You grab coffee with someone and they’re building an agent that automates something nobody thought could be automated a year ago. Walk a few blocks and someone shows you a wearable that listens to everything you say. Open X and there’s a new model, a new wallet, a new blockchain, a new API. A startup that just raised $10 million to build something that barely existed six months ago.

And if you’re an techy dude, that naughty voice starts taking in your head and start connecting the dots.

Wait…

What if I connect this agent to a wallet? What if I use stablecoins for this? What if I mix AI with consumer? What if the phone disappears and all of this runs through a wearable? What if an agent could buy, sell, pay, or invest for you?

And then you feel it.

I think every builder knows this feeling. That little rush when two technologies that used to live in completely different worlds suddenly connect inside your head.

Holy shit. I can build this.

For a long time, I have been seeking the adrenaline gives you that type of feeling.

It was also one of my biggest traps.

When everything looks buildable

I’ve spent most of my career building. I worked on products operating at Venmo scale and in early-stage startups where almost nothing existed yet and we had to build the product, the infrastructure, and sometimes even figure out what the product was supposed to be.

Fintech. Crypto. AI agents. Consumer apps. Wallets. Stablecoins. Onramps. Wearables.

Every new technology gave me the same feeling a kid probably gets when you hand them a new box of LEGO.

You don’t see pieces. You see possibilities.

And for years, I thought this was one of my biggest advantages as a builder. I could look at a new technology and say:

Now I can I build with this faster, without thinking in the theory, just put together the things…

That’s an incredible sensation.

But the real problem is not only the tech.

And it took me a surprisingly long time to realize that.

AI. Crypto. Consumer.

I remember becoming particularly obsessed with crypto. Not just the speculation. The infrastructure fascinated me.

For the first time, you could move value almost like you move information. A wallet could become an account. A stablecoin could cross a border. An asset could live natively on the internet.

Then AI models arrived.

And my brain did exactly what I had trained it to do.

It connected the pieces.

AI. Crypto. Consumer.

Something huge had to exist at that intersection.

So I started building. One experience, then another. Ways to invest. Ways to move money. Agents capable of taking financial actions. Simpler consumer interfaces sitting on top of incredibly complicated financial systems.

And I want to be careful here.

I don’t think any of this was worthless.

I still believe there are enormous opportunities there. Real people used some of the products we built. We found interesting behaviors. I learned a ridiculous amount.

The mistake was somewhere else.

I was almost always starting with:

This technology is incredible. What problem can I solve with it?

Instead of:

This person has a problem. What are they actually trying to do?

That sounds like a small difference.

It isn’t.

Two founders walk into a coffee shop

Imagine two founders walking into a coffee shop. It’s kind of a cliche in SF

The first one is holding a new technology. He looks around and thinks:

How could these people use this?

The second founder walks in with nothing. He sits down. Watches. Listens.

And asks:

What are these people already trying to do, what their are consuming and why?

Both founders might eventually build a product. But they arrived there from completely different directions.

For most of my career, I was the first founder.

And there’s another reason this trap is so easy to fall into.

Building feels incredibly productive

You write code. A screen appears. You deploy something. You buy a domain. You install analytics. Someone downloads the app.

Every day there’s something tangible you can point at and say:

Look. I made this.

Talking to people is much more uncomfortable. You can spend three hours talking to users and end the day with less certainty than you had when you started.

You might discover your hypothesis was wrong. You might realize the feature you spent a week building doesn’t matter. Or worse: the problem you find intellectually fascinating barely occupies any space in another person’s life.

There’s no confetti when that happens.

But you may have just saved yourself the biggest disappointment in your life and a bunch of time

San Francisco makes the trap stronger

And there’s something about SF that amplifies this trap.

Not because SF is bad maybe boring sometimes... Quite the opposite. It might be one of the most important places on Earth if you love building technology.

But that makes it surprisingly difficult to distinguish between two things:

A new technological capability

and

A humans needs

They are not the same thing.

When a technology becomes powerful enough, it’s incredibly easy to imagine hundreds of futures around it. AI can do this. Crypto enables that. Agents can now do something else.

