Stop Reading. Start Playing: The Quantum Pirate’s Guide to Building the Future
A conversation with Sergio Gago on hacking your way through career, AI governance, and why the best innovation comes from being the dumbest person in the room
Referencing to this podcast.
Note: This article is not a “Sitting in with …” version, because Sergio has just too much drive to sit with. So it is a gig-flavored piece. Enjoy.
There’s something fitting about speaking with Sergio Gago, a man who built five companies, led quantum initiatives at Moody’s, and now serves as CTO at Cloudera, on a podcast about entangled health. Because if anyone understands how to navigate the convergence points where quantum science, AI, and human systems collide, it’s him.
But before we get to the sophisticated world of enterprise AI and quantum finance, we need to start where Sergio did: with a cassette tape, a magazine, and a broken game.
The Origins of a Hacker
Picture this: Spain, early 1980s. A young Sergio sits in front of an Amstrad computer with a printed magazine in his lap. The magazine contains source code, line after line of BASIC, for a simple Snake game. There are no downloads, no GitHub repositories. If you want the game, you type. Every. Single. Line.
“These magazines were monthly,” Sergio recalls. “And very often, pretty much every month, they had typos. And today we call them bugs.”
You’d spend hours transcribing code, only to find the program wouldn’t run. Then you’d wait another month for a correction notice: “We’re sorry, line 1003 was wrong.” This wasn’t frustration; this was education.
“That gave me some kind of resilience and started structuring my mind, being an engineer,” he says. But more importantly, it taught him something else. When he finally got the Snake game working, he realized he could change a variable and give himself infinite lives. He could hack the game to do his bidding.
This is the foundational philosophy of everything that follows: Don’t just do the things. Make them your own.
From then on, Sergio’s mind evolved into what he calls a “hacking mindset”, a relentless drive to tinker with everything. Not to break it, but to understand it. To improve it. To bend it to his will, whether it was a small circuit, a sumo-fighting robot, or eventually, entire companies.
Five Failures, Five Lessons
At 17, Sergio founded his first company. It failed miserably.
“I was very young and naive,” he laughs. “Now I am only naive.”
The problem wasn’t technical skill, he could code. The problem was that he “didn’t know anything about absolutely anything” when it came to running a business. But that year of failure taught him more than most MBA programs ever could.
So he did what any good engineer does: He debugged himself. What did he need? Contacts. Network. Industry experience. He got a job, learned everything he could, and built his second company. This time, it worked.
By his early twenties, Sergio had cracked a crucial insight: Consulting companies make money every time you swing the hammer. But he wanted to build something that made money without having to constantly swing.
This led him into the golden era of SEO, digital businesses, and eventually, machine learning. One of those ventures (Aquire Media) used traditional ML to extract signal from real-time news. Think every press release, every SEC filing, every earnings report. Millions of stories a day. The company’s job was to identify which ones mattered and what they meant for credit risk.
Moody’s bought the company. Sergio suddenly found himself inside the financial system that runs the world.
How the World Actually Works
“I learned a ton about how the world actually works,” Sergio says of his time at Moody’s.
Credit risk analysis isn’t just finance, it’s the invisible infrastructure of global commerce. When a ratings agency downgrades a company or a country, markets move. Interest rates shift. Investment decisions cascade. “Needless to say, when any ratings agency says, ‘Hey, this company was a AAA and now it’s a AA,’ it has a massive impact.”
But here’s where it gets interesting for quantum.
Sergio noticed something: The finance world isn’t so different from physics. Monte Carlo simulations. Stochastic processes. Risk modeling. These are problems tailor-made for quantum approaches.
So when Moody’s asked if he wanted to explore quantum computing’s potential in finance, Sergio said yes. Not because he was a quantum expert - he wasn’t. But because he saw a pattern. He saw entanglement.
“Maybe we could be the right partner and mentor in the middle,” he explains. “We can bring this knowledge on what the impact of quantum finance is without being biased about claiming quantum advantage.”
Finance and healthcare are remarkably similar in this way. Both deal with massive, noisy datasets. Both require explainable, interpretable models. Both involve life-altering decisions made under uncertainty. And both are ripe for disruption by quantum and AI, if we can get the governance right.
AI Agents: The HR Problem Nobody’s Talking About
Fast forward to today. Sergio is now CTO at Cloudera, and the conversation has shifted from quantum to another emerging challenge: AI agents in production.
