Podcasts about Pareto

Want to scale your business faster?Join our 2-day, interactive workshop: https://www.acquisition.com/workshop-yt-d?el=yt-alex-485w&htrafficsource=youtubeMost business owners aren't “bad at business.” They're just selling to broke people and then act surprised when the close rate is trash, churn is high, and customers complain nonstop. In this episode of The Game, Alex breaks down the uncomfortable truth: if you want to make money, you have to go where the money is. A small percentage of buyers control a massive percentage of the wealth, which means if you price and position your business for “everyone,” you end up building a business for the people who can't pay. The goal is simple. Pick a better customer, build a bigger offer, and charge in a way that makes you more money with fewer sales.YouTube Timestamps00:00 Why businesses struggle to make money04:32 Applying the Pareto principle in profits07:21 Top-down business and pricing strategy16:10 Sell to the rich - they pay better, complain less28:47 Picking price points: value over cost32:50 How close rates reveal underpriced commodities38:41 Stop selling commodities and raise prices systematicallyMore Value:Discover The Easiest Business I Can Help You Start (Free Trial): https://www.skool.com/hormoziJoin The In-Person Scaling Workshop In Las Vegas: https://www.acquisition.com/o-vegasDownload your free $100M scaling roadmap here: https://www.acquisition.com/roadmap?el=yt-alex-486r&htrafficsource=youtubeGet the $100M Book Bundle: https://shop.acquisition.com/pages/100m-book-bundleTake the $100M Lead Generation Course: https://www.acquisition.com/training/leads?hsLang=enLearn How to Make Offers People Cannot Refuse: https://www.acquisition.com/training/offers?hsLang=enFollow Alex Hormozi's Socials:⁠⁠LinkedIn ⁠⁠ | ⁠⁠Instagram⁠⁠ | ⁠⁠Facebook⁠⁠ | ⁠⁠YouTube ⁠⁠ | ⁠⁠Twitter⁠⁠ | ⁠⁠Acquisition ⁠

Are you the only person your builders call when something goes wrong? Most subcontractor owners don't realize they've accidentally become their company's full-time account manager, and it's the reason they can't step away from the business.In this episode, Khalil and Martin break down why this happens, what an account manager actually does (and how it's different from a project manager), and how to build this role into your company so you can stop being the bottleneck.What You'll LearnThe critical difference between an account manager and a project manager, and why confusing them creates chaosWhat the full account manager workflow looks like from discovery and onboarding through post-installHow to find, develop, and compensate the right person for this role inside your companyWhy proactive communication changes the power dynamic between subs and buildersHow to use the 80/20 rule to decide which builder accounts deserve dedicated managementKey Topics & Timestamps01:00 - Episode Intro06:54 - Account Manager vs. Project Manager: Process vs. People + One Point of Contact14:48 - Hiring & Incentivizing Great Account Managers (Homegrown Traits + Pay Structure)18:49 - What Great Account Managers Actually Do (Advocate, Proactive, Problem-Solver)23:47 - Defining the Role: Not Sales, Not PM — Owning the Builder Relationship26:24 - The Account Manager Workflow: Onboarding → Pipeline → Quote → Production → Post-Install + Scaling Tips Key TakeawaysIf every builder calls you directly when something goes wrong, you've become your company's account manager, whether you intended to or notStart building this role by documenting exactly what you do for your top builder relationships so the process can eventually be transferredGrow your account manager internally; external hires lack the institutional context needed to be effectiveThe account manager must have full context across sales, production, and install to make the same quality decisions you wouldCompensate this role well with a strong base plus account-based incentives; they are essentially an inside salesperson for your most valuable relationshipsBegin with one key account and your most reliable employee before expandingResourcesTodd Hagopian and the 80/20 (Pareto) principle for prioritizing accounts⁠⁠⁠Implementing AI in Your Business Workshop Sign-Up ⁠⁠⁠24 Things⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Construction Business Owners Need to Successfully Hire & Train an Executive Assistant⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Schedule⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ a 15-Minute Roadblock CallBuild a Business that Runs without you. Explore our⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ GrowthKits⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Need Marketing Help? We Recommend⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Benali⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Need Help with podcast production? We recommend⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Demandcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Checkout ⁠⁠⁠⁠⁠Quo⁠⁠⁠⁠⁠ More from Martin Holland⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠theprofitproblem.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠annealbc.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠   ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Email Martin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Meet With Martin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠More from Khalil⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠benali.com ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Email Khalil⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Meet With Khalil⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠More from The Cash Flow ContractorSubscribe to our⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow On Social:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X(formerly Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Visit our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Email⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ The Cashflow Contractor

What if the very laws you were taught to follow are actually keeping you from the life you're meant to live? In Episode 101 of Love Learning You™, cultural psychology researcher, author, and speaker Justine Gonzalez examines the viral "7 Laws for a Liberated Life" posts and goes deep into the heart and intent behind some of the laws we follow. Most people scroll past posts like these without ever asking: Who created these laws? Who benefits from them? And what gets left out when the wisdom of entire cultures, ancestral traditions, and spiritual lineages is replaced with a list of Eurocentric/capitalistic productivity hacks?In this episode, Justine models what it looks like to think critically as a cultural psychology researcher and then offers something radically different: her own 5 personal laws for liberation, built from 15+ years of research, lived experience, and cross-cultural spiritual study.

This week, Bob walks through Javier Milei's 2026 address to the World Economic Forum, explaining the Austrian and neoclassical ideas behind Milei's defense of capitalism—from Rothbard and Kirzner to Pareto efficiency and the welfare theorems.Related:Bob's Breakdown of The Intra-Austrian Debate over Milei: Mises.org/HAP539aThe Mises Institute is giving away 100,000 copies of Hayek for the 21st Century. Get your free copy at Mises.org/HAPodFree

This week, Bob walks through Javier Milei's 2026 address to the World Economic Forum, explaining the Austrian and neoclassical ideas behind Milei's defense of capitalism—from Rothbard and Kirzner to Pareto efficiency and the welfare theorems.Related:Bob's Breakdown of The Intra-Austrian Debate over Milei: Mises.org/HAP539aThe Mises Institute is giving away 100,000 copies of Hayek for the 21st Century. Get your free copy at Mises.org/HAPodFree

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

Eirik Furuseth er gründer og partner i Finansco, med bakgrunn som finansrådgiver fra DNB og Pareto, frittalende kommentator i debatter om skatt, økonomisk politikk og maktstrukturer i Norge. I dag snakker vi om epstein-saken og hva den avslører. Dette er den største krisen for det offisielle Norge i min levetid, sier Eirik. ► NY BOK UTE NÅ: Frykt og Stillhet - jødiske stemmer i Norge etter 7. oktober. Bestill her:  https://bok.norli.no/frykt-og-stillhet► STØTT ARBEIDET PÅ VIPPSOm du ønsker å støtte arbeidet med denne podcasten, kan du bidra med et stort eller lite beløp, etter eget ønske. All støtte settes pris på, og du bidrar til arbeidet med å lage flere episoder. Bruk Vippsnummer: #823278►  ⁠BLI ⁠⁠⁠⁠MEDLEM⁠⁠⁠⁠⁠ Fremover vil de som er støttemedlemmer få tilgang til episodene først. Da støtter du podcasten med det samme som prisen av en kaffe hver måned. Setter stor pris på om du blir støttemedlem. Tusen takk.► Annonsere på Henrik Beckheim Podcast?Send en mail til post@henrikbeckheim.no ► MERCH: Kjøp klær, kopper, capser og mer: https://henrikbeckheim.com/store► Linker:⁠⁠Youtube⁠⁠ | ⁠⁠Nettside⁠⁠ | ⁠⁠TikTok⁠⁠ | ⁠⁠Instagram⁠⁠ | Podimo | ⁠⁠Facebook⁠⁠ | ⁠⁠Apple

Passer le DSCG en 4 mois, en travaillant en CDI à temps plein en audit : ça ressemble à un mythe… Jusqu'à ce qu'Alban l'ait fait. Dans cet épisode des Geeks des Chiffres, je reçois Alban Salas Gordo (21 ans), auditeur junior, qui raconte sans filtre sa méthode, son rythme de travail, sa stratégie de révision et surtout la logique derrière sa réussite : anticipation + stratégie + exécution. Au programme : - Comment il a construit des bases solides dès le DCG pour “accélérer” au DSCG - Sa méthode “activation” : comprendre la matière avant d'apprendre - Comment il a ciblé l'essentiel avec la logique Pareto (80/20) + annales + rapports du jury - Son rythme réel : matin / midi / soir, gestion de l'énergie, sommeil, sacrifices - L'approche “entonnoir” : balayer large, éliminer et ne garder que les points faibles - Sa classification des UE : droit (apprendre) / finance & compta-audit (pratiquer) / management (hybride) - Le mémoire : choix du sujet, agrément, rédaction, préparation de soutenance “pro” - La psychologie : zéro excuses, projet de vie, et le mantra “le regret est plus douloureux que l'échec” - La suite : audit, international (Sydney), et trajectoire long terme Si tu es en DCG/DSCG, en alternance, en cabinet, ou simplement curieux de voir ce que “travailler intelligemment” veut dire, cet épisode est une masterclass. Linkedin d'Alban : https://www.linkedin.com/in/alban-salas-gordo/Code Promo YT1 : - 10% sur toute la plateforme Les Geeks des Chiffres. --------Bienvenue sur le podcast n°1 de la filière comptable et financière ! + 650 000 écoutes.Je suis Nicolas Piatkowski, cofondateur de l'école en ligne Les Geeks des Chiffres, qui a formé plus de 14 000 étudiants au DCG & DSCG : https://www.lesgeeksdeschiffres.comChaque semaine, des pros du chiffre me partagent leur parcours, leurs réussites (et galères !), leurs conseils, et t'aident à décrypter un secteur en pleine mutation.Que tu sois en DCG, DSCG, alternance, BTS ou un professionnel aguerri… Tu trouveras ici des interviews inspirantes, des retours d'expérience concrets, des insights métier et des clés pour te démarquer dès tes premières expériences.Au programme :Réalité du métier d'expert-comptable ou de financier aujourd'hui.Les compétences techniques et digitales de demain.Outils tech, indicateurs clés, culture business.RH, management, soft skills… tout ce qui compte vraiment !Et bien sûr, des conseils pour réussir tes études, tes stages, ton alternance ou ton premier CDI.Si tu veux prendre une longueur d'avance dans tes études et ta carrière, ce podcast est ton nouveau compagnon de route.Bonne écoute… et c'est partiiiiii !  »Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Andrew Hulbert built Pareto from scratch, scaled it to about £50M in revenue, and exited for around $100M, retiring at 37. But the most interesting part of his story is what happened next. In this episode, Andrew explains how he avoided the post-exit crash many founders experience by preparing himself personally, not just preparing the business. We talk about working with a business psychologist, the “Exit Island” concept, how he decompressed after closing, and why the things that looked like success (cars, status, noise) were far less fulfilling than reconnecting with his wife, kids, friends, and health. This is a practical, honest conversation for founders who are approaching an exit and wondering: Who am I without the business, and what comes next? We cover: preparing for exit mentally, clean exits vs earn-outs, identity after exit, relationship repair, health during the sale process, significance and meaning, and what Andrew would do differently if he built it again. Guest: Andrew Hulbert Host: Jerome Myers Learn more about your ad choices. Visit megaphone.fm/adchoices