But humans don’t wake up in the morning thinking:

I need an app combining LLMs with blockchain.

They wake up thinking:

I don’t want to lose money.

I have no idea what I should invest in.

I can’t focus.

I’m exhausted.

I want someone to talk to.

I want to feel like I’m moving forward.

I need to make it to the end of the month.

I don’t want to waste another four hours staring at bullshit on my phone.

The technology comes later.

The uncomfortable question

Recently, I caught myself building again.

New idea. New prototype. Exactly the kind of thing that would normally make me open my editor and disappear for a few days.

But this time, an uncomfortable question appeared.

A very simple one.

What evidence do I actually have that this problem exists?

Not: Can I find people who think this sounds cool?

Not: Can I make a convincing argument for why this should exist?

Not even: Can I make a TikTok about it and get views?

Evidence.

Who experiences this problem? When does it happen? What do they do when it happens? What have they already tried? Why didn’t those solutions work? How much does this actually bother them?

And maybe the most uncomfortable question of all:

Am I discovering something, or am I just looking for an excuse to build again?

So I tried not to build

I started doing something that feels almost unnatural when you’re an engineer.

I tried not to build.

I started talking to people. Reading what they wrote when they were frustrated. One-star reviews. Forums. Comments. Interviews.

I started paying attention to words that kept appearing. Tiny contradictions. Things people repeatedly did even though they told me they didn’t want to do them.

And slowly, something changed.

Problems stopped looking like ideas.

They started looking like behaviors.

That distinction changed the way I look at products.

Because an idea exists inside your head.

A behavior exists whether you build anything or not.

Build something people want

And that made me realize something I’m still processing.

Maybe for years I thought my job as a builder was to produce answers: an app, a startup, a feature, an agent.

But maybe the skill I was missing wasn’t building better answers.

It was learning to ask better questions.

There’s a sentence everyone in startups knows:

Build something people want.

I had spent years obsessing over the first word.

Build.

Maybe I should have spent more time thinking about the other three.

Something people want.

This is the experiment

I’m not starting Builder’s Log because I finally figured out the formula.

Actually, it’s almost the opposite.

I’m starting it because I want to document what happens when someone who has spent years learning how to build starts learning how to observe.

I’m still going to build. Probably too much. I’m still going to fall in love with new technologies. I’m going to chase ideas that turn out to be terrible. I’ll probably mistake noise for signal more than once.

But I want to try something different.

Before asking:

What can I build?

I want to learn to ask:

What is actually the problem here?

Because maybe the real work of a founder begins long before the first commit.

Which leaves me with a much harder question.

If you don’t start with the technology, if you don’t start with the idea, if, for a moment, you force yourself not to build anything…

How do you find a problem actually worth solving?

That’s where this experiment begins…

For years I have been exploring how to enable people to save and access real liquidity. That has been one of the main problems I have seen in my experience: banks fail to maintain retention and prevent users from withdrawing their money quickly. The current financial system was not designed for people with no history, and that leaves out millions of people with real potential.

That is why this manifesto proposes the democratization of financial information as a new asset—a kind of Polymarket—where people can invest in people, and people can use that information for their own benefit, instead of large companies being the only ones to exploit it by driving consumption, issuing credit or credit-products and excluding the vast majority from accessing liquidity due to lack of scores.

Problem

People don’t save.

The primary reason is they have no liquidity.

The second is the lack of education and financial understanding.

No one likes a math teacher or listening to rates or balances. People are consumers—they need money to work with.

But we know how hard it is for someone to make it to the end of the month with their full salary, and debt starts piling up—this also happens because they don’t know their debt has grown, especially those who use credit cards.

Do banks bet on us? What is this element of a bet?

Although betting is demonized by the gambling industry, it is a natural behaviour in human beings: we like it, it excites us, and we are wired by the fact that since our beginnings we have bet with nature. We have to sow several seeds for one to germinate; we have to bet that the climate and conditions align; we have to bet that everything goes well. That is why we discovered that the more we invest in one of them, it will give us the tree— from there the dream starts, from there the bet on the best product: on yourself.

