Their harder problem is determining which data will actually make their models better.

José Nunes built Vetto to solve that problem. Instead of waiting for a lab to specify the data it needs, Vetto studies where a model is underperforming and designs the work required to improve it.

That changes Vetto’s role.

Most data suppliers receive guidelines from the labs and find people to execute them. Vetto works one step earlier: its researchers design the guidelines themselves.

To determine whether those guidelines work, they build benchmarks and train small models on each dataset. The results show the labs how the model performed before and after training.

If the model does not improve, the data is not good.

This ability to demonstrate improvement is also what drives the business. The research gives a lab a reason to buy before a traditional sales process begins.

Today, José is 25 and Vetto is doing eight figures a year. It tripled revenue several months in a row, with no sales team or paid marketing.

As José told me:

“What you do with the data is as important as having the data.”

In the episode, José explains how Vetto became a research partner to the AI labs and why deciding what data to collect is more valuable than collecting it.

Presented by Ashurst Perkins Coie. Paul Navarro works with some of the best founders in Latin America, many of them guests on the podcast. I always ask my guests who I should interview next, and the only time anyone recommended a lawyer, it was Paul. Glad to have them as a founding sponsor.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[03:15] His journey from a small town in Brazil to Silicon Valley

[16:20] How Vetto competes with the labs in research

[18:05] Why the most valuable part of AI data happens before collection

[19:15] How Vetto recruits researchers it cannot outbid the frontier labs for

[57:20] Why researchers hired in Brazil can perform alongside people from DeepMind

Today he’s playing the “main league” of Silicon Valley: building an AI company in SF, against the smartest, best-funded people in the world. In his words, “you’re playing against the Pelés and Kakás.”

That company became Firecrawl, the biggest web-scraping repo on GitHub. 170k stars from a team of 20.

Every founder is dealing with the same question: if AI can clone your product in a weekend, what makes yours worth using?

Firecrawl does one unglamorous job: it reads webpages for AI. It takes a page full of code and turns it into clean text a model can use. Almost every AI agent needs that and agents now browse the web more than people do (according to Cloudflare).

Nicolas and his cofounders built it without connections or a big raise. They open-sourced it, developers picked it up and it spread from there. Tobi Lütke, the founder of Shopify, invested without even taking a meeting.

In the episode, Nicolas explains why the most boring part of the stack is often the most valuable and how open source became his sales team.

Presented by Ashurst Perkins Coie. Paul Navarro works with some of the best founders in Latin America, many of them guests on the podcast. I always ask my guests who I should interview next, and the only time anyone recommended a lawyer, it was Paul. Glad to have them as a founding sponsor.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[02:55] At 12, he had to choose: drop out of school or quit soccer

[31:25] The indie hacker test: if it takes a weekend, it’s a feature

[35:45] The best product is the one your users never see

[50:05] What he means by building the eyes for AGI

Those have been the best and highest-growth years of my life. Today his episode goes live, and I finally get to tell his story, because most people don’t know the arc.

He landed in the US at 18 with almost no English and spent his first year mostly in the library, learning the language and what he calls the OS of American culture.

In the 2000s, he ran Penguin Holdings out of Miami, a circle of young LatAm founders betting on the next generation.

Alongside Wences Casares, Micky Malka and Verónica Serra, they wrote one of the earliest checks into a small Argentine startup called Mercado Libre.

Then he spent 12 years as a founder himself, building two video game studios with Latin American talent and selling to the US, back when everyone said it couldn’t be done from there.

The first was acquired by Activision after a wall of nos, and the next one sold too.

In 2015, he co-founded Reach. The fund just closed its fifth fund at $265M.

The wildest part? He’s too humble to tell you any of this himself.

So I got it out of him on today’s episode. This was a special one for me. Hope you enjoy it!

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[09:40] How Argentina’s first VC circle found and backed Mercado Libre

[45:20] Why Reach invested in Replit at pre-seed in 2016

[49:20] Why AI is raising the bar for technical cofounders

[51:55] The “bloodbath” Esteban expects as companies cut AI tools

[55:05] Who should move to Silicon Valley and how everyone else should spend time here

He just came back a unicorn.

Zero to a $1.2B valuation in three years. HappyRobot raised a $150M Series C led by Prism Capital and Eurazeo.

DHL, Repsol, Koch on the customer list now. Four industries, three continents.

And they got there by doing what the Silicon Valley playbook says not to.

• “Focus on one vertical”. He went multi-vertical: logistics, energy, telcos, insurance.

• “Go global late”. He’s already deep in Europe and LatAm, because his customers are global and he’s European, so why wait.

• “Sell pure software; services don’t scale”. They do both on purpose. Sending FDEs onsite is their biggest growth lever and moat: they shipped in two days what a rival quoted in three months.

The best companies don’t copy the playbook. They write their own.