Hablo de la ley de Pareto como estilo de vida para todo. Enero termina por fin, eso sí, febrero ya empezará a volar y para cuando nos demos cuenta otro año que pasa. Reflexiono contigo sobre la vida, la base de los hábitos y te recuerdo que repito taller Recupera tu foco y atención este miércoles 4 de febrero de 19.30h a 21h online. Toda la información aquí: www.amagoiaeizaguirre.com

Det är den sista dagen på den tyngsta rapportveckan och vi har fått flera riktigt viktiga rapporter både här i Sverige och i USA. Med oss för att analysera dessa och sammanfatta den intensiva veckan som gått har vi idag Per Hedberg, förvaltare på Lancelot och Anders Roslund, analyschef på Pareto. Programledare är Elin Wiker och Nike Mekibes.

Hosts Kevin Palmieri and Alan Lazaros expose a subtle trap that keeps high performers stuck longer than failure ever could. Holding onto what once worked. After years of building Next Level University and coaching thousands through real growth phases, they have seen how progress turns into comfort, and how comfort quietly caps results.This episode cuts through surface-level self-improvement advice and reframes what it actually takes to move from momentum to mastery. The focus is on leverage, standards, and long-term consistency across health, wealth, and relationships. No hacks. No hype. Just the principles required to reach the next level without burning out or drifting backward.Learn more about:Your first 30-minute “Business Breakthrough Session” call with Alan is FREE. This call is designed to help you identify bottlenecks and build a clear plan for your next level. - https://calendly.com/alanlazaros/30-minute-breakthrough-sessionJoin our private Facebook community, “Next Level Nation,” to grow alongside people who are committed to improvement. - https://www.facebook.com/groups/459320958216700_______________________NLU is not just a podcast; it's a gateway to a wealth of resources designed to help you achieve your goals and dreams. From our Next Level Dreamliner to our Group Coaching, we offer a variety of tools and communities to support your personal development journey.For more information, check out our website and socials using the links below.

Den intensiva rapportveckan fortsätter och idag domineras flödet av tunga industrijättar som släpper sina siffror för det fjärde kvartalet. Därför har vi idag med oss Karl Hedberg, aktiechef på DNB Carnegie Private Banking, Niklas Wellfelt, chefstrateg Sverige på Ålandsbanken och Forbes Goldman, analytiker på Pareto. Programmet leds av Elin Wiker och Gabriel Mellqvist.

Mentor Sessions Ep. 049: Jeff Booth vs Simon Dixon: Bitcoin's Abundant Future or Total Dystopian Nightmare?What if Bitcoin's promise of a deflationary free market utopia crushes the global surveillance state—or traps 95% in a multipolar prison of programmable money, elite control, and endless chaos? In this must-watch, visionary Jeff Booth clashes with geopolitical expert Simon Dixon on whether Bitcoin enforces abundance for all through unstoppable privacy tech like Fedi and Nostr, or merely offers an escape hatch for the few amid dollar demise, AI weaponization, and financial industrial complex capture. From Venezuela's turmoil and Iranian protests to UK thought police and precious metals surges, they expose how centralized custody in ETFs and treasury companies co-opts Bitcoin, risking chain forks and surveillance nightmares. Jeff's optimistic blueprint for agency-driven freedom battles Simon's stark warnings of hybrid systems where the masses "own nothing and be happy," tying into Bitcoin self-custody, decentralized mining, and circular economies as your shield against fiscal dominance and currency wars. If you're stacking sats in a Bitcoin-only world, this debate reveals why privacy isn't optional—it's your path to sovereignty or subjugation. Don't miss the white pill vs. black pill showdown that could redefine your Bitcoin strategy!About Jeff BoothWebsite: https://jeffbooth.ca/Nostr: jeffbooth@nostrverified.comAbout Simon DixonX: @SimonDixonTwittYouTube: https://www.youtube.com/@SimonDixon21Chapters:00:01:05 Hook & Guest Introduction00:01:13 Global Chaos Overview00:01:40 Jeff: System Collapse & Deflation00:04:02 Simon: Multipolarity & Dollar Strategy00:07:22 Surveillance State & Resistance00:08:55 Jeff: Control Structures & Elite00:11:02 Fear vs Optimism Messaging00:12:35 Bitcoin Centralization Risks00:14:44 Privacy & Cypherpunk Roots00:16:37 Simon: Banking to Bitcoin Journey00:19:29 Jeff: Parallel Ecosystems00:25:35 Trump's Surveillance Ties00:26:25 Node Risks00:29:30 Bitcoin Protocol Stack00:33:08 Chain Forks & Resistance00:35:40 Federations & Decentralized Banks00:36:43 Imposition vs Escape Hatch00:38:50 Systems Non-Coexistence00:40:02 Pareto & Prison Debate00:42:43 Black Markets & Emergence00:45:21 Gold Lessons & Ethics00:49:18 Free Market Spirituality00:52:03 Thought Traps & Sovereignty00:58:57 Distractions & Community Building01:00:01 Bitcoin's Voluntary Ethics01:01:27 Agency & Time Value01:03:36 Force & Confiscation Risks01:04:46 Privacy Attack Costs01:06:40 Custody Fears01:07:01 Hope vs Fear01:08:29 Simon's Financial Obsession01:10:17 Jeff's Optimism Shift01:13:24 Decentralization Threats01:15:17 VC Journeys01:17:02 Outperforming Bitcoin01:21:23 Spiritual Free Market01:24:08 Decentralized Banks Concept01:24:44 E-Cash & Federation Risks01:29:30 Fedi Privacy Layers01:30:12 Final Advice01:33:50 Guest Contacts & Wrap-Up⚡ POWERED by Abundant Mines: Fully managed Bitcoin mining. Learn more at https://qrco.de/bgYKPB

What if financial planning were approached the same way engineers design aircraft, medical treatments, or complex systems—with clearly defined objectives, constraints, and rigorous trade-off analysis? In this episode, Benjamin Felix is joined by Braden Warwick for a deep dive into what it means to engineer financial outcomes. Drawing on Braden's background as a PhD-trained mechanical engineer and his work building financial planning software at PWL Capital, the conversation reframes financial planning as a design problem rather than a speculative exercise. They explore the critical distinction between a financial plan and a financial projection, why uncertainty does not invalidate good planning, and how professional communication under uncertainty can build trust with clients—especially those from technical backgrounds. The discussion highlights the importance of goals-based planning, sensitivity analysis, and explicitly quantifying trade-offs when clients have multiple competing objectives. Key Points From This Episode: (0:00:04) Introduction to Episode 393 and the return of Braden Warwick (0:02:50) Braden's role at PWL and his experience deploying Conquest Planning software (0:05:46) The tension between low industry entry barriers and professional standards in financial planning (0:07:54) Braden's background in mechanical engineering and academia 0:09:33) Financial plans vs. financial projections: why uncertainty doesn't make a plan "wrong" (0:12:59) Lessons from medicine and engineering on communicating decisions under uncertainty (0:15:15) An engineering framework for financial planning: objectives first, then solutions (0:18:42) Why surface-level goals like "minimize tax" or "maximize returns" often miss what really matters (0:21:19) Evaluating plans against goals using projections, scenario analysis, and sensitivity analysis (0:24:28) Why sensitivity analysis helps planners focus on what actually drives outcomes (0:29:27) Handling multiple competing goals using trade-off analysis and Pareto frontiers (0:36:46) Practical ways planners can present trade-offs without complex math (0:39:25) Case study setup: professional financial planning with corporate clients (0:40:20) Salary vs. dividends for business owners when optimizing for legacy goals (0:44:26) Why financial planning software outputs can be misleading without context (0:48:23) The importance of understanding how planning software calculates key metrics (0:50:22) Using PWL's free retirement tool to analyze CPP and OAS timing decisions (0:53:44) Approximating Monte Carlo outcomes using standard error of the mean (0:56:16) Linking "bad" and "terrible" outcomes to plan success probabilities (0:58:44) How CPP and OAS deferral affects sustainable spending and downside protection (1:02:46) What makes PWL's CPP calculator different from typical break-even tools (1:05:15) Why wage inflation assumptions materially affect CPP deferral decisions (1:07:46) Closing framework: goals, constraints, sensitivity analysis, and quantified trade-offs (1:09:36) Financial planning as an emerging discipline rooted in engineering-style thinking Links From Today's Episode: Meet with PWL Capital: https://calendly.com/d/3vm-t2j-h3p Rational Reminder on iTunes — https://itunes.apple.com/ca/podcast/the-rational-reminder-podcast/id1426530582. Rational Reminder on Instagram — https://www.instagram.com/rationalreminder/ Rational Reminder on YouTube — https://www.youtube.com/channel/ Benjamin Felix — https://pwlcapital.com/our-team/ Benjamin on X — https://x.com/benjaminwfelix Benjamin on LinkedIn — https://www.linkedin.com/in/benjaminwfelix/ Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)

