Podcasts about 200b

Paulo Passoni, Managing Partner at Valor Capital, and Olga Maslikhova sit down with their first-ever TJC Debrief guest — Ivana Delevska, Founder and CIO of Spear Invest and Portfolio Manager of the Spear Alpha ETF (SPRX, Nasdaq), one of the best-performing actively managed AI ETFs. Ivana spent a decade at Tiger Management, Millennium, and Citadel before founding Spear, where she now runs over $100M in AUM as a one-person fund augmented by AI. This is the June 2026 edition of TJC Debrief — a monthly show covering tech, venture, and capital markets through a global lens.We cover where $1 of AI spend actually goes — 50% to compute, 15–20% to networking, 15% to power and physical build-out — and why networking is the most under-the-radar layer of the value chain, why behind-the-meter power and former Bitcoin mining sites (Applied Digital) are the most overlooked plays in AI infrastructure, why Latin America could become a serious data center alternative to the US given cheaper electricity and faster permits, why hyperscaler-backed offtake deals are solving the cost-of-capital problem for data center build-outs, the SpaceX IPO at $1.77 trillion and 60x forward revenue with only 15% growth — and why Paulo thinks the employee lockup wall is the biggest risk, why Anthropic at ~$1T with $15B revenue scaling to $200B in 2027 is the more reasonable bet on a 12-month horizon while SpaceX is the better 10-year hold, why the application layer is where the next wave of billion-dollar revenue companies will emerge — using Higgsfield as a case study going from $0 to nearly $500M in revenue in one year by orchestrating 30 video models, why speed and revenue per employee ($1–10M is the new bar) are the only real moats left in software, why Elon is the "king of hardware" and what the EPC contractor insourcing playbook actually looks like, why community is the anti-AI moat — from independent watchmaker collector groups to Corgi's coffee shop in Silicon Valley, why the air pocket of AI demand is the real risk to watch (token prices are the early signal), and why wealth concentration from the AI boom is the biggest macro risk of all — and what forced-savings products and intelligent wealth transfer mechanisms could prevent it.Subscribe to The J Curve Insider newsletter for deeper insights and follow Olga on LinkedIn and Instagram.

In this episode, Brian Luebben breaks down the exact business philosophy he stole from Walt Disney's 1957 mind map and used to build a portfolio of companies now doing over $15 million per year. Brian walks through his own mind map live - the one he drew in 2023 before making his first dollar in business - and shows how Action Academy, Sexton World, Sexton Hospitality, and his business and real estate fund all connect and feed each other like a flywheel. Plus, he shares the "plant trees, manage orchards" framework that changed how he thinks about building and scaling businesses for the long term.Curious as to how we've bought multiple businesses and built millions in equity? Give this video a watch for a full breakdown: https://www.youtube.com/watch?v=cviipnGtDWI&feature=youtu.beIf you are serious about building a life on your terms and want to surround yourself with people who are actually doing it, go to: https://actionacademy.com?el=action_academy_podcastIf you want to leave corporate America in the next 6-18 months - you should check out our Action Academy Community

SpaceX's IPO filing reveals xAI lost $6.4 billion in 2025 while planning a massive Grok expansion — offering the first public look at Elon Musk's AI financials and more details about his ambitions. Also, the next big thing for Nvidia will be CPUs for AI agents, $200 billion worth, CEO Jensen Huang predicts. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Andrew and Tom discuss the US quantum computing grants going to IBM, Rigetti, and GlobalFoundries, and Nvidia's earnings call highlights including the Vera CPU opening a new $200 billion TAM with $20 billion in visible revenue this year, plus NVDA gaining inference market share against Amazon's Trainium and Google's TPUs thanks to frontier model partners like Perplexity, Cursor, Anthropic, TML, and Reflection.Join our live YouTube stream Monday through Friday at 8:30 AM EST:http://www.youtube.com/@TheMorningMarketBriefingPlease see disclosures:https://www.narwhal.com/disclosure

LexisNexis CEO of Global Legal Sean Fitzpatrick joins David Cowen to unpack the newly announced strategic alliance with Luminance and what it signals about where legal work is actually headed. From the "trust-first" shift replacing better-faster-cheaper, to law firms growing margins while raising rates, to the emergence of an entirely new role (manager of agents), this conversation is a candid read on how AI is reshaping the practice, the workflow, and the talent equation in legal. Key Topics Covered Why the LexisNexis and Luminance integration was customer-demanded, and how authoritative legal content plus contract intelligence changes the workflow equation ChatGPT as a step change, not an incremental shift: the strategy stayed the same, the tools changed everything The three buckets of legal work (repeatable/rules-based, judgment-based, and pure thought leadership) and where AI actually plays The "AI dividend" in practice: a GC reclaiming 10 hours a week to turn warranty claims from cost center into profit driver Why trust now outranks speed and cost as the dominant buying criterion in legal AI How law firms are growing revenue faster than cost base, and pushing high-single-digit rate increases The role that doesn't exist yet: manager of agents, leading a workforce with no human employees "AI fluidity" as the new hiring filter, plus career advice on reputation, partner selection, and taking risks early (with a Shoe Dog recommendation)

AGENDA: 00:05:11 — Anthropic freezes secondary sales, requiring board approval for all transfers. 00:10:45 — Why Anthropic is buying capacity from Elon Musk. 00:15:35 — Anthropic's massive $200B revenue commit to Google. 00:18:55 — Goldman Sachs predicts a 24x surge in token consumption driven by agents. 00:31:05 — Will AI labs eat the app layer? The threat to Legal and CX verticals.  00:37:55 — SaaS public markets: HubSpot tanks 18% while Monday.com finds its footing. 00:42:40 — Growth theft: How Clay is commoditizing ZoomInfo's data business. 00:46:25 — Cerebras prices IPO at $150–$160 with a $48B market cap. 00:52:15 — Real Venture Capital: Celebrating the early bets by Foundation and Benchmark. 00:58:30 — Ramp's valuation vs. the Chapter 7 collapse of e-commerce card Parker. 01:06:20 — Success and Sacrifice: Is mental health the price of building a $20B company?  

Welcome back to the 200th Episode Special of the Talk My Credo Podcast! In part "A" we celebrated the only way we knew how, with shenanigans. With THIS episode, Part "B", expect more of the same! The crew brings you a variety show with different segments and presentations thats a big different from the norm, but a sneak peak into different thing that we'll be introducing in the show! So sit back, enjoy the ride and lets get active!!-------------------------*** CHAPTERS ***00:00 - Welcoming Introduction01:25 - Usher, Chris Brown, & R&B talk08:58 - The Crew shows love to Donte11:03 - Nassor's journey to NC13:35 - Yo Keashia! Pt123:17 - Queen Tings w Dee The Grey (Meatmatized)34:32 - Call The Show Promo35:08 - The Dudecast (Boys Acting Up)1:01:05 - Couldnt Be Me w KT (Mormon Mumbles)1:08:58 - The Girlcast (Setting Boys Straight)1:26:20 - Yo Keashia! Pt21:35:07 - Thank You!!---------------Follow Us:

Creative Strategies CEO Ben Bajarin joins TITV Host Akash Pasricha to discuss AMD's 38% revenue jump and why the agentic AI shift is creating a "share grabbing" environment for CPUs. We also talk with The Information's Sri Muppidi and Erin Woo about Anthropic's massive $200 billion commitment to Google Cloud and whether the AI boom is fueled by circular financing. Rocket Drew provides an update from the Elon Musk v. OpenAI trial on Greg Brockman's testimony, and we get into the $60 million funding round for Tessera Labs with CEO Kabir Nagrecha. Finally, we close with subscriber data on the tightening chatbot race between ChatGPT, Claude, and Gemini.Articles discussed on this episode: https://www.theinformation.com/articles/anthropic-commits-spending-200-billion-googles-cloud-chipsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/00:00 — 01:13 Introduction 01:14 — 11:05 AMD Revenue Rises 38% in Q1 11:06 — 18:41 Anthropic to Spend $200B on Google's Cloud and Chips 18:42 — 22:18 Musk Trial: OpenAI President Testifies 22:19 — 29:48 Tessera Labs Raises $60M in Funding 29:49 — 31:23 Three-Way AI Chatbot Race Emerges 

Trump promised security. America spends trillions on defense, intelligence, surveillance, ICE, and homeland security — so why do we keep getting more chaos? [***APOLOGIZE FOR THE POSTING DELAY!***]In this episode, Matt Robison breaks down the shocking White House Correspondents' Dinner security scare, the gap between security theater and real safety, massive Pentagon spending, cuts to healthcare and medical research, Palantir's growing role in surveillance, and why Americans still feel less secure.Featuring physician and health policy expert Dr. Eric Lullove.

