FabianDP

@FabianDPCUBA

First Who, Then What

Madrid, España

Joined October 2016

199 Following

19 Followers

1.7K Posts

FabianDPCUBA retweeted

GREG ISENBERG

@gregisenberg

3 days ago

I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies. My observations: 1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively. 2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now. 3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention. 4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is. 5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now. 6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text. They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it. 7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy. 8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies. Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here. 9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens. 10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity! 11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it. 12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable. 13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think. 14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data. 15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open. 16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down. 17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building? 18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone. 19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good. 20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped. 21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense. Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era. Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways. Happy to be home and locked in. Until next time!

gregisenberg's tweet photo. I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.

My observations:

1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.

2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.

3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.

4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.

5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.

6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.

They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.

7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.

8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.

Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.

9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.

10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!

11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.

12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.

13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.

14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.

15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.

16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.

17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?

18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.

19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.

20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.

21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.

Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.

Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.

Happy to be home and locked in.

Until next time!

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FabianDPCUBA retweeted

Rose Celine Investments 🌹

@realroseceline

2 months ago

In 2010, I started contributing to my retirement account. Back then, the maximum annual IRA contribution was just $5,000, and I’ve contributed the maximum every year since. Over the last approx 16 years, I’ve invested roughly $100,000 of my own money. Through patient, sensible investing, the account has grown to more than $1.1 million. At first glance, that looks like roughly a 16% CAGR, but that’s actually a misleading way to measure the return because it assumes the entire $100,000 was invested on day one. In reality, money was added every year for 16 years, so much of the capital had less time to compound. The proper way to measure performance is with a money weighted return calculation (XIRR), which would be north of 25% CAGR. Calculating the exact figure requires the precise dates and amounts of every contribution, which I am too lazy to do. For perspective, the S&P 500 compounded at roughly 14% annually over approximately the same period. The lesson isn’t that I beat the market, the lesson is that even a small annual edge, compounded over many years, can produce dramatically different outcomes. To be fair, I was fortunate enough to own two investments that each became 30 baggers, and they accounted for a significant portion of the gains. Luck always plays a role in investing. But luck also tends to favor investors who stay in the game long enough to let exceptional businesses surprise them. One of the biggest lessons I’ve learned is that investing isn’t about being right all the time. I made plenty of mistakes along the way. The overwhelming majority of my wealth came from just a handful of decisions. You don’t need to hit a home run every year. You need to avoid catastrophic mistakes, recognize exceptional businesses when you find them, and then have the discipline to hold them while they continue compounding. The hardest part wasn’t finding those businesses. The hardest part was continuing to hold them after they doubled, tripled, went up 10x, and eventually more than 30x. Every step of the way there were reasons to sell. Every step of the way there were people saying they were overvalued. Looking back, my greatest enemy wasn’t inflation, recessions, or bear markets. It was the temptation to interrupt compounding. I also realized something fascinating as the portfolio grew. In the beginning, my annual contributions were the primary driver of my wealth. Eventually, they became almost irrelevant. The businesses themselves began creating more wealth than I could contribute through new savings. That’s the moment you truly understand the power of ownership. The first $100,000 came from me. The next million increasingly came from the businesses I owned. That’s why I believe the biggest secret in investing isn’t finding hundreds of great stocks. It’s finding a few truly exceptional businesses, buying them at sensible prices, and then having the patience to let time do what almost no investor is willing to allow it to do. Compounding doesn’t look impressive in the beginning. It whispers for years before it roars (borrowed this quote is from my book 😉). The greatest fortunes are rarely built by constantly making brilliant decisions. They’re built by making a handful of great ones… and then having the discipline to get out of their way. 🌹

realroseceline's tweet photo. In 2010, I started contributing to my retirement account. Back then, the maximum annual IRA contribution was just $5,000, and I’ve contributed the maximum every year since. Over the last approx 16 years, I’ve invested roughly $100,000 of my own money. Through patient, sensible investing, the account has grown to more than $1.1 million.

At first glance, that looks like roughly a 16% CAGR, but that’s actually a misleading way to measure the return because it assumes the entire $100,000 was invested on day one. In reality, money was added every year for 16 years, so much of the capital had less time to compound. The proper way to measure performance is with a money weighted return calculation (XIRR), which would be north of 25% CAGR. Calculating the exact figure requires the precise dates and amounts of every contribution, which I am too lazy to do.

For perspective, the S&P 500 compounded at roughly 14% annually over approximately the same period. The lesson isn’t that I beat the market, the lesson is that even a small annual edge, compounded over many years, can produce dramatically different outcomes.

