GPTadvisor

Ready for takeoff 🛫

Thrilled to introduce @boromas, CEO and Founder of @GPTadvisor, as a speaker at AI Rush in London! Salvador is a recognized expert in developing AI-driven financial advisory solutions for institutions. As the CEO and Founder of GPTadvisor, a native AI wealthtech platform, he brings years of experience from roles at Openfinance, Finametrix, and Allfunds. Salvador’s work is at the cutting edge of AI and finance, transforming how institutions provide advisory services. 📅 Save the date: 16 May, in London

La #InteligenciaArtificial está transformando el sector financiero y la #regulación juega un papel clave en su integración. @Cecabank_es y @GPTadvisor trabajamos conjuntamente en el único proyecto de #innovación financiera basado en #IA aplicada a los servicios de inversión de @CNMV_MEDIOS. ¿En qué procesos será más eficiente integrar la IAG?, ¿en qué punto queda la regulación?, ¿cuáles serán los beneficios para el usuario final? 👇🏼 cecabank.es/la-regulacion-…

La wealthtech GPTAdvisor lanza su expansión en Américas y suma al equipo a Camila Rocha @GPTadvisor @FundsSociety fundssociety.com/es/noticias/ne…

🔴ENTREVISTA | Salvador Mas: "Europa está a por uvas en el desarrollo de IA" 🎙️La competencia global en el desarrollo de la inteligencia artificial está dejando a Europa cada vez más rezagada, según el fundador de @GPTadvisor. capitalradio.es/noticias/tecno…

La inteligencia artificial va a ser la mejor amiga del gestor de patrimonios e inversiones Dentro vídeo de @GPTadvisor

Here's our live demo at @Finovate Fall in NYC Check out how our advanced AI portfolio analysis capabilities are dramatically increasing wealth management productivity!

Our team just applied to @SWCPTV #StartupWorldCup #GPTadvisor startupworldcup.io

Ready to showcase our latest genAI tech for wealth management at @Finovate Fall NYC!!

We are arriving in NYC for @Finovate Fall!

En @uchceu hablando de IA generativa en el mundo económico

One thing that we think a lot about at Box is how you build software for the future of AI when you have rapid advancements in what's state of the art. Every day, there are brand new AI models that perform better than the last, and we're witnessing a never ending race to lower AI (token) prices due to the need for model and infrastructure providers to stay competitive and move workloads to their platforms. This is an incredible boon for developers, and it means you can roughly extrapolate out that the AI we will be using in a year or two could be 10X cheaper or 10X higher quality.  Even for software, this is an incredible rate of improvement. Given this rate of change, you can take a fundamentally different approach to building software. For most of the software we build, it's actually incredibly important to understand the constraints of the technology and build with those limitations in mind. For instance, if you're building a complex application you know your database will eventually become a bottleneck, and you have to appropriately design for that. Similarly, if you have a high-bandwidth (video for instance) application, you have to build for the constraints of high latency environments like mobile. In either of these examples, what you build will fundamentally be limited by today's technology, and at best you should only expect improvements at the rate of Moore's Law -- if you're lucky. AI is completely different. While you could of course pare down product ambitions and only build for what's possible today, instead, given the precipitous drop in AI costs and continued breakthroughs in model efficacy, it makes more sense to build solutions more for what will be possible in the future. Practically speaking, this means working backwards from what AI will enable with a 10X gain on some performance characteristic, and figuring out an architecture pattern that lets your product absorb those gains. We spend a lot of time trying to distinguish when we're building a new Box AI capability which axis of improvement (cost, intelligence, etc.) will make this feature much better, and then decide if we need to compensate for today's limitations or will the gains instantly accrue to our feature in the future. This changes what we prioritize building, how we architect what we're building, and the sequence of capabilities we launch. But ultimately, the biggest thing it changes is the ambitions you can have in AI. When you know that things that were literally impossible a year ago will become easily implementable in a year from now, the aperture of the products and services you can deliver widens dramatically. AI breakthroughs will only increase, and it's going to allow us to build software that we barely could've dreamed of.  What matters most now is how you build software today to get the biggest benefits in the future.

NVIDIA has released FREE courses on AI. No payment needed. Here are 8 free courses you don't want to miss:

a16z just released an enterprise AI report after speaking to dozens of Fortune 500 companies. Thanks @sarahdingwang for this. Here are 8 insightful slides: 1. For production use cases, OpenAI still has dominant market share.

LLM-triggered engines are the future of every human-advisor role towards Human/AI-advisors In WM the applications are endless (and instant!)

Here's a sneak peek of our pitch at @Finovate Europe in London, last February 27th Have a look at GPTadvisor's AI proposition for wealth managers!

@migtissera This is their CEO as a kid. Maybe GPT-4 is a wrapper around him?

🤝 @AndbankAND llega a una alianza con @GPTadvisor para aprovechar el potencial de la IA para sus banqueros 🔗 #inteligenciaartificial #bancaprivada #Andbank #RankiaPro rankiapro.com/es/noticias/an…

"La #IA como valor diferencial en los servicios de inversión" es la primera mesa redonda con Salvador Mas de @GPTadvisor, María Vidal de @finReg360, Laura Comas de @CaixaBank y modera Aurora Cuadros, directora corporativa del Área de #SecuritiesServices de @Cecabank_es

Ray Dalio: Are We in a Stock Market Bubble? linkedin.com/pulse/we-stock…