And generally institutions only bet on the best payer or those who have shown they will pay. While this methodology has worked well, we know that they keep doing it in the traditional way: it is impossible to get credit or working capital if you don’t have a history so that big entities will bet on you.

Not only that, but you need collateral, promissory notes, co-signers and a series of documents, plus interviews and bureaucracy.

What is investment?

We see that on the other side of the coin are the investors—those who bet their money, their time or intuition—to invest. They do it for many reasons: for money, curiosity, risk or simply for supporting the dream of others, because they know that by doing so they can also multiply the value of what they hold.

Banks and financial institutions don’t manage money, they manage dreams. They bet that others will achieve theirs, and their business consists of intermediating between those who save and those who need capital.

The real value of money is not in the paper or the digital figure, but in the potential of what you invest in: the time, energy and human purpose behind it.

Big banks and funds don’t create wealth by themselves; they function as middlemen. They make money by lending the capital of those who dream of a more stable future to those who dream of making it grow. Essentially, they transfer the potential of some into the opportunities of others.

But the question is: what are we missing to invest in ourselves, or to help others fulfil their goals? Sometimes we simply wait for more money in return, but there are also investments seeking something deeper: generating savings, boosting projects with real impact and building stronger, more sustainable and humane environments.

And there are also investors who only want more money and are more risk tolerant!

So why do we continue using traditional methods to connect these two types of people—a hidden supply and demand—until less than 20 years ago when nearly all our information came into the cloud?

Vision

Artificial intelligence can redistribute wealth.

Today, bank algorithms are closed and limited; they cannot process the volume of requests nor understand the human context behind them. Decisions still depend on people doing it manually, losing time, objectivity and opportunity.

A specialized agent can validate, analyse and suggest financial decisions more fairly and quickly. An AI properly trained already predicts better; with large-language models (LLMs) we can reach those objectives, giving them enough context and guiding them to avoid the biases of traditional algorithms.

From here arises the idea of an agentic bank, where processes are not determined by bureaucrats but by autonomous agents that act with data, logic and context.

Still, the change is not only technological: it requires a legal base and financial structure. And already exist several peer-to-peer systems for transactions—the most used is blockchain—but they need that autonomous or intelligent layer to avoid falling into the same human/manual problem.

Solution

So how do we solve this problem of the middle-man? While we know that both sides are people and both have the need to borrow and to invest.

Then, although it is obvious that technology plays an important role, the question is: what technology allows us to categorize a person in a public and impartial way and give them a credibility score?

That is where LLMs come in: impartial agents capable of distributing resources and making decisions fairly, without corruption or bureaucracy.

Because, on one hand there is plenty of data, but we don’t have the capacity to process it; therefore a 10× factor is needed, and LLMs have that factor since they don’t depend on being or not being connected to entities to start making predictions.

And if we manage to begin to categorize these scores and show that these people have potential for savings or credit, what would remain to do the same thing a bank does and lend money?

Crypto-assets, like any unregulated financial tool, have fallen into scams: fictitious money, empty collectibles and useless digital land. But Bitcoin has maintained its value because, despite the controversy, an environment has been created where it can be exchanged for real goods and services; it is a network between people, without banks or intermediaries.

Blockchain already allows secure and decentralised transactions, but we still depend on slow systems and human decisions that don’t scale.

It’s not about replacing what exists, but about creating an alternative that works when the system fails, applying the principle of 10×: use technology to do the same thing ten times better, faster and for more people.

Bitcoin has value because it can buy something real, goods or services. It is a human, decentralised, politically uncontrolled medium of exchange, and it is digital native. With this technology we can build a network where specialised agents optimise financial decisions for real communities, without depending on bureaucracy or inefficient institutions, and where investors can find opportunities globally, not only within a stock exchange, a bank or a financial entity.

Then inside those agents could also be credit-agents and matchers that bring these people together, because the technology exists!