Everyone’s excited about AI agents. Companies are building proof-of-concept demos that blow people away. An agent that summarizes earnings calls. An agent that writes discharge instructions. An agent that answers customer support tickets.
“You can generate an agent that does something amazing,” Sergio says, “and everyone falls in love with it.”
But here’s the problem: Taking that from a demo to a production, enterprise-grade environment is a completely different story.
Why? Because most people are thinking about agents as software tools when they should be thinking about them as digital employees.
“If we believe that agents are going to be our digital colleagues very soon,” Sergio explains, “how do we manage those agents? Is that an HR task? Is that an IT task? Or something new that combines those two departments together?”
This isn’t a metaphor. Think about what every human employee brings to a company:
Insider knowledge: Who to talk to, how to unblock problems
Rapport and relationships: Trust built over time
Process understanding: How the company actually works, not just what the org chart says
Skills and judgment: The tacit knowledge that separates a chief economist from someone who can just read English
Now imagine deploying hundreds or thousands of AI agents without any of that context.
“You need a good catalog of your data and metadata,” Sergio says. “A very good authorization and authentication process, who can access what and at what level. Most companies have that solved for humans. So how do we port that to non-human colleagues?”
This is the unsexy part of the AI revolution. It’s not about building smarter models. It’s about ontology, lineage, access control, audit trails, and governance. It’s about ensuring that when an agent makes a recommendation, you can trace exactly why, based on what data, with what permissions.
Healthcare’s Two Sides: Stochastic and Operational
When we turn to healthcare, Sergio sees two distinct challenges, and opportunities.
First, the stochastic side. Medicine is full of uncertainty. You go to a doctor with a diagnosis, and they give you a probability: “You have a 60% chance of...” That number comes from data, from studies, from pattern recognition across thousands of cases. It’s statistics wrapped in human judgment.
“That is, in a way, a perfect training ground for these types of systems that are good at statistics and are good at identifying features that are maybe sometimes hidden from us,” Sergio explains.
Blood analytics from hundreds of thousands of people. Historical pregnancy data. Early indicators of potential complications. These are exactly the kinds of problems where quantum machine learning could shine … not by replacing doctors, but by giving them superpowers.
Second, the operational side. Doctors are burned out. Not because they don’t love medicine, but because they spend 15-20 minutes after every patient visit typing into an EHR system. Nurses are drowning in documentation. Administrative staff are buried under repetitive tasks.
“I think there is a lot of space for empowering our health professionals with these superpowers,” Sergio says, “either by making better decisions with more data, or by reducing some of the operational work.”
Imagine a “Jiminy Cricket on your shoulder”, an AI assistant that surfaces relevant research in real-time, flags potential drug interactions, reminds you of best practices, all without requiring you to look away from the patient.
Or imagine embodied AI in elder care. Sergio describes a Spanish research project using feeding robots for paralyzed patients. The perception from patients was actually better than human caregivers, not because they preferred robots, but because it gave them back dignity. They didn’t feel like a burden.
Another company built a companion robot for lonely elderly people, a talking head with an iPad, trained to remind them to take pills, call for help in emergencies, and most importantly, just talk. Sing karaoke. Discuss the news. Combat the solitude that is one of the biggest health risks for aging populations.
“The reports they get is that these people are happier and more engaged with life,” Sergio says. “And that is also part of the psychological element of health.”
Governance Isn’t Optional
But (and this is crucial) none of this works without governance.
“We’ve gone through the honeymoon phase of AI where we look at all these things this can achieve,” Sergio warns. “But if you don’t add this governance layer, then you let technology drive our world and decisions at the mercy of those who have control of those technologies.”
Would you outsource all your company’s decisions to someone on the other side of the world that you have no control over, who can cut ties anytime, who might be biased? Of course not. But that’s exactly what companies do when they rely entirely on LLMs-as-a-service.
This isn’t just about the EU AI Act or regulatory compliance. It’s about sovereignty, traceability, and control.
“You need to have governance at all levels,” Sergio emphasizes, “from the data governance in the same way that we have today. How do you make sure that you control, manage, and support all your data as it keeps growing and evolving in your company?”
This is what Cloudera does, it’s the unsexy, essential infrastructure that makes AI actually work at scale. Not the flashy demo. The boring, critical stuff: data cataloging, authentication, authorization, audit logs, lineage tracking.
Two Types of People
So how do you stay innovative in this heavily regulated, governance-first world?
Sergio borrows from Clayton Christensen’s The Innovator’s Dilemma: There are two types of people in every company. Those who run the business, and those who change the business.