To be fair, I was fortunate enough to own two investments that each became 30 baggers, and they accounted for a significant portion of the gains. Luck always plays a role in investing. But luck also tends to favor investors who stay in the game long enough to let exceptional businesses surprise them.

One of the biggest lessons I’ve learned is that investing isn’t about being right all the time. I made plenty of mistakes along the way. The overwhelming majority of my wealth came from just a handful of decisions. You don’t need to hit a home run every year. You need to avoid catastrophic mistakes, recognize exceptional businesses when you find them, and then have the discipline to hold them while they continue compounding.

The hardest part wasn’t finding those businesses. The hardest part was continuing to hold them after they doubled, tripled, went up 10x, and eventually more than 30x. Every step of the way there were reasons to sell. Every step of the way there were people saying they were overvalued. Looking back, my greatest enemy wasn’t inflation, recessions, or bear markets. It was the temptation to interrupt compounding.

I also realized something fascinating as the portfolio grew. In the beginning, my annual contributions were the primary driver of my wealth. Eventually, they became almost irrelevant. The businesses themselves began creating more wealth than I could contribute through new savings. That’s the moment you truly understand the power of ownership. The first $100,000 came from me. The next million increasingly came from the businesses I owned.

That’s why I believe the biggest secret in investing isn’t finding hundreds of great stocks. It’s finding a few truly exceptional businesses, buying them at sensible prices, and then having the patience to let time do what almost no investor is willing to allow it to do.

Compounding doesn’t look impressive in the beginning. It whispers for years before it roars (borrowed this quote is from my book 😉). The greatest fortunes are rarely built by constantly making brilliant decisions. They’re built by making a handful of great ones… and then having the discipline to get out of their way.

🌹

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FabianDP @FabianDPCUBA

3 months ago

@CarlosMaslaton Carlos, conoces ese periódico y esa cuerda.

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FabianDP @FabianDPCUBA

3 months ago

@MIXANTHRO @rondon_EA26 Cuantos nacieron en Francia? Los 3 que no nacieron en Francia no son franceses?

Who to follow

FabianDP @FabianDPCUBA

3 months ago

@andresidaho77 Excepto los franceses, colombia y algun africano, el resto de países ha bajado las camadas de tener al menos 2 o 3 crack. Da igual un grande como Italia o un país competitivo como Perú.

FabianDP @FabianDPCUBA

4 months ago

@andresidaho77 Goat

FabianDP @FabianDPCUBA

4 months ago

@GustavoBolsa Si crees q el x-ismo es el problema...

FabianDP @FabianDPCUBA

4 months ago

@CesarLeyess @Polako_mdq88 @TendenciasDepor Etoo un verano? Eso es lo que crees que duró? Jajajan si con el Mallorca le metió 5 al Madrid y todavía no era conocido.

FabianDPCUBA retweeted

TacticzHazel

@TacticzH

4 months ago

I believe strong cashflow, high growth, and strong stable business models are still the way to go. While everyone is chasing the next microcap bottleneck in the same space, I look for value. Quality businesses at a discount because the narrative has changed. The narrative. Not the business. You can find the 15 favorite stocks I would buy right now in this article. I talk about: $NFLX , $NVO $ADYEN, $BABA and a lot more! Check it out here: https://t.co/YPZDDJmIIx

FabianDP @FabianDPCUBA

4 months ago

@DavidAbell18104 @LauraFreixas @carmen_domingo Nooooo que va. Tienes que pasar por muchos test, ser evaluado por la distintas categorías de feminismo y al final te darás cuenta de que a la gente "o escritores" de artículos les importará 0 para determinar lo que eres.

FabianDP @FabianDPCUBA

4 months ago

@disident_ass @LauraFreixas @carmen_domingo Ignorantes alienados...que risas

FabianDPCUBA retweeted

Pakillo Mariscal

@MariscalPakillo

5 months ago

Hilo con las convocatorias finales de las 48 selecciones que disputarán el Mundial.

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FabianDP @FabianDPCUBA

5 months ago

@andresidaho77 si se van todos los que dices es equipo nuevo y a sufrir de nuevo. Kalulu Thuram Conceicao no son fáciles de sustituir y salieron "baratos" para el nivel y potencial que tienen. Los otros que pones como "sumados": un lazo, un abrazo un agradecimiento y adiós.

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FabianDP @FabianDPCUBA

6 months ago

@DanielValverdeC @XMihura

FabianDP @FabianDPCUBA

6 months ago

@Anujbost

571

FabianDPCUBA retweeted

Alberto @albertofierroal

6 months ago

1.📚💻⚖️ I am very excited to share a project I have been working on for many months: Peace Through Law, a blog created to help scholars, students, and any person interested in ICL to find information about the work of international tribunals 🧵⬇️ https://t.co/mQSTs5oX9t