Intention of the Manifesto

The objective is not to replace the existing system, but to offer an alternative when that system doesn’t work.

A model where LLMs and autonomous agents act as impartial mediators, redistributing resources with logic and fairness.

The idea is that these technologies can serve communities—people with growth potential who today don’t receive support because institutions are saturated or biased.

It’s not utopia: it’s a step toward a paralysed financial system that serves this large mass and generates value over time by democratizing credit- or savings-scores of people so that not only large companies can use them commercially, but other investors can also invest in these people, their dreams and their objectives.

Solution by Verticals (or Acts)

Source of truth: To obtain a person’s transactions you only need their device.

Trust: Users must trust in sharing their information, knowing they will be rated fairly, not only by banking data but by more personal and digital dimensions.

Financial awareness: Users use the tool mainly to visualise their spending and receive alerts when they won’t make it to the end of the month.

Connection: With that information, profiles can be categorised and people connected with existing loan-services or platforms.

Specialized financial model:A financial LLM should exist — trained exclusively on consumer data to predict creditworthiness using behavior, for any entity or final user can start getting this insights without barriers

It enables fairer, real-time evaluations tailored to each person’s financial reality.

History: The user improves their financial history and obtains better opportunities over time.

Democratization: Blockchain allows eliminating banking restrictions and opens access to investors more tolerant of risk, willing to invest in people.

Financial Context

Cooperatives and credit-entities usually have higher bad-loan rates than banks, but they still operate. That shows the model works when there is trust. With the right technology we can find that 10× factor, where more people can get alternative financing or credit.

Blockchain Vertical

Create a specialised token for investors to buy with the expectation of obtaining returns from the portfolio recovered from higher-risk users.

Since agents lend the money, the return is optimized, and it stops being just a bet. Instead those agents attempt to determine each person’s risk efficiently.

That money shouldn’t be liquidated, but transformed into a living asset: one that drives goals and maintains its value over time, just like a company, a commodity or any asset with future.

Banking Vertical

Regulated entities can purchase the credit history or pay a fee to access users who previously didn’t qualify.

This turns the system into a platform specialized in recovering opportunities that traditional banking discarded.

Closing

This manifesto is an invitation to redefine how we value people.

To stop measuring risk manually or privately and begin investing in human potential with data.

Because betting on someone is not just a game: it is believing in their capacity to create value.

And when that happens, wealth stops concentrating and begins circulating. What were risky bets become investments with greater potential!

I remember hearing many years ago that there is a veil within society. It’s mentioned in esoteric books, but I know it appears across many contexts. I don’t think it’s a secret that was meant to be hidden.

Instead, I believe we are victims of our social mind — the part of us that makes us lose sight of our purest nature when we confuse glamour, elegance, and social status.

As children, we are taught biology, yet at some point it seems that, out of egocentrism, we convince ourselves that we stopped being animals simply because we became “civilized.”

We begin to live these staged lives in which we imitate others purely based on how they appear, and we in turn shape ourselves to resemble them. But the real question is: why?

At what moment did we forget that we are animals? And I think technology plays a significant role in this.

Something within us makes us believe that we are no longer animals — because we smell good, because we dress well, because we speak eloquently or communicate on a massive scale.

But if the real veil is that we are governed by our instincts, then why, when we see someone, do we first notice their facial expression, their shoes, and their hands? And then, only afterward, does their essence either pull us closer or make us withdraw?

It makes me think that our deepest wiring is still seeking survival — the same mechanism we used to determine whether someone was friendly or a potential threat.

And that’s how the most basic sales tricks work: the new perfume, the new clothes, the expensive shoes that draw the eye.

This natural behavior of following movement with our gaze — hands, hips, posture — happens unconsciously. We adorn our “claws” with gold and diamonds, echoing centuries of war and bloodshed that earned us the right to indulge in these trivial delights.

In the end, it is our most animalistic protocols that define our manners, our greetings, and the tones of our conversations. I often feel like a dog approaching in a certain way to signal whether we are friendly or not.

