Podcasts about Waymo

Google's self-driving car company says the autonomous vehicles are struggling with passengers leaving the door open, and they're partnering with DoorDash to address the problem. Learn more about your ad choices. Visit podcastchoices.com/adchoices

HOUR 3: They might be the way of the future, but do they need Waymo research? full 2237 Mon, 16 Feb 2026 22:00:00 +0000 Nv0KIIElgYThAkXe1Cc8Qn28Ecvd3928 news The Dana & Parks Podcast news HOUR 3: They might be the way of the future, but do they need Waymo research? You wanted it... Now here it is! Listen to each hour of the Dana & Parks Show whenever and wherever you want! © 2025 Audacy, Inc. News False https://player.

In today's bonus episode, Gastor and Shalewa talk about Kid Rock's canceled tour and Waymo being controlled by workers in other countries.PATREON LAUNCH! For all those that have asked how they can help support the pod - it's finally here! Thanks again to all the Troops and Correspondents who rock with us. Check it out - we'll have some exclusive content and fun perks, plus it really does help! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/WarReportPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Many Thanks to our Patreon Troops & Correspondents for helping us bring this show to life.Shouts to the Correspondents!Tanya WeimanFontayne WoodsMark OrellanaCrystall SchmidtB. EmmerichCharlene BankAskewCharlatan the FraudCynthia PongKen MogulSayDatAgain SayDatAgainLaKai DillStephanie GayleUncleJoeStylenoshCato from StonoJennifer PedersenMarcusSarah PiardAna MathambaLooking to further support? Help our data storage/archiving needs here: ⁠https://www.amazon.com/hz/wishlist/ls/23X55OW4CFU8Y?ref_=wl_share⁠Follow The Team:Instagram@SilkyJumbo@GastorAlmonteTwitter:@SilkyJumbo@GastorAlmonteTheme music "Guns Go Cold" provided by Kno of Knomercyproductions Twitter: @Kno Instagram: @KnoMercyProductions

In this episode, I'm joined by Bill Briggs, CTO at Deloitte, for a straight-talking conversation about why so many organizations get stuck in what he calls "pilot purgatory," and what it takes to move from impressive demos to measurable outcomes. Bill has spent nearly three decades helping leaders translate the "what" of new technology into the "so what," and the "now what," and he brings that lens to everything from GenAI to agentic systems, core modernization, and the messy reality of technical debt. We start with a moment of real-world context, Bill calling in from San Francisco with Super Bowl week chaos nearby, and the funny way Waymo selfies quickly turn into "oh, another Waymo" once the novelty fades. That same pattern shows up in enterprise tech, where shiny tools can grab attention fast, while the harder work, data foundations, APIs, governance, and process redesign, gets pushed to the side. Bill breaks down why layering AI on top of old workflows can backfire, including the idea that you can "weaponize inefficiency" and end up paying for it twice, once in complexity and again in compute costs. From there, we get into his "innovation flywheel" view, where progress depends on getting AI into the hands of everyday teams, building trust beyond the C-suite, and embedding guardrails into engineering pipelines so safety and discipline do not rely on wishful thinking. We also dig into technical debt with a framing I suspect will stick with a lot of listeners. Bill explains three types, malfeasance, misfeasance, and non-feasance, and why most debt comes from understandable trade-offs, not bad intent. It leads into a practical discussion on how to prioritize modernization without falling for simplistic "cloud good, mainframe bad" narratives. We finish with a myth-busting riff on infrastructure choices, a quick look at what he sees coming next in physical AI and robotics, and a human ending that somehow lands on Beach Boys songs and pinball machines, because tech leadership is still leadership, and leaders are still people. So after hearing Bill's take, where do you think your organization is right now, measurable outcomes, success theater, or somewhere in between, and what would you change first, and please share your thoughts?   Useful Links Connect With Bill Briggs Deloitte Tech Trends 2026 report Deloitte The State of AI in the Enterprise report

Yes, we gave you a live show on Presidents' Day!On today's MJ Morning Show:Phone test plansFester's drink spillSavannah Guthrie's latest pleaMorons in the newsOld news?Super speeder on Howard Frankland BridgeV-Day for Fester and MJ (separately...)Ever work at a convenience store? MJ took calls. (Phones worked)How long does a virus survive in the air or on surfaces?The Great Phone Test!How much could you spend at Publix on roses?MJ's Instagram videoSavannah Guthrie's mom updateThe leaf testTone Loc hospitalized over the weekendGuy Fieri won't eat these 6 itemsSouthwest Air... were they in the right?Station House BBQ owner Anthony calledRFK Jr's AI tells what items are best to put in your...Waymo's doorsWisconsin restaurant asks that you not dine there if you smell of marijuanaPam lung from using too much Pam gets $25 million jury awardSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Rideshare Rodeo Podcast (episode 548) Topics: DoorDash Fell 25% Over the Past 30 Days Uber Stock Drops 6.4% This Week After Earnings Miss and Robotaxi Expansion Empower Rideshare, what are they trying to accomplish  Waymo is paying DoorDash gig workers to close its robotaxi doors Seattle's gig worker law was supposed to boost pay. It did at first, until it did NOT California man says he learned his identity was stolen when Uber sent him tax forms for money he didn't earn Rideshare Rodeo Brand & Podcast: https://linktr.ee/RideshareRodeo

Our 235th episode with a summary and discussion of last week's big AI news!Recorded on 01/02/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:* Major model launches include Anthropic's Opus 4.6 with a 1M-token context window and “agent teams,” OpenAI's GPT-5.3 Codex and faster Codex Spark via Cerebras, and Google's Gemini 3 Deep Think posting big jumps on ARC-AGI-2 and other STEM benchmarks amid criticism about missing safety documentation.* Generative media advances feature ByteDance's Seedance 2.0 text-to-video with high realism and broad prompting inputs, new image models Seedream 5.0 and Alibaba's Qwen Image 2.0, plus xAI's Grok Imagine API for text/image-to-video.* Open and competitive releases expand with Zhipu's GLM-5, DeepSeek's 1M-token context model, Cursor Composer 1.5, and open-weight Qwen3 Coder Next using hybrid attention aimed at efficient local/agentic coding.* Business updates include ElevenLabs raising $500M at an $11B valuation, Runway raising $315M at a $5.3B valuation, humanoid robotics firm Apptronik raising $935M at a $5.3B valuation, Waymo announcing readiness for high-volume production of its 6th-gen hardware, plus industry drama around Anthropic's Super Bowl ad and departures from xAI.Timestamps:(00:00:10) Intro / Banter(00:02:03) Sponsor Break(00:05:33) Response to listener commentsTools & Apps(00:07:27) Anthropic releases Opus 4.6 with new 'agent teams' | TechCrunch(00:11:28) OpenAI's new GPT-5.3-Codex is 25% faster and goes way beyond coding now - what's new | ZDNET(00:25:30) OpenAI launches new macOS app for agentic coding | TechCrunch(00:26:38) Google Unveils Gemini 3 Deep Think for Science & Engineering | The Tech Buzz(00:31:26) ByteDance's Seedance 2.0 Might be the Best AI Video Generator Yet - TechEBlog(00:35:14) China's ByteDance, Alibaba unveil AI image tools to rival Google's popular Nano Banana | South China Morning Post(00:36:54) DeepSeek boosts AI model with 10-fold token addition as Zhipu AI unveils GLM-5 | South China Morning Post(00:43:11) Cursor launches Composer 1.5 with upgrades for complex tasks(00:44:03) xAI launches Grok Imagine API for text and image to videoApplications & Business(00:45:47) Nvidia-backed AI voice startups ElevenLabs hits $11 billion valuation(00:52:04) AI video startup Runway raises $315M at $5.3B valuation, eyes more capable world models | TechCrunch(00:54:02) Humanoid robot startup Apptronik has now raised $935M at a $5B+ valuation | TechCrunch(00:57:10) Anthropic says 'Claude will remain ad-free,' unlike an unnamed rival | The Verge(01:00:18) Okay, now exactly half of xAI's founding team has left the company | TechCrunch(01:04:03) Waymo's next-gen robotaxi is ready for passengers — and also 'high-volume production' | The VergeProjects & Open Source(01:04:59) Qwen3-Coder-Next: Pushing Small Hybrid Models on Agentic Coding(01:08:38) OpenClaw's AI 'skill' extensions are a security nightmare | The VergeResearch & Advancements(01:10:40) Learning to Reason in 13 Parameters(01:16:01) Reinforcement World Model Learning for LLM-based Agents(01:20:00) Opus 4.6 on Vending-Bench – Not Just a Helpful AssistantPolicy & Safety(01:22:28) METR GPT-5.2(01:26:59) The Hot Mess of AI: How Does Misalignment Scale with Model Intelligence and Task Complexity?See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