Marley Kayden narrows in on Amazon's (AMZN) earnings ahead of Wednesday night's earnings. She tells investors to watch revenue growth and explains why analysts are broadly positive regarding the Mag 7 giant's ecommerce and AI and AWS growth. Prosper Trading Academy's Scott Bauer walks us through example trades for amazon and comments to look at the volatility of the market when looking for direction.======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

AI is now an execution race defined by infrastructure. Patrick Moorhead and Daniel Newman break down how compute shortages, energy constraints, and security risks are reshaping the race from building models to actually running them at scale. From chip supply and hyperscaler strategy to AI-native security and the growing case for regulation, this episode maps the pressure points defining what it really takes to turn AI investment into production reality. Handpicked Topics Include: Meta, Broadcom, and the Reality of the Compute Shortage — Meta's multi-year MTIA partnership with Broadcom reinforces a critical truth, there is no surplus compute. Hyperscalers are simultaneously investing in NVIDIA, AMD, ARM, custom silicon, and networking just to meet demand. The discussion breaks down why "compute deficiency" is now the defining constraint in AI, and why every viable chip, regardless of performance tier, will find a buyer. (The Decode) Anthropic 4.7, Model Degradation, and the Hidden Cost of Scale — The hosts debate performance tradeoffs in Anthropic's latest release, including degraded real-world usability, throttled reasoning quality, and SLA concerns. As token usage increases and compute constraints tighten, model providers are quietly balancing performance against availability, raising questions about reliability for enterprise deployment. (The Decode) Enterprise AI and the Rise of AI-Native Security Architectures — IBM's Autonomous Security platform signals a shift from AI-enhanced tools to fully AI-native security orchestration. As models increase attack surface through agents and prompt injection risks, enterprises must rethink cybersecurity at the system level, not just the application layer. (The Decode) Energy, Not Just Compute, Is the Next Bottleneck — Oracle's partnership with Bloom Energy highlights a parallel constraint, power availability. With data center expansion accelerating, companies are investing in fuel cells, natural gas, and off-grid solutions to sustain AI growth. The discussion makes clear that AI scaling is now equally dependent on energy infrastructure as it is on silicon. (The Decode) Hyperscaler Strategy: Everyone Is Talking to Everyone — Google's reported discussions with Marvell are not an exception, they are the rule. The hosts introduce the principle that every hyperscaler is constantly evaluating every chip partner. With stakes this high, redundancy, diversification, and supplier leverage are mandatory, not optional. (The Decode) The Flip: Should AI Be Regulated as a Public Utility? — One side argues that AI's scale, energy consumption, and societal impact justify utility-style regulation, comparing it to infrastructure like electricity and the internet. With trillion-dollar CapEx commitments and concentration among a few players, the case is made that access and governance will inevitably require oversight. The opposing view warns that premature regulation would lock in incumbents, slow innovation, and weaken global competitiveness, particularly against China. (The Flip) Semiconductor Policy, Tariffs, and Global Leverage — Section 232 semiconductor tariffs emerge as a geopolitical tool rather than pure trade policy. The discussion outlines exemptions, unresolved packaging questions, and how tariffs are being used to influence global supply chains and negotiations with China. (Bulls & Bears) TSMC Signals Unstoppable AI Demand — TSMC's earnings confirm what the market has been debating, AI demand is not slowing. With record margins, increased CapEx, and continued expansion, the company validates long-term infrastructure investment and reinforces that supply, not demand, is the limiting factor. (Bulls & Bears) ASML and the Fragility of the Supply Chain — ASML's performance highlights strong demand but also exposes geopolitical risk, particularly around China restrictions. The conversation expands to include broader supply chain dependencies across equipment makers and the long-term implications of restricting access to advanced manufacturing tools. (Bulls & Bears) Quantum Signals: DARPA, IBM, and the Next Compute Frontier — The episode closes with a look at quantum computing's trajectory, including DARPA contracts and IBM's push toward measurable business value. While still early, quantum is positioned as the next layer of heterogeneous compute that could redefine long-term infrastructure. (Bulls & Bears) The Decode Meta Partners with Broadcom to Co-Develop Custom AI Silicon https://about.fb.com/news/2026/04/meta-partners-with-broadcom-to-co-develop-custom-ai-silicon/ https://247wallst.com/investing/2026/04/14/why-googles-tpu-talks-just-made-marvell-technology-a-must-buy-ai-stock/ https://x.com/danielnewmanUV/status/2044201311915106659 https://x.com/PatrickMoorhead/status/2044180443218546954 https://x.com/PatrickMoorhead/status/2044161631324676401 Anthropic Launches Claude Opus 4.7 Amid Outages and Enterprise Growing Pains https://www.cnbc.com/2026/04/16/anthropic-claude-opus-4-7-model-mythos.html https://aws.amazon.com/about-aws/whats-new/2026/04/claude-opus-4.7-amazon-bedrock/ https://cloud.google.com/blog/products/ai-machine-learning/claude-opus-4-7-on-vertex-ai https://gizmodo.com/anthropic-releases-claude-opus-4-7-to-remind-everyone-how-great-mythos-is-2000747469 https://www.techradar.com/news/live/claude-anthropic-down-outage-april-6-2026 https://help.apiyi.com/en/claude-opus-4-7-release-features-api-guide-en.html IBM Launches Autonomous Security to Defend Against AI-Powered Cyberattacks https://newsroom.ibm.com/2026-04-15-ibm-announces-new-cybersecurity-measures-to-help-enterprises-confront-agentic-attacks Bloom Energy and Oracle Expand to 2.8GW — Fuel Cells Power the AI Data Center Boom https://www.bloomenergy.com/news/bloom-energy-and-oracle-expand-strategic-partnership-to-deploy-up-to-2-8-gw-to-accelerate-ai-infrastructure-build-out/ https://www.cnbc.com/2026/04/13/oracle-expands-bloom-energy-deal-days-after-400-million-stock-warrant.html https://finance.yahoo.com/sectors/energy/articles/bloom-energy-oracle-expand-strategic-210300696.html Google in Talks with Marvell on TPU Development and a Dedicated LLM Inference Chip https://247wallst.com/investing/2026/04/14/why-googles-tpu-talks-just-made-marvell-technology-a-must-buy-ai-stock/ https://x.com/wallstengine/status/2044036448094146733 The Flip: Should AI Be Regulated as a Public Utility? FOR: OpenAI itself said AI should be treated like a utility https://techcrunch.com/2026/04/06/openais-vision-for-the-ai-economy-public-wealth-funds-robot-taxes-and-a-four-day-work-week/ Amazon spending $200B on AI infrastructure proves the utility parallel https://observer.com/2026/04/amazon-andy-jassy-defends-ai-spend/   AGAINST:  Utilities are regulated because they stopped innovating — AI is still accelerating https://hai.stanford.edu/ai-index/2026-ai-index-report Cloudera: Nearly 80% of enterprises say AI is held back by data access  https://www.globenewswire.com/news-release/2026/04/14/3273502/31982/en/Nearly-80-of-Enterprises-Say-AI-Is-Held-Back-by-Data-Access-Challenges-New-Cloudera-Report-Finds.html Bulls and Bears Section 232 Semiconductor Tariff Deadline Passes — What Comes Next?  https://ninescrolls.com/news/section-232-semiconductor-tariff-deadline-arrives-april-14-equipment-makers-brac https://www.whitecase.com/insight-alert/president-trump-orders-narrowly-targeted-25-section-232-tariff-certain-advanced https://www.gibsondunn.com/trump-administration-new-tariffs-on-and-export-licensing-requirements-for-advanced-semiconductors-create-challenging-new-cross-currents-new-opportunities-for-us-manufacturers/ https://x.com/PatrickMoorhead/status/2043432884308992459 TSMC Q1 2026 Earnings: Record Profit, Margins Crush Guidance, AI Demand 'Extremely Robust' https://www.investing.com/news/transcripts/earnings-call-transcript-tsmcs-q1-2026-shows-strong-growth-and-margin-gains-93CH-4617167 https://ca.investing.com/news/stock-market-news/tsmc-q1-net-profit-surges-58-beats-expectations-on-strong-aifueled-demand-4567884 https://www.techi.com/tsmc-q1-2026-earnings-report/ https://finance.yahoo.com/markets/stocks/articles/tsmc-q1-2026-earnings-record-121456920.html ASML Q1 2026: Revenue Beats, Full-Year Guidance Raised — 'Demand Outpacing Supply' https://www.asml.com/en/news/press-releases/2026/q1-2026-financial-results https://www.gurufocus.com/news/8795513/asml-reports-strong-q1-2026-results-with-eur-88-billion-in-sales https://www.cnbc.com/2026/04/15/asml-q1-2026-earnings-report.html https://semiconalpha.substack.com/p/asml-q1-2026-revenue-beat-guidance IonQ Surges 20% on DARPA Quantum Contract — Market Prices In Commercialization https://www.ionq.com/news/ionq-selected-for-darpas-heterogeneous-architectures-for-quantum-harq-program https://www.gurufocus.com/news/8792447/ionq-ionq-secures-darpa-contract-for-quantum-computing-advancement https://economictimes.com/news/international/us/ionq-stock-surges-20-after-bagging-big-contract-heres-all-about-it-and-what-investors-should-know/articleshow/130263057.cms https://www.cnbc.com/video/2026/04/14/ionq-ceo-niccolo-de-masi-on-securing-darpa-contract-and-recent-acquisitions.html https://x.com/danielnewmanUV/status/2044124700586946681

