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It's ev.news Briefly for Tuesday 18 August 2026, only todays headlines and nothing else, in just 4 minutes if you haven't got time for the full show.Patreon supporters fund this show, get the episodes ad free, as soon as they're ready and are part of the ev.news Community. You can be like them by clicking here: https://www.patreon.com/evnewsIONNA AND WALMART PRESSURE FAST-CHARGING PRICESWalmart is expanding its own network with Electrify America, ABB and 400-kW Alpitronic NACS/CCS chargers offering Walmart+ members 10% off, while automaker-backed Ionna targets 30,000 US and Canadian ports by 2030 with stores and restrooms. Paren data shows Ionna averages $0.37/kWh, the cheapest of 17 networks and about 40% below Tesla and Electrify America's $0.56, though home charging still dominates at more than 90% of sessions and $0.18/kWh.ZEEKR TO LAUNCH 8X AND 9X IN AUSTRALIAZeekr will add the 9X and 8X luxury plug-in hybrid SUVs to its all-electric Australian range from late 2026 into 2027, after passing 10,000 local sales in under two years. The three-row 8X targets the BMW X5 and Range Rover Sport below A$100,000 with 900-volt charging, while the 9X moves upmarket at around A$150,000 with six-seat luxury and Naim audio; both are heavy, road-focused monocoque vehicles rather than off-roaders.LI SHUFU LEAVES GEELY AUTO CHAIRLi Shufu has resigned as chairman of Geely Automobile after 40 years, with An Conghui succeeding him on 18 August 2026 and Li becoming honorary chairman for life while remaining controlling shareholder and chairman of Zhejiang Geely Holding. Gan Jiayue becomes CEO as the group shifts from family-centred management toward professional systems, targeting two-thirds of sales outside China and 5% market share by 2030 across Europe, the Americas and Southeast Asia.LEIPZIG PREPARES FOR NEUE KLASSEBMW halted Leipzig production for a five-and-a-half-week summer shutdown in which over 2,000 contractors replaced 240 hydraulic lifting tables, installed 160 body-shop robots and swapped roughly 1,500 tonnes of steel to handle Neue Klasse underbodies integrating the battery and powertrain. BMW spent a low three-digit million sum on top of nearly €2 billion over five years, though it has not named which models Leipzig will build, as new i3 production began in Munich this month.SPARKCHARGE EXPANDS UK ENERGY NETWORKSparkCharge is bringing its US distributed energy model to the UK, serving autonomous fleets, construction and industrial sites that cannot wait for adequate grid connections. Founded by CEO Joshua Aviv and already operating across North America, the company will combine AI-optimised software, battery storage and on-site generation under new UK managing director James Taylor.FEDERAL PACKAGE TARGETS HEAVY EV BARRIERSAustralia has committed $400 million to heavy-vehicle reforms targeting charging barriers, automated permits, curfews and weight limits, announced by Treasurer Jim Chalmers after a Productivity Commission report found road-freight productivity had stalled for over a decade. The Commission estimated wider access for high-productivity and zero-emission trucks could add up to $4 billion to GDP annually, with electric trucks currently under 1% of Australian heavy-vehicle sales.ŠKODA ADDS V2L AND CAMP MODEŠkoda has added Vehicle-to-Load bidirectional charging, which powers external devices via a 230V boot socket, and an app-activated Camp Mode that maintains overnight cabin temperature with at least 30% charge. The new Peaq will also offer a Relax Package with AGR-certified massaging seats, electric leg rests and Sonos audio, plus a Wellbeing app, ventilated Phone Box and up to four 45W USB-C ports.BEACHMAN TARGETS OLD BIKES FOR ELECTRIC CONVERSIONBeachman Bikes is seeking broken motorcycles for electric conversion, building on founders Ben Taylor and Steve Payne's first prototype, a modified 1979 Kawasaki KZ200 completed in 2020, and a production run of 30 bikes on a 1970s 125cc platform. Having sold the 45 mph '64 as a Class II e-bike, the company is moving to pedal-free low-speed motorcycles in Canada and plans to end US e-bike sales next year to become a moped and motorcycle manufacturer.BRISBANE RIVER E-SCOOTERS STAY UNDERWATERAugust low tides exposed dozens of dumped Lime, Neuron and Beam e-scooters in the Brisbane River, including 24 near the CBD, with Ocean Crusaders having collected around 150 in the past year and storing the waterlogged batteries privately. Water experts warned that lithium, copper, nickel, cobalt and manganese could accumulate in sediment and harm macroinvertebrates, while the council caps operators at 7,100 devices and reportedly earned just over AUD$5m last financial year.

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

Matt Putra, managing partner at 8X, shares insights on leveraging AI in e-commerce, the importance of organizational frameworks like EOS, and strategic financial management for growth and exit readiness.AI's impact on marketing and product developmentUsing EOS to organize and prepare for exitFinancial hygiene and data organization for acquisitionStrategic use of AI in ad creation and testingScaling and managing growth in consumer goodsChapters00:00Introduction and Guest Introduction03:44AI in Product Prototyping and Marketing Acceleration07:10Challenges and Limitations of AI in Content Creation08:49Role of 8X as Fractional CFOs and Data Management11:11Upstream Metrics and Data Organization for Growth13:28Using EOS for Business Operations and Exit Preparation15:35Case Study: Two Large Exits and Preparation Strategies17:55EOS and Strategic Frameworks for Large-Scale Growth20:06Financial Habits for Scaling to 8X and Beyond21:49Monitoring and Optimizing Conversion Funnels23:25The Future of AI in CFO and Financial Roles26:03Governance and Security in AI-Driven Financial Processes28:45Automation, Testing, and Human Oversight in AI Systems32:44Lessons from Metal Recycling and Business Resilience36:36Killing Underperforming SKUs and Product Management39:22Retail Strategies and Entry Points for Brands41:50Trends in Global Trade and Market Expansion44:21The Role of Research Content and AI45:02Soccer in Vancouver and Local Sports Culture46:09Contact and Closing Remarks

Something big is happening in short-term rentals, and most operators haveno idea.In this episode, Mike Sjogren returns to the show with Mike Reilly. And they open with atrend that changes everything.In 2022, most short-term rentals in the US were run by hosts with fewer than 20 units. By 2025, that share had dropped hard. The reason is not that hosts quit. It is that private equity quietly started buying up management companies, because the margins are good and the industry is wide open.Mike breaks down what that means for you, and reveals the project STR Secrets has spent 16 months building to help members cash in on it.In this episode:→ The data behind the great short-term rental consolidation, and why the big money is moving in now→ Freedom Collective: the new vehicle built to help operators sell their management company for a 6 to 8X exit instead of a 3 to 5X→ How 22 members generated over $3 million in new management commissions in about 7 weeks, including one who acquired a company and added 122 doors in a single quarter→ The full AI Summit recap→ Why the most important hire of the next few years is an AI mplementation specialist, and why the CEO's job is to cast the vision, not tinker in the tools until 3am→ Mike's own Q2 numbers from the new AI-powered CRM: 1,000 leads, 68 opportunities, 7 new clients, and $211,000 in new commissions, while taking only 10 percent of the sales calls himselfWhether you have your first property or your fiftieth, this episode will change how you think about where this industry is headed.Ready to scale and exit the right way? Apply to work with us: strsecrets.com/apply

Something big is happening in short-term rentals, and most operators haveno idea.In this episode, Mike Sjogren returns to the show with Mike Reilly. And they open with atrend that changes everything.In 2022, most short-term rentals in the US were run by hosts with fewer than 20 units. By 2025, that share had dropped hard. The reason is not that hosts quit. It is that private equity quietly started buying up management companies, because the margins are good and the industry is wide open.Mike breaks down what that means for you, and reveals the project STR Secrets has spent 16 months building to help members cash in on it.In this episode:→ The data behind the great short-term rental consolidation, and why the big money is moving in now→ Freedom Collective: the new vehicle built to help operators sell their management company for a 6 to 8X exit instead of a 3 to 5X→ How 22 members generated over $3 million in new management commissions in about 7 weeks, including one who acquired a company and added 122 doors in a single quarter→ The full AI Summit recap→ Why the most important hire of the next few years is an AI mplementation specialist, and why the CEO's job is to cast the vision, not tinker in the tools until 3am→ Mike's own Q2 numbers from the new AI-powered CRM: 1,000 leads, 68 opportunities, 7 new clients, and $211,000 in new commissions, while taking only 10 percent of the sales calls himselfWhether you have your first property or your fiftieth, this episode will change how you think about where this industry is headed.Ready to scale and exit the right way? Apply to work with us: strsecrets.com/apply