We would love to hear your feedback!News Links for Ep 289A tipping point is here for the gig economy, and we felt it the moment two verdicts landed: Uber facing an $858.5M judgment in a sexual assault case under “apparent authority,” and Instacart ordered to pay nearly $16M after a fatal crash. We unpack what these rulings actually mean—where platform liability starts, where driver accountability remains, and how this could finally force safety to become a real product priority instead of a press release.From there, we widen the lens. A delivery drone fails midair near an apartment window, sending parts and smoke to the ground. Waymo confirms that when its robotaxis get stumped, human “fleet response” agents—including teams abroad—provide guidance while the software “stays in control.” Meanwhile, Tennessee considers doubling sidewalk delivery robot speeds to 20 mph, raising obvious questions about risk to pedestrians. We talk about what responsible autonomy should look like, how to design failure modes that don't maim people, and why public trust depends on clear logs, not vague assurances.On the ground, the work gets messy too. One driver finds illegal pills tucked in a hollowed-out bun for a motel drop, a perfect snapshot of how courier features can be exploited. We share the right playbook—screenshots, immediate police contact, and no returns to sender—and outline the policies platforms should adopt to stop turning drivers into mules. There's levity as well—a parakeet “driving” a Waymo earns a TOS warning—but the point stands: when tech meets everyday chaos, design has to assume mischief.We close with a practical angle: sustainability for high-mileage drivers. If you really live on the road, EVs can beat gas on total cost of ownership—no oil changes, fewer brake jobs, and predictable energy costs—despite pricier out-of-warranty fixes. With real safety investment, better insurance architecture, and smarter autonomy rollouts, gig work can be safer and more sustainable for the people carrying the load.If this resonated, tap follow, share with a friePlease fill out the survey for a chance for a 25.00 Gift Card!   Support the showEverything Gig Economy Podcast Related: Download the audio podcast Newsletter Octopus is a mobile entertainment tablet for your riders. Earn 100.00 per month for having the tablet in your car! No cost for the driver! Want to earn more and stay safe? Download Maxymo Love the show? You now have the opportunity to support the show with some great rewards by becoming a Patron. Tier #2 we offer free merch, an Extra in-depth podcast per month, and an NSFW pre-show https://www.patreon.com/thegigeconpodcast The Gig Economy Podcast Group. Download Telegram 1st, then click on the link to join. TikTok Subscribe on Youtube

In hour 2, Mark, Melynda, and Ed talk about a new announcement about making the pride flag equal to the American flag, and all others protected by congress. As well as Waymo starting to hire gig-workers to close the doors that are left open on their autonomous vehicles. See omnystudio.com/listener for privacy information.

La versione podcast automatica della newsletter #Techy del 16/2/2026 Il panorama tecnologico sta cambiando a una velocità senza precedenti. Se ti occupi di digitale, questi sono i trend e i dati che non puoi ignorare per restare rilevante nel 2025. Ecco l'analisi sintetica di ciò che sta accadendo:1. La Crisi del Software (SaaSgeddon)

On today's episode of Fletch, Vaughan & Hayley's Big Pod, Waymo paying Door Dashers Top 6 - Things smart undies will tell you Winter Olympics Tiramisu SLP - How many times have you been in love Retirement home for Gen Z WTF is boy Kibble August Cassette Do you hate something your family loves? Haley is old Going into debt for dating Fact of the day What's your business turn-off? QLP - Are you still playing Pokémon Go? When ere you ghosted by a professional? Scammy Irish family See omnystudio.com/listener for privacy information.

Ralph welcomes, Robert Weissman co-president of Public Citizen, to discuss his Senate testimony about the many ways the Trump Administration's assault on fraud is itself fraudulent. Plus, Ralph informs us of a report from Aljazeera about the MK-84 weapon the IDF is using in Gaza that is designed to generate so much heat it literally vaporizes people.Robert Weissman is a staunch public interest advocate and activist, as well as an expert on a wide variety of issues ranging from corporate accountability and government transparency, to trade and globalization, to economic and regulatory policy. As the president of Public Citizen, he has spearheaded the effort to loosen the chokehold corporations and the wealthy have over our democracy.Every American should be worried about fraud. So it's fine for the committee to be talking about fraud, but it should be based on actual facts and what's actually happening, which is not what's going on with this focus on Minnesota… And without a doubt, if the concern is about fraud in the public or the private economy right now, the number one problem with fraud is the Trump administration.Robert WeissmanThanks to the Supreme Court decision on Presidential immunity, Trump believes (correctly) that he will not be held criminally accountable for anything that he does while he's President. And that is true so long as that Supreme Court decision stands. And I think it's fair to say that basically everyone who's working for him right now—who I think are committing all kinds of crimes, including through the sale of pardons and through the outrageous use of ICE in Minnesota and around the country—I think they expect they're going to get pardoned before he goes. So I think they think they too will be (and they're probably not wrong in expecting it) that they too will be immune from criminal prosecution (at least federal criminal prosecution) for any crimes they commit while they're in the administration.Robert WeissmanIn Case You Haven't Heard with Francesco DeSantisNews 2/13/26* Our top stories this week concern the Jeffrey Epstein case. According to POLITICO, Democratic Congressman Ro Khanna, who, along with Republican Congressman Thomas Massie has led the charge to release the Epstein files, “took to the House floor Tuesday and read aloud the names of six ‘wealthy, powerful men' whose names were originally redacted,” in the files. These names include billionaire Victoria's Secret owner Leslie Wexner, Emirati shipping magnate Sultan Ahmed Bin Sulayem, and Italian politician Nicola Caputo, among other more mysterious figures like Salvatore Nuara and Leonic Leonov. Khanna used congressional representatives' unique power under the speech and debate clause to make these names public, after combing through the files personally along with Rep. Massie. Khanna added “if we found six men that they were hiding in two hours, imagine how many men they are covering up for in those 3 million files.”* Speaking of hiding names in the files, Axios reports that Representative Jamie Raskin stated that “when he searched President Trump's name in the unredacted Epstein files… it came up ‘more than a million times.'” The implication of this statement is clear: Trump's cronies in the Justice Department are covering up the extent of Trump's relationship and involvement with Jeffrey Epstein. Another member of the administration, Commerce Secretary Howard Lutnick, admitted under Senate questioning that he had lunch with Epstein on his island, along with his family, claiming he “could not recall” why they did. The administration is allowing members of Congress to view the unredacted files within certain hours via a database they describe as confusing, unreliable, and clunky.* Another surprising revelation from the files is that House Minority Leader Hakeem Jeffries apparently solicited campaign donations from Epstein back in 2013. According to MSN, Epstein received a campaign solicitation via email from a fundraising firm touting Jeffries as “one of the rising stars in the New York Congressional delegation,” and offering Epstein “an opportunity to get to know Hakeem better.” Jeffries denies having any knowledge of this firm's outreach to Epstein and decried House Oversight Committee Chairman James Comer's implication that he had any relationship with the late sexual predator and financier, calling Comer a “stone cold liar” and a “malignant clown.”* In non-Epstein related news from Capitol Hill, last week lawmakers held a hearing to probe the operations of autonomous taxi service Waymo. While Republicans chose to focus on Waymo's supposed ties to Chinese companies, Senator Ed Markey of Massachusetts grilled the chief safety officer, Mauricio Peña, on the company's reliance on workers abroad for key safety decisions. Peña admitted that while some operators are located in the US, others – who step in when robotaxis encounter “unusual situations” – work remotely from the Philippines. Markey called this “completely unacceptable,” emphasizing that these workers may need to react “in a split second” during dangerous scenarios. Waymo is just the latest company marketing its services as high tech and autonomous, but later revealed to be reliant on cheap foreign labor. This from Business Insider.* ICE lawlessness continues to roil Congress. Many Democrats are now sounding the alarm that Trump's immigration police – masked, armed, accountable directly to him and backed to the hilt by the administration – could be used as a tool to suppress voter turnout by conducting raids at or near polling locations, thereby scaring citizens into staying home. Senator Chris Murphy of Connecticut said “Trump is trying to create a pretext to rig the election.” Murphy, along with some Senate Democratic allies, pushed leadership to demand that ICE be banned from polling sites as a condition of government shutdown negotiations, but leadership balked, per POLITICO. While such a scenario can sound far-fetched, Trump has “falsely and repeatedly claimed for more than a decade that millions of illegal immigrants vote in the U.S., arguing that was one factor in his 2020 loss,” and, just before the 2020 election, he pledged to send “sheriffs” and “law enforcement” to polling places.* Drop Site News' Jacqueline Sweet reports 70 organizations, Jewish, Christian, Muslim, Hindu, and Unitarian, as well as civil rights, academic, legal, peace, and human rights groups, submitted a formal request to the National Security Division of the Justice Department seeking a “Foreign Agents Registration Act (FARA) investigation into Canary Mission.” Canary Mission is a shadowy, infamous group that tracks pro-Palestine activity on college campuses. In 2018, they appeared at the George Washington University wearing spooky masks in an attempt to intimidate the student government into voting down a BDS resolution. They failed. This latest letter comes on the heels of a Drop Site story from January that “showed among other things that Canary is operated in Israel by a large Israeli team.” As the letter notes, the Foreign Agent Registration Act “exists precisely to address this type of potential activity carried out in the United States for the benefit of a foreign country.”* In more news regarding pro-Palestine activism, last week, six defendants linked to Palestine Action, a direct action protest group in the United Kingdom, were acquitted of aggravated burglary in connection with an alleged break in at Elbit Systems, a defense firm with close ties to the Israeli military, in August 2024. The persecution of Palestine Action has gone far beyond normal law enforcement. Some activists have been in pre-trial detention for over 500 days, more than double the maximum limit set by the Crown Prosecution Service. The case of the Palestine Action protestors has drawn outcry from international human rights groups, including the United Nations and Human Rights Watch. As HRW notes, in July of last year, the British government declared Palestine Action a terrorist organization and have now detained over 2,700 protestors over infractions as minor as holding a sign reading “I oppose genocide, I support Palestine Action.” As of now, over 20 activists are still in detention awaiting trial, many beyond the legal limits, and the six acquitted activists may face retrial. But for now, the group has scored a major victory in the face of overwhelming odds.* Turning back to domestic news, New York Governor Kathy Hochul appears to have pulled off a fait accompli in her reelection campaign. Last year, former Representative Elise Stefanik dropped her bid for the Republican gubernatorial nomination and sitting Rep. Mike Lawler declined to run. Now, Hochul's main primary opponent – Lieutenant Governor Antonio Delgado – has dropped his bid after Hochul secured the endorsements of New York City Mayor and political superstar Zohran Mamdani as well as the entirety of the New York Democratic congressional delegation. This from the New York Times. This is a stunning political feat for a Governor who won the narrowest gubernatorial election in the state since 1994 when she was last up in 2022. It now seems that Hochul will square off against Bruce Blakeman, the Trump-endorsed Republican executive of Nassau County in November.* Meanwhile in Los Angeles, the dynamic of the Mayoral race was upended this week by the last-minute decision of Councilmember Nithya Raman to throw her hat into the ring against incumbent Mayor Karen Bass. Raman, an urban planner by trade, chairs the Council's Housing and Homelessness Committee and has “built her political identity around tenant protections, homelessness policy and efforts to accelerate housing production,” per the Los Angeles Daily News. Raman was the first of several Councilmembers elected with DSA support and she has maintained a strong relationship with the local branch despite tensions with the national organization, primarily over Israel/Palestine issues. Bass, who won a narrow election against billionaire developer Rick Caruso in 2022, has faced harsh criticism over her handling of the devastating fires in 2025 and her inability to make significant progress on the city's homelessness crisis. However, Bass maintains the support of much of the city's Democratic establishment, including the unions and much of the City Council and Raman's late entry will make it difficult for her to consolidate majority support across the sprawling western metropolis.* Finally, in a David-and-Goliath tale, we turn to TJ Sabula, the UAW Local 600 Ford factory line worker who called Trump a “pedophile protector.” Infamously, the president retorted by giving Sabula the finger and mouthing, “F--- you.” Ironically, Trump also trotted out his iconic catchphrase “You're fired.” Well, Sabula was not fired – and in fact “has no discipline on his record,” – because he was protected by his union, per the Detroit News. In a recent address, UAW Vice President Laura Dickerson said “TJ, we got your back,” adding “In that moment, we saw what the president really thinks about working people…As UAW members, we speak truth to power. We don't just protect rights, we exercise them.” UAW President Shawn Fain, who has emerged as a firebrand leader of the revitalized labor movement, commented “That's a union brother who spoke up…He put his constitutional rights to work. He put his union rights to work.”This has been Francesco DeSantis, with In Case You Haven't Heard. Get full access to Ralph Nader Radio Hour at www.ralphnaderradiohour.com/subscribe