คนสองคนที่เกิดวัน เดือน ปีเดียวกัน แต่ชีวิตอาจไปกันคนละทาง บางครั้งอาจไม่ใช่เพราะดวง ไม่ใช่เพราะฐานะ แต่อาจเป็นเพราะ “วิธีคิดในการตัดสินใจ” ที่ต่างกัน Sci x Fi EP. นี้ ต้อง นนทพงศ์ ชวน ดร.โก้ พงศกร สายเพ็ชร์ อาจารย์พิเศษ Scientific Research and Presentation มหาวิทยาลัยมหิดล หลักสูตรนานาชาติ มาเล่ากฎ 5 ข้อ ที่จะช่วยให้คุณตัดสินใจเรื่องงาน เงิน และชีวิต ได้ดีขึ้น แล้วคุณจะเข้าใจว่า...ทำไมบางคนแม้เริ่มจากศูนย์ แต่ไปได้ไกลกว่าคนอื่น 0:00 Intro 0:54 เปิดรายการ 6:31 ‘Opportunity Cost' ต้นทุนค่าเสียโอกาส  13:18 ‘Inversion' การคิดย้อนกลับ 24:18 ‘Pareto' กฎ 80/20 33:09 ‘Probabilistic Thinking' คิดเชิงความน่าจะเป็น  44:43 First Principles คิดจากหลักการพื้นฐาน #WealthMeUp #ScixFi  #DecisionMaking #MentalModels #การเงิน #การลงทุน

What if the reason you're not achieving extraordinary results isn't because you're doing too little, but because you're doing too much? In this encore episode of The Game Changing Attorney Podcast, Michael Mogill sits down with Jay Papasan, Vice President at Keller Williams Realty and bestselling author of The One Thing: The Surprisingly Simple Truth Behind Extraordinary Results. Jay breaks down why the popular concept of balance is a fallacy, how multitasking is actually killing your productivity, and why discipline is not what you think it is. From understanding the truth about willpower to mastering the focusing question that changes everything, this conversation delivers a master class in achieving more by doing less. Here's what you'll learn: Why multitasking is a lie that's costing you 28% of your day and lowering your IQ by 11 points How to use selective discipline and the 66-day habit formation principle to make success automatic What the focusing question is and how it creates clarity around your most leveraged activities Want to achieve extraordinary results? This episode shows you exactly how to get there. ---- Show Notes: 03:52 – The origin story of The One Thing, from a 14-page handwritten essay to a bestselling book 05:59 – Why focusing on one thing is such a challenge despite being simple 09:01 – Walking through the process of using extreme Pareto to narrow down priorities 13:04 – Debunking the myth of multitasking and why it's costing you 28% of your day 19:36 – The Green Beret story: how training creates habits that last decades 28:26 – Defining willpower as different from discipline and why it's a limited resource 30:16 – A powerful study on parole judges that proves willpower depletion is real 36:47 – Counterbalancing instead of balance and why it matters for business and life 47:23 – How purpose gives you direction and a clear sense of priority ---- Links & Resources: The One Thing by Jay Papasan Atomic Habits by James Clear Better Than Before by Gretchen Rubin Willpower Doesn't Work by Benjamin Hardy Grit by Angela Duckworth Eat That Frog by Brian Tracy The Miracle Morning by Hal Elrod The Pareto Principle ---- Do you love this podcast and want to see more game changing content? Subscribe to our YouTube channel. ---- Past guests on The Game Changing Attorney Podcast include David Goggins, John Morgan, Alex Hormozi, Randi McGinn, Kim Scott, Chris Voss, Kevin O'Leary, Laura Wasser, John Maxwell, Mark Lanier, Robert Greene, and many more. ---- If you enjoyed this episode, you may also like: 383. AMMA — Why Comfort Will Quietly Destroy Your Law Firm 334. Dr. Benjamin Hardy — From Limiting Beliefs to Limitless Potential: A Guide to Personal Growth 78. Dr. Katy Milkman — How to Change: The Science of Getting From Where You Are to Where You Want to Be

El Principio de ParetoConviértete en un supporter de este podcast: https://www.spreaker.com/podcast/productividad-maxima--5279700/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

From a council estate in Oxford to a £100 million exit by age 37. Andrew Hulbert's journey isn't a polished Silicon Valley success story—it's raw, real, and packed with hard-won lessons about what actually matters when you're building something from nothing.In this episode, Andrew breaks down the decade-long grind of scaling Pareto from his bedroom to a 500-person, £50 million turnover business serving the world's biggest tech companies. He shares why balance is bollocks when you're building, why bright yellow McLarens don't buy happiness, why you should retire early if you can, and how a council estate upbringing gave him the hunger and community mindset that fueled everything. This is a masterclass in bootstrapping, knowing when to go all-in, and actually achieving the goal you set out to hit.What you'll learn:

Most flower farmers head into a new year full of hope but without a clear plan... and that's how burnout and unpredictable income sneak right back in! In this episode of the Six Figure Flower Farming Podcast, Jenny Marks shares five strategic shifts to help you build a more profitable, efficient, and sustainable flower farm in 2026 - without more land, more flowers, or more chaos. This conversation is about stepping out of busywork and into clarity so your farm supports your life, not the other way around. You'll hear how to plan your flower farm around outcomes instead of endless tasks, choose one high-impact sales outlet to focus on, forecast crops based on real profit data, build simple systems that protect your time and margins, and create a consistent marketing and sales engine before the season starts. If you want steadier revenue, better boundaries, and a farm business that feels intentional and manageable, this episode will help you set the foundation for long-term flower farming success. Listen to Episode 42: Pareto's Law Did you enjoy this episode? Please leave a review on Apple or Spotify. Follow Jenny on Instagram: @trademarkfarmer Find free flower business resources: www.trademarkfarmer.com ​

In today's episode of Next Level University, hosts Kevin Palmieri and Alan Lazaros challenge the way most people think about effort, focus, and progress. The idea of “less is more” is familiar, but rarely understood correctly. This conversation reframes the Pareto Principle as a decision-making standard, not a productivity trick. It confronts why most people stay busy yet stagnant, why real progress feels slower than expected, and why long-term results demand a different mindset entirely. This episode is about leverage, patience, and choosing what actually matters when the payoff is far away. Listen closely. Then cut the noise and commit to the work that compounds when no one is watching.Learn more about:Where learning turns into action. “Next Level Book Club”  every Saturday:https://zoom.us/meeting/register/tJMkcuiupjIqE9QlkptiKDQykRtKyFB5Jbhc_______________________NLU is not just a podcast; it's a gateway to a wealth of resources designed to help you achieve your goals and dreams. From our Next Level Dreamliner to our Group Coaching, we offer a variety of tools and communities to support your personal development journey.For more information, check out our website and socials using the links below.

In this episode of the Always On Podcast, Duncan MacPherson speaks with Natasha Kennedy, Pareto-certified coach and family legacy specialist, for a timely conversation on the greatest risk facing financial advisors during the great wealth transfer. Natasha breaks down why most heirs ultimately leave their parents' advisor, how silence and avoidance around legacy planning erode trust, and why preparing heirs has become a critical differentiator in today's advisory landscape. Together, they unpack how advisors can embed continuity, succession, and family investment legacy into their value proposition in a way that deepens relationships, strengthens referability, and positions them as indispensable stewards of multi-generational wealth. Key takeaways: Why nearly 90% of heirs don't stay with their parents' advisor How legacy and succession conversations increase advisor retention and fee worthiness Practical ways to engage heirs without creating entitlement How continuity planning protects relationships through generational transitions Tune into now to unlock the secrets to keeping clients for generations by addressing the blind spot most advisors overlook. Promotions: Pareto Systems: Turnkey Advisor Membership Connect With Duncan MacPherson: Website: ParetoSystems.com Toll Free: 1.866.593.8020 Learn More: Schedule a Call LinkedIn: Duncan MacPherson Connect With Natasha Kennedy: LinkedIn: Natasha Kennedy Presentation: The Legacy Plan Speaker Introduction: Natasha Kennedy About Our Guest: Natasha Kennedy, who brings more than a decade of financial services experience, beginning her career on Wall Street and then working with First Trust Advisors, and now focused on psychology and counselling. Today, Natasha is a consultant with Pareto Systems helping advisors to strengthen client relationships, and build purposeful businesses. With her background in both finance and psychology, Natasha is the ideal person to guide us through the human side of this great wealth transfer, specifically on adding Intergenerational Planning & Family Meetings into your process.

Bu epizodda irəliləyişimizi ən çox yavaşladan əsas faktoru axtarıram.Pareto qanunu (20/80) və Constraint (darboğaz) yanaşması üzərindən baxaraq, niyə çox çalışsaq da az nəticə aldığımızı və həqiqi problemi necə tapmalı olduğumuzu müzakirə edirəm. Az şeyi düzəltməklə necə daha çox sürət qazanmaq olar?