A Special Industry Update, With Jason Diamond and Mindy Diamond Jason and Mindy Diamond revisit how advisor due diligence is evolving—from AI and enterprise value to firm stability, ownership, and optionality—and why those questions matter more than ever. In Summary Due diligence has always been about finding the right fit. But what advisors are evaluating has expanded considerably. In this replay of an Industry Update, Jason Diamond and Mindy Diamond revisit The Advisor Transition Playbook to explore how advisor priorities continue to evolve. Beyond the traditional reasons advisors consider change, they discuss newer factors shaping decisions today—from artificial intelligence and enterprise value to ownership structure, firm stability, and long-term optionality. The conversation reinforces that while every advisor's motivations are personal, the evaluation process has become far more strategic. Today's advisors aren't simply comparing recruiting deals or platforms. They're considering how today's decisions may influence the value, flexibility, and future of the businesses they're building. The Storyline For years, advisor movement was largely driven by familiar themes: bureaucracy, management changes, technology frustrations, and the desire for greater independence. Those factors remain important. But the conversations Diamond Consultants has with advisors today increasingly include questions that rarely surfaced just a few years ago. How should AI factor into firm selection? What is the long-term value of building enterprise value instead of simply maximizing a recruiting package? How important is a firm's ownership structure? And how should advisors think about stability in a marketplace where acquisitions, recapitalizations, and private equity investment have become commonplace? Jason and Mindy revisit the transition framework introduced in Part 1, focusing less on the mechanics of making a move and more on the evolving criteria advisors are using to evaluate their options. The result is a broader discussion about due diligence—not simply as a transition exercise, but as an ongoing strategic process for advisors seeking to build their best business life. Topics Covered Advisor due diligence Traditional vs. emerging drivers of advisor movement Artificial intelligence in wealth management Enterprise value and advisor ownership Recruiting deals versus long-term economics Reverse due diligence Firm ownership and stability Private equity in wealth management Advisor optionality Building a long-term advisory business Blubrry Player > Download a transcript of this episode… Listen and Learn Highlights for Advisors Why are the traditional drivers of advisor movement still relevant? (4:00) Jason and Mindy revisit the longstanding push-and-pull factors that continue to influence advisor decisions, from bureaucracy and management frustrations to the desire for greater ownership and control. How has AI become part of the due diligence process? (13:50) The discussion explores why advisors increasingly expect firms to demonstrate a clear AI strategy—and why investment, integration, and vision may become meaningful competitive advantages. Why should advisors care about enterprise value, even if they don't technically own their business? (24:30) Jason and Mindy explain why more advisors are evaluating decisions through the lens of long-term business value rather than solely short-term economics. What does reverse due diligence really involve? (37:15) The conversation highlights why advisors should evaluate prospective firms with the same rigor firms use when evaluating advisors. How does firm ownership affect advisor optionality? (38:00) Private equity, acquisitions, and changing ownership structures have made it increasingly important to understand what happens if a firm's strategy changes after an advisor joins. Why has due diligence become more strategic than ever? (45:30) The episode concludes with a broader discussion about defining one's “best business life” and making decisions that align with long-term goals rather than reacting to short-term frustrations. Key Takeaways The reasons advisors evaluate change have expanded well beyond traditional frustrations such as bureaucracy and compensation. AI has become an increasingly important component of firm evaluation, not because it replaces advisors, but because it can enhance productivity and client service. Enterprise value is becoming a consideration even for advisors who currently work within employee models. Reverse due diligence is just as important as a firm's evaluation of an advisor, particularly when assessing ownership structure, capitalization, and long-term stability. The most effective transition decisions balance immediate economics with long-term flexibility, ownership, and optionality. Every advisor's definition of success is different, making clarity around personal goals the foundation of any due diligence process. https://youtu.be/WZbUZJZK1yc Quotable Moments “Every advisor deserves to live their best business life.” “Just because you're frustrated doesn't mean you should move. You need something worth moving toward.” “The question isn't simply what you're paid today. It's what you're building over time.” “Knowledge is power. Understanding what your business is worth should be part of every advisor's decision-making process.” FAQs Why are more advisors expanding their due diligence beyond compensation? While transition economics remain important, advisors are increasingly evaluating technology, AI capabilities, enterprise value, ownership opportunities, and long-term flexibility as part of the decision-making process. How should advisors evaluate a firm's AI strategy? Rather than looking for finished products, advisors should assess whether a firm has a clear vision, meaningful investment, and an integrated approach to using AI to improve advisor productivity and client experience. What is reverse due diligence? Reverse due diligence is the process of evaluating a prospective firm as thoroughly as the firm evaluates the advisor. It includes understanding ownership structure, financial stability, culture, technology, leadership, and long-term strategy. Why does enterprise value matter for employee advisors? Even advisors who do not currently own their businesses may benefit from understanding how different business models create opportunities for ownership, long-term value creation, and future monetization. How has private equity changed advisor due diligence? Private equity has introduced new opportunities for growth and capital, but it has also made it more important for advisors to understand ownership structures, investment horizons, and what future transactions could mean for their business. What does Diamond Consultants mean by an advisor's “best business life”? It refers to aligning an advisor's business model, goals, client experience, compensation, flexibility, and long-term vision in a way that best supports both the advisor and the clients they serve. While transition economics remain important, advisors are increasingly evaluating technology, AI capabilities, enterprise value, ownership opportunities, and long-term flexibility as part of the decision-making process. Rather than looking for finished products, advisors should assess whether a firm has a clear vision, meaningful investment, and an integrated approach to using AI to improve advisor productivity and client experience. Reverse due diligence is the process of evaluating a prospective firm as thoroughly as the firm evaluates the advisor. It includes understanding ownership structure, financial stability, culture, technology, leadership, and long-term strategy. Even advisors who do not currently own their businesses may benefit from understanding how different business models create opportunities for ownership, long-term value creation, and future monetization. Private equity has introduced new opportunities for growth and capital, but it has also made it more important for advisors to understand ownership structures, investment horizons, and what future transactions could mean for their business. It refers to aligning an advisor's business model, goals, client experience, compensation, flexibility, and long-term vision in a way that best supports both the advisor and the clients they serve. 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. Related Resources The Advisor Transition Playbook: The Latest on Due Diligence, the Move, and Everything In Between – Part 1 Annual Advisor Transition Report Top 10 Tips for a Strategic Due Diligence Process Should I Stay or Should I Go? View the transcript of this episode… The Advisor Transition Playbook: The Latest on Due Diligence, the Move, and Everything In Between – Part 2 A Special Industry Update with Jason Diamond and Mindy Diamond. Jason Diamond: Welcome to a replay of one of the most popular episodes from our podcast series for financial advisors, The Advisor Transition Playbook: The Latest on Due Diligence, the Move, and Everything In Between. It's Part 2 of a 2-Part Industry Update with Mindy Diamond. 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: There’s been a noticeable shift in how advisors are approaching decisions about their business, not necessarily in whether they’re exploring change, but in what they’re focused on when they do. Mindy is back with me for a continuation of our earlier conversation on the Advisor Transition Playbook. Last time, we spent time on the mechanics, how due diligence works, what a move actually entails, and how to think through the process. What’s become more apparent since then is that the inputs into that process are evolving. The traditional drivers are still there, but layered on top are a set of considerations that didn’t carry the same weight before. AI is one of them, and not just as a tool, but as a differentiator that advisors are starting to diligence more seriously. Enterprise value is another. Showing up in conversations, even for advisors who don’t technically own their business, but are thinking more critically about what they’re building over time. And then there are questions around stability, ownership, and flexibility. What happens to the firm itself and whether advisors retain the ability to adapt again if circumstances change. None of this is theoretical. It’s showing up in real time conversations. What we want to do here is unpack those new triggers of advisor movement and what they suggest about how decisions are being made today. So let’s get to it. Mindy, the legend, thank you for joining me. So glad to have you on. Mindy Diamond: Thank you. I’m so happy to be here. Jason Diamond: Great. Let’s dive right in. I’ll set the stage really quickly one more time. When we spoke about this topic last, we talked about the drivers of movement, what we’ll call in this conversation as the old or the legacy drivers of movement, and we spoke about the mechanics of the move. Before we get into the new drivers of movement, which I want to be the meat of the conversation, remind us, when we talk about the legacy drivers of movement …. And by the way, by saying legacy, I by no means want to suggest that they’re not valid today, because they’re equally valid, if not more so today than they were then. But when you think of the classic drivers of movement in our industry, what are they? Mindy Diamond: Yeah. So I would say, first of all, let’s start by saying that for every advisor, they’ve got a unique set of needs. So the first thing to say is that while you and I can talk about the categories of frustrations or things that might bother an advisor, they show up differently in each advisor’s life. So it’s important to note that everyone is unique. But generally speaking, if I had to package them, I’d say it’s number one that shows up most of the time is too much bureaucracy. A feeling that a firm or a model is just too hypervigilant in terms of compliance and it’s too bureaucratic and too hard to get things done. Number two would be some sort of change in or frustration with management. Something is going on that the person or persons that are responsible for managing the business are just not … They’re not the wind at their back. They’re obstreperous. They’re causing difficulty and frustration. And probably the third one would be less about a pain point and more about the desire to be something that they couldn’t where they were. The notion that they want to be more independent, they want to be a business owner and they just can’t do that. That doesn’t exist within the model where they work. Those probably have been the three ones top of mind, but I bet you’ll have some … You’ll add to that. Jason Diamond: I’ll add a couple. But before I do, I’ve heard you talk about this topic, maybe said another way as pushes and pulls. Can you explain what you mean by that? Mindy Diamond: Yeah. So I think that we think about the pushes, the frustrations, the things pushing somebody out the door, the factors that make it less easy or less fluid to do business. And there’s almost always pushes that exist when somebody comes to us, where they’re frustrated to some degree or another about certain things. But we tell people all the time that just to be frustrated should never be enough, because if all you’re doing is running from one set of problems, you’re very likely to run into maybe a different set, but still problems elsewhere. So a move needs to be driven in equal part, if not more, by pulls. Being pulled toward an opportunity that can be needle moving enough or better enough than where you are now. Pushes and pull. Jason Diamond: I love it. So let me ask you a little bit of a pointed question. Is a recruiting deal a valid pull factor? Mindy Diamond: So look, it’s different for every person. We’ve had advisors come and say, “I just went through a divorce and the most important thing to me is to recapitalize. And so a recruiting deal is really important.” And while I would never be one to say that’s not valid, it can be … And by the way, any advisor should want to and expect to better their financial situation. There should be economic gain. But it shouldn’t be the only or the primary reason for the move. So you want to monetize. The notion of wanting to monetize in the short term should be a factor in what model you pick, but it shouldn’t be the primary driver for a move. Jason Diamond: I agree with that wholeheartedly. I was going to say something I think maybe would’ve surprised you a little, which is like, yeah, I think recruiting deal is a very valid pull factor because what we’re saying is, it shouldn’t be the only pull factor. And sometimes it is and it makes us a little bit sad, I think, when that’s the case. But all of these factors you mentioned, and the ones I would add, I think that maybe technology would be another kind of factor that drives movement, all of these factors are not one specific reason. If you did the exit interview, either actually conducted the exit interview with advisors or thought exercise exit interview, I think they would point to a confluence of all of these factors. Compliance was a headache. I wanted to launch a podcast. I wanted to be able to send a timely communication to my clients. We used to hear that one during COVID a lot, right? By the time compliance approved something to send to clients, it was already stale. So do you agree with that, that it’s generally a confluence or a combination of these? Or in your experience, is it advisors are like, “No, compliance or the tech is so bad, I’m out”? Mindy Diamond: Yeah. So most often there’s a straw that breaks the camel’s back incident or thing where they’re willing to put up with a series of minor paper cuts, if you will. And then almost always there’s something that happens. You and I got a call the other day from a team that said that they had split from their partner and the management of the firm was favoring the ex-partner, making it harder for them to stay or making it less fun or feel good for them to stay. So while they gave me a laundry list of things that were imperfect, I don’t know that any one of the things that were imperfect up until then would’ve been enough to drive them out. But when that one thing, that feeling that they were a second class citizen came up, that was the straw that breaks the camel’s back and went from a minorly frustrated to, “I’m out of here.” Jason Diamond: Yeah. And there’s probably a hundred examples you could walk us through. And I wanted to just highlight too, this concept is not limited to the wirehouse or employee or captive firm world, this is equally relevant for independent advisors. Granted, some of the pushes and pull factors, some of the triggers are not necessarily the same, but the idea that advisors outgrow a broker dealer or an RIA or either need or want or desire in some way, shape or form, greater autonomy, flexibility, freedom, control is certainly not limited to the employee space. I just wanted to make that point. Mindy Diamond: And I think that’s absolutely right. I think the notion of that frustrations or limitations or bureaucracy only existed if you were a W2 employee at a bulge bracket firm. That went out the window. As the industry landscape has expanded and there’s more and more valid ways to be a financial advisor, there’s more and more ways for a firm or a model or an infrastructure to frustrate an advisor. And that’s not being overly negative. It’s just to say there is no perfection anywhere. Jason Diamond: Yeah, 100%. And by the way, to play a little bit of devil’s advocate on that, and then we’ll move on, I would just say there are pain points that might come from a firm being small and subscale as well. My firm doesn’t have efficient technology. They don’t invest enough in the business. They don’t provide a lead mechanism. They don’t have a robust banking and lending or investment solutions platform. So this stuff cuts both ways. An advisor can be frustrated or limited and an advisor can be excited. Pushes and pulls I think touch on, we’ve heard from advisors in every single pocket of the market, this is a relevant concept. Mindy Diamond: The theme of this is that every advisor deserves to live their best business life. That’s what people are in search of when they reach out to us or when they engage with us. What they’re looking for more than anything, and this is irrespective of where they work or how long they’ve worked or how much they manage, every advisor is in search of their best business life. And what defines their best business life is having the best quality of work life, but also the best ability to do what they want to do with their business, to serve their clients without limitations, to grow the way they want, to be paid a fair wage, and ultimately set up to maximize the value of the business they’ve built. Those are the definitions of one’s best business life. Jason Diamond: I used an even simpler definition of best business life and I stole it from you, which is the true north concept, which is if your true north is maximizing enterprise value and chasing the dollar and trying to build something that’s scalable and saleable, then great. If your true north is to build a lifestyle practice, there’s plenty of advisors who are successful and happy and content in that regard as well. And I think that’s what we’re talking about, is finding your true north and then it’s possible. I mean, that’s the beauty of the landscape. We’re talking about this, a lot of this is pain points or things that advisors experience. The exciting part of this is there’s never been a better time to be an advisor because of the breadth of choice they have and the ecosystem that’s been born to support advisors, to your point, across the spectrum. Mindy Diamond: Yeah. And it’s also, I think, worth saying that it starts with really good crystal clear clarity around not only what’s frustrating you, but what you want ideal to look like. Because I can’t tell you, or I can tell you because … I can’t tell our listeners, I can’t stress enough how often we get calls from advisors that tell us where they think they want to be or tell us they want to move. They have clarity about what’s frustrating them or what they want to change, but they don’t really have clarity about what they want it to look like. And the less clarity you have, the less likely you are to be successful in finding the exact right solution. So our work, the thing we probably do best is really work with advisors to help them. It doesn’t take long. In an hour conversation, we can help them to really get crystal clear on what they’re looking to solve for. Jason Diamond: Absolutely. All right. Great appetizer. We set the table. Let’s dive into the main course now. I want to talk now about what I’m calling the 2.0 triggers or the new triggers of movement. And to be clear, it’s not that these are more important or better or more significant drivers of movement. In fact, you could argue they’re probably at present less significant than the ones we just listed. But I think what we’re saying is these are triggers that are starting to come up more and more in conversations and we expect them to only proliferate further. And in that regard, they’re noteworthy and important for advisors because advisors should be reconciling not just what are the things I need to be worrying about today, but also what are the things I need to be potentially worrying about five years from now. So with that in mind, let’s dive in. I think the first one we have to start with is AI. And I always chuckle a tiny bit when we mention AI, we used to have to specify what are we talking about. Are we talking about artificial intelligence or alternative investments? And now it’s very clear. Everybody knows we’re talking about artificial intelligence. So the direction of the industry, no over-dramatization to say is at stake here. It’s that important of a topic. Let me ask you just very simply first, is this coming up in conversations with advisors? Mindy Diamond: Oh, all the time, but it’s almost table stakes. So I think the way it comes up is that people assume, advisors assume, and by the way, have the right to assume that AI is part of the tech stack. The notion that if I’m evaluating a firm and part of what frustrates me or part of what’s really important to me is cutting edge, really robust technology, part of what I am expecting is that a new firm is going to have really robust technology. And part of that is really robust access to AI. And has honed the AI in a way that’s user-friendly, that really answers or delivers on making me a better … Not replacing me as an advisor, but making me a better, more efficient advisor. Jason Diamond: 100%. And I would also add, so as I think about this AI topic, I don’t want this to become a conversation around, is AI going to replace advisors, because I think we both agree that’s not going to be the case. Especially at the top end of the market for quality advisors, I think they’re not going anywhere. But in my view, when we think about the trigger of movement, AI has the potential to be transformative because a couple kind of use cases or trigger cases come to mind, and I’d love to hear your thoughts. One is, do you think advisors will potentially consider a move because they’re worried about this? So in other words, play this logic out with me. I’m 55 years old and I’m like, “Oh man, AI might be coming from my job.” And there’s firms offering 400% of revenue to move my book. Maybe I should take that check and kind of de-risk and monetize while I can. What are your thoughts on that? Mindy Diamond: I absolutely think we’re already working with that fall into that category, but to say that is the only reason for the move would be wrong. I’m grateful that people trust us enough to be transparent with us. So they let us know that underneath the notion that they want to better serve clients, they ultimately want better access to A, B, and C, they want to be able to do D, E and F with less restriction, is really the main reason for the move. But underneath it, the notion that my book, I want to protect myself. My book may well be the biggest it’s ever going to be. It is going to be worth more today than it could be in the future if things don’t go my way. And if I know I’m going to move and one of my goals is to monetize, I might want to do that now. Jason Diamond: I agree. And that’s where the top deal story comes in also. Firms paying a top deal is a part of that story. It’s what you just said, plus advisors know firms are willing to pay incredible multiples. I mean, as we speak, UBS is in market with one of the largest deals in history. So those two narratives side by side, I agree. I think this becomes more of a kind of catalyst or driver movement. It’s come up in my conversations on both sides of the spectrum. It’s the tech savvy, AI savvy advisors who are excited about this, who are like, “I want to be the most AI enabled version of myself I can be. It’s going to make me a rockstar and it’s going to widen the gap with my peers,” but it’s also come up with the people who are, I think, rightly scared and fearful about what this might mean for their job. Mindy Diamond: Let me ask you, what are examples of the way you’ve seen some of the best firms who have embraced AI? What is their narrative? What is it that they’re saying to advisors that if you come here from a tech or AI perspective, you’ll be better because we’re able to do … Fill in the blank. Jason Diamond: Yeah. So a couple that come up. First of all, I want to make the important point. Advisors do not expect that firms, either their current firm or firms that they are diligencing prospectively, have this figured out or solved. Everybody understands this is a fairly new area that firms are still very much kind of developing their strategies in. What advisors want to see is a few things. They want to see though leadership, they want to see investment, and they want to see a strategy, right? Effectively, they want to see a step in the right direction, really. So I’ll give you a couple examples. There are a number of tech savvy RIAs, very tech-enabled, AI-focused RIAs, because I think this is easier to be nimble. I think where you’ll see this quicker probably is in the independent space. That what they’re doing is things like this. An advisor logs on to their workstation in the morning and their system queues them proactively, Mr. and Mrs. Smith may be good candidates for a Roth IRA conversion. And then if the advisor decides to contact the client in some way about it, the system will of course help them draft the communication, but then it’ll take it a step further and actually help them to process and transact that conversion. So soup to nuts, ultimately driving efficiency. That’s the name of the game. That’s why firms, I think, are excited about AI, at least the good firms. Because what I think they realize it will do is, the stuff that’s a waste of time that could be automated that advisors, and probably even more so their associates, client associates are spending time on, that should be a massive time saver for advisors. And I think if you play that story out, what does that mean? It should mean bigger books of business and therefore more productive advisors because they have more time to prospect and focus on their clients. Thoughts? Mindy Diamond: Yeah. So I think you said it perfectly, but it raises the question then. You say that the RIAs can be more nimble. You’re right. I mean, the big story around the biggest firms was like moving a battleship, it takes a long time to turn it. It’s not as nimble. So what and how are the bigger firms competing against the RIAs with respect to AI? And second question, we still always get questions, and rightly so, about Morgan Stanley has more money to invest… Jason Diamond: That was going to be part of my answer. Mindy Diamond: … than fill in the blank RIA. So how does that all work? Jason Diamond: That is absolutely going to be part of my answer, is that I have heard this question posed almost presumptively both ways. “Oh, it’s got to be that the RIAs are going to be the clear winners in this.” And I’ve also heard, “Oh, it’s got to be that the wirehouses are going to be the clear winners in this.” I don’t think it’s going to be channel specific like that. I think it is going to be firm specific. I think there’s going to be firms that are going to do this well and firms that are going to not do this well. But there’s going to be winners in the wirehouse space. There’s going to be winners in the regional firm space, with firms like Raymond James who are clearly trying to be on the cutting edge of this. There’s certainly going to be winners in the broker-dealer space. LPL is investing heavily in this, as are many of their broker-dealer competitors. And then of course the RIA space, where sometimes they may not have the budgets, but they have a couple things. They have private equity backing, sometimes. They have the custodians that they’re built on, right, or the tech vendors that they’re built on. So Schwab and Fidelity or Orion and Addepar. They have other ways to access these innovations. One of the things that comes up with this that your question I think gets at is, a similar question that was raised around technology stacks, which is strength of offering versus integration. And that’s where I think a firm like Morgan Stanley really will shine, is they should … Because they don’t put anything out that’s not well integrated. The big firms have generally done a pretty good job of that. Versus the RIAs. Sometimes we’ve heard feedback where, yes, you have access to you name it, right? You dream it up, you can go and buy it. But the left hand may not speak to the right hand quite as well. Mindy Diamond: Yeah, that’s actually a really good point. And integration is probably one of the biggest … If you ask an advisor when they talk about technology as either being one of their pushes or pulls, probably what they’re referring to more than anything is not only having the capability, but having the integrated capability. So that’s a great point. And I think your point is right, that the final chapter on this has not been written. Nobody thinks that it has. And so whatever answers you and I can talk about today about who’s winning this race, or this tech race or this AI race, will be totally different tomorrow. We all know that. But I think for purposes of this conversation, to say that an advisor having an expectation that their technology be outstanding and that AI be on the table, that a firm is embracing it and heading in the right direction, if you will, has the right thought leadership and the right willingness to invest in it is what advisors are really looking for right now. Jason Diamond: Absolutely. And this is a question too from the firm’s perspective, if you are a firm of any size, you must be able to answer that. This has become question 1A. And again, I don’t mean to suggest that I think AI is the number one most important factor driving advisor movement today. It very well might be at some point down the road. I don’t think we’re there yet. But I do think it’s the topic du jour or the hot topic, where every advisor is asking about this. So that means if you’re a firm, you need to be prepared to tell the story or at least have the vision. And I think what we’re hearing from both advisors and from firms is this, AI is going to … What is right now a gap between the good and the bad, the quality and the non, is going to become an absolute chasm, right? An absolutely mountainous gap between the best firms and the firms who are able to adapt this technology or this AI. And the same thing at the advisor level, between the AI-enabled superpowered advisor versus those who are in the dinosaur ages, for lack of a better term. Mindy Diamond: Yeah. And we’ll move on, but it is worth saying that the day of the standalone independent, the one man or one woman band who hangs out a shingle, and to use your term, running a lifestyle practice, nothing wrong with that, but it would be near impossible to imagine a world where a standalone independent can compete with a private-equity-backed RIA or an RIA that has a big pool of capital behind them or to compete with the major firms. And our point is the ability to compete is probably more important with respect to this topic than just about any other. Jason Diamond: Totally agree. Thank you for tying a bow on that because I think that’s a good place to leave the AI topic, at least for now. I’m certain we’ll have more to say on this one. By the time we release this episode, we’ll probably have more to say on it. So we’ll have to do a follow-up again. But I want to talk now about enterprise value. And this is one where if you’re an RIA or if you’re an advisor at an independent firm, this might sound like a duh, but hear me out on this one. The idea is as follows, if I’m a wirehouse advisor or any sort of captive advisor, I don’t technically own anything. Agree? Mindy Diamond: Agreed. Jason Diamond: Okay. So if that’s true, that I don’t technically own anything, I technically don’t have any sort of enterprise value or ability to monetize. But my premise here and why I would argue that enterprise value has become a driver of movement is even wirehouse advisors know … They see teams like OpenArc, a massive RIA that launched last year. They see their corner office peers breaking away, starting independent firms. They see them selling to asset managers, private-equity-backed RIAs, private equity firms in their own right for these massive multiples. And what I guess I’m getting at, and I’m curious if you agree is, if a wirehouse advisor, let’s say, sees their colleagues sell to a private equity firm for 20X, doesn’t that have to become a little bit of a catalyst for movement in its own right? Mindy Diamond: Without a doubt. Historically … Actually, let me date myself. When I started this business now 32 years ago, there was zero way for an advisor who was a captive employee of a firm, of any firm, to monetize their business. It’s why there was so much movement, because the only way they could monetize was to get paid a big fat transition deal to move from one firm to the other. Jason Diamond: Yep. Mindy Diamond: Obviously, we all know that first it started with the big firms, and then just about every brokerage firm on the street began to offer a retire-in-place program. And that is the big firms or a traditional brokerage firm’s way of allowing advisor to monetize in place from their perspective to stave off attrition. And for an advisor that believes that the status quo serves them well, that finishing their career, that leaving their legacy, that leaving their team at their firm is the best thing to do, then those retire-in-place programs, like Merrill’s CTP or Morgan’s FAP or UBS’s Alpha or a name at every firm has them, is the best gift to advisors there is. But the problem is that the next generation at those firms are buying an asset they don’t own. And so when we talk about enterprise value or the desire to build enterprise value as a real driver of movement, what we’re talking about is not only that advisors want ownership of an asset, because ownership translates into more control and autonomy and agency over building it the way you want to, but it also translates into maximizing the value of the business that you’ve built. So that’s a long-winded way of saying that the OpenArc deal you are referencing, for anybody not familiar, is a Merrill Lynch team, a legacy Merrill Lynch team in Atlanta that was managing more than 120 billion in assets, part retail, ultra high net worth client assets, and part institutional consulting assets. And believe me, I don’t want to make it sound like it was a snap that one day they’re happy and the next day they’re going independent. Over a 10-year period became more and more aware, driven by the pushes and more aware of the pulse. But ultimately, while there was a long list of things they wanted to be able to do that they couldn’t to best serve clients and grow the business, the real driver at the end of the day, or I shouldn’t say the real driver, but a major driver was the notion of building and owning enterprise value. Yes, they could have all gotten very attractive deals and retired with your Merrill CTP, but they wanted to own the business, they wanted cap gains treatment. And so they went through the sweat equity big time of building what they’re calling OpenArc for the ability for probably five, 10, 20 years, because there’s partners with all different ages, so at all different times, to be able to really maximize the value of the business they’ve built. Jason Diamond: Can I push back on that for … It’s a super helpful example, but my one thought is, okay, yeah, of course, 130 billion in assets, they should be concerned with enterprise value at that size. And the delta between caring about enterprise value and not is too great because those guys have, by all accounts, a phenomenal business that is rivaled by very few in the industry. Most of our audience does not fit into that stratosphere. So what about advisors in, let’s call it the million to $10 million space? Should they still care about this concept? Mindy Diamond: Again, it’s an inside job. It’s a personal thing. Some don’t. But the answer is yes. And if I were them, I would. Why? Because whether I am generating a million a year in revenue or $10 million a year in revenue, at the end of the day, I’ve got an asset. I’ve built a valuable asset. And I have the choice at the end of the day or the middle of the day to decide a million things about that asset. How do I want to live my business life? How do I want to serve my clients? Where do I want to work? But one of the biggest factors to determining where and how they want to work is, ultimately, do I want to be able to maximize the value of the business that I’ve built? And while there are few things that are really definitive in this industry, the one thing that is absolutely indisputably definitive is that if you build an independent practice like the ex-Merrill Lynch churned RIA OpenArc team did, you will ultimately build enterprise value exponential multiples greater than any way you could monetize the business as a traditional employee. Jason Diamond: And that math absolutely still holds up even at numbers smaller than we’ve mentioned. I totally agree with that. I’ll give you one other reason why I think you should care. And I’d love your thoughts on this one. I’ll ask it two ways maybe. I’ll tell you my take and then I’ll ask you yours. Morgan Stanley, let’s use as an example. Who are Morgan Stanley’s competitors? In my opinion, the legacy answer to that is, well, of course the wirehouses are Morgan Stanley’s competitors. Merrill, UBS, Wells Fargo, what maybe used to be a longer list, but today those four. I don’t think that’s the answer anymore. I think those are the direct competitors. But because of this enterprise value conversation, I think Morgan Stanley’s competitors are anyone and everyone who recruits financial advisors with books of business. Because if you think about it, an advisor who has a $3 million business at a wirehouse, even if they’re not actually going to do this, they don’t have any entrepreneurial spirit, no desire to go independent, they still know that they could. This is an option and a viable option. And firms are even figuring out ways to cut out the middle step, right? Because this was historically a two-step process. You’re a wirehouse advisor or a W2 advisor. You break away, launch an independent business to establish your enterprise value, begin building it, and then you monetize it. If you could cut out the middle step, or even if you couldn’t, I still think it’s pretty clear that if you’re an advisor, this is important because the firms know … Like when Morgan Stanley’s writing a recruiting deal, they’re kept honest by RIAs and acquirers just the same as their direct peer set. Do you agree with that or do you think I’m reading too far into this? Mindy Diamond: Oh no, I agree a thousand percent. I think that it is naive for anyone recruiting for or on behalf of a traditional firm to think that the only competition is another traditional firm. The days of pomposity for a senior leader at a traditional firm to say, “We’ve got the best technology, the best everything fill in the blank. We have no competitors.” That’s just naive. Because even if it’s true, you’ve got the best platform infrastructure fill in the blank, there is a multitude of advisors that value things different than what you can provide. Beauty is in the eye of beholder is probably a good way to say that. But at the end of the day, what we’re really talking about is when I started the business, because there was no way, no really good way for an advisor to really monetize their life’s work, the only thing they could or were focused on from a personal financial gain perspective was the short-term deal. What are they paying? What’s the transition deal? Now, of course they’re concerned about that. But almost to a person, they’re equally concerned about what I can build and what will this allow me to build in terms of the value of the business I’m building in the long term. So let me ask you, if we’re talking about an advisor that has the ability to monetize in the short term for what could be 4X and in some cases more than that these days, and we’re talking about the ability to maximize enterprise value, and we talk about the concept of moving once and monetizing twice, what kind of numbers are we talking about? Fill in the blanks there. Jason Diamond: It’s such a hard question to answer because I do genuinely believe recruiting deals, when you talk about 300 to 400% revenue deals in the recruiting space, they vary a little bit, but I feel pretty comfortable quoting those types of numbers that most firms are somewhere in the 300 to 400% of T12 realm. There are some outliers, we mentioned UBS. But the multiple or EBITDA based or enterprise value M&A market where we’re doing these legitimate buyout transactions, the valuations do vary quite a bit. But here’s how I think about it. First of all, most firms are not purchased or sold at top line revenue. Most are sold at some sort of adjusted EBITDA number, which factors in local expenses, platform expenses, but also advisor compensation. And then that adjusted number is typically multipled. The multiples are anywhere from 8X for small kind of, let’s say, million dollar revenue businesses up to, we’ve seen deals struck at north of 20X for some of these mega cap RIAs. Typically, just back of the envelope, if I had to quote, I typically estimate around 5X top line at capital gains is a good kind of ballpark valuation. But there is quite a bit of nuance to it, more so than the traditional recruiting space. And I do think, shameless plug, part of the value in working with somebody who’s an expert on the entirety of the industry landscape is just that. It’s the idea that you need to run the horse race across multiple verticals. The good advisors who work with us typically are looking at a wire like a Morgan Stanley or a Merrill. They’re looking at a boutique firm like a Rockefeller, or they’re looking at a regional like an RBC or a Ray J. They’re looking at an independent firm like an LPL or a Sanctuary. They’re looking all across the spectrum. Mindy Diamond: I think that’s exactly right. But the topic of enterprise value, you can see how powerful it is and how wise it is. For an advisor today, when considering their personal economics to consider not just the short term, but to weigh in or add in or factor in, what could I be building and what ultimately will that business be worth at the end of the day? Jason Diamond: Yeah, 100%. Short of going out and selling your business, what can advisors do then? So I’m an advisor, okay, I’m curious about this. Or is it just as simple as, “Yeah, you should know what your business is worth if you’re an advisor”? Mindy Diamond: Definitively yes, because I mean, we always believe that knowledge is power. And just like it’s important for you to understand what your options are within your own firm, how can I ultimately retire out and monetize my business where I am, I think it’s really hard to make a decision in a vacuum without having other perspective. And getting other perspective doesn’t have to be that you have to go out and take 20 meetings. It’s not that hard for you to figure out what your business is worth to make it a data point for whether or not you’re ultimately best to retire in place or go elsewhere. Jason Diamond: Yeah, that I think is the main takeaway. And the education point is so important. I think because these are relatively new concepts for a lot of advisors that haven’t formally shopped a business before, there’s a lot of resources available. And we’ll certainly link some as well on the page for the episode. Let’s shift gears now, our kind of final trigger 2.0, which is stability and ownership structure of the firm. And this has been a little bit of a hot topic. It’s honestly been a hot topic every year because it seems like things pop up every year. And a lot of times advisors don’t reconcile the question of who owns the firm or how stable is the firm until something happens. The firm gets bought, the firm goes bankrupt, like the First Republic scenario. What should a good advisor do proactively about the idea that if you’re a W2 employee or even an employee who’s affiliated with a broker dealer, you saw this with Commonwealth, you just don’t really have control over what the firm decides to do. Give me your thoughts on this. I know it’s a big topic. Mindy Diamond: Yeah. First of all, using Commonwealth an example, it’s a good one. Because for those unfamiliar, Commonwealth is a boutique broker dealer that was privately owned and whose tagline was, “We love our privately owned status and we are never going to sell,” until one day they did. And not only did they sell, but they sold to the biggest independent broker dealer in the country, ala LPL. That’s not good nor bad, it’s just a fact. So if Commonwealth, who had definitively said we’re never up for sale, suddenly sells, any time you’re an employee of a firm, you never know what tomorrow brings in. You’re not in control over whether it’s sold. So that’s one example. But as you’re talking about this, I’m thinking about, I’m probably going back 20 years, so I’m 10 years into my career and I talked to someone who had been a very successful Merrill advisor. So I’m going to say he was probably generating around $5 million in revenue at the time. Going back 20 years, that’s a pretty significant book of business. He was courted for years by what he thought was a top RIA. And in those days, remember 20 years ago, the RIA space wasn’t nearly as mainstream as it is now. But the story the RIA told him was that ultimately, one, he was going to be a partner in the firm, that was very appealing to him. So he was going to have equity in the firm and much more freedom and control. And locally, by the way, the RIA was a really high quality brand. He worked on a lot of the economics, the short term and the long term with them. They did a ton of due diligence on his book of business. But he failed to ask … And I didn’t represent him. I just know this story. He failed to ask or do enough due diligence about the stability of the firm. What we think is really important, we talk about this expanded landscape. If you’re looking at Morgan Stanley, I don’t think you necessarily need to see Morgan Stanley’s balance sheet. If you are talking to a firm that is anything but a bulge bracket or anything but a large firm, it’s really important to do what we call reverse due diligence and to really understand if a firm expects you to open your kimono and show everything about your business to prove your worth, it is equally important that you do the same for them. In this new world order where private equity has come in and there are so many different ways for a firm to be owned and to be capitalized, it’s very important that an advisor understand what’s going on behind the scenes. And one of the questions around stability, if a firm is private equity backed, is it permanent capital? Is it patient capital? Is the private equity firm going to look to sell and monetize in five years? And then who would the likely buyer be and what does that mean for you? So the question is a big question and it’s really important. Jason Diamond: I love everything you just said, except I do think even the wirehouses, wirehouse advisors, honestly, as much as anybody should be asking these questions. And I’ll give you an example right now, UBS. And UBS, it’s not a story of balance sheet stability. I don’t think anybody has concerns that UBS is going to fail. But UBS management has been very publicly, “Oh, we’re cutting costs.” There’s been some rumors, I think for years, probably dating back 30 years to when you started the business about UBS’s commitment to the US wealth management business. I think those questions about stability and ownership structure are still valid. And to me, the implication of it is twofold. One, what you said, reverse due diligence, ask the questions, plan B. But also the concept of the exits or the off-ramps or how many bites of the apple do you get. So if you’re an advisor and you sell your business to somebody and you sign garden leave and non-competes and non-solicits, the question of ownership structure of that firm becomes less relevant because you have no off-ramps and no ability to exit that business anyway. A lot of times that’s how advisors get comfortable with this concept. And that’s what firms will tell them too, frankly, and we’re living through the middle of this, by the way, with Commonwealth and LPL, is vote with your feet, right? To the extent advisors can, the offer … And this is like, you used the example of private-equity-backed firms. This is how Rockefeller addresses the question of their private equity ownership. If we sell to UBS, all of our advisors will leave. They have that built-in put option. So knowing where the off-ramps are or how many bites of the apple an advisor gets, I think is a big concept that ties into that. But we’re absolutely seeing this pop up, probably largely because of those two examples, Commonwealth and UBS this year, more so Commonwealth, to your point. Janney’s another example last year or two years ago now where KKR comes in and buys Janney. So when these examples happen, it seems like it triggers advisors to say, “Is this something that could happen to me and should I be thinking about this?” Mindy Diamond: Yeah. So let me ask you a question. You’re talking, you’ve mentioned UBS offering this outsized deal. So how does the notion of stability and ownership factor in? If an advisor is considering an unprecedented deal from UBS, what are the caveats or concerns with respect to stability and ownership? Jason Diamond: It’s the same list of considerations you should and would ask of any other firm you’re diligencing, except I think amplified even more in the case … If I was counseling an advisor who was looking at UBS, that would be what I would say, is exactly that. You’re seeing all of these departures and defections, and I would want to have conversations with those advisors and understand exactly why and have guarantees or assurances that I’m not going to suffer from those same pain points that force them to leave. Or, and I say this a little bit flippantly, but it’s a little bit true, I understand the devil that I’m getting into bed with, but for 550%, or whatever the deal might be, I can suck it up. And that’s something that some advisors might well say as well. Mindy Diamond: Yeah. Jason Diamond: I don’t want to end on the negative note of overly large transition, not there’s anything wrong with large transition deals, but as you look out, is there anything that’s coming up in your conversation with advisors that you view as the next wave of this? I’ll give you one that maybe you could touch on, and if you have another one, feel free to offer it in conclusion, but do you think age or advisors starting to succeed out of the business will become more of a driver of movement, even though to your point, advisors can access sunset deals? Mindy Diamond: I do actually, because I think the more the average advisor age increases, the more likely that those advisors are going to want to move on to do something else to monetize the business. And so much of the wave of movement we see is driven not so much by the senior advisor, because many seed advisors are happy enough with the ability to monetize their business in place. Even though it may not maximize the value of the business, it’s a close enough approximation and it means I don’t have to disrupt the apple cart. So we support that 100%. But where we get the calls is from the next generation that says, “Yeah, but hold on a minute. It’s a good way for me to take on a book of assets that I not otherwise have access to. And it’s great for my senior partner, my father, my mother, my whatever to monetize the business. But I’m buying an asset again that I don’t own and I ultimately don’t have control over all these things we’re talking about, the AI investment, the ability to create enterprise value, the stability, the cost cutting, all of it.” So I think it’s all of the above. You say, “What else is there?” I think that’s it. It’s all of the above. It’s anything and everything that drives movement. One, it’s personal, it’s highly unique, it’s different for every advisor. There are certainly themes, and we’re talking about them, but there’s a million different things. It’s personal. And while there are an awful lot of pushes, things that can frustrate an advisor, it is the most exciting time in our view to be an advisor, particularly a high quality one, because the options abound, the ecosystem is big, because the ability to monetize both in the short term and the long term is big, mammoth, exponentially bigger than it ever was before. And the true ability to really build an enterprise has never been greater. And I think all of those things, the desire for an advisor to be the best that they can be and live their best business life is probably the biggest driver of all. Jason Diamond: It’s really true these days, if you can dream it, you can probably build it. And we’ve said in the past, if you build it, they will buy it. It’s a great place to end. This was a really fun topic. I think that’s a spot on kind of fourth trigger, by the way, too. This sort of next gen is almost like the force multiplier or the amplifier of like they see all this other stuff and they’re asking these questions even more so. Because if I’m 60 years old, none of this matters all that much. It matters, but I’m out of the business in five to 10 years. Versus the next gen advisors are the ones who often bear the brunt of this. So I think a lot of really smart stuff. Thank you for sharing your wisdom and expertise. In the episode page, we’ll be sure we have our Industry Transition Report. And we’ve also created a tool, the top 10 tips for a strategic due diligence process, which is a great kind of practical hand-in-hand companion for this topic for advisors looking for more pointed tips on the due diligence process. So Mindy, thank you again. This has been a blast. Mindy Diamond: My pleasure. Thank you. Jason Diamond: Thank you for joining us. We'll be back with a new episode next week, so be sure to listen in. Mindy Diamond: As a financial advisor, you hold yourself to the highest standards of integrity, honesty, and credibility. You are successful because you take your professional responsibility seriously and are dedicated to your clients. But are you living your best business life? Are your goals aligned with your firms or could a better option exist? Should I Stay or Should I Go? is a book written with you in mind. It’s a self-guided journey that walks you through the key steps that we take with our advisor clients. This strategic thought process and roadmap to professional self-discovery is designed to help you ask the right questions and think critically and objectively, whether you’re considering change or not. Learn how to get your copy at diamond-consultants.com/thebook. The Advisor Transition Playbook: The Latest on Due Diligence, the Move, and Everything In Between – Part 2 A Special Industry Update with Jason Diamond and Mindy Diamond. Jason Diamond: Welcome to a replay of one of the most popular episodes from our podcast series for financial advisors, The Advisor Transition Playbook: The Latest on Due Diligence, the Move, and Everything In Between. It's Part 2 of a 2-Part Industry Update with Mindy Diamond. 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: There’s been a noticeable shift in how advisors are approaching decisions about their business, not necessarily in whether they’re exploring change, but in what they’re focused on when they do. Mindy is back with me for a continuation of our earlier conversation on the Advisor Transition Playbook. Last time, we spent time on the mechanics, how due diligence works, what a move actually entails, and how to think through the process. What’s become more apparent since then is that the inputs into that process are evolving. The traditional drivers are still there, but layered on top are a set of considerations that didn’t carry the same weight before. AI is one of them, and not just as a tool, but as a differentiator that advisors are starting to diligence more seriously. Enterprise value is another. Showing up in conversations, even for advisors who don’t technically own their business, but are thinking more critically about what they’re building over time. And then there are questions around stability, ownership, and flexibility. What happens to the firm itself and whether advisors retain the ability to adapt again if circumstances change. None of this is theoretical. It’s showing up in real time conversations. What we want to do here is unpack those new triggers of advisor movement and what they suggest about how decisions are being made today. So let’s get to it. Mindy, the legend, thank you for joining me. So glad to have you on. Mindy Diamond: Thank you. I’m so happy to be here. Jason Diamond: Great. Let’s dive right in. I’ll set the stage really quickly one more time. When we spoke about this topic last, we talked about the drivers of movement, what we’ll call in this conversation as the old or the legacy drivers of movement, and we spoke about the mechanics of the move. Before we get into the new drivers of movement, which I want to be the meat of the conversation, remind us, when we talk about the legacy drivers of movement …. And by the way, by saying legacy, I by no means want to suggest that they’re not valid today, because they’re equally valid, if not more so today than they were then. But when you think of the classic drivers of movement in our industry, what are they? Mindy Diamond: Yeah. So I would say, first of all, let’s start by saying that for every advisor, they’ve got a unique set of needs. So the first thing to say is that while you and I can talk about the categories of frustrations or things that might bother an advisor, they show up differently in each advisor’s life. So it’s important to note that everyone is unique. But generally speaking, if I had to package them, I’d say it’s number one that shows up most of the time is too much bureaucracy. A feeling that a firm or a model is just too hypervigilant in terms of compliance and it’s too bureaucratic and too hard to get things done.

