny. We'll roll this story past Michael Shure and Mo Kelly as we lay out the top Stories of the week in ‘This Week in Politics.' Fingers crossed Albert finds a good gator story this week but whether it's meth, machetes, or mayhem, Friday Fabulous Florida never disappoints. Quick set change… and ou tcomes the rainbow. The Culture Blaster Michael Snyder will slide right in to give us the best advice on movies and streaming options worth your time.

The "Art of the Deal" or the Art of the Fold?

Patrick Moorhead and Daniel Newman return from Dell Technologies World to unpack Google I/O's Gemini-as-operating-system moment, the Blackstone-Google TPU joint venture nobody saw coming, NVIDIA's $81.6 billion quarter with a $91 billion guide, and debate whether or not the "SaaSpocalypse" is finally over. The handpicked topics for this week are: Google I/O 2026: Gemini Becomes the Operating System. Google I/O repositioned Gemini from a product to the operating layer for everything Google does, and the numbers backed it up. 900 million monthly active users, 3.2 quadrillion tokens per month, a 7x jump year over year. Pat's headline: this is about widening distribution, not just model quality. Gemini 3.5 Flash, Antigravity 2.0, Gemini Spark, and Android XR glasses all extend Gemini into surfaces that no competitor can replicate. Daniel's read: the token-cost reckoning is coming, and when enterprise subsidies end, models that can deliver value at a lower cost per token will become the ground zero of the next era. (The Decode) Dell Technologies World 2026: AI Factory Goes Agentic, 1,000 New AI Server Clients. Pat and Dan were both on the ground in Las Vegas and called it the most consequential Dell event in years. Michael Dell and Jensen Huang co-keynoted to launch the next-generation Dell AI Factory with liquid-cooled PowerEdge XE9780 servers, Dell Deskside Agentic AI, and a multi-model ecosystem including Google Distributed Cloud with Gemini 3.0, on-prem OpenAI Codex, and Grok. 1,000 new AI server clients in a single quarter is the cleanest leading indicator of enterprise demand heading into Dell's Q1 print. Pat's biggest takeaway: OpenShell as a control plane for agents spanning from the GB10 all the way to the PowerEdge rack has been the missing orchestration piece. Daniel's read: large enterprises are going to build hybrid AI architectures and want to deliver tokens at the lowest possible on-prem cost, and Dell is ready. (The Decode) Blackstone and Google Launch a $5B TPU Joint Venture. Pat called it the biggest story of the week and the one that went most under the radar. For the first time, a hyperscaler has released its proprietary AI silicon to a third-party distribution entity. The $5 billion deal, up to $25 billion with leverage, targets 500 megawatts of capacity online by 2027. Daniel's framing: Google decided its custom silicon is worth more as a commercially distributed asset than as a captive moat. Pat's note: the proprietary nature of TPU infrastructure means retrofitting existing data centers will require real work, but the sovereign angle gives the JV a natural first market. (The Decode) AMD Helios, $10B Taiwan Investment, and the MI450 Anchor Customer Rumor. AMD dropped a $10 billion Taiwan ecosystem investment alongside confirmation that Helios rack-scale is on track for multi-gigawatt customer deployments beginning 2H 2026. A Citi rumor surfaced Anthropic as the anchor MI450 customer, to be formally announced at AMD's Advancing AI Day in July. Pat's read: Lisa Su has made a commitment and she almost never falls through. The analysts who said AMD would not ship anything in the second half of 2026 are going to be very wrong. (The Decode) OpenAI Guaranteed Capacity: Sam Altman's Moment. OpenAI launched multi-year compute commitment contracts the same week that Anthropic was struggling with capacity outages. Pat called it brilliant and said it makes Sam Altman look like a genius. It's the inference-era analog of cloud reserved instances: guaranteed availability at a locked price for one, two, or three years. Daniel added context: Anthropic's annualized ARR growth is nearly double OpenAI's and is about to lap them, so the model war is far from over. But for enterprises that need reliability, OpenAI just made the most compelling enterprise trust argument of the week. (The Decode) Sovereign AI Crosses $30 Billion at NVIDIA, 14% of Revenue. NVIDIA disclosed sovereign AI as a segment-level line for the first time, at $30 billion in FY26, 3x the prior year. Pat has been tracking sovereign for years and calls this the clearest possible signal that it has moved from marketing term to structural revenue category. Daniel's point: outside of the four or five hyperscalers doing all the major buying, sovereign is where the incremental demand is coming from and it is very real. (The Decode)  The Flip: Is the SaaSpocalypse Over? Daniel took the affirmative and came in loaded. Every earnings report across CrowdStrike, Cloudflare, ServiceNow, Intuit, Salesforce, Atlassian, Notion, and monday.com shows companies growing with the AI tailwind. His core argument: there was a reason SaaS emerged 20 to 30 years ago. Companies do not want to be in the software business. Vibe-coded flat-file apps with no security, no governance, no data lineage look great in a kitchen demo and fall apart at enterprise scale. The SaaSpocalypse is over and he is tired of talking about it. Pat's counter: BofA slapped Salesforce with an Underperform at $160, 8% below where it trades. Snowflake is down 35% year-to-date. A senior Dell executive told him Dell will not buy another SaaS system and is tripling internal software creation. The growth question is real even if the terminal value is not zero. Both agree the tape will tell the real story. (The Flip) NVIDIA Q1 FY27 Results. Record $81.6 billion revenue, up 85% year over year. Data center at $75.2 billion, up 92%. Non-GAAP EPS of $1.87, up 140%. Q2 guide of $91 billion crushed the $86.8 billion consensus by $4 billion at the midpoint. $80 billion buyback authorized, dividend raised 25x. The stock went down after hours for the fifth consecutive time following a massive beat and raise. Pat's read: NVIDIA may be worth $8 to $9 trillion on paper at a sector-average multiple and 75% gross margins held. Daniel's framing: this is the best company in the world, possibly tied with Google, and it is becoming the Apple of this era. He sees a long safe journey of continued growth vs. speculative dollars chasing quantum and space names that can double in a week. (Bulls and Bears) Intuit: Earnings Beat, Revenue Miss. A 17% workforce cut, raised guidance, and $8 billion buyback were authorized. Pat's emerging thesis: these companies are cutting people to afford tokens. Intuit comes at a moment when OpenAI's ChatGPT finance plugin via Stripe is building an intelligence layer that could sit on top of Intuit's products without displacing them directly, at least not yet. (Bulls and Bears) Lenovo: Record $21.6 billion quarterly revenue, up 27% year over year. The company's fastest growth in five years. AI-related revenue is up 84% year over year to 38% of total company revenue. ISG returned to full-year operating profit with a $21 billion AI server pipeline. Pat and Dan both read Lenovo's results as NVIDIA tea leaves, a leading indicator of enterprise AI server demand that directly validates what Dell said on stage about 1,000 new AI server clients. (Bulls and Bears) Analog Devices: Record $3.62 billion revenue, up 37% year over year. EPS up 67%. Q3 guide of $3.9 billion crushed consensus by $270 million. Data center up 90%, industrial up 56%, comms up 79%. The $1.5 billion Empower Semiconductor acquisition adds integrated voltage regulator technology that can reduce AI data center power consumption by 10 to 15% while shrinking the power footprint by up to 4x. Daniel's closing point: you can't build AI servers without players like Analog Devices and Lattice Semiconductor. These essential node companies aren't boring, they're foundational. (Bulls and Bears) Check out all of our Dell Technologies World coverage linked in the show notes including our sit-downs with Michael Dell, Jeff Clark, and key customers. Be part of our community. Hit that subscribe button and see you at Computex.   The Decode Google I/O 2026 — Gemini Becomes the Operating System: 900M MAU, 3.2 Quadrillion Tokens/Month, Gemini Omni, Antigravity 2.0, Gemini Spark, and Android XR Glasses https://blog.google/innovation-and-ai/sundar-pichai-io-2026/ Dell Technologies World 2026 — AI Factory Goes Agentic: Michael Dell + Jensen Huang Unveil PowerEdge XE9780, Dell Deskside Agentic AI, and a Multi-Model Ecosystem; Dell Adds 1,000 AI-Server Clients in the Quarter https://www.dell.com/en-us/blog/dell-technologies-world-a-bright-and-beautiful-road-ahead/ Blackstone + Google Launch $5B (Up to $25B w/ Leverage) JV to Sell Google TPUs Outside Google Cloud — First Time a Hyperscaler Has Released Its Custom Silicon to a Third-Party Distribution Channel; 500 MW Online by 2027, Benjamin Treynor Sloss as CEO https://www.blackstone.com/news/press/blackstone-announces-joint-venture-with-google-to-create-new-tpu-cloud/ AMD Announces $10B+ Taiwan Ecosystem Investment — Helios Rack-Scale Platform With MI450X GPUs and Venice EPYC on TSMC 2nm Targeting Multi-Gigawatt Deployments 2H 2026; the Clearest Second-Source Signal Yet https://ir.amd.com/news-events/press-releases/detail/1286/amd-announces-more-than-10-billion-in-taiwan-ecosystem-investments-to-accelerate-ai-infrastructure OpenAI Launches Guaranteed Capacity — Multi-Year Compute Commitments Turn Inference Capacity Into a New Enterprise Asset Class https://www.cnbc.com/2026/05/19/openai-announces-new-guaranteed-capacity-offering-for-customers-to-secure-compute.html The Sovereign AI Government Investment Wave — NVIDIA Discloses ~$30B Sovereign-AI Revenue (14% of Mix); UAE, Saudi, Japan, Australia, France All in Motion This Week https://finance.yahoo.com/markets/stocks/articles/analog-devices-q2-earnings-beat-153000996.html   The Flip: Is the SaaSpocalypse Officially Over — or Is BofA's Split Call (ServiceNow Buy, Salesforce Underperform) the Real Signal That Platform AI Monetization Is Going to Be Bifurcated, Not Universal? FOR:  BofA Reinstates Coverage of ServiceNow, Salesforce — Barron's (May 18) https://www.barrons.com/articles/servicenow-salesforce-stock-price-ai-7b109396 Embedded workflow + system-of-record stickiness still wins citing ServiceNow Q1 2026 financial results https://newsroom.servicenow.com/press-releases/details/2026/ServiceNow-Reports-First-Quarter-2026-Financial-Results/default.aspx Intuit Q3 revenue up 10%, cuts 17% of staff — SEC 8-K filing (May 20) https://www.stocktitan.net/sec-filings/INTU/8-k-intuit-inc-reports-material-event-b23073259896.html   AGAINST:  BofA Slaps Salesforce With Underperform Rating, $160 Price Target — 24/7 Wall St (May 18) https://247wallst.com/investing/2026/05/18/bofa-slaps-salesforce-with-underperform-rating-160-price-target-is-the-ai-story-falling-flat/ BofA resets Salesforce price target to Underperform — TheStreet (May 19) https://www.thestreet.com/investing/stocks/bofa-resets-salesforce-stock-price-target-to-underperform-at-160 Snowflake -35% YTD heading into May 27 print is the canary that platform stickiness is being repriced https://eciks.org/4640-22295-snowflake-set-to-report-q1-earnings-may-27-with-ai-strategy-in-focus OpenAI Guaranteed Capacity + Dell on-prem Codex create a credible path to displace seat-based SaaS https://www.cnbc.com/2026/05/19/openai-announces-new-guaranteed-capacity-offering-for-customers-to-secure-compute.html Bulls & Bears NVIDIA Q1 FY27 ACTUALS https://www.cnbc.com/2026/05/20/nvidia-nvda-earnings-report-q1-2027.html Intuit Q3 FY26 Actuals https://investors.intuit.com/news-events/press-releases/detail/1312/intuit-reports-strong-third-quarter-results-and-raises-full-year-revenue-guidance Lenovo Q4 FY26 ACTUALS https://www.cnbc.com/2026/05/22/lenovo-shares-jump-15percent-on-record-earnings-as-ai-revenue-nearly-doubles.html Analog Devices Q2 FY26 ACTUALS https://finance.yahoo.com/markets/stocks/articles/analog-devices-q2-earnings-beat-153000996.html  