The holidays are here, and while they are meant to be a time of joy and connection, they often bring along digital distraction, fear of overeating, and anxiety about the impending New Year.In this short and sweet episode, Andrés Preschel breaks down the three most important themes for navigating the holiday season intentionally. You'll learn how to optimize your physiology to enjoy family meals without the guilt, how to create a digital barrier to ensure you are truly present with your loved ones, and exactly how to execute a "Past Year Review" to set yourself up for massive success in the year ahead.Discover your science, optimize your life, and enjoy your holidays.In This Episode, You'll Learn:1. The Gift of Presence (Digital Detox Strategies)Why you should delete social media for the last week of the year (less than 2% of your life!).How to use "intervention" tools to break the dopamine loop and stop doom-scrolling.Tools mentioned: The One Sec app and Shift technology.2. Preventing Holiday Weight Gain (Without Dieting)How to enjoy potlucks and home-cooked meals without "dieting" or counting calories.The Pre-Meal Protein Primer: Consuming 20-30g of lean protein (Greek yogurt, whey, lean beef) 30–60 minutes before a meal to suppress Ghrelin and boost GLP-1 (satiety).Fiber & Bitters: Why eating handfuls of dark leafy greens (arugula, spinach) before your meal slows gastric emptying and reduces glucose spikes.Food Sequencing: The correct order to eat your food to manage insulin response (Fiber/Protein first, Carbs last).The Postprandial Stroll: How a 10–15 minute walk after dinner pulls glucose into the muscles without insulin.3. The "Past Year Review" (Strategic Planning)Why New Year's Resolutions often fail and what to do instead.A step-by-step guide to Tim Ferriss's Past Year Review exercise.How to use the 80/20 rule (Pareto's Law) to identify the people and activities that bring you peak joy—and how to schedule them immediately.Creating a "Not-To-Do" list to eliminate the negative triggers from your life.

In this episode, we break down why modern mobile UA is no longer won by great ideas, but by systems that can ship hundreds or thousands of creatives every month. With real data, real examples, and hard truths, this is a deep dive into the creative arms race dominating mobile games.What you'll learn• Why creative velocity beats creative talent• Why “new creatives in last 30 days” predicts scale• How games produce 23,000 creatives per month• How Meta allocates traffic to new uploads• Why testing frameworks break at scale• Why CEOs underinvest in creative production• How one hit creative can change everything• Why UA financing matters more than everKey takeawayIf you can't ship fast, you can't win, no matter how good your ideas are.Improve your creatives: https://payhip.com/b/tu4nkGet our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Special guest: Ridzki Syahputera/https://www.linkedin.com/in/ridzkisyahputera/Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipg00:00 — Why creative talent no longer wins04:10 — The real bottleneck: creative velocity08:10 — Meta's algorithm & why new creatives get boosted12:40 — How games ship 23,000 creatives per month18:30 — Why testing frameworks fail at scale23:55 — Data proof: revenue vs creative volume29:45 — Why CEOs don't understand creative costs34:20 — The myth of Pareto creatives38:10 — Financing the creative arms race41:20 — Final takeaway---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

A rapid-fire tour through a packed week in AI, from Google's surprise Gemini 3 Flash release and its implications for the model Pareto frontier, to bombshell OpenAI fundraising talks involving Amazon and trillion-dollar valuations, major AI leadership and org changes at Amazon, early signs of stress in data-center financing markets, ChatGPT's push toward an app-platform future, fresh details on the OpenAI–Disney deal, a new US Tech Force for government AI infrastructure, Nvidia's China chip calculus, and Bernie Sanders' call for a data-center construction moratorium. This episode connects the dots between models, money, infrastructure, and politics shaping where AI heads next. Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rovo - Unleash the potential of your team with AI-powered Search, Chat and Agents - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://rovo.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow by Zencoder - Turn raw speed into reliable, production-grade output at ⁠⁠⁠https://zenflow.free/⁠⁠⁠LandfallIP - AI to Navigate the Patent Process - ⁠⁠⁠https://landfallip.com/⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai

Cosa c'entra un economista del 1897 con i buoni propositi per il nuovo anno? In questo episodio proviamo a pianificare il 2026 utilizzando il Principio di Pareto, o "Legge 80/20". L'idea è semplice: l'80% dei risultati deriva dal 20% delle cause. Invece di fare una lista infinita di obiettivi, ho individuato 3 comportamenti chiave ("il mio 20%") che possono trasformare il mio annoUn episodio per riflettere su come cambiare le nostre abitudini, migliorare l'italiano e scoprire perché, a volte, fare meno significa ottenere di più.Buon ascolto! AlessandroClub di conversazione PROSPETTIVE (Livello avanzato): Le iscrizioni sono aperte! 8 settimane di dibattito e attualità. Approfitta del prezzo Early Bird entro il 31 Dicembre. Solo 3 posti rimasti! ⁠https://buy.stripe.com/fZu00c3eTfoLbtr1hD2go00⁠Club di conversazione A RUOTA LIBERA (Livello intermedio): 8 settimane di conversazione libera per perdere la paura di parlare o togliere un po' di ruggine! Approfitta dell'offerta fino al 31 dicembre. Solo 4 posti rimasti!⁠https://buy.stripe.com/6oU9AM7v9gsP9lje4p2go01⁠

I'm a woman trapped in a man's body analysing dating advice for women given by a man. In #505 of 'Meanderings', Juan and I discuss: three dating and relationships books (The New Rules, Get the Guy by Matthew Hussey and Attached), how prescriptive “rules” aimed at women can backfire, why some advice feels outdated (Facebook walls and BBM), how scarcity games tend to attract the very players you might want to avoid, why attachment styles are useful as a lens but less so as a to‑do list, a focus on authenticity over mere effectiveness, watch the influence of your friend circle, understand how strong male sexual drive can shape dating dynamics, apply Pareto principles to health and appearance first, build an interesting life (travel, skills, community) and learning to read yourself so you don't try to fill loneliness with just anyone.No boostagrams this week, very sad puppy.Stan Link: https://stan.store/meremortalsTimeline:(00:00:00) Intro(00:00:47) The books: The New Rules, Get the Guy & Attached(00:04:21) Lot's of Don'ts(00:07:12) Perfectionism and the hunt for Mr Right(00:11:09) Who this attracts: playing games gets game players(00:16:21) What men reportedly dislike(00:20:25) Quick verdict on The New Rules & Switch To Matt Hussey(00:25:48) Practical prompts: compliments, conversations, and friendly vibes(00:30:15) Brief detour to Attached: anxious, avoidant, secure(00:39:24) Boostagram Lounge(00:41:15) Effectiveness vs authenticity: advice for daughters(00:45:00) Masks, faking confidence and why acts won't last(00:48:00) Be interesting: travel, stories and easy conversation openers(00:55:14) Broad advice: the male mind, sex drive, and expectations(01:02:26) Pareto squared: health and appearance(01:07:09) A raw moment: walking through Brisbane and feeling loneliness(01:11:24) Closing reflections Connect with Mere Mortals:Website: https://www.meremortalspodcasts.com/Discord: https://discord.gg/jjfq9eGReUTwitter/X: https://twitter.com/meremortalspodsInstagram: https://www.instagram.com/meremortalspodcasts/TikTok: https://www.tiktok.com/@meremortalspodcastsValue 4 Value Support:Boostagram: https://www.meremortalspodcasts.com/supportPaypal: https://www.paypal.com/paypalme/meremortalspodcast

¿Sientes que llevas meses o años atrapado en un laberinto de ansiedad del que no consigues salir? Has probado muchas cosas, has leído, visto vídeos, quizá has ido a terapia… y aun así te preguntas: “¿Qué es lo que me pasa? ¿Qué estoy haciendo mal? ¿Por qué sigo igual?” En este vídeo te propongo algo diferente: un checklist honesto y profundo para revisar en qué puntos te estás atascando y qué elementos necesitas ajustar para empezar, de verdad, a salir del bucle. No vamos a venderte soluciones mágicas en 7 pasos ni frases de autoayuda vacías. Vamos a mirar la ansiedad con rigor, con humanidad… y con un mapa delante. Llevo más de 25 años ayudando a personas a salir de su laberinto de ansiedad, y también he vivido la ansiedad en primera persona. En este episodio te acompaño a revisar, punto por punto, los factores que hacen que muchas personas sientan que no avanzan, o que siempre terminan volviendo al mismo sitio. En este video vas a ver: 1️⃣ Los mitos que te mantienen atrapado Te explico los tres grandes mitos de la ansiedad: “La ansiedad es una enfermedad” “Soy una persona ansiosa, es mi forma de ser” “La ansiedad viene de fuera, como un virus” Veremos por qué entender la ansiedad como construcción (y no como defecto personal) cambia por completo la forma de trabajar con ella. Empezarás a usar el superpoder de la duda sobre tus creencias. 2️⃣ ¿Estoy siguiendo una ruta o doy vueltas en círculo? Puedes estar intentando volver al “tú de antes” o castigarte por “ser débil”. Hablaremos de por qué no se trata de dejar de tener ansiedad, sino de dejar de tenerle miedo a la ansiedad. Y de cómo pasar de ese punto A al punto B sin quedarte enganchado en el camino, distraído por “las moscas” del proceso. 3️⃣ ¿Sigo un sistema o voy improvisando? La ansiedad, cuando se convierte en trastorno, se vuelve un sistema casi totalitario. Veremos tres grandes patrones: Anticipar y preocuparse de forma constante Evitar lo que da miedo Revisar y comprobar una y otra vez Entender en cuál de estos sistemas te mueves te ayuda a ubicarte en el mapa y dejar de sentir que eres “caso único sin solución”. 4️⃣ ¿La terapia o estrategia que sigo tiene evidencia científica? Hablamos del papel de las terapias cognitivo-conductuales y contextuales, y de por qué no todo lo que alivia momentáneamente es una buena estrategia a largo plazo. No basta con que “algo funcione”; necesitamos saber por qué funciona y qué lugar ocupa dentro de un sistema coherente. 5️⃣ Tu entorno: ¿te ayuda o te sabotea? Te propongo mirar tu vida como una red de nodos: trabajo, relaciones, salud, ocio, descanso… y localizar esos pocos puntos que, si se mueven un poco, cambian muchos otros a la vez. Hablamos de la famosa ley de Pareto aplicada a tu vida y de cómo dejar de obsesionarte con los nodos que hoy no puedes cambiar. 6️⃣ Cómo gestionar las recaídas sin hundirte Las recaídas no significan que hayas fracasado, significan que tu antiguo sistema se ha reactivado. Veremos la diferencia entre tropezar y recaer, y cómo usar la repetición para “fracasar mejor”, aprender y seguir avanzando. Este vídeo no pretende simplificar algo complejo, sino darte una visión más clara de: dónde te estás atascando, qué tiene sentido que cambies, y cómo empezar a hacerlo de forma más estructurada. Y si quieres profundizar y tener un mapa más detallado, puedes apuntarte gratis a nuestro curso: El Mapa de la Ansiedad (curso gratuito) https://escuelaansiedad.com/Cursos/el-mapa-de-la-ansiedad Enlaces importantes Nuestra escuela de ansiedad: www.escuelaansiedad.com Nuestro nuevo libro: www.elmapadelaansiedad.com Visita nuestra página Web: http://www.amadag.com Facebook: https://www.facebook.com/Asociacion.Agorafobia/ Instagram: https://www.instagram.com/amadag.psico/ Youtube AMADAG TV: https://www.youtube.com/channel/UC22fPGPhEhgiXCM7PGl68rw Hashtags #ansiedad,#trastornodeansiedad,#psicología,#saludmental,#elmapadelaansiedad,#AMADAGTV