The 6 biggest stories in tech, business, and macro — week of April 12, 2026.Anthropic's Mythos Preview autonomously found high-severity zero-day vulnerabilities across every major operating system and browser. The offensive/defensive asymmetry in cybersecurity just shifted. Attackers with frontier AI can now run continuous, low-cost vulnerability scans that no human security team can match. Cyber insurance premiums, enterprise security budgets, and the entire defensive tooling stack are mispriced for this.The Strait of Hormuz has been closed for five weeks with 20% of global oil supply blocked, and equity markets are near highs. Trump issued an ultimatum to Iran with a Tuesday 8 PM deadline. Amazon is already rolling out a 3.5% fuel surcharge and airlines are hiking bag fees. The market is betting on a deal. History says those bets don't always pay.The Supreme Court invalidated Trump's IEEPA tariff authority, and a May US-China leaders' summit in Beijing is now the only real mechanism for trade stabilization. China enters that room with more leverage than it had in 2025. For multinationals, the summit outcome is a genuine binary.AWS AI services crossed a $15 billion annualized run rate, the first time Amazon has broken that figure out publicly. Amazon has committed $200B in 2026 capex directed at AI infrastructure, and Jassy says demand is outpacing supply. The custom chip business sits at a $20B run rate. This is AI moving from narrative to line item.Coachella 2026 Weekend 1 is being called one of the most stacked in recent memory. Sabrina Carpenter closed Friday after seven months of production. The Strokes played their first Coachella set in 15 years. Justin Bieber headlined his first major festival in years. Bini made history as the first Filipino group on that stage.Jeff Bezos's stealth AI lab Project Prometheus just hired the co-founder of xAI and former OpenAI infrastructure lead to build its compute architecture. Backed by $6.2B and recruiting from OpenAI, DeepMind, and Meta, Prometheus is targeting engineering, manufacturing, and aerospace, not chat. Physical-world AI with proprietary operational data is a structurally different bet than anything currently valued at frontier multiples.

Rick Watson and Jessica Lesesky break down three stories reshaping the consumer and tech landscape. First, why GLP-1 drugs like Ozempic, Wegovy, and Mounjaro are expected to unlock $13B in new retail spending — and which brands stand to win as 80% of users size down. Then, Nike's latest innovation shake-up: new chief Andy Caine steps in as the stock slides 32%, with Project Amplify, Nike Mind, and Aerofit on the horizon. The Watson Weekly Weekend edition is sponsored by Avalara - the agentic AI platform automating global tax and compliance for leading eCommerce brands. For more details: https://avalaratax.watsonweekly.com/Finally, Amazon's $200B and Google's $185B infrastructure bets — and why Sundar Pichai spends an hour a week personally rationing compute.Subscribe to the newsletter at watsonweekly.com.

In this weekend's episode, three segments from this past week's Washington Journal. First: A discussion about the latest in Iran including the two-week ceasefire and efforts to negotiate a peace deal, with Behnam Ben Taleblu of the Foundation for Defense of Democracies. Then: Sophia Besch of the Carnegie Endowment for International Peace, discusses NATO Secretary General Mark Rutte's visit to Washington and President Trump's threats to leave NATO. And finally: Breaking Defense's Ashley Roque discusses the cost of the U.S.-Israeli war in Iran and the Pentagon's $200B funding request. Learn more about your ad choices. Visit megaphone.fm/adchoices

Now I'm no fan of TSA, as I find them useless. But ICE is their real target.And you know why. ICE represents a real threat to Democrats. The more effective ICE is, the fewer votes Democrats have.As for TSA, Democrats believe that travelers will blame Trump for their delays. The 3rd time in six months Democrats have made TSA work without pay.Airports are setting up food drives for TSA. And now Elon Musk has offered to pay them…[X] SB – Mamdani on ICEThe same is true with gasoline, as Democrats think they can make hay with the price of gas…Already Democrats are saying Trump broke his promise not to raise gas prices. Do you know how hard you have to search for a lifeline to blame Trump for gas prices going back up to Biden levels?But this is their new battle cry.ICE is MEANGas is BACK TO BIDEN LEVELS. Trump broke his promise.In case you haven't noticed, Leftist media is piling on Trump and MAGA. Late night comedians are still at it, as Kimmel quipped about Trump wanting $200B for the war effort, saying give Trump $100B to go away.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Estimates are that $200B of goods have already been diverted away from the region surrounding Yemen in the Gulf of Aden and the Red Sea. The disruptions in travel through the Suez Canal have cascading effects that are felt worldwide, as it puts additional pressure on the supply chain.There is fear of an event drawing Iran into some kind of conflict in the region, which would see the Straight of Hormuz come under the watchful eye of the Iranian government, further adding to shipping issues impacting the oil and LNG exports coming out of that geographic region.It is also worth noting that the expansion of BRICS into this region works to solidify China's connections through the Belt & Road Initiative, while also locking down and securing the shipping lanes to make sure that the oil can continue to leave the region through the Persian Gulf and the Red Sea.—Video ChannelsWatch the video version of Macroaggressions:Rumble: https://rumble.com/c/Macroaggressions YouTube: https://www.youtube.com/@MacroaggressionsPodcastBrighteon: https://www.brighteon.com/channels/macroaggressions/—MACRO & Charlie Robinson LinksHypocrazy Audiobook: https://amzn.to/4aogwmsThe Octopus of Global Control Audiobook: https://amzn.to/3xu0rMmWebsite: www.Macroaggressions.ioMerch Store: https://macroaggressions.dashery.com/ Link Tree: https://linktr.ee/macroaggressionspodcast—Activist Post FamilySign up for the Activist Post Newsletter: https://activistpost.kit.com/emailsActivist Post: www.ActivistPost.comNatural Blaze: www.NaturalBlaze.com —Support Our SponsorsGround Luxe Grounding Mats: https://GroundLuxe.com/MACROReplace Your Mortgage: www.WipeOutYourMortgageNow.comC60 Power: https://go.ShopC60.com/PBGRT/KMKS9/ | Promo Code: MACROChemical Free Body: https://ChemicalFreeBody.com/macro/ | Promo Code: MACROWise Wolf Gold & Silver: https://Macroaggressions.Gold/ | (800) 426-1836LegalShield: www.DontGetPushedAround.comEMP Shield: www.EMPShield.com | Promo Code: MACROChristian Yordanov's Health Program: www.LiveLongerFormula.com/macroAbove Phone: https://AbovePhone.com/macro/Van Man: https://VanMan.shop/?ref=MACRO | Promo Code: MACROThe Dollar Vigilante: https://DollarVigilante.spiffy.co/a/O3wCWenlXN/4471Nesa's Hemp: www.NesasHemp.com | Promo Code: MACROAugason Farms: https://AugasonFarms.com/MACRO—

Social media giants face a legal reckoning, Keppel races toward a $200 billion ambition, and AI stocks are back on fire - what does it all mean for investors? Markets are navigating a collision of regulation, reinvention and rally, hosted by Michelle Martin with Ryan Huang. A landmark ruling puts pressure on platforms like Meta, Instagram and YouTube, raising new risks for Big Tech. Meanwhile, Keppel pivots hard into an asset-light future, chasing scale in infrastructure, energy transition and digital assets. In the US, names like Super Micro, Dell and HPE surge as AI demand fuels a fresh wave of optimism.Plus, UP or DOWN on Microsoft, On Holding, Banyan Group and Japanese IPOs - while Apple teases its next AI leap.See omnystudio.com/listener for privacy information.