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss whether Waymo has finally solved the supply constraint question following reports of a deal for 50,000 Hyundai vehicles by 2028. They break down the economics, theorizing a $50,000 per-vehicle cost that likely includes line-fit sensors, a price point that Grayson argues destroys the bear case that autonomous vehicles cannot cost-effectively scale.The conversation then shifts to hardware as Walt puts on his inspector hat, spotting a hidden Class 8 truck graphic in Waymo's latest blog post. This revelation sparks a debate on if Waymo is planning a return to trucking in 2027 to coincide with the new Daimler Truck's new Freightliner Cascadia redundant chassis platform. They also analyze Waymo's 6th Generation Driver, noting the emphasis on custom silicon and aggressive camera cleaning systems seems to mimic Tesla's approach.On the Foreign Autonomy Desk, they discuss Lyft's plan to launch Baidu RT6 robotaxis in London and Uber's deployment of Chinese robotaxis in Dubai. While Uber touts its partners, Grayson provides ground truth on the Chinese market, arguing that strict geofences and residency restrictions mean the technology is not as far ahead as Western media portrays.Looking at the broader ecosystem, Grayson and Walt analyze Aurora's pivot to upfitting International trucks, a strategy shift that mirrors competitor Kodiak, along with Kodiak's new defense partnership with the United States Marine Corps.Closing out the show, they discuss the current regulatory environment for autonomous vehicles and NHTSA's Automated Vehicle Safety Public Meeting upcoming in March and Waymo calling for D.C. residents to advocate for autonomous vehicles.Episode Chapters0:00 Waymo's Reported 50,000 Robotaxi Hyundai Deal03:26 The $50,000 Robotaxi Economics06:20 Zeekr & Waymo/Magna Mesa Upfitting Plant10:11 Scaling to 750,000 Autonomous Vehicles17:09 Waymo Gen 6: Custom Silicon & Improved Cameras23:21 Uber's Narrative vs. Waymo's Reality28:09 Lyft's Flexdrive Advantage31:52 Inspector Walt: Waymo's Autonomous Truck Tease33:41 Aurora's Pivot & Kodiak's Marine Corps Deal41:39 Foreign Autonomy Desk: Lyft in London & Uber in Dubai45:09 The Regulatory Tide Turns48:38 Hyundai: The Arms Dealer of AutonomyRecorded on Friday, February 13, 2026--------About The Road to AutonomyThe Road to Autonomy is the definitive media brand covering the Autonomy Economy™. Through our podcasts, newsletter, and proprietary market intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth. To learn more, say hello (at) roadtoautonomy.com.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Impact sur l'emploi, utilisation malveillante, perte de contrôle... L'intelligence artificielle soulève toujours autant d'interrogations et d'inquiétudes. Des démissions jettent le trouble et alimentent les questionnements. Et aussi : l'actu de la semaine.