A Spanish founder built a US unicorn and now he’s taking it back into Europe and LatAm, the markets the Valley tends to overlook.

Unlike any tech wave before it, AI is diffusing and getting adopted faster than we’ve ever seen.

So the next generation of great companies won’t wait to go global. They’ll build that way from day one.

In the episode, Pablo breaks it all down, starting with why he almost quit HappyRobot to go work as a freight broker.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[04:35] When he wanted to quit his own company to become a freight broker

[12:29] Why DHL has more in common with a telco than a regional logistics shop

[32:55] They deployed in 2 days what a competitor quoted at 3 months

[44:35] Why he went global this early

5M users across 150 countries. In January, the company raised a $21M Series A led by Matrix.

But Anada Lakra started BoldVoice with a problem that felt much smaller.

She moved from Albania to attend Yale after studying English for almost 10 years. On paper, she was fluent. In person, people constantly asked her to repeat herself.

Those daily “paper cuts” slowly changed how confidently she spoke.

Years later, Anada realized millions of non-native speakers had the same problem.

They already knew English, but their accent was hard to follow

Solving that required more than another language learning app. Existing speech recognition could identify what someone said. It couldn’t diagnose pronunciation, cadence and word stress with the precision of a speech coach.

So BoldVoice – or how she calls it – “an audio AI lab masked as a consumer company” built that capability

What looks like a simple coaching app is powered by proprietary AI models trained to hear how people actually speak English.

That technical foundation became the moat behind Boldvoice.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[18:18] Why BoldVoice built its audio AI in-house

[18:45] What conventional speech recognition misses about non-native speakers

[26:03] How a seven-person team reached $10M+ in revenue

[27:58] How BoldVoice turns AI research into a better coaching product

Not even a vibe coder like me: we mostly live in Claude Code, Cursor, and Codex.

These players are at war to own the coding agent, maybe the biggest AI market there is.

Luis Bitencourt runs GitHub Copilot. So I asked him straight up: is Copilot losing the race?

His answer:

Everyone’s fighting on two layers: the harness and the model. Luis thinks neither is winnable.

No model has stayed on top more than three months. And the harness, in his words, has “no huge secret sauce” long term.

What doesn’t move is the platform underneath. Every one of those tools runs on GitHub.

So instead of locking developers into GitHub’s own agents, they let Claude and Codex now run as agents next to Copilot.

Both harness and model are replaceable. Luis is betting the place where humans and agents come together to ship software is much harder to move.

If you’re building anything on top of AI right now, that’s the reframe: you might be fighting for the layer that resets every 3 months, not the one that lasts.

I lived a mini version of this: my podcast team moved all our work to a GitHub repo this year. I resisted it, tried to make it work in Notion, and I couldn’t.

Where he sees the next battle field of agents:

“The same way that you think of PRs, you think of GitHub. Can we get to a point where you think of sessions, you think of GitHub?”

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[16:20] What Rappi and Loft, two messy physical-world businesses, taught him about humans in the loop

[28:45] Why coding agents are just the beginning: code review, SRE, debugging

[38:25] Why Microsoft’s distribution buys a head start of months now, not years

Fifteen years later he was leading the engineering behind Instagram’s feed, the personalization machine billions of people open every day.

His take on AI agents: they’re making the mistake Instagram almost made. They optimize for what you want right now instead of what you actually want.

At Instagram the easy path was showing people whatever kept them scrolling. His name for it: candy for lunch. Amazing the first time, miserable by week two. The hard part was never the ranking model, it was choosing an objective function that paid off long-term, even at the cost of engagement today.

Agents take the candy. They validate whatever you say and optimize for the next reply. Feels great in the moment. It’s not the relationship you wants with agents.

Then add memory. Everyone’s racing to bigger context windows, assuming more context means better answers. Past a point it’s the opposite: the extra data makes the agent worse.

Rodrigo says forgetting isn’t a flaw in memory, it’s what makes memory usable. The win is knowing what to throw away, and that’s the app’s job, not the model’s.

Rodrigo left Meta to do something about it, co-founding Vetto to build the research-grade data frontier models train on, betting the next leap comes from better signal, not more of it.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[21:30] Why Instagram’s early feed almost fell into the same trap

[33:20] Why a bigger context window can quietly make your agent worse

[35:30] Why forgetting isn’t a bug, it’s what makes memory work at all

[43:20] His lessons from Facebook’s AI Research on building product & research orgs

The room was full of founders spending a few weeks in the Bay trying to crack the city.

So Santiago pushed me on the one thing I get asked most: how do you actually break into Silicon Valley?

Here’s what I think now after 5 years in SF:

• Skip the big events (SF Tech Week, SaaStr, Salesforce). They’re built (mostly) for tourists, but the locals are who you came for.

• San Francisco is a city that happens indoors. Deals close at dinner parties. Friendships start at birthday BBQs and soccer pickup. You have to hack your way into the indoors.