The boys talk about the $200 billion funding request for the war in Iran, the things that money could pay for instead, Pete Hegseth only speaking in Haikus, a Politico article that revealed the Trump admin's consent manufacturing strategy is to post “banger memes”, what's going on with Cuba, and a WSJ article beefing with Hasan. BECOME A PATRON.Early access on Patreon: https://www.patreon.com/headintheofficepodSubstack: https://headintheoffice.substack.com/HITO Merch: https://headintheoffice.com/ Get 40% off Ground News: https://ground.news/checkout/all?fpr=headintheoffice YouTube: https://www.youtube.com/channel/UC4iJ-UcnRxYnaYsX_SNjFJQSubscribe to second channel: https://www.youtube.com/channel/UC3UoTN328OA7fK2dzicP-ZATikTok: https://www.tiktok.com/@headintheoffice?lang=enInstagram: https://www.instagram.com/headintheoffice/Twitter: https://twitter.com/headintheofficeThreads: https://www.threads.com/@headintheofficeDiscord: https://discord.gg/hito Collab inquiries: headintheofficepod@gmail.com(0:00) Pete Hegseth is CORNY(7:03) Intro/reviews(11:09) War with Iran, $200B request(44:19) Poster-occupied government(57:49) US is starving Cuba(1:06:28) WSJ Hasan Piker article(1:12:46) Extras(1:15:55) Ending/reviewsSeen on this episode:Iran updates - https://www.politico.com/news/2026/03/18/white-house-iran-game-online-00834373?utm_source=firefox-newtab-en-us https://www.axios.com/2026/03/21/trump-peace-deal-iran-kushner-witkoff https://www.nbcnews.com/world/iran/iran-unswayed-trumps-48-hour-deadline-threats-obliterate-energy-infras-rcna264607 https://www.nbcnews.com/world/middle-east/live-blog/live-updates-iran-war-trump-hormuz-deadline-energy-crisis-gulf-power-rcna264685Cuba news - https://thehill.com/policy/energy-environment/5794480-us-embassy-cuba-diesel-fuel-iran-conflict/ https://www.cnn.com/2026/03/18/americas/cuba-us-pressure-blackout-latam-intl Incredible piece from the WSJ - https://www.wsj.com/opinion/free-expression/democrats-are-too-cozy-with-hasan-piker-2ecee4cc?mod=e2tw 

Welcome back to the Konfidence in the Klutch Podcast with Donald Nelson (2:30). Konfidence in the Klutch's Deezus gives his Konfident Service Announcement: Living Rent Free In Your Head (5:30).  Deezus then shares his NBA news, including Giannis was hurt for the fourth time this season, and the Bucks want to shut him down, I agree.  Cade Cunnighame and Anthony Edwards are each out for multiple weeks.  Bucks waive Cam Thomas.  PG says he was supporting his mental health when he accidentally took a substance, and he's in a better place now, mentally and physically.  NBPA wants the league to look into the Bucks' urging Giannis to sit (9:00). Deezus then shares his WNBA thoughts, including that the league and the players' association have agreed to a historic new CBA.  Congratulations to the players and the governors (20:15). Deezus checks on his NCAA men's and women's final four picks.  Acuff Jr. to receive a signature show with Reebok (25:20). Deezus discusses 'Politics as usual', including Joe Kent quits.  NATO says no to Trump's request for help.  Tulsey Gabbard CYA'd.  Trump gives Iran a time frame to open up the Strait of Hormuz, Iran says, try it.  What could $200B do for Americans instead of funding a war?  Troops are on the way to the Gulf region (27:00). Deezus shares his thoughts on "Paradise" and how this season has finally turned into why we fell in love with the show (33:00). The podcast was recorded at 4:30 p.m. CT on Wednesday, March 24, 2026.  Host: Donald Nelson Producer/Engineer: Donald Nelson Music by: Konfidence in the Klutch Productions Subscribe, Stream, or Download:

This week on The GovNavigators Show, Robert and Adam are joined by Rob Bocek, Chief Commercial Officer at Virtualitics, for a conversation on how artificial intelligence is reshaping defense readiness.Drawing on his wide experience from Navy Special Warfare to Microsoft, Rob explains how AI can help the Department of Defense move beyond fragmented data and toward faster, more informed decision-making. We explore how these tools surface hidden readiness gaps, improve situational awareness, and support leaders operating in high-stakes, time-sensitive environments.Rob also walks through how Virtualitics is helping defense leaders make sense of massive, fragmented datasets, using AI-powered analytics to surface hidden risks, identify readiness gaps, and support faster, more confident decision-making. He explains how their approach emphasizes explainable AI, enabling operators and commanders to trust and act on insights in real time.Show Notes:DHS budget conflict continuesDoW's $200B supplemental requestFraud Task Force EOHR shared services pushGSA to establish Acquisition QSMOWhat's on the GovNavigators' Radar:Mar 24:The Hill and Valley ForumMar 25: House Oversight Committee hearing: “Doing More with Less: Eliminating Duplicative Programs”

LISTEN and SUBSCRIBE on:Apple Podcasts: https://podcasts.apple.com/us/podcast/watchdog-on-wall-street-with-chris-markowski/id570687608 Spotify: https://open.spotify.com/show/2PtgPvJvqc2gkpGIkNMR5i WATCH and SUBSCRIBE on:https://www.youtube.com/@WatchdogOnWallstreet/featured  A proposed $200B war bill sparks major questions: Is it really “America First,” or fueling debt and inflation? As oil prices surge and spending explodes, Chris talks about the real economic impact—and what it could mean for your wallet.

Mit ihrem Krieg gegen den Iran könnten sich die USA und Israel verspekuliert haben. Die Börse reagiert äußerst nervös, eine neue Inflationswelle rollt an und die Energiepreise erklimmen täglich neue Rekordhöhen. Zudem ist nicht damit zu rechnen, dass die iranische Regierung sich in den letzten Zügen befindet, wie anfangs kolportiert wurde.Die gesperrte Straße von Hormus ist für den Weltenergiemarkt eine Katastrophe, deren Ausmaß noch gar nicht abzuschätzen ist. Wenn eine mit den 1970er-Jahren vergleichbare Energiekrise einsetzen sollte, wird das auch für die Tech-Konzerne, die momentan gigantische Mengen an Energie verschlingen, zu einem Problem. Nicht nur deshalb häufen sich in der republikanischen Partei kritische Stimmen. Das mediale Trump-Vorfeld scheint sich ebenfalls, namentlich Tucker Carlson, immer stärker von Trump zu distanzieren.Heißt es also wieder einmal „It's the economy, stupid“? Mehr dazu von Ole Nymoen und Wolfgang M. Schmitt in der neuen Folge von „Wohlstand für Alle“! Quellen/Literatur:CNN: “Cracks emerge in GOP over Iran war cost as administration floats more than $200B request to Congress”, online verfügbar unter: https://edition.cnn.com/2026/03/19/politics/iran-war-cost-republicans-congress. Friedrich Merz: Presse-Statement vom 2. März 2026, online verfügbar unter: https://www.bundesregierung.de/breg-de/aktuelles/kanzler-statement-naher-osten-2409172Friedrich Merz: Regierungserklärung vom 18. März 2026, online verfügbar unter: https://dserver.bundestag.de/btp/21/21064.pdfTej Parikh: “How the Iran war could derail the AI boom”, online verfügbar unter: https://www.ft.com/content/df3f208a-2512-4a75-b2f3-d3bd27bae2e8Isabella Weber/Gregor Semieniuk: “The world energy shock is coming”, online verfügbar unter: https://www.newstatesman.com/international-politics/geopolitics/2026/03/the-world-energy-shock-is-cominghttps://www.ft.com/content/df3f208a-2512-4a75-b2f3-d3bd27bae2e8Unsere Zusatzinhalte könnt ihr bei Apple Podcasts, Patreon und Steady hören – oder über eine YouTube-Kanalmitgliedschaft. Vielen Dank!Apple Podcasts: https://podcasts.apple.com/de/podcast/wohlstand-f%C3%BCr-alle/id1476402723Patreon: https://www.patreon.com/oleundwolfgangSteady: https://steadyhq.com/de/oleundwolfgang/aboutTermine:Ole und Wolfgang sind am 24.3. in Bielefeld: https://www.instagram.com/astaunibielefeld/p/DVQ-z4gjF-Q/Wolfgang ist am 26.3. in Hamburg: https://hamburgliest.de/programm/#elfenbeinturm-und-barrikade-2„Schlager für Alle“ am 11.4. in Hamburg: https://tickets.centralkomitee.de/product/91257/wolfgang-m-schmitt-ole-nymoen-centralkomitee-hamburg-am-11-04-2026Ole ist am 24. April in Ravensburg:https://www.imblauensessel.de/Ole ist am 27. April in München:https://www.literaturhaus-muenchen.de/veranstaltung/kaempfen-fuer-die-freiheit/