We claim that cultural norms define good manners — but these are the same cues every animal uses to present itself: smelling, displaying, smiling, greeting. Animals do this. So what makes us so sure that doing it makes us civilized?

This leads me to see “race” not as hierarchy but simply as different species of animal:

White cats who wield beauty and glamour that everyone wants to witness.

Latin monkeys who dance and charm.

Mystical dragons who observe, exhaling smoke as they watch.

Once you see this, the social veil dissolves. You understand why commerce targets the innermost animal — selling the flashiest shoes, the brightest smile, the most alluring scent. It builds an illusion of ecstasy for our internal creature. Fashion becomes nothing more than changing fur.

We become slaves to our most primal instincts — which are then exploited to domesticate us into the circuses we call “social circles,” turning us into consumers of yet another new product.

Marketing gurus like to say, “Sell to the mind,” but in reality, they are selling to the inner animal — the part that does not think, only reacts.

That inner animal is the one making the decision the instant it sees the object of desire, whispering through the darkest corridors of the mind, convincing us why we need those shoes to adorn our claws.

So we become addicted to the reward system embedded in our neural wiring — the same system that helped us survive and escape pain.

But why do we do this? I think the mind is merely collateral — because nature itself has always been selling. Nature designs the most vivid fruits, the most seductive flowers, the most poisonous plants. Survival of the strongest is also survival of the most appealing.

Nature creates color to attract mates, dance rituals to court, patterns to hunt, venom to trap.

Anyone who ignores this resemblance, who hides behind the veil and denies their animal nature, is the one living in the matrix.

This is also how markets begin to make sense — they are the evolution of the exchange rituals we’ve had for thousands of years to hunt, mate, and coexist.

The most organized societies are those that allow the consumer to consume freely.

Here in the Bay Area, I see neighborhoods arranged like hives where people work and bring in honey. Cats — the most organized — give permission to be whatever animal one chooses, allowing a vast range of products to flourish: cures, poisons, information.

In contrast, my own society rewards predators, preventing organization. Fear rules consumption: fear of being different, fear of appearing different, yet we egotistically believe we are superior because of how we appear.

Many societies cannot see this — blinded by the veil — and therefore prevent their own evolution of products, exchange, and progress. They believe money is the goal, without understanding that exchange is what generates continuous growth.

Here in the Bay Area, people speak of the economy as a form of artificial intelligence in itself — a system that distributes resources to create disruptive technology. It makes sense. Beyond culture, beyond politics — markets behave like cells moving nutrients.

From the Silk Road, to the battles for maritime control, to the Panama Canal, to air routes — technology has always served the market. Even wars to control these systems only accelerated technological improvement.

So if our relationship with nature is so deeply rooted, what is the parallel with technology?

Nature also preserves, iterates, and improves its creations over time. Trial and error is encoded into the genetics of every living being. Our civilization now mirrors these same evolutionary patterns — except we accelerate them.

Yet these systems also exploit our primal patterns to sell to us at scale, using 10x amplification.

From adorning our claws to whitening our teeth, to refining our scent — it’s always been the same animal, only more sophisticated tools.

And I ask myself: if technologies with catastrophic potential — chemical, biological, nuclear — ultimately ended up serving the consumer rather than destroying us, then perhaps our role as founders is exactly this:

To understand how to use powerful technology, commercialize it, and improve it.

So when I say the consumer always wins, it is because:

If the first automobile didn’t wipe us out…

If nuclear power didn’t extinguish us…

If artificial intelligence is now reshaping our behavior…

Then the real question becomes:

What new market will we create next so that the consumer can continue consuming?

Bibliography (Ai suggestions):

Human Nature, Instinct, and Social Behavior

Morris, Desmond. The Naked Ape: A Zoologist’s Study of the Human Animal. New York: McGraw-Hill, 1967.

→ Classic exploration of how human behavior mirrors animal instincts, even in modern society.

Lorenz, Konrad. On Aggression. New York: Harcourt Brace, 1966.

→ Examines aggression and dominance as evolutionary survival mechanisms across species, including humans.