• Don’t network. Just live a life you enjoy. The most successful people I’ve met in this city, I met playing soccer. Some of them run top venture firms in the Valley.

• Stop asking what you can get from SF. The investors and founders worth knowing are exhausted by coffees from people they’ll never see again. Show up with something to give and the city opens.

• Capital follows the circle of trust. SF investors will pass on a business they love if your LinkedIn shows zero connections in common. The YC and Stanford logos are shortcuts into that network, not substitutes for it.

The real reason to come isn’t the capital or the talent. It’s what this city does to your mindset.

You don’t realize you needed it until you leave and notice you came back a different person.

Santiago and I kept going from there. We talked about AI, the founders I’m betting on now, and what content survives in the AI era.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[10:35] Why San Francisco Runs on Abundance, Not Scarcity

[14:55] Silicon Valley’s Real Advantage Is the Circle of Trust

[22:15] Why VC Conviction Is Becoming More Psychological Than Rational

[34:15] Why AI Makes “Informative” Content a Losing Game

[43:50] Why San Francisco Happens Indoors

But the story isn’t the product. Carlos arrived from Colombia with no network in the US insurance industry. Latin American VCs told him directly that a Latin team couldn’t run commercial processes in the US.

He ignored them. In November 2024, he bet everything on a single design partner in Miami — no charge, five months — while in parallel warming conversations with other prospects. They launched in April. Eight clients in three weeks. 80% demo-to-paid conversion.

What looked like speed was months of invisible work.

Braven raised $6M from Collide Capital, Fiat Ventures, Broom Ventures, Aito Capital, and a16z scout.

Carlos didn’t have Stanford on his resume or a warm intro to a single US insurance company. He showed up in person when everyone else sent emails. He called when nobody calls. All while everyone told him a Latin team couldn’t crack US commercial insurance.

This is one of the most honest conversations I’ve had about what it actually takes to break into a traditional American market from the outside.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[03:30] Moving to the US Gradually vs. Going All In

[08:55] Selling AI Into a Traditional Industry

[15:50] The Sniper Approach to Getting Your First Customers

[28:15] Raising From US VCs as an Outsider

[41:10] Why San Francisco’s “Boring” Culture Can Be a Superpower

Every other investor passed. “The market is too small”, they said.

That company was AuditBoard. In 2024 it sold for $3.1 billion, one of the biggest software exits of the year, on just $43.5M ever raised.

Alejandro didn’t learn to spot that in venture. He learned it in music. His early career was A&R, the people who find talent before anyone signs it. He came up under the man who discovered Linkin Park, a band every label had passed on.

He reads founders the way a scout reads a raw artist. Not by the resume. By whether they’ve lived the thing they’re building.

That instinct isn’t an accident. Alejandro grew up fifteen minutes from the Tijuana border. Son of a mechanic, first in his family, no network.

He backs founders who don’t fit the pattern because he never fit it either.

He’s forty, and he just moved to San Francisco to go all in again. Founders do that. VCs his age don’t.

Full conversation below.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[06:15] - Growing up 15 minutes from Tijuana: how it shapes how he reads founders

[16:10] - The bet that changed everything: $4M fund, week one, two accountants with no traction and why he said yes

[19:00] - "TAM's too small" and why every other investor passed on AuditBoard

[27:25] The Linkin Park analogy for how real pattern matching works

[38:45] Act One's AI super analyst trained on 10 years of their own memos

[53:35] Why 3x growth no longer impresses anyone in 2026

[01:00:20] How a $73M fund accidentally became a native AI fund

His name is Victor Cardenas, founder of Slash.

$250M in revenue. 65 people. Profitable.

2% of all Facebook ad spend runs through Slash. More than 5,000 businesses spend almost $10B a year on its corporate cards.

A year ago, Mickey Malka of Ribbit Capital, the world’s #1 fintech investor (Robinhood, Nubank, Coinbase), said, “fintech is dead.”

Today, Ribbit is leading Slash’s $100M round at a $1.4B valuation.

Why?

Because Victor is executing something no traditional bank can copy.

More than 50% of Slash’s engineering hours go into building internal software. Agents that automate what banks solve by hiring hundreds of people: processing documents, managing disputes, and responding to bank partners.

15% of PRs going to production today are initiated by non-technical people on the team.

Meanwhile, less than 5% of companies in the US use a fintech. Everyone else is still on interfaces that, in Victor’s words, “were last updated in 2003.”

That is the opportunity.

Victor wants Slash to be this generation’s JP Morgan. When you hear Victor’s story and how Slash is being built, you’ll believe him.