Friday, March 20th, 2026 Today, Corey Lewandowski is taking millions of dollars in bribes for government contracts; California renames Ceasar Chavez day amid a backlash from sex abuse allegations; the Pentagon is seeking $200B more from Congress for Trump's war in Iran; Joe Kent is being investigated by the FBI for leaking classified information; ICE is taking DNA samples from arrested protestors; Bank of America settles an Epstein victim's lawsuit for an undisclosed amount; and Allison delivers your Good News. Dana is out and about. Thank You, HomeChef For a limited time, get  50% off and free shipping for your first box PLUS free dessert for life!  HomeChef.com/DAILYBEANS.  Must be an active subscriber to receive free dessert. →We are ending the $3 Daily Beans only subscription effective March 30th. If you are subscribed at $3 before March 30th, you can keep your $3 subscription for as long as you like without any changes. Guest: John FugelsangTell Me Everything|John Fugelsang, The John Fugelsang Podcast, John Fugelsang|Substack, @johnfugelsang|Bluesky, @JohnFugelsang|TwitterSeparation of Church and Hate by John Fugelsang StoriesSome DHS contractors told White House officials they were asked to pay Corey Lewandowski | NBC News Pentagon seeks more than $200 billion in budget request for Iran war | Washington Post FBI investigates intelligence aide who resigned over war | Semafor ICE officers are taking DNA samples from protesters they've arrested | NPR Bank of America settles claims over lawsuits by Jeffrey Epstein victims | AP News Democrats walk out of Pam Bondi briefing on Epstein files over subpoena compliance | NBC News California to Rename Chavez Holiday as ‘Farmworkers Day' | The New York Times People of Twin Cities awarded JFK Profile in Courage Award over resistance to ICE surge | CBS MinnesotaGood Trouble Call you Senators. Tell them to block Markwayne Mullin's Confirmation as DHS Secretary! Our friends, the fine folks at Indivisible.org, have a script and make calling your Senator easy. Block Markwayne Mullin's Confirmation as DHS Secretary | Indivisible Contacting U.S. Senators | Senate.gov   →NoKings March 28th →2026 Primary Election Calendar: All the Dates Ahead of Midterms →Public Comment Period Open: White House Ballroom Proposal →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible →Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List  →iceout.org →2026 Trans Girl Scouts To Order Cookies From! | Erin in the Morning Good NewsMilitary Families Speak Out →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com Subscribe to the MSW YouTube Channel - MSW Media - YouTube Our Donation Links Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Allison is donating $20K to It Gets Better and inviting you to help match her donations. Your support makes this work possible, Daily Beans fam. Donate to It Gets Better / The Daily Beans Fundraiser Join Dana and The Daily Beans with a MATCHED Donation http://onecau.se/_ekes71 More Donation LinksNational Security Counselors - Donate

President Trump downplays Americans' growing pain at the pump and acknowledges that the Pentagon will be asking for an additional $200 billion to fight the war in Iran. The President called it, “a small price to pay to make sure that we stay tippy top.” Plus, what we're learning about the drones that were spotted recently near the Washington Army base where Secretary of State Marco Rubio and Defense Secretary Pete Hegseth live. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Today's Headlines: The Iran war's bill just got a lot bigger — the Pentagon is asking Congress for $200 billion, on top of the $12 billion already spent, while Trump insists he's "not putting troops anywhere" in a statement that inspired exactly zero confidence. Israel struck the South Pars Gas Field — the largest natural gas field in the world, shared by Iran and Qatar — damaging Qatari energy infrastructure, hitting an American F-35, and triggering retaliatory Iranian strikes across the region. Trump posted that the U.S. "knew nothing" about the attack, Israel immediately said that wasn't true, then Trump said he'd actually warned them not to do it — so he did know — and then threatened to blow up the entire gas field himself if Iran touches Qatar. Oil and gas prices climbed further, the stock market dropped, and seven allies — the UK, Japan, Germany, Italy, the Netherlands, Canada, and one more — announced they'd help reopen the Strait of Hormuz, which Trump accepted graciously by screaming in all caps that he doesn't need anyone's help. Also, Trump told Japan's prime minister "who knows better about surprise than Japan, why didn't you tell me about Pearl Harbor" — an actual thing he said out loud. Elsewhere in global chaos: Hungary's Viktor Orban blocked a $100 billion EU loan to Ukraine, potentially threatening the country's ability to keep its government running. Canada announced it's building its own sovereign space program to reduce dependence on Starlink. Two Iranian citizens were charged in the UK with spying on Jewish institutions on behalf of Iranian intelligence. And in one of the most cold-blooded moves yet, the U.S. State Department is reportedly considering withholding HIV medication from 1.3 million people in Zambia as leverage to extract a minerals deal — because apparently that's a negotiating tactic now. Markwayne Mullin's DHS nomination cleared committee 8-7, with Rand Paul voting no and John Fetterman voting yes, because nothing means anything anymore. Full Senate vote is next, outcome predictable. Resources/Articles mentioned in this episode: NYT: Pentagon Seeks Additional $200 Billion to Fund Iran War NYT: Israeli Officials Said U.S. Was Told About South Pars Attack Axios: After Tehran strikes, Trump says Israel won't attack Iran gas fields anymore Axios: Seven U.S. allies back potential Strait of Hormuz coalition NBC News: Trump makes Pearl Harbor joke during meeting with Japanese prime minister NYT: 2 Men Charged With Spying for Iran on Jewish Institutions in UK WSJ: Ukraine Suffers Money Setback After Hungary Blocks $100 Billion From Europe NYT: Canada Takes Its Sovereignty Push to Space NYT: U.S. Considers Withholding H.I.V. Aid Unless Zambia Expands Minerals Access AP News: Mullin's DHS nomination advances to full Senate despite opposition from Republican Rand Paul Subscribe to the Betches News Room and join the Morning Announcements group chat. Go to: ⁠⁠⁠betchesnews.substack.com Morning Announcements is produced by Sami Sage and edited by Grace Hernandez-Johnson Learn more about your ad choices. Visit megaphone.fm/adchoices

Joe Kent Speaks Out. Polls Get Worse. Is Colombia Next? Where's VA's Budget Request? Cesar Chavez Exposed. Drones Over Hegseth & Rubio's Homes. HBD Iraq War. RIP Chuck Norris. Madness is AMAZING. It's episode 470 and Paul Rieckhoff is solo in New York on the first day of spring, bringing his trademark mix of independence, insight, and righteous outrage as March Madness tips off across America. From Kurtis Blow's “Basketball” to bracket-busting Cinderella stories like 12-seed High Point, Paul lays out his “fab five” vibes—integrity, independence, information, inspiration, and impact—and explains why this tournament is one of the few things still uniting the country. But he also exposes a much darker kind of March madness: Trump's escalating war with Iran, the Pentagon's stunning 200 billion dollar funding request, and the hidden lifetime costs of traumatic brain injuries and VA care that the White House refuses to own. Paul rips into Acting “Secretary of Culture War” Pete Hegseth's propaganda briefings, selective right-wing media access, and dangerous religious extremism from the Pentagon podium, while highlighting the veterans, journalists, and unlikely allies—from Joe Kent to Shawn Ryan to Marjorie Taylor Greene—who are beginning to break with Trump's forever war machine. He connects it all to attacks on the free press, the plan to keep National Guard troops in DC through 2029, new targets like Colombia, the fight for open primaries in Alabama, the breaking scandal around Cesar Chavez, and the explosive growth of America's 45 percent of independents. Along the way, Paul previews an emotional upcoming conversation with WWE legend Mick Foley, celebrates the life and complicated legacy of Chuck Norris, salutes everyday toughness, and invites you to join Independent Veterans of America, Defiance.org, and a rising independent movement that is done with forever wars—and determined to stay vigilant. -WATCH full video of this episode here. -Join IVA and stand up to Trump's Forever Wars. -Learn more about Paul's work to elect a new generation of independent leaders with Independent Veterans of America. -Learn more about American Veterans for Ukraine here. -Learn more about The Headstrong Project for Veterans, Tragedy Assistance Program for Survivors (TAPS), and Department of Veterans Affairs resources in your area. Seeking support is not a sign of weakness. It's a show of strength. If you or a loved one are in immediate crisis, dial 988 and press 1, or text 838255. Connect with Independent Americans: Subscribe on YouTube, Spotify, Apple Podcasts, and all podcast platforms Read more at Substack Support ad-free episodes at Patreon  Connect: Instagram  • X/Twitter • BlueSky • Facebook  Follow on social: @PaulRieckhoff on X, Instagram, Threads, and Bluesky -Join the movement. Hook into our exclusive Patreon community of Independent Americans. Get extra content, connect with guests, meet other Independent Americans, attend events, get merch discounts, and support this show that speaks truth to power.  -And get cool IA and Righteous hats, t-shirts and other merch now in time for the new year.  Independent Americans is powered by veteran-owned and led Righteous Media.  And now part of the BLEAV network!  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Rickey Smiley Morning Show Podcast kicks off with major national headlines as the Pentagon prepares to ask Congress for roughly $200 billion in additional funding tied to the escalating U.S. conflict with Iran, sparking bipartisan debate over the cost, scope, and duration of the war. See omnystudio.com/listener for privacy information.

Tom played chess with Billy Shelton; disciplinary complaint filed against Tom by an associate of a Lake County sheriff candidate dismissed; the incredible Afroman trial; Mailbag segment about the Chicago Bears potentially moving to Hammond, Indiana; Trump's joke about Pearl Harbor in front of Prime Minister of Japan; Pete Hegseth requests $200B for Iran war that Trump says we already won.