本期提要：1、敦煌好吃的好玩的有哪些，三刀推荐2、极氪8X与极氪9X都畅销，到底做对了什么3、极氪8X还有哪些需要改进的地方4、30-50万的新能源，为什么要强调两个上限一个定位5、极氪8X适合哪些人，不适合哪些人6、对比竞品车型，极氪8X的优缺点有哪些【本期高光】�Part 1｜试驾奇遇：从敦煌记忆到「发烧」开录00:00:10 开场就是记忆力大考验：你聊过极氪8X吗？00:01:35 在高原「集装箱」房车营地试驾，雨声噼里啪啦是种什么风味？00:03:09 哇！敦煌演出震撼到词穷，文化魅力直接拉满！00:04:09 司机认证：敦煌是中国全球知名度最高的县级市？00:07:38 敦煌也「卷」疯了？学区房价格听得人瞠目结舌！00:10:55 来敦煌不吃手抓羊配杏皮水，这趟算白来了！�Part 2｜极氪深潜：性能、定价与定位谜题00:12:56 售价35万起，「耀影版」竟敢对标劳斯莱斯？00:14:54 五天闪电提车！这交付速度是真实存在的吗？00:22:34 全主动悬架加持，驾驶感真能媲美「公路之王」？00:24:44 顶配近60万还觉超值？极氪9X的价格策略妙在哪？00:29:08 月销破六千冲万辆，极氪9X的势头也太猛了！00:38:37 旗舰VS次旗舰，这模糊定位让谁纠结了？00:43:19 前脸威严、尾部运动，这车是个「设计矛盾体」？�Part 3｜超越驾驶：消费心理与情感共鸣00:36:59 买百万豪车，就为炫耀「我敢买，你不敢」的勇气？00:45:58 高级感设计，是让你觉得「它理应这么贵」！01:10:57 分享试驾感受被疑「充值」，车评人到底有多难？01:13:37 不想要航空座椅？极氪8X「反向选配」懂你！01:19:24 听众分享八年抗抑郁历程，节目成了心灵慰藉？01:25:06 对抗抑郁的偏方：忙到没时间「emo」！【本期主播】三刀：自称 “别人研究车，而我研究人” 的汽车 KOL。2006 年从事汽车销售，2013 年成立播客工作室，靠一支麦克风从播客做到抖音、B 站、小红书、微博等平台。节目里既聊车，也聊人间冷暖，刀友们口中的 “老大哥”。抖音丨快手丨小红书丨视频号：三刀侃车汽车之家丨懂车帝丨 bilibili 丨公众号：百车全说微博：百车全说三刀欢迎在苹果播客、小宇宙、喜马拉雅、网易云音乐、QQ 音乐、蜻蜓 FM、微博音频、微信视频号搜索【百车全说】，马上订阅节目，不错过每次更新。加入听友社群，微信号：46415254想与三刀 1 对 1 交流，扫码加入知识星球：

