Podcasts about Foxconn

A.M. Edition for Aug. 31. The U.S. strikes Iranian targets near the Strait of Hormuz, sending oil prices higher on concerns of a flareup in fighting. Plus, as the leaders of Russia and China converge against the West on supporting Iran, WSJ's Thomas Grove explains how their fortunes are diverging, with Beijing holding Russia's economy hostage. And a look at how President Trump's China tariffs have propelled Vietnam's economy, as manufacturers shift production. Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

- Another Incentive War Coming? - Detroit 3 Losing Market Share - Chrysler Pacifica Sales Up 81% - Used Prices Up $9K Since 2019 - Ford to Stop China Imports - EREV Sales Drying Up in China - Mitsubishi Gets Foxconn EV from Taiwan - Honda Auctions Off Old Race Cars - Ford Revives the Ford Times, Now Electronically

Plus: Foxconn to ramp up global AI server production. And Tencent Holdings' AI spending ends its earnings growth streak. Julie Chang hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Plus: The AI rally is back after Coreweave shares jump off-hours on bumper profits.  But the AI-buildout is hurting some companies' bottom line, with China's biggest company ending its double-digit earnings streak. Luke Vargas hosts. Sign up for WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Consumer prices increased 3.4% year-over-year. Plus: CoreWeave and Super Micro Computer shares soar after Tuesday's earnings reports. Pierre Bienaimé hosts. Sign up for WSJ's free What's News newsletter. An artificial-intelligence tool assisted in the making of this episode by creating summaries that were based on Wall Street Journal reporting and reviewed and adapted by an editor. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Samsung’s Galaxy Buds Hearing Aid feature gains US FDA approval to launch later in 2026, Foxconn reveals AI servers bring in 51% of revenue with Apple manufacturing dropping to under 30%, and OpenAI releases a ChatGPT desktop app for Linux. MP3 Please SUBSCRIBE HERE for free or get DTNS shows ad-free. A special thanks toContinue reading "Samsung Galaxy Buds Hearing Aid Feature Receives FDA Approval – DTH"

In this episode, Madelyn O'Farrell and Santosh Sankar unpack the data center boom and the idea of compute as the next utility powering an “industrial renaissance.” They explore how AI models are commoditizing, shifting value to the application layer, and draw historical parallels to industrialists like Rockefeller and Carnegie in terms of capital intensity, vertical integration, and long-lived infrastructure. The discussion dives into the biggest bottleneck (access to energy and grid capacity) along with underwhelming GPU utilization, the need for better observability and efficiency, and trends like prefab “constructuring” in data center construction. They also highlight labor and skills constraints in specialty construction, tools like Record Lens to digitize field operations, and the potential for a Foxconn-style contract manufacturer for electrical equipment. The episode closes on what excites them about founders in this space: deep problem understanding, real industrial pain points, and the ambition to build in the physical economy rather than chasing AI hype. Highlights from their conversation include: Setting up Compute as a New Utility and AI Data Center Boom (0:38) Why Compute Becomes a Utility and Implications for Trillion Dollar Tech (3:50) Drawing Parallels Between AI Infrastructure and the Industrial Revolution (6:56) Capital Intensity, Supply Chains, and Long Lived Industrial Assets (7:50) Financing Data Centers Like Power Plants and Identifying Key Bottlenecks (11:44) Energy Queue, Grid Constraints, and Alternative Generation Opportunities (12:25) Efficiency, Grid Utilization, and Rising Importance of Operational Arbitrage (15:13) Utilization, ROI vs. Dark Capacity, and Lessons from the Dot Com Era (21:23) Constructuring Trend and Prefab Manufacturing for Data Centers (26:16) Record Lens and AI Native Project Management for Grid Scale Construction (29:24) Idea of a Foxconn Model for Electrical Equipment Manufacturing (32:47) Standardization, Certification, and Cyber Risk in Grid Infrastructure (36:22) Founder Traits, Industrial Ambition, and Solving Top Three Customer Problems (38:00) Gold Rush Dynamics, Real Pain Points, and Building in the Physical Economy (41:31) FInal Thoughts and Takeaways (42:42) Dynamo Ventures is a venture firm backing founders upgrading the physical economy. As intelligence moves into critical infrastructure and technology collides with physics, industry is entering a new era of transformation - the industrial renaissance. Born from the dirt and grit of supply chains and shaped by operations, not spreadsheets, Dynamo focuses on the complex realities of building in the real world. We invest in companies transforming infrastructure, manufacturing, logistics, transportation, and the systems that power global commerce. Dynamo works closely with founders who combine ambition with a bias to action, bringing a builder mindset to venture capital through deep operational insight, systematic pressure-testing and hands-on partnership. Our purpose is simple: to back the relentless shaping the industrial renaissance. Learn more at www.dynamo.vc Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Market news for August 12, 2026: Geopolitical tensions boosted oil and gold, Asian shares gained, driven by South Korean strength and Foxconn’s 35 per cent profit jump. Investors await critical US inflation data following a North Korean missile launch. Synopsis: Market Focus Daily is a closing bell roundup by The Business Times that looks at the day’s market movements and news from Singapore and the region. Written by: Howie Lim (howielim@sph.com.sg) Produced and edited by: Chai Pei Chieh & Claressa Monteiro Produced by: BT Podcasts, The Business Times, SPH Media Produced with AI text-to-speech capabilities --- Follow Market Focus Daily and rate us on: Channel: bt.sg/btmktfocus Amazon: bt.sg/mfam Apple Podcasts: bt.sg/mfap Spotify: bt.sg/mfsp YouTube Music: bt.sg/mfyt Website: bt.sg/mktfocus Feedback to: btpodcasts@sph.com.sg Do note: This podcast is meant to provide general information only. SPH Media accepts no liability for loss arising from any reliance on the podcast or use of third party’s products and services. Please consult professional advisors for independent advice. Discover more BT podcast series: BT Money Hacks at: bt.sg/btmoneyhacks BT Correspondents at: bt.sg/btcobt BT Podcasts at: bt.sg/podcasts BT Lens On: bt.sg/btlensonSee omnystudio.com/listener for privacy information.

The iPhone Air reaches one year on sale next month, and Apple is now closer to launching its successor than it is to the original's debut. On this week's episode of The MacRumors Show, we discuss what's next for the device.The first-generation model has had a difficult run. A KeyBanc Capital Markets survey found virtually no demand for the device within weeks of its September 2025 launch, supply chain analyst Ming-Chi Kuo reported that suppliers were asked to cut capacity by more than 80%between launch and early 2026, and Luxshare and Foxconn both wound down production, leaving the phone believed to be entirely out of manufacture. Apple discounted it heavily in March, and SellCell data put its ten-week depreciation at 44.3% on average, against 34.6% for the iPhone 17series. The Weibo leaker known as "Digital Chat Station" said in May that the device had barely surpassed 700,000 activations even after multiple rounds of price cuts.A single rear camera left a $999 phone looking less capable than the cheaper ‌iPhone 17‌, which has two, and the absence of the iPhone 17 Pro's vapor chamber cooling means the Air runs warmer under load. It also lacks stereo speakers. Battery life falls short of a full day for many users, mitigated in part by the dedicated MagSafe battery accessory Apple sells alongside it. With the ‌iPhone 17 Pro‌ starting at $1,099, the Air occupies a $100 gap that gave buyers little reason to stop there.Almost every change rumored for the second-generation model targets one of those points. Bloomberg reported in June that Apple will add an Ultra Wide lens alongside the existing 48-megapixel Fusion camera, and that the dual-lens design has reached advanced testing with the same exterior as the current model save for the extra lens. Almost everything other than the battery sits inside the ‌iPhone Air‌'s camera plateau, and Apple has reportedly asked suppliers for an ultra-thin Face ID module to free up space. The company also plans to adopt Samsung's CoE display technology, which is thinner and brighter than the current panel and debuts first on the foldable iPhone. Digital Chat Station expects a 3,500mAh battery, up from 3,149mAh, and The Information reported that Apple wants the device to be lighter and to gain vapor chamber cooling despite the added hardware.The device will also likely feature the A20 Pro chip, binned in some form like its predecessor, along with Apple's second-generation N2 wireless networking chip, the C2 modem, and 12GB of RAM, unchanged from the current model. Externally, little is expected to change beyond the additional rear camera, but Pu expects a slightly smaller Dynamic Island, in line with the wider iPhone 18lineup. A lavender color option could replace Sky Blue.Pricing is the biggest open question. The Information reported that Apple is considering a lower price for the second model, but the surrounding lineup is moving too. Pu expects the iPhone 18 Pro and Pro Max to cost $250 to $300 morethan the current Pro models owing to 2nm silicon and memory costs, and the foldable iPhone is rumored to start somewhere between roughly $2,000 and $2,500. A widened gap between the Air and the Pro tier could change the calculation that undermined the original, particularly once the camera disparity with the standard iPhone disappears.The leaker "Fixed Focus Digital" claimed in April that Apple will push through at least two generations of the device no matter how poorly it sells, a position consistent with Bloomberg's Mark Gurman, who expects the Air 2 in spring 2027 alongside the standard iPhone 18 and iPhone 18e. Nikkei Asia reported in January that no second-generation Air was expected before 2027 at the earliest.Apple's confidence in the fall lineup appears to be growing in the meantime. The company reportedly told suppliers to prepare roughly 10 million foldable iPhones this year, up from an earlier forecast of seven to eight million, and has booked parts for around 80 million smartphones in the second half of 2026. Samsung, meanwhile, says the Galaxy Z Fold 8, Z Fold 8 Ultra, and Z Flip 8 drew 1.44 million pre-orders in South Korea across seven days, a company record, with the redesigned Z Fold 8 accounting for close to half of them.The ‌iPhone 18 Pro‌, ‌iPhone 18 Pro‌ Max, and foldable iPhone are expected in September, and Apple has already begun preparing for the event, opening a lottery for U.S. retail employees to staff it.

This week on Autonomy Signals presented by KPMG, Grayson Brulte and Rob Grant discuss NVIDIA open sourcing its Alpamayo 2 foundation model to capture the entire autonomy stack, WeRide expanding into Denmark through a partnership with GreenMobility, and Aurora sidestepping PACCAR with a Roush retrofit pathway to hit its 200 truck year-end target.NVIDIA released Alpamayo 2 Super, an open source 34 billion parameter vision language action foundation model for Level 4 autonomy, giving away the model to sell the silicon and lock developers into Drive Thor hardware and NVIDIA compute, raising the question of whether a Foxconn and Taiwan Semi partnership to build a bespoke NVIDIA robotaxi is next.While NVIDIA powers the autonomy economy, WeRide announced a partnership with GreenMobility, Denmark's leading shared electric mobility provider, to deploy its EU-compliant GXR robotaxi with public service targeted for the first half of 2027. The strategy being deployed by WeRide in Europe is the Autonomous Belt and Road Initiative, using Chinese domestic cash flow to finance a foreign expansion.As WeRide expands across Europe, Aurora turned to Roush to retrofit International trucks as a way around PACCAR's refusal to allow driverless operations, targeting 20 trucks per week to reach 200 driverless trucks by year-end. With Kodiak moving towards Daimler Truck and Bot Auto already there, PACCAR is watching the entire autonomous trucking industry sidestep its factory line while Aurora burns roughly $200 million a quarter racing to scale before its $1.2 billion in liquidity runs out in a little over five quarters.Episode Chapters0:00 KPMG Sponsor Introduction01:11 Signal 1: NVIDIA Open Sources Alpamayo 2 as their Grand Robotaxi Ambitions Come into Focus40:16 Signal 2: WeRide Expands into Denmark with GreenMobility1:01:25 Signal 3: Aurora Sidesteps PACCAR with Roush and InternationalFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

TSMC in Dresden. Foxconn in Bordeaux. A German chemicals giant in Kaohsiung. EU-Taiwan industrial ties are deepening fast, and it's mutual: EU states have invested more in Taiwan than the US and Japan combined. EETO head Lutz Güllner explains what Chips Act 2.0 changes, where Taiwan fits into Europe's industrial strategy beyond chips, and how Taiwan's civil society is countering foreign information manipulation.Episode highlights:07:23 How EU-Taiwan relations reached their most active point yet09:09 The EU Council's landmark Taiwan statement17:01 The Industrial Accelerator Act and the EU Chips Act 2.024:15 FIMI: naming foreign information manipulation and interference32:44 Why understanding Taiwanese politics matters for a diplomatHost: Kwangyin Liu, Deputy Managing Editor, CommonWealth MagazineGuest: Lutz Güllner, Head of the European Economic and Trade Office in TaipeiProducers: Yayuan Chang, Weiru Wang＊Read more：https://english.cw.com.tw＊Share your thoughts：bill@cw.com.tw Powered by Firstory Hosting

The Hide My Email failure is real but nowhere near as significant as it's being portrayed, plus Apple is raising so many prices yet looking at ways to make that palatable, plus Foxconn has begun its annual recruitment drive as it begins producing millions of new iPhones, all on the AppleInsider Podcast.Contact your hosts:@williamgallagher_ on Threads@WGallagher on TwitterWilliam's 58keys on YouTubeWilliam Gallagher on emailWes on BlueskyWes Hilliard on emailWes's blog HillitechSponsored by:MasterClass: Get 15% off annual memberships at MasterClass.comLinks from the Show:Seven years after Apple Card, Samsung leaps into fintech with its own credit cardHide My Email class action lawsuit seeks payout without evidence of any attacksIt's easy to find a real email address behind Hide My Email, but it doesn't really matterHide My Email flaw still worked two weeks after Apple's claimed fixA fake Hide My Email header can expose the address behind your Apple Account -- a different thingApple Music gets first U.S. price hike in four yearsIs Apple One worth it in summer 2026?AAPL capitalization squeaks past NVDA, Apple becomes world's most valuable companyApple's iPhone Upgrade Program was great while it lastedApple Upgrades will let users lease iPhones and Macs with KlarnaIt has begun: Foxconn amassing army of workers for iPhone 18 Pro assemblyUpgraded Mac Mini, Mac Studio, OLED iMac readied, release date hazyApple preps MacBook Pro, MacBook Neo refresh in bigger AI pushApple's $634M payment to Masimo now set in stone after Judge tosses appealCaleb Hammer on YouTubeSupport the show:Support the show on Patreon or Apple Podcasts to get ad-free episodes every week, access to our private Discord channel, and early release of the show! We would also appreciate a 5-star rating and review in Apple PodcastsMore AppleInsider podcastsTune in to our HomeKit Insider podcast covering the latest news, products, apps and everything HomeKit related. Subscribe in Apple Podcasts, Overcast, or just search for HomeKit Insider wherever you get your podcasts.Subscribe and listen to our AppleInsider Daily podcast for the latest Apple news Monday through Friday. You can find it on Apple Podcasts, Overcast, or anywhere you listen to podcasts.Those interested in sponsoring the show can reach out to us at: advertising@appleinsider.com (00:00) - Intro (02:49) - HIde My Email (16:07) - Apple Services and price rises (25:56) - Apple Upgrade (42:09) - Masimo ★ Support this podcast on Patreon ★