SpaceX filed publicly for its IPO on Nasdaq, revealing $18.7B in 2025 revenue, billions in losses, and Musk's 85.1% voting control. Anthropic pays SpaceX $1.25B per month for compute. Nvidia beat estimates again, Spotify launches Reserved ticketing, and Waymo suspends service over flooding. SpaceX files publicly for its IPO, choosing Nasdaq to make its debut under the symbol SPCX; Elon Musk's shares give him 85.1% of the voting power in the company (Bloomberg) SpaceX's S-1 reveals Anthropic is paying $1.25B per month through May 2029 under their Colossus compute deal, with a 90-day termination clause (The Verge) Spotify partners with Live Nation to launch Reserved, a new feature that sets aside tickets for the most dedicated fans, starting with Premium users in the US (Hollywood Reporter) Spotify debuts a desktop app for creating personal podcasts, competing with Google's NotebookLM, with support for daily briefings based on email and calendar (TechCrunch) Nvidia reports Q1 revenue up 85% YoY to $81.62B, above $78.86B est., Data Center revenue up 92% YoY to $75.2B, and announces an $80B share repurchase program (Nvidia) Waymo suspends operations in Atlanta and San Antonio as its robotaxis struggle with flooded roads and says it has yet to develop a "final remedy" for flooding (TechCrunch) Learn more about your ad choices. Visit megaphone.fm/adchoices

