In the previous article, I argued that energy may be the most important foundation for Venezuela’s reconstruction. Reliable and affordable energy enables manufacturing, agriculture, mining, logistics, tourism, and many of the industries that could shape the country’s future. The more I studied the country’s energy ecosystem, the more I realized that rebuilding Venezuela isn’t about finding one “silver bullet.” It’s about creating the conditions that allow many different industries to grow together.

But if energy is the foundation, what becomes the accelerator?

For me, the answer is artificial intelligence.

I’ve spent the better part of the last decade helping large organizations adopt cloud computing, data platforms, and more recently artificial intelligence. During that time, I’ve watched AI evolve from a research topic into something that is beginning to transform almost every industry. What has surprised me most isn’t that AI is replacing people, but that it is making people significantly more productive. The organizations creating the most value aren’t necessarily those with the biggest AI teams. They’re the ones finding practical ways to use AI to improve decision-making, automate repetitive work, reduce costs, and help employees focus on higher-value problems.

That distinction matters because AI isn’t an industry in the traditional sense. It doesn’t compete with healthcare, energy, education, agriculture, or manufacturing. It improves all of them. In many ways, AI is becoming a horizontal capability, much like electricity or the internet. Countries that learn how to apply it broadly will likely see gains across their entire economy rather than within a single sector.

That’s why I don’t think Venezuela should ask, “How do we build an AI industry?”

I think we should ask, “How can AI make Venezuela’s existing strengths more competitive?”

Energy is perhaps the clearest example. In my previous article, I wrote about Venezuela’s unique combination of oil, natural gas, hydroelectric power, solar, and wind resources. Modern energy companies increasingly rely on AI to optimize every part of their operations. Machine learning models predict equipment failures before they happen. Computer vision systems inspect pipelines, substations, and transmission lines using drones. Weather models optimize renewable energy production. AI helps balance electrical grids, forecast demand, reduce downtime, and improve maintenance planning.

Having worked with NextEra Energy, I saw firsthand how technology has become central to operating a modern energy company. The future of energy isn’t simply about building more infrastructure. It’s about operating that infrastructure more intelligently.

The same pattern appears in agriculture. Venezuela has millions of acres of fertile land and a climate capable of supporting year-round production. AI can help farmers monitor crop health using satellite imagery, optimize irrigation, predict weather patterns, detect pests before they spread, and improve yields while reducing costs. None of those technologies replace farmers. They make farmers more productive.

Healthcare offers another compelling example. Like many developing countries, Venezuela faces shortages of specialists and uneven access to medical services. AI won’t replace physicians, but it can help prioritize patients, assist with medical imaging, summarize clinical information, expand telemedicine, and allow healthcare professionals to spend more time treating patients instead of completing administrative work.

Education may ultimately become one of the areas where AI has the greatest long-term impact. One example that caught my attention recently is Google’s new AI-powered learning experiences. Instead of simply answering questions, these tools can build personalized lessons around almost any topic, explain concepts at different levels of complexity, generate quizzes, adapt to a student’s pace, and act as an interactive tutor that is available at any time. In a world where attention spans are becoming shorter and traditional learning methods often struggle to keep students engaged, conversational AI has the potential to create a far more personalized learning experience.

Imagine what that could mean for a student in Barinas, Mérida, Ciudad Guayana, or Maracaibo who suddenly has access to an always-available tutor capable of teaching programming, physics, mathematics, English, or entrepreneurship in whatever way makes the most sense to that individual. AI won’t replace teachers, but it can amplify their impact and help make world-class educational resources accessible regardless of geography.

Government is another area where AI can quietly improve everyday life. One of the biggest frustrations citizens and businesses face in many countries isn’t the absence of services; it’s the friction involved in accessing them. AI can help automate permitting processes, improve document management, detect fraud, simplify interactions with government agencies, and make public services faster and more transparent. These improvements rarely make headlines, but they significantly improve the ease of doing business and the confidence entrepreneurs and investors have when deciding where to deploy capital.

Perhaps the opportunity that excites me most sits at the intersection of energy and artificial intelligence.

One of the biggest consequences of the AI revolution is that computing infrastructure is becoming incredibly energy intensive. Every new generation of AI models requires enormous amounts of electricity to train and operate. Data centers are already consuming unprecedented amounts of power, and that demand is expected to continue growing for years.

That’s one of the reasons I spent so much time discussing Venezuela’s energy ecosystem in the previous article. These aren’t separate conversations. Countries capable of providing abundant, reliable, and affordable electricity will become increasingly attractive locations for AI infrastructure, advanced manufacturing, and other energy-intensive industries. In many ways, AI has become another reason to modernize Venezuela’s energy sector.

One lesson I’ve learned throughout my career is that technological revolutions rarely create value on their own. Electricity transformed manufacturing. The internet transformed communication. Smartphones transformed commerce. Artificial intelligence will transform productivity. But in every case, the greatest value wasn’t created by the technology itself. It was created by entrepreneurs who figured out how to apply that technology to solve real problems.

I believe the same will be true for Venezuela.