[YouTube] → [Spotify] → [Apple Podcast]

Also in this episode:

[6:45] - How to adapt and compete in Silicon Valley’s high-performance environment

[13:25] - How AI is reshaping startup strategy and execution

[15:05] - How to scale niche vertical fintech products using AI

[22:05] - How to build defensibility when AI commoditizes everything

[32:05] - Why Venezuela could become a major business opportunity

Listen the full interview here 👇

Techstars founder David Cohen was an angel investor in Uber. He also had the chance to invest in Lyft — and passed.

Same market. Same timing. Two completely different outcomes in his portfolio.

The difference? At Uber, he met Ryan Graves (employee #1) at Techstars. “I really clicked with him.” He invested without ever meeting the Uber founders.

At Lyft, Matt Van Horn was a Techstars mentor. They were friends. But the idea seemed stupid: “Intercity transport for college kids? I didn’t get it.”

That same team pivoted, fast-followed Uber, and built a multi-billion dollar company.

After 20 years evaluating startups, the pattern behind his misses is always the same.

The Miss Framework: Why “Bad Ideas” Create Unicorns

The problem: We judge founders by their initial ideas, when ideas are what change the most.

The framework: There’s a Venn diagram that explains everything: “Seems like a bad idea” vs “Actually is a good idea.” The tiny overlap in the middle is where real money is made.

Why it works:

1. Uber and Airbnb were “stupid ideas” — No one wanted to ride in a stranger’s car. No one wanted to sleep in a stranger’s house. Today they’re worth hundreds of billions.

2. The idea blinds you to the team — When an idea seems so bad, you never get to properly evaluate the people. And the right people will pivot to something that works.

3. The market matters more than the idea — Cohen now asks: “Do you love the market? Because they’re probably staying in that market.” The specific idea will change. The market won’t.

The tactical shift:

Before: “Is this idea good?”
Now: “Do I love this team? Am I interested in this market?”

The lesson: Great teams can fix bad ideas. Great ideas can’t fix bad teams.

“The commonality in my passes is that they were ‘bad ideas.’ And there’s a Venn diagram I always remember: what seems like a bad idea, what actually is a good idea, and that tiny overlap in the middle — that’s where people make fortunes.”

This is the simplified framework, but David goes much deeper on how AI is changing founder evaluation (spoiler: technical talent matters less, market insight matters more), why he believes we’ll soon see “zero-employee unicorns,” and his contrarian take on why you DON’T need to be in San Francisco to build a world-class company.

🎧 Listen to the full episode: → [Spotify] → [Apple Podcasts] → [YouTube]

Also in this episode:

[15:50] – Why AI creates a “barbell effect” for startups — the middle gets stuck

[26:00] – How to scale networks without destroying them — segmentation is key

[31:30] – Techstars’ internal meme: “Replace ‘AI’ with ‘software’”

[38:35] – Why AI agents will do venture capital — and probably better than humans

[40:10] – Jack Bogle’s philosophy applied to VC: “The finance industry just subtracts value from society”

Instead of freezing, Freepik became one of the first major image platforms to integrate generative AI. Today, Freepik is one of the most widely used AI creative apps, according to a16z.

This is exactly how they did it.

The Playbook: Action First, Strategy Later

Most incumbents respond to disruption with endless analysis, committees, and 18-month plans. Joaquín did the opposite: he acted before having clarity.

1. Accept that you don’t have a plan (and act anyway)
When DALL·E 2 launched, Joaquín had COVID. Isolated, he spent 24 hours a day looking at AI-generated images. He came out of that period without a plan — but with one clear conviction: the existing business model was dead.

“When you don’t know what to do, do something instead of standing still. Every action teaches you something.”

Rather than waiting for clarity, he made a simple next move: put the technology in users’ hands and see what happened.

2. Use your distribution as a laboratory
Freepik had 600,000 subscribers. Instead of protecting the legacy business, they turned that audience into an experiment:

• They integrated Stable Diffusion 1.5 the day it launched

• They were the first major platform to do so

• They listened closely to what users asked for (history, visual styles, thumbnails)

• They iterated on what they could control and waited on what they couldn’t

Their existing distribution became a competitive advantage — not because it defended the old model, but because it accelerated learning about the new one.

3. Strategy emerged from action
Only after acting did Freepik truly understand its position:

• AI models quickly commoditize (many LLMs competing with each other)

• The real value sits in the wrapper: how the model is exposed to the world

• Freepik already had distribution and deep knowledge of creative users

“Strategy came after action. We started by reacting to users, and only then did we develop the strategy.”

4. Avoid self-deception about your ‘advantages’
Adobe, Shutterstock, and Getty fell into the same trap: they built comforting theses (“only we have copyright-safe images”) that turned out to be wrong. Their models were weaker because they restricted their training data.

Joaquín recognized the pattern: companies look for AI narratives that favor them instead of facing reality.

Freepik accepted that its millions of images were not a sufficient moat — and that freed them to compete on product, not protection.

“AI often forces you to rethink everything from scratch. And that’s very hard to do.”

This playbook isn’t about having all the answers. It’s about moving fast enough to find them before your competitors do.