The escalation ladder continues after Israel strikes a major target. Is the president in danger of losing control and will the Pentagon's $200 billion request become a referendum on the war?  Learn more about your ad choices. Visit podcastchoices.com/adchoices

Today on America in the MorningTrump-Japan Meeting Over Iran This week marked the third week we have been at war with Iran, and concerns have been raised over the shutdown of the Strait of Hormuz which has led to gas prices rising, asks by President Trump of world leaders who have all said no, including the Prime Minister of Japan, to help the US get the vital waterway open, and attacks by Iran on Gulf oil and gas facilities.  John Stolnis has details from Washington.    More War Money Needed After spending $11 billion dollars in the initial first few days of the war in the Middle East, the Pentagon is seeking an extra $200 billion in Iran war funding.  As Washington correspondent Sagar Meghani reports, the Pentagon says this is to replenish armaments, but the call to add more supplemental spending is getting a frosty reception on Capitol Hill, where Republican Senator James Lankford and Democrat Senator Tammy Duckworth told CNN that the Pentagon will not receive a blank check.  TSA Lines Get Longer As the stalemate over the Homeland Security Department budget continues, more TSA agents are calling out and others are getting help from their neighbors.  Details from correspondent Rich Johnson.   Latest On Missing Student In Spain A tragic ending to the story of an American college student who was reported missing earlier this week in Spain.  Correspondent Clayton Neville reports the 20-year-old was visiting friends for spring break and planned to return to the United States this weekend when he went missing.   Latest House Epstein Hearing Lawmakers deposed one of Jeffrey Epstein's associates as part of the House investigation into late-sex offender's dealings.  Correspondent Jennifer King reports.   Netanyahu News Conference With internet rumors swirling that he was dead and replaced in Israel's government, Prime Minister Benjamin Netanyahu held a press conference hailing the US and Israeli action against Iran, and vowing to do whatever it takes to stop both the Iranian regime and Hezbollah operating in Lebanon.  Correspondent Clayton Neville reports that Netanyahu emphatically stated his nation did not coerce or drag the US into the conflict.   Targeting Oil & Gas Middle Eastern energy producing nations have been rattled by Iran's attack on oil and liquefied natural gas fields in nations including Kuwait, Qatar, Bahrain, Oman, and Saudi Arabia.  Correspondent Jon Gambrell reports Gulf nations are fearing Iran targeting energy infrastructure could hurt them for years if attacks worsen.   Removing The Chavez Name Los Angeles Mayor Karen Bass on Thursday signed a proclamation renaming the city's Cesar Chavez Day holiday as “Farmworker's Day,” which comes after news of sex assault allegations against the late labor leader.  Student Loans New Home The Trump administration is making a move to push student loan coverage out of the overview of the Department of Education.  Correspondent Ed Donahue reports. Sports – Robert Workman   NCAA Tournament & more.     Learn more about your ad choices. Visit podcastchoices.com/adchoices

The price of Trump's Iran war may be about to explode. The Pentagon is quietly pushing for a staggering $200 billion funding request to Congress—far beyond what's been spent so far—as U.S. and Israeli strikes intensify and weapons stockpiles run low. Inside the White House, there's doubt the request can even pass, and on Capitol Hill, resistance is already building. Lawsuits related to the Trump administration are flying. Former federal prosecutor, now defense attorney, David Katz will join us to examine the latest from the ongoing efforts by Trump to resurrect his tariff policies to the latest legal decisions on the retribution cases he had the DOJ bring against his foes.

The downward sentiment came after WTI oil moved upwards again toward $97 per barrel after further attacks on critical energy infrastructure in the Middle East. ~This episode is sponsored by iTrust Capital~iTrustCapital | Get $100 Funding Reward + No Monthly Fees when you sign up using our custom link! ➜ https://bit.ly/iTrustPaul00:00 Intro00:10 Sponsor: iTrust Capital00:50 Last night01:45 200B for war02:30 Oil chart03:00 Pete Hegseth: It takes money to kill bad guys04:10 Trump considering deploying troops04:30 Best case scenario06:00 Ceasefire odds06:20 Jerome Powell: zero job growth08:00 Mark Cudmore: Markets impact finally registering09:15 Bitcoin drops after FOMC09:50 Rate Hike odds10:40 Short private credit?11:00 Giancarlo: Private credit on a blockchain11:50 Tokenized equities soar12:10 S&P going 24/712:20 China meeting off the table13:30 Is China trying to compete with the US in crypto14:40 Trump: “Why didn't you tell us about Pearl Harbor?”#oil #Bitcoin #Ethereum~Oil Chaos vs Crypto & Stock Market

Tara dives into the staggering cost of U.S. defense spending for Europe—$663 billion over the past decade—and why American taxpayers are footing the bill for allies who won't lift a finger in their own defense. From Ukraine to the Strait of Hormuz, she exposes decades of failed diplomacy, the weakness of European militaries, and the uniparty dynamics that let it all happen. On the domestic front, the SAFE Act fight is reshaping the Republican Party. Grassroots pressure and free speech are forcing entrenched leadership, including John Thune, to finally confront voter roll transparency and election integrity. Tara explains how these two fronts—the international and domestic—are connected in the fight for American sovereignty and security.

We break down Oracle's Q3 FY2026 earnings, highlighting 44% year-over-year cloud revenue growth to $8.9B, driven by IaaS at $4.9B up 84%, while legacy software growth remains slower. We hash over Oracle's remaining performance obligations (RPO) of $553B, up 325% year over year, and discusses how heavy data center capital expenditures are pressuring free cash flow (over $11B negative) and contributing to slower constant-currency EPS growth (16%) versus revenue.Finally review guidance, including a $90B revenue outlook for FY2027 implying 34% growth, and share expectations for 30%+ growth with a longer-term view that Oracle could approach $200B in annual revenue by 2028. Join us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipSupercharge your analysis with AI! Get 15% of your membership with our special link here: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formChapters:Chapters:00:00 Oracle Earnings 01:58 Q3 Highlights RPO Surge03:08 Cloud Segments Explained03:39 EPS Versus Revenue04:50 Guidance And Outlook06:44 200B Revenue Thesis08:28 CapEx And Cash Flow10:44 Debt Raise And Balance SheetIf you found this video useful, please make sure to like and subscribe!*********************************************************Affiliate links that are sprinkled in throughout this video. If something catches your eye and you decide to buy it, we might earn a little coffee money. Thanks for helping us (Kasey) fuel our caffeine addiction!Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal. #oracle #ORCLstock #chipstockinvestor #datacenters #AI infrastructureNick and Kasey own shares of Oracle

March 6, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Amazon Web Services launches Amazon Connect Health, AI-powered system automating healthcare admin work and reducing abandoned calls by 30% UBS projects global longevity spending will reach $8 trillion annually by 2030, with GLP-1 drugs alone surpassing $200B in sales this decade Oura acquires Finnish startup Doublepoint to bring biometric gesture controls to wearable ecosystem, supporting vision of "cloud of wearables" More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

In today's Cloud Wars Minute, I analyze how AI inferencing and custom chips are reshaping the cloud power structure.Highlights00:05 — 2026 is off to a booming start. One of the numbers we saw was that Amazon is committed to spending $200 billion in CapEx in calendar 2026. That will be, by far, the largest CapEx expenditure in a single year that any company in any industry has ever made. So, truly some monumental, groundbreaking stuff going on here. It shows the size of the opportunity.01:11 — Now that total, Jassy said a few times, is for the whole Amazon Corporation, but he said the vast majority — the lion's share — will go to AWS. So I took a little bit of liberty with this and figured that the overall for the whole company is almost $550 million in CapEx every single day. So I figured the portion of that — about 90% for AWS — is about $500 million a day being invested in the CapEx capabilities for AWS to pursue this enormous opportunity.02:23 — Certainly the AI boom is funneling a huge amount of this, but they've also got this core strength. And he talked about how some companies investing in AI are also then pairing that up with increased non-AI workloads. In particular, on the AI side, he said inferencing is becoming huge.03:05 — He said their chip business is at a $10 billion annualized run rate for AWS. He said every tech company in the world is desperately trying to get specialized, customized chips. AWS and Amazon are increasing their investment in their own chip business. He thinks that down the line, especially as the inferencing category really kicks in, this is going to be a huge boost for them.04:51 — But overall, I think this is a tremendous display of courage and confidence on the part of Jassy and Amazon to again invest more in CapEx than any company in any industry has ever done, because he sees if we do this, this incredible market is going to be coming, and we at Amazon and AWS have the best possible chance of getting more than our share of it. Visit Cloud Wars for more.