✅ The biblical reason dads are called to bring order to their homes ✅ How to train your kids like a football coach (M&Ms included!) ✅ The power of a weekly family meeting to solve your biggest friction points ✅ Why setting "impossible" goals actually works SUMMARY Chaos doesn't have to be the norm in your home. In Part 1 of this conversation, Army Ranger turned fatherhood coach Chris Cirullo unpacks the biblical call for fathers to bring order—and shares the practical systems he's built to lead his five sons with both fun and discipline. You'll also hear why setting impossible goals might be the key to real growth. TAKEAWAYS God designed fathers to bring order and strategy to their homes—it's part of our calling, not just a nice-to-have. Training kids in specific behaviors with immediate rewards (like M&Ms) can save decades of frustration. Weekly family meetings with your wife help you identify and solve one key friction point at a time. Setting "impossible" goals narrows your options and forces clarity on what actually needs to change. What gets measured improves—but what gets measured and reported improves exponentially. GUEST Chris Cirullo is a former Army Ranger with four combat tours in Afghanistan, a former collegiate football player, fitness coach, and tech startup leader. He now coaches men through Mission Fit and serves on the team at Forming Men. Chris and his wife Justine homeschool their five sons in Eugene, Oregon, and are expecting their sixth child. LINKS Send a Voice Message to DadAwesome Apply to join the next DadAwesome Accelerator Cohort: Email awesome@dadawesome.org Subscribe to DadAwesome Messages: Text the word "Dad" to (651) 370-8618 FREE copy of Chris' book: https://www.missionfit.co/free15 Mission Fit Scorecard: missionfit.co/scorecard Forming Men Quotes: "Minutes of training can sometimes save decades of headaches for a father." "I have this innate responsibility as a father to bring order. We're not all great at it, but we do have to find ways to make efforts unto that end." "Setting impossible goals is one of the most effective ways to actually make meaningful growth." "What gets measured improves, but what gets measured and reported improves exponentially." "God wanted to partner with Adam to bring about order in the world, and He stopped short of producing complete order so that man as a father and a husband could do some of that work." TAGS fatherhood, intentional parenting, family systems, discipline, order, army ranger, coaching dads, homeschool dad, training kids, goal setting, Parkinson's law, Pareto principle, Pearson's law, accountability, family mission, Christian dad, family meetings, parenting hacks, dadlife, Genesis

Descubre cómo una sencilla intuición de principios del siglo XX —la Ley de Pareto— puede revolucionar tu emprendimiento: ¿qué pasaría si el 80 % de tus resultados proviniera de apenas tres o cuatro acciones clave? En este episodio de Productividad Máxima te entregamos una estrategia práctica para identificar esas tareas de alto impacto, crear plantillas duplicables y automatizar tus procesos. Nada de trabajar más: se trata de dejar de lado el ruido, concentrarte en lo esencial y multiplicar tus ingresos con menos esfuerzo.Te sorprenderá lo rápido que puedes acelerar lanzamientos, mejorar la retención de clientes y liberar horas cada semana con solo un par de atajos de teclado y un sistema modular de plantillas. Desde ejemplos reales de tiendas online hasta un plan paso a paso para aplicar mañana mismo, este resumen te dejará con la curiosidad de saber cómo bastan cinco minutos para adaptar cada proyecto y obtener resultados inmediatos. ¿Listo para transformar tu manera de emprender?Conviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/productividad-maxima--5279700/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

NÍVEL INTERMEDIÁRIOA ciência revela que procrastinar é o cérebro evitando desconforto, e o caos visual no seu espaço de trabalho pode estar esgotando sua mente e exigindo que ela trabalhe em horas extras. Escute mais um episódio do Português em 5 Minutos e descubra como e por quê.Pergunta do episódio:Segundo o Princípio de Pareto, 80% dos resultados vêm de que percentagem de esforço?a) 80%b) 50%c) 20%Vocabulário:• procrastinação ato de adiar tarefas• prioridade uma tarefa que se deve fazer antes das outras• urgente algo que requer atenção imediata, com prazo curto• importante que contribui para seus objetivos a longo prazo• matriz tabela para organizar informação em quadrantes

¿Te imaginas lograr más trabajando en lo justo y necesario? En este episodio de Productividad Máxima descubrirás cómo la famosa Ley del 80/20 de Pareto puede transformar tu negocio: identifica ese 20 % de acciones que genera el 80 % de tus ingresos y clientes felices, y olvídate del ruido que roba tu tiempo. Con ejemplos reales (como una tienda online que redujo a la mitad su ciclo de lanzamientos) y una historia que te enganchará, entenderás por qué menos, a veces, significa muchísimo más.Además, recibirás un plan práctico de cinco pasos para aplicar mañana mismo: desde listar tus procesos semanales hasta convertir tus tareas clave en plantillas duplicables, integrando atajos de teclado y bloques de concentración de 45 minutos. ¿Quieres saber cómo doblar resultados sin aumentar tu carga de trabajo? Dale play y descubre la acción única que puede cambiarlo todo.Conviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/productividad-maxima--5279700/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

In his smart, timely and sharply funny new book, The Wellness Ethic: How to Thrive in an Unpredictable World (Where Stupid Things Can Happen), author and life coach Mark Reinisch challenges the outdated “work ethic” model that values success over health, happiness and soul. His antidote: wellness ethic lifestyle design — a bold, practical approach to building a life that works for you, on your terms.In a world that feels more chaotic, disconnected and demanding by the day, author and life coach Mark Reinisch delivers a refreshingly practical and accessible framework for a well-designed life in his new book, The Wellness Ethic: How to Thrive in an Unpredictable World (Where Stupid Things Can Happen).Backed by science and inspiring, real-world experiences (not just credentials), Reinisch lays out seven essential components — mind, body, spirit, relationships, personal pursuits, professional pursuits and lifestyle maintenance — that form the backbone of what he calls “wellness ethic lifestyle design,” a simple yet powerful concept that empowers readers to build their lives, on their own terms, around what actually brings them joy and fulfillment.“Lifestyle design is the opposite of running on autopilot, and it is essential to your overall wellbeing,” Reinisch said. “With lifestyle design, the objective is to get to the heart of what you really want in life and then make it your reality.” What makes Reinisch's approach stand out from the legions of other self-help books is his remarkably simple approach: the 80/20 rule, also known as the Pareto principle, derived from the work of Italian economist Vilfredo Pareto. It states that approximately 80% of the results (outcomes) are driven by 20% of the actions (inputs).“When you apply the 80/20 rule to your life, it can be a game changer,” Reinisch said. “For example, the 80/20 rule suggests that 80% of the benefits of spirituality can be attained by embracing the most vital 20% of spiritual practices. Similarly, 80% of the benefits of taking care of your body can be realized by simply adopting the most vital 20% of physical wellness practices. It's the key to making wellness attainable and sustainable.”Written to be the antidote to self-help books that are “too damn boring,” The Wellness Ethic is a compelling, fun-to-read book with humor and personal stories that make the wellness concepts spring to life.“It is the rare self-help book that you won't be able to put down, unless the sheer bulk of it tires your arms and you drop it,” Reinisch quipped.You can find Mark Reinisch at his website: TheWellnessEthic.com X/Twitter: https://x.com/Wellness_EthicFaceBook: Wellness EthicTikTok: @The Wellness EthicBlueSky: Wellness EthicBuy his book on Amazon : https://www.amazon.com/s?k=the+wellness+ethic+mark+reinisch&crid=1TUF4PPUSZB4H&sprefix=mark+reinisch%2Caps%2C164&ref=nb_sb_ss_p13n-expert-pd-ops-ranker_1_13

Descubre cómo la misma estrategia que impulsó la carrera lunar puede duplicar tu productividad en bloque de tiempo ultra­cortos. A través de una fascinante anécdota de la NASA y la Ley de Parkinson como eje, entenderás por qué fijar límites claros de tiempo desactiva el perfeccionismo, potencia tu foco y convierte las horas en entregas visibles. Menos tiempo no significa menos resultados: al contrario, impone decisiones críticas y prioriza lo que de verdad mueve la aguja.En solo cinco pasos prácticos —desde definir un resultado concreto hasta plantar plantillas duplicables— y dos reglas operativas infalibles (la adaptación de los dos minutos y ciclos de iteración rápida), transformarás tu caos diario en sistemas ultrafuncionales. Aplica mañana mismo bloques de 45 minutos, usa Pareto para aislar ese 20 % que genera el 80 % de tu impacto, y verás cómo cada microacción bien estructurada crea una inercia imparable. ¿Te animas a convertir tu lista de tareas en resultados tangibles?Conviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/productividad-maxima--5279700/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

A core principle shapes the success of every capital campaign, and this conversation clarifies exactly how it works and why it matters.In this episode of All About Capital Campaigns, co-hosts Amy Eisenstein and Andrea Kihlstedt talk with each other about the strategic order of solicitation and how top gifts drive momentum, confidence, and overall campaign performance.Andrea explains why campaigns depend on gifts of varied sizes and how a thoughtful gift range chart helps leaders understand what it will take to reach a major goal. Amy expands on the Pareto principle and the 90/10 pattern that appears so frequently in campaign fundraising, reinforcing why the top group of donors must be approached early.Together, they illustrate the concepts of top-down and inside-out solicitation (beginning with the largest donors and the most committed insiders) so the quiet phase can build meaningful early progress. They share examples of how organizations can get stuck when they start by asking everyone at once, including a story about an animal shelter that initially relied on broad direct mail outreach before learning how to focus on individual conversations with high-capacity supporters.Listeners also hear how early board commitments strengthen the case for support, how confidence shapes donor response, and how a clear strategy influences staffing, timing, and long-term relationship building. Andrea and Amy outline the anxiety many teams feel when approaching top donors, and how a well-run feasibility study helps leaders prepare for these pivotal conversations.By the end of the episode, you will understand the structure behind a successful quiet phase and how this approach sets the stage for a strong public launch and stronger fundraising overall.To see if your organization is truly ready for a capital campaign, download this free Readiness Assessment. This guide will help you evaluate six aspects of your organization, including the board and your case for support.