Wilson, Edward O. Sociobiology: The New Synthesis. Cambridge, MA: Harvard University Press, 1975.

→ Foundational text explaining how social organization and cooperation evolved biologically.

Dawkins, Richard. The Selfish Gene. Oxford: Oxford University Press, 1976.

→ Describes how genes drive behavior and reproduction strategies—relevant to your idea of instinct and marketing evolution.

Consumer Psychology and Neuromarketing

Damasio, Antonio. Descartes’ Error: Emotion, Reason, and the Human Brain. New York: Putnam, 1994.

→ Argues that emotion—not logic—drives most human decision-making.

Zaltman, Gerald. How Customers Think: Essential Insights into the Mind of the Market. Boston: Harvard Business School Press, 2003.

→ Demonstrates that 95% of purchase decisions are subconscious and emotional.

Lindstrom, Martin. Buyology: Truth and Lies About Why We Buy. New York: Doubleday, 2008.

→ Neuromarketing classic showing how sensory cues and primal instincts shape consumption.

Ariely, Dan. Predictably Irrational: The Hidden Forces That Shape Our Decisions. New York: HarperCollins, 2008.

→ Explains the psychological biases that make consumer behavior irrational but predictable.

Technology, Markets, and Evolution

McLuhan, Marshall. Understanding Media: The Extensions of Man. New York: McGraw-Hill, 1964.

→ Introduces the concept that technology is an extension of human biology and perception.

Kelly, Kevin. What Technology Wants. New York: Viking Press, 2010.

→ Suggests that technology evolves like a living organism, with its own “wants” and evolutionary direction.

Harari, Yuval Noah. Sapiens: A Brief History of Humankind. London: Harvill Secker, 2011.

→ Explores how shared fictions, trade, and technology transformed humans from animals into world builders.

Wiener, Norbert. Cybernetics: Or Control and Communication in the Animal and the Machine. Cambridge, MA: MIT Press, 1948.

→ The foundational theory connecting animal behavior, feedback systems, and the birth of artificial intelligence.

Kurzweil, Ray. The Singularity Is Near: When Humans Transcend Biology. New York: Viking, 2005.

→ Bridges biological and technological evolution, echoing your idea of technology as nature’s next iteration.

Cultural Evolution, Systems, and the Marketplace

Diamond, Jared. Guns, Germs, and Steel: The Fates of Human Societies. New York: W.W. Norton, 1997.

→ Explains how environment and resource competition shaped civilizations — resonant with your view of markets as ecosystems.

Taleb, Nassim Nicholas. Antifragile: Things That Gain from Disorder. New York: Random House, 2012.

→ Argues that systems (including markets) evolve and strengthen through volatility — similar to your take on technology as iterative nature.

Gladwell, Malcolm. The Tipping Point: How Little Things Can Make a Big Difference. Boston: Little, Brown, 2000.

→ On social contagion and how behaviors or trends spread like biological patterns.

I had been working at Venmo for some years and when doing my math: to maintain that monster the payroll was millionaire.

How did only two people manage it?

It was because of that same company that I received a call from one of the most famous names in the world.

After my time at Y Combinator, HF0 and FoundersINC, in less than six months, I was astonished: San Francisco is an industrial machine of producing startups. Venture capital seems endless and, for the first time, I saw how they bet directly on the founder.

I remember that in Ecuador, to apply for an innovation credit of just 5k dollars, you had to fill out ten forms and return everything even if the company went bankrupt. I didn’t even bother to imagine what a bank would ask for.

It was not only the cultural shock, but also seeing how they gave a 20-year-old kid a 150k check just to stay in SF investigating, or how an angel invested simply because he liked the idea.

San Francisco was the one that answered my billion-dollar questions: the famous founder mode, the daily bread at Y Combinator. Create a tool that solves a problem and that people use.

The three batches I was in coincided exactly with the explosion of AI. I saw live how startups received millions of dollars just for developing on top of OpenAI APIs.

I, in my engineer naivety, saw it as absurd, anti-business: systems without testing, without software design, without basic questions about databases, infrastructure or languages. It seemed like another universe.