Today, we discuss the ongoing "AI Overlay" trade, note another AI-related company that is not a hyperscaler, but is set to spend up to USD 200 billion on capacity expansions in coming years. Elsewhere, we discuss the strength of US treasury and Japans' government bond markets and whether this is contributing to pressure on precious metals. As well, we ponder whether both the US dollar and yen might strengthen against the other major currencies and the next keys for sterling direction. Today's pod features Saxo Head of Commodity Strategy Ole Hansen and is hosted by Saxo Global Head of Macro Strategy John J. Hardy. Links discussed on the podcast and our Chart of the Day can be found on the John J. Hardy substack (within one to four hours from the time of the podcast release). Read daily in-depth market updates from the Saxo Market Call and the Saxo Strategy Team here. Please reach out to us at marketcall@saxobank.com for feedback and questions. Click here to open an account with Saxo. Intro and outro music by AShamaluevMusic DISCLAIMER This content is marketing material. Trading financial instruments carries risks. Always ensure that you understand these risks before trading. This material does not contain investment advice or an encouragement to invest in a particular manner. Historic performance is not a guarantee of future results. The instrument(s) referenced in this content may be issued by a partner, from whom Saxo Bank A/S receives promotional fees, payment or retrocessions. While Saxo may receive compensation from these partnerships, all content is created with the aim of providing clients with valuable information and options.

Medical training is still stuck in the arcade era: expensive, basement-bound simulators and outdated software that rarely capture the real stakes of clinical decision-making. In this episode, host Alexandra Takei, Studio Director at Ruckus Games, sits down with Sam Glassenberg, founder of Level Ex (now part of Relevate Health), to unpack how game developers can modernize healthcare learning by truly embracing the craft of video game design, not “gamification” lipstick. The opportunity and the market here are much bigger than you might assume. Healthcare is a trillion-dollar industry in the US alone, and if you can create products that save the medical system money while also growing the $200B video game industry, that's a win-win. The conversation explores why even mediocre games outperform traditional training (the bar is shockingly low), and how live-ops principles let teams update clinical guidance fast. The pair also discusses who plays these games, and it turns out that it's not only doctors but “normal people” who have found these games on the app store. They go deep on design: mapping real clinical challenges to proven genres (diagnosis as reductive-reasoning puzzles, ventilators as rhythm games), and why domain experts often describe what's hard for residents, not what triggers adrenaline for experts, which is the source of “fun” in games. Finally, Sam breaks down the business: sponsored content by clients like Pfizer and Merck, free-to-play for doctors gameplay, and playable ads. We'd also like to thank Overwolf for making this episode possible! Whether you're a gamer, creator, or game studio, Overwolf is the ultimate destination for integrating UGC in games! You can check out all Overwolf has to offer at https://www.overwolf.com/.If you like the episode, please help others find us by leaving a 5-star rating or review! And if you have any comments, requests, or feedback shoot us a note at podcast@naavik.co. Watch the episode: YouTube ChannelFor more episodes and details: Podcast WebsiteFree newsletter: Naavik DigestFollow us: Twitter | LinkedIn | WebsiteSound design by Gavin Mc Cabe.

Silver, Gold and Crypto (oh my) Hang on – Wild ride here Superbowl, Olympics- Wait until you hear about the CAPex spending! Shakeup in Dietville PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter Interactive Brokers  Warm-Up - Silver, Gold and Crypto (oh my) - Need a stock for CTP - Hang on - Wild ride here - Superbowl, Olympics- Wait until you hear about the CAPex spending! - Shakeup in Dietville Markets - Massive moved during the week - - Bitcoin clipped $60k before rebounding - DJIA tops 50,000 for the first time - Wait until you hear about the CAPex spending! - CAT == 1,100 points on the DJIA in 2026 Superbowl and Superbowl ads - Game review - Any ad stick out? - $10M per ad this year - Half Time with Bad Bunny? - Anthropic busting on OpenAi Last Week! - Massive moved - quick calc showed that about $1T was wiped from market caps in the sell-off, particularly in tech names. - HOWEVER - Friday alone is estimated to have added $1.5T to market cap AI Ripping Through - Plenty of names getting cooked over AI announcements - First it was the software companies - Now there are names in legal and finance that got clocked - Today - Altruist.ai can do tax planning and that hurt companies in financial space Earnings Season Update - Reporting so far: 59% of S&P 500 companies have reported Q4 2025 results. - Beat rate: 76% have topped EPS estimates (vs. 5-yr average: 78% (slightly lower) vs. 10-yr average: 76% (in line) - Magnitude of beats (aggregate): earnings are 7.6% above estimates vs. 5-yr average: 7.7% (about the same) vs. 10-yr average: 7.0% (a bit better) - Nothing great,  like Goldilocks Earnings Highlights - Palantir (PLTR): Reported strong Q4 results early in the week , beating estimates with revenue ~$1.41B (vs. ~$1.33B expected) and EPS $0.25 (vs. $0.23). Guidance for 2026 was upbeat (~61% revenue growth). Shares rallied sharply initially (~7–11% post-earnings), but gave back some gains amid broader tech volatility (e.g., down ~11–22% in parts of the week from peaks). - AMD: Reported mid-week, beating EPS (~$1.53 vs. lower expectations) with solid data center growth (~39%). However, Q1 guidance disappointed relative to high expectations in the AI chip space. Shares sank dramatically — down ~15–17% the next day, with some reports noting up to 20%+ drops at points, contributing to broader chip sector pressure. - Alphabet (GOOGL/GOOG): Reported beating on revenue (~$113.8B) and EPS (~$2.82), with strong core performance. But capex guidance for 2026 ($175–$185B, roughly double prior levels) sparked AI spending worries. Shares dipped post-earnings (down ~0.5–5% initially, flat to lower the next day, with some volatility pulling it below key moving averages). - Amazon (AMZN): Reported after hours on February 5, with mixed results — EPS ~$1.95 (narrow miss vs. ~$1.97 expected), but solid overall. The big negative was a surprise $200B capex forecast for 2026 (well above expectations), tied to AI/cloud buildout. Shares plunged sharply — down ~7–10% in after-hours/extended trading, with Friday moves around -5–8% in some sessions. Recent Tech CAPEX announcements - Amazon (AMZN) — Guided to approximately $200 billion in capex for 2026 (a massive jump from ~$125–131 billion in 2025, with ~80% likely AI-related per analyst commentary). This was the largest single-company figure and a major surprise, contributing heavily to the week's "wild" reactions. - Alphabet (GOOGL/GOOG) — Guided to $175–185 billion in capex for 2026 (roughly double the $91 billion spent in 2025, far above analyst expectations of ~$115–119 billion). Emphasis was on AI compute capacity, servers, data centers, and networking to meet demand for Gemini and cloud services. - Meta Platforms (META) — Guidance from late January (but heavily discussed last week): $115–135 billion for 2026 (up significantly from ~$70–72 billion in 2025, potentially an ~87% increase). - Microsoft (MSFT) — No new full explicit 2026 guidance in early February (fiscal year runs July–June), but recent quarterly run-rate and analyst projections put it around $97–145 billion (with some sources citing ~$105 billion or higher based on Q2 spending trends and signals of continued growth from prior levels of ~$88 billion in FY2025). ------!!!!Combined 2026 capex projected at $635–665 billion (low/high ends) or up to $650–700 billion in some reports — a ~60–74% increase from their collective ~$381 billion in 2025. Market Reaction from all of this.... - Markets were a bit spooked on the Anthropic announcement earlier in the week - software sold off and set a sour mood - Microsoft dumped pretty hard as the amount of spend was higher than anticipated, especially with some slower growth in Azure. - Amazon took a beating on the increased spend they anticipate *(extra by $50B) - BUT: Friday markets rallied as there was realization that the $200B spend by Amazon would seep into the economy and fuel infrastructure spending along with chips, tech etc. Other Earnings of Interest -  Reddit reported fourth-quarter earnings on Thursday in which the social media company beat on the top and bottom lines. - The company said it expects first-quarter sales to come in the range of $595 million to $605 million, which is higher than Wall Street expectations of $577 million. - Reddit also announced a $1 billion share repurchase program. - Reddit gets about $250 million a year from OpenAi and Google to have your data for training their LLMs While we are on the subject - Friday, DJIA hit 50,000 - first time ever! - Up 1,200 point of which approx 350 was from caterpillar and 280 was from Goldman Sachs Hats off to WalMart - Walmart Inc. shares pushed its market capitalization past $1 trillion on Tuesday for the first time ever| - Big transformation over the pst year - Walmart has maintained its appeal to households looking for value, its online offerings are drawing new, wealthier shoppers seeking convenience. Google Bond Offering - Issuing several tranches of bonds, denominated in Stirling - one as long as 100 years - Would you buy that? - The Google parent is set to raise $20 billion from a US dollar bond offering on Monday — more than the $15 billion initially expected — and is also pitching investors on what would be its first ever offerings in Switzerland and the UK. - The latter would include a rare sale of 100-year bonds, the first time a tech company has tried such an offering since the dotcom frenzy of the late 1990s Fat Profits in Dietville - Really interesting sequence of events happening... - Hims launches compounded pill at prices as low as $49 per month - Analysts cite questions on efficacy, legality of pill - Hims' move shifts focus from Novo's strong Wegovy pill launch - Broader obesity market whipsawed as pricing pressure rises THEN.. - Hims and Hers Health shares dive 14% after hours on Friday (Down 25% on Monday) - FDA cites concerns over quality, safety, federal law - The U.S. Food and Drug Administration said on Friday it would take action against telehealth provider Hims & Hers, for its $49 weight-loss pill, including restricting access to the drug's ingredients and referring the company to the Department of Justice for potential violations of federal law. AND.... - Eli Lilly last Wednesday posted fourth-quarter earnings and revenue and 2026 guidance that blew past estimates, as demand for its blockbuster weight loss drug Zepbound and diabetes treatment Mounjaro soars. - The pharmaceutical giant anticipates its 2026 revenue will come in between $80 billion and $83 billion. Analysts expected revenue of $77.62 billion, according to LSEG. - Meanwhile, NOVO had a really bad outlook that took the shares down 13% after the report. Japan Markets Soar - Japanese stocks jumped to a record high Monday, leading gains in the region after Prime Minister Sanae Takaichi won a landmark election victory. - The ruling Liberal Democratic Party captured a two-thirds supermajority in the 465-seat lower house, public broadcaster NHK reported. - Japan's Nikkei 225 jumped past 57,000 for the first time before paring gains to close 3.9% higher at 56,363.94, while the Topix also notched a record high, closing at 3,783.94, up 2.3%. Employment Report? - Government shutdown is forcing them to postpone again (Which is dumb) - Number due this Wednesday - Maybe because of this:U.S. employers announced 108,435 layoffs for the month, up 118% from the same period a year ago and 205% from December 2025. The total marked the highest for any January since 2009. - At the same time, companies announced just 5,306 new hires, also the lowest January since 2009, which is when Challenger, Gray & Christmas began tracking such data. - Also, job openings fell sharply in December to 6.54 million, to their lowest since September 2020. - Available jobs are down by more than 900,000 just since October. - NO! Ai and advancements in tech have noting to do with this! NO NO NO M&A - Texas Instruments Inc. has reached an agreement to buy Silicon Laboratories Inc. for about $7.5 billion, deepening its exposure to several markets for chips. - Silicon Labs investors will receive $231 in cash for each share of the company's common stock and the transaction is expected to close in the first half of 2027. - The transaction still needs to win approval by investors in Silicon Labs and shares of Silicon Labs surged by 51% to $206.48 after the announcement. Inflation - This helps - PepsiCo (PEP.O), opens new tab will cut prices on core brands such as Lay's and Doritos by up to 15% following a consumer backlash against several previous price hikes, the snacks and beverage maker said on Tuesday after it topped fourth-quarter results. Miran - Moving - Federal Reserve Governor Stephen Miran is leaving his post as chair of the Council of Economic Advisers, CNBC has confirmed. - He joined the CEA in January 2025, but had been on leave from that post since last September when he filled the unexpired term of former Fed Governor Adriana Kugler.- He reamins on Fed board No Biggie???? - There are some astonishing cased being reported of Bad AI in the operating room - JNJ's TruDi Navigation System - Since AI was added to the device, the FDA has received unconfirmed reports of at least 100 malfunctions and adverse events. - At least 10 people were injured between late 2021 and November 2025, according to the reports. Most allegedly involved errors in which the TruDi Navigation System misinformed surgeons about the location of their instruments while they were using them inside patients' heads during operations. - Cerebrospinal fluid reportedly leaked from one patient's nose. In another reported case, a surgeon mistakenly punctured the base of a patient's skull. In two other cases, patients each allegedly suffered strokes after a major artery was accidentally injured. Cuba - The main airport has putt out a bulletin that they are out of Jet Fuel - Blackouts and lack of other fuels are creating big problems - No airlines have stopped running at this point, but many will as they cannot refuel - This is a bigger problem for cargo planes (supplies) that may not be able to risk flying to Cuba as they will not be able to get out. Love the Show? Then how about a Donation? ANNOUNCING THE WINNER OF THE THE CLOSEST TO THE PIN CUP 2025 Winners will be getting great stuff like the new "OFFICIAL" DHUnplugged Shirt!     FED AND CRYPTO LIMERICKS   See this week's stock picks HERE Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter

In this episode, we explore the intense CapEx spending by tech giants like Amazon, Google, and Meta in the AI compute arms race. We also discuss how Amazon's AWS cloud business is outperforming and expanding, despite investor concerns about the massive expenditures.Chapters00:00 Introduction to the AI Spending Race01:57 AIbox Announcements and Tier Updates03:56 Amazon's Massive Capital Expenditure05:39 Competitor Spending and Investor Skepticism11:51 AWS Performance and Growth18:09 Wall Street and the Future of AI LinksGet the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

In this episode, we explore the intense CapEx spending by tech giants like Amazon, Google, and Meta in the AI compute arms race. We also discuss how Amazon's AWS cloud business is outperforming and expanding, despite investor concerns about the massive expenditures.Chapters00:00 Introduction to the AI Spending Race01:57 AIbox Announcements and Tier Updates03:56 Amazon's Massive Capital Expenditure05:39 Competitor Spending and Investor Skepticism11:51 AWS Performance and Growth18:09 Wall Street and the Future of AI LinksGet the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

This could open up homebuying for millions of Americans. The question is: Is it worth it? A new housing proposal from the Trump administration adds yet another lever that first-time buyers can pull to pay for their first house. But it's got financial advisors sweating.  We're back with another headline episode, talking about recent moves shaking up the housing market. First, some good news from Redfin that shows the housing market is actually getting more… affordable? That's right. A substantial decline in housing costs may be just the start as homebuyer purchasing power grows year over year. We're on the right track…but will it continue? Next, why mortgage rates went back up after Trump's proposed $200B bond-buying exercise—when many expected rates to keep falling. Using a 401(k) to buy a home? One new proposal could make it penalty-free, opening up access to hundreds of thousands of dollars for average Americans. Finally, the big investor ban begins, but here's what the actual executive order says. In This Episode We Cover Penalty-free 401(k) down payments? The On the Market panel is sharply divided Affordability sees a massive win, but will it keep improving? Why mortgage rates didn't keep declining after Trump's $200B bond purchase proposal President Trump signs the long-awaited big investor ban—but will it actually change anything for homebuyers? And So Much More! Links from the Show Join the Future of Real Estate Investing with Fundrise Join BiggerPockets for FREE Join us at the BiggerPockets Conference October 2-4 in Orlando. Buy tickets Sign Up for the On the Market Newsletter Find Investor-Friendly Lenders On the Market 392 - Trump's Housing Proposals Could Work, There's Just One Problem Redfin: Monthly Housing Costs Start the Year Down 5%, the Biggest Decline in Over a Year Reuters: Trump's mortgage-backed bond purchases not moving needle on housing costs HousingWire: Tapping a 401(k) for homeownership is risky business, experts say TIME: Trump Is Moving to Bar Wall Street Firms From Buying Single-Family Homes. Dave's BiggerPockets Profile Henry's BiggerPockets Profile James' BiggerPockets Profile Kathy's BiggerPockets Profile Grab Dave's Book, "Real Estate by the Numbers" Check out more resources from this show on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BiggerPockets.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.biggerpockets.com/blog/on-the-market-394 Interested in learning more about today's sponsors or becoming a BiggerPockets partner yourself? Email ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠advertise@biggerpockets.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Trump pulls no punches. From Europe's dependence on Russian energy to billions lost in Ukraine, this episode breaks down what's really going on in global geopolitics.

Mortgage rates slipped below a key psychological threshold after President Trump ordered $200 billion in mortgage-backed securities purchases through Fannie Mae and Freddie Mac. In this episode of Real Estate News for Investors, Kathy Fettke breaks down what the announcement means for mortgage rates, housing demand, and real estate-related stocks. We cover how markets reacted, why rates falling into the 5% range matters for buyers and investors, and what analysts say could happen next if mortgage bond purchases move forward as planned. If you're tracking affordability, transaction volume, or housing momentum heading into 2026, this is a development you'll want to understand. Want to learn more? Visit www.NewsforInvestors.com  JOIN RealWealth® FOR FREE https://realwealth.com/join-step-1  SOURCE: https://www.barrons.com/articles/opendoor-rocket-trump-mortgage-bond-plan-home-builders-bcd6b456?gaa_at=eafs&gaa_n=AWEtsqfBhoAAN7AfkaRyohPy6nDeTqp9Z0MBR-TjpySKnFAtD9LJyObnXlxwB-cSyTw%3D&gaa_ts=696148c5&gaa_sig=y7XD1dM_VslqoFUu58pjPGO_jUy2kL61XCW1cwKuRQLd00VF6zZa7ZoNrdP0F7k_Ga59lMf9xdIF1wtTyp6YIw%3D%3D 

Mortgage rates dropping to nearly 3 year lows, as President Trump announces he's ordering Fannie and Freddie Mac to buy $200B in mortgage bonds. The impact on housing and rates, and what the moves mean for affordability as would-be buyers sit on the sidelines. Plus Meta inking more nuclear deals, as the tech giant looks to power its AI ambitions. The names they're teaming up with, and what the data center demand could look like this year.Fast Money Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.