The country doesn’t need to build the world’s leading AI model to benefit from artificial intelligence. The tools already exist, and they are becoming more capable every month. Venezuela’s opportunity is to adopt those technologies intelligently, applying them across the industries where the country already possesses natural advantages.

That’s also one of the reasons I started ODS.

While we build AI solutions for organizations across different industries, I increasingly find myself thinking about how many of those same capabilities could eventually contribute to Venezuela’s reconstruction. Whether it’s helping utilities improve operations, enabling smarter logistics, modernizing healthcare, streamlining government workflows, or giving entrepreneurs access to tools that were once available only to large enterprises, AI has the potential to dramatically lower the barrier to innovation.

For me, that’s the most exciting part of this moment.

Countries rebuilding today don’t have to recreate the infrastructure of the twentieth century. They have an opportunity to leapfrog directly to modern technologies, avoiding many of the legacy systems that wealthier nations are now spending billions to replace.

None of this happens automatically. Artificial intelligence is not a substitute for good institutions, sound economic policy, investment, education, or entrepreneurship. Technology amplifies what already exists. Used thoughtfully, it can accelerate progress. Used poorly, it can amplify inefficiency.

That’s why I don’t see AI as another industry competing for attention.

I see it as an enabling technology.

Just as electricity transformed every industry during the last century, artificial intelligence has the potential to transform every industry during this one.

If energy becomes one of the foundations of Venezuela’s reconstruction, I believe AI can become one of its greatest accelerators.

In the next article, I’ll move beyond energy and technology to explore the industries that can help diversify Venezuela’s economy over the coming decades. Agriculture, tourism, logistics, advanced manufacturing, critical minerals, and the blue economy all represent opportunities to build a more resilient and diversified country. Together, they form the next chapter in understanding how Venezuela can create sustainable, long-term prosperity.

In the previous article, I wrote about why I’ve become increasingly optimistic about Venezuela’s future. That optimism isn’t rooted in politics or nostalgia. It’s rooted in something much more practical: over the past several months, I’ve made a conscious effort to better understand the country’s opportunities before deciding where I want to invest my time, my company, and ultimately my career.

That journey has taken me in directions I never expected. I’ve spent evenings reading industry reports instead of news articles, studying maps instead of headlines, and having conversations with entrepreneurs, engineers, investors, and operators who understand Venezuela far better than I do. Every discussion seemed to uncover another layer of the country that I hadn’t fully appreciated before.

What surprised me most was that regardless of where the conversation started, it almost always ended in the same place.

Energy.

It didn’t matter whether we were discussing agriculture, artificial intelligence, manufacturing, tourism, mining, desalination, logistics, or data centers. Sooner or later, every conversation came back to the same question: where will the energy come from?

That’s when I realized that if I wanted to understand Venezuela’s future, I first had to understand its energy ecosystem.

Interestingly, I approached that question from two very different directions.

Professionally, I spent several years consulting with NextEra Energy, one of the world’s largest renewable energy companies. Before working there, I thought I had a reasonable understanding of what an energy company looked like. I expected conversations about turbines, transmission lines, substations, and generation capacity.

Instead, I found myself surrounded by conversations about artificial intelligence, cloud computing, predictive maintenance, digital twins, weather forecasting, computer vision, drones, machine learning, and advanced analytics.

The longer I worked with their teams, the more I realized that NextEra didn’t think of itself simply as a company that generated electricity. It thought of itself as a technology company whose product happened to be energy.

That distinction stayed with me.

The future of energy isn’t just about building more generation. It’s about using technology to make generation more efficient, transmission more reliable, maintenance more predictive, and the entire grid more intelligent. Software has become just as important as physical infrastructure.

Around the same time I started looking more seriously at Venezuela, I also had the opportunity to begin learning from David Moran, an energy expert who has spent decades working in the sector and who has become both a mentor and a friend. David has an extraordinary understanding of Venezuela’s energy resources and has been incredibly generous sharing both his knowledge and his long-term vision for the country.

Our conversations rarely stay focused on a single topic. We may begin discussing natural gas production and end up talking about renewable energy, water infrastructure, manufacturing, AI, logistics, or economic development. What I’ve come to appreciate is that people who spend their lives working in energy don’t think in terms of isolated industries. They think in systems.

That systems perspective completely changed the way I look at Venezuela.

Like many people, I grew up thinking Venezuela’s energy story was oil. Oil certainly deserves its place in that story. Venezuela possesses the largest proven oil reserves in the world, and regions such as the Orinoco Oil Belt, Lake Maracaibo, Monagas, and Anzoátegui have supported the country’s economy for decades. Existing infrastructure, production facilities, pipelines, and refining capacity represent assets that, despite years of underinvestment, still provide an important foundation for the future.

But the more I learned, the more I realized that oil is only one chapter.

Natural gas may be one of the country’s most under appreciated opportunities. Venezuela holds the largest proven natural gas reserves in Latin America, yet much of that potential remains undeveloped. Before beginning this journey, I thought about natural gas primarily as an export opportunity. Today, I think exports are only part of the story.