Wyndham Rewards Earner Premier Card Review | 120K Bonus, 25% Award Discount, $400 Credits & $395 Fee Justin Vacula of the Hurdy Gurdy Travel Podcast is joined by James from the Churn and Burn Podcast to discuss the newly released Wyndham Rewards Earner Premier Card. This new premium Wyndham credit card has a $395 annual fee and offers up to a 120,000-point welcome bonus, with 90,000 Wyndham Rewards points after $6,000 in spend and another 30,000 points after $750 in Wyndham purchases. Justin and James explain how the bonus works, who may be eligible, and whether the card makes sense for travelers who like Wyndham Rewards, casino status matches, and high-value hotel redemptions. They also discuss the card's biggest ongoing benefits, including a 25% discount or rebate on Wyndham award bookings, 30,000 anniversary points, a possible 30,000-point free night certificate after five Wyndham nights, and the potential for Wyndham points to avoid expiration while holding the card. The episode also covers the card's earning rates, including 8X points at Wyndham, 4X on grocery stores, 4X on dining, and 4X on travel, with special attention to the uncapped grocery category. Timestamps 00:00 Podcast Intro Theme 00:32 Wyndham Premier Card Overview 01:26 First Impressions And Barclays 02:54 Welcome Bonus Breakdown 04:32 Core Perks And Rebates 06:51 Earning Rates And Value 07:54 Statement Credits Deep Dive 13:07 Apply Or Pass Decision 16:46 Sponsor Break And Links 19:09 Best Wyndham Redemptions 23:08 Comparisons And Who It Fits 25:43 Listener Question And Business Card 27:27 Status And Caesars Angle 30:03 Wrap Up And Guest Plugs 34:40 Outro And Support The Show —

Content becomes forgettable when the key message isn't wrapped in storytelling.People don't retain information anywhere near as well as they remember stories they hear, and this is where manufacturing is missing a trick.Manufacturers are battling false perceptions that the industry is dirty, dingy, not innovative and not creative. But what's really happening is a failure to communicate the good stuff - the advanced systems and problem solving going on behind factory walls. The things that impact almost everything we touch in our lives.Chris is joined by Danny Gonzales, CEO at IndustrialSage – a video production company focusing on telling those manufacturing stories. Danny noticed that the sector was massively underserved and saw an opportunity to change peoples' perceptions. Using his background in B2B video production to fill the empty space with tales of meaningful work, and to show them to people in a way that they can receive it as something cool. Not a brain dump of stagnant information.For anyone thinking about how manufacturing companies can better communicate their value, attract talent, build stronger brands or create connections through storytelling, this episode is a look at how media and manufacturing are merging.In this episode, find out:How IndustrialSage was born out of the discovery that the machines, technology and processes happening in factories was both very cool and underrepresented in media.IndustrialSage and Danny's mission to change the common misconceptions about the manufacturing industry through storytelling and thought leadership.How true opportunity is found in the niches, despite the instinct to gravitate towards the aesthetic and techy industries which are usually overserviced.How Danny's career has evolved from his college dream of becoming a Hollywood producer, and how an actual Hollywood producer set him on his current path.Why product-user content filmed on an iPhone is outperforming brand films and how leveraging these tools can save time and money while embracing the shift towards authenticity.Why Danny believes the key to creating quality video content is in the strategy and distribution and what companies can do to implement these effectively.Why storytelling is the best way to communicate the value proposition and the psychology behind why it is so effective in overcoming the flood of content.Enjoying the show? Please leave us a review here. Even one sentence helps. It's feedback from Manufacturing All-Stars like you that keeps us going!Tweetable Quotes:“IndustrialSage is on a mission to change the perception of the industry. There are a lot of people that think it's dingy, grimy, not innovative, not creative, and they're dead wrong. We want to show what is going on. We want to do that through storytelling.” – Danny Gonzales “Most people will upload videos, and they just sit there on YouTube, maybe embedded on their website, and that's it. There are so many other use cases – social media, in your trade shows, in your email campaigns.” – Danny Gonzales“For anyone that's going to be a little bit more storytelling driven, there's like an 8X chance that people are going to remember it better when you wrap it inside of a story. That's just how we communicate, and it's easier to be able to transmit.” – Danny GonzalesLinks & mentions:IndustrialSage, industry-leading media company, publishing compelling content for industrial & manufacturing professionalsSierra Nevada Brewing, their Mills River Taproom in Asheville is the Willie Wonka of craft breweriesConnect with Danny on LinkedInMake sure to visit http://manufacturinghappyhour.com for detailed show notes and a full list of resources mentioned in this episode. Stay Innovative, Stay Thirsty.