Joaquín goes much deeper into how he handled conversations with his team (some needed psychological support), why he believes humans will remain essential in creative work, and the framework he uses to think about what AI will never be able to replace.

🎧 Listen to the full episode: → [Spotify] → [Apple Podcasts] → [YouTube]

Also in this episode:

[10:00] Why launching AI without a clear strategy beat staying frozen

[23:00] The “Humans vs AI” framework and what machines will never replicate

[31:00] The hardest conversations with artists who thought their careers were over

[45:00] The strategic mistake that made Adobe, Getty, and Shutterstock fall behind on AI

[58:00] Why distribution is a temporary advantage, not a real moat

[01:00:00] How building Freepik from Málaga—not Silicon Valley—became a competitive edge

Manny’s path took him from a shrimp farm in Ecuador to the heights of Silicon Valley, where he built Outreach into a $4.4B SaaS. But in 2023, a conversation with the CEO of DocuSign handed Manny a “Red Pill” moment: if AI agents do the work of humans, companies will hire fewer people, and the seat-based SaaS model will collapse .

He realized the current infrastructure—giants like Salesforce, Netsuite, and Datadog—was “conspiring against him.” They could track closed deals or API calls, but they couldn’t distinguish between an agent’s cost (tokens) and its value (reasoning) .

Manny founded Paid to solve this: a way to interpret agent behavior so builders can stop selling cheap software and start selling valuable work. Paid has raised +$30M from investors like Sequoia, Lightspeed and EQT.

Spotify | YouTube | Apple Podcasts

🗒 What we covered:

[13:00] – How the tech recession forced Outreach to reinvent itself

[18:30] – Why seat-based pricing is dead and what replaces it

[34:00] – Why the Agent Economy is impossible without a new metering layer

[45:30] – Why generalist agents lose to specific domain experts

🧠 Big Ideas

#1 Tech Is a Great Starting Market, But a Terrible Place to Stay

Outreach grew by selling seat-based SaaS to tech companies. But when the market crashed and those companies cut staff, Outreach’s revenue stalled overnight. He realized tech gives you PMF, not durability. Founders must ask: Is my business built on early adopters, or on industries that survive downturns? If you want a long-term company, your revenue has to cross the chasm long before you think you need to.

#2 The Question from DocuSign’s CEO That Proved Seat-Based SaaS Is Dead

DocuSign CEO told Manny: “If your agents work, why would I hire more people next year?” That single sentence exposed the flaw behind the entire SaaS model. If headcount shrinks, seats shrink. If seats based pricing shrink, SaaS dies. Founders building AI products must stop pricing like old software. The future is not per-seat, it’s per outcome.

#3 The 10× Opportunity: Ultra-Vertical Agents That Replace Salaries, Not Seats

Manny argues that the real AI opportunity isn’t general-purpose agents, it’s vertical agents that perform specific workflows so well they replace human headcount. And when an agent replaces five SDRs or automates a mortgage workflow, it isn’t a $49/mo seat, it’s a slice of payroll. That shift from IT budgets to labor budgets is what makes the agent economy a 10× larger market than SaaS.

😲 My Favorite Quote:

“At Outreach I was paying fortunes to Zuora, Salesforce, and Netsuite, yet it felt like those systems were working against me. The status quo wasn’t neutral; it was my enemy. It was in my face, laughing, taking money out of my pocket, and reminding me I couldn’t do anything about it.

And when you get that kind of punch in the mouth, something changes. That taste of blood wakes you up. You stop thinking in terms of market opportunities and start thinking in terms of refusing to feel powerless again. You promise yourself you won’t get hit like that anymore, and you begin fixing what those systems will never let you solve.”

Prior to FJ Labs, Fabrice built multiple companies including Zingy, which he scaled from near bankruptcy into a $200M exit, and OLX, which he grew to 300M users across +40 countries, before it was acquired by Naspers.

Today he runs FJ Labs as an “angel investor at venture scale,” evaluating hundreds of deals a week and backing 150–200 startups a year with a fast, founder-friendly process.

Spotify | YouTube | Apple Podcasts

🗒 Show notes:

[01:00] – Why being a public company CEO stopped being interesting
[22:00] – Will AI kill marketplaces? The real impact on e-commerce
[27:00] –The next big play: buying offline businesses and 10x’ing them with AI

[33:00] – The trillion-dollar opportunity in B2B digitization
[49:00] – Do AI startups really need less capital?

🧠 Key Takeaways

#1 Stop Chasing “Success” If You Hate Your Tuesdays

Fabrice built a massive company, reached financial freedom, and won his market, yet felt miserable. He realized success isn’t about scale or recognition, but whether your daily work energizes you. His “life audit” forces founders to ask: Do I still enjoy the work this success demands? If not, the business isn’t the problem, the role is.