- Foxconn Reportedly Hiring Up for Fall iPhone Production - Second Beta of blankOS 27 Seeded to Public Testers - Bloomberg's Gurman Lays Out Mac Roadmap Into 2027 - Early '27 MacBook Neo Could Have 12GB of RAM - Dan Moren: Two Troubling Issues with Rumored Apple Upgrade Program - Part of Apple Park Visitor Center Temporarily Closed - Comedy and Romance Series Headed to Apple TV - THR Goes In-Depth on "Ted Lasso" Ahead of Season-Four Premier - Sponsored by Copilot Money: Get a two month free trial with Offer Code MACOSKEN at copilot.money/macosken - Catch Ken on Mastodon - @macosken@mastodon.social - Send Ken an email: info@macosken.com - Chat with us on Patreon for as little as $1 a month. Support the show at Patreon.com/macosken

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Don’t Fade and Die in AI Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ Matt Yanchyshyn, VP AWS Marketplace, Rekha Thangelapalita, Elastic GSI Leaders; Allison McFadden, Accenture AWS Leader; and James Kang of Nvidia join Ultimate Partner. In this panel discussion, leaders from Elastic, Accenture, Nvidia, and AWS dissect the urgent shifts in the ecosystem, emphasizing that partners must adapt to AI and agentic co-selling or risk fading away completely. The conversation explores the necessity of deep co-engineering, the power of multi-product solutions in the AWS marketplace, and how automated agents are now replacing traditional human sales pipeline progression. By embracing data readiness and strategic collaboration, organizations can survive the “token maxing” era, effectively scale their enterprise opportunities, and align with NVIDIA’s five-layer strategy to dominate the new cloud landscape. https://youtu.be/zUkL4Wqsa68 Key Takeaways AI agents will automate the majority of AWS partner co-selling attachments and opportunity progressions this year. Partners who fail to embrace agentic workflows and automated governance face the existential risk of fading into obsolescence. Successful multi-product offerings require a “blood to all organs” approach that benefits the client, the ISV, the GSI, and the hyperscaler simultaneously. Nvidia’s “five-layer cake” model emphasizes that successful outcomes at the application layer automatically drive growth for all underlying infrastructure. The “token maxing” phenomenon is forcing enterprises to seek cost-effective, open-model alternatives to scale their generative AI securely. Integrating GSIs and ISVs on the AWS marketplace significantly increases enterprise deal sizes and long-term customer renewal rates. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags strategic collaboration agreement, data readiness engine, agentic co-sell, semantic layer, token maxing, five layer cake, accelerated computing platform, open models, cloud consumption, multi-product solutions, partner central agents, propensity data, automated opportunity progression, generative AI governance Transcript Matt Y and Panel Audio Podcast [00:00:00] Vince Menzione: You have a choice. You can embrace them and figure it out and get governance and, and make your data available. Um, use the partner, central agent, move to Agen Co-sell, or you can fade and die. [00:00:11] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:22] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi. Own your host. And each week I sit down with leaders at the intersection of technology, partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:44] Vince Menzione: It is the strategy because [00:00:46] Vince Menzione: being in the room changes everything. Let’s start. [00:00:51] Vince Menzione: We’ve got some amazing leaders joining us. So I think probably for a little bit of context, maybe just start with Rika. You can introduce yourself, your role and, uh, what, what you’ve been doing at Elastic. Yeah. [00:01:03] Rekha Thangellapalli: Yeah, sounds great. [00:01:04] Rekha Thangellapalli: Hi everyone. I’m Reka and I lead GSI Alliances at Elastic. Um, for the past 14 years, I’ve had the pleasure of building different kinds of partner ecosystems across companies such as SAP. MuleSoft, Salesforce, Coupa, and now Elastic. Um, I wanna thank Ultimate partner and Vince for having us here today. Thank you and the panel of these incredible speakers for joining me on stage. [00:01:31] Rekha Thangellapalli: Um, very excited for the conversation today. [00:01:33] Vince Menzione: We love Elastic, and you’ve had some of your other leaders on stage at other events. As such, the quality of your leadership team is amazing. Thank you. [00:01:42] Rekha Thangellapalli: I wholeheartedly agree. [00:01:45] Allison McFadden: Excellent. Um, hello everyone. Allison McFadden. I lead our North America AWS practice at Accenture. [00:01:52] Allison McFadden: Uh, I’ve been there for five years, and truth be told, it was my first partnership role, my first formal partnership role. Uh, so I can take some tips from all of you in the room here today. Prior to that, I was 21 years with IBM, and I got into partnerships because my last role at IBM was actually trying to build. [00:02:14] Allison McFadden: Linux business on the mainframe, and I had to have partners. I had to have partners to help me with workloads to run there. So I kind of learned, uh, trial by fire. But I’m excited for the conversation today. Excited to be in this room and excited to talk about what we’re doing with, uh, elastic. Thank you. [00:02:34] James Kang: Uh, my name is James Kang. Nice to see and meet everyone here. Vince, thank you for the opportunity. Thank you [00:02:38] Vince Menzione: for being here. [00:02:39] James Kang: Um, I’m with Nvidia, so I help manage the AWS partnership at Nvidia all up. Um, I guess fun fact, I’m former AWS and so I see a lot of very familiar faces here in the front row. Uh, former colleagues and then current friends. [00:02:56] James Kang: And so, uh, looking forward to the conversation. [00:02:59] Vince Menzione: Great. Well, we’ll start with an easy tia. Matt. This is not directed to you, directed to the others. So what does a successful AWS partnership look like from your C? So we’ll start with Eureka. [00:03:09] Rekha Thangellapalli: Sure. So from an ISV perspective, I think we really are looking at three things. [00:03:15] Rekha Thangellapalli: Uh, mutual investment building together. And scaling together. So when we talk about mutual investment, elastic recently signed a five-year SCA or strategic collaboration agreement with AWS. And while that is a significant milestone in our partnership, for us, what matters more is what it represents, and that is really a long-term commitment from both companies. [00:03:39] Rekha Thangellapalli: Towards product engineering, um, and joint go to market initiatives to deliver value to customers over time. And that’s what we see is that the best partnerships really compound and they build upon each other every year. Um, they don’t necessarily kind of reset every year. Um, next we talk about building together. [00:03:59] Rekha Thangellapalli: So, um. When we talk about joint solutions, we want to deliver solutions that are better together and the customers have to see us that way. And so whether it’s search, observability, or security, we’re looking at taking to market solutions that we can’t or necessarily don’t wanna take on our own. And finally we talk about scaling together. [00:04:22] Rekha Thangellapalli: And this is where marketplace, for instance, plays a big role, um, when customers can draw down on their cloud commitments, transact online and go from, you know, pilot to enterprise scale adoption in hours, not days. Um, this is when really everyone wins. Um, and this is also where partners like Accenture play a critical role. [00:04:47] Rekha Thangellapalli: Um, you know, the incredible amount of expertise that they bring, uh, the managed services capabilities and, um, their data assets actually play a huge role in having our customers realize that value faster. And, um, like Vince mentioned, at the end of the day, best partnerships are all all about creating kind of that. [00:05:07] Rekha Thangellapalli: Self-sustaining flywheel. And so it starts with investing together, building something unique, and having the customers realize that success faster because that success is really the only thing that’s gonna keep that flywheel going for everyone involved. I [00:05:26] Vince Menzione: absolutely. [00:05:26] Allison McFadden: Okay, amazing. I’m gonna riff off a few things Ika said, but from a GSI perspective. [00:05:32] Allison McFadden: A relationship with a WSA successful relationship with AWS looks slightly different. Um, so I think the first thing that we think of in the GSI Community common thread is that the client outcome and delivering value for clients is what we, what we’re striving for. Um, and so the partnership with AWS in that case, um, um, it has to, it has to. [00:06:01] Allison McFadden: Look like one team in front of our clients. So we have to show up indistinguishable, and that’s with AWS and with an ISV partner, it has to look like one solution in front of the client, especially moments that matter. So board meetings, um, you know, the time we’re gonna sign a deal, like we have to look like one team, uh, and keep our our client outcome, um, first and foremost in mind. [00:06:24] Allison McFadden: The second thing, and this is I think where the magic of all the people in this room comes into play. We can have as many discussions at a CEO level as we want. And if our client teams on the ground are not working together, it falls apart. Falls apart directly in front of the client. Yes. And that is a really hard thing to do. [00:06:45] Allison McFadden: So I’m passionate about the alliance work because that that work is what makes it happen at the corporate level. [00:06:53] James Kang: Cool. Um. I’ll start here. So in Nvidia is a accelerated computing platform company. Um, if you asked. Anyone on the, on the street about a year ago, what is ai? A lot of times they would say AI is, is open ai, or it’s philanthropic. [00:07:12] James Kang: Um, Jensen and I’ll, I’ll reference Jensen a lot today, um, because he is our leader, um, but he also sets the strategy in the direction for Nvidia. He talks a lot about AI in the metaphor of a five layer cake. And in terms of the five layer cake, you start off with the foundational bottom layer being power and energy, which sustains. [00:07:32] James Kang: All of our data centers, you move up the stack in terms of chips. So things think of Foxconn, think of TSMC. Next you have the infrastructure layer. So obvious choice is AWS, and then you get to the models where you do have the philanthropics and the open ais. But finally in at the precipice, you have the application layer. [00:07:53] James Kang: Ultimately, the reason why I mentioned all different stacks of the layers, the five layer cake, is the fact that the application layer is the most important. And so when you think about. Partners like Elastic or ServiceNow Trend, ai, CrowdStrike. Every time you pull from the application layer and you see a success, it pulls all five different components of that layer up. [00:08:13] James Kang: And so ultimately, as I think about success, it’s it’s being able to develop these co-sell wins at the application layer and really demonstrating that through extreme co-engineering and co-design with all the different application. Infrastructure, power and energy layers in mind. Um, Jensen also likes to think of himself not only as the CEO and founder, but also as the, the chief Marketing Officer. [00:08:35] James Kang: We are a very event driven company, and so at our big events like GTC or at big industry events like CES or Computex, he likes to show up on the biggest stage, biggest stages and showcase the partnerships with not only ISVs and GSIs, but also with end customers. And so that’s what I think about when I think of SA success. [00:08:56] Vince Menzione: That’s a really good point. You talked about, Allison, you talked about having an alliance strategy, or at least you teed it up, so I thought maybe we would go there for a second. Right? Like, what does a great alliance strategy look like and why is it important to the success of the partnership? [00:09:11] Allison McFadden: Man, I, uh, I have so many opinions on this. [00:09:13] Allison McFadden: We could probably be up here all day. That’s [00:09:15] Vince Menzione: okay. [00:09:16] Allison McFadden: Um, no, I think. Uh, there, there are a couple things, and the first one that comes to mind is focus. We cannot be all things to all people. Um, so when it comes to think about some of the, the work we’re doing with Elastic, we have a very, very clear point of view on what client problem we’re solving, what clients we want to talk to. [00:09:38] Allison McFadden: It helps if, um, from an ISV perspective, if there’s a very clear fit in. The Accenture portfolio or whatever, you know, SI consulting partner. You’re working with a very clear fit in the portfolio and we know what we’re not gonna go after, what we’re not gonna spend our time on because we have, we have this tendency, there’s millions of people. [00:10:00] Allison McFadden: The ecosystem chart that, you know, Vince, you showed up there, there’s so many connections. There’s probably more connections there than there are atoms in the universe, right? So, um. Defining what we do together and what we don’t do together is the first thing that pops to my mind. [00:10:19] Vince Menzione: Reka, do you have a perspective on it since we’re gonna, we’re gonna talk next about what you’ve done together, but, and I also wanna get mass perspective as a hyperscaler partner here as well. [00:10:29] Rekha Thangellapalli: Yeah, I mean from my perspective, I, I’m gonna, you know, kinda echo what Allison said is to be just maniacally focused. Yep. Um, because, especially from my perspective, so Elastic has three different solutions, right? We’ve got search, we’ve got observability, we’ve got security that map to completely different business units within Accenture. [00:10:47] Rekha Thangellapalli: And of course Accenture does a lot of things. And so, you know, when we first came together it was like. Okay, what are we gonna focus on? What industries are we gonna go after? Which segments are we gonna go after? Which customers, you know, um, outcomes are we trying to solve? And I think that sort of maniacal focus is the number one contributing factor to, to the fact that I’m like, up here on stage today. [00:11:12] Rekha Thangellapalli: Great. [00:11:14] Vince Menzione: Matt? Perspective? [00:11:16] Matt Yanchyshyn: Yeah, I, I, I guess I was trying to. To add something, uh, additional from an AWS perspective, uh, when it comes to, you know, what does a great alliance look like? Uh, AWS is obsessed with data, you know, in data we trust. And, and so the best, um, and, and this goes sales business problem, and it’s not just the engineering teams. [00:11:34] Matt Yanchyshyn: And so, uh, you know, Accenture does a good job of this elastic, definitely. And if you can come to the table with, um, quantifiable proof of the value of customer outcomes and partnerships. Um, you’ll win all the time and it’ll be a durable relationship with AWS ’cause we really are this data obsessed company and, and even the most senior sales leaders. [00:11:54] Matt Yanchyshyn: Uh, and so what I mean by that specifically is like if you, if you can show like your a RR to land an a RR conversion ratio, like in in numerical format, it’ll light up our sales leaders and, and they’ll be all, and they will co-sell with you all day long. If you can show the, I mentioned this earlier, like the AWS service, uh, whether you’re consulting company or, um, elastic and, and how the shape of customer accounts change positively when we work together. [00:12:15] Matt Yanchyshyn: That type of sort of quantifiable data works particularly well from an alliance perspective. With AWS as a partner, we, we really are like this data in sort of results out company. Um, so I, yeah, that’s just adding to the great points that were already made. I would say specific to AWS that that’s key. [00:12:30] Matt Yanchyshyn: Yeah. And I’m gonna bring up one more thing. I want to dive in on the, the joint value proposition, but you mentioned something that made a lot of sense and resonated to me about the organizations once you get out of partner, the partner world that we all know and love. Mm-hmm. Once you get down into a field organization or account management organization. [00:12:49] Matt Yanchyshyn: Not as much understanding and really organizations do a bad job here, honestly, in terms of enabling the field organizations. Do you agree? [00:12:58] Allison McFadden: I agree because I, I agree. And, um, you know, I think that’s one of the things, and, and I, I, when I joined Accenture, what we had was a lot of wicked smart architects delivering programs to clients in the field. [00:13:15] Allison McFadden: Very smart, very deep in AWS knowledge. Um, and that was awesome for the 10 clients they were staffed on and to get that understanding of how AWS works and I dream about lar, right? Like, this is a good, you know, but that takes real effort and real work. Yeah. And it’s, it’s um, almost like being a language translator. [00:13:37] Allison McFadden: Yes. For me. Yeah. So, you know, I had to deeply learn AWS so that I could. [00:13:42] Rekha Thangellapalli: Sure. [00:13:42] Allison McFadden: Teach my account teams. My account teams are really smart. They know who they’re selling to. They know their customers. They know what their customers need. They do not know what AWS has to offer always because they’ve got 20 partners lining up to try to tell their stories. [00:13:57] Allison McFadden: Um, they don’t know how to ask of the AWS team or the elastic team or the Nvidia team. Yeah. What they need [00:14:02] Vince Menzione: this co-selling piece. Yeah. [00:14:04] Allison McFadden: And so that is where, um. We had to build that muscle even around our AWS practice, which was a huge practice at Accenture, but we didn’t necessarily surround it with that kind of enablement and um, almost deal coaching layer. [00:14:21] Vince Menzione: So Elastic and Accenture came together. I dunno which one of you wants to lead this part of the conversation, but you will, right? Yeah. So tell us about the genesis of this and why. And a lot of people dunno what Elastic does, but you do some really incredible work. Like I, somebody told me one day was like, oh, you know, Uber, like, that’s elastic, powering all that. [00:14:41] Vince Menzione: Like, we don’t think about that. That the engines that you have and the, the backend to the customers, huge customers. [00:14:48] Rekha Thangellapalli: Yeah, absolutely. Um, so when AWS launched this feature last, um, reinvent where basically it allowed, you know, channel partners such as Accenture to be able to bundle up their services, their data assets with an ISV solution and put it on marketplace, um, you know, Accenture and Elastic immediately saw an opportunity. [00:15:09] Rekha Thangellapalli: Um, at the time most customers were doing gen ai. But they were running into the same challenge, which was that their data just was not ready. And by the way, this is a problem we were solving. Outside of marketplace. I think the, the feature that you guys launched just gave us a way to package it up and to be able to create this repeatable solution, which we call data readiness engine for gen ai and put it on marketplace. [00:15:40] Rekha Thangellapalli: And, um, this to me was a success because. Each company had a clear reason to invest. Um, so for Accenture, they were able to, you know, create a very differentiated services led offering. Uh, for Elastic, we were able to expand on our AI story. And for AWS, um, you know, it drives marketplace adoption, increases cloud consumption, all of that great stuff. [00:16:07] Rekha Thangellapalli: And customers, of course get. A solution to a very real problem that, that they were having. Um, and you know, the surprising part for me going through that journey was that, um. The pitching, the idea, getting the budget, getting the executive sponsorship was actually the easy part. The hard part was getting all three companies to come together, uh, to go from idea to launch in a very ambitious timeline of six weeks. [00:16:37] Rekha Thangellapalli: Nice. And so, you know, this was very much like. Doesn’t matter your title. We’re rolling up our sleeves and we are on this outcome together. Um, and so we literally built a RACI matrix, a project plan, and you know, we had daily standup calls for six weeks where literally. At least one person from each three of these companies called in, you know, got rid of any blockers and we made sure we were on target for that timeline. [00:17:07] Rekha Thangellapalli: Um, and you know, at the end we had a successful launch. But I think my favorite part about the story is the impact that we’re having and, um. My favorite story comes from a global pharmaceutical company that, you know, had basically nine petabytes of data spread across six different continents. Wow. And by working with Accenture and Elastic, they were able to build that trusted foundation that their AI and their agents can, you know, kind of safely tap into and be accessible at scale. [00:17:41] Rekha Thangellapalli: Um, so that’s my version. Allison. [00:17:44] Allison McFadden: Yeah. Well, I don’t have a lot to add. I just, I would say this is a good example of a couple of principles, right? One is having a forcing function is never a bad idea. Sign up for a big event, sign up. I’m like, I’m here with my, you know, Nvidia guys saying, sign up for the event. [00:17:58] Allison McFadden: It’ll make you move quick, right? [00:18:00] Audience Member: Yes. [00:18:00] Allison McFadden: Um, so that is one, but two, one of my mentors once told me, when you’re designing any kind of, you know, offering go to market motion, it has to get blood to all organs. If it does not get blood to all organs, it does not go [00:18:14] Vince Menzione: nice. [00:18:14] Allison McFadden: Um, [00:18:14] Vince Menzione: I love that analogy. [00:18:15] Allison McFadden: Oh, I love it. And I can talk all day. [00:18:17] Allison McFadden: That guy was brilliant. I love him. But, um, no, and, and so Elastic did a really nice job of bringing the tech to the table. Um, our team has to trust in that technology and its ability to scale, right? Um, because at Accenture we have to be able to deploy across 700,000 consultants. Um. And yeah, so I think those are the two, two things that really worked well here is we had, uh, trust in the technology solved a customer need. [00:18:50] Allison McFadden: Um, it drives, we don’t even talk about, like, yes, it drives marketplace revenue, but it unlocks work that we do that drives even more revenue to our AWS Friends. Right. So this is a, this is a, um, product that’s getting your data ready for AG agentic. It’s a messy problem that everyone’s dealing with, and it removes blockers for clients and it unlocks more, you know, ag agentic work on top of that. [00:19:15] Allison McFadden: So, blood to all organs. [00:19:17] Vince Menzione: So, was that the proposal going forward to say we need to have, we need to have trust in the solution. We need to drive significant revenue. It needs to be something all of our, you know, seven, 700,000 people. Can be a part of and help drive? Is that how you think about? [00:19:32] Allison McFadden: Yeah, and for us right now, um, it’s an interesting time for Accenture. [00:19:36] Allison McFadden: Our clients are asking a lot of us, and what it does is it having some of these accelerators helps us deliver cheaper, better, faster to our clients, which is what they’re demanding of us right now. Um, so it’s an accelerator to client outcomes. [00:19:55] Vince Menzione: James, what is NVIDIA’s role and how do, how do you enter the equation here? [00:20:00] James Kang: Yeah, it’s, um, it’s a good question. Um, I, I would say that Nvidia is probably one of the most misunderstood organizations in the world. Um, despite the, uh, the market capitalization in the valuation of the company, we have a very tiny organization. Um, what I mean by that is, um, if you think about. [00:20:20] James Kang: Salesforces and field sales organizations. Um, we’ll take Salesforce as the account or the customer. As an example, we have one account manager at NVIDIA that no, not only covers and is responsible for the relationship with Salesforce, um, but also manages. Automation Anywhere as well as DocuSign. Whereas at AWS, in contrast, like there are full armies and teams Yeah. [00:20:45] James Kang: That are supporting the Salesforce relationship. And so as you think about partnering and working with Nvidia, the focus has to be on really. Extreme co-design, but also being very prescriptive in terms of what are the very specific customer outcomes that we are solving for. And the guidance that I would give is bring in Nvidia into that equation and that conversation as early as possible because that [00:21:10] James Kang: co-engineering and co-design needs to be part of the foundational building blocks in order for you to come out with a end solution that checks all those different requirements. [00:21:20] James Kang: And so I think. Again, like going back to Nvidia, um, we like to talk about two different types of brains. A brain one and a brain two. Uh, brain One you think about the next quarter and making sure that you’re hitting the revenue targets for the next quarter. Brain two, you think about a long-term goals and potentials looking around corners and being very strategic. [00:21:41] James Kang: The saying internally is without Brain one, there is no oxygen, but without brain two, there is no future. And everyone at NVIDIA is trained to think in that brain two mentality. [00:21:52] Vince Menzione: Wow, Matt. [00:21:54] Matt Yanchyshyn: Yeah, I, I was just thinking I love the blood doll organs. Uh, and so just on, on that note, um, and, and, you know, the multi-product solutions that, that you, you built together, uh, that is a really good example of blood do organs because like we all know, that’s how customers buy. [00:22:07] Matt Yanchyshyn: They, they buy solutions and increasingly they’re looking for combinations of ISV, sometimes multiple products from multiple ISVs with services. Uh, often they’re buying it through a resell motion. You know, and they, and, and so that from a customer perspective, they want a single place to go. And so that’s the multi-product solution. [00:22:24] Matt Yanchyshyn: They wanna find everything they need, they need Accenture, they need Elastic to solve a specific solution. And I think where that’s headed is even more specific listings, like with AI powered listing experience, like, you know, elastic Plus Accenture for, I’ll make something up like a manufacturing workload. [00:22:37] Matt Yanchyshyn: And so this solution based. Uh, sort of buying is, is very customer centric. It’s what customers want. We all know that. But that’s, that’s the customer sort of organ, I guess. Um, but then, you know, you all have SCAs and those SCAs have marketplace commits. It helps if that gets transacted through marketplace helps the AWS relationship, you know that that’s an organ. [00:22:55] Matt Yanchyshyn: It’s the relationship. It’s, it’s the commercial construct and that you have, uh, that that’s another organ. You’re marketing people. They, that’s another organ. They don’t wanna land, uh, leads on a static marketing page. They wanna land a lead on a, a storefront with a multi-product solution that can actually convert and that you can actually buy it through that. [00:23:12] Matt Yanchyshyn: So the marketing person’s happy because they, they have less churn. Uh, and then, you know, our reps are happy ’cause guess how they get paid? They retire quota when they sell Marketplace. And they, we also, Jay McMain will tell you, that’s another organ called Jay or on, on you now. Um, [00:23:27] Matt Yanchyshyn: he’ll like that. I’ll call him up and tell him that. [00:23:29] Matt Yanchyshyn: Yeah, [00:23:30] Matt Yanchyshyn: but he, he’ll tell you, you know, don’t believe me. Obviously, never believe Matt, believe, believe the, the data and, and his data shows that. Those deals will close faster and larger if you use marketplace. So that’s, that’s a lot of organs. That’s the whole body. Um, but you know, when you have your customer happy ’cause that’s how they wanna buy your field happy. [00:23:45] Matt Yanchyshyn: Um, and, you know, the relationship happy and you know, your marketing team happy. Uh, and, and Jay happy. Um, and, and you know, I think that multi-product construct and, and the way you kind of use it to model a partnership and the way buyers ultimately wanna buy is, is really powerful. And so I, I think it’s, you know, it’s really a manifestation of how. [00:24:04] Matt Yanchyshyn: We kind of intend and to go to market anyway. Uh, so I think, you know, and thanks for leading the way, by the way. You’re, you’re amongst the very first, so that’s great to see. [00:24:11] Matt Yanchyshyn: So these storefronts are really helping this drive, drive this. Well, [00:24:13] Matt Yanchyshyn: that’s the next evolution. Like we’re talking about the multiproduct solution. [00:24:16] Allison McFadden: I’m JJ Accenture storefront. [00:24:17] Vince Menzione: Yeah. Oh, there you go. I mean, j and j Accenture storefront. [00:24:20] Allison McFadden: We’re gonna talk about that. [00:24:20] Matt Yanchyshyn: Yeah. I mean, [00:24:21] Matt Yanchyshyn: Accenture also leading the way yet again with storefronts. And so I think the combination of. You know, again, I was talking a lot about conversion. Yeah. And you know, buyers know sometimes they know what they wanna buy and, but if you really wanna convert that lead, you wanna land them again, something that combines, you know, elastic Accenture’s services plus software, but in a storefront that is, you know, surrounding with just the solutions they want so they don’t need to kind of go searching. [00:24:42] Matt Yanchyshyn: So, you know, ultimately reducing that time to close, I guess, really ’cause meeting the customer where they are with what they need. [00:24:51] Matt Yanchyshyn: So we talk about co-selling a little bit. We, Jay and I talk about this all the time. We gotta keep looping Jay in here, even though he is not even in town this week, but Reko, um, what does co-sell look like inside Elastic? [00:25:02] Matt Yanchyshyn: You’ve got, we talked about an incredible leadership team. I’ve gotten meet some of your leaders. Seems like you drive, you do a good job internally driving that. Let’s talk a little bit about it. [00:25:11] Rekha Thangellapalli: Yeah, and this is something I’m, I’m personally very passionate about. Um, co-sell is. Very much a journey, not a destination. [00:25:20] Rekha Thangellapalli: And I think step one for us is recognizing the different partner types that we have. Because at Elastic we work with, you know, OEMs, MSPs, resale distributors, GSIs, um, and they all bring something very unique. To the customer lifecycle and they all contribute very differently within, you know, our own sales cycle and sales process. [00:25:45] Rekha Thangellapalli: And so, you know, figuring out what is the unique benefit they bring, how do we enable them? So training and enablement is a huge piece of it, and so is making sure we’ve got the right metrics to measure success. Um, I know a lot of companies look at partner sourced as the north star, and that’s great, right? [00:26:06] Rekha Thangellapalli: Because that is undeniable. You can say, Hey, that would not exist if it wasn’t for my partner team. Um, but we’ve also noticed that when we bring in GSIs, it actually increases renewal rates. It significantly increases. Um, a RR over time. Um, it expands deal sizes and so these are very real metrics that we can point to, um, beyond just the co-sell and the partner sourced number. [00:26:32] Rekha Thangellapalli: Um, so for us it’s looking at it from a very holistic perspective, but also catering it towards that unique partner and making sure we’re doing everything we can to set them up for success and setting up the partnership for success. [00:26:47] Vince Menzione: So clo close win ratios, deal size and renewal rates? [00:26:52] Rekha Thangellapalli: Yes. For specifically for geos size. [00:26:54] Rekha Thangellapalli: Yeah. [00:26:55] Vince Menzione: Very interesting. Allison, uh, what had to change internally to produce these co-selling? We talked a little bit about the field organization and enabling a, a group of, and, you know, account sellers that are very customer focused and enabling them on the co-sell side. What had to change internally to drive that? [00:27:13] Vince Menzione: Yeah. [00:27:14] Allison McFadden: I, I might have already alluded to this a little bit in a previous answer, but, um, creating the capacity to develop, build, and sell these solutions, um, inside of a large GSI, where billable hours is kind of the number one metric on the table. Um. Is part of the investment that we had to make within Accenture to get this done? [00:27:36] Audience Member: Yeah, [00:27:36] Allison McFadden: so expert technology time. So we have technologists that understand the elastic technology. We do similar with Nvidia, by the way, we. We released some of their time to go co-develop the solution because it has to hold technical water, right? It can’t just be a marketing pitch. It can’t just be, it has to be a real, um, what’s the there, there. [00:27:59] Allison McFadden: So in order to actually do proper co-sell, we had to release some of that time. Um, to invest in those partnerships. Um, we’ve also done similar with some industry aligned business development leaders recently, so we have freed their time up to go. Uh. Open new conversations, educate client, account teams, go to clients, have conversations. [00:28:26] Allison McFadden: Um, so that, that’s a new motion that we, uh, have just kind of recently made, um, to allow them, I love this brain one, brain two also, right? So to allow them to focus on brain two, because a lot of our time. Typically spent delivery issues, you know, getting my hours, where am I charging my time? And so just freeing up a little of that capacity to do this work, um, helps get us in this brain two mode where we’re not just living to survive. [00:28:56] Vince Menzione: I. So, Matt, you’ve removed a lot. I mean, one of the things I admire, I admire AWS for being first to market and removing the most friction in marketplace of any of the vendors. Really, truly that. You talked about some of the announcements. How does some of, how does some of this tie PC central agents propensity sales plays, MCP, how does some of this tie to how, how you’re thinking about the future? [00:29:18] Vince Menzione: And how to enable more motions like this. [00:29:20] Matt Yanchyshyn: Yeah. Well, I, I think if you know my boss, UBA Borno, uh, you’ll know that she has a maniacal focus on automation. Yeah. Um, and, uh, co-sell is increasingly automated. You know, you were asking earlier about propensity data. You can get that propensity data in addition to sales plays and, uh, opportunity scores through the partner central agents. [00:29:38] Matt Yanchyshyn: So things that used to require multiple calls to A PDM, if you’re lucky to have one. Yeah. Or a p sm. Uh, you, you can now get through, through these agents, you know, uh, tech Systems, TGS, they, they manage what, over 5,500 customer opportunities with agents that they built on top of our partner Central APIs. [00:29:55] Matt Yanchyshyn: Um, and work Span has built a whole product and business that’s right on leveraging, uh, our APIs, our capabilities to sort of tie into your CRM. So, majority of all opportunities will be progressed and managed by agents. This year at AWS, we already have a majority of all customer opportunities, all app have a partner attached and I, I took a personal goal for a majority of those partner attachments, not to happen from a human. [00:30:22] Matt Yanchyshyn: But from our solution matching engine. And how do you get recommended by that solution? Matching engine, having a healthy ACE pipeline, thanks to partner central agents and the integrations you’re doing. And in addition to being the specializations and doing things like multi-product solutions and ultimately closing opportunities, you dream of LAR and so LAR will help that. [00:30:40] Allison McFadden: It’s more like a nightmare. [00:30:41] Vince Menzione: And so, you know, [00:30:42] Allison McFadden: it’s more like a nightmare, but [00:30:44] Vince Menzione: nightmare. Well, it’s, it’s, yeah. Nightmare of Laura and, and. Nice dreams of PRM, but the, um, but that’s the loop, right? I, I think, uh, increasingly co-sell for us, and in my mind, is largely a hundred percent automated. Yeah. Except for what matters most, those most largest, most strategic, most complex deals. [00:31:01] Vince Menzione: Where our highly paid and very skilled salespeople are most effectively used. [00:31:05] Vince Menzione: Yeah. [00:31:05] Vince Menzione: You know, the days of, you know, this person with 20 years experience selling, clicking, progressing opportunities through a pipeline, uh, should be over. Uh, and, and we need those people out, out selling and, and co-selling. And so that for me. [00:31:19] Vince Menzione: Yeah. That, you know, we talk a lot about co-sell, but I, I’m obsessed with automating as much of the co-sell as possible. [00:31:24] Vince Menzione: I remember going back to the ex Excel spreadsheets and, and that, that seems to be be Viva became spreadsheet jockeys. [00:31:31] Vince Menzione: Yeah. [00:31:32] Vince Menzione: And, and they stopped selling. They forgot how to sell. [00:31:34] Vince Menzione: Yeah. And people spend all this time doing lunch and learns and things like that. [00:31:36] Vince Menzione: And then, you know. Then the salespeople rotate out after 18 months and, and it, that’s, that’s the old days. Uh, you know, the new days are, are AI powered matching algorithms, uh, ag agentic co-sell, using the partner essential agents to get your data and, and putting that data to use automatically and, and what sounded like magic. [00:31:51] Vince Menzione: 12 months ago is being done, you know, by partners at massive scale across thousands of opportunities. You can do it today. And you know, I, there’s a guy named another Mike, right? Mike another Mike who they have, there’s like a guy who’s doing all this and I’m picking on Mike ’cause I, I know their system really well and I know the guy Mike grew easily built it for them. [00:32:08] Vince Menzione: Um, but, you know, I think, yeah, again, in the days of having 10 people sort of doing lunch and learn could be replaced by one or two people, building agents, uh, managing a massive pipeline. And, and that’s the future. [00:32:18] Vince Menzione: Exactly. James, your perspective on what breaks with co-selling? [00:32:22] James Kang: Oh, what breaks co-sell? Um, I would say. [00:32:25] James Kang: It, it starts and finishes with just misalignment and a loss of trust with the customer, especially when you have multiple partners or stakeholders involved. If you’re trying to do a three-way deal with a end customer and you’re not on the same page, you’re not gonna get to a successful outcome on, on the backend. [00:32:44] James Kang: Uh, the fix is a much more complicated story. I would say that to take a step back, um. We’ve talked about the five layer cake. We’ve talked about where NVIDIA kind of fits within the equation. We are invested in the ecosystem and so as different players and application organizations win and see these outcomes for end customers, we celebrate that success. [00:33:07] James Kang: Um, and as part of that kind of ethos of where NVIDIA fits within the ecosystem, we wanna make sure that not only. Our customers, but our partners like ISVs and GSIs are set up for success. Um, we do not as Nvidia sell hardware or GPUs directly to customers We use. Hyperscalers like AWS as kind of our force multiplier. [00:33:31] James Kang: And similarly we think of ISVs and GSIs as the force multipliers in terms of our extensions of how we, we kind of leverage the relationships and build the trust with our end customers. And so going back to kind of the question, Vince, I would say that it all comes back to trust and being able to build that mutual trust. [00:33:48] James Kang: Um, a lot of what we do when we co-sell with AWS is really on the software layer. Um, we actually have more software engineers at NVIDIA than we have hardware engineers, which is a weird thing to say, um, because everyone knows us for our GPUs. But because of that fact, we are heavily invested in Cuda and making sure that Cuda becomes the foundational layer for how not only our ISVs and GSIs, but also our end customers are building. [00:34:12] Vince Menzione: Very cool. So Reiki, you and James together on this production. Versus pilot with the Gentech ai. Tell us a little bit more about that. Where, where are you in the process? [00:34:24] Rekha Thangellapalli: Yeah. So I mean, in general, what we’re seeing out in the market in, in relation to sort of AI and, and customer’s journeys is that, um, at least from an elastic perspective, um, we’re seeing people very much in production when it comes to, you know, kind of AI assistant co-pilot use cases. [00:34:42] Rekha Thangellapalli: So, you know, things like, um, software development, customer support is a big one. Um, any sort of employee productivity use cases where there’s. Still a human in the loop somewhere. Um, and there’s a very like, clear path to value. And so we see the customers being in production excelling there. Um, no problem. [00:35:01] Rekha Thangellapalli: Where we’re seeing people still kind of in the pilot phase is those fully autonomous workflows where there is no human involved. The agent is reasoning on its own. Um, accessing multiple systems and taking an action on the user’s behalf. And what we’re seeing is that it’s not the intelligence of the agent that’s holding it back. [00:35:26] Rekha Thangellapalli: It’s more about giving the right context to the agent and having the right. Security kind of governance controls in place for the company to feel comfortable in putting these fully autonomous workflows into production. And that’s really the conversation we’re having is all right, what are the controls you need in place? [00:35:47] Rekha Thangellapalli: For you to release this to your business unit. Um, and what is the context that the agent is needed before we can comfortably let the agent make the decision on the user’s behalf? Um, James, I’d be interested to hear what you’re, what you’re seeing in the market [00:36:03] James Kang: plus one on all things context. I, I would even go so far as to say, um. [00:36:09] James Kang: H how many folks in the audience have heard of token maxing? Like this new term? [00:36:13] Rekha Thangellapalli: Yeah. Yeah. [00:36:14] James Kang: Um, I’ll, I’ll give a very specific example of, of Uber that went public. With the example of Claude, like they allowed all of their employees to use as many tokens as possible, and within the span of four months, they exhausted their full budget for the year, and so they had to pull back, and now there’s a cap on every employee. [00:36:33] James Kang: I think the number that’s circulating is $1,500 per month per employee, and so I think that is at least. In this multi-phase evolution of where we’re going to be and where we’re today, cost has become kind of the prohibitive force in terms of agentic AI at scale. Um, I think we are working on some very creative solutions in-house and Nvidia. [00:36:55] James Kang: Um. And we saw some really dynamic announcements this week when it comes to all things agent core, um, where we want to focus on very nimble ways for customers to be able to execute and go to market. And one extreme example of that is our investment within our open model strategy. So Nvidia, not only, again, providing GPUs, we actually offer our own op open models, which we call our Nitron models. [00:37:21] James Kang: And through our Nitron models, we are allowing customers to really develop and fine tune their own proprietary models in a cost effective manner. So right alongside the frontier models like OpenAI and Anthropic. It’s not a if then, it’s not an either or statement. It’s a, it’s a permutation, it’s an and So we’re giving you a cost effective alternative to not only bring your AgTech applications at scale by training on Nibo tron, which is open source, but then once you’ve kind of finished and fine tuned that specific training job to be able to. [00:37:53] James Kang: Go ahead and utilize your frontier models, whether it be OpenAI or Claude. And I know there’s other partners here that are providing those kind of different model capabilities. And so I think for us it’s, it’s a matter of choice. We know that this market is dynamic. It’s gonna be evolving over the next coming months as well as the next coming years. [00:38:10] James Kang: Uh, but we believe that we are positioned for a really unique dynamic expansion of AgTech use cases over the, at least the next three to six months. [00:38:20] Vince Menzione: Allison, for the partners in the room who are glazed over right now going, what do I, what do I do over the next 12 months? [00:38:26] Allison McFadden: Should I wake everybody up by saying, yeah, please. [00:38:27] Allison McFadden: Say go hurricanes. [00:38:28] Vince Menzione: Yes. [00:38:29] Allison McFadden: Is there anyone, anybody? Everyone’s like, boo. I get to leave the parade today to go home to parade. I live in Raleigh, so we’ve got our parade on Saturday. Nice. [00:38:39] Vince Menzione: Nice. [00:38:40] Allison McFadden: All right. Wake up. Um, all right. So for the $50 million partners in the room, um. $50 million is not small. You have something that works. [00:38:50] Allison McFadden: Right. This is great. What I would be thinking about is, you know, we’ve talked about focus before, but really doubling down on, you know, what is, what is your industry, what is your client like, ideal client that you serve. And build, um, almost that kind of community. You know, the, the clients we have move from firm to firm to firm. [00:39:17] Allison McFadden: And if you’ve done good work at one, you’re gonna follow ’em to the next. Um, so build that client demand in a specific place or specific client profile that is just like really knocking it out out of the park for you. Um. Scale with marketplace, right? So if you, I, I love some of the data that you were sharing in your talk earlier, um, because it’s like no overhead scaling mechanism. [00:39:45] Allison McFadden: I mean, it’s, it’s fantastic. Um, Accenture, other GSIs like us, we are investing in marketplace. So we’re investing in resources, um, to help us. Use marketplace more with our clients and we’re gonna capture, right, those storefronts. And if you’re present on marketplace, you’re gonna be able to catch, uh, yourself in that wheel. [00:40:09] Allison McFadden: So I think those are the, the kind of couple of things I would say is focus, focus, focus to drive that client demand and use scaling mechanisms like marketplace to really kind of, uh, accelerate. [00:40:24] Vince Menzione: Matt, anything to add there on the. [00:40:26] Vince Menzione: Well just, you know, Ja, James, you, I love the token maxing reference in Uber and it reminds me, you remember when cloud came out and everyone was like, oh, all these people are, are gonna use the cloud and costs are outta control and. [00:40:39] Vince Menzione: Um, a lot of people pulled back from the cloud and, and a lot of those companies no longer exist. And it’s similar with, with, uh, token maxing, like, oh, these agents are outta control. You have a choice. You can embrace them and figure it out and get governance and, and make your data available. Um, use the partner, central agent, move to agent to co-sell, or you can fade and die. [00:40:58] Vince Menzione: And, and that’s, that’s where we’re at. Uh, is, is the, the companies sitting here today embraced the cloud years ago and won. Uh, and and there’s a set of companies here today who are gonna embrace agents in the, for both buyers and sellers, and will win. And there are those who won’t and they won’t win. And so for me, it’s like we’re, we’re at a, we’re at a crossroads. [00:41:18] Vince Menzione: And, and if you’re gonna win, you gotta leap into that, you know? I love it. And, uh, and, and, and it’s, it means the cost of experimentation is so much lower now. Development and, and even business development or software development is, is agent enabled. And so you can take risks, you can experiment and, and you have to, it’s, it’s an existential moment. [00:41:37] Vince Menzione: Agreed. We’ve got a couple minutes left over for any questions. What do you think? Sure. Are there any here. I think there are a couple. Yeah, we’ve got, we’ve got a co-sell question I’m sure coming up here. [00:41:51] Audience Member: Um, I’m Cassandra, I’m the CEO of Partner Tap. And one of the questions I had was, I think, you know, the co-selling between the sellers is where things get. Really, really hard when you’re multi-partner. And so when I was listening, um, with, you know, the Accenture and Elastic together, you talked about how you had, you, you had to get these BD business development people. [00:42:22] Audience Member: Um, is this a new team that is over the client team? And how do these teams interact like with the elastic sellers? Are you doing a lot of coaching to the field and then with if AWS sellers are, are involved, like what is that whole picture? What does look like, [00:42:43] Allison McFadden: like [00:42:44] Audience Member: on the ground? I mean, that is the hardest part, I think, and that’s what we hear. [00:42:48] Allison McFadden: It’s so, it’s so, it’s so tough. Um, and I will, I’ll just say, so our business development leaders that we now have kind of. Expanded their capacity. They have always been, they have always been there. Um, but they have not been well resourced. They haven’t, they haven’t had very clear kind of job description. [00:43:12] Allison McFadden: I’m gonna say I, in the past they have been kind of focused on partner relationship. And so like more like an alliance manager and maybe working on some of the data. Right? So when I say I have nightmares about Lars, because we’re always trying to increase the LAR for Accenture and, and they were focused like in those detailed weeds of like trying to pass ACE and trying to call the PDM and all this stuff. [00:43:39] Allison McFadden: What we are doing is really pivoting them to be proper sales, business development focused on client outcomes and focused on. Technical skills to be able to describe what this solution is to the field. So, um, and because we need, I have many, many questions about, I gotta get agents to work with Eurogen co-sell so that that part somehow goes away. [00:44:05] Allison McFadden: So that’s a, that’s the thing we gotta solve still, but, um, so we’re pivoting them to be kind of driving. More of that co-sell enablement with the field, um, and taking that message to the field rather than being there, waiting for questions to come in from the field, waiting for like our field teams to discover, oh, I saw something that we’re doing with Elastic, like on a press release on LinkedIn. [00:44:30] Allison McFadden: Right. So we’re kind of trying to pivot them to be more proactive. [00:44:33] Vince Menzione: Very cool. [00:44:34] Rekha Thangellapalli: Yeah. And uh, Cassandra, that’s an excellent question because I think. Multi-party, you know, sort of tri-party offerings. The hardest part is operationalizing it at scale, right? Yeah. And so for this particular offering, we are basically having three routes to market. [00:44:51] Rekha Thangellapalli: So one is seeing how this offering fits into our existing elastic go to market. And so I am constantly enabling our field sellers to say, okay, within our three field sales place, here’s exactly where this fits in. Here are, you know, uh. Keywords that you hear in customer conversations where you bring up this offering and here’s a process of how it works. [00:45:14] Rekha Thangellapalli: Um, exactly At what sales stage do I bring in Accenture, how, you know, what are the roles and expectations? Right? So that’s on the elastic side. We’re doing the same thing on the Accenture side. So we’re doing a ton of training enablement and lunch and learns, and we’re also looking at how do we fit into. [00:45:31] Rekha Thangellapalli: Uh, Accenture’s AI transformation projects, we are the semantic layer, right, of their enterprise brain. And so it’s a whole different sales motion, um, and, you know, having the right assets, having the right process again to make sure that that goes smoothly. And then finally, we’re going directly to the customer. [00:45:49] Rekha Thangellapalli: So we are launching multiple external campaigns where, you know, if the customer raises their hand. We will, we will line up immediately. Right. Um, and so, [00:46:01] Allison McFadden: I mean, I can’t, I can’t, I can’t say how important that third leg of the stool is. ’cause the second part, she talked about getting into our catalog is the first thing. [00:46:09] Allison McFadden: ’cause my BU business development leaders have the catalog. Right. And that’s what they’re selling. So what Elastic has done has gotten into one of those offerings and then. If we have a customer that asks for it, that is the fastest way to alignment. That is like the number one thing that we respond to [00:46:26] Vince Menzione: customer at the center. [00:46:27] Vince Menzione: This is great. Well, I think we’re up to time. This was a great session. I want to thank you. This is what a great, what a great group. [00:46:34] Vince Menzione: Thanks for listening to the Ultimate Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. Subscribe where [00:46:43] Vince Menzione: you listen, and head over to the ultimate partner.com. [00:46:47] Vince Menzione: For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything [00:47:09] I.

A researcher found an unlocked server...and inside it, working VPN credentials for nearly 74,000 corporate firewalls spanning Chevron, Samsung, Foxconn, and thousands more. In this episode of Darnley's Cyber Café, Darnley breaks down the FortiBleed leak: what was exposed, how attackers allegedly cracked their way in at a billion-attempt scale, and why this story matters even if your organization has never touched a FortiGate. Spoiler: the lesson is bigger than one vendor...Show Notes:If you were affected https://socradar.io/free-tools/fortibleedClick here to send future episode recommendationSupport the showSubscribe now to Darnley's Cyber Cafe and stay informed on the latest developments in the ever-evolving digital landscape.

Wisconsin governor candidate Joel Brennan joins to discuss the fallout from opponent Sara Rodriguez firing her campaign manager over massive campaign finance discrepancies, his take on AI data centers, and his firsthand role in renegotiating the Foxconn deal. Over the weekend, Lt. Gov. Rodriguez — one of five Democrats in the governor primary — announced she let her manager go over hundreds of thousands of dollars in mismanaged funds. Brennan reacts to the news, explaining why he views it as a failure of "competence 101." He details how his own campaign handles its books to ensure accountability before Wednesday's filing deadline, and discusses the political impact on the primary now that a "coronation" has been disrupted. Later, we dive into Brennan's role as Department of Administration Secretary in helping renegotiate the failed $2.85 billion Foxconn contract under Tony Evers. A deal originally put in place by former Republican Gov. Scott Walker and touted as the "Eighth Wonder of the World" by President Donald Trump during his first term. Lastly, we tackle the wild west of AI data centers in Wisconsin. With La Crosse County and its 18 municipalities fending for themselves to pass local moratoriums, Brennan reacts to the state legislature "hanging local government out to dry" on regulation. He breaks down his four-part plan to establish guardrails — including utilizing union labor, implementing strict green standards, banning non-disclosure agreements (NDAs) that keep locals in the dark, and ensuring residential ratepayers aren't stuck subsidizing massive corporate energy bills. Plus, he explains why he supports a targeted, 90-day statewide pause on approvals to write these rules into law, rather than an arbitrary, year-long ban. Though all that would have to wait until after the November election, since the GOP-controlled state Legislature hasn't been in session since April and won't resume work until January of 2027.See omnystudio.com/listener for privacy information.

„Inovace bez implementace je halucinace,“ říká Libor Witassek, kybernetik, podnikatel a patriot z Opavy, který má za sebou transformace fabrik od Vítkovic po celosvětově známý Foxconn — s ním spolupracoval 9 let, během kterých vyrostl z 300 tisíc na 1,2 milionu zaměstnanců ve 30 zemích světa.V nové epizodě podcastu ROŽNI uslyšíte, jak Libor před pěti lety převzal malou strojírnu na letecké díly Strojcar a jak ji pandemie během jediného týdne připravila o všechny zakázky. Libor se s tím vypořádal tak, že nechal vyrábět hliníkové protihlukové panely. Dnes k nim ve Strojcaru přidávají fotovoltaiku nebo senzory a staví je pro energeticky soběstačná města.V krizi se musel rychle rozhodnout, kterých lidí se zbavit a které si naopak ponechat.

In der heutigen Folge sprechen die Finanzjournalisten Daniel Eckert und Holger Zschäpitz über die Rekordjagd beim Dax und magische 26.000 Punkte, Übernahmefantasie bei Easyjet und die Hoffnung auf eine Sonderdividende bei Conti. Außerdem geht es um Foxconn, Nvidia, Lufthansa, Ryanair, Airbus, Brookfield, SK Hynix, Micron Technology, PepsiCo, Delta Air Lines, Levi Strauss, TSMC, Volkswagen, GEA, Repsol, Bank of America, UBS, JPMorgan Chase, Goldman Sachs, Spotify, Walmart, Ford, Ionis Pharmaceuticals, Moody's. Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und – ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Anzeige: Diese Folge enthält Werbung für Smartbroker+. Depot eröffnen, 30 € ETF als Bonus sichern und aus tausenden ETFs wählen. Smartbroker+ macht Investieren einfach. Alle Informationen gibt es unter: https://get.smartbrokerplus.de/triple-aaa-podcast2/ Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

We kunnen astronomische winstcijfers verwachten bij Samsung als gevolg van hun chips-productie, die het afgelopen kwartaal extreem veel meer winst heeft behaald dan een jaar geleden. Joe van Burik vertelt erover in deze Tech Update. Verder in deze Tech Update: Samsung praat na Anthropic nu ook met Meta over een nieuwe deal voor de productie van chips Foxconn boekt een flinke omzetwinst met dank aan AI-serverracks voor Nvidia, maar dat komt met een waarschuwing See omnystudio.com/listener for privacy information.

Although many sunbelt markets have been excessively challenged for multifamily, not all have been equally impacted. Houston, for example, has continued to see major job and population growth. Companies like Nvidia, Apple, and Foxconn have made large AI-related investments in Houston. Houston has one of the country's major ports and Is the fourth largest city in the country. It is predicted to surpass Chicago as the third largest city in the U.S. by 2035. Sam Morris is a partner at LSCRE, a multifamily real estate company with a large presence in Houston. All of LSCRE's properties are in Texas, 80% of which are in Houston, which is becoming an attractive market for institutional investors.

ลองจินตนาการถึงบริษัทที่เป็นความภาคภูมิใจของคนทั้งประเทศ มีประวัติศาสตร์ยาวนานกว่าศตวรรษ และเป็นผู้สร้างนวัตกรรมเปลี่ยนโลกอย่างทีวีและเครื่องคิดเลขพกพาเครื่องแรก แต่สุดท้ายกลับขาดทุนยับเยินเกือบสูญสิ้นชื่อ และต้องยอมขายกิจการให้กับบริษัทรับจ้างผลิตจากต่างชาติ เกิดอะไรขึ้นกับ Sharp อดีตยักษ์ใหญ่แห่งวงการเทคโนโลยีญี่ปุ่น ทำไมบริษัทที่เคยเป็นเบอร์หนึ่ง ถึงก้าวพลาดจนต้องยอมให้ Foxconn เข้ามา Takeover กิจการไปแบบเบ็ดเสร็จ มหากาพย์ความพ่ายแพ้ครั้งประวัติศาสตร์นี้ซ่อนบทเรียนราคาแพงอะไรไว้บ้าง และพวกเขาติดกับดักความสำเร็จของตัวเองได้อย่างไร คลิปนี้มีคำตอบครับ เลือกฟังกันได้เลยนะครับ อย่าลืมกด Follow ติดตาม PodCast ช่อง Geek Forever's Podcast ของผมกันด้วยนะครับ #Sharp #ชาร์ป #Foxconn #ประวัติธุรกิจ #กรณีศึกษา #วิเคราะห์ธุรกิจ #ธุรกิจญี่ปุ่น #บทเรียนธุรกิจ #ธุรกิจเครื่องใช้ไฟฟ้า #เทคโนโลยีจอภาพ #แบรนด์ญี่ปุ่น #จุดจบแบรนด์ดัง #ธุรกิจเจ๊ง #ฟ็อกซ์คอนน์ #RiseAndFall #TheRiseAndFall #geekstory #geekforeverpodcast

Flex Ltd., ticker FLEX, surged roughly eighty percent in a single month — and the company hasn't even completed the spinoff that sparked it. Nick and Kasey cover this electronics manufacturing services giant for the first time at Chip Stock Investor, breaking down what drove the run-up, what the proposed spinoff actually is, and whether there is anything left for long-term fundamental investors at today's valuation.Flex is one of the world's largest electronics manufacturing services companies, competing with Foxconn, Jabil, Celestica, and Sanmina across a global footprint spanning over ninety locations in Asia, Europe, the Middle East, Africa, and the Americas. Unlike the perception that contract manufacturing means cheap labor in Asia, Flex's business increasingly runs on automation and robotics — a structural shift that is compressing cost parity across geographies and driving genuine margin improvement. The spinoff is the centerpiece of this episode. Flex is separating its Cloud and Power Infrastructure segment — referred to as SpinCo in the materials — into a standalone company expected to begin trading by the first quarter of calendar year 2027. This segment posted thirty-eight percent year-over-year revenue growth in fiscal year 2026, with guidance pointing to sixty-five to seventy-five percent growth in fiscal 2027 and over eighty percent in fiscal 2028. The business covers critical power products for utility companies, embedded power systems inside data center servers and racks, thermal management solutions that compete in the same market as Vertiv, and cloud power infrastructure for hyperscalers and neo clouds. SpinCo also carries nearly ten percent adjusted operating margins — roughly double the margin profile of the remaining Flex business.What stays with Flex after the split is the larger but slower-growing core: twenty-one billion in revenue across Regulated Manufacturing Solutions, covering healthcare and automotive, and Integrated Technology Solutions serving customers like Cisco, Juniper Networks, now part of Hewlett Packard Enterprise, and Teradyne. Growth there is expected in the low to mid-single digits. Margins are trending in the right direction, but this is not a high-margin business.Nick and Kasey also zoom out on the broader industrial conglomerate breakup theme reshaping the market — from GE Vernova to Honeywell — and how Flex's spinoff fits squarely into that playbook. The prior Flex spinoff, NextPower in 2024, has performed very well for shareholders and gives the SpinCo story some historical credibility. The balance sheet is in reasonable shape for a manufacturer, with enough cash on hand to support bolt-on acquisitions as SpinCo looks to consolidate market share.The valuation discussion is honest: at roughly sixty to seventy times current earnings, this is a momentum trade. The forward picture for fiscal 2028 could look closer to thirty times earnings if growth delivers, but the stock is not cheap by traditional measures.For in-depth stock research and the Semiconductor Insider membership, visit chipstockinvestor.com. Use fiscal.ai/csi for 15% off any paid plan.

- Peugeot Invests €1 BILLION in France - Foxconn Takes Orders for 2nd EV - Bentley Reveals New Flying Spur - MG Announces 1st EU Plant - Tesla Faces FSD Lawsuit in China - Tesla's China-Made Deliveries Up Again - Tesla Shows Signs of EU Recovery - Kia Sets Sales Record Thanks to Hybrids

Antoine Legault discusses the quantum computing trade, highlighting growing industry adoption, U.S. government support and new technology developments from companies like Nvidia (NVDA) and Foxconn. He names IBM Corp. (IBM), IONQ, Inc. (IONQ) and D-Wave (QBTS) as key stocks to watch, while noting that leadership in the space could shift significantly over the next three years. Antoine also warns that slower than expected technological progress remains a big risk factor. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe 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

- Peugeot Invests €1 BILLION in France - Foxconn Takes Orders for 2nd EV - Bentley Reveals New Flying Spur - MG Announces 1st EU Plant - Tesla Faces FSD Lawsuit in China - Tesla's China-Made Deliveries Up Again - Tesla Shows Signs of EU Recovery - Kia Sets Sales Record Thanks to Hybrids

Emmanuel Le Roux, directeur général de Bull, était l'invité dans Tech & Co, la quotidienne, présentée par François Sorel, ce lundi 1er juin. Il s'est penché sur le rapprochement entre Bull et Foxconn afin de développer des infrastructures d'IA souveraines en Europe sur BFM Business. Retrouvez l'émission du lundi au jeudi et réécoutez-la en podcast.

Welcome to SIT Podcast. Just a few hours ago, the eyes of the global tech world turned to the Taipei Music Center, where NVIDIA CEO Jensen Huang delivered a GTC Taipei keynote that sent a jolt through the industry. As we speak, the doors of Computex 2026 have yet to officially open — but NVIDIA has already seized the moment, declaring the arrival of a "new era of PC."In this episode, we take a close look at three defining trends:1. NVIDIA moves into laptop silicon. After more than a decade away, NVIDIA returns to the consumer CPU arena with the N1 and N1X chips. According to supply-chain reports, the high-performance N1X is said to feature a 20-core Arm CPU and Blackwell-architecture graphics, with performance reportedly compared to the desktop-class RTX 5070. More significantly, this could mean the CUDA ecosystem running natively on a Windows-on-Arm laptop for the first time.2. Taiwan — the center of global AI. In his keynote, Huang revealed that NVIDIA's annual spending in Taiwan has grown to roughly $100 billion. The company is also planning an overseas headquarters called "Constellation," reportedly slated to open around 2030 and house some 4,000 employees. From TSMC's manufacturing to Foxconn's assembly, Taiwan has become the heart of what Huang envisions as the AI factory producing computational tokens.3. The rivals respond, and an industry test. Faced with NVIDIA's momentum, Intel has rolled out its Arc G3 chips built for handheld gaming devices, while Qualcomm defends its ground with a $300 entry-level Windows laptop platform. With DRAM and SSD costs climbing, Gartner projects PC prices will rise a notable 17% in 2026 — a real test of what every maker can deliver.歡迎來到 SIT Podcast。就在幾個小時前，全球科技界的目光都聚焦在台北流行音樂中心，NVIDIA 執行長黃仁勳發表了震撼產業的 GTC Taipei 主題演講。此時此刻，Computex 2026 的展覽大門尚未正式開啟，但 NVIDIA 已經先聲奪人，宣告了「PC 新紀元」的到來。在本集節目中，我們將深入解析三大關鍵趨勢：NVIDIA 跨足筆電矽晶片： NVIDIA 睽違十年重回消費型 CPU 戰場，推出 N1 與 N1X 晶片。根據供應鏈報告，高性能的 N1X 據傳搭載 20 核 Arm CPU 與 Blackwell 架構繪圖核心，其性能甚至被拿來與桌機等級的 RTX 5070 相比。更重要的是，這可能代表 CUDA 生態系將首度原生運行於 Windows-on-Arm 筆電。台灣——全球 AI 的中心： 黃仁勳在演講中透露，NVIDIA 每年在台灣的支出已增長至約 1,000 億美元。此外，NVIDIA 正計畫興建名為「Constellation」（星座）的海外總部，預計 2030 年啟用，將容納約 4,000 名員工。從台積電的製造到 Foxconn 的組裝，台灣已成為黃仁勳眼中生產「計算代幣」的 AI 工廠核心。競爭對手的回擊與產業逆風： 面對 NVIDIA 的強勢，Intel 隨即推出專為掌上型遊戲機設計的 Arc G3 晶片，Qualcomm 則以 300 美元的低價 Windows 筆電平台防守市場。然而，在 DRAM 與 SSD 成本飆升的壓力下，Gartner 預測 2026 年 PC 價格將大幅上漲 17%，這對所有廠商來說都是嚴峻的考驗。

Nvidia se ha convertido en la gran protagonista del panorama empresarial tras su participación en la feria Computex celebrada en Taipéi, donde presentó una serie de innovaciones tecnológicas destinadas a marcar el futuro del sector. La elección de Taiwán como escenario principal no ha sido casual. Su director ejecutivo, Jensen Huang, ya adelantó días atrás un ambicioso plan de inversión que podría alcanzar los 150.000 millones de dólares anuales en la isla. Durante la presentación inaugural se proyectó un vídeo en el que se mostraban varias instalaciones de la compañía en territorio taiwanés, además de poner en valor las alianzas estratégicas que mantiene con empresas locales de referencia como Foxconn y TSMC. Entre los anuncios más destacados figura el nuevo chip RTX Spark, destinado al mercado de ordenadores personales y fabricado por TSMC. Con este lanzamiento, Nvidia aspira a reforzar su posición frente a competidores de primer nivel como Intel, AMD, Qualcomm y Apple. El procesador, conocido como N1X, incorporará una GPU basada en la arquitectura Blackwell. Uno de sus principales atractivos será la integración avanzada de inteligencia artificial, permitiendo ejecutar modelos lingüísticos de hasta 120.000 millones de parámetros y manejar contextos de hasta un millón de tokens. En este proyecto, la colaboración con Microsoft desempeñará un papel fundamental mediante la adaptación de Windows 11 para aprovechar plenamente las capacidades de estos nuevos chips. Huang también dedicó parte de su intervención a presentar Vera Rubin, la nueva plataforma de inteligencia artificial de Nvidia. El directivo la definió como una auténtica supercomputadora diseñada específicamente para agentes de IA y destacó que representa un importante avance en la evolución de la informática moderna. Según explicó, empresas como OpenAI, Anthropic y SpaceX ya figuran entre los primeros usuarios de esta tecnología. Además, defendió que la expansión de los agentes inteligentes incrementará significativamente la demanda de capacidad computacional y contribuirá al crecimiento económico sin provocar una destrucción masiva de empleo. Otro de los anuncios relevantes de Computex fue la colaboración entre Nvidia y la compañía china Unitree, especializada en robots humanoides. Gracias a este acuerdo, investigadores de la Universidad de Stanford y de la Universidad de California en San Diego podrán utilizar estas plataformas robóticas en distintos proyectos de investigación. Sin embargo, la relación de Unitree con las autoridades chinas genera ciertas dudas regulatorias, especialmente en Estados Unidos, donde dicha vinculación podría dificultar o ralentizar la adopción de esta tecnología en determinados ámbitos.

Chris and Hector break down a major ransomware attack on Foxconn, the growing strain AI data centers are putting on power grids, and new allegations surrounding Microsoft security and cloud infrastructure. They also discuss insider threats, ransomware leaks, BitLocker concerns, and why cybersecurity vendors continue to overwhelm the industry with noise instead of solutions. Join our Patreon for weekly bonus episodes: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/c/hackerandthefed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Send HATF your questions at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠questions@hackerandthefed.com

Damos un repaso rápido a Apple TV antes de meternos en harina. Ángel entrevistó a Kyle Andeer, vicepresidente de Apple, y explica cómo las exigencias de Bruselas para abrir el sistema operativo a terceros chocan frontalmente con las políticas de privacidad y seguridad de la compañía. Además, debatimos sobre el conflicto emergente con OpenAI, cuyos directivos estarían considerando tomar medidas legales o expresar su frustración debido a que los retrasos en la nueva versión de Siri han diluido la exclusividad y el beneficio esperado de su acuerdo inicial con Apple.Nos quedamos ojipláticos ante el nuevo Googlebook, y encajamos la verdad sobre el Macbook Neo de Álex.Por otro lado, exploramos los rumores más llamativos de cara a la próxima WWDC, destacando las novedades que podríamos ver en el ecosistema móvil, como la llegada de los "Genmojis" generados por IA, una interfaz de cámara mucho más profesional y personalizable, y un esperado botón en pantalla para deshacer acciones.erminamos comentando el sorprendente éxito en los premios de diseño de Liquid Glass, y analizamos el reciente hackeo a una fábrica de Foxconn en Estados Unidos, fantaseando con humor sobre lo que implicaría una filtración masiva de los secretos industriales y proyectos cancelados de Cupertino. Anthropic's Mythos Found Bugs in Apple's MacOS - WSJ Apple's F1 Streaming Ambitions Hit Wall as Sky Renews European Rights - MacRumors How Apple TV's Upcoming Fantasy Show Could Run for 10+ Seasons (& Might Not End Until the 2040s) - ComicBook.com MacBook Neo Processor Benchmarks: A18 Pro CPU vs M1 and M4 Apple criticises EU measures to help AI rivals access Google services Reuters Apple Project Files Allegedly Stolen in Foxconn Ransomware Attack - MacRumors EU iPhone Users Get AirPods-Like Pairing and Notification Forwarding for Third-Party Wearables in iOS 26.5 - MacRumors Googlebook: Designed for Gemini Intelligence Coming Fall 2026 - Googlebook Meet - awm-zzhz-fda Foxconn confirms cyberattack after Nitrogen claims Apple, Nvidia data theft Apple criticises EU measures to help AI rivals access Google services Reuters Apple frente a la Ley de Mercados Digitales: "Nos están obligando a dejar funciones fuera de Europa" Creadores EU iPhone Users Get AirPods-Like Pairing and Notification Forwarding for Third-Party Wearables in iOS 26.5 - MacRumors Dazn se hace con los derechos de la Fórmula 1 en España hasta 2026 Palco23 Anexo:Derechos audiovisuales de Fórmula 1 - Wikipedia, la enciclopedia libre TV Shows on Apple TV (May 2026) Rotten Tomatoes How Apple TV's Upcoming Fantasy Show Could Run for 10+ Seasons (& Might Not End Until the 2040s) - ComicBook.com Satisfacción garantizada - Apple TV Press (ES) OpenAI preparing ‘legal action' against Apple over Siri partnership: report - 9to5Mac La cámara del iPhone sería totalmente personalizable en la próxima actualización de Apple - Infobae What award did Liquid Glass win? Apple Project Files Allegedly Stolen in Foxconn Ransomware Attack - MacRumors

Het lijkt wel 2007. De kapitaalmarktrente staat op niveaus die al decennia niet zijn gezien. De rente op Amerikaanse schuldpapieren stijgen in rap tempo, vanwege een aankomende inflatieschok, oorlog en oplopende staatsschulden. Maar het lijkt alsof het op de aandelenmarkt allemaal niks uitmaakt. Daar blijven de recordstanden gewoon overeind. Worden die risico's wel serieus genomen? Je hoort het deze aflevering. Dan hoor je ook over Tesla. Dat heeft een probleem: topman Elon Musk. Met zijn andere hobby - SpaceX - dreigt hij Tesla-beleggers weg te lokken. Analisten denken dat die Musk-fanboys hun geld straks uit Tesla gaan halen, en in SpaceX gaan steken zodra de beursgang een feit is. Zou dat een slimme zet zijn voor die beleggers? En wat betekent het voor het aandeel Tesla? Verder hebben we het over een plan van de Amerikaanse beurswaakhond. Die wil de cryptomarkt verder vervlechten met de beurs. Het moet mogelijk worden om ook tokens van aandelen te kopen. We schakelen vriend van de show Daniël Mol van de Cryptocast in om een oordeel te vellen over de plannen. Te gast: Jim Tehupuring, van 1Vermogensbeheer BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. Van Musk tot Microsoft en van Ahold tot ASML. Wij vertellen je wat beleggers bezighoudt, wie de markten in beweging zet en wat dat betekent voor jouw beleggingsportefeuille. See omnystudio.com/listener for privacy information.

Sailpoint, Skoda, Best Western Hotels, DigiCert, Foxconn, and OpenAI are having a not great week...

Foxconn confirms North American factory attack BitLocker zero-day accesses protected drives MDASH patches 16 Windows flaws Get the show notes here: https://cisoseries.com/cybersecurity-news-foxconn-factory-attacks-bitlocker-zero-day-accesses-protected-drives-mdash-patches-windows-flaws/↗ Huge thanks to our episode sponsor, Doppel  Social engineering attacks look trustworthy — a routine request, an internal email, a familiar face on a call.   But Doppel sees through the disguise. Our AI-native platform detects and disrupts attacks across every channel, while training employees to recognize deepfakes and deception.   We fight relentlessly to protect your business, brand, and people.   Doppel. Outpacing what's next in social engineering.   Learn more at doppel.com.  

El programa 2873 de Radiogeek, les habló de varios temas importantes. El teléfono Trump T1 se enviará a finales de esta semana, según el director ejecutivo de la compañía; WhatsApp incorporará un modo de "chat incógnito" para conversaciones privadas con inteligencia artificial; Samsung se enfrenta a una huelga; Un Samsung Galaxy S24 explotó en la mano de su usuario en Corea del Sur; OpenAI en el banquillo: una familia demanda a la empresa tras la muerte de su hijo por consejo de ChatGPT; Foxconn sufrió un ciberataque en sus fábricas norteamericanas y por último La aplicación Cámara de iOS 27 finalmente incorpora los controles profesionales que los fotógrafos de iPhone tanto deseaban. Toda esta información la pueden encontrar desde nuestra web www.infosertec.com.ar o bien desde el canal de Telegram/Whastapp, o Instagram. Esperamos sus comentarios.

Send us Fan MailEight terabytes of stolen schematics is not just a scary number, it is a reminder that cyber risk becomes business risk fast. We start with the Wired report on the Foxconn ransomware attack and unpack what a claim like that could mean in the real world: intellectual property exposure, supply chain disruption, customer impact, and the uncomfortable truth that recovery is only one part of the story when data walks out the door.From there, we switch into CISSP Domain 7 Security Operations mode and work through practical exam-style questions with the “how would this hold up at work” mindset. We break down why live forensics imaging can be the right call during an insider threat investigation, using the order of volatility and the kinds of RAM artifacts that disappear the moment you shut a machine down. We also tackle a Patch Tuesday nightmare scenario where a CVSS 9.8 vulnerability is already being exploited but the change advisory board will not meet for ten days, and we explain why an emergency change process plus compensating controls is the mature security operations answer.We also cover a common privileged access failure where a domain admin uses an elevated account for email and browsing, and how least privilege plus a privileged access workstation (PAW) architecture can prevent a single phish from becoming domain compromise. Finally, we sharpen the fundamentals with an RTO/RPO recovery timeline question and a SIEM brute force threshold miss that illustrates false negatives and the need for better tuning and behavioural baselines.Subscribe for weekly CISSP training, share this with a study partner, and leave a review so more security pros can find the show. What topic do you want me to turn into practice questions next?Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox!  Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

Patch Tuesday. Global agencies update SBOM guidance. Iran-linked espionage group Seedworm breached a major South Korean electronics manufacturer. A telehealth platform breach affects 716,000. Foxconn confirms a cyberattack. Maria Varmazis has an update on orbital data centers. A lawmaker questions surveillance pricing. Brandon Karpf, friend of the show, is talking with Dave about "Japan's space systems face growing cybersecurity threats." Robotic lawnmowers on the cutting edge. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today Brandon Karpf, friend of the show, is talking with Dave about "Japan's space systems face growing cybersecurity threats." Selected Reading Microsoft Fixes 17 Critical Flaws in May Patch Tuesday (Infosecurity Magazine) Microsoft Patches Critical Zero-Click Outlook Vulnerability Threatening Enterprises (SecurityWeek) Adobe Patches 52 Vulnerabilities in 10 Products (SecurityWeek) Fortinet, Ivanti Patch Critical Vulnerabilities (SecurityWeek) Chipmaker Patch Tuesday: Intel and AMD   70 Vulnerabilities (SecurityWeek) ICS Patch Tuesday: New Security Advisories From Siemens, Schneider, CISA (SecurityWeek) Global Cyber Agencies Issue New SBOMs for AI Guidance to Tackle AI Supply Chain Risks (Infosecurity Magazine) Seedworm: Iran-Linked Hackers Breached Korean Electronics Maker in Global Spying Campaign (SECURITY.COM) 716,000 Impacted by OpenLoop Health Data Breach (SecurityWeek) Foxconn confirms cyberattack after ransomware crew claims it stole confidential Apple, Nvidia files (The Register) Congressman launches inquiry into how food retailers use surveillance pricing (The Record) Orbital Inference Data Center Bets On Space GPUs (IEEE Spectrum) Cowboy Space raises $275 million to launch AI data centers on brand-new rocket (Space.com) Yarbo responds to robot flaws that could mow down their owners (Malwarebytes) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

Geert Potjewijd wordt de nieuwe voorzitter van de Autoriteit Persoonsgegevens. De privacy-advocaat verdedigde eerder TikTok, Meta, Uber en Avast in privacyzaken, soms met de AP zelf als tegenpartij. Stijn Goossens bespreekt het in deze Tech Update. Geert Potjewijd volgt Aleid Wolfsen op als voorzitter van de Autoriteit Persoonsgegevens, na tien jaar onder diens leiding. Potjewijd is momenteel nog partner bij advocatenkantoor De Brauw Blackstone Westbroek, waar hij gespecialiseerd is in privacy en digitale veiligheid. Zijn achtergrond is opvallend: Potjewijd vertegenwoordigde grote techbedrijven als TikTok, Meta, Uber en Avast in privacyzaken, terwijl de AP in sommige van die zaken juist de aanklager was. De AP zelf benadrukt die buitenwereld-ervaring als een pluspunt: de vice-voorzitter stelt dat Potjewijd "letterlijk de buitenwereld binnenbrengt." Potjewijd zal de komende vijf jaar voorzitter zijn. Hij noemt AI en digitalisering als centrale thema's. De AP kampt al langere tijd met personeelstekorten, wat de slagkracht als toezichthouder onder druk zet. De vraag is of Potjewijd, met zijn ervaring aan de andere kant van de tafel, die slagkracht kan versterken. Verder in deze Tech Update ECB roept Europese banken op zich voor te bereiden op AI-gedreven cyberaanvallen. ECB-bestuurslid Frank Elderson wijst op het risico van het AI-model Mythos van Anthropic, dat razendsnel beveiligingslekken kan opsporen. Europese banken hebben nog geen toegang tot het model, maar Elderson stelt dat dit geen reden is om niet nu al actie te ondernemen. Hackersgroep Nitrogen Group claimt meer dan 8 terabyte aan data gestolen te hebben bij Foxconn. De aanval zou ruim 11 miljoen documenten hebben opgeleverd, waaronder technische documenten van Apple, Intel, Google en Nvidia. Foxconn heeft de aanval bevestigd en meldt IT-storingen bij diverse Amerikaanse fabrieken. See omnystudio.com/listener for privacy information.

Thursday, May 9th, 2024 Marjorie Taylor Greene is beginning her effort to remove Speaker Johnson; the Georgia court of appeals has granted Trump's motion to reconsider the removal of DA Fani Willis; Judge Aileen Cannon indefinitely postpones the Trump espionage case; Nikki Haley nets 22% of the vote in the Indiana Republican primary; Biden takes aim at Trump for failed Foxconn project with a visit to Microsoft's new site in Wisconsin; the Pentagon confirms that the Biden administration stopped a shipment of bombs to Israel; Kevin McCarthy is escalating his war against Matt Gaetz; RFK Jr has literal brain worms; plus Allison and Dana deliver your Good News. Our Guest Jena Friedman: Not Funny by Jena Friedman https://www.simonandschuster.com/books/Not-Funny/Jena-Friedman/9781982178291 Twitter https://twitter.com/JenaFriedman Instagram https://www.instagram.com/jenafriedman TikTok https://www.tiktok.com/@jenafriedman Soft Focus with Jena Friedman | Adult Swim (Youtube) Haley won 1 in 5 Indiana Republican voters in the presidential primary. She left the race in March (AP News) Court of Appeals to consider DA Fani Willis removal in Trump Georgia election case (AJC) McCarthy vs. Gaetz: The GOP's never-ending feud (Politico) Biden takes aim at Trump for failed Foxconn project with visit to Microsoft's new site in Wisconsin (CNN) Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Paris Marx is joined by Brian Merchant to discuss Apple's announcement that Tim Cook is stepping down as CEO, including his history and legacy, and what may be next for the company. Brian Merchant is the author of The One Device and Blood in the Machine and writes a newsletter of the same name. Tech Won't Save Us offers a critical perspective on tech, its worldview, and wider society with the goal of inspiring people to demand better tech and a better world. Support the show on Patreon. The podcast is made in partnership with The Nation. Production is by Kyla Hewson. Also mentioned in this episode: Brian's most recent newsletter covers Tim Cook's stepping down, as well as Palantir's manifesto. Brian has previously written about Foxconn's working conditions. Brian mentioned Patrick McGee's book Apple in China.

In this episode, Ray Cochrane unpacks Anthropic’s Mythos model and the Treasury’s emergency meetings with Wall Street, then digs into Apple’s vibe-coding crackdown and a gaming-anxiety study that hit way too close to home. Also covered: Verge’s solid-state motorcycle, UBTech humanoid robot sales jumping 23-fold, Japan’s first osmotic power plant, Finland’s permanent nuclear waste vault, Ghostty landing in Ubuntu, Cloudflare’s EmDash CMS, and a Claude Code skill that talks like a caveman. – Want to start a podcast? It’s easy to get started! Sign up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show by framing Anthropic’s new Mythos model as the AlphaGo moment for cybersecurity. From there, the episode moves through Apple’s pushback against AI-generated apps, a gaming anxiety study with a deeply personal hook, a series of “first to ship” energy and robotics wins out of Finland, China, and Japan, and several developer-tool stories that show how quickly the economics of software are shifting. Mythos, the Detection Ceiling, and Wall Street’s Emergency Response Anthropic’s Mythos model has Wall Street rattled. Operating autonomously, Mythos found and demonstrated the exploitation of a 27-year-old TCP SACK bug in OpenBSD, an operating system famous for being one of the most security-focused on the planet. Per Anthropic’s red team, over 99% of the vulnerabilities Mythos has identified remain unpatched. The researchers’ conclusion is blunt: “the moat in AI cybersecurity is the system, not the model.” The policy response moved fast. On April 7th, Treasury Secretary Bessent and Fed Chair Jerome Powell pulled the CEOs of Goldman Sachs, Citi, Bank of America, and Morgan Stanley into Treasury headquarters on short notice. All four banks are now testing Mythos internally. Treasury CIO Sam Corcos is also seeking direct access. Anthropic is gating distribution through Project Glasswing, a limited-access program with JPMorgan, Apple, Google, Microsoft, and Nvidia. Cochrane comes down firmly behind Anthropic’s gated approach. Because a 5.1-billion-parameter open model can apparently recover the core analysis chain for the OpenBSD flaw, this capability is not locked behind Frontier Compute. He wants the critical infrastructure hardened before the public gets keys. However, he also notes the bigger lesson is about human wisdom: people offloading all their thinking to AI lose out on the wisdom that makes any of these tools genuinely useful. Apple Bans Vibe Coding Apps from the App Store Apple has been quietly pushing back against what people are calling “vibe coding” apps. Replit, Vibecode, and an app called Anything all run AI models on the phone and produce working software that runs inside the host app. Apple cites Guideline 2.5.2, in effect since 2017, which requires apps to be self-contained. Replit and Vibecode had their App Store updates blocked. Anything was pulled in late March, briefly restored on April 3rd, and then pulled the same day again. The forcing function is volume. App Store submissions jumped 84% in a single quarter as vibe coding tools flooded Apple’s review queue with AI-generated apps. Cochrane thinks Apple is justified, given the security issues swirling around the Vibe coding ecosystem. Even a beautiful diamond gets lost in a sea of sand, and that flood is exactly what Apple is trying to manage. The company behind Anything is now pivoting to iMessage, desktop, and Android. Playing Video Games to Win Is Linked to Higher Anxiety Cochrane gets personal on this one. Through high school and his early 20s, he was deeply addicted to League of Legends. His dad teased him about it constantly. In the last few years of that addiction, his body would go ice cold and shake every ranked match before. His partner identified it as a panic attack. The moment that happened, he quit. Today, he no longer shakes. The new study lines up with his experience. Researchers Kayleigh Watters and Mikael Rubin at Palo Alto University analyzed a publicly available database of 13,464 adult gamers, most of whom primarily played League of Legends. Players who game to win show higher generalized anxiety but actually play fewer hours, since performance pressure pushes them out. Players who game to relax show strong links between social anxiety avoidance and more hours played. The study appeared in the Journal of Affective Disorders. The headline framing of “playing to win makes you anxious” misses the point. The real finding is more interesting: gaming for avoidance and gaming for competition are both warning signs, for different reasons. Cochrane notes that the League of Legends community’s toxicity has been a running joke for years, and this study suggests the game’s structure may have been manufacturing the anxiety that fueled it. Sponsor: GoDaddy Economy hosting is $6.99/month, WordPress hosting is $12.99/month, and domains are $11.99. Both hosting plans include a free domain, professional email, and SSL certificate. Go to geeknewscentral.com/godaddy for the best pricing and to directly support this independent show. Verge Motorcycle: World’s First Production All-Solid-State Battery Cochrane filled his tank for $60 today, which made this story land especially hard. His mom has driven electric for years and patiently manages a 90-mile real-world range. The next-generation answer is already shipping. Verge Motorcycles, a Finnish company, is the first production vehicle of any kind with an all-solid-state battery. Their 2026 bikes ship in Q1 with a pack from Donut Lab, another Finnish outfit spun out of Verge. The numbers are bonkers. The pack delivers an energy density of 400 Wh/kg, roughly double that of current Tesla cells. It sustains 100kW charging, hits full charge in about 5 minutes in the lab and 12 minutes on the actual bike, and the long-range version covers 600 kilometers (about 370 miles) per charge. Toyota, QuantumScape, and Samsung SDI have all been telling us that solid-state is coming in 2027 to 2030. A Finnish motorcycle company shipping in Q1 2026 just embarrassed them all. UBTech Humanoid Robot Sales Jump 23-Fold UBTech dropped its 2025 annual earnings on April 1st. Humanoid robot revenue hit 820 million yuan, roughly $119 million USD, up 2,203% from 35.6 million yuan the year before. Unit sales went from 3 robots in 2024 to 1,079 in 2025. Shares jumped 14% on the announcement. The customer list is a real industrial deployment: BYD, Foxconn, Geely, FAW-Volkswagen, and Audi. The flagship is the Walker S2, with UBTech targeting 5,000 units in 2026 and 10,000 in 2027. Cochrane is honest about what this means. He does not think we are heading for an extinction event, but worker displacement is a real concern. The US has no universal income or universal healthcare. The people affected are not white-collar managers. They are everyday line workers who already make the least on the ladder. Work efficiency reportedly doubles when these robots arrive, which is a company-side win, but the humans they replace are not getting half a year of gardening leave to retrain. He invites the listener to take on this one directly. Japan Switches On Asia’s First Osmotic Power Plant In August 2025, Fukuoka’s Seawater Desalination Center quietly opened Asia’s first osmotic power facility. It generates about 880,000 kilowatt-hours per year, enough for roughly 220 homes. It is only the second operational osmotic plant in the world, after Mariager, Denmark, in 2023. Osmotic generation uses a salinity gradient: fresh water on one side of a membrane, salt water on the other, and the pressure difference spins a turbine. The clever part is what Fukuoka does with desalination brine. Instead of regular seawater, the plant uses concentrated brine left over from the desalination process. This amplifies the salt gradient and squeezes more energy out of the same membrane. The result is a closed-loop partnership: the desalination facility produces drinking water and leaves brine behind, the osmotic plant turns the brine into electricity, and that electricity runs the desalination facility. Every desalination plant on Earth produces brine, so if Fukuoka’s co-located model works, the same pattern could be replicated across hundreds of plants worldwide. Japan’s Luna Ring Solar Moon Proposal Goes Viral Again Shimizu Corporation’s Luna Ring concept is making the rounds again. The pitch: a 6,800-mile belt of solar panels around the Moon’s equator, beaming microwave power back to Earth. Project lead Tetsuji Yoshida has long argued that a full ring could eliminate fossil fuel dependence entirely. The proposal first surfaced in 2013, has no funding, no government endorsement, and no concrete cost estimate. Shimizu has not put any active development behind it. Cochrane finds the concept fun every time it resurfaces. However, this would have to be a worldwide effort in the truest sense, with treaties, a new generation of launch economics, and microwave power transmission at a scale nobody has demonstrated. Beaming the power back to Earth has always been one of the biggest practical holdbacks. The Luna Ring is inspirational, but not shipping. Finland’s Onkalo Nuclear Waste Vault Opens Finland’s Onkalo facility is the world’s first permanent deep geologic repository for spent nuclear fuel. Operated by Posiva, the facility is buried about 430 meters down in 1.9-billion-year-old bedrock. It is designed to hold up to 6,500 tons of spent fuel and operate until the 2120s. The construction costs about €1 billion, with operating and closure adding roughly €4 billion more before the program is done. The catch is that radioactivity remains dangerous for hundreds of thousands of years. Edwin Lyman, director of nuclear power safety at the Union of Concerned Scientists, warned that the copper canisters will eventually corrode, with different scientific opinions on how fast. Geologic disposal remains “fraught with uncertainties,” and we have never validated an engineered system across a 100,000-year time frame. The bet is that the rock and copper outlast the radioactivity. Cochrane sees Onkalo as time-buying rather than a final answer. It is more of a bank holding spent fuel while science catches up. He prefers it to Japan’s ongoing approach of releasing tritium-treated water from Fukushima Daiichi into the Pacific, even though the dilution is well below WHO drinking water guidelines. Burying the waste in an insurmountable containment strikes him as the more honest answer to a problem nobody knows how to truly solve. Ghostty Terminal Lands in the Ubuntu Repos Ghostty 1.3.0 is now available in Ubuntu 26.04 LTS’s universe repository. The install is simply `sudo apt install ghostty`, no PPAs, no Snap, no Nix, no building from source. Ghostty was created by Mitchell Hashimoto, co-founder of HashiCorp. It is GPU-accelerated, uses native Swift on macOS and native GTK4 with libadwaita on Linux, and supports tabs, splits, profiles, ligatures, and the Kitty graphics protocol. Cochrane recently caught Hashimoto on a podcast, where he walked through his agentic coding workflow. Ghostty is being actively built using AI harnesses like Claude Code and Codex. Hashimoto told a story in which Codex fixed a six-month-old bug in 45 minutes, for a total API cost of $4.14. Personally, Cochrane uses WezTerm, but he is excited to see Ghostty become more widely available with a native UI rather than Electron. Borgo: Rethinking Go Using Rust Analytics India Magazine profiled Borgo, a programming language by developer Marco Sampellegrini (GitHub: alpacaaa). Borgo is statically typed with Rust-like syntax, but it compiles to Go and uses the Go runtime and garbage collector. It includes sum types (Option and Result), pattern matching, and full compatibility with existing Go packages. Notably, it removes Rust’s borrow checker and lifetimes entirely. Borgo is not new. It first appeared on Hacker News in 2023, with a RustLab talk in 2024. The 2026 angle is a renewed look at it through the lens of AI coding agents, since type-rich languages like Rust have been showing outsized productivity gains. Cochrane is a fan of Rust and stands by the borrow checker, but he enjoys these exploratory languages for what they reveal about what developers actually want. Caveman: A Claude Code Skill That Cuts 65% of Tokens Developer Julius Brussee built a Claude Code skill called Caveman that forces Claude to respond in stripped-down fragments. No articles, no “just,” no “really,” no pleasantries, no hedging. The tagline is “why use many token when few token do trick.” Across 10 real dev tasks, Caveman mode averaged 294 tokens per response, compared to 1,214 in normal mode. That is a 65% drop in output tokens. The project is MIT licensed with three intensity levels: lite, full, and ultra. Cochrane stumbled across the project online and shared it with a classmate who had been complaining about token costs. The classmate now insists that “the caveman is the only way to live.” Cochrane has not made the switch, but the bigger point lands. If a community plugin can cut 65% of tokens without correctness regressions, the labs are shipping verbose-by-default and charging users for the privilege. He suspects verbose output makes models feel more trustworthy, even when the token math says otherwise. Cloudflare Launches EmDash as a WordPress Successor Cloudflare released EmDash on April 9th, an open-source, MIT-licensed, TypeScript-based CMS pitched as the spiritual successor to WordPress. The big flex is that it was built in 60 days using AI coding agents. EmDash runs on Astro 6.0, either on Cloudflare’s edge platform or on a standard Node.js server. The plugin security model uses sandboxed Dynamic Workers with explicit permissions, addressing the architecture flaw that Cloudflare says causes 96% of WordPress vulnerabilities. Cochrane could not resist pointing out the irony of the name. The em dash has become the trademark giveaway that an AI was involved in writing. He has reservations about whether EmDash will succeed. WordPress is extremely hard to unseat, plenty of “WordPress killers” have come and gone, and the ecosystem is twenty-plus years deep. He is curious to see what comes next but not optimistic. Google Open-Sources the DESIGN.md Format Google Labs open-sourced the DESIGN.md format used by Stitch, their AI UI design tool. DESIGN.md is a declarative file capturing a project’s design system, colors, typography, and spacing in a way AI agents can read and apply. Cochrane has tried Stitch personally and finds it impressive at producing web designs. He has also seen DESIGN.md-style files already start appearing in repositories. He sees this kind of file becoming a new paradigm for agentic design, alongside robots.txt and llms.txt. However, he worries about a side effect. If everyone uses the same standardized format and the same AI tools, the web could become a homogeneous set of sites that all look the same. He is enthusiastic about the standardization but hopes designers continue to push for genuinely unique work. A 13-Liter PC With a Water Loop Built Into the Case Geeky Gadgets covered a build by “Visual Thinker”, a 13-liter mini-ITX case with custom SLA-printed water distribution plates built directly into the chassis. Instead of traditional soft tubing, plates channel coolant between the CPU and GPU blocks and are sealed with TPU and silicone molds. The case supports a full-size GPU and an SFX power supply. No thermal benchmarks, parts list, or pricing have been published. It is a one-off you cannot buy. Cochrane sees this as a sign of where PC building has gone in 2026. Modern mid-grade GPUs run nearly every recent game, so raw performance is no longer the differentiator. He likes seeing builders lean into design and craft rather than just stuffing the most powerful parts into a box. He admits he is the traditional type and built his own machine to maximize parts, but the design-first direction is a healthy evolution for the hobby. To close out the show, Cochrane recommends Pocket Casts as a podcast app. He finds it picks up new episodes very quickly. Big thanks to GoDaddy for over twenty years of keeping this show on the air, and a reminder that every promo code use is like writing a check to the show. The post Mythos: Cybersecurity’s AlphaGo Moment #1862 appeared first on Geek News Central.

Rivian Automotive's stock soared by more than 8% in premarket trading on Thursday, after Uber announced it would invest up to $1.25 billion in the electric vehicle maker—whose shares have plummeted in a years-long rout—to deploy tens of thousands of robotaxis across the U.S. by the next decade. Key Facts Shares of Rivan jumped 8.2% in premarket trading on Thursday, marking what would be a slight rebound for the stock after stumbling by more than 14% this year. Uber said Thursday it would invest up to $1.25 billion in Rivian through 2031, with plans to purchase 10,000 of Rivian's upcoming R2 vehicle and an option to buy an additional 40,000 robotaxis in 2030. An initial $300 million investment from Uber to Rivian is expected shortly after the deal's signing and is subject to regulatory approval, Uber said. The R2 robotaxis are expected to be available through Uber in 25 cities across the U.S., Canada and Europe, with San Francisco and Miami as the launching sites in 2028, the companies said.  Uber's Robotaxi Expansion: From Rivian To Nvidia Uber has announced several partnerships over the last year as it competes with the Alphabet-backed Waymo in the robotaxi market. The company announced a strategic partnership with the Amazon-backed Zoox last week, with plans for Zoox's robotaxis to be made available through Uber by 2027. In October, Stellantis announced a joint project with Uber, Nvidia and Foxconn, with plans for Uber to deploy robotaxis from the automotive conglomerate—spanning Jeep, Dodge, Chrysler and more—in the U.S. That same day, Nvidia said it was partnering with Uber to increase Uber's autonomous vehicle fleet to 100,000, starting in 2027. Lucid, in September 2025, announced a $300 million investment from Uber, which said it would later deploy Lucid's robotaxis. Read the full story on Forbes: https://www.forbes.com/sites/tylerroush/2026/03/19/rivian-shares-rally-8-after-uber-invests-up-to-125-billion-in-robotaxi-deal/ Learn more about your ad choices. Visit megaphone.fm/adchoices

WWJ auto analyst John McElroy reports Ceer is staffed by executives with global automotive experience including several from GM. The automaker will come out with an EV that will be sold throughout the Mideast. Foxconn, Hyundai and BMW are all involved.

What if “reindustrialization” delivers fabs, data centers, and subsidies—but not the jobs? We sit down with Marxist geographer Phil Neel to unpack Hell World, a sweeping account of how deindustrialization, gigified services, and AI deskilling have rewired the global labor map. Drawing on years of on-the-ground research and a panoramic read of supply chains, Neel explains why factories employ far fewer people, why service work resists productivity gains, and how rents—especially real estate—shape cities and politics more than we admit.We follow the trail from Foxconn's peaks to muted booms in Vietnam and India, from “Chinese investment” myths in East Africa to the very real power of trade networks, wholesale warehouses, and e-commerce hubs. Along the way, Neel dismantles comforting periodizations—neoliberalism, monopoly capital, neo-feudalism—that blur structural continuities in accumulation. The state is growing, but not as a cure: military contracts, healthcare complexes, and subsidized tech now anchor a reindustrialization that largely bypasses wage earners.So where does strategy live? Neel argues for a Promethean, developmental communism that treats production and complexity as political terrain. That means credible plans for electrification, clean water, durable housing, and transit—paired with the organizational muscle to win space: assemblies, strike capacity, and the willingness to cross today's legal tripwires that have long neutralized labor. Electoral wins can blunt repression at the margins, but they won't substitute for power built in services, logistics, and the everyday circuits where value and control actually move.If your city's future looks like a shiny battery plant and an even larger rent bill, this conversation offers a sharper map. We trace commodities back to ports and smelters, expose the limits of jobless growth, and sketch a politics that aims higher than nostalgic compacts and faster than the next subsidy cycle. Listen, share with a friend, and tell us: where would you place power to make material gains possible today? Subscribe for more deep dives and leave a review to help others find the show.About Phil NeelPhil A. Neel is an author and researcher known for his "communist geography." Raised in the rural Siskiyou Mountains, his work is grounded in the material realities of the American hinterland and the global logistics industry. He is the author of Hinterland: America's New Landscape of Class and Conflict and Hellworld: The Human Species and the Planetary Factory.Send a text Musis by Bitterlake, Used with Permission, all rights to BitterlakeSupport the showCrew:Host: C. Derick VarnIntro and Outro Music by Bitter Lake.Intro Video Design: Jason MylesArt Design: Corn and C. Derick VarnLinks and Social Media:twitter: @varnvlogblue sky: @varnvlog.bsky.socialYou can find the additional streams on YoutubeCurrent Patreon at the Sponsor Tier: Jordan Sheldon, Mark J. Matthews, Lindsay Kimbrough, RedWolf, DRV, Kenneth McKee, JY Chan, Matthew Monahan, Parzival, Adriel Mixon, Buddy Roark, Daniel Petrovic,Julian

How Taiwan rose to global prominence in high tech manufacturing, from computer maker to the world's leading chip manufacturer. How did Taiwan, a former Japanese colony and the last fortress of the defeated Chinese Nationalists, ascend to such heights in high-tech manufacturing? In Island Tinkerers: Innovation and Transformation in the Making of Taiwan's Computing Industry (MIT Press, 2024), Honghong Tinn tells the critical history of how hobbyists and enthusiasts in Taiwan, including engineers, technologists, technocrats, computer users, and engineers-turned-entrepreneurs, helped transform the country with their hands-on engagement with computers. Rather than engaging in wholesale imitation of US sources, she explains, these technologists tinkered with imported computing technology and experimented with manufacturing their own versions, resulting in their own brand of successful innovation. Defying the stereotype of “the West innovates, and the East imitates,” Tinn tells the story of Taiwanese technologists' efforts over the past six decades. Beginning in the 1960s, they grappled with the “black-boxed” computers that were newly available through international technical-aid programs. Shortly after, multinational corporations that outsourced transistor and integrated circuit assembly overseas began employing Taiwanese engineers and factory workers. Island tinkerers developed strategies to adapt, modify, assemble, and work with computers in an inventive manner. It was through this creative and ingenious tinkering with computers that they were able to gain a better understanding of the technology, opening the door to future manufacturing endeavors that now include Acer, Foxconn, Asus, and Taiwan Semiconductor Manufacturing Company (TSMC). Honghong Tinn is Assistant Professor in the Program in the History of Science, Technology, and Medicine and the Department of Electrical and Computer Engineering at the University of Minnesota, Twin Cities. Li-Ping Chen is a visiting scholar in the Department of East Asian Languages and Cultures at the University of Southern California. Her research interests include literary translingualism, diaspora, and nativism in Sinophone, inter-Asian, and transpacific contexts. Li-Ping's NBN episodes on Taiwan Studies are supported by the Chun and Jane Chiu Family Foundation Taiwan Studies Program at Oregon State University. Relevant Links: Open Access for Island Tinkerers here Island Tinkerers' Book Talk with Honghong Tinn here Chinese language translation of Island Tinkerers 科技造浪者: 一部奇蹟般的台灣科技產業史，揭開全球都想知道的人脈網絡 here Fly up with Love (1978) here “Labour and (De)Industrialisation in East Asia” in Gateway To Global China Podcast here Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

How Taiwan rose to global prominence in high tech manufacturing, from computer maker to the world's leading chip manufacturer. How did Taiwan, a former Japanese colony and the last fortress of the defeated Chinese Nationalists, ascend to such heights in high-tech manufacturing? In Island Tinkerers: Innovation and Transformation in the Making of Taiwan's Computing Industry (MIT Press, 2024), Honghong Tinn tells the critical history of how hobbyists and enthusiasts in Taiwan, including engineers, technologists, technocrats, computer users, and engineers-turned-entrepreneurs, helped transform the country with their hands-on engagement with computers. Rather than engaging in wholesale imitation of US sources, she explains, these technologists tinkered with imported computing technology and experimented with manufacturing their own versions, resulting in their own brand of successful innovation. Defying the stereotype of “the West innovates, and the East imitates,” Tinn tells the story of Taiwanese technologists' efforts over the past six decades. Beginning in the 1960s, they grappled with the “black-boxed” computers that were newly available through international technical-aid programs. Shortly after, multinational corporations that outsourced transistor and integrated circuit assembly overseas began employing Taiwanese engineers and factory workers. Island tinkerers developed strategies to adapt, modify, assemble, and work with computers in an inventive manner. It was through this creative and ingenious tinkering with computers that they were able to gain a better understanding of the technology, opening the door to future manufacturing endeavors that now include Acer, Foxconn, Asus, and Taiwan Semiconductor Manufacturing Company (TSMC). Honghong Tinn is Assistant Professor in the Program in the History of Science, Technology, and Medicine and the Department of Electrical and Computer Engineering at the University of Minnesota, Twin Cities. Li-Ping Chen is a visiting scholar in the Department of East Asian Languages and Cultures at the University of Southern California. Her research interests include literary translingualism, diaspora, and nativism in Sinophone, inter-Asian, and transpacific contexts. Li-Ping's NBN episodes on Taiwan Studies are supported by the Chun and Jane Chiu Family Foundation Taiwan Studies Program at Oregon State University. Relevant Links: Open Access for Island Tinkerers here Island Tinkerers' Book Talk with Honghong Tinn here Chinese language translation of Island Tinkerers 科技造浪者: 一部奇蹟般的台灣科技產業史，揭開全球都想知道的人脈網絡 here Fly up with Love (1978) here “Labour and (De)Industrialisation in East Asia” in Gateway To Global China Podcast here Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/east-asian-studies

We discuss how Democratic candidates for Governor are competing with each other to embrace the BadgerCare Public Option bill, which will soon be introduced in the state legislature. We are joined by Citizen Action's Healthcare Action Coordinator, Kristie Tweed, to discuss widespread public support for bold action on healthcare costs and coverage. Kristie tells us about town halls Citizen Action members are holding throughout the state and how you can get involved as we push for passage of BadgerCare Public Option. We expose the power flex by big business hacks at the Milwaukee Metropolitan Association of Commerce (MMAC) which has filed suit to block proposed Port Washington ordinance to give residents a vote on public subsidies in big economic development deals. The business lobby, which promoted Foxconn, has a history of challenging democratic decision making, believing big corporations have a right to our tax money. Trump doubles down on threats to seize control of state elections, as lackey Congressman Brian Steil introduces a shocking voter suppression bill that does the bidding of the would-be dictator in the White House. We discuss why Wisconsin will be ground zero for the attack on the 2026 election and what we can do about it. Also, we discuss the ICE kidnapping of a Madison area soccer player's mom (who had legal status) at a soccer tournament, yet another example of the searing human toll of Trump's lawless crackdown.

Plus: Ubisoft Entertainment shares plunge after major structural overhaul announcement. And Elon Musk takes the stage at Davos. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Claude Opus 4.5 has entered the Chat.