Earn 8x or redeem with Chase Travel℠ Points Boosts? Frequent Miler reader Bryant wrote in saying...With the Chase Sapphire Reserve® Card providing 8X points on travel and hotel, doesn't it always make sense to book through Chase and get the multiplier? That being said, what then is the best way to utilize the points to maximize their value?We've been answering a reader or listener question at the end of each Frequent Miler on the Air podcast episode. Now, we've turned the question of the week into its own weekly episode. Tune in every Sunday at 5pm for our Question of the Week podcast. And if you have your own question you'd like to submit, you can send it to mailbag@frequentmiler.com.Mentioned in this episode:Check out all of our other travel podcasts from around the worldThis podcast is part of Voyascape, a podcast network that brings together the world's best travel podcasts. You can find all of our podcasts from around the world at Voyascape.com. If you are interested in advertising or sponsored content on any of our shows you can find out more at the link below.Voyascape Podcast Network

What if the breakthrough you've been praying for is still possible — even after years of struggle, burnout, and uncertainty? Just 12 months ago, Christine Davis was questioning whether she could even stay in the mortgage industry after nearly two decades in the business. She was exhausted, financially stressed, and stuck wondering if things would ever turn around. Today? She's completely transformed her business — increasing her monthly income over 8X while creating more consistency, confidence, freedom, and momentum than she's had in years. And she didn't do it through cold calling, chasing Realtors, or grinding herself into the ground. In this episode, Christine shares the real story behind her turnaround: The mindset shifts, daily habits, relationship strategies, and uncomfortable growth moments that helped her go from survival mode… to THRIVE MODE and building a business she's genuinely proud of again. Inside this episode: • Why waiting for the market to “get better” keeps most LOs stuck. • The simple daily “Hour of Power” habit that helped Christine rebuild momentum. • How to stop chasing referrals and become a true growth partner Realtors WANT to work with. • Why confidence comes from repetition — even when you feel awkward at first. • The systems and structure that gave Christine more income, less stress, and more time with family. • The biggest mistake struggling LOs make when trying to grow in a down market. If you've been feeling discouraged, inconsistent, or uncertain about your future in this industry… This episode will remind you that your next breakthrough may be a lot closer than you think.

Description: In this episode, listen to Bill Levinson as he hosts Arnold "Tre" Tarpley, live from the Levinson Headquarters! Trey is the owner and founder of Lead Lab, one of the fastest-growing Life & Annuity lead programs in the industry! Trey and his team have developed unique marketing strategies & AI-powered verification processes to help Agents around the USA increase the quantity and quality of their leads. As a result, this has led to agents seeing their Life & Annuity sales increase by 4-8X on average! The results speak for themselves! *Active Levinson & Associates agents can purchase discounted & exclusive SMS-Verified leads for IUL, Annuity, Final Expense, Mortgage Protection & Aged Leads! Arnold's Biography: Arnold (Tre) is the Founder of Lead Lab, where he and his team help life insurance agencies scale using proven systems and market development strategies. His focus is on building real infrastructure, stronger relationships, and sustainable growth. Arnold has thousands of recurring clients every month. Their results are built on trust, execution, and putting agents first. Trey was born in Pittsburgh and played professional Football in the NFL. He was drafted by the Atlanta Falcons in 2024. Check us out online: Agent Back Office Site: LevinsonAndAssociates.com Facebook: @levinsonandassociates X: @levinsonassoc Instagram: @levinsonandassociates Threads: @levinsonandassociates LinkedIn: @bilevinson  Podcast: levinson.libsyn.com YouTube Library: @thelevinson1

Hey y'all, Alex here, let me catch you up!Jensen Huang went on Lex and said AGI has been achieved. We'll get to that.The biggest demo moment: Gemini 3.1 Flash Live launched - Google's omni model that sees, hears, and searches the web in real time. We tested it live and I said “what the f**k” on air. It was really impressive!Google Research also dropped TurboQuant (6x KV cache compression) which crashed Samsung and Micron stocks - we had Daniel Han from UnSloth help us make sense of why that's overblown. OpenAI killed Sora - the app, the API, and the $1B Disney deal. Claude felt noticeably dumber this week AND max account quotas are melting as 500+ people confirmed on my X and Reddit. We have an official word from Anthropic as to why. Mistral launched Voxtral TTS (open weight, claims to beat ElevenLabs), Cohere shipped an ASR model, and Google's Lyria 3 Pro now generates full 3-minute music tracks inside Producer AI.This and a lot more in today's episode, let's dive in (as always, show notes and links in the end!) ThursdAI - Let me catch you up! Gemini 3.1 Flash Live: The Real-Time AI Companion Is HereGoogle dropped a breaking news on the show today, with Gemini 3.1 Flash - LIVE version. This one is an omni-model, that means it can receive text/audio/video on input and respond in text and voice. It has Google search grounding, and it felt... immediate! I was blown away, really, check out the video, the speed with which it was able to “see” me, respond to my query, look up something on the web, was mind blowing. I don't often get “mind blown” anymore, there's just too many news, but this one did the trick! With the pricing being around 10x cheaper than GPT-real-time, and the Google search grounding being super fast, I can absolutely see this model being hooked up to... robots (like ReachyMini), SmartGlasses that can see what you see, and a bunch more! Gemini Live is available on Google AI studio and has been rolled out globally inside the Google Search app! So now when you pull up the Google Search app, just open it and point at anything. Truly a remarkable advancement.Google research publishes TurboQuant - 6x reduction in KV cache with 0 accuracy lossGoogle research posted some work (based on an Arxiv paper from almost a year ago) that shows that with geometry tricks, combining two other techniques like PolarQuant and QJL, they are able to compress the KV cache of running LLMs by nearly 6x, and show an 8X speed up for model inference with zero accuracy loss. If you ever watched silicon valley the HBO show, this sounds like the fictional middle-out algorithm from PiedPiper. If this scales (and that's a big if, we don't know if this applies to other, bigger models yet), this means significant decreases in memory requirements to run the current crop of LLMs for longer context. The claim is big, so we'll continue to monitor if this indeed scales, but the most interesting thing about this piece of news is, that it broke the AI bubble and went to wall street, with finance brows deciding that this means that memory will not be needed as much any more and it tanked Samsung and Micron stocks. Which I found particularly ridiculous on the show, did they not hear about Jevons Paradox? This is reminiscent of the DeepSeek R1 saga that tanked Nvidia stocks over a year ago. Daniel Han from Unsloth, who joined us on the show, pointed out that the approach is mathematically interesting even if it's not necessarily better than existing open-source techniques like DeepSeek MLA. LDJ noted that the baseline comparison (16-bit KV cache) isn't really fair since most production systems are already compressing beyond that. Yam implemented it himself and confirmed the speedups are real, but so is the trade-off.Anthropic updates: Opus dumber? Quotas lower! Injunction won! Computer.. used. Anthropic folks, especially on the Claude code side are shipping like crazy, we won't be able to cover all the updates, but there was a few notable things I have to keep you up to date on. Claude Opus seems to be getting “dumber”, againI have to talk about this because it affected my work directly this week and hundreds of people confirmed the same experience.I use Claude Opus for my standard ThursdAI prep workflow — generating the TL;DR with 10 bullet points and an executive summary for every topic we cover, creating episode pages, etc. The format has not changed for over a year and yet this week I asked for 10 factoids. I got 4. It says “10” right there in the prompt. Four bullet points. On the website builder, I've asked Opus to create a page for last weeks episode, and instead of adding it to the other episode, Opus decided to ... replace the last episode with this one. This would be funny if it wasn't sad. This is Opus 4.6 we're talking about, not some quantized open source LLM from last year! The reason is unclear, and it's not only me, Wolfram noticed that it's easier to see these types of things in other languages and that for the last week Opus would forget to add Umlauts in German!? and Yam also felt it. Pro/Max plan quotas burning up, Anthropic confirmed that they are tightening them for “peak hour” usageThis week, so many people started posting that something is wrong with their Claude Codes, I did a survey, and it blew up. Hundreds of people replied and confirmed that for the first week, they are hitting their session quotas on Pro and 20x $200/mo MAX accounts much much quicker than before. When I say much quicker, I mean, some fokls have hit the quota in as little as 5 minutes. While some others had no issues. I personally btw did not have this. A few days later, Thariq from the Claude code team, and later an official post, confirmed that Anthropic had been rolling out a “tightening” of the Pro/Max accounts to accomodate for growth. This is of course, a huge bummer to the folks who pay $200/mo for the 20x max tier, as they tend to run agents and subagents overnight. But here's the thing, I don't think that folks from Anthropic see what we see, some folks got no issues with hitting quota, and some are barely able to use their subscription. I hope that they will find and resolve these bugs quick, because some folks are switching to Codex, and the Anthropic IPO is coming up! I will say, I don't envy Thariq's job, he's doing it gracefully, and maybe one of the only ones in Anthropic that does it at all. Judge granted Anthropic an injunction against DoW and the whole “Supply chain risk” designation!Just in as I'm writing this, a district judge in CA, granted Anthropic an injunction against being designated as a supply-chain-risk company. If you haven't been following, the US Department of War, specifically Pete Hegseth, threatened and then designated Anthropic as a supply chain risk company, while us president Trump “fired” Anthropic and banned its use in any gov agencies. Well, no so fast says Judge Lin, from CA District court. In this Order, she shows that Dept. of war didn't meet any legal requirements for this designation. It's really a fascinating read, but the highligth is this: When asked why Hegseth made a public statementthat had no legal effect and that did not reflect the immediate intent of DoW, counsel stated, “I don't know.”This is just the first court and will likely be escalated further up the judicial system. This is still developing and apparently the Pentagon declared Anthropic a supply chain risk under two different statutes, and this only affects one of them. So while it's good news, it's not over yet. Voice & Audio Explosion: Three Releases in One HourI had to hit the breaking news button mid-TLDR because three major voice releases dropped simultaneously during the show.Mistral Voxtral TTS — Mistral's first text-to-speech model, 3 billion parameters, open weight. They claim it beats ElevenLabs Flash v2.5 in human preference tests (58% win rate on flagship voices, 68% on zero-shot voice cloning). We tested it live on the show — it's decent, with emotion controls for neutral, happy, and frustrated voices. I was not super impressed tbh, it sits somewhere between the very good big labs TTS and the very small open source 82M param TTS. Cohere Transcribe — Cohere enters the ASR game with a 2 billion parameter open-source model (Apache 2.0!) that immediately grabbed the #1 spot on HuggingFace's Open ASR Leaderboard with a 5.42% word error rate, beating Whisper Large v3's 7.44%. In human evaluations, it wins 61% of the time on average, and 64% specifically against Whisper. For anyone in regulated industries needing local inference for compliance, this could genuinely replace Whisper as the default.Google Lyria 3 Pro — Google's most advanced music model is here.It can now generate full 3-minute tracks with structural control — intros, verses, choruses, bridges. We generated a ThursdAI opening theme live on the show using Producer AI, and it was... honestly not bad? It followed our instructions perfectly: drum and bass, 174 BPM, high energy podcast opener with vocals and introduction. The instruction-following was spot on. Nisten said it's the best music generation model right now. It's available to Gemini subscribers and via Producer AI and gemini, and it can even compose music from images. SynthID watermarked, royalty-free. We might actually use one of the generated tracks as a new show opener.The craziest thing is, since Google acquired Composer, the team has been shipping. I only generated the audio during the live show, but now went back there to download it for you guys, and whoah, it can now generate whole clips by using other Google tech, this is really cool! OpenAI kills SORA (and Atlas?)Last week we reported on about OpenAI's focus shift towards Codex and productivity, and this week we see the first casualty. OpenAI is killing SORA, the app, the Sora 2 and Sora 2 pro models and APIs. Many AI haters are celebrating this as through “ai videos” is dead, but honestly, this is obviously about the GPU power and the other things OpenAI needs to do to win the fight against Anthropic. OpenAI is also apparently going to IPO this year (like Anthropic) and they absolutely need to win the productivity/agents in enterprise market. As part of this shut down, the Disney + OpenAI partnership, is also dissolving, and Disney will no longer invest 1B into OpenAI.So, say bye bye to having digital selfies with Sam Altman. I've generated this SORA vid to hear from Sam himself: Atlas browser, OpenAI's native browser endeavor is supposedly also going to transform, together with Codex and OpenAI native app into one super app that includes all three according to the same memo. AGI is here according to Jensen, AGI is far away, according to ARC-AGI-3 The back to back this week can give anyone whiplash. First, Lex Friedman had Jensen Huang on the podcast, and asked him a very specific “WhenAGI” question, to which Jensen said “I believe it's already here” Then just a few short days layer, ArcPrize, released the 3rd version of Arc-AGI, Arc-AGI 3 a series of puzzle games, where humans get 100% pass-rate and the current LLM, top tier frontier LLMs, are getting less than 1%! It's an interactive, agentic reasoning benchmark designed to test human-like generalization and intelligence in novel, abstract, turn-based environments.The puzzles all look simple enough to do, and are actually fun, and while the wild claims of “AGI is not here yet” from the ArcPrize folks are quite interesting. The stated goal of the foudation is to release evaluations that are completely un-saturated, and this seems like one such thing at first glance. There's a bit of a debate in the community about the way Arc Prize went about this specific benchmark (no harnesses, raw LLM outputs), saying that humans got a “game” while the LLMs get just raw JSON and minimal and no extra tools. For context, a agentic harness startup claims to have solved 35% already of the games in ArcAGI, but that result is unverified and self reported, becuase they are an agentic harness, which ArcAGI apparently disqualifies. AI Art and DiffusionI wanted to finish but I think these are important releases so I'll include them briefly. Luma Labs Uni-1 — thinks and generates pixels simultaneously, #1 human preference Elo (X, Announcement)This was a surprising release, we previously seen Luma Labs do video, but this time they are posting their Uni-1 which is a… image model but it's based on an LLM, so you talk to it, iterate together until you get results. Yes, Nano Banana via AI studio is kind of like this as well ,but Uni feels a bit different. It can also generate infographics, which I haven't tried yet. You can try Uni herePhota Labs launches Phota Studio + API — a photography-focused image model with identity-preserving personalization (X, try it)There's tons of photo startups, but this one looks kind of crazy! You upload a bunch of your pictures, they train a “model” for you, and then you can create a whole bunch of images, and they do actually resemble you. Yes, Nano Banana can take a few reference pictures, but this somehow seems more accurate! You can create professional photos, fix photos you like, add others to your photos. I do feel there's a jump in capabilities here, specifically because of the personalization! Give them a try if you're not worried about them training on your pics and let me know.Modular made Flux.2 run in

En este episodio del Carlos Castillo Podcast, conversamos con Mario Samperio, gerente general de Olga de Torrebiarte y Asociados, sobre cómo logró escalar una empresa inmobiliaria de 15–20 millones a 125 millones de dólares colocados, multiplicando resultados 8X en 8 años, prácticamente con el mismo equipo.Hablamos de liderazgo, estrategia, rendición de cuentas, cultura de alto desempeño, coaching ejecutivo y cómo ejecutar una visión de crecimiento sin que la estrategia se quede guardada en una gaveta.Este episodio está lleno de ideas prácticas para empresarios, gerentes, líderes comerciales y cualquier persona que quiera llevar su organización a entregar resultados excepcionales.Temas que vas a encontrar en este episodio:00:00 Introducción01:43 Cómo Mario llegó a la empresa03:22 Primeros cambios clave07:22 La estrategia que cambió el rumbo07:51 De meta de 100M a cierre de 125M08:26 Cómo crecieron 8X en 8 años10:03 Estrategia, enfoque y cultura14:18 Cómo mantener viva la estrategia15:49 Rendición de cuentas semanal19:22 Dashboards e indicadores23:36 Cómo desarrollaron al mismo equipo27:12 One-on-ones efectivos36:36 Bono colectivo y alineación38:16 Transparencia de resultados40:40 Gobernanza en empresa familiar44:17 El valor del coaching48:33 Expansión a Antigua Guatemala50:17 CierreSi este episodio te aportó valor, suscríbete, compártelo y déjanos en comentarios cuál fue la idea más poderosa que te llevas.#Liderazgo #Estrategia #CulturaOrganizacional #Ventas #EmpresaFamiliar #CoachingEjecutivo #GestiónEmpresarial #ResultadosExcepcionales #RealEstate #CarlosCastilloPodcast

Jim and Allan disagree on how new hard drive tech is likely to work, more on storage and compute in the same box, and how we set up disk encryption on laptops. Plugs Support us on patreon and get an ad-free RSS feed with early episodes sometimes OpenZFS Monitoring and Observability News/discussion Western Digital doubles the performance of hard drives with dual-actuator High-Bandwidth, with path to 8X performance increase — Power-Optimized HDDs will reduce power by 20 percent Free consulting We were asked about how we set up disk encryption on laptops. A quick-start guide to OpenZFS native encryption – Ars Technica Keeping Data Safe with OpenZFS: Security, Encryption, and Delegation See our contact page for ways to get in touch.