Bienvenido al podcast Productividad Máxima. Hoy te revelo una estrategia de productividad basada en la ley de Parkinson: el tiempo que das a una tarea tiende a expandirse, así que fijar límites reales obliga a priorizar y a convertir incertidumbre en entregas concretas. Veremos cómo, en situaciones de alta presión como la carrera espacial, un marco de tiempo bien diseñado transforma horas de duda en resultados palpables. Te propongo un proceso práctico: define un resultado concreto en lugar de una tarea genérica, asigna un plazo corto e innegociable, usa la regla de Pareto para quedarte con el 20% que genera el 80% del impacto y duplica páginas o plantillas para no empezar desde cero.Además, te doy dos reglas operativas para sostenerlo: la versión adaptada de la regla de los dos minutos y un plan de iteraciones rápidas; integra Parkinson con la técnica Pomodoro para bloques de alta intensidad y protégelos como si fueran reuniones con clientes importantes. Aprovecha plantillas duplicables, atajos de teclado y respuestas predefinidas para reducir fricción y acelerar entregas; y aprende a decir no a lo accesorio mediante una lista de exclusión. El episodio incluye casos reales y señales simples para saber si te está funcionando. Para empezar ya, en 30 minutos elige una tarea pendiente, ponle un límite claro y usa una plantilla duplicable para completarla; así ganarás claridad y tiempo desde la primera semana.Conviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/productividad-maxima--5279700/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

Bienvenido al episodio de Productividad Máxima. Hoy descubrimos La Tracción de los Pequeños Hábitos: convertir microacciones en resultados visibles. Empezamos con una historia: antes del primer alunizaje, la NASA avanzaba gracias a miles de microajustes y rutinas repetidas, no a saltos enormes. Esa idea aplica a los negocios: no siempre hacen falta jornadas interminables; con hábitos pequeños y consistentes, lo imposible se vuelve tangible. Combina la ley de Pareto (el 20% de las acciones que generan la mayor parte de los resultados), la ley de Parkinson (limitar el tiempo) y la filosofía de Hábitos Atómicos para construir microhábitos que empujan tu proyecto sin quemarte.El plan práctico es simple: identifica las acciones clave que más pesan, diseña microhábitos tan pequeños que sea fácil empezar y acota el tiempo en bloques de 30–45 minutos. Duplica y documenta: plantillas y un botón de duplicar para emails, páginas y propuestas. En la vida real, Laura lanzó tres módulos en un mes gracias a dos bloques diarios de microtareas y a duplicar plantillas; Marcos redujo su estrés con Pomodoros y revisiones breves. Si quieres probarlo, toma una tarea de alto impacto, divídela en microacciones de 20–45 minutos y programa mañana dos bloques para la primera. ¿Qué microacción cambiará tu semana?Conviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/productividad-maxima--5279700/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

Este episodio propone una estrategia de productividad que desafía la intuición: menos tiempo puede significar mejores resultados si juntas la Ley de Parkinson, la Ley de Pareto y el poder de duplicar plantillas. La idea es que cuando el plazo es limitado, el trabajo se contrae y se aclara: se eliminan distracciones, se prioriza lo esencial y se deja de perder tiempo en mil detalles. La historia de la NASA en 1969 ilustra esto a la perfección: ante una misión imposible, se descartó lo accesorio y se enfocó en lo crítico para lograr entregas que funcionaban. Y aquí viene el truco clave: al aplicar ese mismo principio al “botón duplicar” y a las plantillas, pasas de hacer una cosa a repetirla una y otra vez con velocidad escalable. Para empezar a aplicar el método ya, sigue este proceso práctico: 1) enumera en diez minutos todo lo que repites semanalmente (propuestas, emails, páginas, plantillas de factura, respuestas de soporte). 2) marca con una estrella el 20% que genera ingresos o clientes. 3) asigna un tiempo máximo realista a cada ítem (por ejemplo, dos bloques de 45 minutos para una página mínima viable). 4) convierte lo que funciona en plantillas o duplicados y documenta tres pasos para adaptarla en menos de 20 minutos. 5) protege tu primer bloque del día para esa tarea de alto impacto y maneja interrupciones con una regla: si es urgente, delega o decide en cinco minutos. m, y mide el progreso con tres señales claras: menor tiempo medio por entrega, más entregas publicables por semana y mayor conversión o facturación. Si ves esos indicadores mejorar, la estrategia está funcionando: casos como Paula, que duplicó velocidad y ventas con plantillas y bloques de enfoque, muestran que no se trata de trabajar más, sino de trabajar mejor. ¿Listo para empezar? Elige hoy el activo que más vende y conviértelo en una plantilla duplicable; mañana úsalo y observa cuánto tiempo ahorras.Conviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/productividad-maxima--5279700/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