Reliable natural gas can stabilize the electrical grid, support fertilizer production for agriculture, provide feedstock for petrochemical industries, power desalination plants that improve water security along the Caribbean coast, reduce methane emissions by capturing gas that is currently flared, and eventually position Venezuela as an LNG supplier to a world looking for diversified sources of energy. It isn’t simply another commodity. It’s a resource capable of enabling multiple industries simultaneously.

Hydroelectric power tells a similar story. Venezuela already possesses one of the largest hydroelectric systems in the world through the Guri, Caruachi, and Macagua dams, with Tocoma representing another important long-term opportunity once completed and fully integrated. While discussions often focus on the challenges these facilities have faced over the years, it’s easy to overlook the strategic advantage they represent. Few countries have access to this scale of renewable baseload generation. Modernizing generation equipment, strengthening transmission infrastructure, and improving operational reliability could provide Venezuela with decades of low-carbon electricity that supports both industrial development and future electrification.

Renewable energy is another area where my own assumptions have changed considerably. Before speaking with David, I largely viewed solar and wind as alternatives to fossil fuels. Today, I see them as complementary pieces of a much larger energy portfolio.

Venezuela’s geography creates exceptional conditions for utility-scale renewable generation. The Paraguaná Peninsula in Falcón receives some of the strongest and most consistent trade winds in the Caribbean, making it one of the country’s most attractive locations for wind energy development. Similar conditions exist in La Guajira, in the state of Zulia, where decades of studies have highlighted the region’s outstanding wind potential.

Solar opportunities are equally compelling. States such as Falcón, Guárico, Lara, Zulia, and Anzoátegui benefit from high levels of solar irradiation, large expanses of available land, and proximity to existing industrial corridors and transmission infrastructure. Rather than replacing oil or natural gas, these regions could complement Venezuela’s hydroelectric system by diversifying generation, improving resilience, and reducing long-term operating costs.

One idea that has consistently emerged from my conversations with David is that the future shouldn’t be framed as oil versus renewables. That’s a false choice.

The countries that will have the greatest competitive advantage are likely to be those capable of intelligently integrating multiple energy sources based on their geography, infrastructure, and economic needs. Venezuela is in the rare position of possessing world-class oil reserves, the largest proven natural gas reserves in Latin America, significant hydroelectric capacity, exceptional wind corridors, and excellent solar resources. Very few countries enjoy that level of diversification.

As I continued connecting these ideas, another realization began to emerge.

The conversation about energy isn’t really about energy.

It’s about everything energy enables.

Reliable and affordable electricity makes manufacturing more competitive. It supports modern agriculture through irrigation, fertilizer production, cold-chain logistics, and food processing. It makes desalination economically viable for coastal communities facing water challenges. It allows mining operations to become more efficient and environmentally responsible. It strengthens ports, transportation networks, telecommunications, and industrial parks.

Perhaps most interesting to me, it also creates the conditions required for entirely new industries.

Artificial intelligence is transforming nearly every sector of the global economy, but AI also has a physical footprint. Data centers require enormous amounts of electricity. High-performance computing depends on reliable power, modern transmission infrastructure, fiber connectivity, and increasingly sustainable sources of generation. Countries capable of providing abundant, affordable, and reliable electricity will become increasingly attractive locations for AI infrastructure over the coming decades.

That’s a connection I probably wouldn’t have made a few years ago.

Working with NextEra taught me that energy companies are becoming technology companies.

Learning from David has taught me that countries rich in energy resources have an opportunity to become technology platforms if they think beyond extraction and focus on value creation.

That distinction has fundamentally changed the way I think about Venezuela.

For decades, much of the national conversation revolved around what Venezuela could extract from the ground. Increasingly, I find myself asking a different question.

What can Venezuela build because of what lies beneath the ground?

Those are two very different visions for the future.

One is centered on exporting resources.

The other is centered on creating industries.

None of this should be interpreted as suggesting that success is inevitable. Energy alone will not rebuild Venezuela. Institutions matter. Education matters. Rule of law matters. Transparent regulation matters. Entrepreneurship matters. Investment matters. Without those ingredients, even the greatest natural advantages remain unrealized.

But after months of studying the country’s opportunities, I’ve become convinced that energy is where the conversation should begin. Not because it’s the only opportunity Venezuela has, but because it is the foundation upon which so many other opportunities depend.

Every industrial transformation begins with abundant and reliable energy.

If Venezuela can modernize its existing infrastructure, intelligently integrate oil, natural gas, hydroelectric power, solar, and wind, and leverage technology to manage that ecosystem more efficiently, it won’t simply rebuild its energy sector.

It will create the conditions for rebuilding much of the country’s economy.

In the next article, I’ll explore one of those opportunities in greater depth. As someone who has spent the last several years building AI solutions for large organizations, I believe artificial intelligence could become one of the most powerful accelerators of Venezuela’s reconstruction—not as a standalone industry, but as a force multiplier across energy, agriculture, healthcare, government, logistics, and entrepreneurship.