Jim and Allan disagree on how new hard drive tech is likely to work, more on storage and compute in the same box, and how we set up disk encryption on laptops. Plugs Support us on patreon and get an ad-free RSS feed with early episodes sometimes OpenZFS Monitoring and Observability News/discussion Western Digital doubles the performance of hard drives with dual-actuator High-Bandwidth, with path to 8X performance increase — Power-Optimized HDDs will reduce power by 20 percent Free consulting We were asked about how we set up disk encryption on laptops. A quick-start guide to OpenZFS native encryption – Ars Technica Keeping Data Safe with OpenZFS: Security, Encryption, and Delegation See our contact page for ways to get in touch.

Generating alpha in "boring" businesses often outperforms chasing the latest tech trend. In this solo episode of Mechanics of Money, Sam Silverman breaks down the Private Equity Roll-Up Strategy, specifically how consolidating fragmented industries like commercial paving can generate returns of 3X to 8X.We look past the unglamorous nature of asphalt to reveal the financial mechanics of buying cash-flowing assets at low multiples and exiting at institutional valuations.In this episode, we cover:The Math of Arbitrage: How to buy small operators at 3-4X earnings and exit to institutional buyers at 8X or higher, turning $20M of invested capital into a $56M exit.The "Paving Thesis": Why the $110 billion Bipartisan Infrastructure Law and the essential nature of road maintenance make this a recession-resistant asset class.Economic Moats: Why high equipment costs and supply chain relationships create barriers to entry that protect investor value from new competition.The "Silver Tsunami": How the retirement of baby boomer business owners, over half of the industry, is creating a massive acquisition opportunity for well-capitalized buyers.Execution Risk: The reality of integration failure, culture clashes, and why "simple to explain" does not mean "easy to execute".Links & Resources:Newsletter: Join the Mechanics of Money weekly deep dive: https://www.mechanicsofmoney.coInvest: Invest with Silverman Capital: https://silvermancapital.coAbout the Host: Sam Silverman is the Founder of Silverman Capital, a private equity and real estate investment firm. Mechanics of Money is the audio playbook for high-net-worth individuals moving from "High Earner" to "Sophisticated Allocator."

A mom arrived drunk to pick up her child from daycare, she 8X over the limit at a .419...and a man could face at least 5 years in prison for throwing a ketchup packet at another man!

In this episode, Patrick McKenzie (patio11) walks through how perpetual futures work, from funding rates to liquidations to the surprise of automatic deleveraging. Perps are the dominant trading mechanism in crypto (6-8X larger than spot volume) and exist primarily to let exchanges and market makers run casinos more capital-efficiently. He explains why this intellectually interesting innovation probably won't escape crypto, despite what crypto enthusiasts might expect.–Full transcript available here: www.complexsystemspodcast.com/understanding-perpetual-futures/–Sponsor: Framer is a design and publishing platform that collapses the toolchain between wireframes and production-ready websites. Design, iterate, and publish in one workspace. Start free at framer.com/design with code COMPLEXSYSTEMS for a free month of Framer Pro.–Links:Bits about Money, Perpetual futures, explained www.bitsaboutmoney.com/archive/perpetual-futures-explained/ –Timestamps:(00:00) Intro(02:36) Beginning with the problem(06:49) Perps predate crypto but found a home there(08:19) Multiple settlements a day(10:30) Convergence in prices via the basis trade(13:44) Sponsor: Framer(15:11) Leverage and liquidations(18:46) We have altered the terms of your unregulated futures investment contract(21:50) An aside about liquidations(25:14) Will crypto successfully "export" perps

James Lavish, co-managing partner of the Bitcoin Opportunity Fund and author of The Informationist newsletter, joins Episode 305 of the Julia La Roche Show. In this episode, Lavish explains how the government shutdown has locked nearly $1 trillion in the Treasury General Account, draining liquidity from financial systems and raising concerns about a 2019-style repo crisis as bank reserves fall to dangerous levels. He argues Americans have lost 25% of their purchasing power from 2020 to 2025, and while technology should bring deflation, we instead have persistent 3% inflation because it's necessary to manage $38 trillion in debt through currency debasement. Lavish explains the K-shaped economy where the top 1% gained 8X wealth since 1990 versus 4X for the bottom 50%, noting commercial real estate defaults are spiking and subprime auto lenders are collapsing. When the TGA liquidity eventually floods back into markets, he warns not to mistake it for prosperity—it's currency debasement, which is why he recommends positioning in hard assets like Bitcoin, gold, and real estate. The Fed is trapped between dual mandates with no way out, and while AI stocks may have gotten ahead of themselves risking a market shock, his message is clear: own assets because he's not bullish on the economy, he's bearish on the currency.This episode is brought to you by VanEck. Learn more about the VanEck Rare Earth and Strategic Metals ETF: http://vaneck.com/REMXJuliaLinks: Twitter/X: https://x.com/jameslavish The Informationist: https://jameslavish.substack.com/ The Bitcoin Opportunity Fund: https://www.bitcoinopportunity.fund/ Timestamps: 0:00 - Introduction and welcome1:20 - Big picture macro view: Fed battling dual mandates4:30 - Stagflation risk: prices rising as economy rolls over5:10 - Government shutdown removing liquidity from markets7:19 - Treasury General Account (TGA) explained14:21 - 2019 repo crisis explained21:31 - Current concerns about overnight lending market26:18 - Will Fed do QE again?29:03 - Credit markets29:07 - K-shaped economy explained37:08 - Position for currency deterioration38:28 - Why people think 2% inflation is normal40:11 - Lost 25% purchasing power from 2020 to 202540:41 - Technology should bring deflation, not inflation46:30 - Why we need inflation: $38 trillion debt problem50:59 - What's keeping James up at night55:27 - Closing remarks and contact information

Join Marvin Cash and Mac Brown on another segment of Casting Angles on The Articulate Fly fishing podcast as they tackle the technical challenges of North Carolina's extremely low water conditions during delayed harvest season. Mac Brown, veteran guide and casting instructor, shares essential leader construction techniques for technical low-water presentations, breaking down common misconceptions about leader design and energy transfer. Learn why adding tippet to store-bought leaders creates presentation problems, and discover Mac's proven formula for building effective 20-foot leaders using stiff monofilament like Maxima Chameleon—10 feet of 15-pound butt section, 5 feet of 12-pound, 2.5 feet of 0X, then your tippet of choice. Mac explains why modern nylon doesn't require complex tapered sections like old gut leaders did, debunks the "leader hinge" myth and reveals why he keeps the same leader on his rod for the entire fly line's lifespan. The discussion covers critical low-water stealth tactics including staying out of the water entirely, lengthening leaders for distance and using lighter tippets down to 7X and 8X nylon for superior knot strength over fluorocarbon in small diameters. With DH season underway and freestone streams running low across the Smokies, these expert adjustments will help you adapt your approach and connect with selective trout in challenging autumn conditions.Related ContentS7, Ep 41: Navigating High Water: Strategies for Success with Mac BrownS6, Ep 130: Casting in Color: Mac Brown's Fall Fly Fishing StrategiesS7, Ep 20: Practice Makes Perfect: Mac Brown on Mastering Casting TechniquesS6, Ep 141: Mastering Cold Weather Fly Fishing with Mac BrownAll Things Social MediaFollow Mac on Facebook, Instagram and Twitter.Follow us on Facebook, Instagram, Twitter and YouTube.Support the Show Shop on AmazonBecome a Patreon PatronSubscribe to the PodcastSubscribe to the podcast in the podcatcher of your choice.Advertise on the PodcastIs our community a good fit for your brand?

RECRUITERS: SAY HELLO TO YOUR NEW CO-WORKERS ARCHER, CHASE & THENA! (LIVE DEMO OF AI AGENTS)   Is your recruitment supplier talking to you about AI Agents? Well this is the show where we challenge the founders to come on screen to live demo their new AI workers to the brainfood audience!   Not content will one agent, our friends at hackajob are have launched no less than three at the same time:   Meet:  

After more than 100 episodes, #Coach2Scale wraps with a powerful closing message from host Matt Benelli, one that goes far beyond sales tactics. In this final episode, Matt shares four hard-earned truths from hundreds of conversations with CROs, enablement leaders, and frontline managers. He challenges the myth of the “super rep turned manager,” breaks down the true ROI of coaching (7–8X when done right), and reminds us that one-on-ones aren't just a task, they're the operating system for growth.Matt also reflects on why performance loops aren't enough without practice loops, and how great teams aren't built on pressure, but on preparation. This episode connects the dots between personal development and business outcomes; it's a call for CROs and GTM leaders to stop managing through dashboards and start developing their people with purpose. If you're serious about building a high-performing team that lasts, this episode is your blueprint.Top Takeaways1. The human side of coaching is non-negotiable.Vulnerability-based trust isn't soft; it's the foundation for accountability, belief, and long-term performance.2. Properly equipped managers deliver 7–8X ROI.Coaching isn't a “nice to have”; consistent, structured 1:1s lead to higher engagement, lower attrition, and stronger pipeline performance.3. Stop promoting super reps into management without support.Selling and coaching are completely different skills; without systems and training, you set managers (and their teams) up to fail.4. Practice beats performance.Top teams don't just execute, they review, adapt, and improve with immediate, behavior-focused feedback that drives lasting change.5. Coaching isn't a tool; it's a behavior change engine.Technology alone doesn't drive growth; tying behavior improvement directly to outcomes is what makes a coaching culture truly effective.6. One-on-ones are not optional; they're the operating system.When coaching becomes the standard cadence, it shifts manager behavior from reactive firefighting to proactive development.7. Performance grows when reps feel developed, not just measured.The best leaders strike a balance between empathy and accountability, investing in long-term careers rather than just meeting short-term quotas.8. Coaching is how you scale without breaking your team.Growth doesn't come from dashboards or pressure; it comes from developing people who are confident, capable, and aligned.

In this week's Chit Chat Across the Pond, Adam Engst of TidBITS joins me to talk about his decision process on choosing an iPhone 17 over the 17 Pro. One of the things I like best about Adam is how, just like me, when he starts digging for an answer which should be knowable, he's like a dog with a bone and won't stop until he succeeds. The main decision point between the 17 and the 17 Pro is whether someone really does care about the 8X telephoto lens vs. the macro lens. We tried to figure out how many photos we really took with the 5X on the iPhone 16 Pro, but that turned out to be a much harder task than you would think. We tried Apple Photos smart albums, but for reasons you'll hear, you can't really do it that way. Using the open source command line ExifTool from exiftool.org/... (also available through Homebrew) and his able assistant ChatGPT, he was able to create a script that queried our Photos Libraries to determine how many photos we had taken in the last year using the different camera/lens combos on the iPhone 16 Pro. While you hear us talking he hadn't yet perfected his script but he eventually beat it into submission! Of course, Adam has written up an article describing this descent into madness and even published the script if you care to download it. You can find his article at tidbits.com/.... Read an unedited, auto-generated transcript with chapter marks: CCATP_2025_09_25 Join the Conversation: allison@podfeet.com podfeet.com/slack Support the Show: Patreon Donation Apple Pay or Credit Card one-time donation PayPal one-time donation Podfeet Podcasts Mugs at Zazzle NosillaCast 20th Anniversary Shirts Referral Links: Setapp - 1 month free for you and me PETLIBRO - 30% off for you and me Parallels Toolbox - 3 months free for you and me Learn through MacSparky Field Guides - 15% off for you and me Backblaze - One free month for me and you Eufy - $40 for me if you spend $200. Sadly nothing in it for you. PIA VPN - One month added to Paid Accounts for both of us CleanShot X - Earns me $25%, sorry nothing in it for you but my gratitude

In this week's Chit Chat Across the Pond, Adam Engst of TidBITS joins me to talk about his decision process on choosing an iPhone 17 over the 17 Pro. One of the things I like best about Adam is how, just like me, when he starts digging for an answer which should be knowable, he's like a dog with a bone and won't stop until he succeeds. The main decision point between the 17 and the 17 Pro is whether someone really does care about the 8X telephoto lens vs. the macro lens. We tried to figure out how many photos we really took with the 5X on the iPhone 16 Pro, but that turned out to be a much harder task than you would think. We tried Apple Photos smart albums, but for reasons you'll hear, you can't really do it that way. Using the open source command line ExifTool from exiftool.org/... (also available through Homebrew) and his able assistant ChatGPT, he was able to create a script that queried our Photos Libraries to determine how many photos we had taken in the last year using the different camera/lens combos on the iPhone 16 Pro. While you hear us talking he hadn't yet perfected his script but he eventually beat it into submission! Of course, Adam has written up an article describing this descent into madness and even published the script if you care to download it. You can find his article at tidbits.com/.... Read an unedited, auto-generated transcript with chapter marks: CCATP_2025_09_25 Join the Conversation: allison@podfeet.com podfeet.com/slack Support the Show: Patreon Donation Apple Pay or Credit Card one-time donation PayPal one-time donation Podfeet Podcasts Mugs at Zazzle NosillaCast 20th Anniversary Shirts Referral Links: Setapp - 1 month free for you and me PETLIBRO - 30% off for you and me Parallels Toolbox - 3 months free for you and me Learn through MacSparky Field Guides - 15% off for you and me Backblaze - One free month for me and you Eufy - $40 for me if you spend $200. Sadly nothing in it for you. PIA VPN - One month added to Paid Accounts for both of us CleanShot X - Earns me $25%, sorry nothing in it for you but my gratitude

Ellie Holbert, founder and principal of Empact Advisory Services, is a scientist, researcher and organizational change expert who has mastered unlocking full potential of organizations including transforming some of the largest health systems in the U.S. as well as large enterprises with over 250,000 employees. In this latest episode, she highlights the key factors to her system of discovering the human potential to perform in organizations with KPI structure, culture, and habits that drive results.0:00 Episode Highlight 1 - Redefining Failure & Circle of Safety0:42 Episode Highlight 2 - Case: Wrong KPIs harmed Customer Satisfaction in a Large U.S. Healthcare System03:34 Personal Inspiration: Lessons from Motherhood & Reading“Redefining strength, resilience, and inspiration.”04:53 Academic Excellence & Early Career Discipline“Why structure, early mastery, and emotional intelligence shaped her career.”07:46 Metrics vs. Culture: Avoiding the Wrong Incentives“How poorly designed KPIs can backfire—and what to measure instead.” Importance of balancing individual and team metrics. Measuring trust, approachability of leaders and emotional intelligence factors is linked to observable leadership that drives business outcomes.10:30 Unintended Problems of Procurement KPIs in Largest US Health System. Created Customer Dissatisfaction despite opposite intentions.11:40 When to Invest in Team Effectiveness Interventions“Times of change, fear, and stagnation demand cultural investment.”Employees are operating from self-protection which is slowing innovation, and performance.16:42 Today's Need to Protect Top Performers & Measure ROI“Why high performers deliver 8X output and how to retain them.”19:49 Building Self-Driven Teams That Last Beyond ConsultingEllie anchors specific techniques to “Psychological safety, retrospectives, and boldness as cultural cornerstones.”20:30 Leadership Power of Describing Reality Accurately without BlameSelf-regulation is a norm in the most successful teams, centered on creating structure with clarity, safe environment and analysis of emotional intelligence.24:48 Keys to Thrive in Uncertainty: Mindset, Delegation & Letting Go27:57 Redefining the Modern OrganizationEllie reacts to Netflix custom to encourage executives to make $1 million dollar mistakes. Failure is a critical part of our success, as well as sharing lessons from failures because of the benefits of learning. “The four pillars: psychological safety, belonging, clarity, and purpose.”33:03 Case Study: From Chaos to Clarity in 8 WeeksEllie tells how she created champions in a financial services organization with 5000 employees handling a major cybersecurity initiative, pressure and uncertainty. “How one healthcare team improved ROI by 45X and revenue by 11X.”37:35 – Final Words & Team Health Assessment“Start small: measure and improve team health."