Aaron Benanav discusses the second part of his ‘Beyond Capitalism' essay series in the New Left Review. In this part he lays out the institutional design of his proposal of a multi-criterial economy. Shownotes Aaron at Cornell University: https://cals.cornell.edu/people/aaron-benanav Aaron's personal website: https://www.aaronbenanav.com/ Access to Aaron's paywalled publications: https://www.aaronbenanav.com/papers Mailing List to join the Movement for Multi-Dimensional Economics: https://docs.google.com/forms/d/e/1FAIpQLSeUF7MZ2jQJXY_wHKn5xSIo-_L0tkMO-SG079sa5lGhRJTgqg/viewform Benanav, A. (2025). Beyond Capitalism—1. New Left Review, Issue 153, 65–128. https://newleftreview.org/issues/ii153/articles/aaron-benanav-beyond-capitalism-1 Benanav, A. (2025). Beyond Capitalism—2. New Left Review, Issue 154, 97–143. https://newleftreview.org/issues/ii154/articles/aaron-benanav-beyond-capitalism-2 Benanv, A. (2020). Automation and the Future of Work. Verso. https://www.versobooks.com/products/2682-automation-and-the-future-of-work on economic stagnation, see especially chapter 3, “In the Shadow of Stagnation”. on Marx's concept of the Value-Form: https://www.marxists.org/archive/marx/works/1867-c1/appendix.htm Moore, J.W. & Patel, R. (2020). A History of the World in Seven Cheap Things. A Guide to Capitalism, Nature, and the Future of the Planet. Verso. https://www.versobooks.com/products/817-a-history-of-the-world-in-seven-cheap-things on the abstract domination of capitalism: Postone, M. (1993). Time, Labor and Social Domination. A Reinterpretation of Marx's Critical Theory. Cambridge University Press. https://files.libcom.org/files/Moishe%20Postone%20-%20Time,%20Labor,%20and%20Social%20Domination.pdf Mau, S. (2023). Mute Compulsion. A Marxist Theory of the Economic Power of Capital. Verso. https://www.versobooks.com/products/2759-mute-compulsion Leipold, B. (2024). Citizen Marx. Republicanism and the Formation of Karl Marx's Social and Political Thought. Princeton University Press. https://press.princeton.edu/books/hardcover/9780691205236/citizen-marx on GDP (Gross Domestic Product): https://en.wikipedia.org/wiki/Gross_domestic_product on the Five-Year Plans in the Soviet Union: https://en.wikipedia.org/wiki/Five-year_plans_of_the_Soviet_Union Katsenelinboigen, A. (1977). Coloured Markets in the Soviet Union. Soviet Studies. Vol. 29, No.1. 62-85. https://www.jstor.org/stable/150728 Uvalić, M. (2018). The Rise and Fall of Market Socialism in Yugoslavia. https://www.researchgate.net/publication/331223694_The_Rise_and_Fall_of_Market_Socialism_in_Yugoslavia on Friedrich Hayek: https://en.wikipedia.org/wiki/Friedrich_Hayek Hayek, F. A. (1945). The Use of Knowledge in Society. The American Economic Review, 35(4), 519–530. https://www.jstor.org/stable/1809376 on the Pareto Optimum: https://en.wikipedia.org/wiki/Pareto_efficiency on Rational Choice Theory: https://en.wikipedia.org/wiki/Rational_choice_model on Behavioral Economics: https://en.wikipedia.org/wiki/Behavioral_economics on Otto Neurath: https://en.wikipedia.org/wiki/Otto_Neurath on Neurath's technocratic tendencies: https://jacobin.com/2023/02/technocratic-socialism-otto-neurath-utopianism-capitalism on Joseph Raz: https://en.wikipedia.org/wiki/Joseph_Raz on Utilitarianism: https://en.wikipedia.org/wiki/Utilitarianism on the Capability Approach by Amartya Sen and Martha Nussbaum: https://en.wikipedia.org/wiki/Capability_approach on the Human Development Index (HDI): https://hdr.undp.org/data-center/human-development-index#/indicies/HDI on the Sustainability Development Goals (SDGs): https://sdgs.un.org/goals on Multi-Objective Optimization: https://en.wikipedia.org/wiki/Multi-objective_optimization Saros, D. E. (2014). Information Technology and Socialist Construction. The End of Capital and the Transition to Socialism. Routledge. https://www.routledge.com/Information-Technology-and-Socialist-Construction-The-End-of-Capital-and-the-Transition-to-Socialism/Saros/p/book/9780415742924 on Neoclassical Economics: https://en.wikipedia.org/wiki/Neoclassical_economics on Citizen Assemblies and Sortition: https://www.sortitionfoundation.org/ on John Stuart Mill: https://en.wikipedia.org/wiki/John_Stuart_Mill Mill, J. S. (2011). On Liberty. Cambridge University Press. https://www.cambridge.org/core/books/on-liberty/62EC27F1E66E2BCBA29DDCD5294B3DE0 McCabe, H. (2021). John Stuart Mill, Socialist. McGill-Queen's University Press. https://www.mqup.ca/john-stuart-mill--socialist-products-9780228005742.php on Degrowth: https://degrowth.info/ on Nick Land and Right Accelerationism: https://youtu.be/lrOVKHg_PJQ?si=Q4oFbaM1p4fhcWP0 on Left Accelerationism: https://criticallegalthinking.com/2013/05/14/accelerate-manifesto-for-an-accelerationist-politics/ Devine, P. (2002). Participatory Planning through Negotiated Coordination. Science & Society, Vol. 66, No. 1, 72-85. https://guilfordjournals.com/doi/abs/10.1521/siso.66.1.72.21001?journalCode=siso on Oskar R. Lange: https://en.wikipedia.org/wiki/Oskar_R._Lange on Lange's neoclassical approach to Socialism: https://jacobin.com/2022/10/oskar-lange-neoclassical-marxism-limits-of-capitalism-economic-theory Kowalik, T. (1990). Lange-Lerner Mechanism. In: Eatwell, J., Milgate, M., Newman, P. (eds). Problems of the Planned Economy. Palgrave Macmillan. https://link.springer.com/chapter/10.1007/978-1-349-20863-0_21 on Joseph Schumpeters concept of Creative Destruction: https://en.wikipedia.org/wiki/Creative_destruction Shaikh, A. (2016). Capitalism. Competition, Conflict, Crises. Oxford Academic. https://academic.oup.com/book/1464 Kornai, J. (1980). “Hard” and “Soft” Budget Constraint. Acta Oeconomica, 25(3/4), 231–245. https://www.jstor.org/stable/40728773 on the Cobb-Douglas Production Function: https://en.wikipedia.org/wiki/Cobb%E2%80%93Douglas_production_function on Adam Smith: https://en.wikipedia.org/wiki/Adam_Smith Lutosch, H. (2025). Embracing the Small Stuff. Caring for Children in a Liberated Society. In: Groos, J., & Sorg, C. (Eds.). (2025). Creative Construction. Democratic Planning in the 21st Century and Beyond. Bristol University Press. https://bristoluniversitypress.co.uk/creative-construction Hahnel, R. (2021). Democratic Economic Planning. Routledge. https://www.routledge.com/Democratic-Economic-Planning/Hahnel/p/book/9781032003320 Cockshott, P. & Cottrell, A. (1993). Towards a New Socialism. Spokesman. https://users.wfu.edu/cottrell/socialism_book/new_socialism.pdf on Universal Basic Services (UBS): https://en.wikipedia.org/wiki/Universal_basic_services https://autonomy.work/ubs-hub/ Fraser, N. & Sorg, C. (2025). Socialism, Planning and the Relativity of Dirt. In: Groos, J., & Sorg, C. (Eds.). (2025). Creative Construction. Democratic Planning in the 21st Century and Beyond. Bristol University Press. https://bristoluniversitypress.co.uk/creative-construction on Milton Friedman: https://en.wikipedia.org/wiki/Milton_Friedman on John Maynard Keynes: https://en.wikipedia.org/wiki/John_Maynard_Keynes Aaron on what to learn from radical Keynesianism for a transitionary Program: Benanav, A. & Henwood, D. (2025). Behind the News. Beyond the Capitalist Economy w/ Aaron Benanav. https://open.spotify.com/episode/2diIiFkkM4x7MoZhi9e0tx on Socializing Finance: McCarthy, M. A. (2025). The Master's Tools. How Finance Wrecked Democracy (And a Radical Plan to Rebuild It). Verso. https://www.versobooks.com/products/755-the-master-s-tools Future Histories Episodes on Related Topics S3E47 | Jason W. Moore on Socialism in the Web of Life https://www.futurehistories.today/episoden-blog/s03/e47-jason-w-moore-on-socialism-in-the-web-of-life/ S03E29 | Nancy Fraser on Alternatives to Capitalism https://www.futurehistories.today/episoden-blog/s03/e29-nancy-fraser-on-alternatives-to-capitalism/ S03E04 | Tim Platenkamp on Republican Socialism, General Planning and Parametric Control https://www.futurehistories.today/episoden-blog/s03/e04-tim-platenkamp-on-republican-socialism-general-planning-and-parametric-control/ S02E33 | Pat Devine on Negotiated Coordination https://www.futurehistories.today/episoden-blog/s02/e33-pat-devine-on-negotiated-coordination/ S03E10 | Aaron Benanav on Associational Socialism and Democratic Planning https://www.futurehistories.today/episoden-blog/s02/e10-aaron-benanav-on-associational-socialism-and-democratic-planning/ S01E32 | Daniel E. Saros on Digital Socialism and the Abolition of Capital (Part 2) https://www.futurehistories.today/episoden-blog/s01/e32-daniel-e-saros-on-digital-socialism-and-the-abolition-of-capital-part-2/ S02E31 | Daniel E. Saros on Digital Socialism and the Abolition of Capital (Part 1) https://www.futurehistories.today/episoden-blog/s01/e31-daniel-e-saros-on-digital-socialism-and-the-abolition-of-capital-part-1/ --- If you are interested in democratic economic planning, these resources might be of help: Democratic planning – an information website https://www.democratic-planning.com/ Sorg, C. & Groos, J. (eds.)(2025). Rethinking Economic Planning. Competition & Change Special Issue Volume 29 Issue 1. https://journals.sagepub.com/toc/ccha/29/1 Groos, J. & Sorg, C. (2025). Creative Construction - Democratic Planning in the 21st Century and Beyond. Bristol University Press. [for a review copy, please contact: amber.lanfranchi[at]bristol.ac.uk] https://bristoluniversitypress.co.uk/creative-construction International Network for Democratic Economic Planning https://www.indep.network/ Democratic Planning Research Platform: https://www.planningresearch.net/ --- Future Histories Contact & Support If you like Future Histories, please consider supporting us on Patreon: https://www.patreon.com/join/FutureHistories Contact: office@futurehistories.today Twitter: https://twitter.com/FutureHpodcast Instagram: https://www.instagram.com/futurehpodcast/ Mastodon: https://mstdn.social/@FutureHistories English webpage: https://futurehistories-international.com   Episode Keywords #AaronBenanav, #JanGroos, #Interview, #FutureHistories, #FutureHistoriesInternational, #futurehistoriesinternational, #Transition, #DemocraticPlanning, #Keynes, #Efficiency, #Economics, #NeoclassicalEconomics, #NeoclassicalSocialism, #OttoNeurath, #DemocraticEconomicPlanning, #Capitalism, #Economics, #Socialism, #Socialisation, #Investment, #Degrowth, #UniversalBasicServices, #CareWork

How To Lower Your ACoS in Amazon PPC starts here on That Amazon Ads Podcast.If Amazon PPC and Amazon Ads are draining profit, learn the two levers that cut ACoS fast without killing sales.Stephen and Andrew show a click-by-click system: lower CPC with precise bid and placement controls, and raise RPC by pruning non-converting traffic and reallocating to what converts.You'll see why sorting by highest ACoS fails, how to follow the spend, and where Top of Search multipliers, auto/broad, and negatives fit.We demo CPC vs RPC diagnostics (e.g., CPC up 10%, RPC down 10% → ~20% ACoS surge) and a Pareto workflow that fixes the few campaigns driving most spend.Watch now to master Amazon PPC inside That Amazon Ads Podcast, and bookmark this if you need a refresher on How To Lower Your ACoS in Amazon PPC with Amazon Ads.

My guest this week is Andrew Hulbert, founder of Pareto FM, who started his business from his bedroom at 27 with no name, network, or funding and grew it to a £42 million turnover before exiting less than a decade later.Andrew's story is one of calculated risk, relentless focus, and smart scaling. From landing his first £200,000 contract with the Bulgari Hotel to winning major clients like Twitter, ASOS, and Deliveroo, his approach to business growth was simple: do what big companies get wrong, and do it exceptionally well.In this episode, we discuss the realities of starting from zero, why being “too small” can become your biggest advantage, and how creating sweet equity helped him retain every senior hire across nine years. Andrew also shares what life looks like post-exit. Family, friends, and purpose and what it truly takes to let go without losing identity.If you're at the stage where you're thinking of starting, scaling, or selling, this conversation will help you think more strategically about risk, people, and purpose.Key Takeaways:Start with One Win: Focus on getting your first deal, not the perfect business plan. Momentum starts with movement.People Are Everything: Hire for motivation, not CVs. Pareto's 17 senior leaders stayed through to exit because they shared the vision and had skin in the game.Be Willing to Bet on Growth: Andrew's decision to reinvest £1 million into overheads helped double the company's value in two years.Know When to Step Back: The biggest challenge isn't starting, it's letting go. Learning to trust others is what takes a founder from operator to leader.Redefine Success After Exit: Freedom doesn't come from a payout, it comes from presence—being there for family, friends, and yourself.