One of the questions I’ve been asked most often since returning from Venezuela is why I’ve become so optimistic about the country’s future. It’s a fair question because, on the surface, very little has changed. The country continues to face enormous political, economic, and institutional challenges, and the recent earthquake only made the road to recovery even more difficult. If anything, the headlines would suggest that now is the worst possible time to be talking about opportunity. Yet the more time I spend learning about Venezuela, the more convinced I become that understanding the country’s long-term potential is one of the most worthwhile things I can be doing right now.

What’s interesting is that this optimism isn’t new. I didn’t suddenly discover that Venezuela has abundant natural resources or a strategic geographic location. Like most Venezuelans, I grew up hearing that we lived in one of the richest countries in the world. I knew we had oil. I knew we had fertile land, hydroelectric power, beautiful beaches, mountains, and extraordinary biodiversity. Those weren’t revelations. What changed over the past year wasn’t my belief that Venezuela has potential. What changed was my decision to stop treating that potential as an abstract idea and start understanding what it actually means.

That shift happened for a practical reason. When I started ODS earlier this year, I wasn’t thinking about Venezuela as a topic to write about. I was thinking about it as a place where I genuinely wanted to build. If I was serious about hiring talent, investing my time, developing partnerships, and eventually contributing to the country’s reconstruction, then I needed to answer a much harder question than whether Venezuela had opportunity. I needed to understand where those opportunities actually were, which industries mattered most, what problems were worth solving, and where someone with my background in technology could make a meaningful contribution. General optimism wasn’t enough. It needed to become informed conviction.

That realization completely changed the way I approached my time in Venezuela. Venezuela Tech Week became less about attending talks and more about meeting people. I deliberately spent time with founders, engineers, investors, operators, academics, and business leaders, asking as many questions as I could. What surprised me wasn’t that everyone shared the same perspective, they didn’t. It was that almost every conversation revealed another layer of the country that I hadn’t fully appreciated before. One person would talk about natural gas, another about tourism, another about agriculture, another about logistics. At first those felt like independent conversations. Eventually I realized they were all describing different pieces of the same system.

When I came back to Miami, those conversations didn’t end. They became the starting point for a much deeper process of learning. Over the past several months I’ve found myself reading industry reports that I never imagined I’d care about, studying maps of pipelines and electrical grids, looking at agricultural production, shipping routes, mineral deposits, weather patterns, and demographic trends. I’ve also been incredibly fortunate to spend time learning from people who have dedicated their careers to understanding these industries. One of the people who has had the biggest influence on my thinking throughout this process has been David Moran, an energy expert who has spent decades working in the field and understands Venezuela’s energy ecosystem better than almost anyone I’ve met. Over the past several months, David has been incredibly generous with his time, sharing his vision for the country’s future and helping me understand the opportunities that exist across the energy sector, particularly in renewables.

Before these conversations, I tended to think about Venezuela’s energy story primarily through the lens of oil. David helped me see a much broader picture. We’ve talked about natural gas, hydroelectric power, solar, wind, energy storage, grid modernization, desalination, and how all of those pieces could work together as part of a modern energy strategy. More importantly, he has helped me understand not only what resources Venezuela possesses, but why they matter and how they could become the foundation for entirely new industries over the coming decades.

That interconnectedness is probably the biggest lesson I’ve learned so far. It’s easy to think about oil, tourism, agriculture, mining, technology, or infrastructure as separate industries competing for attention. In reality, they reinforce each other. Affordable and reliable energy makes manufacturing viable. Manufacturing creates demand for better logistics. Better logistics improve exports and agriculture. Technology increases productivity across every sector. Education develops the engineers, scientists, and entrepreneurs who make all of those industries more competitive. Prosperity isn’t the result of one successful industry. It’s the result of an ecosystem where each part strengthens the others.

That perspective has also changed the way I think about Venezuela’s greatest asset. Most people would probably answer oil, and it’s certainly one of the country’s defining resources. But the more I study Venezuela, the more convinced I become that its greatest advantage is its people. Over the past twenty-five years, millions of Venezuelans have built remarkable careers across the world. They’ve become engineers, founders, physicians, researchers, executives, investors, and scientists. They haven’t stopped contributing to Venezuela simply because they live somewhere else. Many are already investing, mentoring, advising, building businesses, or sharing knowledge from abroad. If the country is able to create the right environment over time, that global network of talent could become one of its most valuable competitive advantages.

This is ultimately why I’ve decided to write this series. I’m not trying to convince anyone that Venezuela’s future is guaranteed, and I’m certainly not suggesting that the challenges are behind us. If anything, the earthquake reminded all of us how much work remains. My goal is much simpler. I want to document what I’m learning as I try to understand where Venezuela’s opportunities lie and how someone like me can contribute to them. Some of these ideas may prove wrong. Others may evolve as I continue learning. But I suspect that process of learning in public is more valuable than pretending I’ve already reached the destination.

The next article will begin where almost every conversation about Venezuela begins: energy. But instead of looking at oil in isolation, I want to explore why Venezuela’s energy ecosystem (from oil and natural gas to hydroelectric power, renewables, and the infrastructure that connects them) may be one of the country’s greatest enablers for everything that comes next.