Every successful founder has a story worth learning from. In this episode, Jake & Gino sit down with Brian Smith to unpack his incredible entrepreneur journey—from surviving political challenges in Denver real estate to building and scaling businesses across the U.S. and Mexico.Brian shares candid lessons on entrepreneurship, resilience, and growth. Discover how his early days in commercial real estate shaped his mindset, why burnout forced him to pivot, and how he eventually launched Strategy Ladders to help founder-led B2B service businesses scale beyond the owner. You'll also hear his perspective on Mexico City as an entrepreneurial hub, the shift from consulting to implementation, and how he helps businesses achieve 2–8X growth. If you're an entrepreneur or investor, you won't want to miss Brian's insights.Connect with Brian Smith: StrategyLadders.com | LinkedIn: S. Brian Smith Chapters:00:00 – Introduction03:12 – Breaking into Denver real estate with master leases07:39 – Politics, burnout, and lessons from failure14:43 – Redefining success: survivalist roots to investment banking20:24 – Founders Organization & why many groups miss real entrepreneurs32:52 – Myths vs. reality of living in Mexico as an entrepreneur42:45 – Why implementation beats coaching in today's consulting market We're here to help create multifamily entrepreneurs... Here's how: Brand New? Start Here: https://jakeandgino.mykajabi.com/free-wheelbarrowprofits Want To Get Into Multifamily Real Estate Or Scale Your Current Portfolio Faster? Apply to join our PREMIER MULTIFAMILY INVESTING COMMUNITY & MENTORSHIP PROGRAM. (*Note: Our community is not for beginner investors)

US-based trading giant Robinhood announced the listing of Hedera Hashgraph $HBAR last Friday, triggering an instant rally despite lukewarm market momentum. Meanwhile, RWA is heating up on Hedera, could OpenAI be planning integration with the hbar hashgraph?~This episode is sponsored by Gemini~Sign up for The Gemini Credit Card and get an extra $50 in crypto!➜ https://bit.ly/GeminiPBNGuest: Gregg Bell, Chief Business Officer at HederaHedera website ➜ https://hedera.com/00:00 intro00:06 Sponsor: Gemini00:57 Hedera TVL Growth01:30 HBAR Staking Lacking01:59 Staking Rewards03:06 Boosting Liquidity Pools?04:03 Core Developers Growing05:13 A.I. & Financial Services Benefits06:50 Fees Raising by 8X???08:59 OpenAI x Hedera Meeting?11:23 White House Crypto Report12:57 New Council Memeber14:24 Nvidia & Palantir15:39 Money Market Funds on Lloyds Bank17:00 Legal Finality17:28 Hedera ETFs & Treasury Companies18:28 Hedera Roadmap18:05 Ethereum Compatibility Coming20:49 Why Robinhood Listed $HBAR22:40 outro#Crypto #chatgpt #hedera ~OpenAI + Hedera?

For Patrick Ta Beauty, the key to growth isn't just great marketing — it's an experience that educates, inspires and converts.In this episode of Retail Remix, host Nicole Silberstein chats with Heidi Lim, VP of Ecommerce at Patrick Ta Beauty. With experience leading digital growth at brands like ThirdLove, Estée Lauder and Fashion Nova, Heidi shares how she's now helping Patrick Ta Beauty stand out in the crowded, trend-driven beauty market. From breaking through the noise on TikTok to building smarter marketing flows, she reveals what it really takes to scale a prestige beauty brand today.Together, they discuss:How the brand turns product education into a powerful driver of conversion and trust;Why a diversified channel mix — DTC, Sephora, TikTok Shop — is key to building both reach and resilience;What makes creator partnerships work — and how to keep them aligned with your brand story;How the team achieved 8X year-over-year growth during BFCM by doubling down on retention;PLUS the beauty trend you'll be hearing about all summer. RELATED LINKSExplore Patrick Ta Beauty's latest products and digital experiencesRead more about ecommerce innovation at Retail TouchPointsCheck out past episodes of Retail Remix

How to Build a Membership That Fuels Freedom, Stability, and Long-Term Growth If you've ever wondered whether a membership could work in your business model — or if you're still sitting on the sidelines debating when to make the leap — this episode is your permission slip to stop waiting and start validating. Stu McLaren is here for an unfiltered, insight-packed conversation on what's changing in the world of memberships, how to launch one even if you're just starting out, and the fastest path to turning what you know into monthly recurring revenue. This isn't just a pep talk — it's a masterclass in how to build predictability, profitability, and long-term sustainability into your business model. You'll learn why memberships are more relevant than ever (even in the age of AI), how to create real transformation for your customers (not just provide more content), and the exact strategy Stu teaches to validate your idea before you build a thing. Whether you're brand new to memberships or looking to scale your existing one, this episode is packed with innovative marketing strategies, real-world case studies, and mindset shifts to help you grow.  HERE ARE THE 3 KEY TAKEAWAYS FROM THIS EPISODE: 1️⃣ Information is everywhere — but implementation is what sets you apart. In the AI era, your value as a course or membership creator comes from helping people apply what they learn and get real results, not just giving them more “how-to” content. 2️⃣ Recurring revenue gives you freedom, confidence, and a business that's worth 8X more. Stu breaks down why predictable monthly income changes everything—from reducing stress to increasing your company's valuation. 3️⃣ You don't need a huge audience to launch. Stu shares the exact “founding member” script and strategy to validate your idea and welcome your first paying members (before you even create a single piece of content!) RESOURCES MENTIONED IN THIS EPISODE: Years ago, Stu helped me create a membership that now generates millions in recurring revenue. And it's not just because he's brilliant (though, he is). It's because he truly cares about helping people like you build a business that feels good, works long-term, and gives you the freedom you've been dreaming about. And now? He's teaching a free masterclass called: “3 Secrets to Turn What You Already Know, Love, and Do Into Recurring Revenue” It's happening May 18 at 8pm ET — and whether you're in the beginning stages of your business or you've been building for a while, it's going to be powerful. You don't need to have it all figured out. You just need the next step — and this is it. Click this link to save your seat now! amyporterfield.com/stu MEMBERSHIPS MENTIONED IN THIS EPISODE:  Nonprofit Leadership Lab Ali Kay Studio Tony's Acoustic Challenge Launch Your Box Autism Little Learners MORE FROM STU Get your copy of Stu's book, Predictable Profits: stu.me/predictable-profits Follow Stu on Instagram @stumclaren Learn more about Membership.io membership.io MORE FROM ME Follow me on Instagram @amyporterfield SUBSCRIBE & REVIEW If you loved this episode, please take a moment to subscribe and leave a review on Apple Podcasts! Your support helps us reach more entrepreneurs who need these insights.

It's strange how something as simple as the vibe of a waiting room can completely change how people feel about going to the dentist. When a place feels stiff and impersonal, it just adds to the anxiety that so many already bring with them. But when you walk into a space that feels warm and familiar—like your favorite neighborhood spot—it's like your guard drops a little. That kind of atmosphere doesn't just make people more comfortable, it makes them want to come back.In this episode of the Dental Flow Podcast, host Benjamin Suggs explains why your reception area can make or break a patient's first impression. He shares how small upgrades—like a clean, clutter-free space, friendly greetings, and simple comforts like coffee or Wi-Fi—can ease anxiety and build instant trust. It's not just about appearances; it's about creating a calm, welcoming vibe that helps patients feel cared for before they ever sit in the chair. Benjamin also highlights the impact of showcasing reviews and using personalized touches to boost confidence and increase treatment acceptance. A few thoughtful changes can go a long way. WHAT TO LISTEN FOR[00:00] Why your front desk might be losing you patients[06:29] Stop calling it a waiting room and do this instead[07:04] Simple ways to make patients feel instantly at ease[10:15] Snacks, Wi-Fi and the secret to five-star reviews[16:02] Turn reviews into trust while patients sit and wait CONNECT WITH USLearn more about improving dental practice marketing on Flow.Benjamin Suggs on LinkedInFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

In this conversation, Benjamin Suggs emphasizes the critical role of positive patient reviews in dental practices. He discusses how online reviews have become as trusted as personal referrals and outlines various strategies for showcasing these reviews effectively. From utilizing Google Business profiles to social media, email marketing, and print media, Suggs provides actionable insights on how to leverage patient testimonials to build trust, attract new patients, and enhance practice growth.Takeaways85% of consumers trust online reviews as much as referrals.Online reviews are the new word of mouth.Displaying reviews prominently can enhance first impressions.Use Google Business posts to showcase five-star reviews.A carousel of positive reviews on your website can boost credibility.Social media is a valuable platform for sharing reviews.Incorporate testimonials into email marketing for new patients.Display reviews in the reception area to build trust.Use relevant testimonials for specific services to improve SEO.Broadcast reviews across all platforms to maximize visibility.Chapters00:00The Importance of Positive Patient Reviews02:54Where to Showcase Reviews for Maximum Impact06:06Leveraging Social Media and Paid Ads09:04Integrating Reviews into Email Marketing and Reception Areas12:05Utilizing Testimonials in Print Media and Treatment PlansFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

There's no shortage of knowledge in today's world. But if knowledge alone created success, we'd all be billionaires with six-pack abs. Darren Hardy breaks down the essential truth: knowledge alone isn't enough to create real, lasting change. Learn how small daily behaviors shape massive results. Don't miss this crucial insight!

Your potential is staggering—far beyond what you've yet achieved. Darren Hardy exposes the surprising forces holding you back and shares how to remove them. Tune in now to know what's quietly limiting your success and how to unlock the extraordinary capabilities already within you!

What will you wish you had done? Darren Hardy delivers a stirring reminder to seize the moment, inspired by the honest reflection of a man nearing the end of his journey. Find out how to live with no regrets in today's episode.

SUMMARYIn this episode of the Dental Flow podcast, Benjamin Suggs interviews Andrew Riepe from Crown Tenant Advisors, a leading dental broker specializing in tenant representation in healthcare real estate. Andrew shares his journey from a minor league baseball player to a successful broker, emphasizing the importance of aligning real estate decisions with the goals of dental practices. The conversation covers various topics including the unique role of tenant representation, navigating lease agreements, choosing the right location for dental practices, understanding tenant improvement allowances, and the value of dental practices to landlords. Andrew also discusses the importance of renegotiating leases and planning for the future, providing valuable insights for dental startups and established practices alike.TakeawaysCrown Tenant Advisors focuses exclusively on healthcare clients.Tenant representation ensures conflict-free negotiations for clients.Understanding lease clauses can prevent potential issues for practices.Location choice impacts the success of dental practices significantly.Rural markets may offer less competition for new practices.Tenant improvement allowances are crucial for startup budgets.Dental practices are considered high-value tenants by landlords.Renegotiating leases should start 1.5 to 2 years before expiration.Planning ahead is essential for successful practice management.Chapters00:00 Introduction to Dental Real Estate Expertise03:09 The Unique Role of Tenant Representation06:07 Navigating Lease Agreements and Clauses08:58 Choosing the Right Location for Dental Practices11:52 Understanding Tenant Improvement Allowances14:55 The Value of Dental Practices to Landlords18:00 Renegotiating Leases and Future Planning21:03 Conclusion and Contact InformationARE YOU LOOKING TO OPEN A DENTAL PRACTICE OR RENEGOTIATE YOUR LEASE? CONNECT WITH ANDREW REIPE AND CROWN TENANT ADVISORS FOR YOUR REAL ESTATE NEEDS.https://crowntenantadvisors.com404.216.4017ariepe@crowntenantadvisors.comFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

Elaine Zelby is Co-Founder and CRO at Tofu. Tofu puts top-of-funnel on autopilot for B2B marketing teams, so you can ship integrated marketing campaigns 8X faster. Series A funded (recent). Also check out Ep. 96 “First Time Founders Think Product Wins, Second Time Founders Know GTM Wins”.Here's what we cover:Your recent funding round, what did it take to get there (HINT: 4 main things);How do you use tofu at tofu;How do you structure your nurture sequences;How do you incorporate customer research at tofu (HINT: product roadmap, website, GTM plan);Both companies and people are building agents and bots, how do you break away and ahead of everyone else;Where are you placing big bets in 2025;How do you know your content is good.Elaine on LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠www.linkedin.com/in/elainezelbyTofu: www.tofuhq.comFor more content, subscribe to Building With Buyers on Apple or Spotify or wherever you like to listen, and don't forget to leave a review if you're lovin' the show. Music by my talented daughter.Anna on LinkedIn: ⁠⁠⁠⁠⁠⁠www.linkedin.com/in/annafurmanov⁠⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠⁠furmanovmarketing.com

Our guest this week is a DEEJAY and Socialite from the New York Scene. She specialist in planning, managing, and creating events within the fashion industry. From product launches and pop-ups to in-store events and strategic partnerships, she brings over a decade of experience. Her expertise spans every detail of a successful Fashion Week show or party. Which includes venue selection, set design, catering, lighting, gifting, photo/film crews, security, talent booking, and entertainment.Five years ago, she co-founded Dune Suncare, an 8X award-winning suncare brand. Their revolutionary invisible gel is clinically proven to perfect and protect all skin tones while delivering skincare benefits. With the motto-“Be Smart, Look Good, Cover Your Assets”, Dune Suncare has quickly made its mark in the industry. Please welcome Mei Kwok from Dune Suncare!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