We kick things off in FOLLOW UP with the ongoing "nuclear war" between Automattic and WP Engine, where discovery has revealed Matt Mullenweg's alleged hit list of competitors and a desperate attempt to bully payment processors—because nothing says "open source" like an eight-percent royalty shakedown. Meanwhile, the Harvard Business Review confirmed what we already knew: AI isn't reducing our work; it's just compressing it until we're all working through lunch and burning out faster while Polymarket turns our collective brain rot into a literal "attention market" where you can bet on Elon's mindshare.Transitioning to IN THE NEWS, Elon has officially pivoted SpaceX from Mars to the Moon, presumably because building a "self-growing lunar city" is easier than admitting the Red Planet is hard, though his xAI all-hands rant about "ancient alien catapults" suggests he's been staring at the sun too long. Between X allegedly taking blue-check lunch money from sanctioned Iranian leaders, Meta facing trials for creating "predator-friendly hunting grounds," and Russia finally pulling the plug on WhatsApp, the internet is looking more like a digital dumpster fire than ever. Add in Discord leaking 70,000 government IDs, OpenAI shoving ads into ChatGPT while safety researchers flee the building like it's on fire, and a "cognitive debt" crisis eroding our ability to think, and you've got a recipe for a tech-induced psychosis that even crypto-funded human trafficking can't outpace.In MEDIA CANDY, we're wondering about the soft-core porn intro in the latest Star Trek: Starfleet Academy while Apple buys the total rights to Severance for seventy million dollars—because in-house production is the only way to keep those ballooning budgets under control. Super Bowl trailer season gave us a glimpse of The Mandalorian and Grogu and a Project Hail Mary teaser, while Babylon 5 has finally landed on YouTube for free, proving that even 90s serialized sci-fi eventually finds its way to the clearance bin.Over in APPS & DOODADS, Meta Quest is nagging us for our birthdays like a needy relative, while Roblox had to scrub a mass-shooting simulator—because "AI plus human safety teams" is apparently just code for "we missed it until it hit the forums." Ring's Super Bowl ad for "Search Party" accidentally terrified everyone by revealing a mass surveillance network for pets that's a slippery slope toward a police state, and Waymo is now paying DoorDashers ten bucks just to walk over and close the car doors that autonomous tech still can't figure out.Wrapping up with THE DARK SIDE WITH DAVE, we dive into the Mandalorian Hasbro reveal where Sigourney Weaver's action figure comes with no accessories because her existence is enough of a flex. We explore the grim reality of "RentAHuman," where humans are paid pittance to pretend AI agents are actually doing work, and look at "Trash Talk Audio," which sells a $125 microphone made out of a literal old telephone for that authentic Gen-X "get off the line, I'm expecting a call" aesthetic. From Marcia Lucas finally venting about the prequels and a rare book catalog specifically for our aging generation, we're reminded that while the future is a chaotic mess of "GeoSpy" AI and corporate reshuffling at Disney, at least we still have our cynical memories and some free versions of Roller Coaster Tycoon to keep us from losing it completely.Sponsors:CleanMyMac - Get Tidy Today! Try 7 days free and use code OLDGEEKS for 20% off at clnmy.com/OLDGEEKSDeleteMe - Get 20% off your DeleteMe plan when you go to JoinDeleteMe.com/GOG and use promo code GOG at checkout.Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/733FOLLOW UPAutomattic planned to target 10 competitors with royalty fees, WP Engine claims in new filingAI Doesn't Reduce Work—It Intensifies ItPolymarket To Offer Attention Markets In Partnership With Kaito AIIsrael Arrests Members of Military for Placing Polymarket Bets Using Inside Information on Upcoming StrikesIN THE NEWSUnable to Reach Mars, Musk Does the Most Musk Thing PossibleWe'll Find the Remnants of Ancient Alien Civilizations': Read Musk's Gibberish Rant from His xAI All-Hands MeetingElon Musk's X Appears to Be Violating US Sanctions by Selling Premium Accounts to Iranian LeadersMeta Faces Two Key Trials That Could Change Social Media ForeverWhatsApp is now fully blocked in RussiaRussia is restricting access to Telegram, one of its most popular social media apps. Here's what we knowDOJ may face investigation for pressuring Apple, Google to remove apps for tracking ICE agentsDiscord Launches Teen-by-Default Settings GloballyDiscord says hackers stole government IDs of 70,000 usersFree Tool Says it Can Bypass Discord's Age Verification Check With a 3D ModelTesting ads in ChatGPTOpenAI Researcher Quits, Warns Its Unprecedented ‘Archive of Human Candor' Is DangerousOpenAI Fires Top Safety Exec Who Opposed ChatGPT's “Adult Mode”Anthropic AI Safety Researcher Warns Of World ‘In Peril' In ResignationMusk's xAI loses second co-founder in two daysAmerica Isn't Ready for What AI Will Do to JobsMonologue: No, Something Big Isn't ComingThe Scientist Who Predicted AI Psychosis Has a Grim Forecast of What's Going to Happen NextCrypto-Funded Human Trafficking Is ExplodingMEDIA CANDYShrinkingStar Trek: Starfleet AcademyPoor ThingsProject Hail Mary | Final TrailerMinions & Monsters | Official TrailerDisclosure Day | Big Game SpotThe Mandalorian and Grogu | A New Journey Begins | In Theaters May 22Babylon 5 Is Now Free to Watch On YouTubeApple acquires all rights to ‘Severance,' will produce future seasons in-houseOptimizing your TVAPPS & DOODADSTumbler Ridge Shooter Created Mall Shooting Simulator in RobloxHere's how to disable Ring's creepy Search Party featureWaymo Is Getting DoorDashers to Close Doors on Self Driving CarsTikTok US launches a local feed that leverages a user's exact locationApple just released iOS 26.3 alongside updates for the Mac, iPad and Apple WatchTHE DARK SIDE WITH DAVEDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingWe Call It ImagineeringYour First Look at Hasbro's 'Mandalorian and Grogu' Figures Is Here (Exclusive)I Tried RentAHuman, Where AI Agents Hired Me to Hype Their AI StartupsTrash Talk AudioRoger Reacts to Star Wars - A New HopeMarcia Lucas Finally Speaks Out | Icons Unearthed: Unplugged (FULL INTERVIEW)What's wrong with the prequels?Rare Books, Gen X editionGeoSpyCLOSING SHOUT-OUTSRobert Tinney, who painted iconic Byte magazine covers, RIPBud CortSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This is a free preview of a paid episode. To hear more, visit andrewsullivan.substack.comZaid is a young center-left journalist (after the young center-right journo we had on last week, Jason Willick). Zaid worked as a reporter for The Intercept and as a reporter-blogger for ThinkProgress, United Republic, the Progressive Change Campaign Committee, and Alternet. He's now on Substack at “The American Saga” — subscribe!For two clips of our convo — on what the Dems should do on immigration, and whether Ossoff and Buttigieg could be strong contenders for the presidency — head to our YouTube page.Other topics: his parents immigrating from Pakistan; born and raised outside Atlanta in Newt Gingrich country; growing up Muslim in the South; tithing and agape; starting a student magazine at UGA; Mamdani and affordability; higher taxes on the rich; universal childcare; Ossoff and “the Epstein class”; the Dems' denialism over Kamala; identity politics killing the party; how Dems should respond to AI; data centers hiking energy bills; Waymo; Trump's success at closing the border; asylum reform; the left crying wolf over racism; Stephen Miller the wolf; Eric Kaufmann's Whiteshift; pushing left-racism on a racially tolerant public; Jasmine Crockett; Dem leaders cowed by activists; transqueer ideology; Bad Bunny; Israel and the Dems; foreign aid; Tom Massie; Ro Khanna; gerontocracy; Obama's success in red states; rumors of Stacey Abrams being closeted; AOC; Warnock; Newsom's left-wing baggage; the silo of Bluesky; Renee Good; and the indoctrination of kids on gender.Browse the Dishcast archive for an episode you might enjoy. Coming up: Sally Quinn on the WaPo and silent retreats, Michael Pollan on consciousness, Jeffrey Toobin on the pardon power, Derek Thompson on abundance, Matt Goodwin on the UK political earthquake, Jonah Goldberg on the state of conservatism, Tom Holland on the Christian roots of liberalism, Adrian Wooldridge on “the lost genius of liberalism,” Tiffany Jenkins on privacy, and Kathryn Paige Harden on the genetics of vice. An abundance of riches! And a lot of reading for yours truly! As always, please send any guest recs, dissents, and other comments to dish@andrewsullivan.com.

Send a textWatch the top undergraduate and master's teams in the 2026 Case Competition World Cup Finals go head-to-head in a live, high-pressure strategy showdown.5 finalist teams take on The Waymo Challenge, presented by Grant Thornton Stax: build a 3–5 year growth strategy to scale Waymo's autonomous ride-hailing business – without sacrificing safety, strong unit economics, or public trust.You'll see:5-minute pitches from undergrad/MS competitorsLive Q&A and evaluation from Celebrity Judges at EY-Parthenon, Grant Thornton Stax, KPMG, L.E.K. Consulting, and Simon-KucherHow top employers assess structured thinking, judgment, and storytellingPresented by Grant Thornton Stax, a strategy consulting firm specializing in analytics-driven growth strategy and commercial diligence. Stax partnered to develop the case and is actively hiring talent interested in strategy, data, and real-world impact.Helpful Stax Links:Careers siteLife at Grant Thornton StaxContact: recruiting@stax.comAdditional Resources:Learn more about running a case competition for your university or club (Career Services & Club Leaders only)Book a free 15-minute call with Katie to explore Management Consulted prep support optionsEmployee Survival Guide®A Podcast only for employees. Mark shares information your employer does not want you knowListen on: Apple Podcasts SpotifyConnect With Management Consulted Schedule free 15min consultation with the MC Team. Watch the video version of the podcast on YouTube! Follow us on LinkedIn, Instagram, and TikTok for the latest updates and industry insights! Join an upcoming live event - case interviews demos, expert panels, and more. Email us (team@managementconsulted.com) with questions or feedback.

Three Big Conversations: The hills are alive with the sound of kids saying "chicken, banana," - 14:38 A new AI tool called OpenClaw has an iron grasp on data - 20:20 Backlash took center stage after Bad Bunny's halftime show. - 49:04 Song of the Week: "The Great Divide" - Noah Kahan - 01:16 Click here to read the lyrics. In Other News.. - 1:04:02 James Van Der Beek passed away this week after battling colorectal cancer. Best known for his role as Dawson Leery on Dawson's Creek, he helped define late 90s teen drama, and his legacy continues through streaming nostalgia and even one of the internet's earliest viral reaction GIFs.  At a congressional hearing last week, self-driving car company Waymo admitted that sometimes its cars are being remote-controlled by workers based in the Philippines. An emerging star of the 2026 Winter Olympics is American figure skater Alysa Liu, who won gold and then promptly broke her medal. Cool guys have bangs now, according to GQ. Fellas are trading the long-popular long-on the-top, short-on-the sides haircut for softer, more lived-in fringe. This weekend, the long-awaited, R-rated adaptation of the gothic romance novel Wuthering Heights will hit 3,600 screens and is projected to hit $50 million on its opening weekend.  Axis Resource → A Parent's Guide to Talking About Hell

- China to Ban Yoke Steering Wheels and Mandate Physical Buttons - Trump Administration Eliminates EPA Endangerment Finding in Historic Deregulation - Rivian Stock Surges 25% On 2026 Growth Guidance Despite 2025 Revenue Slump - Waymo Rolls Out 6th-Gen AV Tech Stack Targeting 1 Million Weekly Rides - Canada's Project Arrow Debuts Next-Gen EV Prototypes - Maextro S800 Outsells Mercedes-Maybach and BMW 7 Series in China - White House Considers Lowering Steel and Aluminum Tariffs to Ease Auto Manufacturing Costs - Mercedes-Benz To Sell Daimler Truck Stake to Boost Finances After 50% Profit Drop

- China to Ban Yoke Steering Wheels and Mandate Physical Buttons - Trump Administration Eliminates EPA Endangerment Finding in Historic Deregulation - Rivian Stock Surges 25% On 2026 Growth Guidance Despite 2025 Revenue Slump - Waymo Rolls Out 6th-Gen AV Tech Stack Targeting 1 Million Weekly Rides - Canada's Project Arrow Debuts Next-Gen EV Prototypes - Maextro S800 Outsells Mercedes-Maybach and BMW 7 Series in China - White House Considers Lowering Steel and Aluminum Tariffs to Ease Auto Manufacturing Costs - Mercedes-Benz To Sell Daimler Truck Stake to Boost Finances After 50% Profit Drop

Reggie, Paul, & Collin give their takes on WayMos.