Over the past several months, I’ve been traveling to Venezuela, meeting founders, investors, engineers, and industry experts, while studying the country’s opportunities across multiple industries.

This series is my attempt to document that journey. I’ll share what I’m learning, the people I’m learning from, and why I believe rebuilding Venezuela will require far more than government policy. It will require entrepreneurs willing to build, investors willing to commit capital, and a global Venezuelan diaspora willing to reconnect its talent, experience, and networks with the country that shaped us.

The recent earthquake didn’t change that conviction. If anything, it made it more urgent.

Subscribe now

A few days ago, Venezuela experienced one of the most devastating natural disasters in its recent history. Two powerful earthquakes struck within seconds of each other, leaving destruction across several cities, including my hometown of Caracas and the neighboring coastal city of La Guaira.

Like many Venezuelans living abroad, I spent the next several hours staring at my phone, refreshing news feeds, and waiting for a message from home.

Telecommunications had gone down and it took almost five hours before I finally heard from my father. Those five hours reminded me how fragile certainty feels when the people you love are thousands of miles away. My family was fortunate but many others were not.

Over the following days, I couldn’t stop thinking about the families who never received the phone call they were waiting for. Families who lost loved ones, homes, businesses, or years of hard work in just a few minutes. My thoughts have been with them ever since and the images are truly heartbreaking. It has been a few tough and emotional days.

The timing of the earthquake made it especially emotional for me. Earlier this year I traveled back to Venezuela to attend Venezuela Tech Week and spend time with entrepreneurs, engineers, investors, and people working across different industries. It wasn’t meant to be a nostalgic trip. I wanted to understand the country through a different lens; one focused less on politics and more on its future.

When I returned home, I wrote about something I hadn’t felt in a long time: optimism. After nearly three decades of economic decline, institutional deterioration, and one of the largest migration crisis in modern history, I sensed that something had begun to shift. The conversations I had were no longer centered on surviving the next year. They were about building the next decade.

People were launching companies again. International investors were quietly paying attention. Members of the Venezuelan diaspora were beginning to ask not if they would ever contribute again, but how.

It wasn’t blind optimism. Venezuela’s challenges remain enormous, and everyone I spoke with understood that. But for the first time in years, rebuilding no longer felt like an abstract idea. It felt possible.

Then the earthquake happened.

The human and economic cost is difficult to comprehend. Reconstruction was already going to be one of the defining challenges of our generation, and now the country faces the additional burden of rebuilding homes, schools, hospitals, roads, and entire communities that had only just begun to look toward the future again.

As I watched the images coming out of Venezuela over the following days, my mind kept returning to the same question:

How can I help?

Not because I believe one person can solve problems of this scale, but because moments like these make it impossible to stand on the sidelines. Seeing the suffering and devastation reinforced something I had already been feeling for months: I want to be part of rebuilding my country.

Rebuilding a country isn’t only about restoring physical infrastructure. It’s about rebuilding opportunity, trust, institutions, and hope. It’s about creating reasons for people to stay, reasons for others to return, and reasons for the next generation to believe they can build a meaningful future at home.

I’ve spent a lot of time asking myself what role I can realistically play in that process. The truth is, I won’t rebuild bridges or apartment buildings. I won’t design public policy or restore the national electrical grid. But I can build businesses, I can create jobs, I can mentor young professionals, and I can invest my time in people who are choosing to build something meaningful.

Over the last few months, that’s become one of the driving motivations behind my own company. Not because one business can transform a country, but because thousands of businesses can. Economic recovery doesn’t happen through speeches or grand announcements. It happens when entrepreneurs hire their first employee, when engineers mentor younger engineers, when investors back founders willing to take risks, and when people choose to solve problems instead of waiting for someone else to solve them.

Those actions may seem small on their own, but multiplied across thousands of people, they become reconstruction.

One of the most encouraging moments this past week came while volunteering in Miami to help organize relief efforts for families affected by the earthquake. I was surrounded by people from different backgrounds, professions, and communities, all working toward the same goal. There was no concern about who received credit or whose idea was better. Everyone simply asked, “What needs to be done?” and got to work.

It reminded me that rebuilding always begins with people. Before governments, before infrastructure, and before investment, there has to be a shared sense of purpose. Communities recover because ordinary people decide they are going to help one another.

Those are the values I hope continue to shape Venezuela’s future: cooperation instead of division, integrity instead of corruption, and service instead of self-interest.

One story that has stayed with me over the years was Puerto Rico after Hurricane Maria. Red Ventures CEO Ric Elias launched a program to support the island recovery effort. Like every major disaster, the immediate priority was humanitarian aid. Food, water, medicine, and temporary shelter couldn’t wait. But what impressed me most came after the emergency phase with the launch of FWD787, an initiative that brought together Puerto Rican young talent living and working in the US to build companies, create jobs, and invest back into the island. Several friends of mine have been part of that initiative, and seeing the positive impact they’ve had over the years has been insipirational

Humanitarian aid helps people survive.

Economic opportunity helps communities thrive.