#2 Why AI’s Biggest Wins Will Come From Unsexy B2B Workflows

Fabrice argues the real AI gold mine isn’t in flashy verticals but in the offline, analog processes that run the global economy. Factories, suppliers, logistics, marketplaces, and procurement still rely on emails, spreadsheets, and phone calls. AI will create the most value by modernizing these slow, invisible workflows, not by reinventing consumer apps.

#3 Don’t Compete in “The Game of Kings”: Play a Vertical You Can Actually Win

Founders outside Silicon Valley shouldn’t compete with OpenAI, or Google on foundational models, they lack capital, talent, and infrastructure. The winnable path is going deep in a specific vertical, owning its workflows, and using AI to make them better. Investors should avoid infrastructure fantasies and back applied-AI companies with real customers and real problems.

#4 AI Won’t Kill Marketplaces—Because Shopping Is Human, Not Logical

Fabrice argues GPT won’t replace ecommerce because people don’t just search, they browse, discover, and entertain themselves. And the messy backend of fulfillment, payments, and logistics is something AI won’t own. Marketplaces aren’t dying; they’re getting augmented.

😲You won’t hear this anywhere else

“Most investors think of AI as a race to replace human labor, but the reality on the ground looks very different. When I look at how companies are deploying AI, I don’t see mass layoffs. I see teams hiring even more engineers to build better products, faster. Technology has always made work cheaper and more efficient, yet historically it has created more jobs, not fewer.

The real value isn’t in removing people, but in reshaping roles: AI handles the diagnosis, the doctor provides empathy; AI delivers the lesson, the teacher becomes a guide. The shift will be profound, but it will unfold far more slowly than people expect and its ultimate impact will be far greater than we imagine.”

Born in Buenos Aires and raised in the US, Marcelo’s career took him from finance at J.P. Morgan (where he invested in Mercado Libre) to Funny or Die, a comedy startup backed by Will Ferrell, and later to Real Influence, a platform that connected brands with YouTubers long before influencer marketing was mainstream.

Today, he’s building Evalion, a testing and monitoring platform for voice AI agents, based on the belief that in the near future millions of companies will build their own agents, as voice becomes the primary interface for how we interact with technology.

Spotify | YouTube | Apple Podcasts

🗒 Show notes:

[10:30] – The moment Marcelo realized you can’t outcompete Google.

[11:48] – How Ipsy started: turning creators’ attention into a business model

[23:00] – Founder–market fit vs. curiosity-driven founders

[26:10] – Why passion for the process matters more than passion for the industry

[32:40] – Voice as the next interface: from websites → apps → agents

[48:40] – Building Evalion: why trust and reliability will be the next real moat in AI.

🧠 Key Takeaways

#1 Curiosity as the Real Founder Superpower

Marcelo didn’t start companies because he lived with the problem, he did it out of pure curiosity. At Ipsy, that mindset helped him see how creators, brands, and supply chains connected before the “creator economy” existed. Now in AI, that same drive pushes him to understand how voice tech is built from scratch.

#2 The Next Platform Shift: Voice
Marcelo has seen every interface shift: web, mobile, and social. Now, with Evalion, he’s betting on voice as the next one. As AI gets better, people will talk to brands instead of typing, and each company will need its own “voice identity.” The screen won’t vanish, it’ll merge with conversation. Those designing for voice-first today will lead tomorrow.

#3 The Reliability Moat: Why Trust Is the Next Advantage in AI
Evalion is building the reliability layer for voice agents, testing and monitoring how they perform. Marcelo believes trust will matter more than intelligence as AI scales. Just like Datadog became essential for cloud reliability, AI will need its own backbone for consistency. The real opportunity isn’t making AI smarter, but making it dependable.

😲You won’t hear this anywhere else

“Most people think the future of conversational AI will be dominated by a few giants like OpenAI or Google. But I see a world with millions of builders creating their own voice agents. It’s not that different from what happened with the web or mobile apps.

At first, everyone thought Yahoo would build every website, but that never happened. Companies will want to build their own voice experiences because they’re the direct interface with their customers.

You won’t talk to ChatGPT to book a flight with United; you’ll talk directly to United, through a personalized voice that reflects its brand. When that happens, human–machine interaction will fundamentally change. We’ll move from looking down at our phones to a world where screens are everywhere and voice is omnipresent.”

Shawn led early investments in Uber, Roku, Siri, and Chime, generating over $2 billion in returns for Menlo’s limited partners.

Behind those wins is a clear philosophy, what Shawn calls his Consumer Utilitarian Framework: a model for backing companies that use breakthrough technologies to meet timeless human needs in faster, cheaper, and better ways.

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🗒 Show notes:

[16:20] — His Consumer Utilitarian Framework and Menlo Ventures’ 4 investing pillars.

[20:10] — Core principles: timeless human needs × evolving technology.

[22:06] — AI as the next “ingredient technology” unlocking new markets.