Modèles IA de la semaine, la crise de la RAM et l’automatisation des emplois. Discussions sur Waymo et l’IA utilisée pour la génération de long-métrages documentaires historiques.  Me soutenir sur Patreon Me retrouver sur YouTube On discute ensemble sur Discord Modèles de la semaine Skintoken, Kling 3 et Lucy 2. Les world models pour les voitures. Darren des bourdes ? L'IA arrive à la TV… Des IA juges aux JO. Pour mesurer les penis ? Les rumeurs d’apocalypse seraient un peu exagérées ? Et si votre ami avait une backdoor ? Giteubé : l'IA met la pression… négative. Metal Gears Western digital, des disques passés au peigne fin… Plasmon quoi ? Et où ? Tic et TACC : il y a 64 bits et 64 bit, soyons précis. DDRAMA : la crise arrive sur les téléphones. Où sont les renforts ? Temu du genou : JDD chopé comme un millionnaire. Flash est toujours vivant… En quelque sorte. Participants Une émission préparée par Guillaume Poggiaspalla Présenté par Guillaume Vendé

Les voitures autonomes actuelles ne sont pas encore capables de rouler partout sans préparation. Waymo affirme franchir une étape clé grâce aux “World Models” capables de générer des situations de conduite ultra-réalistes pour mieux affronter l'inattendu.Pourquoi les voitures autonomes ne sont pas encore universellesLes véhicules autonomes qui circulent aujourd'hui aux États-Unis ou en Chine sont de niveau 4. Cela signifie qu'ils fonctionnent dans des zones précises, après avoir été longuement entraînés dans ces environnements. Ils ne disposent pas encore de la capacité d'adaptation universelle d'un conducteur humain, capable de faire face à n'importe quelle situation, dans n'importe quelle ville et sous n'importe quelle météo.Un entraînement encore trop dépendant du réelLa limite des systèmes actuels tient à leur apprentissage. Ils excellent dans des contextes qu'ils connaissent déjà, mais peuvent être pris en défaut face à des événements rares : véhicule à contresens, conditions météorologiques extrêmes, obstacle inattendu ou comportement imprévisible d'un autre usager. Pour viser le niveau 5 — l'autonomie totale — il faut élargir considérablement la palette des situations rencontrées pendant l'entraînement.Des “World Models” pour simuler toutes les routes du mondeWaymo mise sur une approche fondée sur un modèle génératif capable de créer des environnements de conduite photoréalistes et interactifs à partir de simples vidéos en deux dimensions. Le système reconstitue des scènes en trois dimensions dans lesquelles le logiciel de conduite autonome peut évoluer comme en conditions réelles. Ce dispositif permet de générer à la demande des scénarios très variés : tempête de neige sur le Golden Gate, tornade soudaine, rue tropicale enneigée ou événements improbables comme des objets mal arrimés sur un toit de voiture, un animal sauvage surgissant sur la chaussée ou un piéton déguisé de manière insolite. L'intérêt est de confronter le système à des milliards de variations d'un même scénario, afin d'améliorer sa capacité d'adaptation.Une étape vers le niveau 5 ?Selon l'entreprise, cette méthode serait plus rapide, moins coûteuse et plus stable que les simulateurs traditionnels. Elle permettrait d'accélérer l'apprentissage tout en testant des situations difficiles, voire dangereuses, impossibles à reproduire facilement dans le monde réel. Reste une question centrale : un entraînement massif dans des univers simulés suffira-t-il à reproduire la souplesse de jugement d'un conducteur humain ? Car face à une situation extrême, les réactions varient d'une personne à l'autre. Les World Models représentent sans doute une avancée majeure. Mais la route vers une autonomie totale, capable de s'adapter partout et en toutes circonstances, demeure un défi technologique et éthique de premier plan.-----------♥️ Soutien : https://mondenumerique.info/don

Waymo hat doch Menschen im Hintergrund: Remote-Operatoren auf den Philippinen übernehmen, wenn ein Fahrzeug nicht weiterkommt – inklusive Infinite-Money-Glitch über DoorDash. Anthropic sammelt weitere $30 Mrd. ein bei $380 Mrd. Bewertung – praktisch jeder große Investor ist dabei. Bloomberg berichtet vom Tabubruch, in OpenAI und Anthropic gleichzeitig zu investieren. X erreicht $1 Mrd. Subscription-ARR, lag als Twitter aber mal bei $5 Mrd. Werbeumsatz. Spotify behauptet, die besten Entwickler hätten seit Dezember keine Zeile Code geschrieben – die R&D-Kosten sind aber tatsächlich um fast 40% gesunken. OpenAI und Google warnen US-Abgeordnete vor chinesischer Modell-Destillation. Die EU eröffnet ein neues Antitrust-Verfahren gegen Googles Werbeauktionen, während AI Overviews das offene Web weiter austrocknen. Die FTC attackiert Apple News wegen angeblichem Links-Bias. Robinhood enttäuscht mit schwachem Krypto-Geschäft, Cloudflare glänzt mit 34% Umsatzwachstum und 40% Kundenwachstum. Die EPA streicht unter Trump die wissenschaftliche Basis für die Schädlichkeit von Treibhausgasen. Eine Juniper-Research-Studie zeigt: Jede 10. Social-Media-Anzeige in Europa ist ein Scam. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Waymo (00:08:59) Anthropic $30 Mrd. Funding (00:15:11) X erreicht $1 Mrd. Subscription ARR (00:18:02) Spotify: Beste Entwickler schreiben keinen Code mehr (00:21:07) Jonas Andrulis und Roland Berger Joint Venture (00:26:55) ai.com Domain für $70 Mio. verkauft (00:30:01) China destilliert OpenAI und Google Modelle (00:40:24) Distillation Attacks: Die Debatte um Content-Klau (00:41:59) Google Antitrust: EU untersucht Werbeauktionen (00:46:07) AI Overviews und das Sterben des Open Web (00:51:49) FTC vs. Apple News (01:07:34) Robinhood und Coinbase Earnings (01:13:12) Cloudflare Earnings (01:19:55) Verbraucherschutz: Elster Phishing und Scam Ads Studie (01:25:41) EPA streicht Klimaschutz-Grundlage Shownotes Waymo setzt menschliche Agenten im Ausland ein - cybernews.com Waymo stellt DoorDash-Fahrer ein, um Autotüren zu schließen. - x.com Anthropic schließt $30 Milliarden Finanzierungsrunde für KI-Startups ab. - cnbc.com Anthropic erhält $30 Milliarden in Serie-G-Finanzierung. - anthropic.com VCs brechen Tabu: Unterstützung für Anthropic und OpenAI. - bloomberg.com 1. X Subscriptions - theinformation.com Elon Musks xAI verliert zweiten Mitgründer in 48 Stunden. - businessinsider.com Spotify: Beste Entwickler schreiben seit Dezember keinen Code dank KI - techcrunch.com Roland Berger and Jonas Andrulis start start-up - handelsblatt.com AI domain - x.com OpenAI beschuldigt DeepSeek, US-Modelle zur Vorteilsgewinnung zu destillieren. - bloomberg.com Google says attackers used 100,000+ prompts to try to clone AI chatbot Gemini - nbcnews.com EU untersucht Google wegen Suchanzeigen-Preisen erneut auf Kartellverstöße - bloomberg.com Apple steht vor neuen Spannungen mit Trump-Regierung - ft.com FTC Apple - x.com Apple News bevorzugt linke Medien, schließt konservative aus: Studie - nypost.com Tech companies pressured to share data on Trump critics, according to reports - msn.com ‘What Oligarchy Looks Like' - commondreams.org Google übermittelte persönliche und finanzielle Daten eines Studentenjournalisten an ICE - techcrunch.com EPA - nbcnews.com Scam Ads - juniperresearch.com