Over the past several months, I’ve found myself studying Venezuela in a way I never had before. I’ve been reading about our energy sector, our natural resources, our geography, our infrastructure, and the entrepreneurs already working to build the country’s future. Along the way, I’ve met founders, investors, engineers, and industry experts whose knowledge has challenged many of my assumptions and expanded my perspective.

What started as curiosity has gradually become a sense of responsibility. If I genuinely want to contribute to Venezuela’s reconstruction, then I have an obligation to understand where the country’s greatest opportunities lie and how someone with my background in technology and entrepreneurship can create meaningful value.

That’s ultimately why I’m writing this series.

Over the coming months, I’ll share what I’m learning as I continue exploring the opportunities I believe can play a meaningful role in rebuilding Venezuela. We’ll dive into the country’s energy ecosystem, the role artificial intelligence can play in accelerating development, the opportunities in agriculture, tourism, entrepreneurship, and infrastructure, and the conversations I’m having with people who have spent decades building businesses and industries in Venezuela.

I don’t claim to have all the answers. In fact, one of the goals of this series is to challenge many of my own assumptions. The more I learn, the more I realize how much I still don’t know. But I also believe that rebuilding Venezuela starts with understanding it, and understanding it requires curiosity, humility, and a willingness to learn from the people who have dedicated their lives to solving these problems.

My hope is that this series encourages more people (particularly those of us in the Venezuelan diaspora) to begin asking a different question. Not whether Venezuela can recover, but what role each of us can play in that recovery.

Some will build companies. Some will invest. Some will mentor. Some will return. Others may continue living abroad while creating opportunities from wherever they are. Every contribution matters, because rebuilding a country isn’t the responsibility of a government or a handful of business leaders. It’s the cumulative effect of millions of individual decisions made over many years.

The earthquake reminded me how fragile life can be, but it also reminded me why this work matters. It reinforced my belief that Venezuela’s future won’t be built by waiting for the right moment. It will be built by people who decide that, despite the challenges, their country is still worth investing in.

I hope you’ll join me on that journey.

Next: Why I’m Bullish on Venezuela.

Lately I’ve been getting an unusual amount of requests from friends and people in my network who are building impressive products entirely on their own, with zero technical background, using tools like Lovable or Claude. They reach out asking for a sanity check from a pro, wanting to make sure they’re doing things right. Some of them are genuinely surprised that what they built actually works.

And honestly, what they’re building is good. Not “good for someone with no coding experience” good. Actually good.

I know some engineers are uncomfortable watching this happen. There’s real anxiety in the industry right now about whether technical skills still matter, whether AI is coming for their jobs, whether what took years to learn is suddenly worth less. I understand that feeling, but I don’t share it. I believe in something called Jevons Paradox: when a resource becomes easier and cheaper to use, total demand for it goes up, not down. Making software easier to build doesn’t shrink the need for engineers. It expands what people want to build, and someone has to build it right.

What this moment has created is an incredible thing to watch. People who deeply understand their own industries, whether that’s hospitality, logistics, healthcare, or finance, are now able to build the tools they always knew their industry needed. That’s not a threat. That’s new surface area.

So I wanted to write the guide that saves you from having to call your engineer friend every time something comes up. Here’s what you actually need to know.

First: Know What You’re Working With

If you used Lovable, you have a complete, live app. It built you a front end (what users see), wired it to a database through a service called Supabase, and deployed everything automatically. You’re live. The URL it gave you, something like yourapp.lovable.app, is a real working address on the internet.

If you used Claude (the chat at claude.ai), you have excellent code and a live preview inside the chat, but by itself Claude doesn't deploy or host anything. Think of Claude as the architect: it designs the building, but you need someone to construct it. The good news is that Claude now connects directly to Replit, which means you can go from a conversation in Claude to a live, deployed app without ever leaving the workflow. Replit handles the hosting and deployment on Claude's behalf. Alternatively, you can take Claude's code into Lovable or hand it to a developer. The point is that Claude alone is a starting point, not a finish line.

Subscribe now

The 10 Tips

1. Run a security check before anyone uses it.
Ask Claude directly: “Run a security assessment of my code and flag any vulnerabilities.” Takes 30 seconds. Do it before you share the link with anyone outside your circle.

2. Keep your API keys secret, and check if they already aren’t.
When you connect to outside services like payments, email, or maps, you get an API Key. Think of it as a password tied to your credit card or your account. Every tool you're using (Lovable, Replit, Supabase, Cloudflare) has a dedicated section in its settings called Secrets, Keys, or Environment Variables. That is the only place your API keys should ever live. If you find yourself copying a key directly into a code file, stop. Ask Claude: "Where should I store this API key in my current setup?" and it will tell you exactly where to put it for your specific tools. Then, before you share your code with anyone on GitHub, ask Claude: "Are there any API keys or secrets exposed in my codebase?" because the most common mistake is pasting a key into a file once, forgetting about it, and pushing it to the world.

3. Lock your database.
If anyone on the internet can pull data from your app without logging in, you have a serious problem. Supabase has a feature called Row Level Security. Ask Claude or Lovable to confirm it’s turned on. This single step prevents the most common way vibe-coded apps get exploited.