Then I received one of the best pieces of advice of my life, from a former Netflix founder:

“Don’t worry about technology, worry about your company being worth millions. Then there will be enough money to pay engineers to do it.”

From then on I saw this environment with different eyes. I understood those who asked me to code simple ideas and that I, with an enterprise mindset, didn’t get. I wanted to charge what AAA software was worth, but they were only looking to validate a market, solve a problem, prototype and, if they went viral, raise almost infinite capital to scale.

I saw how “dumb” programs made in weeks became worth millions in less than six months, while in the enterprise world everything was planned in years.

A 25-year-old guy in Venezuela, who made liquor and had never worked in tech, received 2.5 million dollars with an almost manual product. They sold stablecoins in an underserved market and, although my calculations said they had to eat the whole country to generate revenue, SF saw the potential and bet on them.

Then everything made sense. I understood my former funders, my mistakes advising early stages, and how Venmo was born.

I asked myself what I was missing to develop something of my own.

I have academic knowledge, titles and experience. I remembered how 10 years ago I had a product idea and left it because it seemed impossible to execute in Ecuador, tempted by the low salaries of local companies.

My eyes opened. I became obsessed with understanding not only the SF environment, but also why they gave millions. I remembered a conference from the Nvidia founder: “Ignorance is fundamental to make the impossible. If we knew everything we would face, we probably would never try.”

Networking

Being in accelerators and demos I was forced to learn to speak, to do networking, something that seemed useless to me as an engineer in Ecuador, but valuable in SF and in life.

In the office people constantly came and I had to repeat the same pitch of the company I was in. An older man brought his son to do an intern at FoundersINC, in some rare science because of how otaku the guy was.

—“Are you from Latam? Surely a bank?”

The comment hit my Latin ego. Of course, that’s what we think there: that only a bank or something financial will get us out of poverty. It made me rethink if those limits force us to only enter traditional companies just to pay bills.

So I asked myself: why keep doing the same after 10 years?

Here, people research how to get to Mars, how to build robots, how to extend longevity or even how to create AI girlfriends. What for me was science fiction was already happening. I remembered Being Digital by Nicholas Negroponte: everything I read 15 years ago at university was coming true.

In six months I understood that the AI era had begun, and I was late.

On my way home in a Waymo, after my first ETH hackathon, I kept asking myself the same question: where do AI and crypto fit into the consumer? I obsessed. I spent entire days turning it around.

I realized I needed to understand AI in depth. My first glimpses were seeing how it coded for me, my WTF AI moment. But I needed to go deeper: why were so many people doing this here?

That’s why, after almost a year researching and working in a crypto + AI company, and after a near-million scam attempt cloning my X account with my name and content, I understood it:

The only thing I was missing was to start.

I had to take myself out of the equation and focus on what the consumer wants with AI.

I understood that I had to use my content not only to get a job, but to distribute.

I realized I have everything to see myself as a venture in AI + consumer.

What I see:

Consumer:

Agents will manage our behaviors automatically. Apps exist because phones were dumb: they needed to capture human attention with flows and forms. Now, any app without a conversational intelligent chat is destined to disappear.

Companies already know it: by opening emails, messaging and social networks you gave access to everything. An agent can instantly learn who you are, what you do and what you want.

UIs will slowly disappear: an agent will know when you need an Uber just by listening to you.

It will be your digital butler: emails, meetings, taxis, food, payments. One single app, and you tell your agent what you want.

An AI like a PhD at home: it guides you to cook, repair a pipe or diagnose your car just by looking at it.

Never again manuals: an instructor that teaches you whatever you want.

It can guide you in adventures, like renting a Tesla you never touched, showing you step by step how to set it up, or translating traffic signs in any language.

B2B:

Agents are already taking colossal processes. With all the data of a company, they can detect optimization problems in seconds. Where before you needed 100 humans, now one agent is enough.

Finance:

AI will distribute wealth, not only creating apps and agents to optimize trading, but also educating anyone on how money works, how to save and how to start a business from scratch.