Story of the Week (DR):Trump is bringing Tim Cook, Elon Musk, and a dozen other CEOs to Beijing for his Xi summitTechnology & AIElon Musk – CEO, Tesla and SpaceXTim Cook – CEO, AppleJensen Huang – CEO, Nvidia (joined as a last-minute addition after a personal call from the President)Cristiano Amon – CEO, QualcommSanjay Mehrotra – CEO, Micron TechnologyDina Powell McCormick – President, MetaJim Anderson – CEO, CoherentFinance & InvestmentLarry Fink – CEO, BlackRockStephen Schwarzman – CEO, BlackstoneDavid Solomon – CEO, Goldman SachsJane Fraser – CEO, CitigroupAerospace & ManufacturingKelly Ortberg – CEO, Boeing (reportedly finalizing a massive 500-jet deal during the trip)Larry Culp – CEO, GE AerospacePayments & ServicesMichael Miebach – CEO, MastercardRyan McInerney – CEO, VisaAgriculture & BiotechBrian Sikes – CEO, CargillJacob Thaysen – CEO, IlluminaPaypal agrees to $30 million settlement with Trump's Justice Department over 'illegal DEI'The company launched a $530M Economic Opportunity Fund in 2020 for Black and underrepresented minority businessesDid not fight this in court, just surrenderedTo make the DOJ happy, PayPal had to ditch its race-based criteria; instead, it now funnels that financial support to veteran-owned businesses and companies in farming, manufacturing, or technology. A direct “black” to “white” transferAny company that launched a race-specific grant or loan program after 2020 is now officially in the DOJ's crosshairs, and "social justice" is being litigated as "civil rights fraud."PayPal board:“Independent” chair David W. Dorman (2015-; 17%)member of the Dell Technologies BoardMichael Dell and Donald Trump are BFFs: Dell pledged $6.25B to Trump AccountsJonathan Christodoro (2015-; 13%): a disciple of billionaire Carl Icahn (former Managing Director at Icahn Capital), one of Trump's oldest and most vocal alliesFounder PayPal Mafia Trump BFFs: Musk (DOGE), David Sacks (AI and Crypto Czar), Peter Thiel (JD Vance creator)Frank Yeary (2015-; 12%): Intel director since 2009 and chair since 2023It Was One of DOGE's Most Absurd Abuses. A Court Finally Exposed ItThis whole saga centers on a major legal showdown between the Trump administration's Department of Government Efficiency (DOGE) and the National Endowment for the Humanities (NEH). The case is a consolidated lawsuit (often called the NEH-DOGE lawsuit) filed in May 2025 by groups including the Authors Guild, the American Historical Association, and the Modern Language Association. On May 7, 2026, U.S. District Judge Colleen McMahon issued a massive 143-page ruling. She essentially nuked DOGE's attempt to defund hundreds of humanities projects, calling their process a "textbook example of unconstitutional viewpoint discrimination."The AI Purge: Instead of a professional review, DOGE staffers (described in court as young "technologists" with no background in humanities) ran thousands of grant descriptions through ChatGPT.DOGE staffers—mostly described as 20-somethings with "zero experience in the humanities"—attempted to dodge government transparency laws by conducting official business on Signal with auto-delete enabled. The court found this was a blatant violation of the Federal Records Act, proving that "efficiency" is often just code for "avoiding a paper trail."The Woke Filter: They told the AI to flag anything related to "DEI." This backfired spectacularly when the AI flagged projects on Holocaust survivors, Appalachian history, and Italian-American archives simply because they used words like "identity," "culture," or "women."DOGE didn't actually read the grants they cut. Instead, they used ChatGPT and basic keyword searches to flag any program containing "incriminating" words like "history," "culture," "identity," or "BIPOC." If the AI thought it sounded "woke," the funding was axed—a move Judge Colleen McMahon called a "textbook example of unconstitutional viewpoint discrimination."In perhaps the most "mask-off" moment of the proceedings, it was revealed that DOGE staffers flagged and canceled a documentary about Jewish women's slave labor during the Holocaust. The reason? Their AI-driven filter decided that focusing on "Jewish cultures" and "female voices" made it an illegal DEI program. Apparently, documenting Nazi atrocities is now "radical identity politics."The ruling highlighted a minor detail the administration seemed to forget: DOGE isn't a real government agency. The judge noted that DOGE had absolutely no lawful authority to terminate congressionally appropriated funds. They were essentially a group of private-sector bros playing President with the NEH checkbookThe Redirect: The court found that the $100 million "saved" wasn't actually returned to the Treasury. Instead, it was being funneled into the administration's own projects, like the "National Garden of American Heroes."Why Two Big Companies Just Cut Paid Family Leave MMFor the last decade, a tight labor market forced companies to compete for talent with generous perks. Now, with the job market cooling and employees having less leverage to quit, companies like Deloitte and Zoom are quietly rolling back benefits.Zoom, the company that became the face of remote work, has slashed its paid parental leave. Birthing parents saw their leave drop from up to 24 weeks to 18 weeks, while non-birthing parents were cut from 16 weeks down to 10.Deloitte is making deep cuts, but not for everyone. The reductions specifically target “Center” employees—the administrative, IT, and finance support staff who generally earn less—rather than the high-earning consultants. Their leave was halved from 16 weeks to just eight.Beyond just time off, Deloitte is axing its $50,000 reimbursement program for adoption, surrogacy, and IVF for these support roles.I Hate Working 5 Days': Zoom CEO Eric Yuan Says AI Could Shrink Workweeks To 3 Days In A Major Future ShiftGoodliest of the Week (MM/DR):DR: Chipotle CEO [Scott Boatwright] tells customers to ‘just ask' if they want bigger portions after downsizing accusations: “You should ask for a little more ... We serve big, beautiful bowls and burritos. Full stop, no questions asked. If you want more, just ask the team member. I promise you there's never a team member on that line that's going to say no.” 886 to 1MM: Oil shortages DR MMBeer demand stumbles as gas prices surge, data showsI mean, isn't this the double best? Less idiots driving drunk AND less idiots DRIVING!Oil shortages are even hitting colored snack bagsUgly snacks, maybe less eating!Assholiest TRIGGERIEST of the Week (MM):Brett BlundyVictoria's Secret unveils allegations against activist investor, loses board directorBlundy, Australian billionaire who launched Bras N Things, a classy establishment sold to Hanes, and currently chairs Lovisa, a fast fashion jewelry business, bought 13% of VS and thinks he can run it betterHe's disappointed with VS acquisition of Adore Me (online retailer) and the drop in earningsMeanwhile, Lovisa's 1Y market returns: -22% vs. ASX +4% TRIGGERED:Blundy, a fucking Australian billionaire blowhard, chairs LovisaLovisa board: Blundy, Mark McInnes (“deputy chair”), John Cheston (CEO), Bruce Carter, Tracey Blundy (wife), John Charlton, Sei Jin Alt (woman, Asian)Brett and Tracey own 40%+ of sharesZero merit directorsExec team: John, Mark, Victor, Chris - zero womenBlundy is targeting VS, whose board is…Donna James, Hillary Super (CEO), Irene Britt, Sarah Davis, Jacqueline Hernandez, Rod Little, David McCreight, Mariam Naficy, Lauren Peters, Anne SheehanExec team: 4 women, 1 manThis is the ultimate mansplain - some chest thumping billionaire walks into a room full of women, pushes them out, takes over… and this from the filing:“On November 13, 2025, members of the Board held a videoconference call with Mr. Blundy to inform him that the Board had determined, in accordance with its fiduciary duties, that appointing Mr. Blundy to the Board would not be in the best interests of VS&Co or its stockholders. In an effort to reach amutually agreeable resolution, the Board proposed collaborating with BBRC and Mr. Blundy on (i) adding one mutually-agreed new independent directornot affiliated with BBRC to the Board, (ii) Mr. Blundy's participation in a review with the Board of the Company's capital allocation, (iii) entering into alonger-term information sharing agreement and, in the context of a negotiated resolution with BBRC and Mr. Blundy, an agreement on customary standstill restrictions, and (iv) taking down the Rights Plan. After this call, the Board delivered to Mr. Blundy the following letter explaining its rationale for rejecting his candidacy and proposing a new framework for a mutually agreeable resolution:“The potential for significant reputational and legal risk to Victoria's Secret arising from (1) your pattern of hiring executives with a history of serious allegations of sexual harassment or other misconduct, and (2) the reported and alleged instances of harassment and highly inappropriate employee policies that occurred under your oversight at companies you controlled or effectively controlled.The proxy should just say, “Australian white male billionaire who is cool sexually harassing women while selling them underwear wants to take over massive underwear store run by women”Elon Musk and Sam AltmanMusk first…Sam Altman Accuses Elon Musk of Laughing at Memes During Important OpenAI MeetingsMusk's China trip during OpenAI trial prompts apology from his lawyer for CEO's absenceTRIGGERED: This is the man child trillionaire we're supposed to take seriously - does his mom fold his socks for him? Does he eat Cheerios out of a frisbee for breakfast? These are our male adult role models?Musk apparently was too busy for the trial, but during talks of absorbing OpenAI into Tesla, he wasn't too busy to spend a long time forcing everyone to look at his fucking dopey idiot manboy memes that made him laughReminder time: Musk is in charge of who gets internet in military conflict (Starlink), gutted the government (DOGE), is trying to implant chips in brains (Neurolink), and used everyone else to get his billions (Tesla was bought, subsidized, SpaceX subsidies, Boring Company steals municipal money to dig holes…)Altman next…Sam Altman faces awkward grilling over 'toxic culture of lying'ChatGPT Told a 19-Year-Old How to Mix Drugs — His Mother Found Him Dead the Next MorningWHEN YOU PUT A SOCIOPATH AND MANCHILD IN CHARGE OF A WORLD DESTROYING DEVICE, IT TURNS OUT IT'S BADWarren Buffett DRPut the folksy “I'm just a guy eating a werther's original candy making money” schtick aside, where he says they pick great management and let them do their thing - this is “their thing”:TRIGGERED: Electric Company Says It's Cutting Off an Entire Town So It Can Sell All Its Power to Data CentersThere is so much to hate here:Tech billionaires building data centers for AI: checkNV Energy is wholly owned by Berkshire Energy which is owned by Warren Buffett: checkTrump appointed asshole running regulatory agency that represented utilities: checkThe town is Lake Tahoe - 50,000 residents have to find a new source of electricity in ONE YEAR because Buffett/Berkshire/NV Energy decided the re-route all energy to data centers for AIGoogle, Apple, MSFT all have facilities, 12 data center projects in Northern NevadaNevada would have to ask woke California to build hundreds of millions of dollars worth of transmission lines in a year to get to Tahoe, FERC would have to approve other changes (Chair Laura Swett, Trump appointee, represented electric utilities and the firm wrote pieces about the glory of data centers - one of the Amicus Briefs they wrote in 2024 was on behalf of… NV Energy)Of the fines issued by FERC this year, 99% are one company: an energy efficiency companySince Trump was elected, FERC has issued fines targeting blue state utilities and renewables at a more than 2:1 rateSo the people are fucked - maybe Warren can tell them to power their town on See's Candy sugar rushesHeadliniest of the WeekDR: Kids with fake mustaches can fool high-tech age verification systemsMM: Waymo recalls 3,800 robotaxis after glitch allowed some vehicles to 'drive into standing water'Who Won the Week?DR: Steve Roth, the CEO of Vornado Realty Trust, expressed his support for fellow billionaire and the Citadel CEO Ken Griffin: “I must say that I consider the phrase tax the rich — quote tax the rich — when spit out with anger and contempt by politicians both here and across the country, to be just as hateful as some disgusting racial slurs”MM: Lawyers - literally everything now is a lawsuit and everyone is a lawyer. PredictionsDR: NYC Mayor Mahmdani asks Steve Roth for “just little more” and Roth replies: “I'm not a fucking Chipotle, commie scum.”MM: Chili's CEO wakes up at 5 a.m., runs daily, and uses that time to generate ideas for the business: On a run next Thursday, May 21, Chili's CEO Kevin Hochman stops short and says out loud, “What if the Big Crispy Chicken Sandwich was BIGGER???”