4. Push your code to GitHub today.
If your code only lives on your laptop, one accident and it’s gone. GitHub is like Google Drive for code: free, keeps full history, and lets you roll back if something breaks. If your project is private or confidential, mark the repository as Private.

5. Keep the code lean.
Ask your AI tool to generate the minimum code necessary to make something work. Less code means easier to fix, easier to hand off, and cheaper to maintain. If you ever bring in a developer, they’ll thank you. Bloated AI-generated codebases can cost weeks of cleanup before real work begins.

6. Ask for error handling.
When you’re prompting Claude or Lovable to build your API layer, the layer that connects your app to your database, add this: “Build a robust error handling framework.” In plain terms: if something breaks, you want a clear message explaining what went wrong, not a blank white screen and a confused user.

7. Set a spending cap on every service you connect.
Supabase, your hosting platform, your email service, they all charge based on usage. A traffic spike or a bug that accidentally loops thousands of requests can generate a surprise bill. Before you go live, set a spending cap or billing alert on every service. Most of them offer this in their settings for free.

8. Get your own domain.
Lovable gives you a default URL, but you’ll want your own eventually. Buy a domain on GoDaddy or Namecheap, think of it like buying a street address for your app. I use Cloudflare to manage mine: it’s free for the basics, fast, and adds a security layer on top of everything else.

9. Don’t over-engineer early.
If your app is working on the stack your AI tool chose, leave it alone. There is no prize for adding complexity to something that’s already running. The time to upgrade your infrastructure is when your current setup starts failing, not before.

10. Don’t build your own login system.
Authentication, the system that handles sign-ups, logins, and passwords, is notoriously hard to get right, and the consequences of getting it wrong fall on your users, not just you. The good news: you don’t need to build it. Supabase has authentication built in and supports email, password, Google login, Apple login, and more, all with a single prompt. Ask Claude or Lovable: “Set up authentication using Supabase Auth with Google login.” Never store passwords yourself. Never build a custom login flow from scratch. This is one area where using what already exists is always the right call.

When to Call a Professional

If you’re still building and testing, keep going on your own. If you’re getting real users, run through the security tips above before you go any further. If things are breaking under load, get a fractional engineer for a few hours, not a full team, just someone to diagnose and fix. If revenue, investors, or enterprise clients are in the picture, that’s the inflection point. You need a professional to review your architecture, harden your security, and migrate your V1 into something built to scale.

The vibe coding era didn’t make engineers irrelevant. It moved them to where they matter most: scaling what you already built.

When it’s time to grow it, you know where to find me.

Subscribe now

Every week there’s a new headline about AI costs spiraling out of control. Token bills in the millions. Agentic systems burning through compute. Enterprise AI budgets doubling.

And every week, I watch founders and executives read those headlines and quietly decide that AI is too complicated, too expensive, or too risky to touch right now.

That’s a mistake. And I think it comes from a fundamental confusion about what kind of AI we’re actually talking about.

The discourse is about a different problem than yours

The cost horror stories are real, but they’re almost exclusively about one thing: overly complex, multi-agent orchestration pipelines built at scale, often without clear ROI guardrails. We’re talking about autonomous systems making thousands of LLM calls per task, with memory layers, tool chains, and retry logic compounding at every step.

There are valid use cases for that kind of complexity, but it represents a small slice of how AI actually gets used in practice. Most businesses (including mine) need something much simpler: targeted automation for specific, repeatable workflows. The economics of that are completely different.

What my actual setup looks like

I run ODS Tech with a handful of tools that took days to set up, not months. And I try to bring custom agents to the platforms I already use, like Slack.

My recruiting agent “Zizou” is a Slack bot connected to a backend that uses the Claude Agent SDK. It has a detailed prompt describing the talent profile I’m looking for and tools to search and evaluate candidates. I describe a need in Slack. It shows me a set of profiles, then I reach out personally to assess.

My engineering agent “Ronaldo” is another Slack bot. I’ve created Slack channels that map to specific GitHub repositories so the agent has the right context. I drop a task in the right channel, the bot opens a Pull Request in the respective repo, and GitHub Copilot reviews it for anything I might have missed. If Ronaldo can’t do it or I just feel like doing it myself then I use Claude Code, which has genuinely changed how fast I can move on a greenfield problem. Yes, that one has a real cost but it fully justifies the productivity gain.

For prototyping and validating new ideas, I use Lovable. When a concept is worth exploring but not yet worth a full build, I can have a working prototype in front of a client or stakeholder in hours. It’s changed how I approach early-stage validation entirely: less “let me scope this out” and more “let me just show you.”

For the business side (SOWs, proposals, project briefs, recurring documents) I have Projects in Claude, GPTs in ChatGPT, and a set of reusable prompts and templates that handle the boilerplate. What used to take hours now takes minutes.

None of this is exotic. None of it requires an AI engineer on staff.

The hidden cost isn’t compute. It’s configuration.

Here’s what I actually spend time on: writing good prompts, being precise about what I want each tool to do, and connecting the right systems together with intention.