Crypto:

An agent can eliminate human corruption and act autonomously. In public processes, there would no longer be a need to pay bribes or negotiate with bureaucrats: AI, with explicit contracts, releases the money.

The best reference is the divine: a human cannot meet all the needs of a community, but an AI can, being impartial and benefiting everyone.

Where I am now:

I understand the environment and have a network of contacts in SF thanks to accelerators.

I became a 10x developer by understanding these industrial machines, programming day and night to launch apps in record time.

I learned to move fast, stop thinking like an engineer and think in product: create something that people love and become your ambassador.

What I think is happening:

OpenAI and Y Combinator are subsidizing the use of AI to discover business models.

We already saw how consumer exploded: you don’t need to know systems to create your own app. Sales and customer service agents are everywhere.

That’s why it’s important to start now and raise giant capital. That money will inevitably be burned in this new space race.

The big question is: who is going to win it… and am I running in it?

Now that I’m starting to document in public and talk about my experiences with AI, I think I should start here: what was mine?

Back then there was no Bolt, Grok, Claude, not even the term vibecoding, but ChatGPT was already all over the media. And me? I was curious to sabotage it, to say it was just another hype tool, another smoke-and-mirrors fad that was going to crash and burn trying to bring this complexity to consumers.

I slammed face-first into reality when I saw how it understood, improved, and gave me the code for some financial math formulas—the kind you make in Excel and then query from some crappy database just to display them in dashboards.

For me, this was a nightmare in every sense, and it always had been. You had to work with fake data and pray it was close enough to the real thing, but usually the production code never matched. That meant manual validations after combing through thousands of lines of code from some “genius” who hadn’t written a single test—so any change took days to validate just to keep the charts from showing fake numbers and the product owners from losing their minds.

Sounds easy, but it’s another nightmare when you’re working with tables you’ve never seen before and an inhuman amount of data.

Seeing the result in a single second blew my mind. Two days of work—plus validations and product owner corrections—shrunk down to one hour.

My brain went into overdrive thinking about what I was going to do with this superpower. My limits as a developer and consultant were shattered. Every time I wanted to build something, I used to be reminded of my human limits: I couldn’t be backend, frontend, devops, and a mathematician all at once.

Since university, I’d been limited by not being a mathematician. In job interviews, they’d throw Fibonacci, factorial, or GCD problems at me just to get a first interview—and even if I could code, failing one math test meant nothing. Now AI handed me those answers in a second.

When quoting work, I’d always price frontend higher—not just because I didn’t like it, but because it was an eternity to code or tweak a UI so a button had the right color, shape, and look.

Now all that was simplified. I could finally try all those open-source tools I’d ignored just because I didn’t have the time—or the patience to read their docs.

No more excuses to dive into what motivated me most and why I got into fintech in the first place: the stock market and crypto. I’d bought books, taken courses, and had all the theory, but until that moment it was hard to even imagine creating a strategy. The math and theory were there, but the time needed to learn concepts, set up the tools, deploy, and read documentation just to get my first trading bot? Forget it.

Sometimes manual trading was easier. I’d researched crypto so much because people had asked me for bots—but building one practically for free for someone versus working for a company? Not even close. Leaving my job for a bot when I didn’t even know if their strategy worked in the market made no sense.

Now I realized I could bring my ideas to life, build not just code but entire products to test or play with. All those projects I’d thought of but abandoned for lack of time—I could start them. And even now, I still feel the same rush I did that first time, seeing the possibilities.

Another one of my hobbies was writing, and what had always stopped me from publishing a book was not having the time to edit it.

Today, I can have my own lab—coding and researching at the same time, bringing to life all those ideas I’d buried under “I’m not a writer,” “I’m not a mathematician,” “I’m not a designer.” All those projects that used to haunt me in my sleep can finally see the light.

Since then, I keep asking myself how this industrialization will change the consumer. What’s going to be the next B2C AI consumer product? Now I’ve got AIs creating products—the only thing I need to do is validate them in the market.