Devora is one of the most influential voices in consumer insights today, shaping how brands — from Netflix to Pepsico, TikTok, and Waymo — understand and influence shopper behavior.As Chief Strategy Officer at Alter Agents, Devora designs research studies to solve the toughest brand challenges — leading 3X brand growth — and is part of an exciting revolution in research called agile neuroscience testing that uses biometrics and AI to reveal subconscious consumer reactions in real time. Shopper insights and strategy have been Devora's passion for 15 years, during which time she has worked with top brands like Snapchat, Activision, Nespresso, Bose, and Schwab. She's also the brains behind the methodology used by Google for their groundbreaking ZMOT research. Whether it's decoding consumer choice, the rise of "shopper promiscuity," or how brands can future-proof their strategies — Devora goes beyond surface-level data to tap into how people buy, why they switch brands, and what companies must do to stay ahead. She has co-authored retail and shopping insights books like Fire in the Zoo and Influencing Shopper Decisions, and her TEDx on the Future of Shopping and Retail has nearly 300K views.Connect with Devora here:https://www.linkedin.com/in/devorarogers/https://www.facebook.com/AlterAgents/mentions/?_rdrhttps://www.instagram.com/alter_agents/?hl=enhttps://alteragents.com/Download our FREE Optimize Your LinkedIn Profile Guide here:https://www.thetimetogrow.com/ecsoptimizeyourprofile

We visit the capital of the Dominican Republic to see how its historic centre is balancing residential and tourist appeal. We also stop by Sydney’s newly reopened fish market and hear from the co-CEO of Waymo.See omnystudio.com/listener for privacy information.

The hills are alive with the sound of kids saying "chicken, banana," a new AI tool called OpenClaw has an iron grasp on data, and backlash took center stage after Bad Bunny's halftime show. Song of the Week: "The Great Divide" - Noah Kahan Click here to read the lyrics. In Other News.. James Van Der Beek passed away this week after battling colorectal cancer. Best known for his role as Dawson Leery on Dawson's Creek, he helped define late 90s teen drama, and his legacy continues through streaming nostalgia and even one of the internet's earliest viral reaction GIFs.  At a congressional hearing last week, self-driving car company Waymo admitted that sometimes its cars are being remote-controlled by workers based in the Philippines. An emerging star of the 2026 Winter Olympics is American figure skater Alysa Liu, who won gold and then promptly broke her medal. Cool guys have bangs now, according to GQ. Fellas are trading the long-popular long-on the-top, short-on-the sides haircut for softer, more lived-in fringe. This weekend, the long-awaited, R-rated adaptation of the gothic romance novel Wuthering Heights will hit 3,600 screens and is projected to hit $50 million on its opening weekend.  → Become a monthly donor today, join the Table. → Check out the podcast now on our YouTube Channel! → Get your question on Ask Axis! Send in your questions to ask@axis.org. → For more Axis resources, go to axis.org.

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

Waymo co-CEO Tekedra Mawakana speaks exclusively to Bloomberg Tech's Ed Ludlow about global expansion efforts for the robotaxi firm, raising $16 billion dollars and ongoing safety concerns.See omnystudio.com/listener for privacy information.

This episode is sponsored by Airia. Get started today at ⁠⁠⁠⁠⁠⁠⁠⁠⁠airia.com⁠⁠⁠⁠⁠⁠⁠⁠⁠. Jason Howell and Jeff Jarvis break down Claude Opus 4.6's new role as a financial‑research engine, discuss how GPT‑5.3 Codex is reshaping full‑stack coding workflows, and explore Matt Shumer's warning that AI agents will touch nearly every job in just a few years. We unpack how Super Bowl AI ads are reframing public perception, examine Waymo's use of DeepMind's Genie 3 world model to train autonomous vehicles on rare edge‑case scenarios, and also cover OpenAI's ad‑baked free ChatGPT tiers, HBR's findings on how AI expands workloads instead of lightening them, and new evidence that AI mislabels medical conditions in real‑world settings. Note: Time codes subject to change depending on dynamic ad insertion by the distributor. Chapters: 0:00 - Start 0:01:59 - Anthropic Releases New Model That's Adept at Financial Research Anthropic releases Opus 4.6 with new ‘agent teams' 0:10:00 - Introducing GPT-5.3-Codex 0:14:42 - Something Big Is Happening 0:33:25 - Can these Super Bowl ads make Americans love AI? 0:36:52 - Dunkin' Donuts digitally de-aged ‘90s actors and I'm terrified 0:39:47 - AI.com bought by Crypto.com founder for $70mn in biggest-ever website name deal 0:42:11 - OpenAI begins testing ads in ChatGPT, draws early attention from advertisers and analysts 0:48:27 - Waymo Says Genie 3 Simulations Can Help Boost Robotaxi Rollout 0:53:30 - AI Doesn't Reduce Work—It Intensifies It 1:02:08 - As AI enters the operating room, reports arise of botched surgeries and misidentified body parts 1:04:48 - Meta is giving its AI slop feed an app of its own 1:06:53 - Google goes long with 100-year bond 1:09:18 - OpenAI Abandons ‘io' Branding for Its AI Hardware Learn more about your ad choices. Visit megaphone.fm/adchoices

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A mix of escalating geopolitical cyber risks, the changing landscape of defensive security, and a series of high-profile incidents demonstrating the enduring threat of human-driven flaws.Cyber Espionage and Geopolitics:A year-long, sprawling espionage campaign by a state-backed actor (TGR-STA-1030) compromised government and critical infrastructure networks in 37 countries, utilizing phishing and unpatched security flaws, and deploying stealth tools like the ShadowGuard Linux rootkit to collect sensitive emails, financial records, and military details. Simultaneously, the threat environment has extended to orbit, where Russian space vehicles, Luch-1 and Luch-2, have been reported to have intercepted the communications of at least a dozen key European geostationary satellites, prompting concerns over data compromise and potential trajectory manipulation.AI and Security:AI has entered a new chapter in defensive security as Anthropic's Claude Opus 4.6 model autonomously discovered over 500 previously unknown, high-severity security flaws (zero-days) in widely used open-source software, including GhostScript and OpenSC. This demonstrates AI's rapid potential to become a primary tool for vulnerability discovery. On the cautionary side, the highly publicized Moltbook, a social network supposedly run by self-aware AI bots, was revealed as a masterclass in security failure and human manipulation. Cybersecurity researchers uncovered a misconfigured database that exposed 1.5 million API keys and 35,000 human email addresses, and found that the dramatic bot behavior was largely orchestrated by 17,000 human operators running bot fleets for spam and coordinated campaigns.Automotive Security and Autonomy:New US federal rules are forcing a major, complex shift in the automotive supply chain, requiring carmakers to remove Chinese-made software from connected vehicles before a 2026 deadline due to national security concerns. This move is redefining what "domestic technology" means in critical industries. In a related development, Waymo's testimony revealed that when its "driverless" cars encounter confusing situations, they communicate with remote assistance operators, some based in the Philippines, for guidance—a disclosure that immediately raised lawmaker concerns about safety, cybersecurity vulnerabilities from remote access, and the labor implications of overseas staff influencing US vehicles.Insider Threat and Legal Lessons:The importance of the security principle of "least privilege" was highlighted by an insider incident at Coinbase, where a contractor with too much access improperly viewed the personal and transaction data of approximately 30 customers. This incident reinforces that the highest risk often comes not from external nation-state hackers, but from overprivileged internal humans. Finally, two security researchers arrested in 2019 for an authorized physical and cyber penetration test of an Iowa courthouse settled their civil lawsuit with the county for $600,000. However, the county attorney's subsequent warning that any future similar tests would be prosecuted delivers a chilling message to the security testing community about legal risks even when work is authorized.

INTRO (00:24): Kathleen opens the show drinking a Bad Birdie Juicy Golden Ale from Four Peaks Brewing Company. She reviews her Super Bowl weekend in Nashville cooking chili and watching the game with friends.   TOUR NEWS: See Kathleen live on her “Day Drinking Tour.”   TASTING MENU (3:51): Kathleen samples Goldfish Hot Buffalo Seasoned Pretzels, Hormel Dill Pickle Pepperoni, and Kettle Brand “Special Sauce” chips.    COURT NEWS (28:30): Kathleen shares news involving Chappell Roan's response to critics of her Grammy outfit, Martha Stewart is making cookies for Team USA in the Olympic Village, and Snoop Dogg is crushing it financially on his NBC Olympic coverage.    UPDATES (45:26) : Kathleen shares updates on Waymo's tech support location, and the Alcatraz Coyote is heading back to the mainland,.   FRONT PAGE PUB NEWS (52:36): Kathleen shares articles on the 2026 Vegas Sphere lineup, Target has a disturbing new staff policy, Pizza Hut is closing hundreds of locations, a study relates drinking beer to increased brain intelligence, Eddie Bauer files for bankruptcy, Starbucks launches new international menu items, Twisted Sister's Dee Snider is retiring, and a mystery buyer purchases a ranch 4x the size of NYC in Wyoming.   HOLY SHIT THEY FOUND IT (59:35): Kathleen reads about a gray wolf found in LA County for the first time in 100 years.    WHAT ARE WE WATCHING (1:18:16): Kathleen recommends watching the 2026 Milan Winter Olympic coverage on NBC and Peacock, and “Victoria” on Netflix.    SAINT OF THE WEEK (1:20:20): Kathleen reads about St. James the Apostle, patron saint of pilgrims, vets, pharmacists and people with arthritis.    FEEL GOOD STORY (1:22:25): Kathleen shares a story about the history of cats in the White House.   