With Ricky Smith—Founder & Managing Partner, Inspired Wealth Planning Overview Jason Diamond speaks with Ricky Smith of Inspired Wealth Planning about leaving Edward Jones after 30 years, evaluating 12 firms, and building an independent business that grew to $1.25B in assets under care in less than three years. Listen in… > Download a transcript of this episode… NOTE: The views and opinions expressed by the guests on this podcast are their own and do not necessarily reflect the views and opinions of Diamond Consultants. Neither Diamond Consultants nor the guests on this podcast are compensated in any way for their participation. Watch… https://youtu.be/cobAfEl0_To About this episode… What happens when you stop thinking like a renter and start thinking like an owner? Not just in theory, but in how you run your business, make decisions, and show up for clients. For Ricky Smith, that question didn't come at the beginning of his career. It came 30 years later, after building a highly successful practice at Edward Jones and beginning to see the business through a different lens. Today, Ricky is the founder and managing partner of Inspired Wealth Planning, the independent firm he built with Kestra Private Wealth Services. Since launching in March 2023, the firm has grown to over $1.25B in assets under its care across seven locations. What makes this story interesting isn't just the move—it's how intentional it was. Ricky didn't rush into independence. He spent a year evaluating 12 different firms and paths, clarifying what mattered most, and ultimately making a decision based on people and alignment, not just economics. Ricky shares his journey with Jason Diamond, including: His approach to due diligence—and why he dove deeper into the weeds before he was satisfied with his next steps. Reconsidering the wirehouse model—and why he felt independence was the best path forward. The “ownership mindset”—and how that drives his values and processes. The early phase of independence—and why it's less about growth and more about getting the structure right. Growing by 50%—and what “breakthroughs” he had in less than three years. Ricky offers the perspective that making the leap to independence may be “short-term hard,” but you're working toward building a business that's designed to be “long-term easy.” And there's another broader idea worth paying attention to: Most advisors don't lack options; they hesitate to act on them. Listen in for sage advice from an advisor who has lived in the wirehouse world and is now independent—and has realized the value of ownership. Want to learn more about where, why, and how advisors like you are moving? Click to contact us or call 908-879-1002. Related Resources Diamond Consultants Edward Jones Advisor Transition Report 2025This “firm-focused report” seeks to look under the hood at movement to and from Edward Jones from January to June of 2025. The Cost of Clarity: What Advisors Stand to Gain and Lose When Their Firm Shows Its HandWhen firms become explicit about who and what they value, it's time for advisors to read those signals and respond. The Advisor Transition Playbook: The Latest on Due Diligence, the Move, and Everything In Between – Part 2Jason and Mindy Diamond revisit the transition playbook, this time focused on how advisor priorities are shifting. From AI and enterprise value to stability and flexibility, they unpack what's changing in due diligence and what it means for advisors evaluating their next move. Ricky SmithManaging Partner Ricky Smith is the founder and Managing Partner of Inspired Wealth Planning. Inspired Wealth Planning is group of like minded veteran financial advisors who serve their clients and local communities across Georgia and now even Ohio. Before founding Inspired, Ricky worked as a financial advisor for 39 years. Primarily as an employee of a nationwide financial firm. Wanting to have more control over the outcomes for clients, his team and his own career, he left the employee model to join an independent firm – Kestra Private Wealth Services. After opening the Kestra based office, other advisors inquired about joining Inspired. Within the first 36 months, Inspired grew to 7 locations, 10 advisors, 14 support staff and over $1.2 billion in assets under care. In February 2026, Inspired was selected as the Outstanding Business of the Year for Kestra Financial (the parent company of Kestra Private Wealth). This was the first time that any firm from Kestra Private Wealth had ever been selected for that award. In early April the firm was on the cover of Advisor Hub magazine and in mid-April, Ricky was selected for the Forbes/Shook Best in State Wealth Advisors for the state of Georgia. An Honor that he has received 3 times in the past 5 years. Ricky lives in Cordele Georgia with his wife, Patti and their tuxedo cat Oreo. They have a daughter, Brooke, who lives in Maryland. Ricky has been a loyal member and participant with the local Chamber of Commerce for 42 years, serving as chairman in 1999. He and Patti are long-time members of Cordele First Church and supporters of the local chapter of Celebrate Recovery.

4.3 million industrial robots are already deployed globally. Robot costs have dropped 50% in 30 years. Payback periods are now 1 to 3 years. The reshoring of American manufacturing isn't a forecast — it's a buy order.This week on Money On Tap, Ben Brayshaw and Dan Michelon continue the series with The Railroads of Robotics — the picks-and-shovels playbook for physical AI and the next great industrial build-out.What you'll learn:Why three forces — reshoring, labor shortage, and 1–3 year robot payback — make automation inevitableThe four investable layers: robots · AI systems · software · hardwareA walk-through of the public names: Rockwell Automation, Teradyne, Emerson Electric, NVIDIA, Tesla (Optimus), AeroVironment, Applied Materials, AutodeskHow cobots are reshaping skilled-trades work — and what the NVIDIA CEO's "three-day work week" prediction really meansFive robotics-themed ETFs walked through: ROBO, BOTZ, IBOT, ARKQ, ROBTWhat to tell the kids and grandkids about which jobs will actually exist in 10 yearsThe geopolitical risk that could shelve this entire build-out overnightPlus Money In The News:United Airlines hikes fares up to 20% — CEO admits passing 100% of jet-fuel cost to consumersMusk vs. Altman: a $134B suit heading to court while SpaceX ($1.25T) and OpenAI ($850B) IPOs loomAdobe announces a $25B buyback (25% of market cap) while Big Tech keeps laying off — and the buyback nuance most investors missRead the companion blog: brayshawfinancial.com/blogSchedule a free consultation: app.greminders.com/t/9f3ce72e/initialconsultaFull Money On Tap episode library: brayshawfinancial.com/money-on-tapContact UsPhone: 855-226-8551Email: info@yourmoneyontap.comOffice: 116 South River Road, Bedford, NH 03110Web: brayshawfinancial.comWhat is "physical AI" and why does it matter for investors? Physical AI is the application of artificial intelligence to machines that operate in the real world — industrial robots, cobots, autonomous vehicles, drones, and humanoid robots. Unlike AI software that lives only on a screen, physical AI directly performs labor: assembling products, moving materials, inspecting quality, and operating equipment. For investors, it converts the AI thesis into measurable productivity gains and physical reshored capacity.

Welcome back to the Konfidence in the Klutch Podcast with Donald Nelson (2:15). Konfidence in the Klutch's Deezus gives his Konfident Service Announcement: Be You (2:30).  Deezus gives his thoughts on "Michael", and he doesn't understand the hate towards the movie (5:30).  Deezus then shares his NBA news, including the league releasing anti-tanking measures that will be voted on.  Masai Ujiri named Mavs team president.  Jahmal Mosley fired in Orlando.  Detroit advances. Is Orlando a Giannis destination?  Cavs advance.  Is it now or never for Spida Mitchell?  Knicks advance.  Did they figure it out, and the Hawks should be proud of their season?  Sixers advance.  Who gets traded on the Celtics?  Lakers advance.  Are the Rockets having buyer's remorse knowing they could've used those assets to land Giannis?  Wolves advance.  The Nuggets just had an identity and reality check.  Spurs advance.  Blazers ownership hires Splitter, or you will be cursed by the basketball gods.  Deezy finishes up with series previews and action from the Eastern and Western Conference semi-finals (14:15). Deezy then shares his WNBA thoughts, including A'Ja, Angel, and Paige at the Met.  Deezus shares his KITK WNBA Fantasy roster (32:15). Deezus discusses 'Politics as usual,' including how the U.S. has spent over $25B on a sixty-plus-day exercise.  Does anyone know WTF is going on over there, anyway? (33:50). Deezus shares his Quick Ones: Meg and/or Klay step up for Lexie; Stefon Diggs vs. The Chef is OC.  Doechi at the Met (37:00). The podcast was recorded at 5:00 p.m. CT on Tuesday, May 5, 2026.  Host: Donald Nelson Producer/Engineer: Donald Nelson Music by: Konfidence in the Klutch Productions Subscribe, Stream, or Download:

May 2, 2026, 9 AM ; Trump's claim came on what was the 60-day deadline for him to gain approval from Congress to continue the war with Iran. Meanwhile, in his first Capitol Hill appearances since the start of the war, Defense Secretary Pete Hegseth was grilled about a host of issues, including the cost of the war, which the Pentagon put at $25B dollars over two months. Massachusetts Democratic Rep. Seth Moulton joins The Weekend to discuss Hegseth's testimony to congress and the conflict's mounting price tag. For more, follow us on social media: Bluesky: @theweekendmsnow.bsky.social Instagram: @theweekendmsnow TikTok: @theweekendmsnow To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Thursday, April 30th, 2026 Today, the Supreme Court gutted what was left of the Voting Rights Act in a 6-3 ruling over Louisiana maps; Trump's FCC ordered a review of ABC's broadcasting license because Jimmy Kimmel makes jokes; the DOJ is dismissing conspiracy charges against the Broadview six including Kat Abughazaleh; a Pentagon official tells Congress the war in Iran has cost $25B so far; right after Democrats filed contempt charges against Pam Bondi she announced she'll be appearing for a deposition May 29th; a new Colorado conversion therapy ban with a clever mechanism is close to passing in the state; and Allison and Dana deliver your Good News. Thank You, Mint Mobile Make the switch! MINTMOBILE.com/DAILYBEANS Thank You, Coyuchi Get 15% off your first order when you visit Coyuchi.com/dailybeans The Daily beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser   Guest: Traci Feit Love - Lawyers for Good Government@tracifeitlove.bsky.social National Law Day of Action 2026: Find an Event Near You National Law Day of Action Lawyers for Good Government - @lawyers4goodgov.bsky.social The Latest Breakdown:Todd Blanche's Unhinged Midnight Ballroom Motion StoriesFlorida Approves Redistricting Map That Could Add 4 Republican House Seats | The New York Times FCC Orders a Review of ABC's Licenses Amid Feud Between Trump and Kimmel | The New York Times Feds to Dismiss Conspiracy Charges Against Remaining ‘Broadview Six' Defendants | Chicago News | WTTW New Colorado Conversion Therapy Ban With Clever Mechanism Close To Passing | Erin In The Morning   Good Trouble National Law Day of Action 2026: Find an Event Near You National Law Day of Action MAY DAY STRONG Find a march or rally in your area:May Day Participation Guide | Indivisible →Letter Carriers' “Stamp Out Hunger“ Food Drive →FieldTeam6.org →Palmetto State Abortion Fund - Midland Gives  →2026 Primary Election Calendar: All the Dates Ahead of Midterms →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List  →iceout.org Good NewsLeavingMAGA.org   →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com Subscribe to the MSW YouTube Channel - MSW Media - YouTube Harry Dunn is running for CongressHarry Dunn for Maryland Our Donation Links The Daily beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans with a MATCHED Donation http://onecau.se/_ekes71 More Donation LinksNational Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG at fedoath@pm.me and let me know what you're going to do, or just vent. I'm always here to listen.  Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The CEO of Qualcomm joins the show with the stock surging after results. The company saying it has a new mystery hyperscalers customer which it will reveal in the coming months. Plus, the CEO of commercial real estate firm JLL on earnings and the opportunity in data centers. Then CNBC speaking with sources, confirming Meta plans to tap the bond market in an effort to raise $20-25B. The stock falling on concerns over its capex plans. And can consumers expect any relief at the pump with gas prices at 2022 highs? We discuss. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Matt Cornwell — 130+ credits, 20+ years in the Southeast, and the man behind Get Taped's 350+ videos — joins Get Scene host Jesse Malinowski to unpack the real reason most actors aren't booking, the insurance backdoor inside casting offices, and why he refuses to coach during your self-tape. Plus: how Georgia still beats LA by $1.25B in tax incentives, his Steve Martin philosophy on staying in the top 10%, and the celebrity-set booking he believes belonged to another actor.Join Our Newsletter:https://getscenestudios.us7.list-manage.com/subscribe?u=7660af20fdb3c04d6b6516591&id=eecb804958Monthly Promo :Use code BOOKEDPOD for $10 off the 4-week Booking Challengehttps://www.getscenestudios.com/getsceneonline/booked-it-challenge-5zka6Join out Patreon!⁠https://www.patreon.com/cw/GetScenePod⁠

In this episode of Tank Talks, Matt Cohen and John Ruffolo break down one of the biggest economic policy announcements in Canada's innovation economy: Mark Carney's proposed $25 billion Canada Strong Fund, a sovereign wealth fund designed to invest in nation-building projects, strategic industries, Canadian technology companies, and long-term economic sovereignty. John, who previously argued for this type of fund in his Substack piece Canada's Missing Pot of Gold, explains why Canada's biggest structural problem is undercapitalization and why relying on foreign direct investment for critical industries creates serious sovereignty risks.Matt and John dig into the hard questions behind the fund: Where does the money come from? Can Canada borrow at low rates and invest for long-term returns? How should the fund be governed so it does not become a political slush fund? And can this vehicle finally force a more serious conversation around Canadian pension funds, domestic capital formation, and backing companies like Cohere, Kepler, and Xanadu before they are pushed toward foreign capital markets?The episode also covers Cohere's acquisition of German AI firm Aleph Alpha, the rise of sovereign AI alternatives outside the U.S. and China, Xanadu's volatile post-SPAC quantum stock run, SpaceX's reported Cursor acquisition talks, Meta's 8,000-person AI-driven workforce reduction, and Thoma Bravo's massive Medallia equity wipeout. From sovereign wealth and AI infrastructure to quantum financing and private equity pain, this episode asks the real question: can Canada build the capital systems needed to own its future?Canada Strong Fund: Carney's $25B sovereign wealth fund announcement (00:31)Matt opens the episode by laying out the breaking news: Mark Carney has launched the proposed Canada Strong Fund, a $25 billion sovereign wealth fund aimed at giving Canadians a stake in strategic national projects and critical industries.Why John Ruffolo says Canada is dangerously undercapitalized (01:22)John argues that Canada's core economic problem is not a lack of ideas, talent, or companies, but a lack of domestic capital formation. He explains why foreign-controlled capital in sovereign industries is a bad idea and why Canada needs its own funding mechanism.The biggest risk: governance or political slush fund? (03:14)John explains that the Canada Strong Fund will only work if it is independently governed, similar to CPPIB or CDPQ. Without strong governance, he warns, the fund could collapse into politically motivated pet projects.Can Canada borrow at 3.5% and earn 7% long term? (04:59)John breaks down the financial logic behind using Canada's strong credit rating to borrow at lower rates and invest through a professionally managed fund targeting long-term returns similar to major pension funds.Why the fund fails if returns do not materialize (08:15)Matt raises concerns about launching a sovereign wealth fund during a deficit environment. John says the idea only works if the fund is independently managed and capable of generating real long-term returns.No more grants: John's blunt plan for government funding (14:02)John calls for Canada to stop giving grants, especially to foreign-based companies, and instead convert government support into equity investments that create long-term ownership and capital recycling for the country.Cohere acquires Aleph Alpha and makes a sovereign AI play (16:12)Matt breaks down Cohere's acquisition of German AI firm Aleph Alpha, the new Berlin European headquarters, and the reported $600 million financing commitment from Schwarz Group as part of a broader sovereign AI strategy.Xanadu's quantum stock surge and post-SPAC volatility (19:59)Matt explains Xanadu's post-SPAC trading action, including its sharp rise, options activity, and SEC filing registering nearly 300 million Class B shares for sale after the lockup period expires.SpaceX, Cursor, and peak AI paper-deal froth (24:25)Matt and John react to reports that SpaceX could acquire AI coding startup Cursor for $60 billion, with John arguing that SpaceX shareholders should be furious about the growing complexity and governance concerns.Meta layoffs and the real cost of AI capital spending (27:56)Matt highlights Meta's reported 10% workforce reduction tied to massive AI capital spending. John argues the “AI efficiency” explanation often masks bad capital allocation and failed strategic bets.Thoma Bravo's $5.1B Medallia equity wipeout (29:55)The episode closes with Thoma Bravo handing Medallia back to creditors after a major private equity software deal collapses, raising questions about SaaS valuations, debt structures, and exit assumptions in the AI era.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

The AI world is moving fast… maybe faster than most people realize.In this episode, I break down 4 major stories that are shaping where AI, software, and business are headed right now:A powerful new model from Anthropic that was built… then locked awayOpenAI hitting $25B in revenue and what an IPO could meanThe $5B vibe coding boom (and why most builders aren't developers)Elon Musk's $250B power move with xAI, SpaceX, and CursorThis isn't just news — these shifts are changing how apps get built, who builds them, and who controls the future of AI.If you're building, investing, or even just paying attention… this one matters.

This week on Ricker and Bon: Tim Cook exits Apple, Tesla raises its 2026 capex to $25B, OpenAI ships GPT-5.5 "Spud," SpaceX eyes a $60B acquisition of Cursor, and consumer sentiment hits a 74-year low.

This week, we discuss agents taking over at Google Cloud Next, Apple's new CEO, and Cursor getting acquired (sort of). Plus, Coté's e-waste has no exit strategy. Watch the YouTube Live Recording of Episode 569 Runner-up Titles I love throwing stuff in the trash. Dillo dirt's a thing. BurgerOps. Thomas opens for Richard. Department of “No” people Starfish Stomach Model Enterprise — come into me The Organization will Assimilate it Gold plaques all around He can let his freak flag fly Take the first billion dollar offer Rundown Google Next Welcome to Google Cloud Next26 Google's AI adoption — Steve Yegge X Post Tanzu Platform 10.4: a private cloud platform for AI harnesses (or, "agentic AI") Apple becomes a $4 Trillion under Tim Cook Cursor Watch Cursor in talks to raise $2B+ at $50B valuation as enterprise growth surges There's No Time for SpaceX to Buy Cursor SpaceX says it can buy Cursor later this year for $60 billion or pay $10 billion for 'our work together' Relevant to your Interests Poland street sees humanoid robot chasing boars in unusual AI showcase Someone planted backdoors in dozens of WordPress plug-ins used in thousands of websites Snapchat owner cuts 16% of global staff in latest round of job cuts Email for agents - Cloudflare Email Service now in public beta DeployBar — Free CI monitoring. Unsolicited platypus included. Let them tinker - hacking developer resistance to sound enterprise architecture and platforms China's DeepSeek is raising funds at $10 billion valuation, The Information reports Sources: Cursor in talks to raise $2B+ at $50B valuation as enterprise growth surges AI chipmaker Cerebras files to go public after scrapping IPO plans last year Amazon to invest up to $25B in Anthropic as part of expanded cloud partnership - SiliconANGLE Amazon to invest up to $25 billion more in Anthropic; Claude developer to spend more than $100 billion on AWS AI technology Amazon and Anthropic expand strategic collaboration Anthropic CPO leaves Figma's board after reports he will offer a competing product OpenAI loses multiple executives in latest leadership shakeup Scoop: NSA using Anthropic's Mythos despite Defense Department blacklist The scientific case for being nice to your chatbot Anthropic's redesigned Claude Code desktop app lets you burn through tokens even faster OpenAI's Codex Mac app adds three key features that go beyond agentic coding Introducing Claude Opus 4.7 Anthropic Sponsors WebRTC.ventures – Real-time communication & Voice AI integration WeAreDevelopers World Congress North America Sept 23–25, San José, CA Use Code DEVPOD26 — 15% off, stacks with group rates for 4+ Listener Feedback Subscribe to Failover New Nonsense Struggling shoe retailer Allbirds makes bizarre pivot from shoes to AI, stock explodes more than 700% Allbirds Stock Now Crashing as Reality Sets in About Its Delusional AI Pivot Conferences DevOpsDays Austin, May 5-6, 2026 DevOpsDays + AI Nashville, May 14-15, 2026 KCD Texas, May 15, 2026, use code MEDIA_THANK_YOU for free pass WeAreDevelopers Europe, July 8-10, 2026 Berlin, Coté speaking. DevOpsDays Graz, Sept 4-5, 2026 DevOpsDays Dallas, Sept 28-29, 2026 DevOpsDays Rockies, Sept. 22 – 23, 2026, Discount Code: 26DODSWEDEFTALK WeAreDevelopers NA, Sept 23-25, 2026, Discount Code: DEVPOD26 DevOpsDays Vilnius, Sep 30 - Oct 1. 2006 DevOpsDays Istanbul, October 24th, 2026 - Coté keynoting. VMware User Groups (VMUGs): Toronto (May 12-14, 2026) Dallas (June 9-11, 2026) Orlando (October 20-22, 2026) SDT News & Community Join our Slack community Email the show: questions@softwaredefinedtalk.com Free stickers: Email your address to stickers@softwaredefinedtalk.com Follow us on social media: Twitter, Threads, Mastodon, LinkedIn, BlueSky Watch us on: Twitch, YouTube, Instagram, TikTok Book offer: Use code SDT for $20 off "Digital WTF" by Coté Sponsor the show Sponsor more podcasts with Failover Media Recommendations Brandon: Claude /team-onboarding The Junior Dev Crisis: Who Inherits the Code When AI Does the Work Matt: Resident Alien Coté: and

Tesla Q1 2026 earnings are hot off the press, and the biggest announcement wasn't in the results but on the conference call. Elon Musk and Tesla are now upping CAPEX guidance to $25B in 2026! That's right, they want to spend $25BILLION this year on investments on AI and robotics. This means they will be losing free cash flow and could mean they will have to raise more capital in the future. All to fund ambitious projects like Cybercab, Robotaxi, Optimus training, Terafab AI Chips & more! What are your thoughts on this massive news!??!My X:   / gfilche  HyperChange Patreon :)   / hyperchange   Disclaimer: Nothing in this show is financial advice I'm long Tesla.

Apple named John Ternus as its next CEO, with Tim Cook stepping up to executive chairman on September 1. Amazon agrees to invest up to $25B more in Anthropic, Bezos' Project Prometheus nears a $10B raise, and SpaceX's IPO prospectus reveals Musk's power moves. John Ternus, senior VP of Hardware Engineering, will become Apple's next CEO on September 1; Tim Cook will become executive chairman of Apple's board (CNBC) Amazon agrees to invest up to $25B in Anthropic, on top of the $8B that it has already invested; Anthropic commits to spend $100B+ on AWS over the next 10 years (CNBC) Sources: Jeff Bezos' Project Prometheus is close to a $10B fundraising deal, which includes an initial $6.2B raise in November, at a $38B post-money valuation (FT) Draft of SpaceX's confidential IPO prospectus: Elon Musk increased his stake in SpaceX last year by purchasing $1.4B of stock from current and former employees (The Information) Learn more about your ad choices. Visit megaphone.fm/adchoices

D.A. Davidson's Gil Luria talks with TITV Guest Host Anita Ramaswamy about Apple's CEO transition and Amazon's $25B investment in Anthropic. We also talk with The Information's Valida Pau and Cory Weinberg about Elon Musk's increased stake in SpaceX and the company's upcoming IPO. On the ground at Adobe Summit 2026, TITV Host Akash Pasricha joins Adobe CMO Rachel Thornton and Amit Ahuja about the launch of new AI agents and enterprise partnerships with Nvidia, Microsoft and Anthropic. Lastly, we get into agentic commerce with DICK'S Sporting Goods' VP of Product Jason Cherok and the macro state of AI adoption with Adobe's Senior Director & Head of Adobe Digital Insights Taylor Schreiner.Articles discussed on this episode: https://www.theinformation.com/articles/musk-bought-1-4-billion-spacex-shares-helping-boost-controlSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Netflix beat on revenue and income but dropped 10%+ on weak Q2 guidance as Reed Hastings exits the board. Anthropic launches Claude Design, OpenAI overhauls Codex Desktop with computer control, and DeepSeek seeks its first outside funding at $10B+. Netflix reports Q1 revenue up 16% YoY to $12.25B, vs. $12.2B est., net income up 83% YoY to $5.28B, and forecasts Q2 EPS and revenue below est.; NFLX drops 10%+ (Bloomberg) Anthropic launches Claude Design, a new experimental product that lets users create visuals like prototypes, slides, one-pagers, and more using Claude (TechCrunch) Sources: Dario Amodei is set to meet with WH Chief of Staff Susie Wiles on Friday, a breakthrough in Anthropic's effort to resolve its fight with the Pentagon (Axios) OpenAI updates its Codex desktop app with features like computer control, an in-app browser, image generation, automation memory, plugin support, and more (ZDNet) Sources: DeepSeek is in talks to raise outside capital for the first time, seeking at least $300M at a valuation of at least $10B (The Information) Longreads India produces 1.5M+ CS graduates annually, but AI coding tools are forcing its $315B IT outsourcing industry into an existential reckoning (Bloomberg) Doug Liman's $70M movie Bitcoin: Killing Satoshi uses AI for sets, lighting, and more in post-production, cutting costs from an estimated $300M (The Wrap) Defunct startups are being liquidated for their Slack archives, Jira tickets, and email threads—operational exhaust that AI labs now treat as premium training data (Forbes) Learn more at liquid.trade/techbrew. Disclaimer: ● Initial 3 week subscription and 4 weeks of medication from $79 plus tax and $179 per month plus tax for 12 week subscription thereafter. Final pricing depends on program selection. ● Noom GLP-1Rx Program involves healthy diet, exercise and support. Individual results vary. Meds & personalization based on clinical need. Not reviewed by FDA for safety, efficacy, or quality. No affiliation with Novo Nordisk Inc., the only US source of FDA-approved semaglutide. Not available in all 50 US states ● Based on an analysis of self reported data from 1,254 engaged Noom users. Learn more about your ad choices. Visit megaphone.fm/adchoices

The NIA boys discuss Claude Mythos, The Clipping Economy & Anthropic's $30B vs. OpenAI's $25B (?)Timestamps(00:00:00) - Intro(00:03:20) - Claude Mythos(00:13:27) - The Clipping Economy(00:23:16) - Claude Mythos (pt2) (00:27:09) - Anthropic's $30B vs. OpenAI's $25B (?)What Is Not Investment Advice?Every week, Jack Butcher, Bilal Zaidi & Trung Phan discuss what they're finding on the edges of the internet + the latest in business, technology and memes.Subscribe + listen on your fav podcast app:Apple: https://pod.link/notadvicepod.appleSpotify: https://pod.link/notadvicepod.spotifyOthers: https://pod.link/notadvicepodListen into our group chat on Telegram:https://t.me/notinvestmentadviceLet us know what you think on Twitter:http://twitter.com/bzaidihttp://twitter.com/trungtphanhttp://twitter.com/jackbutcherhttp://twitter.com/niapodcast Hosted on Acast. See acast.com/privacy for more information.

Today we were pleased to welcome Doug Lambert, Co-Founder and Chief Operating Officer of Saronic Technologies. Doug, alongside Dino Mavrookas (CEO), Rob Lehman (CCO), and Vibhav Altekar (CTO), co-founded Saronic in 2022 to advance maritime superiority through intelligent autonomous systems. Saronic's mission is to equip the U.S. and its allies with advanced autonomous surface vessels, enhancing situational awareness and enabling more effective detection, tracking, and response to emerging maritime threats. The company recently closed a $1.75B Series D at a $9.25B valuation and announced a new downtown New Orleans office to support its expanding shipbuilding operations in Louisiana. We were thrilled to spend time with Doug and explore autonomy, maritime innovation, and the future of naval and offshore operations. In our conversation, Doug provides an overview of Saronic, their product range, and rapid growth to ~1,500 employees. We discuss the convergence of enabling technologies (AI, machine learning, edge compute, and advanced sensors) that have made true maritime autonomy possible, why this moment is different from prior attempts, and the combination of technology breakthroughs, market tailwinds, and geopolitical developments that have accelerated adoption. We explore Saronic's approach to designing purpose-built autonomous vessels, as well as their decision to vertically integrate across design, manufacturing, and operations, highlighting how scale production, control of the full system, and a data-driven flywheel are critical to driving down costs and unlocking broader adoption. We examine the strategic implications of autonomy and how these platforms act as force multipliers across defense, offshore energy, and critical infrastructure. Doug shares his perspective on the concept of a hybrid fleet, where autonomous systems augment traditional assets, extend reach, and improve safety, and how this shift could reshape maritime strategy over time. We cover the importance of edge-based decision making versus cloud reliance, and how real-world deployment and data collection underpin both performance and competitive advantage. We also touch on the broader industrial and cultural backdrop, including the reindustrialization of U.S. shipbuilding, the blending of software and skilled trades, and the growing importance of building in the physical world. We discuss workforce dynamics, labor constraints in maritime, adoption challenges, the gap between technical readiness and real-world trust as autonomy moves from concept to scaled deployment, and much more. It was a wide-ranging discussion and we're thankful to Doug for sharing his time and unique insights. Mike Bradley started the show by noting that the 10-year bond yield had moved down to ~4.27% following a softer-than-expected March PPI report (YoY +4%). While still elevated, the print came in well below consensus and remains far below the 11.7% peak seen during the Biden Presidency. On the oil market front, WTI was trading at ~$92/bbl, down $6–$7 on the day and $4–$5 since President Trump announced the Strait of Hormuz blockade over the weekend. He noted that global oil prices also moved lower on optimism around a potential second round of Iranian peace talks, as well as a meaningful downward revision (~730 kbpd) to the IEA's 2026 demand outlook. Traders are now less focused on how high prices could go and more focused on how low they could fall if and when the Strait of Hormuz reopens. On the broader equity market front, the S&P 500 was up ~1% on the day and trading within ~0.5% of its all-time high, highlighting a notable divergence between energy market concerns and broader market optimism. Within equities, Energy was the worst-performing S&P sector on the day and has effectively round-tripped since the onset of the Iran conflict, now down ~11% from its March peak. Looking ahead, Oil Services Q1 earnings begin next

Why can't you just use canola oil from your kitchen to kill spider mites? If you've ever wondered about the true science behind oil-based pesticides, this episode is a deep dive into the physics and chemistry of plant protection. Tad and Julie Graesch explore why oils remain one of the most effective tools for managing resistant pest populations like two-spotted spider mites and thrips. In this episode, we cover: Physical Modes of Action: How oils kill through suffocation and desiccation, and why pests can't develop resistance to being physically smothered. Mineral vs. Essential Oils: The functional differences between horticultural mineral oils (like SuffOil-X) and 25B essential oils (like Epishield). The Power of Formulation: Why inert ingredients, surfactants, and molecular weight are just as important as the active ingredients for preventing phytotoxicity. Dipping vs. Spraying: The high-stakes world of plant dips and how to target specific life stages—including the elusive thrips egg. Tank Mixing & Compatibility: Best practices for combining oils with microbials like Beauveria bassiana to create a synergistic knockdown effect. Whether you are a small-scale hobbyist or a large-scale commercial cultivator, understanding how to master oils will help you maintain a cleaner garden with fewer chemical interventions. You can order Bioworks products through KiS Organics, just reach out if you have any questions. About the Guest: Julie Graesch is the Technical Services Manager for BioWorks. With over 18 years of experience as a biological scientist, Julie has worked in laboratory, greenhouse, and field research, specializing in integrated pest management (IPM) for the horticulture industry. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

OpenAI locks in the largest private funding round in history, Google disrupts memory economics with a major efficiency breakthrough, and NVIDIA continues to consolidate control over AI infrastructure. This week, Patrick Moorhead and Daniel Newman unpack the clear shift from model competition to full-stack execution.

The global healthcare system is heading for a catastrophic workforce shortage — 10 to 15 million workers short by 2030. In this episode, financial analyst and Tesla expert Cern Basher (@cernbasher) returns to break down how humanoid robots like Tesla's Optimus could be the only scalable solution, why New Zealand is uniquely positioned to lead a healthcare robotics pilot, and what the Terafab chip factory and Digital Optimus mean for the timeline. From fleet learning to the privacy concerns, from Moxy robots already in 25+ US hospitals to Elon Musk's vision of billions of robots powered by space-based AI — this is the conversation that healthcare, tech, and policy leaders need to be having right now. IN THIS EPISODE: The global healthcare workforce crisis — why no recruitment drive can fix it Robots already in hospitals: Moxy's 1M+ deliveries across 25 US hospitals Tesla Optimus: where the technology is right now The three components: physical body, AI brain, and language model Fleet learning explained — 40 robots learn in 6 months what takes a human 25 years Why New Zealand is the ideal proving ground for healthcare robotics Digital Optimus and Macrohard: the software robot that runs businesses Terafab: Tesla's $25B chip factory with SpaceX and xAI This episode is sponsored by MitoSynergy Copper 1+ Most copper supplements use poorly absorbed oxidised forms that can actually increase free radical damage. MitoSynergy's patented BioCopper1 (Cunermuspir) is a copper-niacin chelate that delivers reduced copper (Cu1+) directly to your mitochondria, supporting ATP production at cytochrome c oxidase. I've been personally testing MitoActivator EX and have noticed a real difference in training power and energy output. Try it: https://mitosynergy.com/lisaTamati (10% off with this link) ABOUT CERN BASHER: NZ-born financial analyst and one of the most influential voices on X at the intersection of AI, Bitcoin, Tesla, and macroeconomics. Follow Cern: @cernbasher on X and YouTube -------------------------------------------- PTL SIGNAL — AI, Tech, Bitcoin and Markets: https://ptlsignal.com Free founding member access to our AI-powered Financial Document Analyzer — earnings transcripts, annual reports, Fed minutes analysed in 60 seconds. Take control of your health and unlock the secrets to a longer, healthier and more vibrant life: https://www.lisatamati.com/healthspan-hacks-course/ SHOP Longevity Supplements: https://shop.lisatamati.com -------------------------------------------- PODCAST — Pushing The Limits: https://www.lisatamati.com/podcast https://podcasts.apple.com/nz/podcast/pushing-the-limits/id1207975008 https://open.spotify.com/show/6mc5BfQispXYMxd4AaYXYL FOLLOW LISA: Instagram: @lisatamati X/Twitter: @lisaytamati YouTube: @LisaTamati Enquiries: support@lisatamati.com Website: www.lisatamati.com Shop: shop.lisatamati.com PTL Signal: ptlsignal.com

This week on Swimming with Allocators, Earnest and Alexa chat with Anthony Giambrone, Partner at StepStone Group. Anthony shares his unconventional path from gas station manager and nightclub worker to leading a major global venture allocation platform. The conversation covers his break into investment banking, the scaling of GreenSpring into StepStone, and why relationships, EQ, and consistency across vintages matter more than market timing in venture. Key takeaways include the power-law nature of VC returns, how emerging managers and spinouts can stand out with a real edge and long-term relationship-building, why asset quality matters more than discounts in secondaries, and how AI, liquidity pressures, and longer private company lifecycles are reshaping the next decade of venture capital. Also, Rebecca Stuart, an employment-focused partner at Sidley, explains how she helps venture-backed companies navigate complex employment and co‑founder separations, equity and vesting pitfalls, evolving worker classification and pay transparency laws, and the fast-changing regulatory landscape around AI in hiring and employment decisions. Highlights from this week's conversation include: Anthony's Background and Humble Beginnings (0:42) Importance of Empathy and Relationships in Venture (4:16) Applying Greenspring/StepStone Experience to Today's Market (6:15) StepStone Venture Team, AUM, and Global Footprint (8:07) Why You Can't Time Early Stage Venture (9:38) Vintage Volatility and Power Law in Venture Outcomes (11:23) How Founder Ambition Affects GP and Fund Diligence (14:28) Insider Segment: Co‑Founder Divorce (18:04) Using New Investments to Clean Up Equity and IP (21:43) Employees Demanding Human Review in AI‑Driven Processes (25:43) Fund Slot Constraints and LP Down‑Selection (28:33) Advice for New LPs on Capturing Upper Quartile Returns (31:36) Is Top Quartile Performance Still Good Enough? (33:08) Secondaries Strategy and Asset Quality Over Discounts (34:31) Liquidity Pressures, DPI, and GP‑Led Solutions (38:37) StepStone's 10‑Year Lifecycle Partner Vision (40:35) StepStone Group is a global private markets firm focused on providing customized investment solutions and advisory and data services to its clients worldwide. The firm's venture capital and growth equity platform, built on the foundation of Greenspring Associates, manages $25B+ in AUM across primary fund investments, secondaries, and co-investments, as of June 30, 2025. Learn more at www.stepstonegroup.com. Sidley Austin LLP is a premier global law firm with a dedicated Venture Funds practice, advising top venture capital firms, institutional investors, and private equity sponsors on fund formation, investment structuring, and regulatory compliance. With deep expertise across private markets, Sidley provides strategic legal counsel to help funds scale effectively. Learn more at sidley.com. Swimming with Allocators is a podcast that dives into the intriguing world of Venture Capital from an LP (Limited Partner) perspective. Hosts Alexa Binns and Earnest Sweat are seasoned professionals who have donned various hats in the VC ecosystem. Each episode, we explore where the future opportunities lie in the VC landscape with insights from top LPs on their investment strategies and industry experts shedding light on emerging trends and technologies.  The information provided on this podcast does not, and is not intended to, constitute legal advice; instead, all information, content, and materials available on this podcast are for general informational purposes only. Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to The Daily Wrap Up, an in-depth investigatory show dedicated to bringing you the most relevant independent news, as we see it, from the last 24 hours (3/26/26). As always, take the information discussed in the video below and research it for yourself, and come to your own conclusions. Anyone telling you what the truth is, or claiming they have the answer, is likely leading you astray, for one reason or another. Stay Vigilant. !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v75hm4a","div":"rumble_v75hm4a"}); Source Links (In Chronological Order):  (7) The Last American Vagabond on X: "@DBrozeLiveFree One of MANY obvious and well-documented cases of Americans being illegally detained or abused by ICE. https://t.co/hhurrVqnej" / X DHS Lies About Detaining/Deporting US Citizens & Trump Reportedly Readying To Attack Venezuela New Tab (7) The Last American Vagabond on X: "@DropSiteNews I am glad you guys are covering this, it needs more attention. Here is TLAV's coverage on this story from February: https://t.co/jVJjiONeZa" / X She uncovered a terrifying lab hidden in California, with alleged ties to China - Los Angeles Times Israeli Citizen Charged In The Las Vegas "Biolab" Case As Fort Detrick Investigates Sabotage (7) Las Vegas Locally

In Episode 242 of China EVs & More, Tu Le and Lei Xing break down a pivotal week in the global EV industry — one defined by accelerating innovation, new partnerships, and intensifying competition across China, the U.S., and beyond.  XPeng reaches a major milestone with its first quarterly profit, joining NIO, Li Auto, and Leapmotor in demonstrating that China's EV startups can achieve profitability — even amid one of the most competitive markets in the world.Meanwhile, Rivian secures a $1.25 billion partnership with Uber, signaling a major push into the robotaxi ecosystem and raising questions about whether EV startups can remain viable without tapping into autonomy and mobility platforms.The hosts also dive into Xiaomi's refreshed SU7 launch, the growing wave of EV announcements ahead of the Beijing Auto Show, and how Chinese automakers continue to iterate products 2–3x faster than legacy competitors.Other key topics include:The rise of “physical AI” and next-generation autonomy platforms from XPengNVIDIA's expanding role in global AV ecosystemsThe future of robotaxis and whether margins will hold as competition growsThe coming battle for large electric SUVs in China and globallyHow Chinese EV technology is increasingly influencing global vehicle design and developmentWith Chinese OEMs scaling faster, launching more products, and expanding globally, Tu and Lei highlight a clear shift: the EV race is no longer about catching up — it's about survival and adaptation.

What does it take to scale technology across one of the world's largest marketing organizations? In this episode of Technovation, Peter High speaks with Craig Cuyar, Global CIO of Omnicom, about leading technology transformation across a $25B global enterprise. Craig shares how Omnicom is evolving from a holding company to a more centralized operating model, particularly in the wake of its acquisition of Interpublic Group. Key topics include: Managing post-acquisition integration at global scale Leveraging first-party data and proprietary platforms Using AI’s predictive models to agentic workflows, to transform marketing Balancing centralization, outsourcing, and business-aligned IT leadership

Market update for Thursday March 19, 2026Check out the Public app for incredible investing tools and to support the show (LINK)Follow us on Instagram (@TheRundownDaily) for bonus content and instant reactions.In today's episode:Fed Meeting recap: rate cut hopes fadeUber invests $1.25B* in Rivian to launch robotaxisMicron crushes earnings but stock still fallsFive Below jumps, Red Cat drops on earningsMeta shuts down the metaverse (yes, really)

(0:00) Travis Kalanick: Officially exiting stealth mode, what he's been working on (5:52) How to automate the physical world, markets to go after (11:00) Return to self-driving: Tesla, Waymo, and the autonomous race (16:17) Leaving Los Angeles for Austin, the decline of truth and justice in California (25:51) Actuators, robot hands, "Capital as a weapon," Middle East SWF impacted by Iran War (36:00) Michael Dell: Dorm room to $140B in annual revenue, why Texas attracts founders (43:46) Dell's $50B AI infrastructure bet (1:03:50) Invest America: Michael Dell's $6.25B gift - A 401k from birth for 25M kids This podcast was recorded LIVE at Arena Hall in Austin, Texas. Thanks to our partners for making this event possible!: EY: Austin vibes meet AI innovation. Thanks to EY for co‑hosting with us at #SXSW. Discover what executives are saying about AI transformation in the latest AI Pulse Survey. https://ey.com/en_us/insights/emerging-technologies/pulse-ai-survey Forge Global: We're proud to highlight our partners at Forge Global, who are helping the world's most innovative private companies and their teams gain #liquidcourage on their terms. Learn more here: https://forgeglobal.com/who-we-serve/private-companies/ De'Longhi Athena Polymarket

When an AI agent makes a decision that costs your company millions in a lawsuit, who do you fire?Agility requires both the speed to adopt new technologies like AI agents, as well as the foresight to build the guardrails that prevent that speed from driving your brand off a cliff.Today, we're going to talk about the hidden crisis brewing behind the AI revolution: the accountability gap. As companies race to replace roles with autonomous AI agents, a critical question is being ignored: when an agent makes a biased, unethical, or simply wrong decision that harms a customer or an employee, who is actually responsible? This isn't a future problem; it's happening right now, and it poses a massive threat to brand trust, customer relationships, and legal standing.To help me discuss this topic, I'd like to welcome, Albert Castellana, Co-Founder & CEO at GenLayer. About Albert CastellanaAlbert Castellana is Co-Founder & CEO at GenLayer. A serial crypto entrepreneur since 2013, Albert has co-founded and led major blockchain projects including Radix DLT, NEM.io, BadgerDAO, and StakeHound, reaching over $25B in combined market value. Albert brings extensive experience in decentralized finance and governance. Albert's leadership is driven by firsthand insight into how existing legal systems fall short for digital assets, fueling his passion to create a trustless, global arbitration layer. Albert Castellana on LinkedIn: https://www.linkedin.com/in/acastellana/ Resources GenLayer: https://www.genlayer.comTake your personal data back with Incogni! Use code AGILE at the link below and get 60% off an annual plan: ⁠https://incogni.com/agile⁠  The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow Drive your customers to new horizons at the premier retail event of the year for Retail and Brand marketers. Learn more at CRMC 2026, June 1-3. https://www.thecrmc.com/ Enjoyed the show? Tell us more at and give us a rating so others can find the show at: https://ratethispodcast.com/agileConnect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company Hosted on Acast. See acast.com/privacy for more information.