[38:20] — How AI gives apps like MyFitnessPal a second life

[56:20] — Shawn’s key lessons from 20+ years of consumer investing.

🧠 Key Takeaways

#1 The Consumer Investor’s Mindset: Shawn reframes consumer investing through Maslow’s hierarchy of needs. He argues that human behavior has barely changed in 100,000 years, what changes are the “ingredient technologies” (like AI) that unlock new ways to meet the same timeless needs: safety, belonging, self-expression, mastery, etc. Don’t chase trends or viral behaviors. Anchor your product in a permanent human job-to-be-done. Then ask: “What new technology now makes it 10x better, faster, or cheaper to meet that same need?

#2 Delight is a leading indicator, but not a moat: Shawn describes his process as starting from emotional intuition, moments when a product feels “magical”, but quickly layering on analytical discipline. Many investors stop at delight; he goes further to test whether delight connects to a repeatable, scalable business model.
Emotional resonance gets you noticed; operational excellence keeps you alive. Founders should design for delight, but validate for durability, ask what real problem it solves, how often it repeats, and how the economics scale beyond the early adopter bubble.

#3 AI will turn every personal habit into a market: Shawn predicts that AI will unlock entire consumer categories that failed before because they demanded too much user effort, nutrition tracking, fitness planning, time management, personal CRM, relationships.

These “failed apps” (like MyFitnessPal, Evernote, or Salesforce) are now viable again because AI removes the friction between intent and action.
Revisit old consumer pain points that once looked “too boring” or “too hard to scale.” AI transforms friction into opportunity. A product that used to require constant manual input can now anticipate, auto-complete, and coach.

😲You won’t hear this anywhere else

“We talk about AI like it’s going to make us infinitely productive, but maybe that’s not what we actually need. We’ve had decades of productivity gains from the internet, from mobile, from every app that promised to ‘save time’and yet people seem more stressed, more distracted, less connected. Maybe what’s missing isn’t more output, but better rails for how we live. Technology that doesn’t just accelerate work, but helps us pause, recognize when we’re overwhelmed, nudges us to take a break, or reminds us to be present with each other.

If AI could give us that—tools that restore empathy and awareness instead of eroding them it would be the most important shift of all. Not more productivity, but more humanity.”

Pablo studied industrial engineering and pursued a Ph.D. in computer vision in Munich. Alongside his brother Javier Palafox (COO) and Luis Paarup (CTO), they were part of Y Combinator’s in 2023, where they pivoted from their initial idea to what Happy Robot is today.

Recently, Happy Robot raised $44M in its Series B, led by funds like Base10 and a16z. Their “Forward Deployed Engineering” approach allowed them to grow their revenue 20x since their Series A. Today, they work with over 100 clients, including the largest freight brokerage firms in the U.S.

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Today, Pablo and I talked about:

• Why implementation is more important than technology.

• How to get your clients to see you as an “AI partner” instead of just a vendor.

• How the “Forward Deployed Engineering” model drove their 20x growth.

• How they went from being a “threat” to a “strength” for their clients.

• Why building a blame-free culture is essential when working with imperfect technology.

🤯 Key Insight: The key to automating traditional industries lies in proper implementation.

While most AI startups sell demos over Zoom, Pablo understood that to transform a traditional industry, the solution couldn’t be 100% remote.

His bet was Forward Deployed Engineering (FDE). This partner-implementer model solves the biggest barrier to adoption: the human factor. By working from the inside, AI stops being perceived as an external “threat” and becomes an internal “strength,” validated by the team itself. This approach helped them grow 20x since their Series A.

⭐ Favorite Quote: “The truth is, customers don’t want an AI vendor; they want an AI partner. Someone who is willing to go the extra mile to understand their use case and make it work.

It’s fascinating to see how they go from viewing us as a threat in their SWOT analysis to considering us a strategic opportunity in just one year. It’s not just about implementing technology; it’s about guiding them through the transformation of how they work.”

🤔 Fun Fact: Before founding Happy Robot, Pablo dedicated 19 years of his life to the piano, reaching a near-professional level. His teacher wanted him to pursue a musical career, but despite his talent, he chose to study industrial engineering.

Interestingly, this musical discipline taught him about perseverance and commitment, qualities he now applies as the CEO of his startup. He admits he would probably repeat the same approach with his own children because of the valuable lessons it taught him.

I learned a lot from Pablo. Follow him on LinkedIn for more ideas and advice.

Before Sendgrid, Isaac started multiple startups that failed and faced a ton of challenges as a first time founder, with very little business background and an introverted personality.

Today, we discussed Isaac’s challenges in transitioning from founder to CEO, his decision to bring in an external CEO to SendGrid, and the lessons he learned along the way. We also covered the company-building lessons he gained at SendGrid, including insights on product development, market timing, and team building.