ObjectiveTo establish a standardized procedure for capturing and using before and after photos in dental marketing, ensuring that the practice maximizes its marketing potential, attracts more patients, and enhances overall productivity and accuracy in task execution.Key StepsUnderstand the Importance of Full Face PhotosRecognize that full face before and after photos are more impactful than close-up shots of teeth.Acknowledge the transformative effect these images can have on potential patients.Educate Team MembersConduct a training session on the significance of before and after photos in marketing.Share success stories of practices that effectively utilized these photos.Set Up a Photography SpaceDesignate a specific area in the practice for taking before and after photos.Ensure the area is well-lit and equipped with necessary photography tools (camera, lighting, backdrops).Develop a Patient Consent ProcessCreate a consent form for patients to sign, granting permission to use their images for marketing purposes.Ensure that the form clearly explains how the photos will be used.Capture High-Quality PhotosTrain staff on how to take high-quality before and after photos, focusing on full face shots.Use a consistent angle and lighting for all photos to maintain uniformity.Incentivize PatientsOffer incentives (e.g., discounts) to patients who agree to have their full face photos taken.Communicate the benefits of showcasing their transformation to encourage participation.Utilize Technology for SimulationsInvest in dental photography apps that can simulate post-treatment appearances.Incorporate these simulations into patient consultations to enhance the selling process.Create a Library of PhotosOrganize and store all before and after photos systematically for easy access.Ensure that photos are labeled with patient information (with consent) and treatment details.Incorporate Photos into Marketing MaterialsUse before and after photos in social media posts, website galleries, and advertisements.Create video content using the photos to further engage potential patients.Review and Update RegularlySchedule regular reviews of the photo library to ensure it remains current and relevant.Update marketing materials with new photos as they become available.Cautionary NotesAlways obtain written consent from patients before using their images.Be sensitive to patient privacy and ensure that photos are used ethically.Avoid using photos thatFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

IntroductionIn this episode, we examine Online Booking / Scheduling Apps for dentists. These are third-party platforms that allow patients to book appointments directly on a dental practice's calendar. Based on data analysis and client feedback, we analyze their pros and cons.Note: Flow New Patient Marketing does not sell this service, so you can be sure we are giving you an unbiased opinion!Pros of Online Booking SystemsEase of Action for PatientsSimplifies the appointment booking process, increasing the likelihood of patients taking action.Immediate Booking CapabilityPatients can book appointments in real-time, reducing the chance of forgetting their intention to schedule.Preference of Younger GenerationsYounger generations prefer online interactions over phone conversations, making online booking more appealing.Cost EfficiencyReduces missed opportunities due to busy phone lines and allows for more appointments to be filled.24/7 AvailabilityPatients can book appointments outside of business hours, increasing convenience and accessibility.Cons of Online Booking SystemsHigher No-Show RatesOnline bookings may lead to more no-shows compared to personal interactions with front desk staff.Loss of Schedule ControlSome practitioners feel they lose control over their scheduling preferences and nuances.Reduced Human InteractionLess personal connection may hinder the development of long-term relationships with patients.Client Spotlight:Vibrant Smiles - Dentist in Mableton GA Website: https://vibrantsmilesga.comGoogle: https://g.co/kgs/9Z74VKLFacebook: https://www.facebook.com/vibrantsmilesgaFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

Check out this episode wherever you like to listen or watch podcasts! Episode Page: vinneychopra.com/podcast/ Youtube: https://youtu.be/VXX0fqMEjMU Spotify: spoti.fi/423B4fz iTunes: apple.co/3tQ9Tsf   ——  

In this episode of Create Like The Greats, we dive into why publishing research can be a game-changer, especially for those in AI, marketing, and SEO. The host uncovers how leveraging research-driven content can generate buzz, backlinks, and unmatched visibility for brands. Using examples from industry leaders like Google DeepMind and NVIDIA, this episode provides actionable steps to create, repurpose, and distribute impactful research. If you're ready to learn how research can transform your marketing strategies and build authority in your field, this is a must-listen! Key Takeaways and Insights: 1. The Rise of AI and Its Impact Across Industries (02:00) AI is influencing professions beyond marketing, including healthcare, education, and law. Google Trends data highlights an 8X increase in searches for AI research over the last four years. Online communities like Reddit's r/Futurology and Artificial Intelligence have seen significant growth since 2022. 2. Why Research-Driven Content Matters (10:42) Major companies like Google and NVIDIA are publishing groundbreaking AI research to generate links, press mentions, and authority. Google's AI research hubs (Google Research and DeepMind) showcase the power of free, accessible industry data. Example: Google DeepMind has journal articles with over 500-2,000 referring domains, driving backlinks, traffic, and credibility. 3. Different Types of Research-Driven Content Proprietary Research: Conduct in-depth studies with your team or academics to develop unique insights. Repurposing Data: Don't let research sit idle in PDFs—turn it into blog posts, LinkedIn infographics, social media videos, and more. Curated Research: Take existing industry studies or journals and reframe them for broader audiences in your niche. 4. Case Study: Unlocking Engagement Through Simplified Research Example: A blog post combining coffee productivity studies with up-tempo music research led to a viral response. Translating complex journals into relatable concepts amplifies visibility and audience resonance. 5. The SEO and Brand Visibility Advantages Research publications draw backlinks from reputable sources (e.g., Microsoft, universities) and are frequently cited in blogs and press. Example: DeepMind's journals generate massive referral traffic due to their accessibility and simplicity. Insights from Stanford's AI Index: The number of AI research articles tripled from 2010 to 2022. 6. Pro Tips for Distributing Research Findings Distribute research summaries via subreddit communities, LinkedIn posts, and news outlets. Convert large datasets into visual aids like charts, infographics, and videos to maximize distribution potential. Leverage PR strategies by pitching your findings to publications and blogs for additional reach. Actionable Advice for Creating Research-Driven Content Create Research: Invest in proprietary studies, user surveys, or experiments in your niche. Repurpose Content: Tailor findings into blog posts, social media visuals, and bite-sized infographics for different platforms. Collaborate: Partner with academics or industry experts to co-create compelling research studies. Translate Research: Simplify complex academic papers into relatable and engaging content for non-technical audiences. Pitch Your Study: Reach out to journalists, bloggers, and influencers who may amplify and share your findings. Resources and Links Mentioned:  Google Research – Explore Google's latest AI publications and studies. DeepMind Research – Access over 183 AI-focused journal articles. Stanford AI Index 2024 – Annual report on AI trends and insights. Tool Mentioned: Distribution.ai – Automate your content distribution processes. Related Subreddits: r/Futurology r/ArtificialIntelligence —

SummaryIn this episode of the Dental Flow Podcast, host Benjamin Suggs interviews Matt Poppert from DDS Match South, a dental practice brokerage. They discuss the importance of confidentiality in the selling process, the dynamics between selling to DSOs versus private buyers, and the various types of dental practices that can be sold. Matt shares insights from his extensive experience in the dental industry, emphasizing the need for proper planning and preparation when considering a practice sale. He also highlights the importance of understanding lease agreements and the impact of real estate on practice valuation.Chapters00:00 Introduction to Dental Practice Brokerage05:57 Matt Poppert's Journey to DDS Match South12:04 The Importance of Confidentiality in Selling Practices17:57 Understanding the Market: DSOs vs. Private Buyers23:58 Types of Dental Practices and Their Valuation29:55 Preparing for a Successful Practice SaleWant to watch this episode? https://www.youtube.com/watch?v=_2HKnTGDAHcTakeawaysConfidentiality is crucial when selling a dental practice.Most practices are sold from doctor to doctor, not to DSOs.Planning for an exit strategy should start early in a dentist's career.The value of a practice is based on its current revenue, not past performance.Lease agreements can significantly impact the sale of a practice.Understanding the market dynamics is essential for sellers.There are many buyers available, not just DSOs.Proper planning can prevent loss of practice value due to unforeseen circumstances.Matching the right associate with a practice is key to successful transitions.Networking with professionals in the industry can aid in the selling process.Sound Bites"Confidentiality is of the utmost importance.""Landlords are professional business people.""It's never too early to start planning."Connect with Dental Practice Broker Matt Poppert and DDSMatch SouthWebsite - https://ddsmatchsouth.comInstagram - https://www.instagram.com/ddsmatch.south/Facebook - https://www.facebook.com/ddsmatchsouth For 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

Planning Your Dental Practice's 2025 Digital Dental Marketing PlanOverviewThe podcast discusses the importance of planning a comprehensive digital dental marketing plan for 2025.Emphasis is placed on enhancing digital experiences, utilizing automation and AI, and ensuring HIPAA compliance.Three overarching themes to keep in mind: enhanced digital experiences, automation, and HIPAA compliance.AssumptionsFocus on UX website design, clear calls to action, and personalized content.SEO strategies include on-page optimization, off-page strategies, content creation, and video integration.Paid ads offer hyper-focused targeting and effectiveness in driving key services.Conversion strategies involve sales processes, phone skills, automation, and online booking.Reviews and referrals are crucial for building reputation and attracting new patients.Loyalty and re-care strategies include reminders, database reactivation, and social media engagement.Testing StrategyUtilize Google Business Profile page optimization for local SEO.Implement strategies for reviews, referrals, loyalty, and re-care to engage patients.Ensure personalized and engaging content on social media and newsletters.Consider automation for reminders, database reactivation, and engaging patients.Focus on enhancing digital experiences, leveraging automation, and ensuring HIPAA compliance for successful digital marketing in 2025.Dentist Spotlight:Robson Crossing Dentistry - Dentist in Flowery Branch GAFind them on Google - Dentist Flowery BranchFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

To view this podcast on YouTube, please go to: https://www.youtube.com/watch?v=5ZZ_xBXEpYILeveraging YouTube for Dental PracticesOverviewYouTube has become a valuable platform for businesses, including dental practices, due to its search engine capabilities and vast user base.Creating video content is essential for leveraging YouTube effectively to reach potential patients.Different types of videos, such as long-form and short-form, can be used to engage with viewers and promote dental services.Client testimonials in video format can be a powerful tool for building trust and credibility.AssumptionsThe success of using YouTube for dental practices requires consistent content creation and posting to build a presence over time.Integrating video content on both YouTube and the practice's website can enhance visibility and engagement.Running YouTube ads through Google Ads provides targeting opportunities to reach specific audiences effectively.Testing StrategyUtilize skippable ads on YouTube to reach a broad audience and measure view-through rates for different video lengths.Implement demographic targeting, interest-based targeting, and keyword targeting to optimize ad placements.Monitor the effectiveness of YouTube ads for brand building and direct response to assess the impact on practice visibility and patient acquisition.Dentist SpotlightKnox Valley Dental - Dentist in Farragut - Knoxville TN - https://knoxvaleydental.comGoogle Page - https://g.co/kgs/YXZUM8eFacebook - https://www.facebook.com/KnoxvalleydentalFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

To view this podcast on YouTube, please go to: https://www.youtube.com/watch?v=BeIRxrmRJVE&tOptimizing Your Practice Name: A Guide for Dental PracticesIntroductionImportance of choosing a business/practice nameRelevance for both startups and established practicesExamples of RebrandingDentist renaming practice due to new additionsConsolidating multiple practices under one nameKey Considerations for Practice NameMemorable and professional nameDifferentiation from competitorsTarget audience alignmentURL AvailabilityImportance of securing a matching URLPreferable extension (.com) for online presenceInclusion of Town Name in Practice NameHistorical value vs. current SEO practicesPotential confusion for search engines and patientsConclusionImportance of thorough research before finalizing a practice nameConsider professional assistance for naming or renaming processesPractice SpotlightSugar Hill Corners Dentistry - Sugar Hill, GAWebsite: https://sugarhillcornersdentistry.comGBP: https://g.co/kgs/t5StzuPFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

To view this podcast on YouTube, please go to: https://www.youtube.com/watch?v=KPbx6EpVedoResponding to Positive Reviews for Dental PracticesImportance of Responding to Positive ReviewsGoogle likes to see activity on its platformsShows engagement and activity on your Google Business ProfilePoliteness and gratitude towards patients who leave positive reviewsWhy Responding to Positive Reviews is EssentialGoogle's preference for active engagementPoliteness and gratitude towards patientsImpact on SEO and reputation among potential new patientsHow to Respond to Positive ReviewsLog in to the back end of your Google Business ProfileClick on "reply to reviews" or select a specific review to respond toKeep responses generic, HIPAA compliant, and appreciativeStart with "Thank you," include a generic comment, and end with an invitation to returnOptionally include a keyword for your location but avoid sounding forced or roboticBenefits of Responding to Positive ReviewsImproves SEO by showing activity on your profileDemonstrates appreciation to patients and potential new patientsEnhances the overall reputation of your dental practiceConclusionRegularly respond to all positive reviews to maintain engagementPositive impact on Google's perception and patient relationshipsContributes to a positive online presence and reputation for your dental practicePractice SpotlightBrielle Dentistry Co.Website: https://brielledentistryco.comGBP: https://g.co/kgs/46hqVNLIG: https://www.instagram.com/explore/locations/1774419222621823/brielle-dentistry-co/Youtube: https://www.youtube.com/@thedentistrycoFor 12+ years, Flow New Patient Marketing has had a simple mission… to bring you as many quality new patients as possible. We believe in ensuring that when a potential patient needs a provider, you are the one they find, choose, and set an appointment with. We Get You More *Profitable* New Patients! No long-term contracts. Our clients average an 8X return on investment. Personalized, non-corporate approach. 5-star reviewed. Find us: Website: https://newpatientsflow.com Google: https://g.co/kgs/zqWTc5a Facebook: https://www.facebook.com/newpatientsflow Instagram: https://www.instagram.com/newpatientsflow/ Linkedin: https://www.linkedin.com/company/newpatientsflow

Send us a texthttps://www.coreyquinn.com/anyone-not-everyone-bookLeading an agency through growth is tough, but nothing bottlenecks that growth like founder-led sales. In this episode, I sit down with Corey Quinn, a seasoned expert in scaling agencies, to discuss the painful challenges of agency founders trapped in founder-led sales and how to break free. Corey shares his journey, from micromanaging leadership driven by ego to guiding a team that grew an agency's revenue 8X, scaling it to $150 million.We dive deep into the pivotal moments when Corey realized his leadership style needed to change, and the steps he took to foster an environment where his team could thrive. He also breaks down the power of niche specialization, explaining how the right data-driven focus can exponentially improve agency growth. Finally, Corey unveils his game-changing gift-based outbound strategy that played a significant role in building Scorpion's success, offering actionable steps for agency founders to implement today.If you're struggling to escape founder-led sales or scale your agency, this episode provides invaluable insights and practical strategies to set your business on the path to sustainable growth.Connect with Corey Quinn:Corey Quinn generously offers listeners a free copy of his audiobook, Anyone Not Everyone, along with helpful workbooks and videos. Head to AnyoneNotEveryone.com to grab your copy and start building a scalable agency today.  Books Mentioned:Anyone Not Everyone by Corey Quinn Ready to Finish 2024 Strong?Don't wait until December to address your challenges. There's a few months left in 2024, now is the time to plan for a strong finish! Book a free strategy call with Dr. William Attaway to create a plan for impactful results. Support the showJoin Dr. William Attaway on the Catalytic Leadership podcast as he shares transformative insights to help high-performance entrepreneurs and agency owners achieve Clear-Minded Focus, Calm Control, and Confidence. Free 30-Minute Discovery Call:Ready to elevate your business? Book a free 30-minute discovery call with Dr. William Attaway and start your journey to success. Special Offer:Get your FREE copy of Catalytic Leadership: 12 Keys to Becoming an Intentional Leader Who Makes a Difference. Connect with Dr. William Attaway: Website LinkedIn Facebook Instagram TikTok YouTube

Michael Cooper forged a Hall of Fame career w/ Los Angeles because he embraced his role. Jerry West told him as a rookie, "there are not enough balls to go around to shoot...I need you as a defender." And as Coop said, "I put the suit on, and it fit extremely well." It sure did. 8X he was NBA All Defensive team, and he won 5 rings playing for the Showtime Lakers. Learn more about your ad choices. Visit podcastchoices.com/adchoices