If you’re a “change the business” person trapped in a “run the business” environment, you’ll be miserable. This is why most startup founders leave after an acquisition, they can’t adapt to the culture of process and quarterly results.
But Sergio has figured out the balance. He keeps “a leg and three fingers” in the startup world, angel investing, advising, staying connected to the hacking mindset, while working in large enterprises.
“The way I try to do it is to balance myself with those things,” he explains. “If I had a very corporate job, I would also work in an investment fund or as a business angel.”
Music is another outlet. Sergio plays guitar (loudly and imperfectly, by his own admission) in a band called Ion Maiden, which performs at quantum computing conferences. “Sometimes you’re not prepared. I’m definitely not a professional musician. But man, didn’t we play well at London?”.
His advice for staying curious? “Try to be the stupidest person in the room.”
“Many people try to be the smartest one, right? I think it should be the other way around. You have to surround yourself with people that are smarter and more intelligent than you.”
And then: “Be brave.”
“Sometimes you’re just outside of your comfort zone, doing something that you’re definitely not prepared to. But yeah, let’s go and do it, and things will happen.”
The Ottoman Decision Method
We can’t talk about Sergio without mentioning what he calls the “Ottoman way of making decisions.”
(Disclaimer: His Turkish friends insist this isn’t actually Ottoman. But the method itself is brilliant.)
Here’s how it works: You have to agree with yourself, both sober and drunk.
If there’s a disagreement between your sober self and your intoxicated self, you go back to the drawing board. You reset. You run the experiment again.
“There is some sense to it,” Sergio explains. “When you are in a dubious state of mind, you are also unlocking some filters. But at the same time, you don’t want to make decisions just when you are in that state.”
Think of it like Kahneman’s two brains, System 1 and System 2, but with cocktails. It’s about accessing different cognitive modes, different sources of insight, and making sure your decision holds up across all of them.
“So you entangle yourself,” I joke.
“Exactly,” Sergio laughs. “Entangle and then detangle. Compute and uncompute.”
Would You Buy a Humanoid Robot?
At the end of our conversation, Sergio poses a question to the audience, one that may define the next decade:
Would you buy a humanoid robot that helps around the house with everything from laundry to emergency response, but has complete access to your calendar, browser history, and personal life?
His answer? Absolutely yes.
“I think we will have something between 5,000 to 10,000 euros, less than a car. The ROI will be calculated on how much home assistance costs today, especially for the elderly.”
He predicts he’ll have one at home before 2030.
This isn’t science fiction. It’s the logical next step when you combine embodied AI, aging populations, rising healthcare costs, and the increasing sophistication of robotics. The question isn’t if, but when, and more importantly, how we govern it.
The Signal in the Noise
Sergio started writing The Quantum Pirate newsletter because he was drowning in quantum hype. “Every 10 articles, nine of them are ‘What are the best quantum companies to invest your money in?’” he says.
So he built a system to curate signal from noise. He automated it. He turned it into a newsletter. Not for others, for himself. “I would use Substack to just put that on a newsletter so I can receive the newsletter I would like to receive.”
People started subscribing. The community grew.
His advice for cutting through the AI hype today is the same:
“Forget about all that. Stop reading and start playing.”
“Even if you don’t know how to program, it’s easier than ever. So forget about all these experts on LinkedIn. Tinker yourself. Solve problems yourself.”
This is the essence of the hacking mindset. Don’t just consume information. Don’t just follow trends. Build. Break. Fix. Repeat.
Keep Rocking and Change the World
As our conversation winds down, Sergio leaves us with a simple message:
“Massive absolute curiosity and passion for change in the world. If there’s something to take home, it’s this: Keep rocking and change the world. There’s a lot of things to make better. So let’s bloody do it.”
We’re living through a convergence moment. Quantum computing is maturing. AI is moving from demos to production. Healthcare is desperate for innovation. Robotics is entering the home. Each of these technologies alone could spark an industrial revolution. Together, they’re creating something we can barely comprehend.
Sergio Gago has been at the center of this convergence for years, not as a theorist, but as a builder. From typing buggy code off magazine pages to leading quantum strategy at Moody’s to shaping the AI infrastructure of the future at Cloudera, he’s lived the hacker’s creed:
Don’t just do the things. Make them your own.
And maybe, just maybe, make big decisions both sober and drunk.
Sergio Gago is CTO at Cloudera and author of The Quantum Pirate newsletter. Connect with him on LinkedIn.