Finally, we talked about his latest venture, Memo, and how he aims to empower introverts to build their personal brands and social capital in a world dominated by social media.

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🗒 Show notes:

[04:01] - How did Isaac decide to bring in an external CEO

[09:18] - Transition from founder to becoming a CEO

[19:01] - The role of the founder

[29:51] - Business lessons from SendGrid's success

[51:19] - What does it mean to build a social network platform for introverts

🧠 Key Takeaways

#1 Hire Your Boss: Isaac knew early on he wasn’t the ideal long-term CEO, preferring to focus on product development. Supported by mentors and being the transparent with the board, he successfully transitioned leadership to Jim Franklin, who shared the company’s core values and mentored Isaac, ensuring both his growth and SendGrid’s success.

#2 Building a Strong Foundation: Isaac emphasized the importance of a great team, mentors, and clear values. He learned that even with a solid team and funding, success isn't guaranteed. Mentors help navigate unknown challenges, and strong values guide decisions when mentors can't. 

#3 Empowering the Quiet Majority: Isaac's Memo is a social network for introverts, tackling the issue that 90% of social media content is created by 10% of users. Memo focuses on meaningful connections, allowing users to be found on their terms, prioritizing user control and privacy over invasive engagement tactics.

#3 Empowering the Quiet Majority: Isaac's Memo is a social network for introverts, addressing the issue that 90% of social media content is created by 10% of users. Memo focuses on meaningful connections, allowing users to be found on their terms, prioritizing user control and privacy over invasive engagement tactics.

😲You won’t hear this anywhere else

[About hiring an external CEO] “I think I would start with the end: hire your boss. You need to see this person as your boss, even though you created the company and have many ideas. Ultimately, they need the ability to make the decisions required for a CEO.

Accepting this makes everything easier. A smart CEO will always seek the founder's opinion, especially on gut feelings. Founders have the best gut instincts for their company by far. If CEOs leverage this, you can create a great relationship where the founder feels included in decisions.”

Before Valor, Paulo played a protagonic role in developing LatAm’s growth equity ecosystem as a Managing Partner at SoftBank, managing their $8 billion LatAm Fund. Paulo invested and acted as a board member of several Brazilian unicorns including VTEX, Loggi and Creditas. He also invested over a decade in Latin America’s public and private equities at Third Point Management and Eton Park.

In this episode, we talked about the non-obvious investing principles he’s learned over his career, why he’s bullish on LatAm’s venture and growth ecosystem, unlike other investors, and his reflections from his time at Softbank. We also discussed his frameworks for investing in growth-stage companies in Brazil and LatAm, including markets, regulatory tailwinds, business models, metrics, and teams; and how this has changed since 2020 and 2021.

Learn about investing principles in growth-stage companies today:

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🗒 Show notes:

[02:15] - Transitioning from Private to Public Investments

[05:48] - Understanding the Dynamics of Private Equity and Venture Capital

[07:56] - Insights into LatAm’s Growth Ecosystem

[11:55] - Reflecting on Investment Lessons and Market Dynamics

[22:18] - The Role of Local vs. Global Investors in LatAm

[29:31] - Regulatory Changes and the Rise of Fintech in Brazil

[39:11] - Rethinking Business Models and Capital Efficiency

[55:18] - The Impact of AI on Business Models

🧠 Key Takeaways

#1 Adapt for Local Success: The companies that succeed in LatAm are the ones who don't think about the other countries' success cases, but companies that tailor their strategies to local regulations, taxation, logistics, theft issues, and income levels. Copying models from the U.S. or Europe can be counterproductive. 

#2 Don’t Try to Win Every Deal: As an investor, you’ve got to be losing deals, almost as a ‘sanity’ check for knowing that you're doing the right thing. There’s something to be calibrated with your risk appetite and valuation analysis if you put out 10 term sheets in a year and every single term sheet is accepted.

#3 How AI Will Shape the Venture Industry: Producing code is going to get cheaper, and the barrier to entry will be lowered, making it more challenging to maintain margins and protect excess economic profits. Investors might need to get involved in software companies at an earlier stage (up to Series B), as companies will become profitable sooner and need less external capital. Although this dynamic affects pure digital goods, like fintech and software companies, other types of businesses will still require capital.

😲You won’t hear this anywhere else

What challenges do founders face when transitioning from startup growth to scaling a company, and how can they address them?

“It's common for them to take a company from zero to one hundred, but not necessarily from one hundred to one thousand. This transition can be problematic and often traumatic. Founders might either manage to step out gracefully or inadvertently destroy value during the process. Some founders are versatile enough to handle the entire journey, but it's rare.

Most founders believe they are versatile, but the truth often proves otherwise. Realizing this sooner allows them to move to an executive chairman position and hire a capable CEO. This mindset should trickle down, with continuous upgrades at the C-level being necessary. Although it's a ruthless and challenging process, it's essential for the company's growth."