Spike, Zuckerman, and Jonny put the 2026 BMW M2 CS through its paces. The crew dives into press car crashes, the dangers of pedal confusion, Waymo's overseas monitoring practices, and discusses the incoming invasion of Chinese cars in North America. ______________________________________________

Just got back from 4 days in Austin, Texas testing Tesla's Robotaxi network. It was very interesting to show the app to my friends and see their reactions and compare the experience with Waymo/Uber. Robotaxi demand was very high because of low prices, and this mean that about 50% of the time I was unable to call a car due to "high service demand", the other 50% of the time Robotaxi was available with a ~15 minute wait time. I'm STOKED to see this expand! It may take some time, but this will truly transform transportation as we know it ... especially if the price sticks! What are your thoughts on the Robotaxi rollout so far.0:00 Just got back from Austin0:40 Official Robotaxi data2:48 Tesla Robotaxi review vs Waymo & Uber4:59 Robotaxi demand is off the charts7:28 Funny Robotaxi Supervisor story9:06 Watching Robotaxi develop over timeMy X:   / gfilche  HyperChange Patreon :)   / hyperchange   Disclaimer: I'm long Tesla stock and nothing in this show is financial advice.

Shoot us a Text.Episode #1266: Ford posts its biggest earnings miss in years but bets big on a 2026 rebound. Robotaxis scale nationwide while public trust hangs in the balance. And NADA partners with Northwood to strengthen the next generation of dealership leadership.Ford just posted its biggest quarterly earnings miss in four years and its worst net loss since 2008. But beneath the headline loss, the company's trucks and commercial vehicles are still carrying the load—and 2026 is being framed as a rebound year.The Q4 adjusted EPS (Earnings Per Share) came in at 13 cents versus the expected 19 cents, the largest miss in four years.Revenue remained strong, with $45.9B in Q4 and a record $187.3B for the full year, but about $900M in unexpected tariff costs and aluminum supply disruptions pressured margins.The company reported an $11.1B net loss in Q4 and an $8.2B loss for the full year, largely driven by $15.5B in EV-related special charges and restructuring actions.Ford Pro and Ford Blue have projected 2026 pre-tax earnings of up to $7.5B and $4.5B respectively, while the Model e unit is expected to lose up to $4.5B.CFO Sherry House noted that the Novelis aluminum plant disruption is not expected to fully resolve until mid-2026, meaning the company will continue sourcing alternative supplies at a higher cost.Waymo, Tesla, Zoox and others are racing to scale robotaxis across the U.S., but recent crashes and investigations show that winning public trust may be harder than winning market share.A Waymo vehicle struck a child who ran into the street from behind a parked SUV in California, prompting a federal investigation. Zoox also reported a crash after a driver opened a door into its path. Both companies say their systems reacted appropriately.A majority of Americans say they're unlikely to try a self-driving taxi, though younger consumers are more open to the idea.“When something goes wrong, people don't experience it as a statistical issue — they experience it as a moral and emotional one,” said Professor William Riggs.Northwood University and NADA are teaming up to expand education access for franchised dealers, their employees and their families — with discounted tuition, scholarships and a clear focus on building the next generation of dealership leadership.NADA dealer members can enroll in Northwood's online undergraduate programs at $350 per credit hour, with the benefit extending to eligible spouses and dependents.Northwood's DeVos Graduate School is offering 20% MBA tuition scholarships, discounted master's programs and up to $15,000 toward a Doctor of Business Administration.Both organizations say the goal is strengthening the leadership pipeline in a people-driven, capital-intensive retaiJoin Paul J Daly and Kyle Mountsier every morning for the Automotive State of the Union podcast as they connect the dots across car dealerships, retail trends, emerging tech like AI, and cultural shifts—bringing clarity, speed, and people-first insight to automotive leaders navigating a rapidly changing industry.Get the Daily Push Back email at https://www.asotu.com/ JOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/

Driverless taxis are coming to DC! At least, that's what Waymo, the California-based autonomous vehicle company, announced last year. The company revealed hopes of  expanding into the District, despite DC's current laws requiring a human driver behind the wheel for all vehicles. So will these robotaxis actually arrive? Andy Hawkins has been covering Waymo for The Verge, and CityCast's own Priyanka Tilve has logged serious hours riding in Waymos around Austin. They're bringing their expertise front and center to tell us if DC is serious about driverless cars. Want some more DC news? Then make sure to sign up for our morning newsletter Hey DC. You can text us or leave a voicemail at: (202) 642-2654. You can also become a member, with ad-free listening, for as little as $10 a month. Learn more about the sponsors of this February 11th episode: Library of Congress Nace Law Group Johns Hopkins University Baltimore Museum of Art Interested in advertising with City Cast? Find more info HERE.

Episode 285: Join us this week on TechTime Radio with Nathan Mumm: The Show That Makes You Go "HMMM." Welcome to our show as we guide you through all things tech with a lil' whiskey on the side.This week on TechTime Radio, we cut through a week where algorithms, automation, and accountability all collided. We opened with TikTok's regulatory shakeup, where EU pressure and U.S. oversight triggered an algorithm reset that left creators scrambling. The conversation centered on what responsible design looks like when addictive features meet real duty of care, especially for younger users.We shifted to the automotive world this week, from Waymo scraping parked cars to a D.C. robo‑minibus that froze in the middle of the lane after a minor crash. The show explained how fragile edge cases and confusing human handoffs still make these systems unreliable, even as automation becomes more common. We wrapped up with enterprise updates, new security concerns, and a hands-on look at Gwen Ways Gadget, the Ziea-One, the calendar-organizer clock robot, all finished off with a lively American whiskey tasting that sparked plenty of debate.Feed fatigue, robo-fender-benders, and a desk gadget with egg eyes take center stage as we untangle a week where regulation, automation, and attention collide. We start with TikTok's new reality: EU regulators label its design addictive, while U.S. oversight and ownership shifts trigger a jarring algorithm reset. Creators see their niche content vanish, reach plummet, and feeds feel sanitized or broken. We explore what accountability looks like when infinite scroll and autoplay meet duty of care—especially for younger users—and whether smarter design can keep discovery without weaponizing compulsion.Then we pivot to the streets, where autonomy hit a pothole. A Waymo vehicle, even with a specialist onboard, scraped parked cars; a D.C. robo-minibus froze mid-lane after a minor crash; and an AI-enhanced used-car listing offered up cobblestone floor mats and two gear shifters. It's funny until it isn't. We cut through the headlines to the heart of the problem: brittle edge cases, unclear handoffs, and the non-negotiable need for human-in-the-loop safeguards. From staged rollouts to geofencing and real-world failover plans, we map the practices that separate novelty from reliability.On the enterprise side, Microsoft's long goodbye to Exchange Web Services sounds mundane—until your calendar syncs and SaaS bridges hiccup. We explain the timeline, what's replacing EWS, and how to audit your hidden dependencies before 2027 arrives. To actually tame your day, we test-drive Zia One, a Kickstarter AI calendar that merges Google, Outlook, and more into a glanceable desktop display with voice commands, Pomodoro timers, and playful animations. It's a focused bet on ambient computing—and we share how to evaluate crowdfunded hardware for real-world viability.Security stakes stay high as Coinbase reports a contractor-enabled data access incident, complete with leaked screenshots of internal tools. We detail why outsourced support is a prime attack surface and lay out a practical blueprint for least privilege, session monitoring, and vendor governance. And yes, we sip through a four-bottle American whiskey flight, trade takes on flavor and finish, and crown a winner—with a few confident opinions that may not age well.Hit play for a fast, clear, and funny tour through the week's most consequential tech shifts, grounded in practical steps you can apply today. If you enjoy the show, subscribe, share it with a friend, and leave us a review—then tell us: which trend needs the toughest guardrails right now?Support the show

The crew tackles a legendary Homie Helpline where Randy is labeled a "greedy" fool for trying to ditch his sick, hard-working nurse girlfriend for a boys' night at an Ashanti concert. Then the "studious foos" also investigate the "creepy" news that Waymo self-driving cars are being remotely piloted by workers in the Philippines and debate why 35% of college kids are addicted to scrolling TikTok during intimacy. [Edited by @iamdyre

Waymo co-CEO Tekedra Mawakana discusses the robotaxi firm's global expansion efforts, $16 billion fundraise and ongoing safety concerns. She speaks exclusively with Ed Ludlow in this special edition of "Bloomberg Tech".See omnystudio.com/listener for privacy information.

The company plans to offer rides to the public in Nashville sometime this year. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Matt, Ryan, Drew, and Shannon talk Waymo driverless cars, conspiracy theories, and take your calls.See omnystudio.com/listener for privacy information.

