Podcasts about Alessio

Conoce a la escritora peruana Roxana Gil Ramón y su libro 'Memorias de un alebrije'. También, Alessio del Pozo nos habla de su trabajo como animador 2D. Y la ingeniera Julia Cárdenas se lanza a la política por el partido morado.

In the end, we are all either Standees or Meeples. It's just that "meeple" is kind of trademarked. No, really, go checking if you need. Done? Well, welcome to episode 109 of The Last Standee Podcast! In the grand scheme of things, we belong to the standee category. Mostly copy-left, done with whatever arts&craft project brought us into existence. It was messy. Anyway! This is the episode where we talk about sources and resources! After the usual catch-up, we have Cara talk about Neta-tanka, a resource management game with some noteworthy mechanichs she... liked? Disliked? Look, it's complicated. That's why she made a review. It has to do with archetypes. Everything has. It's the meeple/standee thing from above, cranked up to "existential". But speaking of archetypes, there's Alexis with Bubzium, the next great sourcebook from Hollow Press, you'll remember them from Vermis, maybe. Anyway, I won't even start describing it here, because words will fail the experience. All we can comfortably say is that Hollow Press found their niche, and comfortably so. We kept the dessert for last. Mix in resources, and a good source worldbuilding, in the archetypal old kingdom, to talk about The Old King's Crown with Alessio, a dear, endearing game from late summer 2025 which is getting on and off the BGG Hotness these last days. Good timing you say? Remember that the day right after this episode is out, the "reprint + expansion" campaign will start! Hey-hey, you are welcome! As usual, happy gaming until next time!

Recorded 10 minutes before i went to the Airport in our squat in Earlscourt.

Fluent Fiction - Italian: The Hidden Aroma: Solving Mysteries at Caffè dei Sogni Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-02-26-23-34-02-it Story Transcript:It: Il sole filtrava attraverso le finestre del "Caffè dei Sogni", un'antica torrefazione situata nel cuore di Firenze.En: The sun filtered through the windows of the "Caffè dei Sogni", an ancient coffee roasting shop located in the heart of Firenze.It: L'aria era impregnata dell'aroma caldo e avvolgente del caffè appena tostato.En: The air was filled with the warm and enveloping aroma of freshly roasted coffee.It: Alessio, l'orgoglioso proprietario, stava sistemando i sacchi di chicchi accanto al bancone di legno scuro, mentre Marco, suo cugino, preparava un espresso per un turista curioso.En: Alessio, the proud owner, was arranging the sacks of coffee beans next to the dark wooden counter, while Marco, his cousin, was preparing an espresso for a curious tourist.It: Ma qualcosa non andava.En: But something was wrong.It: Giulia, una delle clienti abituali e abile detective, notò la fronte corrugata di Alessio mentre sistemava i registri.En: Giulia, one of the regular customers and a skilled detective, noticed Alessio's furrowed brow as he organized the ledgers.It: Si avvicinò con un sorriso e una tazza di caffè in mano.En: She approached with a smile and a cup of coffee in hand.It: "Problemi, Alessio?En: "Problems, Alessio?"It: " chiese con tono amichevole.En: she asked in a friendly tone.It: Alessio sospirò, rendendosi conto che la piacevole facciata del suo caffè non poteva nascondere il problema a portata di mano.En: Alessio sighed, realizing that the pleasant façade of his café could not hide the problem at hand.It: "Una spedizione di chicchi rari è sparita, Giulia," confessò Alessio, con la voce piena di preoccupazione.En: "A shipment of rare beans has disappeared, Giulia," confessed Alessio, his voice full of concern.It: "È un grosso guaio per la nostra reputazione di famiglia.En: "It's a big problem for our family's reputation."It: " Giulia annuì, i suoi sensi di detective già in allerta.En: Giulia nodded, her detective instincts already on alert.It: "Posso aiutarti a cercare," suggerì, sapendo che Alessio era orgoglioso e preferiva tenere i problemi in famiglia.En: "I can help you look," she suggested, knowing that Alessio was proud and preferred to keep problems within the family.It: "Va bene," disse Alessio, esitante.En: "Okay," said Alessio, hesitant.It: "Ma solo perché non so da dove cominciare.En: "But only because I don't know where to start."It: " Così, Giulia iniziò a indagare.En: So, Giulia began to investigate.It: Tra una chiacchiera e l'altra con i clienti, osservava attentamente chi entrava e usciva dalla torrefazione.En: Between chats with customers, she carefully observed who entered and exited the roasting shop.It: Nel frattempo, Marco sembrava distratto.En: Meanwhile, Marco seemed distracted.It: Versava l'espresso con più lentezza del solito, e uno strano sguardo inquieto gli attraversava il volto ogni volta che il nome della spedizione veniva menzionato.En: He poured the espresso more slowly than usual, and a strange uneasy look crossed his face every time the name of the shipment was mentioned.It: Giulia lo notò, ma non disse nulla subito.En: Giulia noticed this but said nothing immediately.It: Dopo qualche giorno, Giulia raccolse abbastanza indizi da confrontare Marco.En: After a few days, Giulia gathered enough clues to confront Marco.It: Lo trovò nel retro della torrefazione, accanto ai sacchi di caffè.En: She found him in the back of the roasting shop, next to the sacks of coffee.It: "Marco," iniziò, con tono calmo.En: "Marco," she began, in a calm tone.It: "Devo farti delle domande sulla spedizione.En: "I need to ask you some questions about the shipment."It: "Marco sbiancò.En: Marco turned pale.It: "La spedizione?En: "The shipment?It: Io.En: I...It: io non so niente," balbettò, evitando il suo sguardo.En: I don't know anything," he stammered, avoiding her gaze.It: Ma Giulia non era convinta.En: But Giulia was not convinced.It: Con gentile pressione, rivelò di aver scoperto che Marco aveva contratto debiti e aveva sottratto il caffè per rivenderlo e recuperare denaro.En: With gentle persuasion, she revealed that she had discovered Marco had contracted debts and had taken the coffee to sell it and recover money.It: Alessio, che era stato chiamato da Giulia, si presentò in tempo per sentire la confessione completa di Marco.En: Alessio, who had been called by Giulia, arrived in time to hear Marco's full confession.It: Un silenzio carico di tensione cadde nella stanza.En: A silence heavy with tension fell in the room.It: "Perché non me lo hai detto?En: "Why didn't you tell me?"It: " chiese Alessio, ferito.En: asked Alessio, hurt.It: "Purtroppo avevo paura di deluderti," spiegò Marco, con gli occhi bassi.En: "Unfortunately, I was afraid of disappointing you," explained Marco, with his eyes downcast.It: Alessio chiuse gli occhi un istante, poi fece un respiro profondo.En: Alessio closed his eyes for a moment, then took a deep breath.It: "Possiamo sistemarlo insieme," disse infine, prendendo una decisione importante.En: "We can fix it together," he finally said, making an important decision.It: Con l'aiuto di Giulia, trovarono un modo per ripagare i debiti di Marco senza compromettere la torrefazione.En: With Giulia's help, they found a way to repay Marco's debts without compromising the roasting shop.It: Il giorno dopo, Alessio si alzò di buon umore, grato per l'aiuto che non sapeva di poter accettare.En: The next day, Alessio rose in good spirits, grateful for the help he didn't realize he could accept.It: La famiglia, alla fine, era il legame più forte.En: Family, after all, was the strongest bond.It: Giulia sorrideva, bevendo il suo caffè giornaliero, soddisfatta di aver aiutato a risolvere il mistero e, soprattutto, di aver visto Alessio aprirsi agli altri.En: Giulia smiled, sipping her daily coffee, satisfied that she had helped solve the mystery and, most importantly, had seen Alessio open up to others.It: Il Caffè dei Sogni continuò a prosperare, un simbolo di tradizione e unità nella storica città di Firenze.En: Il Caffè dei Sogni continued to thrive, a symbol of tradition and unity in the historic city of Firenze. Vocabulary Words:the sun: il solethe windows: le finestrethe heart: il cuorethe aroma: l'aromathe owner: il proprietariothe counter: il banconethe detective: l'investigatorethe ledgers: i registrithe shipment: la spedizionethe beans: i chicchireputation: la reputazioneconcern: la preoccupazionehesitant: esitantethe chats: le chiacchierethe clues: gli indizithe gaze: lo sguardouneasy: inquietopersuasion: la pressionethe confession: la confessionethe silence: il silenziotension: la tensionedebts: i debitithe decision: la decisionethe spirits: l'umorethe bond: il legamethe tradition: la tradizionethriving: prosperarethe city: la cittàthe family: la famigliathe facade: la facciata

In questo episodio di

Partendo da una storica scommessa tra i cowboys di Buffallo Bill, in trasferta in Italia e i Butteri italiani, la nuova pellicola degli autori di Re Granchio si snoda poi in un'avventura fatta di amore, incomprensioni, erotismo, poesia, vendetta e ambienti fantastici e affascinanti, dove spiccano le ottime interpretazioni di tutto il cast artistico, Alessandro Borghi e Nadja Tereszkiewicz in testa.Una pellicola di stampo italiano, che omaggia opere oltreoceano e che possiede una vera e propria identità solida, inducendo importanti riflessioni, senza dimenticare l'intrattenimento.Ringrazio il regista Alessio Rigo De Righi che, con gentilezza e disponibilità, ha accettato l'invito del vostro CaRfa per parlare del film e di tutto il cinema di ieri e di oggi. Una chiacchierata senza scaletta e genuina, che spero possiate apprezzare come il sottoscritto.E Testa O Croce? è un film da recuperare sicuramente, per i tanti che hanno mancato la visione al cinema.

Le ferie illimitate sono davvero il simbolo della cultura aziendale moderna? O nascondono una trappola invisibile?In questo episodio di Confidenze Imprenditoriali, Alessio e Giulia raccontano la loro esperienza diretta in Startup Geeks. Quello che sulla carta sembrava libertà pura si è rivelato un paradosso: il team prendeva meno ferie, non di più.Perché la libertà senza chiarezza genera ansia, e il benessere non è un benefit, ma una responsabilità di chi guida.Se vuoi capire perché le policy da sole non bastano, come la leadership deve dare l'esempio per legittimare il riposo, e perché dieci cervelli lucidi valgono più di venti esausti, questa puntata ti aiuterà a ripensare le "regole non scritte" del tuo team.-------------

Ci siamo, si scaldano i motori e le vetture entrano in pista. A barcellona prima, poi in Bahrain abbiamo avuto il primo assaggio di F1 2026, del nuovo regolamento e delle soluzioni inedite dei team, ma le carte sono ancora tutte da scoprire; vi lasciamo questa chiacchierata di introduzione alla stagione con anche una novità importante!Di questo e molto altro parliamo ampiamente nella nuova puntata di ZonaDRS con Alessio, Angelo e Giacomo!Grazie e buon ascolto!

Toute l'actu des sélections nationales et des championnats anglais, espagnol, italien et allemand avec nos légendaires "Drôles de Dames" : Julien Laurens, Fred Hermel, Polo Breitner et Johann Crochet.

El hijo de #JorgeD’Alessio vivió una auténtica pesadilla luego de que un tiburón lo ATACARA ¡tres veces! La rápida reacción de colocarle un torniquete fue clave para evitar que se DES4NGR4R4, convirtiéndose en la diferencia entre la vida y la tragedia. No te pierdas lo mejor del espectáculo de lunes a viernes De Primera Mano a las 3 p.m. con Gustavo Adolfo Infante, Addis Tuñón, Érika González y Lalo Carrillo por Imagen Televisión. Visita también nuestra página: www.imagentv.comSee omnystudio.com/listener for privacy information.

Protagonistas, análisis y reacciones del triunfo del Atlético de Madrid ante el RCD Espanyol por 4-2. Además, los miembros de 'El Sanedrín' desgranan todas las claves de la derrota del Real Madrid ante Osasuna por 2-.1 Por último, entrevista a Alessio Lisci, técnico del conjunto navarro.

Protagonistas, análisis y reacciones del triunfo del Atlético de Madrid ante el RCD Espanyol por 4-2. Además, los miembros de 'El Sanedrín' desgranan todas las claves de la derrota del Real Madrid ante Osasuna por 2-.1 Por último, entrevista a Alessio Lisci, técnico del conjunto navarro.

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

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Fluent Fiction - Italian: Cupid's Mishap: How a Valentine's Gaffe Sparked Romance Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-02-15-08-38-20-it Story Transcript:It: Nell'ufficio aziendale di una grande società a Milano, l'aria era piena di tensione e eccitazione.En: In the company office of a large corporation in Milano, the air was filled with tension and excitement.It: Era il giorno di San Valentino e, nonostante il freddo invernale, il profumo di cioccolatini e rose riempiva l'ambiente.En: It was Valentine's Day and, despite the winter cold, the scent of chocolates and roses filled the environment.It: Alessio, un giovane impiegato un po' goffo ma sveglio, sedeva alla sua scrivania.En: Alessio, a somewhat awkward but sharp young employee, sat at his desk.It: Il rumore delle tastiere lo distraeva mentre si perddeva nei suoi pensieri su Gianna, la sua collega di cui aveva una cotta segreta.En: The sound of keyboards distracted him as he got lost in his thoughts about Gianna, his colleague on whom he had a secret crush.It: Alessio aveva preparato un biglietto romantico per Gianna, sperando di farle sapere i suoi sentimenti in modo discreto.En: Alessio had prepared a romantic card for Gianna, hoping to let her know his feelings in a discreet way.It: Ma, distratto e nervoso, commise un terribile errore.En: But, distracted and nervous, he made a terrible mistake.It: Invece di inviare il biglietto solo a Gianna, lo inviò per errore a tutta l'azienda.En: Instead of sending the card only to Gianna, he accidentally sent it to the entire company.It: "Oh no!"En: "Oh no!"It: esclamò Alessio, vedendo la notifica sullo schermo del suo computer.En: exclaimed Alessio, seeing the notification on his computer screen.It: Il rumore dell'ufficio si fermò per un attimo mentre tutti leggevano il biglietto.En: The noise in the office paused for a moment as everyone read the card.It: Le guance di Alessio erano rosse dall'imbarazzo.En: Alessio's cheeks were red with embarrassment.It: Tutti iniziarono a bisbigliare e guardarlo con sorrisi divertiti.En: Everyone started whispering and looking at him with amused smiles.It: "Cosa devo fare adesso?"En: "What should I do now?"It: pensò Alessio, in preda al panico.En: thought Alessio, in a panic.It: Il capo, Lorenzo, convocò una riunione straordinaria.En: The boss, Lorenzo, called an extraordinary meeting.It: Alessio capì che era il momento di affrontare la situazione.En: Alessio realized it was time to face the situation.It: Durante la riunione, Alessio si alzò.En: During the meeting, Alessio stood up.It: Con un sorriso nervoso, disse: "Cari colleghi, credo che tutti abbiate ricevuto una mia... corrispondenza speciale.En: With a nervous smile, he said, "Dear colleagues, I believe you all received some of my... special correspondence.It: Non era il mio intento pubblicare i miei sentimenti in questo modo."En: It wasn't my intention to publish my feelings in this way."It: La sala scoppiò in una risata collettiva.En: The room burst into collective laughter.It: Alessio continuò, approfittando del momento: "Sapete, l'amore a volte ci fa compiere gesti folli.En: Alessio continued, taking advantage of the moment: "You know, love sometimes makes us do crazy things.It: Volevo solo dire che, se non vi dispiace, stasera, vorrei invitare Gianna a cena.En: I just wanted to say that, if you don't mind, this evening, I'd like to invite Gianna to dinner.It: Accetti, Gianna?"En: Will you accept, Gianna?"It: chiese, finalmente guardandola negli occhi.En: he asked, finally looking her in the eyes.It: Gianna era sorpresa, ma colpita dalla sincerità di Alessio.En: Gianna was surprised but touched by Alessio's sincerity.It: Gli sorrise e rispose con gioia: "Sì, Alessio, mi piacerebbe molto."En: She smiled at him and joyfully replied, "Yes, Alessio, I would love to."It: L'ufficio esplose in un applauso e tutti si congratularono con Alessio per il suo coraggio.En: The office erupted in applause, and everyone congratulated Alessio on his courage.It: La giornata di San Valentino, che poteva trasformarsi in un disastro, era invece diventata un ricordo prezioso per Alessio.En: Valentine's Day, which could have turned into a disaster, instead became a cherished memory for Alessio.It: Aveva imparato che l'onestà e l'umorismo possono trasformare anche le situazioni più imbarazzanti in qualcosa di bello.En: He had learned that honesty and humor can turn even the most embarrassing situations into something beautiful.It: Da quel giorno, Alessio camminava per l'ufficio con più fiducia, consapevole che mostrare il vero sé non era solo liberatorio, ma anche incredibilmente attraente.En: From that day on, Alessio walked around the office with more confidence, aware that showing his true self was not only liberating but also incredibly attractive. Vocabulary Words:tension: la tensioneexcitement: l'eccitazionewinter: l'invernalescent: il profumoenvironment: l'ambienteawkward: goffosharp: svegliokeyboard: la tastierathoughts: i pensierisecret crush: la cotta segretadiscreet: discretomistake: l'errorenotification: la notificared: rosseembarrassment: l'imbarazzowhispering: bisbigliareamused smiles: i sorrisi divertitipanic: il panicoextraordinary meeting: la riunione straordinariacollective laughter: la risata collettivaadvantage: il vantaggiocrazy things: i gesti follicourage: il coraggiodisaster: il disastrocherished memory: il ricordo preziosohonesty: l'onestàhumor: l'umorismoembarrassing situations: le situazioni imbarazzanticonfidence: la fiduciaincredibly attractive: incredibilmente attraente

Alessio Santella presenta "Continuo a camminare" su Radio Delta 1 con Daniele Di Ianni.

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]:

L'atmosfera è elettrica e gli occhi di tutto il mondo sono puntati sull'Italia! Con l'inizio delle Olimpiadi Invernali di Milano-Cortina, Katia e Alessio ci accompagnano in un viaggio tra le vette innevate e le piste di ghiaccio per scoprire il lessico degli sport invernali. In questo episodio  di

Hai creato un MVP ma nessuno lo compra? Il problema non è il prodotto: è che non sai come accompagnare le persone dal "non ti conosco" al "ti pago".In questo episodio di Confidenze Imprenditoriali, Alessio e Giulia spiegano la Customer Journey attraverso le tre fasi del funnel di vendita: TOFU (catturare attenzione), MOFU (costruire fiducia) e BOFU (convertire in vendita). Ogni fase richiede contenuti e strategie specifiche, e saltarne anche solo una significa perdere clienti. Dal post LinkedIn che risveglia un problema alla guida gratuita che ti fa diventare autorevole, fino alla pagina di vendita che trasforma la fiducia in acquisto. Se vuoi capire come strutturare contenuti che guidano il cliente da una fase all'altra e come costruire un funnel integrato che non lascia buchi, questa puntata ti dà il metodo completo per passare dall'idea ai primi clienti paganti.-------------

Super Bowl LX is in the books and we're ready to break down the big game from every angle on the Just End The Suffering podcast! Host Mike Phillips (⁠⁠⁠@MPhillips331⁠⁠⁠) kicks off the show by recapping the Seahawks' dominant win over the Patriots (1:35) with Joe D'Aloisio (@Joe__DAloisio). Mike then continues his big game coverage by recapping Bad Bunny's halftime show (30:51) and the latest batch of commercials with Nick D'Alessio.Subscribe to the Just End The Suffering podcast on ⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠Amazon⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠TuneIn⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠ and⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠!Subscribe to ⁠⁠⁠⁠⁠⁠⁠⁠⁠Mike Phillips's channel⁠⁠⁠⁠⁠⁠⁠⁠⁠ on YouTube!

Ajo që ndodh në shtëpinë e “Big Brother VIP”, padyshim që është më e ndjekura e më e komentuara në rrjetet sociale, si edhe në jetën e përditshme. Në lidhje me audiencën shumë të madhe të këtij spektakli ka mendime të ndryshme. Suksesi ka kaluar kufijtë e një spektakli në ekranin e televizorit e tani jemi përballë një fenomeni social që mund të quhet edhe si “Big Brother Mania”.

Eccoci! #EGOriferiti #podcast #LiaPipitone #AlessioCordaro #Libera

Alessio si è presentato in studio con una camicia nuova, molto colorata e... decisamente vistosa! L'acquisto diventa l'occasione perfetta per Katia e Alessio per esplorare il colorato mondo dei modi di dire italiani legati all'abbigliamento.In questo episodio di

Fluent Fiction - Italian: Mask Mysteries Unveiled at Venice's Enchanting Carnevale Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-02-01-08-38-20-it Story Transcript:It: Le campane di Venezia suonavano festosamente, riempiendo l'aria invernale di suoni gioiosi.En: The bells of Venezia rang festively, filling the winter air with joyful sounds.It: Era Carnevale, e le strade brulicavano di persone in maschera, un arcobaleno di colori che si rifletteva nell'acqua dei canali.En: It was Carnevale, and the streets swarmed with people in masks, a rainbow of colors reflecting in the water of the canals.It: Alessio camminava fra la folla, la sua macchina fotografica pronta a catturare ogni dettaglio.En: Alessio walked through the crowd, his camera ready to capture every detail.It: Giornalista curioso, non poteva fare a meno di notare i volti nascosti dietro le maschere, ogni sguardo una storia da scoprire.En: A curious journalist, he couldn't help but notice the faces hidden behind the masks, each gaze a story to discover.It: Accanto a lui camminava Giulia, elegante e riservata.En: Beside him walked Giulia, elegant and reserved.It: La sua famiglia custodiva da generazioni un'antica maschera, il cui segreto era noto solo a pochi membri.En: Her family had guarded an ancient mask for generations, its secret known only to a few members.It: Ora quella maschera era stata rubata, e Giulia temeva per l'onore e la sicurezza della sua eredità.En: Now that mask had been stolen, and Giulia feared for the honor and safety of her heritage.It: Alessio, con la sua passione per il mistero, l'aveva convinta a unirsi a lui nella ricerca.En: Alessio, with his passion for mystery, had convinced her to join him in the search.It: "Giulia," disse Alessio, fermandosi davanti a una bancarella di carnevale, "dimmi di più sul segreto della maschera.En: "Giulia," said Alessio, stopping in front of a carnival stall, "tell me more about the secret of the mask.It: Perché è così importante?"En: Why is it so important?"It: Giulia esitò.En: Giulia hesitated.It: "Non mi fido facilmente," rispose, "ma dobbiamo recuperarla.En: "I don't trust easily," she replied, "but we must recover it.It: La maschera ha un antico simbolo che può rivelare un tesoro nascosto."En: The mask has an ancient symbol that can reveal a hidden treasure."It: Mentre si addentravano nei vicoli stretti, trovarono il primo indizio: una piuma color smeraldo, legata a una vecchia porta.En: As they ventured into the narrow alleys, they found the first clue: an emerald-colored feather tied to an old door.It: "Conosci qualcuno che apprezza le piume di questo tipo?"En: "Do you know anyone who appreciates feathers like this?"It: chiese Alessio.En: asked Alessio.It: Giulia annuì.En: Giulia nodded.It: "Forse qualcuno del passato... un vecchio amico di famiglia.En: "Perhaps someone from the past... an old family friend.It: Ma non lo vedo da anni."En: But I haven't seen him in years."It: La caccia li portò da Piazza San Marco fino al Ponte di Rialto, seguendo una scia di indizi criptici.En: The hunt led them from Piazza San Marco to the Ponte di Rialto, following a trail of cryptic clues.It: Ogni pista li avvicinava di più al loro obiettivo.En: Each lead brought them closer to their goal.It: Nel frattempo, la folla di Carnevale divenne sempre più fitta, rendendo la ricerca ancora più complicata.En: Meanwhile, the Carnevale crowd grew ever denser, making the search even more complicated.It: Infine, arrivarono al gran ballo in maschera.En: Finally, they arrived at the grand masquerade ball.It: Tutto sembrava una danza magica di colori e suoni.En: Everything seemed like a magical dance of colors and sounds.It: Alessio capì che era il momento giusto per aprirsi con Giulia.En: Alessio realized it was the right moment to open up to Giulia.It: "Siamo una squadra," le disse, "voglio solamente aiutarti."En: "We are a team," he said to her, "I just want to help you."It: Giulia guardò Alessio negli occhi.En: Giulia looked into Alessio's eyes.It: Il suo sguardo era sincero, e lei decise di fidarsi.En: His gaze was sincere, and she decided to trust him.It: "C'è una stanza segreta nel vecchio teatro," sussurrò.En: "There is a secret room in the old theater," she whispered.It: "La maschera potrebbe essere lì."En: "The mask might be there."It: Entrarono nel teatro, guidati dagli indizi e dai suoni del carnevale.En: They entered the theater, guided by the clues and the sounds of the carnival.It: Lì, svelarono l'identità del ladro: un uomo del passato di Giulia, geloso del segreto di famiglia.En: There, they uncovered the identity of the thief: a man from Giulia's past, jealous of the family secret.It: Confrontarlo non fu facile, ma la solidarietà dei partecipanti al Carnevale si rivelò decisiva.En: Confronting him was not easy, but the solidarity of the Carnevale participants proved decisive.It: Alessio e Giulia, insieme agli altri, recuperarono finalmente la maschera.En: Alessio and Giulia, along with the others, finally recovered the mask.It: Con il mistero risolto, Alessio scrisse un articolo avvincente sulla storia della maschera e la loro avventura.En: With the mystery solved, Alessio wrote a compelling article about the story of the mask and their adventure.It: Giulia, invece, riportò la maschera alla sua famiglia, con la promessa di proteggerla meglio.En: Giulia, on the other hand, returned the mask to her family, with the promise to protect it better.It: La loro avventura dimostrò quanto fosse importante la fiducia reciproca.En: Their adventure demonstrated the importance of mutual trust.It: Mentre il Carnevale di Venezia proseguiva con i suoi festeggiamenti, Alessio aveva scoperto il valore del lavoro di squadra, e Giulia aveva trovato un alleato fidato.En: As the Carnevale di Venezia continued with its festivities, Alessio had discovered the value of teamwork, and Giulia had found a trusted ally.It: Le maschere avevano rivelato non solo un segreto, ma anche un'amicizia nata tra gli stretti vicoli di una Venezia incantata.En: The masks had revealed not only a secret but also a friendship born among the narrow alleys of an enchanted Venezia. Vocabulary Words:the bells: le campanefestively: festosamenteto swarm: brulicarethe mask: la mascherathe canals: i canalicurious: curiosothe heritage: l'ereditàto hesitate: esitarethe treasure: il tesorothe clue: l'indiziothe feather: la piumaemerald-colored: color smeraldoto appreciate: apprezzarecryptic: cripticothe trail: la sciathe masquerade ball: il ballo in mascherathe gaze: lo sguardoreserved: riservatathe theater: il teatrothe thief: il ladrojealous: gelosothe solidarity: la solidarietàto uncover: svelarecompelling: avvincentethe friendship: l'amicizianarrow: strettoto trust: fidarsito protect: proteggereteamwork: lavoro di squadrathe adventure: l'avventura

Fluent Fiction - Italian: Bridging Worlds: Art and Connection in Oaxaca's Mercato Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-01-29-23-34-02-it Story Transcript:It: Il mercato di Oaxaca era un mare di colori.En: The mercato of Oaxaca was a sea of colors.It: Le bancarelle straripavano di tessuti intricati, realizzati con un'arte che parlava direttamente al cuore di chi sapeva ascoltare.En: The stalls overflowed with intricate textiles, created with an art that spoke directly to the heart of those who knew how to listen.It: Alessio camminava lentamente tra la folla, la sua macchina fotografica pronta a catturare ogni dettaglio.En: Alessio walked slowly among the crowd, his camera ready to capture every detail.It: Tuttavia, non riusciva a trovare l'immagine perfetta, quella che potesse raccontare la vera essenza dell'arte indigena.En: However, he couldn't find the perfect image, the one that could tell the true essence of indigenous art.It: Dall'altra parte del mercato, Giulia osservava incantata i tessitori locali.En: On the other side of the mercato, Giulia watched the local weavers with enchantment.It: Anche se non parlava spagnolo, la sua passione per l'arte l'aveva spinta a venire qui.En: Even though she didn't speak Spanish, her passion for art had driven her to come here.It: Si sentiva un po' isolata, incapace di comunicare, ma non voleva arrendersi.En: She felt a bit isolated, unable to communicate, but didn't want to give up.It: Con il suo sketchbook in mano, iniziò a disegnare gli intricati motivi che vedeva intorno a lei, sperando di poter esprimere la sua ammirazione attraverso le immagini.En: With her sketchbook in hand, she started drawing the intricate patterns she saw around her, hoping to express her admiration through images.It: Alessio notò una giovane donna intenta a disegnare, circondata da tessitori sorridenti.En: Alessio noticed a young woman intent on drawing, surrounded by smiling weavers.It: Era Giulia, che aveva finalmente trovato un modo per interagire con gli artigiani, mostrando loro i suoi schizzi.En: It was Giulia, who had finally found a way to interact with the artisans by showing them her sketches.It: La scena aveva un'atmosfera magica, quasi come se una storia silenziosa si stesse svolgendo davanti ai suoi occhi.En: The scene had a magical atmosphere, almost as if a silent story was unfolding before his eyes.It: Senza pensarci troppo, Alessio alzò la macchina fotografica e scattò una foto.En: Without thinking too much, Alessio raised his camera and took a picture.It: Quando Giulia sollevò lo sguardo, i loro occhi si incrociarono.En: When Giulia lifted her gaze, their eyes met.It: Fu un incontro silenzioso ma pieno di significato.En: It was a silent but meaningful encounter.It: Alessio si avvicinò, incuriosito dai suoi disegni.En: Alessio approached, curious about her drawings.It: "I tuoi schizzi sono incredibili", disse, ammirando la profondità con cui aveva catturato i dettagli e l'anima dell'artigianato.En: "Your sketches are incredible," he said, admiring the depth with which she had captured the details and soul of the craftsmanship.It: Giulia sorrise timidamente, trovando conforto nella possibilità di comunicare.En: Giulia smiled timidly, finding comfort in the opportunity to communicate.It: Iniziarono a parlare, condividendo le loro esperienze e le loro sfide.En: They began to talk, sharing their experiences and challenges.It: Alessio le raccontò della sua ricerca della foto perfetta e di come avesse trovato la sua ispirazione nel vederla all'opera.En: Alessio told her about his search for the perfect photo and how he had found his inspiration by seeing her at work.It: "La tua presenza qui è ciò che mi ha dato una nuova prospettiva", ammise Alessio.En: "Your presence here is what gave me a new perspective," Alessio admitted.It: Nel frattempo, Giulia scoprì una nuova fiducia in sé stessa grazie alla connessione stabilita con gli artigiani e con Alessio.En: Meanwhile, Giulia discovered a newfound confidence in herself thanks to the connection established with the artisans and with Alessio.It: La loro conversazione si trasformò in una lezione di vita, insegnando a entrambi il valore della connessione umana e culturale.En: Their conversation turned into a life lesson, teaching them both the value of human and cultural connection.It: Poco dopo, Alessio scattò un'altra foto, questa volta di Giulia mentre discuteva animatamente con un anziano tessitore che sorrideva orgoglioso dei suoi lavori.En: Shortly after, Alessio took another photo, this time of Giulia while she animatedly discussed with an elderly weaver who smiled proudly at his work.It: L'immagine catturava perfettamente l'unione di due mondi artistici e culturali.En: The image perfectly captured the union of two artistic and cultural worlds.It: Quando il sole iniziò a tramontare, tingeva il cielo di arancio e oro, Alessio e Giulia si allontanarono dal mercato con nuove ispirazioni.En: When the sun began to set, painting the sky orange and gold, Alessio and Giulia left the mercato with new inspirations.It: Lui aveva trovato il soggetto perfetto per la sua fotografia, lei aveva trovato un nuovo modo di essere parte di un mondo a volte incompreso.En: He had found the perfect subject for his photograph, she had found a new way to be part of a sometimes misunderstood world.It: Il mercato si preparava a chiudere, eppure l'energia vibrante delle storie e delle culture che lo animavano rimanevano vive.En: The mercato was getting ready to close, yet the vibrant energy of the stories and cultures that animated it remained alive.It: Alessio e Giulia sapevano che quel giorno non avevano solo trovato l'arte, ma anche una nuova comprensione di sé stessi e degli altri.En: Alessio and Giulia knew that that day they had not only found art, but also a new understanding of themselves and others. Vocabulary Words:the market: il mercatothe colors: i colorithe stalls: le bancarellethe textiles: i tessutiintricate: intricatithe heart: il cuorethe crowd: la follathe weavers: i tessitorienchanted: incantataisolated: isolatato communicate: comunicarethe patterns: i motivithe sketchbook: il sketchbookthe eyes: gli occhisilent: silenziosodrawing: disegnaredepth: profonditàthe soul: l'animatimidly: timidamentechallenges: le sfideto discover: scoprireconfidence: fiduciaelderly: anzianoproudly: orgogliosothe sunset: il tramontoorange: aranciogold: orovibrant: vibranteto animate: animareunderstanding: comprensione

In questa puntata di Italiano ON-AIR, Katia e Alessio ci portano nel cuore del Rinascimento italiano per scoprire la figura di Fra' Giovanni da Fiesole, meglio conosciuto come Beato Angelico.Perché questo frate domenicano è considerato uno dei più grandi maestri del Quattrocento? Scopriremo insieme le caratteristiche uniche della sua pittura, fatta di luce divina, colori delicati e una prospettiva che unisce realismo e spiritualità. Parleremo anche dell'origine del suo nome e vi daremo un prezioso consiglio di viaggio per visitare i suoi capolavori a Firenze, lontano dalla folla, in un museo spesso fuori dalle rotte turistiche.

Fluent Fiction - Italian: Navigating Nature's Fury: A Journey of Discovery and Respect Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-01-26-08-38-20-it Story Transcript:It: Nella vastità della foresta pluviale brasiliana, Alessio e Giuliana avanzano tra le foglie fitte e il canto degli uccelli esotici.En: In the vastness of the Brazilian rainforest, Alessio and Giuliana advance through the thick leaves and the song of exotic birds.It: È estate e il caldo è opprimente, mentre il sole splende alto sopra di loro.En: It's summer and the heat is oppressive, while the sun shines high above them.It: Alessio, con i suoi occhi attenti, valuta ogni passo.En: Alessio, with his attentive eyes, evaluates every step.It: È uno scienziato ambientale e vede il mondo attraverso i dettagli.En: He is an environmental scientist and sees the world through details.It: Giuliana, invece, con il suo taccuino in mano, guarda tutto con curiosità.En: Giuliana, on the other hand, with her notebook in hand, looks at everything with curiosity.It: È una giornalista, sempre a caccia di una storia straordinaria da raccontare.En: She is a journalist, always on the hunt for an extraordinary story to tell.It: Il loro scopo è chiaro: Alessio vuole tornare al campo con i preziosi dati raccolti.En: Their purpose is clear: Alessio wants to return to the camp with the precious data collected.It: Giuliana spera di trovare un racconto avvincente.En: Giuliana hopes to find a compelling story.It: Ma la foresta è ingannevole, e il loro percorso diventa incerto.En: But the forest is deceptive, and their path becomes uncertain.It: Le nuvole cominciano a formarsi rapidamente, e un improvviso temporale si abbatte su di loro con tutta la sua furia.En: The clouds begin to form rapidly, and an unexpected storm crashes down on them with all its fury.It: "La bussola non mente," dice Alessio, cercando di mantenere la calma.En: "The compass doesn't lie," says Alessio, trying to stay calm.It: Sa che devono tornare al campo, ma i loro telefoni non funzionano più.En: He knows they must return to the camp, but their phones no longer work.It: La pioggia cade forte, cancellando le tracce del sentiero.En: The rain falls hard, erasing the tracks of the trail.It: Giuliana trema leggermente, ma è determinata a non arrendersi.En: Giuliana trembles slightly, but she is determined not to give up.It: "Meglio esplorare nuovi sentieri," propone Giuliana con vigore.En: "It's better to explore new paths," suggests Giuliana vigorously.It: "Potremmo scoprire qualcosa di unico!"En: "We might discover something unique!"It: Alessio è titubante, ma non possono permettersi di disperdersi nel buio che avanza rapidamente.En: Alessio is hesitant, but they can't afford to get scattered in the quickly approaching darkness.It: Mentre il fulmine illumina il cielo, Giuliana nota qualcosa di insolito tra le ombre: una piccola apertura nel fianco della montagna.En: As the lightning illuminates the sky, Giuliana notices something unusual among the shadows: a small opening in the side of the mountain.It: "Potremmo ripararci lì!"En: "We could shelter there!"It: suggerisce entusiasta.En: she suggests enthusiastically.It: Contro la sua abituale logica, Alessio decide di fidarsi dell'istinto di Giuliana.En: Against his usual logic, Alessio decides to trust Giuliana's instinct.It: Trovano rifugio nella grotta mentre il temporale imperversa fuori.En: They find refuge in the cave while the storm rages outside.It: Con il suono della pioggia che risuona nelle profondità, Alessio e Giuliana parlano con più tranquillità.En: With the sound of the rain echoing in the depths, Alessio and Giuliana speak more calmly.It: Le loro differenze diventano un ponte di comprensione.En: Their differences become a bridge of understanding.It: Lui vede quanto è importante la prospettiva coraggiosa di Giuliana.En: He sees the importance of Giuliana's bold perspective.It: Lei comprende il valore del ragionamento ponderato di Alessio.En: She comprehends the value of Alessio's thoughtful reasoning.It: Quando il temporale passa e il mattino giunge, la foresta ritorna alla sua solita sinfonia di vita.En: When the storm passes and morning comes, the forest returns to its usual symphony of life.It: Insieme, escono con cautela dalla grotta, guidati ora non solo da una bussola, ma da un nuovo rispetto reciproco.En: Together, they cautiously exit the cave, guided now not only by a compass but by a newfound mutual respect.It: Ritornano al campo con i dati intatti e una storia pronta a prendere forma nella penna di Giuliana.En: They return to the camp with the data intact and a story ready to take shape in Giuliana's pen.It: La foresta, con i suoi segreti e avventure, ha insegnato loro più di quanto avrebbero potuto immaginare.En: The forest, with its secrets and adventures, has taught them more than they could have imagined. Vocabulary Words:the vastness: la vastitàthe rainforest: la foresta pluvialethe heat: il caldooppressive: opprimentethe details: i dettaglithe curiosity: la curiositàextraordinary: straordinariathe purpose: lo scopoprecious: preziosicompelling: avvincentedeceptive: ingannevolethe storm: il temporalethe fury: la furiato tremble: tremaredetermined: determinatavigorously: con vigorehesitant: titubantethe darkness: il buiothe lightning: il fulmineunusual: insolitoenthusiastically: entusiastalogic: logicathe instinct: l'istintothe refuge: il rifugioto rage: imperversarethe depths: le profonditàthe differences: le differenzeto comprehend: comprenderethoughtful: ponderatothe symphony: la sinfonia

Rieccoci con un'altra puntata veramente TOP de IL CORTOCIRCUITO, e questa volta diciamo sul serio, visto che finalmente torna in presenza il buon Alessio Pianesani dopo la sua avventura di 1 mese in Cambogia: in questo appuntamento scoppiettante si è parlato in primis della Crisi di UBISOFT dopo la recente ristrutturazione e la cancellazione di vari giochi, tra cui il remake di Prince of Persia Le Sabbie del Tempo, delle Bombe sparate da Xbox durante il suo ultimo Developer Direct, e le novità (purtroppo anche negative) nel versante Film e Cinema!

In questo episodio di

Fluent Fiction - Italian: Silent Snow, Loud Lessons: Building Bridges in the Dolomites Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-01-21-23-34-02-it Story Transcript:It: Le Dolomiti si coloravano di bianco, come un dipinto silenzioso carico di immensa bellezza.En: The Dolomiti were turning white, like a silent painting full of immense beauty.It: Il cielo era limpido, e il sole invernale brillava sopra il paesaggio innevato.En: The sky was clear, and the winter sun shone over the snowy landscape.It: Alessio e Bianca erano in viaggio verso una piccola baita incastonata tra i monti.En: Alessio and Bianca were traveling to a small cabin nestled between the mountains.It: Lì, avrebbero trascorso alcuni giorni insieme, inviati dall'azienda per migliorare la loro capacità di lavorare in team.En: There, they would spend a few days together, sent by the company to improve their teamwork skills.It: Alessio era un lavoratore capace.En: Alessio was a capable worker.It: Tuttavia, il suo carattere testardo spesso lo metteva in contrasto con gli altri.En: However, his stubborn nature often put him at odds with others.It: Bianca, al contrario, era creativa e sempre aperta a nuove idee, ma trovava difficile lavorare con Alessio, che raramente ascoltava le opinioni altrui.En: Bianca, on the other hand, was creative and always open to new ideas, but found it difficult to work with Alessio, who rarely listened to others' opinions.It: La baita era accogliente, con un fuoco che scoppiettava nel camino e una vista mozzafiato sulle montagne innevate.En: The cabin was cozy, with a fire crackling in the fireplace and a breathtaking view of the snowy mountains.It: I due colleghi si sedettero su un grande divano di fronte al camino.En: The two colleagues sat on a large couch in front of the fireplace.It: Parlare apertamente non era facile con tutto quel silenzio attorno, ma dovevano provarci.En: Speaking openly wasn't easy with all that silence around, but they had to try.It: Il mattino seguente, il sole brillava su un mare di neve fresca.En: The next morning, the sun shone on a sea of fresh snow.It: Un team-building era stato organizzato: i due dovevano costruire una struttura di neve.En: A team-building activity had been organized: the two had to build a snow structure.It: Alessio e Bianca si misero a lavoro, ma presto iniziarono i contrasti.En: Alessio and Bianca got to work, but soon conflicts arose.It: Alessio voleva sovrastare con la sua idea, mentre Bianca proponeva una struttura più innovativa e collaborativa.En: Alessio wanted to dominate with his idea, while Bianca proposed a more innovative and collaborative structure.It: La tensione era palpabile.En: The tension was palpable.It: Alessio continuava a respingere le proposte di Bianca, insistendo sulla sua visione.En: Alessio kept rejecting Bianca's proposals, insisting on his vision.It: La situazione sembrava destinata al fallimento, finché una parte della struttura non iniziò a cedere.En: The situation seemed destined for failure, until part of the structure began to collapse.It: Bianca cercò di rimediare, ma senza successo.En: Bianca tried to fix it, but without success.It: Era un momento critico, e Alessio si trovò di fronte a una scelta: lasciare che il progetto di Bianca crollasse o mettere da parte il suo orgoglio e aiutarla.En: It was a critical moment, and Alessio found himself facing a choice: let Bianca's project collapse or put aside his pride and help her.It: Con un profondo respiro, Alessio decise di agire.En: With a deep breath, Alessio decided to act.It: Insieme, lavorarono senza sosta per rinforzare la struttura.En: Together, they worked tirelessly to reinforce the structure.It: Sembrava quasi che, mentre la neve prendeva forma, anche il loro rispetto reciproco crescesse.En: It seemed almost as if, while the snow was taking shape, so too was their mutual respect growing.It: Alessio ascoltò per davvero le idee di Bianca, accettando per la prima volta che collaborare poteva portare a risultati eccezionali.En: Alessio truly listened to Bianca's ideas, accepting for the first time that collaboration could lead to exceptional results.It: Quando finirono, la struttura di neve era solida e unica, riflettendo il contributo di entrambi.En: When they finished, the snow structure was solid and unique, reflecting the contribution of both.It: Bianca si sentì finalmente apprezzata nelle sue capacità mentre Alessio comprese il valore della collaborazione e della fiducia.En: Bianca finally felt appreciated for her abilities while Alessio understood the value of collaboration and trust.It: L'atmosfera tra i due era cambiata.En: The atmosphere between the two had changed.It: Ritornarono verso la baita, mentre il sole calava dolcemente dietro le Dolomiti.En: They returned to the cabin, as the sun gently set behind the Dolomiti.It: Il silenzio che li avvolgeva era ora sereno e colmo di un nuovo rispetto reciproco.En: The silence surrounding them was now serene and full of a newfound mutual respect.It: Avevano scoperto la forza di lavorare insieme, sapendo che combinando le loro visioni potevano affrontare qualsiasi sfida.En: They had discovered the strength of working together, knowing that by combining their visions, they could face any challenge.It: E così, in quella fredda ma luminosa giornata d'inverno, Alessio e Bianca capirono che le montagne erano belle non solo quando le si osservava da lontano, ma anche quando si lasciavano esplorare.En: And so, on that cold but bright winter day, Alessio and Bianca realized that the mountains were beautiful not only when viewed from afar, but also when allowed to be explored. Vocabulary Words:the cabin: la baitato nestle: incastonarestubborn: testardobreathtaking: mozzafiatoproposal: la propostato dominate: sovrastareconflict: il contrastopalpable: palpabileto collapse: cederepride: l'orgoglioto reinforce: rinforzareexceptional: eccezionalemutual: reciprocoto explore: esplorarecompany: l'aziendacreative: creativoto propose: proporreinnovative: innovativocritical: criticoto fix: rimediaresea of snow: mare di neveteamwork: lavoro in teamstructure: la strutturacozy: accoglientecapability: capacitàto appreciate: apprezzaretrust: la fiduciaatmosphere: l'atmosferato shine: brillaresilence: il silenzio

If you learn how to live with intention, you can lead with purpose and manifest all of your goals in life. This is exactly what Bianca D'Alessio did, which led to her becoming the number one real estate broker in New York City. She joins J.R. Lowry to share all about her journey centered on authenticity, leading with lasting impact, and discovering gratitude even in her biggest failures. Bianca also talks about her new book, Mastering Intentions, wherein she breaks down daily practices to amplify your own power. Discover how to manage your narrative, be comfortable with vulnerability, and unlock resilience both in business and in life.Check out the full series of “Career Sessions, Career Lessons” podcasts here or visit pathwise.io/podcast/. A full written transcript of this episode is also available at https://pathwise.io/podcasts/bianca-dalessio.Become a PathWise member today! Join at https://pathwise.io/join-now/

Join Ivoclar (AND US!) this February at LMT Lab Day in Chicago. Ivoclar will be offering 16 different educational lectures over the three-day event, giving dental professionals plenty of opportunities to learn, connect, and grow. Visit labday.com/Ivoclar to view the full schedule and register, and be sure to stop by and see the Ivoclar team in the Windy City. Cal-Lab Association Meeting in Chicago Feb 19-20 https://cal-lab.org/ LMT Lab Day Chicago Feb 19-21 https://lmtmag.com/lmtlabday Almost three years after his last appearance, Rob Nazzal returns to Voices From the Bench, this time joined by Mike Alessio of Bonadent Dental Laboratory (https://bonadent.com/). The conversation dives deep into lab leadership, culture, transparency, and how data—when used the right way—can empower teams instead of policing them. Mike shares his 32-year journey with Bonadent, from starting as a pickup-and-delivery driver to leading the Danaren division, and explains how a family-owned lab has grown into a multi-location organization without losing its people-first culture. Rob and Mike unpack the realities of tracking productivity on the lab floor, the challenges of sharing metrics openly, and why transparency builds trust, alignment, and accountability when done with intention. The discussion shifts to quality vs. productivity, the difficulty of truly measuring “quality,” and why labs must lead with craftsmanship before numbers. They also explore how digital workflows, QC processes, and proactive communication with doctors impact remakes, efficiency, and relationships. On the sales side, Rob breaks down how icortica (https://www.icortica.com/voices) helps labs grow by focusing on existing customers, improving retention, and giving sales teams real-time insights into what conversations they should be having—right before they walk into an office. Mike and Elvis share firsthand experiences using icortica (https://www.icortica.com/voices), highlighting how real-time data, centralized notes, and smart alerts change the way sales reps prepare, prioritize, and perform. The episode wraps with a look at Bonadent's unique culture (including their famous converted Walmart lab), long employee tenure, and why investing in people, transparency, and the right technology is the real key to sustainable growth in today's dental lab landscape. If you want to grow your business, you need clear insight into what's happening inside your operation and across your customer journey. That's where Icortica comes in. At Canadian Dental Labs, Icortica has become a cornerstone of how we operate—giving us at-a-glance visibility into performance, helping us focus our efforts, spot opportunities early, and solve problems before they grow. It takes the guesswork out of decision-making and shows us what to do next. Plus, the Icortica team is incredibly responsive and feels like a true partner in our success. If you're serious about growing your business and understanding your customers better, Icortica can get you there. Learn more at icortica.com/voices — Icortica, helping dental labs grow. Special Guests: Mike Alessio and Rob Nazzal.

Fluent Fiction - Italian: Thwarting Election Fraud: A Cold Day in Collevecchio Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-01-17-23-34-02-it Story Transcript:It: Il cielo era grigio sopra il piccolo centro comunitario di Collevecchio.En: The sky was gray above the small community center of Collevecchio.It: Era inverno, e il vento portava con sé un freddo pungente.En: It was winter, and the wind carried with it a biting cold.It: All'interno del centro, la scena era ben diversa.En: Inside the center, the scene was quite different.It: Il calore umano riempiva l'aria mentre i cittadini, avvolti nei loro cappotti e sciarpe, si mettevano in fila per votare.En: Human warmth filled the air as the citizens, wrapped in their coats and scarves, queued to vote.It: La sala era addobbata con manifesti elettorali di vari colori.En: The hall was decorated with election posters of various colors.It: Alessio, un funzionario elettorale diligente e attento, stava verificando i documenti dei votanti con attenzione.En: Alessio, a diligent and attentive electoral official, was carefully checking voters' documents.It: Proprio mentre il giorno di voto procedeva senza intoppi, Alessio ricevette un messaggio anonimo.En: Just as the voting day was proceeding smoothly, Alessio received an anonymous message.It: Diceva che ci sarebbe stato un tentativo di frode elettorale.En: It said there would be an attempt at electoral fraud.It: Alessio lesse il messaggio due volte.En: Alessio read the message twice.It: Era preoccupato.En: He was worried.It: Non voleva causare panico inutilmente, ma non poteva ignorare l'avvertimento.En: He didn't want to cause panic unnecessarily, but he couldn't ignore the warning.It: Nel frattempo, Giuliana, una giovane giornalista dal piglio deciso, aveva sentito del messaggio.En: Meanwhile, Giuliana, a young journalist with a determined approach, had heard about the message.It: Voleva scoprire la verità.En: She wanted to uncover the truth.It: Era determinata a pubblicare la storia, ma le mancavano prove concrete.En: She was determined to publish the story, but she lacked concrete evidence.It: Decise di parlarne con Alessio.En: She decided to talk to Alessio about it.It: "Alessio, possiamo parlare?"En: "Alessio, can we talk?"It: chiese Giuliana, avvicinandosi a lui.En: asked Giuliana, approaching him.It: "Certo, Giuliana.En: "Certainly, Giuliana.It: Che succede?"En: What's going on?"It: Alessio rispose, anche se era visibilmente preoccupato.En: Alessio replied, although he was visibly concerned.It: "Questo messaggio anonimo... Credi che sia vero?"En: "This anonymous message... Do you think it's true?"It: chiese Giuliana.En: asked Giuliana.It: Voleva sapere se Alessio avrebbe indagato, ma anche se avrebbe collaborato con lei.En: She wanted to know if Alessio would investigate and if he would collaborate with her.It: Alessio sospirò, incerto sul da farsi.En: Alessio sighed, uncertain about what to do.It: "Non lo so.En: "I don't know.It: Ma non possiamo ignorarlo completamente.En: But we can't completely ignore it.It: Potrebbe essere solo un falso allarme."En: It might just be a false alarm."It: "Ma se fosse vero?En: "But what if it's true?It: Dobbiamo fare qualcosa," insistette Giuliana.En: We need to do something," insisted Giuliana.It: Nel corso della giornata, Giuliana osservò una figura sospetta vicino alle urne.En: Throughout the day, Giuliana observed a suspicious figure near the ballot boxes.It: Sembrava strano.En: It seemed odd.It: Scriveva qualcosa su un foglio in modo furtivo.En: The person was writing something on a sheet of paper furtively.It: Giuliana scattò rapidamente alcune foto e corse da Alessio.En: Giuliana quickly snapped a few photos and ran to Alessio.It: "Alessio, presto!En: "Alessio, quickly!It: C'è qualcosa che non va!"En: Something is wrong!"It: Giuliana esclamò concitata.En: Giuliana exclaimed, agitated.It: Alessio si precipitò con lei.En: Alessio hurried with her.It: Vide l'individuo e decise di intervenire.En: He saw the individual and decided to intervene.It: Lo fermò e chiese spiegazioni.En: He stopped him and asked for an explanation.It: Dopo un breve controllo, scoprirono che la persona stava tentando di manipolare i voti con nuove schede false.En: After a brief check, they discovered that the person was attempting to manipulate the votes with new fake ballots.It: Fu un momento teso, ma grazie alla prontezza di Giuliana e alla risolutezza di Alessio, la situazione venne risolta senza intaccare i risultati delle elezioni.En: It was a tense moment, but thanks to Giuliana's alertness and Alessio's decisiveness, the situation was resolved without affecting the election results.It: A fine giornata, Alessio e Giuliana si trovarono a discutere dell'accaduto.En: At the end of the day, Alessio and Giuliana found themselves discussing the incident.It: "Grazie a te, abbiamo sventato la frode," disse Alessio, riconoscendo il valore del lavoro di Giuliana.En: "Thanks to you, we thwarted the fraud," Alessio said, acknowledging the value of Giuliana's work.It: "E grazie a te, la tua attenzione ha evitato il panico," replicò Giuliana, rispettandolo per la sua calma e decisione.En: "And thanks to you, your attentiveness avoided panic," replied Giuliana, respecting him for his calmness and decisiveness.It: Quella sera, Alessio camminò verso casa sentendo un nuovo rispetto per il lavoro dei giornalisti.En: That evening, Alessio walked home with a newfound respect for the work of journalists.It: Giuliana tornò in redazione con una lezione importante sull'importanza dell'integrità.En: Giuliana returned to the newsroom with an important lesson about the importance of integrity.It: Entrambi sapevano di aver fatto la cosa giusta, consapevoli che vigilanza e collaborazione erano essenziali per la giustizia.En: Both knew they had done the right thing, aware that vigilance and collaboration were essential for justice. Vocabulary Words:the sky: il cielobiting: pungentethe scene: la scenawrapped: avvoltito queue: mettersi in filathe hall: la salathe official: il funzionariodiligent: diligenteto check: verificarethe document: il documentosmoothly: senza intoppithe message: il messaggioanonymous: anonimothe attempt: il tentativofraud: la frodeworried: preoccupatothe warning: l'avvertimentothe journalist: la giornalistadetermined: decisoto uncover: scoprireconcrete: concreteevidence: le proveto investigate: indagareto ignore: ignorarefalse alarm: falso allarmesuspicious: sospettafurtively: furtivoto manipulate: manipolaredecisiveness: risolutezzathwarted: sventato

Sometimes, an episode recording goes south. Maybe it takes the entire episode or waits for the weekend, but it happens. There's no Last Standee anymore, only Unquackable. Do you expect me to write a synopsis of... this? Fine, I'll try. We begin with a Standee Catch-up, par for the course. Then Fen does a recollection of a lot of stuff (Seance of the Blake Manor - wishlisted on Steam!), but mostly talks about Grimcoven and snow (unrelated). Then - oh dear how I missed those - it's time for the irregular space of Cara's Reviews, with Dark Romance. Listen to it! It's maybe a bit long, but it's highly educational. Then, Alessio has 10 minutes to finish with Toy Battle and Unstoppable. No worries, maybe it can still be done... but you really should keep Fen away from photo editors during that time. Now all I see are Toy Battle rubber duckies - everywhere. You cannot stop the quacking. It's UNQUACKABLE. ...so we publish this out of schedule, because the cake is a lie and all there is is QUACK.

In this episode, we are joined by Alessio Pagliai and Stepan Drunks. Brought to you by Sovol (https://jle.vi/sovol) and OctoEverywhere (https://octoeverywhere.com/welcome?id=podcast).

Ogni anno in Svizzera oltre 2'000 persone finiscono in coma. Poco più della metà di loro sopravvive. La terapia intensiva salva vite umane, ma è un'esperienza molto pesante da vivere, con strascichi fisici, cognitivi e psicologici, oggi riconosciuti sotto il termine PICS (Post Intensive Care Syndrome). Per aiutare i pazienti in coma, alcuni ospedali introducono all'interno del percorso di cura l'utilizzo di diari narrativi che, scritti da infermiere e infermieri, altro personale sanitario e parenti, riducono l'insorgenza della PICS e aiutano i pazienti a riappropriarsi della propria storia, interrotta dalla malattia e dal coma. “Buongiorno signor Alessio*, sono Sergio, l'infermiere che si sta occupando di lei in questo momento… Non conosco nulla di lei, vedo solo che è giovane e spero che potrà svegliarsi presto. Nel frattempo, faccio – in realtà, facciamo tutti - il tifo per lei”.I diari di terapia intensiva hanno un valore terapeutico e umano. In questo documentario li apriamo, sfogliamo e leggiamo con rispetto ed emozione, guidati da Sergio Calzari, infermiere di Terapia intensiva presso il Cardiocentro dell'EOC e fondatore del progetto postintensiva per un'umanizzazione della terapia intensiva; Flavia Pegoraro, infermiera in Urgenza Emergenza presso l'IRRCS San Gerardo dei Tintori di Monza, docente, ricercatrice e autrice di una serie di interventi pubblicata sui Sentieri nelle Medical Humanities e Mirko Achermann che, sopravvissuto a un'esperienza di coma durata più mesi, ha scelto di condividere la sua storia e il suo diario sul blog. *Nome d'invenzione, a tutela della privacyMontaggio e sound design a cura di Thomas Chiesa.undefined

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

Fluent Fiction - Italian: Finding Balance: A Tale of Exams & Friendship in Firenze Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-12-30-08-38-20-it Story Transcript:It: La biblioteca di Firenze era un luogo magico.En: The biblioteca of Firenze was a magical place.It: Il soffitto alto, le pareti foderate di quercia scura e il profumo di libri antichi rendevano l'ambiente speciale.En: The high ceiling, the walls lined with dark oak, and the smell of ancient books made the atmosphere special.It: In uno dei tavoli in legno, sotto la morbida luce verde di una lampada, Alessio stava studiando.En: At one of the wooden tables, under the soft green light of a lamp, Alessio was studying.It: Fuori faceva freddo, l'inverno era arrivato, ma dentro la biblioteca c'era calore e tranquillità.En: Outside it was cold, winter had arrived, but inside the biblioteca there was warmth and tranquility.It: Alessio era concentrato sui suoi libri.En: Alessio was focused on his books.It: I suoi esami finali erano vicini e lui doveva mantenere la borsa di studio.En: His final exams were approaching, and he had to maintain his scholarship.It: Sentiva una grande pressione.En: He felt a great deal of pressure.It: Giulia, invece, era seduta poco distante, con un sorriso sereno sul volto.En: Giulia, on the other hand, was sitting not far away, with a serene smile on her face.It: Lei studiava, ma sembrava non avere nessuna preoccupazione.En: She was studying, but seemed to have no worries.It: Alessio non poteva fare a meno di sentirsi geloso di quanto fosse rilassata.En: Alessio couldn't help feeling jealous of how relaxed she was.It: Una sera, in prossimità di Capodanno, la tensione diventò troppa.En: One evening, near Capodanno, the tension became too much.It: Alessio chiuse il suo libro di scienze e si avvicinò a Giulia.En: Alessio closed his science book and approached Giulia.It: "Come fai a essere così tranquilla?"En: "How do you stay so calm?"It: chiese, cercando di nascondere la sua ansia.En: he asked, trying to hide his anxiety.It: Giulia alzò lo sguardo e sorrise.En: Giulia looked up and smiled.It: "Cerco di bilanciare lo studio con la vita.En: "I try to balance study with life.It: Faccio delle pause, parlo con gli amici, e mi godo le piccole cose," rispose dolcemente.En: I take breaks, talk with friends, and enjoy the little things," she replied sweetly.It: Alessio sospirò.En: Alessio sighed.It: "Io non riesco mai a rilassarmi.En: "I can never relax.It: Ho paura di perdere la mia borsa di studio."En: I'm afraid of losing my scholarship."It: Giulia lo ascoltò attentamente, poi aggiunse: "Alessio, siamo tutti preoccupati.En: Giulia listened attentively, then added, "Alessio, we are all worried.It: Anche io ho delle difficoltà.En: Even I have difficulties.It: A volte mi diverto troppo e poi mi ritrovo con poco tempo per studiare."En: Sometimes I have too much fun, and then I find myself with little time to study."It: Questa confessione sorprese Alessio.En: This confession surprised Alessio.It: Non aveva mai pensato che lei potesse avere problemi simili.En: He had never thought that she might have similar problems.It: I due parlarono a lungo quella sera.En: The two talked for a long time that evening.It: Capirono di non essere soli nelle loro paure e si promisero di sostenersi a vicenda.En: They realized they were not alone in their fears and promised to support each other.It: Insieme, crearono un piano di studio che includeva pause e momenti di svago.En: Together, they created a study plan that included breaks and leisure moments.It: Decisero persino di festeggiare insieme la vigilia di Capodanno, per rilassarsi prima degli esami.En: They even decided to celebrate New Year's Eve together, to relax before the exams.It: Mentre i giorni passavano, Alessio iniziò a notare la differenza.En: As the days passed, Alessio began to notice the difference.It: Studiare diventava un po' meno pesante, e lui si sentiva più sereno.En: Studying became a little less burdensome, and he felt more at ease.It: Arrivò il giorno dell'esame e Alessio si sentì pronto.En: The exam day arrived, and Alessio felt ready.It: Sapeva di aver fatto del suo meglio.En: He knew he had done his best.It: Alla fine, capì che non si trattava solo di studio, ma anche di vivere.En: In the end, he understood that it wasn't just about studying but also about living.It: Balance era la parola chiave.En: Balance was the key word.It: Grazie a Giulia, Alessio scoprì che un equilibrio tra il lavoro e il benessere personale era essenziale.En: Thanks to Giulia, Alessio discovered that a balance between work and personal well-being was essential.It: I due uscirono dalla biblioteca, pronti ad affrontare il futuro e i suoi esami, insieme.En: The two of them left the biblioteca, ready to face the future and its exams, together. Vocabulary Words:the library: la bibliotecathe ceiling: il soffittothe wall: la paretethe oak: la querciathe table: il tavolothe lamp: la lampadathe warmth: il caloretranquility: la tranquillitàthe scholarship: la borsa di studiopressure: la pressioneserene: sereno/athe smile: il sorrisothe tension: la tensionecalm: tranquillo/aanxiety: l'ansiato balance: bilanciaresweetly: dolcementeto sigh: sospirareto relax: rilassarsiattentively: attentamentedifficulties: le difficoltàconfession: la confessioneto promise: prometterethe break: la pausaleisure: lo svagoto celebrate: festeggiareburdensome: pesanteease: sereno/ato face: affrontarebalance: l'equilibrio

Fluent Fiction - Italian: Finding Inspiration: A Christmas Tale in Piazza Navona Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-12-26-08-38-20-it Story Transcript:It: Nel cuore di Roma, sotto un cielo grigio d'inverno, Piazza Navona brillava di luci natalizie.En: In the heart of Roma, under a gray winter sky, Piazza Navona shone with Christmas lights.It: C'erano bancarelle ovunque, che esponevano dolci profumati e regali fatti a mano.En: There were stalls everywhere, displaying fragrant sweets and handmade gifts.It: L'aria, fredda e pungente, era piena di musica, risate e l'inebriante aroma di caldarroste.En: The air, cold and sharp, was filled with music, laughter, and the intoxicating aroma of roasted chestnuts.It: Alessio camminava lentamente tra la folla.En: Alessio walked slowly through the crowd.It: Portava un cappello di lana che nascondeva i suoi riccioli scuri e una sciarpa avvolta stretta intorno al collo.En: He wore a wool hat that hid his dark curls and a scarf wrapped tightly around his neck.It: Cercava ispirazione per il suo nuovo quadro.En: He was looking for inspiration for his new painting.It: Era un giovane artista in cerca della sua musa.En: He was a young artist searching for his muse.It: Al suo fianco c'era Giulia.En: Beside him was Giulia.It: Pratica e premurosa, lei osservava Alessio con un misto di preoccupazione e affetto.En: Practical and caring, she watched Alessio with a mix of concern and affection.It: "Devi pensare al futuro, Alessio," gli disse, scuotendo la testa mentre un carretto di zucchero filato passava accanto a loro.En: "You need to think about the future, Alessio," she said, shaking her head as a cotton candy cart passed by them.It: In quel momento, Matteo, il loro amico d'infanzia, tornato dall'estero, si unì a loro con un grande sorriso.En: At that moment, Matteo, their childhood friend, returned from abroad and joined them with a big smile.It: "Roma mi è mancata!En: "Roma really missed me!It: E voi ancora di più!En: And you guys even more!It: ", esclamò abbracciandoli forte.En: ", he exclaimed, embracing them tightly.It: Alessio sorrise, ma dentro di sé si sentiva frustrato.En: Alessio smiled, but inside he felt frustrated.It: La piazza era piena di vita, ma lui non riusciva a trovare l'immagine perfetta da dipingere.En: The square was full of life, but he couldn't find the perfect image to paint.It: Decise di fermarsi e osservare le persone attorno.En: He decided to stop and observe the people around.It: Vide famiglie che si scattavano foto davanti alla grande fontana, bambini che correvano intorno agli alberi decorati, e coppie che si scambiavano piccoli doni dal mercato.En: He saw families taking photos in front of the big fountain, children running around the decorated trees, and couples exchanging small gifts from the market.It: Eppure, niente sembrava colpire l'immaginazione di Alessio.En: Yet, nothing seemed to strike Alessio's imagination.It: Poi, all'improvviso, vide una scena che gli fece battere il cuore.En: Then, suddenly, he saw a scene that made his heart race.It: Un'anziana coppia, mano nella mano, si avvicinava a una bancarella.En: An elderly couple, hand in hand, approached a stall.It: I loro volti erano solcati dal tempo, ma si illuminavano di gioia mentre due bambini si lanciavano loro tra le braccia.En: Their faces were lined with time, but they lit up with joy as two children threw themselves into their arms.It: Le risate dei due piccoli e l'affetto sincero tra i nonni accesero una scintilla in Alessio.En: The laughter of the little ones and the sincere affection between the grandparents sparked something in Alessio.It: "È questo!En: "That's it!"It: ", esclamò, sorpreso dalla forza della sua visione.En: he exclaimed, surprised by the strength of his vision.It: Giulia lo guardò attentamente.En: Giulia looked at him carefully.It: "Hai visto qualcosa, vero?"En: "You saw something, didn't you?"It: Alessio assentì, con gli occhi pieni di nuova luce.En: Alessio nodded, his eyes full of new light.It: "I volti, le emozioni, le connessioni... sono l'essenza di questo Natale."En: "The faces, the emotions, the connections... they are the essence of this Christmas."It: Con rinnovata energia, Alessio si mise in un angolo tranquillo della piazza e cominciò a disegnare, perdendosi nei dettagli e nei sentimenti della scena che aveva visto.En: With renewed energy, Alessio found a quiet corner of the square and began to draw, losing himself in the details and feelings of the scene he had witnessed.It: Matteo lo guardava, impressionato dalla dedizione dell'amico ritrovato.En: Matteo watched him, impressed by the dedication of his rediscovered friend.It: Giulia, sollevata, gli diede una pacca sulla spalla.En: Giulia, relieved, gave him a pat on the shoulder.It: "Forse hai davvero trovato il modo di far funzionare la tua arte."En: "Perhaps you've truly found a way to make your art work."It: Quella sera, mentre le luci di Natale si riflettevano sui ciottoli umidi della piazza, Alessio capì che c'era un modo per bilanciare la passione e la realtà.En: That evening, as the Christmas lights reflected on the wet cobblestones of the square, Alessio realized there was a way to balance passion and reality.It: La sua tela si riempiva lentamente di vita, mentre una nuova determinazione cresceva dentro di lui.En: His canvas slowly filled with life as new determination grew within him.It: Questa volta, non solo aveva trovato la sua ispirazione, ma anche un cammino che univa il cuore e la ragione.En: This time, not only had he found his inspiration, but also a path that united heart and reason. Vocabulary Words:the heart: il cuorethe square: la piazzathe stalls: le bancarellefragrant: profumatithe gifts: i regalisharp: pungentethe scarf: la sciarpathe muse: la musapractical: praticacaring: premurosathe future: il futurothe childhood: l'infanziaabroad: l'esterofrustrated: frustratocouples: coppieto strike: colpirethe imagination: l'immaginazioneto approach: avvicinarsilined: solcatithe joy: la gioiasincere: sincerothe spark: la scintillato witness: assistereto draw: disegnarethe details: i dettaglithe feelings: i sentimentirenewed: rinnovatadetermination: determinazionethe canvas: la telato unite: unire

In questa puntata di Italiano On Air, Katia e Alessio ci portano alla scoperta di uno dei simboli più amati del Natale italiano: il panettone. Nato a Milano, questo dolce alto e soffice ha una storia antica, fatta di tradizioni, leggende e curiosità.Quando nasce il panettone? Chi lo ha inventato? E com'è diventato il protagonista indiscusso delle feste natalizie in Italia e nel mondo? Tra Medioevo, corti ducali, fornai innamorati e lunghe lievitazioni, ripercorriamo l'evoluzione di questo “pane di lusso” così speciale.E alla fine… il dibattito più gustoso: canditi sì o canditi no? Una puntata perfetta per entrare nell'atmosfera natalizia e scoprire un pezzo goloso della cultura italiana.La trascrizione la puoi trovare nella pagina dell'episodio, scorrendo in basso.I nostri contatti

Bianca D'Alessio discusses the importance of embracing vulnerability and authenticity in leadership. She reflects on her personal journey of breaking down societal expectations of perfectionism and strength, and how this transformation has positively impacted her business and personal growth. Through storytelling and connection, she emphasizes the power of being true to oneself and the strength found in shared experiences. Bianca D'Alessio, the star of Selling the Hamptons on HBO Max, is the top-ranked real estate broker in both New York City and state, and the founder of one of highest producing brokerage teams in the U.S. She oversees a $10 billion international real estate portfolio, is a global speaker, and the author of, Mastering Intentions: 10 Practices to Amplify Your Power and Lead with Lasting Impact. Bianca is a frequent expert voice in Forbes, The New York Times, Fox Business, Medium, and The Real Deal. Get in Touch with Bianca:WEBSITE: biancadalessio.com    SOCIAL MEDIA:    facebook.com/bianca.dalessio.3      https://www.linkedin.com/in/biancadalessio/      https://www.instagram.com/biancadalessio/

1 hour and 55 minutes The Sponsors Thank you to Underground Printing for making this all possible. Rishi and Ryan have been our biggest supporters from the beginning. Check out their wide selection of officially licensed Michigan fan gear at their 3 store locations in Ann Arbor or learn about their custom apparel business at undergroundshirts.com. Our associate sponsors are: Peak Wealth Management, Matt Demorest - Realtor and Lender, Ann Arbor Elder Law, Michigan Law Grad, Human Element, Sharon's Heating & Air Conditioning, The Sklars Brothers, Champions Circle, Winewood Organics, Community Pest Solutions, Venue by 4M where record this, and Introducing this season: Radecki Oral Surgery, and Long Road Distillers. 1. Offense vs Purdue Starts at :57 This podcast starts out telepathically but then Brian's intrusive thoughts got telepathed so it had to stop. Dave introduces the Snack of the Week. Would you rather talk about this game or Dunkaroos? Bryce Underwood - not good in the first half. A fumble on the sideline is usually harmless unless it involves the silliest rule in football. His scrambling was good but you can't build a business in this industry by scrambling, that will get you killed against Ohio State. Too many missed passes, he doesn't really settle in. By the Georgia game, JJ was probably where Bryce is now - many mistakes but you can see the talent. On the flip side, the offensive line had a great game. Purdue loaded the box but Jordan Marshall rushed for 185 yards anyways. You can't tackle him with just one guy, he will emerge from piles. This is the fourth straight game where Sprague has been incredible. Bryson Kuzdzal had some nice runs on the game-sealing drive. Tight ends were fine, more catches by Zack Marshall. There's not a lot of separation between Marshall and Klein. Semaj had way fewer snaps, Goodwin saw more time. You have six 2nd or 3rd year players on this offensive line that can absolutely play in this conference. The future of the offensive line is bright.  2. Defense vs Purdue Starts at 41:43 How do we even feel about the defensive performance? We've seen Purdue all season be an offense that moves the ball down the field but can't score. That happened but it felt bad. Cam Brandt was too far upfield on a couple big run plays. Why are the good defensive ends not on the field for 70% of the snaps that they should be out for? Why are the starters rotating out so much throughout the game? Assuming he's healthy, do you put Jaishawn Barham at DE or LB against Ohio State? Michigan didn't commit to a position for him and it's hurting his play. Way fewer three defensive tackle sets, yay. If your name is going to be "Michael Jackson" you need to go by "Mike". Jyaire Hill got sealed a couple times but was otherwise fine. The endzone DPI was DPI. Metcalf got sucked in during the touchdown.  3. Hot Takes, Game Theory, and Special Teams Starts at 1:06:04 Takes hotter than the amount of trouble Jason would get into if he did the Hot Takes voice at a golf tournament where he was during recording. Michigan has not been good at Special Teams Things, why are they running kickoffs out of the middle of the endzone? Another punt that Semaj didn't field that gave up 20 yards. Did Jay Harbaugh have a heat map for punting? We've never had to talk so much about shield punting positioning but now we have to. Clock management at the end of the first half was pretty on-point. Purdue's 4th down decision making was aggressive which you do if you want to try to win the game. Shout out to Michigan fans for feeding energy back into the team in the 4th quarter. The students did the shirtless thing that's become a college football thing. Also shout out to Barry Odom for getting the Purdue bench fired up.  4. Around the Big Ten with Jamie Mac Starts at 1:28:22 Indiana 55, Maryland 10 This is a typical Indiana game these days. Indiana's offense is a machine. The defense is... also a machine?? Every week, Indiana has some weird defensive stat that's historical and worth tracking. Mendoza threw and interception on his first play, the game was wobbly for about a quarter.  Ohio State 38, Penn State 14 Briefly competitive in the 2nd quarter. Penn State is the first top five team in the history of college football to lose five straight games. Julian Sayin had 14 yards per attempt. Ohio State finally catches a break and gets an obvious targeting call to not get enforced.  Minnesota 23, Michigan State 20 (OT) MSU benches Aidan Chiles for Alessio Milivojevic. The Spartans lose this game despite outgaining Minnesota by about 160 yards. The final two minutes of this game are worth watching. Northwestern QB Aidan Chiles?? Alessio had a better EPA than Chiles any other game this season. USC 21, Nebraska 17 If you like offense, don't look at this game. We are suddenly having feelings about Wink Martindale. Dylan Raiola is done for the season and USC is able to grind out a win. Raiola's backup went 5/7 for 7 yards.  Illinois 35, Rutgers 13 A solid victory for Illinois, most of Rutgers' yards are when it was 35-6. Bert: "I put us as good as any 6-3 team out there. That doesn't mean anything." Bowl eligible in consecutive seasons for the first time since 2011. Illinois is the new Wisconsin.  MUSIC: "On & On"—The Marcus King Band "Husbands"—Geese "Don't Forget That I Love you"—Pale Jay “Across 110th Street”—JJ Johnson and his Orchestra