Work with Jordan personally at ⁠⁠www.ecommerceos.co⁠⁠Work with social commerce club at ⁠⁠www.socialcommerceclub.com⁠⁠Get 27 strategies in 27 days at ⁠⁠https://socialcommerceclub.com/pages/27-strategies⁠⁠Join Tiktok shop elites mastermind at ⁠⁠https://www.skool.com/tiktokshopelite/aboutUnlock the power of TikTok Shop as your ultimate top-of-funnel engine. In this video, Jordan West reveals the proven system his agency uses to turn TikTok Shop into a content machine that fuels Meta ads, Amazon, DTC, and retail growth. Learn how to harvest winning creator content, whitelist it, scale it, and syndicate it everywhere—so you never run out of high-performing ads again.Whether you're running a DTC brand or managing enterprise-level campaigns, this strategy will help you stop guessing which ads will work and start scaling with precision.

https://www.astralcodexten.com/p/sources-say-bay-area-house-party [previously in series: 1, 2, 3, 4, 5, 6, 7] Something is off about this Bay Area House Party. There are . . . women. “I've never seen a gender balance like this in the Bay Area,” you tell your host Chris. “Is this one of those fabled ratio parties?” “No - have you heard of curtfishing? It's the new male dating trend. You say in your Bumble profile that you're a member of the Dissident Right who often attends parties with Curtis Yarvin. Then female journos ask you out in the hopes that you'll bring them along and they can turn it into an article.” “What happens when they realize Curtis Yarvin isn't at the party?” “Oh, everyone pools their money and hires someone to pretend to be Curtis. You can just do things. Today it's Ramchandra.” You follow his gaze, and there is Ramchandra, hair greased back, wearing a leather jacket, surrounded by a crowd of young women. “When I say I'm against furries,” he's explaining, staccato, at 120 wpm, “I mean the sort of captured furries you get under the post-Warren-G-Harding liberal order, the ones getting the fat checks from the Armenians at Harvard and the Department of Energy. I love real furries, the kind you would have found in 1920s New Mexico eating crocodile steaks with Baron von Ungern-Sternberg! Some of my best friends are furries, as de Broglie-Bohm and my sainted mother used to say! Just watch out for the Kikuyu, that's my advice! Hahahahahaha!” Some of the women are taking notes. “But enough about me. When I was seventeen, I spent seven weeks in Bensonhurst - that's in the Rotten Apple, in case you can't tell your Nepalis from your Neapolitans. A dear uncle of mine, after whom I was named…” “Ramchandra is pretty good,” you admit. “Still, if it were me I would have gone with a white guy.” “It's fine,” says Chris. “Curtis describes himself as a mischling, and none of the journos know what that means.” Ramchandra is still talking. “Of course, strawberries have only been strawberries since after the Kronstadt Rebellion. Before that, strawberries were just pears. You had to get them hand-painted red by Gypsies, if you can believe that. Gypsies! So if you hear someone from west of Pennsylvania Avenue mention ‘strawberries', that's what we in the business call il significanto.” “I admit he has talent,“ you say. “But this curtfishing thing - surely at some point your date realizes that you're not actually a high-status yet problematic bad boy who can further her career just by existing, and then she ghosts you, right?” “That's every date in San Francisco. But when you curtfish, sometimes she comps your meal from her expense account. It's a strict Pareto improvement!” After some thought, you agree this is a great strategy with no downsides, maybe the biggest innovation in dating since the invention of alcohol. Having failed to bring your own journo to the party, you look for one who seems unattached. You catch the eye of a blonde woman who introduces herself as Gabrielle, and you try to give her the least autistic “Hello” of which you are capable.

Key Highlights from the Episode:0:00 – Introduction3:21 – Professional scarcity: why focusing on fewer clients creates greater impact5:12 – Shifting from book of business to real business7:10 – Why client experience outweighs investment performance9:15 – The law of familiarity and loyalty fatigue14:23 – How to reset client relationships during a transition18:16 – Fee worthiness and setting rules of engagement25:03 – How processes and intellectual property drive valuation29:12 – Depersonalizing your practice to build enterprise value31:40 – The role of AI in practice management34:17 – Balancing high-tech with high-touch in client relationships40:23 – Breaking the status quo to unlock potentialResources:Elite Consulting Partners | Financial Advisor Transitions: https://eliteconsultingpartners.com  Elite Marketing Concepts | Marketing Services for Financial Advisors: https://elitemarketingconcepts.com  Elite Advisor Successions | Advisor Mergers and Acquisitions: https://eliteadvisorsuccessions.com  JEDI Database Solutions | Data Intelligence for Advisors: https://jedidatabasesolutions.com  Connect with Duncan Macpherson on LinkedIn: https://www.linkedin.com/in/duncanmacpherson  Visit Pareto Systems: https://www.paretosystems.com  Download Duncan's AI Whitepaper: https://paretosystems.com/ai-strategies-for-financial-professionals.html  Listen to more Advisor Talk episodes: https://eliteconsultingpartners.com/podcasts/  Follow us on LinkedIn: https://linkedin.com/company/eliteconsultingpartners   

Summary In this episode, Clayton Cuteri explores the concept of momentum in personal growth and spirituality, drawing on teachings from Jesus and the Pareto principle. He emphasizes the importance of focusing on positive thoughts and actions to create momentum in life and how this can lead to significant transformations. The conversation also touches on the interconnectedness of individuals in their spiritual journeys and the importance of supporting one another.Clayton's Social MediaLinkTree | TikTok | Instagram | Twitter (X) | YouTube | RumbleTimecodes00:00 - Intro01:04 - The Power of Momentum in Life03:51 - Understanding the Pareto Principle06:04 - Creating Positive Momentum12:00 - Transforming Your Life Through Focus15:40 - Supporting Each Other's Spiritual JourneysIntro/Outro Music Producer: Don KinIG: https://www.instagram.com/donkinmusic/Spotify: https://open.spotify.com/artist/44QKqKsd81oJEBKffwdFfPSuper grateful for this guy ^Send Clayton a text message!Support the showNEWSLETTER - SIGN UP HERE

In this episode of The Conference Room, host Simon is joined by serial entrepreneur  Kasim Aslam.  Kasim shares his inspiring journey from humble beginnings in Albuquerque to building multiple seven and eight-figure businesses, including the top-ranked Google ad agency Solutions 8, which he sold in 2022. He discusses his entrepreneurial origin story, the challenges he faced, and how he cracked the code on hiring exceptional talent globally.  Kasim introduces his upcoming book Higher, a step-by-step framework for hiring top remote talent, and dives deep into why most businesses struggle to attract extraordinary employees and how to fix that. He also shares practical hiring strategies, including how to find, vet, and retain the best people worldwide, especially in emerging markets. This episode is a must-listen for entrepreneurs and business leaders looking to scale with high-impact talent.Key Moments:Introduction to  Kasim Aslam: Serial entrepreneur, author, and founder of Solutions 8, with multiple successful exitsCassim's entrepreneurial origin story: Growing up in poverty, early business lessons selling candy, and the impact of his upbringing.Overcoming adversity: From losing everything in the banking collapse to starting over with web work and building Solutions 8.Building and selling Solutions 8: Growing the largest dedicated Google ad agency and lessons learned from the exit.Post-exit ventures and passion for offshore staffing: How  Kasim builds multiple service businesses and his focus on empowering talent in emerging markets.The Higher book and hiring philosophy: Why treating people as commodities is wrong and the power of the Pareto (80/20) distribution in talent.Why most businesses repel top talent: Fear of competence, mediocrity, and the importance of paying more to attract exceptional employees.Global talent arbitrage: How hiring internationally can provide access to highly skilled, motivated workers at competitive rates.Practical hiring framework: Writing compelling job posts, using “fly traps” to filter candidates, paying for trial projects, and testing real job skills.Final tips and book launch: Top three hiring tips—be a place people want to work, have high expectations, and trust your team. How to get the Higher book and connect with Cassim.To learn more about  Kasim Aslam please visit her Linkedin ProfileTo learn more about  Pareto Talent please visit her websiteYOUR HOST - SIMON LADER    Simon Lader is the host of The Conference Room, Co-Founder of global executive search firm Salisi Human Capital, and lead generation consultancy Flow and Scale. Since 1997, Simon has helped cybersecurity vendors to build highly effective teams, and since 2022 he has helped people create consistent revenue through consistent lead generation.      Get to know more about Simon at:    Website: https://simonlader.com/  Twitter: https://twitter.com/simonlader  LinkedIn: https://www.linkedin.com/in/headhuntersimonlader/    The Conference Room is available onSpotifyApple podcastsAmazon MusicIHeartRadio

On this episode of Next Level CRE, Matt Faircloth interviews Paul Moore. Paul shares his remarkable journey from selling his first company and chasing “shiny objects” that left him $2.5M in debt, to giving his way out during the 2008 crash, and eventually pivoting into real estate. He explains why multifamily wasn't the “perfect investment,” how Wellings Capital now focuses on fund-of-funds strategies using Pareto's principle to back only top-tier operators, and why diversification across operators, geographies, and asset classes is key. Paul also highlights how private equity firms vet operators, what passives should know about due diligence (including NOI audits), and how Wellings has raised over $800K to fight human trafficking through AIM Paul Moore Current role: Founder & Managing Partner, Wellings Capital Based in: Lynchburg, Virginia Say hi to them at: LinkedIn| Wellings Capital| AIM Free Get 50% Off Monarch Money, the all-in-one financial tool at www.monarchmoney.com with code BESTEVER Join the Best Ever Community  The Best Ever Community is live and growing - and we want serious commercial real estate investors like you inside. It's free to join, but you must apply and meet the criteria.  Connect with top operators, LPs, GPs, and more, get real insights, and be part of a curated network built to help you grow. Apply now at ⁠www.bestevercommunity.com⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices