Podcasts about Aitor

En este nuevo episodio de Fusiones y Adquisiciones, Ricardo, Aitor y Fran, socios de Albia IMAP, hacen balance del primer trimestre de 2025: - ¿Qué operaciones hemos cerrado? - ¿Cómo vemos el mercado? - ¿Qué operaciones nos han llamado más la atención de este periodo en España? - ¿Qué esperamos de lo que queda de año?

Después de dos semanas de ausencia, Aitor Albizua regresa con energía para su auditoría. Celebra el éxito del EGM, con conga de por medio; habla sobre el viaje del equipo a Roma y el precio del café; de los debates lingüísticos a raíz del 'steak tartar' o sobre la manos y sus habilidades, como las del colaborador Manuel Delgado. 

En la Biblioteca de Antonio Martínez Asensio recibimos a Marcos Giralt, autor que nos dona 'Los ilusionistas', novela epistolar con tintes autobiográficos. En la hora musical, celebramos el cincuenta aniversario del primer disco de Triana, 'El Patio', con un reportaje de Severino Donate para el que ha contado con la colaboración de Eduardo Rodríguez Rodway, el único miembro original vivo de la banda. Aprovechando la efeméride del considerado por muchos críticos como el mejor disco de la historia de la música española, Fernando Neira repasa otros álbumes de aquel mágico 1975 en la industria musical. Y cerramos el programa con la 'Auditoría' de Aitor Albizua.

Aitziber eta Malenek podcastera gonbidatzen duten laugarren Markina-Xemeindarra da. Aitor Nieto polimaitasunaz hitz egitera gonbidatu dute.Zer da polimaitasuna? Nola bizi du Nietok? Zer desberdintasun dago polimaitasuna eta harreman irekien artean? Zer iritzi du harreman monogamoen inguruan? Zer uste du polimaitasuna ligatzeko erabiltzen dutenez? Zer da “polifake”?Polimaitasun harremanetako akordioez eta komunikazioaren garrantziaz aritu dira, besteak beste.

Hablamos con Aitor Retolaza, portavoz de Futuro Ciudadanos, haciendo balance del primer año del pacto de Gobierno entre PP y Futuro Ciudadanos en Alcobendas

El Cid (Murciano)- Maquetos y mentiras, Aitor (22-04-2025) Más contenido inédito en: https://www.es-tv.es Aportaciones a Raúl: https://www.patreon.com/user?u=40527138 Nº de cuenta: ES75 3018 5746 3520 3462 2213 Bizum: 696339508 o 650325992 Aportaciones a David: https://www.patreon.com/davidsantosvlog Nº de Cuenta: ES78 0073 0100 5306 7538 9734 Bizum: +34 644919278 Aportaciones a Equipo-F: TITULAR: EQUIPO F CUENTA: ES34 1465 0100 9417 5070 9106 C ÓDIGO SWIFT: INGDESMM Conviértete en miembro de este canal para disfrutar de ventajas: https://www.ivoox.com/podcast-un-murciano-encabronao-david-santos-los-audios_sq_f11099064_1.html Canales de U.M.E.: El Cid

Rueda de prensa Aitor Calle (entrenador Numancia ) en Guijuelo - El Numancia cumple y gana en Guijuelo 1-4 para asegurar la segunda plaza

Este viernes, Aitor Albizua llega con todo un repertorio de historias para contar. Como las nuevas sillas del estudio o los sueños raros de Ana Uslé. Además, Aitor se mete en polémica, Sergio Castro ha destapado que hay parte del equipo que se va de cañas a modo de “planes secretos”... aunque parece que Castro tiene un pequeño lío con los nombres.

Hoy viajamos hasta la Sierra de Gredos para conocer un proyecto que representa la esencia de los vinos de montaña, la recuperación del viñedo y el amor por la Garnacha. Nos acompaña Aitor Fernández, quien recientemente ha tomado el testigo de Las Bacantes, un proyecto iniciado por Carlos Sánchez hace más de una década, y que ahora sigue su curso con la misma pasión y compromiso por la tierra. Las Bacantes es un homenaje al viñedo viejo de Gredos, una zona de extraordinario valor vitícola donde la Garnacha y la Albillo Real encuentran su máxima expresión. Trabajando con viñas de Cadalso de los Vidrios, Aitor sigue una filosofía de mínima intervención, respetando el carácter del terruño y apostando por una viticultura sostenible. Pero su historia no comienza aquí: Aitor es un apasionado del vino que dejó atrás su carrera en el periodismo para sumergirse en el mundo de la enología, pasando por proyectos como Vega Sicilia, Soto Manrique y Compañía de Vinos Telmo Rodríguez. Más allá de la elaboración de vino, Las Bacantes representa una lucha por mantener con vida el viñedo en una zona donde el 80% de la superficie cultivada ha desaparecido en los últimos 50 años. Problemas como la falta de relevo generacional, el éxodo rural o la ausencia de apoyo institucional hacen que cada cepa superviviente sea un símbolo de resistencia. Aitor asume este desafío con la convicción de que el vino puede ser un motor de cambio, una forma de proteger el paisaje y dar futuro a las generaciones venideras. Antes de comenzar nuestra conversación con Aitor, os recordamos que podéis seguir disfrutando de Gourmet FM en la 92.0 FM local de la provincia de Sevilla desde Radio Tomares, y también en plataformas digitales como iVoox, Spotify e iTunes. No olvidéis seguirnos en redes sociales para compartir vuestras ideas y propuestas, y si queréis contactar directamente, podéis escribirnos a fran@franleon.es. ¡Nos encanta escucharos y seguir creciendo junto a vosotros! Con Fran León.

Con Aitor Ferrnández, Portavoz de Tax Down, repasamos las principales novedades que trae la renta este año, una de ellas afecta sobre todo a los propietarios de inmuebles. Nuestro invitado nos destaca que “los propietarios de inmuebles se podrán deducir el 50%, en una zona tensionada más”. Una de las zonas es Cataluña, donde estas deducciones como nos explica Aitor, pueden llegar incluso al 90%. El concepto de zona tensionada está generando muchas dudas en esta campaña de la renta. El Portavoz de Taz Down nos aclara que “aunque se califiquen de zona tensionada muchas comunidades, la única de verdad es Cataluña”. Otra de las grandes novedades de esta campaña para Aitor Fernández es el aumento de la reducción para rentas bajas y los cambios que afectan a la tributación de los donativos. Aquí nos cuenta que “aumenta la deducción o el derecho a la misma, que será del 80% de los primeros 250 euros”. Una de las que aparecerán este último año en la renta será la deducción por adquisición por vehículo eléctrico y por eficiencia energética, que incluye la colocación de placas solares. Nuestro invitado nos cuenta que “estas no entrarán en los próximos años y que en cuanto a los autónomos, aunque hayan estado de alta aunque solo un día, tendrán que hacer la declaración de la renta”.

Un vapor británico que naufraga en plena Ría de Bilbao. Un rescatador y un consignatario que no llegan a un acuerdo para su rescate. Esos son los hilos de los que parte hoy el relato de Juanmari Rekalde en su sección biográfica de buques. Aitor Francesena, Gallo ha ganado en Byron Bay, Australia, con un surf brillante, la primera prueba del Campeonato del Mundo de Surf para personas sin visión. Para conseguir su sexto título, Aitor debe todavía competir en Hawai y California. Él mismo nos hace, desde Sydney, la crónica de la prueba. La cultura marítima polinesia es fascinante. Su relación con el Océano y sus criaturas es única y maravillosa. Guillermo Lekunberri, surfista y "waterman" nos presenta una de las embarcaciones polinesias más características: la canoa para surf de cuatro remeros. Capucine Trochet es una periodista, escritora y navegante francesa que ya pasó por nuestro programa en 2015. Ahora, con motivo de la publicación en castellano de su libro "TARA TARI", Jorge Ríos nos recuerda el inmenso coraje y la pericia marinera de esta mujer, navegante excepcional.

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Santiago González recuerda cómo el PNV se acercó a los nazis y cómo Aitor Esteban lo negaba.

Isaías Lafuente presenta el informe 829 de la Unidad de Vigilancia.

Isaías Lafuente presenta el informe 829 de la Unidad de Vigilancia.

Hoy contamos con la presencia de Aitor Karanka, un invitado que nos cuenta cómo su vida futbolística tiene dos nombres a destacar: Athletic Club y Real Madrid, los dos grandes clubes en los cuáles ha podido vivir momentos únicos, como ser campeón de Europa en repetidas ocasiones. Además, Aitor nos cuenta cómo fue su final de carrera viviendo una gran experiencia en Estados Unidos y cómo fue su transición al mundo del entrenador, algo que, en un principio, no tenía muy claro. Real Madrid con Mourinho, ascensos con el Middlesbrough, un año en Israel....y muchas anécdotas más que nuestro invitado nos ha permitido conocer de primera mano. Muchas gracias Aitor por querer compartir tu historia con nosotros, ha sido un verdadero placer poder contar contigo! Puedes seguirnos y apoyarnos en: - YOUTUBE: https://www.youtube.com/@offsiders.project/playlists - Instagram: https://www.instagram.com/offsiders.podcast/ - TIK TOK: https://www.tiktok.com/@offsiders_podcast?_t=8aI0IbPe2Fi&_r=1 - X: https://x.com/Offsiders_PRJ - Contacto: comunicacion@offsiderspodcast.com MARCAS DE TIEMPO: 0:00 Intro 1:10 La exigencia de su padre 4:30 Formación en el Athletic Club 16:40 Cuatro años en el primer equipo 22:55 Su salida del Athletic 26:30 Años en el Madrid con buenos y malos momentos 41:55 Qué significó Jupp Heynckes para él 47:40 Salida del Madrid y vuelta al Athletic 49:30 De vuelta en casa y lesión de cruzado 54:05: Retirada en la MLS 55:40 Transición al "mundo entrenador": Fernando Hierro 57:40 Empezar en categorías inferiores de la Selección Española 1:01:30 Cuerpo técnico con José Mourinho 1:10:30 Middlesbrough, dos ascensos hasta Premier League 1:21:00 Cómo se define Aitor Karanka 1:26:00 Granada, un recuerdo muy bonito 1:27:40 ¿Qué está haciendo actualmente? 1:37:40 Final del episodio: La pregunta del millón Learn more about your ad choices. Visit megaphone.fm/adchoices

Rueda de prensa de Aitor Calle (entrenador Numancia) - El Numancia gana 3-0 y vuelve a sonreír en Los Pajaritos

Aitor Albizua se ríe de los lapsus en directo de esta semana, de la obsesión musical que han tenido estos días por ABBA o de la polémica que ha surgido alrededor de una simple pluma caligráfica que Pascual Donate se ha comprado... y es que dos de los integrantes del equipo creen que 'no todo el mundo vale para escribir con pluma'.

En este segundo episodio de La Gran Victoria, profundizo en uno de los retos más importantes de la vida consciente: saber distinguir lo esencial de lo accesorio.Me acompaña Aitor Contreras, casi hermano, compañero de batalla y uno de los estrategas de marketing digital y performance más brillantes que he conocido. Juntos hemos recorrido un sinfín de proyectos, aventuras y aprendizajes. Y hoy, él me plantea dos preguntas que son clave para cualquier persona que busca vivir con intención y claridad: ¿Cómo decides qué hacer y qué no hacer durante el día? Una conversación sobre foco, sabiduría práctica, decisiones conscientes y la esencia de la Ultraproductividad.¿Cómo cuentas al mundo lo que haces? Un viaje hacia la propuesta única de valor, el poder del relato personal y la autenticidad como marca.Este episodio trata sobre encontrar esa pequeña gran diferencia que lo cambia todo. En tu día, en tu vida, en tu historia.Si tienes alguna pregunta, matiz o cuestión en la que profundizar, hazlo en los comentarios y responderé.Únete a La Gran Victoria de cada día.Recursos y notas del podcast:⁠Quién es Aitor Contreras⁠ - LinkedIn.⁠Reciente sobre La Gran Victoria⁠ - última entrevista en podcast Inevitable.⁠Sobre el Método Ultraproductividad⁠.Entrevista a Seth Godin.Forma parte de la Liga del 1% y recibe chispazos, sacudidas, experimentos, viajes, expediciones e invitaciones exclusivas.Podcast #266 Disrupt Everything - La Súper Liga del 1% | La Gran Victoria - Episodio 2.

El fisioterapeuta vitoriano está trabajando con una de las estrellas del fútbol en Arabia y se ocupa también de la selección senegalesa de fútbol

Hoy Aitor Albizua se adentra en las entrañas de la actualidad, como una censura inesperada hasta una ardilla que parece estar robando frutos secos en la redacción... Aitor no deja nada fuera del radar. Las situaciones surrealistas de esta semana y hasta un juego de adivinanzas con el polivalente Pepe Rubio.

Guillermo Saccomanno dona a la Biblioteca de Don Antonio Martínez Asensio su última novela, 'Arderá el viento'. En la hora musical con Fernando Neira recibimos a los chicos de Alcalá Norte, que se marcan un directo sin complejos. Y cerramos la semana con La Auditoría de Aitor Albizua para poner orden en el programa.

Rueda de prensa de Aitor Calle (entrenador del Numancia) en Pontevedra - El Numancia reclama ‘alineación indebida' por la entrada de Yelko Pino al terreno de juego

02 19-03-25 LHDW Aitor Bilbao nos cuenta su experiencia con el nacionalismo vasco: 'Enfadado con el blanqueo de ETA', El daño que ha hecho el nacionalismo vasco

Haciendo que el dinero baile para mí (con la mayor elusión permitida) para hacer con mi tiempo lo que me dé la puta gana. ¿Quieres usar bien tu tiempo para ser rico y libre?

En este episodio de La Auditoría Aitor Albizua habla de la reciente visita de Àngels al Museo del Prado, su encuentro con "Las Meninas" y un retrete histórico. Aitor tampoco ha podido dejar de lado los juegos vocales improvisados por Pepe Rubio 'El Pescaílla' o el asco que algunos oyentes sienten por cosas tan peculiares como la textura del plátano o con laboratorios.

En el tiempo de 'Historias musicales', con Fernando Neira, recibimos a Dulzaro, que se marca un temazo en directo al piano del estudio central de la SER. Sumamos a la biblioteca de Antonio Martínez Asensio 'El buen mal', libro que dona la escritora Samanta Schweblin. Y Aitor Albizua pasa revista al programa con los gazapos de la semana.

Nuestro auditor Aitor Albizua nos trae buen humor para arrancar el viernes. Críticas a los momentos más divertidos del programa, como el despiste de Estefanía Molina sobre los Oscar, donde no reconoció ninguna de las películas nominadas, o el momento en que Mariola Urrea relacionó el uso de zapatillas con la crisis de los 50, desatando un 'zas' de Pascual Donate hacia Brian Pérez. Además, Aitor no puede evitar reírse con el “aisberg” de Fernando Bayo, y nos recuerda lo bien que el equipo maneja el contacto con los oyentes, como cuando Bob Pop agradeció a la radio su paciencia. También los temas de economía de Javier Ruiz, que, aunque siempre claro y riguroso, nunca deja de sorprender con sus comparaciones. 

As we head into a new era under President Donald Trump, migration and rights are very much in the news, even as we celebrate Hispanic Heritage Month. Threatened deportations and the end of some existing paths to legal status, as well as the Supreme Court decision against university use of affirmative action, create new challenges. Aitor Bouso Gavín, a lecturer of Latinx Studies at Harvard's Ethnicity, Migration, Rights (EMR) and the faculty coordinator for the Latinx Studies Working Group, discusses the important role of Latinx Studies.

Atlas32 | En esta guía del fútbol remoto, Víctor nos lleva al Vaticano, Jordi hace geopolítica en Rusia y Aitor trae un mordisco en los testículos (literalmente). Además, también repasamos la situación del fútbol en Oceanía y hablamos del secuestro de un portero en África. Casi nada. To hear more, visit www.brazaletenegro.com

La escritora Florencia del Campo dona a la biblioteca de Antonio Martínez Asensio su último libro, 'Que tenga una casa'. Este 14 de febrero Fernando Neira nos cuenta la historia de la mejor canción de amor y María Terremoto rompe a cantar en directo. Por último, Aitor Albizua se ríe con las últimas meteduras de pata en el 'Hoy por Hoy'.

Antonio, Cristina, Carolina, Guillermo, Zigor, Antonio, Aitor y Egoitza empiezan una aventura incierta con respaldo de las cooperativas aquí y las ayudas institucionales. Entre los nuevos agricultores encontramos a jóvenes y mayores de cuarenta años que aspiran a trabajar en explotaciones innovadoras, vitivinícolas, con las abejas incluso seguir con el legado ganadero familiar 

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Hago terapia con mi psicólogo Aitor Bilbao sobre la polémica arbitral tras la nota publica del MadridEscucha este episodio completo y accede a todo el contenido exclusivo de Podcast de La Hora de Walter. Descubre antes que nadie los nuevos episodios, y participa en la comunidad exclusiva de oyentes en https://go.ivoox.com/sq/79870

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Charla con mi psicólogo Aitor Bilbao sobre la polémica arbitral tras la carta del Real Madrid. Vaya lio!Escucha este episodio completo y accede a todo el contenido exclusivo de Podcast de La Hora de Walter. Descubre antes que nadie los nuevos episodios, y participa en la comunidad exclusiva de oyentes en https://go.ivoox.com/sq/79870

Llega el primer viernes de febrero y no hay manera más especial que con la visita de Amaral a historias musicales, presentan su nuevo disco 'Dolce Vita'. Por otro lado, la reunión bibliotecaria de la mano de Don Asensio, que trae, junto a su autor Jorge Fernández Díaz, ''El secreto de Marcial'', libro ganador del Premio Nadal 2025. Y cómo no, no se puede cerrar una semana sin que nuestro auditor Aitor Albizua repase los mejores y los peores momentos de estos días. 

Aitor Albizua nos trae la primera auditoria de febrero. Y es que ha sido una semana que lo ha tenido todo, una semana para la historia de la radio, desde grandes análisis de la actualidad geopolítica, hasta chistes verdes por aquí y por allí. Sin duda Aitor ha puesto los puntos sobre los íes al equipo en esta semana tan completita. 

En la Biblioteca de Hoy por Hoy repasamos los libros de ciencia ficción, con especial atención a Solaris. Además, Severino Donate viaja hasta una de las mayores colecciones de libros de ciencia ficción de toda España. En las Historias Musicales, Fernando Neira nos habla del disco menos vendido de la historia. Sólo se vendió una copia... porque así lo quiso el autor. También disfrutamos de una fantástica entrevista y mejor actuación de Jorge Marzu. Y cerramos el programa con la auditoría de Aitor Albizua, que nos habla del festival del humor en el que se ha convertido Hoy por Hoy.

La versión juvenil de El juego de los Detectives. Una historia enviada por Aitor, de 11 años. 

La versión juvenil de El juego de los Detectives. Una historia enviada por Aitor, de 11 años. 

En la Biblioteca de 'Hoy por Hoy' recibimos a Pola Oloixarac, dispuesta a donarnos su libro 'Bad hombre'. Fernando Neira nos cuenta la intrahistoria de las canciones de Stevie Wonder que tienen que ver con los coches. Morgan nos presunta su nuevo disco, 'Hotel Morgan'. Y Aitor 'el auditor' repasa los momentos "más bochornosos" del programa. 

Abrimos en directo un iMac 27 " de finales del 2014 para poder utilizarlo de monitor, en esta primera parte solo intentaremos abrir la pantalla para poder sacar un código, y saber si es compatible con una pieza que se tiene que comprar en estados unidos. Esta es solo la primera parte y habrá una segunda parte donde instalaremos la pieza y probaremos en directo si funciona o no como monitor, todo esto gracias a la ayuda de mi amigo Aitor, espero que disfrutéis de este episodio. //Donde encontrarnos Canal Youtube https://www.youtube.com/c/ApplelianosApplelianos/featured Grupo Telegram (enlace de invitación) https://t.me/+U9If86lsuY00MGU0 Correo electrónico applelianos@gmail.com Canal Telegram Episodios https://t.me/ApplelianosFLAC Mi Shop Amazon https://amzn.to/30sYcbB Twitter https://twitter.com/ApplelianosPod ( (https://twitter.com/ApplelianosPod)https://twitter.com/ApplelianosPod ) Apple Podcasts https://podcasts.apple.com/es/podcast/applelianos-podcast/id993909563 Ivoox https://www.ivoox.com/podcast-applelianos-podcast_sq_f1170563_1.html ( (https://www.ivoox.com/podcast-applelianos-podcast_sq_f1170563_1.html ) https://www.ivoox.com/podcast-applelianos-podcast_sq_f1170563_1.html

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Conversación con Enrique y Aitor sobre la figura de David Lynch, el cineasta recientemente fallecidoEscucha este episodio completo y accede a todo el contenido exclusivo de Podcast de La Hora de Walter. Descubre antes que nadie los nuevos episodios, y participa en la comunidad exclusiva de oyentes en https://go.ivoox.com/sq/79870

Hoy os traemos una charla que es puro ejemplo de cómo compaginar trabajo, familia y pasión por el deporte. Estamos en una acogedora cafetería, disfrutando de un buen café, mientras hablamos con un auténtico estratega, Aitor. Aitor, con 30 años, es funcionario, vive en Madrid y trabaja por turnos, lo que ya suena a un rompecabezas organizativo. Pero eso no es todo: hace un año, él y su pareja dieron la bienvenida a un pequeñín que ahora forma parte de su vida. Desde entonces, Aitor no solo ha tenido que ajustar su rutina, sino también optimizarla para mantener su nivel de entrenamiento y su compromiso con el deporte. Entre sus idas y venidas semanales entre Madrid y Barcelona, Aitor ha aprendido a sacarle partido a cada situación. Cuando está en Barcelona, aprovecha la cercanía de la montaña para realizar entrenamientos de trail, disfrutando de paisajes que Madrid no le ofrece. Sin embargo, en la capital, lo compensa con sesiones de gimnasio y carreras urbanas. Todo, claro, a primerísima hora de la mañana, cuando su familia aún duerme. Aitor es un ejemplo vivo de organización. Cada semana planifica sus entrenos según el lugar en el que se encuentre y las condiciones que tenga. Nos cuenta que, aunque madrugar tanto puede ser un desafío, la recompensa de tener el resto del día para dedicarse a su familia y proyectos personales hace que valga la pena. Lo más inspirador es su filosofía: el deporte no es una obligación ni un sacrificio; es su estilo de vida. Con el apoyo de su pareja y su determinación inquebrantable, Aitor demuestra que con planificación y actitud todo es posible. ¡Gracias, Aitor, por compartir tu historia con nosotros! Nos vemos en el próximo episodio. ¡Hasta pronto, Estratega! _________________________________________________________________ ‍♀️ ‍♂️ ¡Motivación en cada paso de tu viaje! Descubre más en: https://www.instagram.com/estrategas.Trail/ ¿Amante de los videos? Suscríbete aquí: https://www.youtube.com/c/XimEscanellasEstrategas/videos Regalo especial: Las 5 claves para un entrenamiento efectivo. ¡Regístrate! https://ximescanellas.com/pagina-registro-5-claves/ Sigue nuestra cuenta personal en: https://www.instagram.com/xim_escanellas/ https://ximescanellas.com/ Alcanza tus de manera inteligente y eficiente. ****Enviamos un mensaje de what's app si quieres que te ayudemos de forma individual**** http://ximescanellas.com/hablamos-pod/

Entrevista en el podcast de KM ZERO Food Innovation Hub en el que hablamos sobre la infancia de Aitor, su pasión por la nutrición, el futuro de la alimentación y desmentir algunos mitos sobre el tema. Programa original en YouTube: https://youtu.be/6GKmVmcaCuQ KM ZERO Food Innovation Hub: https://www.instagram.com/kmzerohub/ https://www.kmzerohub.com/ 📌 VIAJETAL: Gastronomía y viajes 100% vegetales -Ivoox: https://www.ivoox.com/podcast-viajetal-gastronomia-viajes-100-vegetales_sq_f11809058_1.html -YouTube: https://www.youtube.com/channel/UCG2i9bO4xksDxPoiChYIRzQ -Instagram: https://www.instagram.com/viajetal/ -Spotify: https://open.spotify.com/show/0giAlYsGKs2GWSmXb3ZlJf 📖 Mi quinto libro, '¿Qué pasa con la nutrición?', ya a la venta: https://amzn.to/3KkuNp8 Todos los programas en el podcast del blog: https://goo.gl/2dKYA0 Blog: https://www.midietacojea.com Twitter: https://bit.ly/twitter-mdc Instagram: https://instagram.com/midietacojea/ Facebook: https://www.facebook.com/Midietacojea Canal de Youtube: https://www.youtube.com/midietacojea TikTok: https://bit.ly/TikTok-mdc

El elefante está en la habitación. Todos lo ven, a todos les cambia el paso, pero no lo nombran. Lo rodean con palabras esquivas, con gestos que pretenden señalar sin señalar, como si al evitar nombrar al elefante en la habitación se evitara la carga que genera en las relaciones personales. Como si vivir en el silencio atronador fuera mejor que la opción de una herida que escuece antes de curarse.Aitor Echeverría dedica su primer largometraje a esos silencios, a lo que es doloroso nombrar en familia, a lo que quizá merece la pena señalar para aligerar la carga. Lo hace de la mano de Emma Suárez y Natalia de Molina, en una película que se estrenará el próximo viernes: Además Conxita Casanovas nos cuenta los estrenos de esta semana y Sonia Castelani, el cine que viene en 2025. Y cuando en la sala se apague la gran pantalla y se enciendan las luces, nos iremos al teatro porque llega a España una obra desde el Festival de Aviñón. Terminamos con Jesús Marchamalo para el primer paseo literario del año. Y hacia el final del camino, escucharemos la música que nos trae Leyre Guerrero cara al fin de semana. Escuchar audio

Applications for the 2025 AI Engineer Summit are up, and you can save the date for AIE Singapore in April and AIE World's Fair 2025 in June.Happy new year, and thanks for 100 great episodes! Please let us know what you want to see/hear for the next 100!Full YouTube Episode with Slides/ChartsLike and subscribe and hit that bell to get notifs!Timestamps* 00:00 Welcome to the 100th Episode!* 00:19 Reflecting on the Journey* 00:47 AI Engineering: The Rise and Impact* 03:15 Latent Space Live and AI Conferences* 09:44 The Competitive AI Landscape* 21:45 Synthetic Data and Future Trends* 35:53 Creative Writing with AI* 36:12 Legal and Ethical Issues in AI* 38:18 The Data War: GPU Poor vs. GPU Rich* 39:12 The Rise of GPU Ultra Rich* 40:47 Emerging Trends in AI Models* 45:31 The Multi-Modality War* 01:05:31 The Future of AI Benchmarks* 01:13:17 Pionote and Frontier Models* 01:13:47 Niche Models and Base Models* 01:14:30 State Space Models and RWKB* 01:15:48 Inference Race and Price Wars* 01:22:16 Major AI Themes of the Year* 01:22:48 AI Rewind: January to March* 01:26:42 AI Rewind: April to June* 01:33:12 AI Rewind: July to September* 01:34:59 AI Rewind: October to December* 01:39:53 Year-End Reflections and PredictionsTranscript[00:00:00] Welcome to the 100th Episode![00:00:00] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co host Swyx for the 100th time today.[00:00:12] swyx: Yay, um, and we're so glad that, yeah, you know, everyone has, uh, followed us in this journey. How do you feel about it? 100 episodes.[00:00:19] Alessio: Yeah, I know.[00:00:19] Reflecting on the Journey[00:00:19] Alessio: Almost two years that we've been doing this. We've had four different studios. Uh, we've had a lot of changes. You know, we used to do this lightning round. When we first started that we didn't like, and we tried to change the question. The answer[00:00:32] swyx: was cursor and perplexity.[00:00:34] Alessio: Yeah, I love mid journey. It's like, do you really not like anything else?[00:00:38] Alessio: Like what's, what's the unique thing? And I think, yeah, we, we've also had a lot more research driven content. You know, we had like 3DAO, we had, you know. Jeremy Howard, we had more folks like that.[00:00:47] AI Engineering: The Rise and Impact[00:00:47] Alessio: I think we want to do more of that too in the new year, like having, uh, some of the Gemini folks, both on the research and the applied side.[00:00:54] Alessio: Yeah, but it's been a ton of fun. I think we both started, I wouldn't say as a joke, we were kind of like, Oh, we [00:01:00] should do a podcast. And I think we kind of caught the right wave, obviously. And I think your rise of the AI engineer posts just kind of get people. Sombra to congregate, and then the AI engineer summit.[00:01:11] Alessio: And that's why when I look at our growth chart, it's kind of like a proxy for like the AI engineering industry as a whole, which is almost like, like, even if we don't do that much, we keep growing just because there's so many more AI engineers. So did you expect that growth or did you expect that would take longer for like the AI engineer thing to kind of like become, you know, everybody talks about it today.[00:01:32] swyx: So, the sign of that, that we have won is that Gartner puts it at the top of the hype curve right now. So Gartner has called the peak in AI engineering. I did not expect, um, to what level. I knew that I was correct when I called it because I did like two months of work going into that. But I didn't know, You know, how quickly it could happen, and obviously there's a chance that I could be wrong.[00:01:52] swyx: But I think, like, most people have come around to that concept. Hacker News hates it, which is a good sign. But there's enough people that have defined it, you know, GitHub, when [00:02:00] they launched GitHub Models, which is the Hugging Face clone, they put AI engineers in the banner, like, above the fold, like, in big So I think it's like kind of arrived as a meaningful and useful definition.[00:02:12] swyx: I think people are trying to figure out where the boundaries are. I think that was a lot of the quote unquote drama that happens behind the scenes at the World's Fair in June. Because I think there's a lot of doubt or questions about where ML engineering stops and AI engineering starts. That's a useful debate to be had.[00:02:29] swyx: In some sense, I actually anticipated that as well. So I intentionally did not. Put a firm definition there because most of the successful definitions are necessarily underspecified and it's actually useful to have different perspectives and you don't have to specify everything from the outset.[00:02:45] Alessio: Yeah, I was at um, AWS reInvent and the line to get into like the AI engineering talk, so to speak, which is, you know, applied AI and whatnot was like, there are like hundreds of people just in line to go in.[00:02:56] Alessio: I think that's kind of what enabled me. People, right? Which is what [00:03:00] you kind of talked about. It's like, Hey, look, you don't actually need a PhD, just, yeah, just use the model. And then maybe we'll talk about some of the blind spots that you get as an engineer with the earlier posts that we also had on on the sub stack.[00:03:11] Alessio: But yeah, it's been a heck of a heck of a two years.[00:03:14] swyx: Yeah.[00:03:15] Latent Space Live and AI Conferences[00:03:15] swyx: You know, I was, I was trying to view the conference as like, so NeurIPS is I think like 16, 17, 000 people. And the Latent Space Live event that we held there was 950 signups. I think. The AI world, the ML world is still very much research heavy. And that's as it should be because ML is very much in a research phase.[00:03:34] swyx: But as we move this entire field into production, I think that ratio inverts into becoming more engineering heavy. So at least I think engineering should be on the same level, even if it's never as prestigious, like it'll always be low status because at the end of the day, you're manipulating APIs or whatever.[00:03:51] swyx: But Yeah, wrapping GPTs, but there's going to be an increasing stack and an art to doing these, these things well. And I, you know, I [00:04:00] think that's what we're focusing on for the podcast, the conference and basically everything I do seems to make sense. And I think we'll, we'll talk about the trends here that apply.[00:04:09] swyx: It's, it's just very strange. So, like, there's a mix of, like, keeping on top of research while not being a researcher and then putting that research into production. So, like, people always ask me, like, why are you covering Neuralibs? Like, this is a ML research conference and I'm like, well, yeah, I mean, we're not going to, to like, understand everything Or reproduce every single paper, but the stuff that is being found here is going to make it through into production at some point, you hope.[00:04:32] swyx: And then actually like when I talk to the researchers, they actually get very excited because they're like, oh, you guys are actually caring about how this goes into production and that's what they really really want. The measure of success is previously just peer review, right? Getting 7s and 8s on their um, Academic review conferences and stuff like citations is one metric, but money is a better metric.[00:04:51] Alessio: Money is a better metric. Yeah, and there were about 2200 people on the live stream or something like that. Yeah, yeah. Hundred on the live stream. So [00:05:00] I try my best to moderate, but it was a lot spicier in person with Jonathan and, and Dylan. Yeah, that it was in the chat on YouTube.[00:05:06] swyx: I would say that I actually also created.[00:05:09] swyx: Layen Space Live in order to address flaws that are perceived in academic conferences. This is not NeurIPS specific, it's ICML, NeurIPS. Basically, it's very sort of oriented towards the PhD student, uh, market, job market, right? Like literally all, basically everyone's there to advertise their research and skills and get jobs.[00:05:28] swyx: And then obviously all the, the companies go there to hire them. And I think that's great for the individual researchers, but for people going there to get info is not great because you have to read between the lines, bring a ton of context in order to understand every single paper. So what is missing is effectively what I ended up doing, which is domain by domain, go through and recap the best of the year.[00:05:48] swyx: Survey the field. And there are, like NeurIPS had a, uh, I think ICML had a like a position paper track, NeurIPS added a benchmarks, uh, datasets track. These are ways in which to address that [00:06:00] issue. Uh, there's always workshops as well. Every, every conference has, you know, a last day of workshops and stuff that provide more of an overview.[00:06:06] swyx: But they're not specifically prompted to do so. And I think really, uh, Organizing a conference is just about getting good speakers and giving them the correct prompts. And then they will just go and do that thing and they do a very good job of it. So I think Sarah did a fantastic job with the startups prompt.[00:06:21] swyx: I can't list everybody, but we did best of 2024 in startups, vision, open models. Post transformers, synthetic data, small models, and agents. And then the last one was the, uh, and then we also did a quick one on reasoning with Nathan Lambert. And then the last one, obviously, was the debate that people were very hyped about.[00:06:39] swyx: It was very awkward. And I'm really, really thankful for John Franco, basically, who stepped up to challenge Dylan. Because Dylan was like, yeah, I'll do it. But He was pro scaling. And I think everyone who is like in AI is pro scaling, right? So you need somebody who's ready to publicly say, no, we've hit a wall.[00:06:57] swyx: So that means you're saying Sam Altman's wrong. [00:07:00] You're saying, um, you know, everyone else is wrong. It helps that this was the day before Ilya went on, went up on stage and then said pre training has hit a wall. And data has hit a wall. So actually Jonathan ended up winning, and then Ilya supported that statement, and then Noam Brown on the last day further supported that statement as well.[00:07:17] swyx: So it's kind of interesting that I think the consensus kind of going in was that we're not done scaling, like you should believe in a better lesson. And then, four straight days in a row, you had Sepp Hochreiter, who is the creator of the LSTM, along with everyone's favorite OG in AI, which is Juergen Schmidhuber.[00:07:34] swyx: He said that, um, we're pre trading inside a wall, or like, we've run into a different kind of wall. And then we have, you know John Frankel, Ilya, and then Noam Brown are all saying variations of the same thing, that we have hit some kind of wall in the status quo of what pre trained, scaling large pre trained models has looked like, and we need a new thing.[00:07:54] swyx: And obviously the new thing for people is some make, either people are calling it inference time compute or test time [00:08:00] compute. I think the collective terminology has been inference time, and I think that makes sense because test time, calling it test, meaning, has a very pre trained bias, meaning that the only reason for running inference at all is to test your model.[00:08:11] swyx: That is not true. Right. Yeah. So, so, I quite agree that. OpenAI seems to have adopted, or the community seems to have adopted this terminology of ITC instead of TTC. And that, that makes a lot of sense because like now we care about inference, even right down to compute optimality. Like I actually interviewed this author who recovered or reviewed the Chinchilla paper.[00:08:31] swyx: Chinchilla paper is compute optimal training, but what is not stated in there is it's pre trained compute optimal training. And once you start caring about inference, compute optimal training, you have a different scaling law. And in a way that we did not know last year.[00:08:45] Alessio: I wonder, because John is, he's also on the side of attention is all you need.[00:08:49] Alessio: Like he had the bet with Sasha. So I'm curious, like he doesn't believe in scaling, but he thinks the transformer, I wonder if he's still. So, so,[00:08:56] swyx: so he, obviously everything is nuanced and you know, I told him to play a character [00:09:00] for this debate, right? So he actually does. Yeah. He still, he still believes that we can scale more.[00:09:04] swyx: Uh, he just assumed the character to be very game for, for playing this debate. So even more kudos to him that he assumed a position that he didn't believe in and still won the debate.[00:09:16] Alessio: Get rekt, Dylan. Um, do you just want to quickly run through some of these things? Like, uh, Sarah's presentation, just the highlights.[00:09:24] swyx: Yeah, we can't go through everyone's slides, but I pulled out some things as a factor of, like, stuff that we were going to talk about. And we'll[00:09:30] Alessio: publish[00:09:31] swyx: the rest. Yeah, we'll publish on this feed the best of 2024 in those domains. And hopefully people can benefit from the work that our speakers have done.[00:09:39] swyx: But I think it's, uh, these are just good slides. And I've been, I've been looking for a sort of end of year recaps from, from people.[00:09:44] The Competitive AI Landscape[00:09:44] swyx: The field has progressed a lot. You know, I think the max ELO in 2023 on LMSys used to be 1200 for LMSys ELOs. And now everyone is at least at, uh, 1275 in their ELOs, and this is across Gemini, Chadjibuti, [00:10:00] Grok, O1.[00:10:01] swyx: ai, which with their E Large model, and Enthopic, of course. It's a very, very competitive race. There are multiple Frontier labs all racing, but there is a clear tier zero Frontier. And then there's like a tier one. It's like, I wish I had everything else. Tier zero is extremely competitive. It's effectively now three horse race between Gemini, uh, Anthropic and OpenAI.[00:10:21] swyx: I would say that people are still holding out a candle for XAI. XAI, I think, for some reason, because their API was very slow to roll out, is not included in these metrics. So it's actually quite hard to put on there. As someone who also does charts, XAI is continually snubbed because they don't work well with the benchmarking people.[00:10:42] swyx: Yeah, yeah, yeah. It's a little trivia for why XAI always gets ignored. The other thing is market share. So these are slides from Sarah. We have it up on the screen. It has gone from very heavily open AI. So we have some numbers and estimates. These are from RAMP. Estimates of open AI market share in [00:11:00] December 2023.[00:11:01] swyx: And this is basically, what is it, GPT being 95 percent of production traffic. And I think if you correlate that with stuff that we asked. Harrison Chase on the LangChain episode, it was true. And then CLAUD 3 launched mid middle of this year. I think CLAUD 3 launched in March, CLAUD 3. 5 Sonnet was in June ish.[00:11:23] swyx: And you can start seeing the market share shift towards opening, uh, towards that topic, uh, very, very aggressively. The more recent one is Gemini. So if I scroll down a little bit, this is an even more recent dataset. So RAM's dataset ends in September 2 2. 2024. Gemini has basically launched a price war at the low end, uh, with Gemini Flash, uh, being basically free for personal use.[00:11:44] swyx: Like, I think people don't understand the free tier. It's something like a billion tokens per day. Unless you're trying to abuse it, you cannot really exhaust your free tier on Gemini. They're really trying to get you to use it. They know they're in like third place, um, fourth place, depending how you, how you count.[00:11:58] swyx: And so they're going after [00:12:00] the Lower tier first, and then, you know, maybe the upper tier later, but yeah, Gemini Flash, according to OpenRouter, is now 50 percent of their OpenRouter requests. Obviously, these are the small requests. These are small, cheap requests that are mathematically going to be more.[00:12:15] swyx: The smart ones obviously are still going to OpenAI. But, you know, it's a very, very big shift in the market. Like basically 2023, 2022, To going into 2024 opening has gone from nine five market share to Yeah. Reasonably somewhere between 50 to 75 market share.[00:12:29] Alessio: Yeah. I'm really curious how ramped does the attribution to the model?[00:12:32] Alessio: If it's API, because I think it's all credit card spin. . Well, but it's all, the credit card doesn't say maybe. Maybe the, maybe when they do expenses, they upload the PDF, but yeah, the, the German I think makes sense. I think that was one of my main 2024 takeaways that like. The best small model companies are the large labs, which is not something I would have thought that the open source kind of like long tail would be like the small model.[00:12:53] swyx: Yeah, different sizes of small models we're talking about here, right? Like so small model here for Gemini is AB, [00:13:00] right? Uh, mini. We don't know what the small model size is, but yeah, it's probably in the double digits or maybe single digits, but probably double digits. The open source community has kind of focused on the one to three B size.[00:13:11] swyx: Mm-hmm . Yeah. Maybe[00:13:12] swyx: zero, maybe 0.5 B uh, that's moon dream and that is small for you then, then that's great. It makes sense that we, we have a range for small now, which is like, may, maybe one to five B. Yeah. I'll even put that at, at, at the high end. And so this includes Gemma from Gemini as well. But also includes the Apple Foundation models, which I think Apple Foundation is 3B.[00:13:32] Alessio: Yeah. No, that's great. I mean, I think in the start small just meant cheap. I think today small is actually a more nuanced discussion, you know, that people weren't really having before.[00:13:43] swyx: Yeah, we can keep going. This is a slide that I smiley disagree with Sarah. She's pointing to the scale SEAL leaderboard. I think the Researchers that I talked with at NeurIPS were kind of positive on this because basically you need private test [00:14:00] sets to prevent contamination.[00:14:02] swyx: And Scale is one of maybe three or four people this year that has really made an effort in doing a credible private test set leaderboard. Llama405B does well compared to Gemini and GPT 40. And I think that's good. I would say that. You know, it's good to have an open model that is that big, that does well on those metrics.[00:14:23] swyx: But anyone putting 405B in production will tell you, if you scroll down a little bit to the artificial analysis numbers, that it is very slow and very expensive to infer. Um, it doesn't even fit on like one node. of, uh, of H100s. Cerebras will be happy to tell you they can serve 4 or 5B on their super large chips.[00:14:42] swyx: But, um, you know, if you need to do anything custom to it, you're still kind of constrained. So, is 4 or 5B really that relevant? Like, I think most people are basically saying that they only use 4 or 5B as a teacher model to distill down to something. Even Meta is doing it. So with Lama 3. [00:15:00] 3 launched, they only launched the 70B because they use 4 or 5B to distill the 70B.[00:15:03] swyx: So I don't know if like open source is keeping up. I think they're the, the open source industrial complex is very invested in telling you that the, if the gap is narrowing, I kind of disagree. I think that the gap is widening with O1. I think there are very, very smart people trying to narrow that gap and they should.[00:15:22] swyx: I really wish them success, but you cannot use a chart that is nearing 100 in your saturation chart. And look, the distance between open source and closed source is narrowing. Of course it's going to narrow because you're near 100. This is stupid. But in metrics that matter, is open source narrowing?[00:15:38] swyx: Probably not for O1 for a while. And it's really up to the open source guys to figure out if they can match O1 or not.[00:15:46] Alessio: I think inference time compute is bad for open source just because, you know, Doc can donate the flops at training time, but he cannot donate the flops at inference time. So it's really hard to like actually keep up on that axis.[00:15:59] Alessio: Big, big business [00:16:00] model shift. So I don't know what that means for the GPU clouds. I don't know what that means for the hyperscalers, but obviously the big labs have a lot of advantage. Because, like, it's not a static artifact that you're putting the compute in. You're kind of doing that still, but then you're putting a lot of computed inference too.[00:16:17] swyx: Yeah, yeah, yeah. Um, I mean, Llama4 will be reasoning oriented. We talked with Thomas Shalom. Um, kudos for getting that episode together. That was really nice. Good, well timed. Actually, I connected with the AI meta guy, uh, at NeurIPS, and, um, yeah, we're going to coordinate something for Llama4. Yeah, yeah,[00:16:32] Alessio: and our friend, yeah.[00:16:33] Alessio: Clara Shi just joined to lead the business agent side. So I'm sure we'll have her on in the new year.[00:16:39] swyx: Yeah. So, um, my comment on, on the business model shift, this is super interesting. Apparently it is wide knowledge that OpenAI wanted more than 6. 6 billion dollars for their fundraise. They wanted to raise, you know, higher, and they did not.[00:16:51] swyx: And what that means is basically like, it's very convenient that we're not getting GPT 5, which would have been a larger pre train. We should have a lot of upfront money. And [00:17:00] instead we're, we're converting fixed costs into variable costs, right. And passing it on effectively to the customer. And it's so much easier to take margin there because you can directly attribute it to like, Oh, you're using this more.[00:17:12] swyx: Therefore you, you pay more of the cost and I'll just slap a margin in there. So like that lets you control your growth margin and like tie your. Your spend, or your sort of inference spend, accordingly. And it's just really interesting to, that this change in the sort of inference paradigm has arrived exactly at the same time that the funding environment for pre training is effectively drying up, kind of.[00:17:36] swyx: I feel like maybe the VCs are very in tune with research anyway, so like, they would have noticed this, but, um, it's just interesting.[00:17:43] Alessio: Yeah, and I was looking back at our yearly recap of last year. Yeah. And the big thing was like the mixed trial price fights, you know, and I think now it's almost like there's nowhere to go, like, you know, Gemini Flash is like basically giving it away for free.[00:17:55] Alessio: So I think this is a good way for the labs to generate more revenue and pass down [00:18:00] some of the compute to the customer. I think they're going to[00:18:02] swyx: keep going. I think that 2, will come.[00:18:05] Alessio: Yeah, I know. Totally. I mean, next year, the first thing I'm doing is signing up for Devin. Signing up for the pro chat GBT.[00:18:12] Alessio: Just to try. I just want to see what does it look like to spend a thousand dollars a month on AI?[00:18:17] swyx: Yes. Yes. I think if your, if your, your job is a, at least AI content creator or VC or, you know, someone who, whose job it is to stay on, stay on top of things, you should already be spending like a thousand dollars a month on, on stuff.[00:18:28] swyx: And then obviously easy to spend, hard to use. You have to actually use. The good thing is that actually Google lets you do a lot of stuff for free now. So like deep research. That they just launched. Uses a ton of inference and it's, it's free while it's in preview.[00:18:45] Alessio: Yeah. They need to put that in Lindy.[00:18:47] Alessio: I've been using Lindy lately. I've been a built a bunch of things once we had flow because I liked the new thing. It's pretty good. I even did a phone call assistant. Um, yeah, they just launched Lindy voice. Yeah, I think once [00:19:00] they get advanced voice mode like capability today, still like speech to text, you can kind of tell.[00:19:06] Alessio: Um, but it's good for like reservations and things like that. So I have a meeting prepper thing. And so[00:19:13] swyx: it's good. Okay. I feel like we've, we've covered a lot of stuff. Uh, I, yeah, I, you know, I think We will go over the individual, uh, talks in a separate episode. Uh, I don't want to take too much time with, uh, this stuff, but that suffice to say that there is a lot of progress in each field.[00:19:28] swyx: Uh, we covered vision. Basically this is all like the audience voting for what they wanted. And then I just invited the best people I could find in each audience, especially agents. Um, Graham, who I talked to at ICML in Vienna, he is currently still number one. It's very hard to stay on top of SweetBench.[00:19:45] swyx: OpenHand is currently still number one. switchbench full, which is the hardest one. He had very good thoughts on agents, which I, which I'll highlight for people. Everyone is saying 2025 is the year of agents, just like they said last year. And, uh, but he had [00:20:00] thoughts on like eight parts of what are the frontier problems to solve in agents.[00:20:03] swyx: And so I'll highlight that talk as well.[00:20:05] Alessio: Yeah. The number six, which is the Hacken agents learn more about the environment, has been a Super interesting to us as well, just to think through, because, yeah, how do you put an agent in an enterprise where most things in an enterprise have never been public, you know, a lot of the tooling, like the code bases and things like that.[00:20:23] Alessio: So, yeah, there's not indexing and reg. Well, yeah, but it's more like. You can't really rag things that are not documented. But people know them based on how they've been doing it. You know, so I think there's almost this like, you know, Oh, institutional knowledge. Yeah, the boring word is kind of like a business process extraction.[00:20:38] Alessio: Yeah yeah, I see. It's like, how do you actually understand how these things are done? I see. Um, and I think today the, the problem is that, Yeah, the agents are, that most people are building are good at following instruction, but are not as good as like extracting them from you. Um, so I think that will be a big unlock just to touch quickly on the Jeff Dean thing.[00:20:55] Alessio: I thought it was pretty, I mean, we'll link it in the, in the things, but. I think the main [00:21:00] focus was like, how do you use ML to optimize the systems instead of just focusing on ML to do something else? Yeah, I think speculative decoding, we had, you know, Eugene from RWKB on the podcast before, like he's doing a lot of that with Fetterless AI.[00:21:12] swyx: Everyone is. I would say it's the norm. I'm a little bit uncomfortable with how much it costs, because it does use more of the GPU per call. But because everyone is so keen on fast inference, then yeah, makes sense.[00:21:24] Alessio: Exactly. Um, yeah, but we'll link that. Obviously Jeff is great.[00:21:30] swyx: Jeff is, Jeff's talk was more, it wasn't focused on Gemini.[00:21:33] swyx: I think people got the wrong impression from my tweet. It's more about how Google approaches ML and uses ML to design systems and then systems feedback into ML. And I think this ties in with Lubna's talk.[00:21:45] Synthetic Data and Future Trends[00:21:45] swyx: on synthetic data where it's basically the story of bootstrapping of humans and AI in AI research or AI in production.[00:21:53] swyx: So her talk was on synthetic data, where like how much synthetic data has grown in 2024 in the pre training side, the post training side, [00:22:00] and the eval side. And I think Jeff then also extended it basically to chips, uh, to chip design. So he'd spend a lot of time talking about alpha chip. And most of us in the audience are like, we're not working on hardware, man.[00:22:11] swyx: Like you guys are great. TPU is great. Okay. We'll buy TPUs.[00:22:14] Alessio: And then there was the earlier talk. Yeah. But, and then we have, uh, I don't know if we're calling them essays. What are we calling these? But[00:22:23] swyx: for me, it's just like bonus for late in space supporters, because I feel like they haven't been getting anything.[00:22:29] swyx: And then I wanted a more high frequency way to write stuff. Like that one I wrote in an afternoon. I think basically we now have an answer to what Ilya saw. It's one year since. The blip. And we know what he saw in 2014. We know what he saw in 2024. We think we know what he sees in 2024. He gave some hints and then we have vague indications of what he saw in 2023.[00:22:54] swyx: So that was the Oh, and then 2016 as well, because of this lawsuit with Elon, OpenAI [00:23:00] is publishing emails from Sam's, like, his personal text messages to Siobhan, Zelis, or whatever. So, like, we have emails from Ilya saying, this is what we're seeing in OpenAI, and this is why we need to scale up GPUs. And I think it's very prescient in 2016 to write that.[00:23:16] swyx: And so, like, it is exactly, like, basically his insights. It's him and Greg, basically just kind of driving the scaling up of OpenAI, while they're still playing Dota. They're like, no, like, we see the path here.[00:23:30] Alessio: Yeah, and it's funny, yeah, they even mention, you know, we can only train on 1v1 Dota. We need to train on 5v5, and that takes too many GPUs.[00:23:37] Alessio: Yeah,[00:23:37] swyx: and at least for me, I can speak for myself, like, I didn't see the path from Dota to where we are today. I think even, maybe if you ask them, like, they wouldn't necessarily draw a straight line. Yeah,[00:23:47] Alessio: no, definitely. But I think like that was like the whole idea of almost like the RL and we talked about this with Nathan on his podcast.[00:23:55] Alessio: It's like with RL, you can get very good at specific things, but then you can't really like generalize as much. And I [00:24:00] think the language models are like the opposite, which is like, you're going to throw all this data at them and scale them up, but then you really need to drive them home on a specific task later on.[00:24:08] Alessio: And we'll talk about the open AI reinforcement, fine tuning, um, announcement too, and all of that. But yeah, I think like scale is all you need. That's kind of what Elia will be remembered for. And I think just maybe to clarify on like the pre training is over thing that people love to tweet. I think the point of the talk was like everybody, we're scaling these chips, we're scaling the compute, but like the second ingredient which is data is not scaling at the same rate.[00:24:35] Alessio: So it's not necessarily pre training is over. It's kind of like What got us here won't get us there. In his email, he predicted like 10x growth every two years or something like that. And I think maybe now it's like, you know, you can 10x the chips again, but[00:24:49] swyx: I think it's 10x per year. Was it? I don't know.[00:24:52] Alessio: Exactly. And Moore's law is like 2x. So it's like, you know, much faster than that. And yeah, I like the fossil fuel of AI [00:25:00] analogy. It's kind of like, you know, the little background tokens thing. So the OpenAI reinforcement fine tuning is basically like, instead of fine tuning on data, you fine tune on a reward model.[00:25:09] Alessio: So it's basically like, instead of being data driven, it's like task driven. And I think people have tasks to do, they don't really have a lot of data. So I'm curious to see how that changes, how many people fine tune, because I think this is what people run into. It's like, Oh, you can fine tune llama. And it's like, okay, where do I get the data?[00:25:27] Alessio: To fine tune it on, you know, so it's great that we're moving the thing. And then I really like he had this chart where like, you know, the brain mass and the body mass thing is basically like mammals that scaled linearly by brain and body size, and then humans kind of like broke off the slope. So it's almost like maybe the mammal slope is like the pre training slope.[00:25:46] Alessio: And then the post training slope is like the, the human one.[00:25:49] swyx: Yeah. I wonder what the. I mean, we'll know in 10 years, but I wonder what the y axis is for, for Ilya's SSI. We'll try to get them on.[00:25:57] Alessio: Ilya, if you're listening, you're [00:26:00] welcome here. Yeah, and then he had, you know, what comes next, like agent, synthetic data, inference, compute, I thought all of that was like that.[00:26:05] Alessio: I don't[00:26:05] swyx: think he was dropping any alpha there. Yeah, yeah, yeah.[00:26:07] Alessio: Yeah. Any other new reps? Highlights?[00:26:10] swyx: I think that there was comparatively a lot more work. Oh, by the way, I need to plug that, uh, my friend Yi made this, like, little nice paper. Yeah, that was really[00:26:20] swyx: nice.[00:26:20] swyx: Uh, of, uh, of, like, all the, he's, she called it must read papers of 2024.[00:26:26] swyx: So I laid out some of these at NeurIPS, and it was just gone. Like, everyone just picked it up. Because people are dying for, like, little guidance and visualizations And so, uh, I thought it was really super nice that we got there.[00:26:38] Alessio: Should we do a late in space book for each year? Uh, I thought about it. For each year we should.[00:26:42] Alessio: Coffee table book. Yeah. Yeah. Okay. Put it in the will. Hi, Will. By the way, we haven't introduced you. He's our new, you know, general organist, Jamie. You need to[00:26:52] swyx: pull up more things. One thing I saw that, uh, Okay, one fun one, and then one [00:27:00] more general one. So the fun one is this paper on agent collusion. This is a paper on steganography.[00:27:06] swyx: This is secret collusion among AI agents, multi agent deception via steganography. I tried to go to NeurIPS in order to find these kinds of papers because the real reason Like NeurIPS this year has a lottery system. A lot of people actually even go and don't buy tickets because they just go and attend the side events.[00:27:22] swyx: And then also the people who go and end up crowding around the most popular papers, which you already know and already read them before you showed up to NeurIPS. So the only reason you go there is to talk to the paper authors, but there's like something like 10, 000 other. All these papers out there that, you know, are just people's work that they, that they did on the air and they failed to get attention for one reason or another.[00:27:42] swyx: And this was one of them. Uh, it was like all the way at the back. And this is a deep mind paper that actually focuses on collusion between AI agents, uh, by hiding messages in the text that they generate. Uh, so that's what steganography is. So a very simple example would be the first letter of every word.[00:27:57] swyx: If you Pick that out, you know, and the code sends a [00:28:00] different message than that. But something I've always emphasized is to LLMs, we read left to right. LLMs can read up, down, sideways, you know, in random character order. And it's the same to them as it is to us. So if we were ever to get You know, self motivated, underlined LLMs that we're trying to collaborate to take over the planet.[00:28:19] swyx: This would be how they do it. They spread messages among us in the messages that we generate. And he developed a scaling law for that. So he marked, I'm showing it on screen right now, the emergence of this phenomenon. Basically, for example, for Cypher encoding, GPT 2, Lama 2, mixed trial, GPT 3. 5, zero capabilities, and sudden 4.[00:28:40] swyx: And this is the kind of Jason Wei type emergence properties that people kind of look for. I think what made this paper stand out as well, so he developed the benchmark for steganography collusion, and he also focused on shelling point collusion, which is very low coordination. For agreeing on a decoding encoding format, you kind of need to have some [00:29:00] agreement on that.[00:29:00] swyx: But, but shelling point means like very, very low or almost no coordination. So for example, if I, if I ask someone, if the only message I give you is meet me in New York and you're not aware. Or when you would probably meet me at Grand Central Station. That is the Grand Central Station is a shelling point.[00:29:16] swyx: And it's probably somewhere, somewhere during the day. That is the shelling point of New York is Grand Central. To that extent, shelling points for steganography are things like the, the, the common decoding methods that we talked about. It will be interesting at some point in the future when we are worried about alignment.[00:29:30] swyx: It is not interesting today, but it's interesting that DeepMind is already thinking about this.[00:29:36] Alessio: I think that's like one of the hardest things about NeurIPS. It's like the long tail. I[00:29:41] swyx: found a pricing guy. I'm going to feature him on the podcast. Basically, this guy from NVIDIA worked out the optimal pricing for language models.[00:29:51] swyx: It's basically an econometrics paper at NeurIPS, where everyone else is talking about GPUs. And the guy with the GPUs is[00:29:57] Alessio: talking[00:29:57] swyx: about economics instead. [00:30:00] That was the sort of fun one. So the focus I saw is that model papers at NeurIPS are kind of dead. No one really presents models anymore. It's just data sets.[00:30:12] swyx: This is all the grad students are working on. So like there was a data sets track and then I was looking around like, I was like, you don't need a data sets track because every paper is a data sets paper. And so data sets and benchmarks, they're kind of flip sides of the same thing. So Yeah. Cool. Yeah, if you're a grad student, you're a GPU boy, you kind of work on that.[00:30:30] swyx: And then the, the sort of big model that people walk around and pick the ones that they like, and then they use it in their models. And that's, that's kind of how it develops. I, I feel like, um, like, like you didn't last year, you had people like Hao Tian who worked on Lava, which is take Lama and add Vision.[00:30:47] swyx: And then obviously actually I hired him and he added Vision to Grok. Now he's the Vision Grok guy. This year, I don't think there was any of those.[00:30:55] Alessio: What were the most popular, like, orals? Last year it was like the [00:31:00] Mixed Monarch, I think, was like the most attended. Yeah, uh, I need to look it up. Yeah, I mean, if nothing comes to mind, that's also kind of like an answer in a way.[00:31:10] Alessio: But I think last year there was a lot of interest in, like, furthering models and, like, different architectures and all of that.[00:31:16] swyx: I will say that I felt the orals, oral picks this year were not very good. Either that or maybe it's just a So that's the highlight of how I have changed in terms of how I view papers.[00:31:29] swyx: So like, in my estimation, two of the best papers in this year for datasets or data comp and refined web or fine web. These are two actually industrially used papers, not highlighted for a while. I think DCLM got the spotlight, FineWeb didn't even get the spotlight. So like, it's just that the picks were different.[00:31:48] swyx: But one thing that does get a lot of play that a lot of people are debating is the role that's scheduled. This is the schedule free optimizer paper from Meta from Aaron DeFazio. And this [00:32:00] year in the ML community, there's been a lot of chat about shampoo, soap, all the bathroom amenities for optimizing your learning rates.[00:32:08] swyx: And, uh, most people at the big labs are. Who I asked about this, um, say that it's cute, but it's not something that matters. I don't know, but it's something that was discussed and very, very popular. 4Wars[00:32:19] Alessio: of AI recap maybe, just quickly. Um, where do you want to start? Data?[00:32:26] swyx: So to remind people, this is the 4Wars piece that we did as one of our earlier recaps of this year.[00:32:31] swyx: And the belligerents are on the left, journalists, writers, artists, anyone who owns IP basically, New York Times, Stack Overflow, Reddit, Getty, Sarah Silverman, George RR Martin. Yeah, and I think this year we can add Scarlett Johansson to that side of the fence. So anyone suing, open the eye, basically. I actually wanted to get a snapshot of all the lawsuits.[00:32:52] swyx: I'm sure some lawyer can do it. That's the data quality war. On the right hand side, we have the synthetic data people, and I think we talked about Lumna's talk, you know, [00:33:00] really showing how much synthetic data has come along this year. I think there was a bit of a fight between scale. ai and the synthetic data community, because scale.[00:33:09] swyx: ai published a paper saying that synthetic data doesn't work. Surprise, surprise, scale. ai is the leading vendor of non synthetic data. Only[00:33:17] Alessio: cage free annotated data is useful.[00:33:21] swyx: So I think there's some debate going on there, but I don't think it's much debate anymore that at least synthetic data, for the reasons that are blessed in Luna's talk, Makes sense.[00:33:32] swyx: I don't know if you have any perspectives there.[00:33:34] Alessio: I think, again, going back to the reinforcement fine tuning, I think that will change a little bit how people think about it. I think today people mostly use synthetic data, yeah, for distillation and kind of like fine tuning a smaller model from like a larger model.[00:33:46] Alessio: I'm not super aware of how the frontier labs use it outside of like the rephrase, the web thing that Apple also did. But yeah, I think it'll be. Useful. I think like whether or not that gets us the big [00:34:00] next step, I think that's maybe like TBD, you know, I think people love talking about data because it's like a GPU poor, you know, I think, uh, synthetic data is like something that people can do, you know, so they feel more opinionated about it compared to, yeah, the optimizers stuff, which is like,[00:34:17] swyx: they don't[00:34:17] Alessio: really work[00:34:18] swyx: on.[00:34:18] swyx: I think that there is an angle to the reasoning synthetic data. So this year, we covered in the paper club, the star series of papers. So that's star, Q star, V star. It basically helps you to synthesize reasoning steps, or at least distill reasoning steps from a verifier. And if you look at the OpenAI RFT, API that they released, or that they announced, basically they're asking you to submit graders, or they choose from a preset list of graders.[00:34:49] swyx: Basically It feels like a way to create valid synthetic data for them to fine tune their reasoning paths on. Um, so I think that is another angle where it starts to make sense. And [00:35:00] so like, it's very funny that basically all the data quality wars between Let's say the music industry or like the newspaper publishing industry or the textbooks industry on the big labs.[00:35:11] swyx: It's all of the pre training era. And then like the new era, like the reasoning era, like nobody has any problem with all the reasoning, especially because it's all like sort of math and science oriented with, with very reasonable graders. I think the more interesting next step is how does it generalize beyond STEM?[00:35:27] swyx: We've been using O1 for And I would say like for summarization and creative writing and instruction following, I think it's underrated. I started using O1 in our intro songs before we killed the intro songs, but it's very good at writing lyrics. You know, I can actually say like, I think one of the O1 pro demos.[00:35:46] swyx: All of these things that Noam was showing was that, you know, you can write an entire paragraph or three paragraphs without using the letter A, right?[00:35:53] Creative Writing with AI[00:35:53] swyx: So like, like literally just anything instead of token, like not even token level, character level manipulation and [00:36:00] counting and instruction following. It's, uh, it's very, very strong.[00:36:02] swyx: And so no surprises when I ask it to rhyme, uh, and to, to create song lyrics, it's going to do that very much better than in previous models. So I think it's underrated for creative writing.[00:36:11] Alessio: Yeah.[00:36:12] Legal and Ethical Issues in AI[00:36:12] Alessio: What do you think is the rationale that they're going to have in court when they don't show you the thinking traces of O1, but then they want us to, like, they're getting sued for using other publishers data, you know, but then on their end, they're like, well, you shouldn't be using my data to then train your model.[00:36:29] Alessio: So I'm curious to see how that kind of comes. Yeah, I mean, OPA has[00:36:32] swyx: many ways to publish, to punish people without bringing, taking them to court. Already banned ByteDance for distilling their, their info. And so anyone caught distilling the chain of thought will be just disallowed to continue on, on, on the API.[00:36:44] swyx: And it's fine. It's no big deal. Like, I don't even think that's an issue at all, just because the chain of thoughts are pretty well hidden. Like you have to work very, very hard to, to get it to leak. And then even when it leaks the chain of thought, you don't know if it's, if it's [00:37:00] The bigger concern is actually that there's not that much IP hiding behind it, that Cosign, which we talked about, we talked to him on Dev Day, can just fine tune 4.[00:37:13] swyx: 0 to beat 0. 1 Cloud SONET so far is beating O1 on coding tasks without, at least O1 preview, without being a reasoning model, same for Gemini Pro or Gemini 2. 0. So like, how much is reasoning important? How much of a moat is there in this, like, All of these are proprietary sort of training data that they've presumably accomplished.[00:37:34] swyx: Because even DeepSeek was able to do it. And they had, you know, two months notice to do this, to do R1. So, it's actually unclear how much moat there is. Obviously, you know, if you talk to the Strawberry team, they'll be like, yeah, I mean, we spent the last two years doing this. So, we don't know. And it's going to be Interesting because there'll be a lot of noise from people who say they have inference time compute and actually don't because they just have fancy chain of thought.[00:38:00][00:38:00] swyx: And then there's other people who actually do have very good chain of thought. And you will not see them on the same level as OpenAI because OpenAI has invested a lot in building up the mythology of their team. Um, which makes sense. Like the real answer is somewhere in between.[00:38:13] Alessio: Yeah, I think that's kind of like the main data war story developing.[00:38:18] The Data War: GPU Poor vs. GPU Rich[00:38:18] Alessio: GPU poor versus GPU rich. Yeah. Where do you think we are? I think there was, again, going back to like the small model thing, there was like a time in which the GPU poor were kind of like the rebel faction working on like these models that were like open and small and cheap. And I think today people don't really care as much about GPUs anymore.[00:38:37] Alessio: You also see it in the price of the GPUs. Like, you know, that market is kind of like plummeted because there's people don't want to be, they want to be GPU free. They don't even want to be poor. They just want to be, you know, completely without them. Yeah. How do you think about this war? You[00:38:52] swyx: can tell me about this, but like, I feel like the, the appetite for GPU rich startups, like the, you know, the, the funding plan is we will raise 60 million and [00:39:00] we'll give 50 of that to NVIDIA.[00:39:01] swyx: That is gone, right? Like, no one's, no one's pitching that. This was literally the plan, the exact plan of like, I can name like four or five startups, you know, this time last year. So yeah, GPU rich startups gone.[00:39:12] The Rise of GPU Ultra Rich[00:39:12] swyx: But I think like, The GPU ultra rich, the GPU ultra high net worth is still going. So, um, now we're, you know, we had Leopold's essay on the trillion dollar cluster.[00:39:23] swyx: We're not quite there yet. We have multiple labs, um, you know, XAI very famously, you know, Jensen Huang praising them for being. Best boy number one in spinning up 100, 000 GPU cluster in like 12 days or something. So likewise at Meta, likewise at OpenAI, likewise at the other labs as well. So like the GPU ultra rich are going to keep doing that because I think partially it's an article of faith now that you just need it.[00:39:46] swyx: Like you don't even know what it's going to, what you're going to use it for. You just, you just need it. And it makes sense that if, especially if we're going into. More researchy territory than we are. So let's say 2020 to 2023 was [00:40:00] let's scale big models territory because we had GPT 3 in 2020 and we were like, okay, we'll go from 1.[00:40:05] swyx: 75b to 1. 8b, 1. 8t. And that was GPT 3 to GPT 4. Okay, that's done. As far as everyone is concerned, Opus 3. 5 is not coming out, GPT 4. 5 is not coming out, and Gemini 2, we don't have Pro, whatever. We've hit that wall. Maybe I'll call it the 2 trillion perimeter wall. We're not going to 10 trillion. No one thinks it's a good idea, at least from training costs, from the amount of data, or at least the inference.[00:40:36] swyx: Would you pay 10x the price of GPT Probably not. Like, like you want something else that, that is at least more useful. So it makes sense that people are pivoting in terms of their inference paradigm.[00:40:47] Emerging Trends in AI Models[00:40:47] swyx: And so when it's more researchy, then you actually need more just general purpose compute to mess around with, uh, at the exact same time that production deployments of the old, the previous paradigm is still ramping up,[00:40:58] swyx: um,[00:40:58] swyx: uh, pretty aggressively.[00:40:59] swyx: So [00:41:00] it makes sense that the GPU rich are growing. We have now interviewed both together and fireworks and replicates. Uh, we haven't done any scale yet. But I think Amazon, maybe kind of a sleeper one, Amazon, in a sense of like they, at reInvent, I wasn't expecting them to do so well, but they are now a foundation model lab.[00:41:18] swyx: It's kind of interesting. Um, I think, uh, you know, David went over there and started just creating models.[00:41:25] Alessio: Yeah, I mean, that's the power of prepaid contracts. I think like a lot of AWS customers, you know, they do this big reserve instance contracts and now they got to use their money. That's why so many startups.[00:41:37] Alessio: Get bought through the AWS marketplace so they can kind of bundle them together and prefer pricing.[00:41:42] swyx: Okay, so maybe GPU super rich doing very well, GPU middle class dead, and then GPU[00:41:48] Alessio: poor. I mean, my thing is like, everybody should just be GPU rich. There shouldn't really be, even the GPU poorest, it's like, does it really make sense to be GPU poor?[00:41:57] Alessio: Like, if you're GPU poor, you should just use the [00:42:00] cloud. Yes, you know, and I think there might be a future once we kind of like figure out what the size and shape of these models is where like the tiny box and these things come to fruition where like you can be GPU poor at home. But I think today is like, why are you working so hard to like get these models to run on like very small clusters where it's like, It's so cheap to run them.[00:42:21] Alessio: Yeah, yeah,[00:42:22] swyx: yeah. I think mostly people think it's cool. People think it's a stepping stone to scaling up. So they aspire to be GPU rich one day and they're working on new methods. Like news research, like probably the most deep tech thing they've done this year is Distro or whatever the new name is.[00:42:38] swyx: There's a lot of interest in heterogeneous computing, distributed computing. I tend generally to de emphasize that historically, but it may be coming to a time where it is starting to be relevant. I don't know. You know, SF compute launched their compute marketplace this year, and like, who's really using that?[00:42:53] swyx: Like, it's a bunch of small clusters, disparate types of compute, and if you can make that [00:43:00] useful, then that will be very beneficial to the broader community, but maybe still not the source of frontier models. It's just going to be a second tier of compute that is unlocked for people, and that's fine. But yeah, I mean, I think this year, I would say a lot more on device, We are, I now have Apple intelligence on my phone.[00:43:19] swyx: Doesn't do anything apart from summarize my notifications. But still, not bad. Like, it's multi modal.[00:43:25] Alessio: Yeah, the notification summaries are so and so in my experience.[00:43:29] swyx: Yeah, but they add, they add juice to life. And then, um, Chrome Nano, uh, Gemini Nano is coming out in Chrome. Uh, they're still feature flagged, but you can, you can try it now if you, if you use the, uh, the alpha.[00:43:40] swyx: And so, like, I, I think, like, you know, We're getting the sort of GPU poor version of a lot of these things coming out, and I think it's like quite useful. Like Windows as well, rolling out RWKB in sort of every Windows department is super cool. And I think the last thing that I never put in this GPU poor war, that I think I should now, [00:44:00] is the number of startups that are GPU poor but still scaling very well, as sort of wrappers on top of either a foundation model lab, or GPU Cloud.[00:44:10] swyx: GPU Cloud, it would be Suno. Suno, Ramp has rated as one of the top ranked, fastest growing startups of the year. Um, I think the last public number is like zero to 20 million this year in ARR and Suno runs on Moto. So Suno itself is not GPU rich, but they're just doing the training on, on Moto, uh, who we've also talked to on, on the podcast.[00:44:31] swyx: The other one would be Bolt, straight cloud wrapper. And, and, um, Again, another, now they've announced 20 million ARR, which is another step up from our 8 million that we put on the title. So yeah, I mean, it's crazy that all these GPU pores are finding a way while the GPU riches are also finding a way. And then the only failures, I kind of call this the GPU smiling curve, where the edges do well, because you're either close to the machines, and you're like [00:45:00] number one on the machines, or you're like close to the customers, and you're number one on the customer side.[00:45:03] swyx: And the people who are in the middle. Inflection, um, character, didn't do that great. I think character did the best of all of them. Like, you have a note in here that we apparently said that character's price tag was[00:45:15] Alessio: 1B.[00:45:15] swyx: Did I say that?[00:45:16] Alessio: Yeah. You said Google should just buy them for 1B. I thought it was a crazy number.[00:45:20] Alessio: Then they paid 2. 7 billion. I mean, for like,[00:45:22] swyx: yeah.[00:45:22] Alessio: What do you pay for node? Like, I don't know what the game world was like. Maybe the starting price was 1B. I mean, whatever it was, it worked out for everybody involved.[00:45:31] The Multi-Modality War[00:45:31] Alessio: Multimodality war. And this one, we never had text to video in the first version, which now is the hottest.[00:45:37] swyx: Yeah, I would say it's a subset of image, but yes.[00:45:40] Alessio: Yeah, well, but I think at the time it wasn't really something people were doing, and now we had VO2 just came out yesterday. Uh, Sora was released last month, last week. I've not tried Sora, because the day that I tried, it wasn't, yeah. I[00:45:54] swyx: think it's generally available now, you can go to Sora.[00:45:56] swyx: com and try it. Yeah, they had[00:45:58] Alessio: the outage. Which I [00:46:00] think also played a part into it. Small things. Yeah. What's the other model that you posted today that was on Replicate? Video or OneLive?[00:46:08] swyx: Yeah. Very, very nondescript name, but it is from Minimax, which I think is a Chinese lab. The Chinese labs do surprisingly well at the video models.[00:46:20] swyx: I'm not sure it's actually Chinese. I don't know. Hold me up to that. Yep. China. It's good. Yeah, the Chinese love video. What can I say? They have a lot of training data for video. Or a more relaxed regulatory environment.[00:46:37] Alessio: Uh, well, sure, in some way. Yeah, I don't think there's much else there. I think like, you know, on the image side, I think it's still open.[00:46:45] Alessio: Yeah, I mean,[00:46:46] swyx: 11labs is now a unicorn. So basically, what is multi modality war? Multi modality war is, do you specialize in a single modality, right? Or do you have GodModel that does all the modalities? So this is [00:47:00] definitely still going, in a sense of 11 labs, you know, now Unicorn, PicoLabs doing well, they launched Pico 2.[00:47:06] swyx: 0 recently, HeyGen, I think has reached 100 million ARR, Assembly, I don't know, but they have billboards all over the place, so I assume they're doing very, very well. So these are all specialist models, specialist models and specialist startups. And then there's the big labs who are doing the sort of all in one play.[00:47:24] swyx: And then here I would highlight Gemini 2 for having native image output. Have you seen the demos? Um, yeah, it's, it's hard to keep up. Literally they launched this last week and a shout out to Paige Bailey, who came to the Latent Space event to demo on the day of launch. And she wasn't prepared. She was just like, I'm just going to show you.[00:47:43] swyx: So they have voice. They have, you know, obviously image input, and then they obviously can code gen and all that. But the new one that OpenAI and Meta both have but they haven't launched yet is image output. So you can literally, um, I think their demo video was that you put in an image of a [00:48:00] car, and you ask for minor modifications to that car.[00:48:02] swyx: They can generate you that modification exactly as you asked. So there's no need for the stable diffusion or comfy UI workflow of like mask here and then like infill there in paint there and all that, all that stuff. This is small model nonsense. Big model people are like, huh, we got you in as everything in the transformer.[00:48:21] swyx: This is the multimodality war, which is, do you, do you bet on the God model or do you string together a whole bunch of, uh, Small models like a, like a chump. Yeah,[00:48:29] Alessio: I don't know, man. Yeah, that would be interesting. I mean, obviously I use Midjourney for all of our thumbnails. Um, they've been doing a ton on the product, I would say.[00:48:38] Alessio: They launched a new Midjourney editor thing. They've been doing a ton. Because I think, yeah, the motto is kind of like, Maybe, you know, people say black forest, the black forest models are better than mid journey on a pixel by pixel basis. But I think when you put it, put it together, have you tried[00:48:53] swyx: the same problems on black forest?[00:48:55] Alessio: Yes. But the problem is just like, you know, on black forest, it generates one image. And then it's like, you got to [00:49:00] regenerate. You don't have all these like UI things. Like what I do, no, but it's like time issue, you know, it's like a mid[00:49:06] swyx: journey. Call the API four times.[00:49:08] Alessio: No, but then there's no like variate.[00:49:10] Alessio: Like the good thing about mid journey is like, you just go in there and you're cooking. There's a lot of stuff that just makes it really easy. And I think people underestimate that. Like, it's not really a skill issue, because I'm paying mid journey, so it's a Black Forest skill issue, because I'm not paying them, you know?[00:49:24] Alessio: Yeah,[00:49:25] swyx: so, okay, so, uh, this is a UX thing, right? Like, you, you, you understand that, at least, we think that Black Forest should be able to do all that stuff. I will also shout out, ReCraft has come out, uh, on top of the image arena that, uh, artificial analysis has done, has apparently, uh, Flux's place. Is this still true?[00:49:41] swyx: So, Artificial Analysis is now a company. I highlighted them I think in one of the early AI Newses of the year. And they have launched a whole bunch of arenas. So, they're trying to take on LM Arena, Anastasios and crew. And they have an image arena. Oh yeah, Recraft v3 is now beating Flux 1. 1. Which is very surprising [00:50:00] because Flux And Black Forest Labs are the old stable diffusion crew who left stability after, um, the management issues.[00:50:06] swyx: So Recurve has come from nowhere to be the top image model. Uh, very, very strange. I would also highlight that Grok has now launched Aurora, which is, it's very interesting dynamics between Grok and Black Forest Labs because Grok's images were originally launched, uh, in partnership with Black Forest Labs as a, as a thin wrapper.[00:50:24] swyx: And then Grok was like, no, we'll make our own. And so they've made their own. I don't know, there are no APIs or benchmarks about it. They just announced it. So yeah, that's the multi modality war. I would say that so far, the small model, the dedicated model people are winning, because they are just focused on their tasks.[00:50:42] swyx: But the big model, People are always catching up. And the moment I saw the Gemini 2 demo of image editing, where I can put in an image and just request it and it does, that's how AI should work. Not like a whole bunch of complicated steps. So it really is something. And I think one frontier that we haven't [00:51:00] seen this year, like obviously video has done very well, and it will continue to grow.[00:51:03] swyx: You know, we only have Sora Turbo today, but at some point we'll get full Sora. Oh, at least the Hollywood Labs will get Fulsora. We haven't seen video to audio, or video synced to audio. And so the researchers that I talked to are already starting to talk about that as the next frontier. But there's still maybe like five more years of video left to actually be Soda.[00:51:23] swyx: I would say that Gemini's approach Compared to OpenAI, Gemini seems, or DeepMind's approach to video seems a lot more fully fledged than OpenAI. Because if you look at the ICML recap that I published that so far nobody has listened to, um, that people have listened to it. It's just a different, definitely different audience.[00:51:43] swyx: It's only seven hours long. Why are people not listening? It's like everything in Uh, so, so DeepMind has, is working on Genie. They also launched Genie 2 and VideoPoet. So, like, they have maybe four years advantage on world modeling that OpenAI does not have. Because OpenAI basically only started [00:52:00] Diffusion Transformers last year, you know, when they hired, uh, Bill Peebles.[00:52:03] swyx: So, DeepMind has, has a bit of advantage here, I would say, in, in, in showing, like, the reason that VO2, while one, They cherry pick their videos. So obviously it looks better than Sora, but the reason I would believe that VO2, uh, when it's fully launched will do very well is because they have all this background work in video that they've done for years.[00:52:22] swyx: Like, like last year's NeurIPS, I already was interviewing some of their video people. I forget their model name, but for, for people who are dedicated fans, they can go to NeurIPS 2023 and see, see that paper.[00:52:32] Alessio: And then last but not least, the LLMOS. We renamed it to Ragops, formerly known as[00:52:39] swyx: Ragops War. I put the latest chart on the Braintrust episode.[00:52:43] swyx: I think I'm going to separate these essays from the episode notes. So the reason I used to do that, by the way, is because I wanted to show up on Hacker News. I wanted the podcast to show up on Hacker News. So I always put an essay inside of there because Hacker News people like to read and not listen.[00:52:58] Alessio: So episode essays,[00:52:59] swyx: I remember [00:53:00] purchasing them separately. You say Lanchain Llama Index is still growing.[00:53:03] Alessio: Yeah, so I looked at the PyPy stats, you know. I don't care about stars. On PyPy you see Do you want to share your screen? Yes. I prefer to look at actual downloads, not at stars on GitHub. So if you look at, you know, Lanchain still growing.[00:53:20] Alessio: These are the last six months. Llama Index still growing. What I've basically seen is like things that, One, obviously these things have A commercial product. So there's like people buying this and sticking with it versus kind of hopping in between things versus, you know, for example, crew AI, not really growing as much.[00:53:38] Alessio: The stars are growing. If you look on GitHub, like the stars are growing, but kind of like the usage is kind of like flat. In the last six months, have they done some[00:53:4

Santiago González comenta la ceguera de Aitor Esteban, las mentiras sobre Aldama de la portavoz del PSOE y la ignorancia de Pilar Alegría.