We start with headlines from Waymo, Kaiser Permanente, San Francisco teachers, Los Angeles teachers, REI, Seven Stars Bakery, Starbucks, and the state of Nebraska. New York nurses have been on strike for over a month, we discuss the state of their strike and the possible deals announced on Monday. VW workers in Chattanooga won a historic victory for organizing in the South with their recent contract win, we break down the gains. Workers across Europe blocked ports for Palestine this week, even as Western governments and media outlets try to ignore it.  Finally, we discuss the targeted attacks on workers organizers by ICE and the way students and rank and file union members are organizing to stop it. Join the discord: discord.gg/tDvmNzX  Follow the pod at instagram.com/workstoppage, @WorkStoppagePod on Twitter,  John @facebookvillain, and Lina @solidaritybee

Jeremy Bird, Executive Vice President, Global Growth at Lyft joined Grayson Brulte on The Road to Autonomy podcast to discuss the company's strategic partnership with Waymo in Nashville and the deployment of a hybrid network that integrates human drivers with autonomous vehicles. The operational backbone of this strategy is FlexDrive. A best-in-class operation that manages depots, charging, and maintenance for robotaxis. FlexDrive gives Lyft the operational rigor needed to scale robotaxis globally. In Nashville, FlexDrive is supporting the Waymo partnership, while in Europe, Lyft is utilizing FlexDrive to power expansion, including a key partnership with Baidu in the UK and Europe.Looking ahead, Jeremy envisions a marketplace defined by customer obsession where luxury experiences and robotaxis coexist, utilizing operational excellence to fuel future growth.Episode Chapters0:00 Lyft's Partnership with Waymo in Nashville4:44 Robotaxi Fleets & Depots8:50 Freenow11:15 Deploying Robotaxis in the UK and Europe14:41 Autonomous Vehicle Policy in Europe17:35 Expanding Robotaxi Deployments in Europe19:05 Baidu Partnership23:09 Global Robotaxi Partnerships & Lyft's Marketplace 26:04 Luxury Market27:53 Future of LyftRecorded on Wednesday, January 28, 2026--------About The Road to AutonomyThe Road to Autonomy provides market intelligence and strategic advisory services to institutional investors and companies, delivering insights needed to stay ahead of emerging trends in the autonomy economy™. To learn more, say hello (at) roadtoautonomy.com.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Patrick Daugherty (@RotoPat), Denny Carter and Kyle Dvorchak (@kyletweetshere) break down Super Bowl LX from every angle. First, was this a surprising result? Second, did the Patriots and Drake Maye get “exposed”? Looking beyond the game, they debate the futures of Kenneth Walker and the Pats’ skill corps, as well as Sam Darnold’s place in the current quarterback pantheon. Is he someone we would prefer over Brock Purdy and Jared Goff, for instance? Pat and Denny also reminisce on their time in San Francisco and ponder what went right and wrong with their “big game” predictions. (2:10) – Pat details an interesting run-in with a waiter while in San Francisco (3:55) – The crew reflects on their Waymo experiences on the West Coast (9:15) – Surprised by the Seahawks dominance? (19:10) – Examining Drake Maye’s playoff run (time) – How the Patriots can improve their skill corps (28:50) – Kenneth Walker III secures Super Bowl MVP as free agency looms (37:20) – Rather have Sam Darnold or Brock Purdy?See omnystudio.com/listener for privacy information.

Vinnie thinks the big game would have been better if the Patriots got off the plane. Sarah took her first Waymo and enjoyed a star studded, lobster rolled weekend.

Season 6 kicks off with the latest in the long line of Epstein unveilings. Fitz and McShane take a swing at what they have seen so far and how none of it matters unless people start going to jail for it. Did you know Arizona State University is all over the Epstein files? We'll tell you why. The Olympics is back. One day in and male Ski Jumpers have a little problem due to some injections they are taking. WAYMO employees Philipeanos to help you with your "automated" car. Bitcoin crashed and then it kinda bounced back? Fitz calls his shot on it. Jefe has Superbowl predictions galore!Become a supporter of this podcast: https://www.spreaker.com/podcast/razor-wire-news--5683729/support.www.razorwirenews.com

In this week's FOLLOW UP, Bitcoin is down 15%, miners are unplugging rigs because paying eighty-seven grand to mine a sixty-grand coin finally failed the vibes check, and Grok is still digitally undressing men—suggesting Musk's “safeguards” remain mostly theoretical, which didn't help when X offices got raided in France. Spain wants to ban social media for kids under 16, Egypt is blocking Roblox outright, and governments everywhere are flailing at the algorithmic abyss.IN THE NEWS, Elon Musk is rolling xAI into SpaceX to birth a $1.25 trillion megacorp that wants to power AI from orbit with a million satellites, because space junk apparently wasn't annoying enough. Amazon admits a “high volume” of CSAM showed up in its AI training data and blames third parties, Waymo bags a massive $16 billion to insist robotaxis are working, Pinterest reportedly fires staff who built a layoff-tracking tool, and Sam Altman gets extremely cranky about Claude's Super Bowl ads hitting a little too close to home.For MEDIA CANDY, we've got Shrinking, the Grammys, Star Trek: Starfleet Academy's questionable holographic future, Neil Young gifting his catalog to Greenland while snubbing Amazon, plus Is It Cake? Valentines and The Rip.In APPS & DOODADS, we test Sennheiser earbuds, mess with Topaz Video, skip a deeply cursed Python script that checks LinkedIn for Epstein connections, and note that autonomous cars and drones will happily obey prompt injection via road signs—defeated by a Sharpie.IN THE LIBRARY, there's The Regicide Report, a brutal study finding early dementia signals in Terry Pratchett's novels, Neil Gaiman denying allegations while announcing a new book, and THE DARK SIDE WITH DAVE, vibing with The Muppet Show as Disney names a new CEO. We round it out with RentAHuman.ai dread relief via paper airplane databases, free Roller Coaster Tycoon, and Sir Ian McKellen on Colbert—still classy in the digital wasteland.Sponsors:DeleteMe - Get 20% off your DeleteMe plan when you go to JoinDeleteMe.com/GOG and use promo code GOG at checkout.SquareSpace - go to squarespace.com/GRUMPY for a free trial. And when you're ready to launch, use code GRUMPY to save 10% off your first purchase of a website or domain.Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/732FOLLOW UPBitcoin drops 15%, briefly breaking below $61,000 as sell-off intensifies, doubts about crypto growBitcoin Is Crashing So Hard That Miners Are Unplugging Their EquipmentGrok, which maybe stopped undressing women without their consent, still undresses menX offices raided in France as UK opens fresh investigation into GrokSpain set to ban social media for children under 16Egypt to block Roblox for all usersIN THE NEWSElon Musk Is Rolling xAI Into SpaceX—Creating the World's Most Valuable Private CompanySpaceX wants to launch a constellation of a million satellites to power AI needsA potential Starlink competitor just got FCC clearance to launch 4,000 satellitesAmazon discovered a 'high volume' of CSAM in its AI training data but isn't saying where it came fromWaymo raises massive $16 billion round at $126 billion valuation, plans expansion to 20+ citiesPinterest Reportedly Fires Employees Who Built a Tool to Track LayoffsSam Altman got exceptionally testy over Claude Super Bowl adsMEDIA CANDYShrinkingStar Trek: Starfleet AcademyThe RipNeil Young gifts Greenland free access to his music and withdraws it from Amazon over TrumpIs it Cake? ValentinesAPPS & DOODADSSennheiser Consumer Audio IE 200 In-Ear Audiophile Headphones - TrueResponse Transducers for Neutral Sound, Impactful Bass, Detachable Braided Cable with Flexible Ear Hooks - BlackSennheiser Consumer Audio CX 80S In-ear Headphones with In-line One-Button Smart Remote – BlackTopaz VideoEpsteinAutonomous cars, drones cheerfully obey prompt injection by road signAT THE LIBRARYThe Regicide Report (Laundry Files Book 14) by Charles StrossScientists Found an Early Signal of Dementia Hidden in Terry Pratchett's NovelsNeil Gaiman Denies the Allegations Against Him (Again) While Announcing a New BookTHE DARK SIDE WITH DAVEDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingThe Muppet ShowDisney announces Josh D'Amaro will be its new CEO after Iger departsA Database of Paper Airplane Designs: Hours of Fun for Kids & Adults AlikeOnline (free!) version of Roller Coaster tycoon.Speaking of coasters, here's the current world champion.I am hoping this is satire...Sir Ian McKellen on Colbert.CLOSING SHOUT-OUTSCatherine O'Hara: The Grande Dame of Off-Center ComedyStanding with Sam 'Balloon Man' MartinezSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this Friday edition of 2 Pros & A Cup Of Joe, Jonas Knox, Brady Quinn, & LaVar Arrington, discuss some of their stories from the previous day in San Francisco, such as using Waymo to drive around. Plus, the guys cover the NFL awards from last night, another edition of ICYMI, and more!See omnystudio.com/listener for privacy information.