The businesses losing money on AI aren’t losing it on tokens. They’re losing it on scope: building general-purpose systems when specific-purpose tools would have done the job at a fraction of the cost.

The question worth asking isn’t “can we afford AI?” It’s “do we know what we actually want AI to do?”

If you have an answer to that second question, the economics almost always work out.

What this means for your business

You don’t need a six-figure platform contract or a dedicated ML team to get real leverage from AI right now.

You need clarity about your highest-friction workflows, a willingness to experiment with focused tools, and someone who can connect the pieces without overbuilding.

The gap between businesses getting real ROI from AI and those still watching from the sidelines isn’t budget. It’s specificity.

If you’re trying to figure out where to start, happy to share what’s worked. Reach out or leave a comment.

Subscribe now

A few weeks ago, I picked that codebase back up. Hadn’t touched it in seven years. I rebuilt the entire backend on a modern framework, overhauled the infrastructure, and shipped a significant mobile upgrade.

I had a working version in a weekend. Nearly production-ready in about two weeks.

What would have taken a solid team six months to a year, I did largely on my own, in a fraction of the time, with better architecture than the original. I’m not writing this to brag. I’m writing this because it genuinely made me think about the cost of building and in the service providing business, how do you accurately estimate your work.

The Formula Everyone Is Still Using

For decades, software development and consulting ran on one equation:

Time × Rate = Price

It made sense. More complexity meant more hours. More hours meant more cost. You estimated conservatively, padded for unknowns, and presented a number. Clients compared vendors by that number. Vendors competed on it.

AI just quietly dismantled one side of that equation, and most procurement cycles, vendor contracts, and client expectations haven’t moved an inch.

A senior engineer working with the right AI tooling today isn’t 10 or 20 percent faster. On the right problems, they are three, four, sometimes five times faster, with better documentation, fewer bugs, and more consistent output than before. And here’s the part that still surprises me every time: it doesn’t feel that complex anymore. Not because the problems are simpler, but because the tools have fundamentally changed what execution feels like.

That’s the exciting part. You can deliver more, at higher quality, in less time than ever before in the history of this industry.

I’m currently about a month into a complex enterprise engagement. The kind of project that historically carried serious weight in time and budget. We’re nearly done.

But that excitement comes with a question I haven’t been able to shake. If building feels less complex and time is shrinking, what exactly are you charging for?

This isn’t just something small operators like me are wrestling with. McKinsey recently disclosed that a quarter of its global fees now come from outcomes-based pricing. Clients aren’t arriving with a scope anymore. They’re arriving with the outcome they want and asking McKinsey to price against actually delivering it. If that’s happening at the top of the consulting pyramid, the rest of the industry needs to pay attention.

What Clients Are Actually Buying

Here’s something that surprised me when I started my own business. Something I didn’t fully expect.

Not every client is buying outcomes. Some are buying presence. Visible effort. A human being engaged over time. Hours on an invoice as a proxy for care and seriousness.

When I first encountered this as an operator it caught me off guard. I came in with an outcomes-first mentality, focused entirely on results and value delivered. And then I realized that for some clients, the human behind the work matters as much as the work itself. The relationship, the availability, the feeling that someone is genuinely invested alongside them.

I understand it now. And I accept it. It comes from decades of clients being trained to equate time and presence with value. It’s not irrational. It’s human.

But it adds another layer to an already complicated billing question. Because if part of what you’re selling is yourself, your judgment, your experience, your presence, how does that change when AI compresses the execution side of the equation? I don’t have a clean answer. What I do know is that it makes the relationship side of this business more important, not less.

One Idea That Stuck With Me

I picked up The Consulting Bible by Alan Weiss not long ago. One idea stopped me cold:

Your price is not determined by your hours. It is determined by your value to the client and what they are willing to pay for your expertise.

I’m still working out what that looks like in practice in the AI era. But it points at something real. The consultant who knows what to build and why, and can now build it in a third of the time, isn’t worth less. The expertise didn’t shrink. The hours did.

And yet. If building no longer feels as complex as it once did, does that change the value equation? I think about this a lot. My honest answer is no, because the complexity was never really in the execution. It was always in knowing what to build, why to build it, and how to make sure it actually solves the right problem. AI handles more of the former. It doesn’t touch the latter.

But that argument only works if the client trusts you enough to believe it. Which brings everything back to the same place it always has. Relationships first. Trust before pricing. That part hasn’t changed and I don’t think it ever will.

Where I’m Landing, For Now

Here’s what I know. AI lets you deliver more in less time. That part is exciting. What I’m still figuring out is the billing side. If complexity is no longer the anchor, and time is no longer the measure, what is? Is there even a right formula anymore?

That’s the question I’m sitting with as an operator. And I’m not coming at it from a place of having figured it out. I’m coming at it from a place of building every day, watching what AI actually does in practice, and trying to stay honest about what that means for how I run my business and serve my clients.

I suspect a lot of you are in the same place.

If you’re a consultant, a technology leader, or a business owner navigating this, I’d genuinely love to hear how you’re thinking about it. Not looking for a definitive answer. Just an honest conversation with people who are figuring it out in real time, same as me.

Last week, I traveled to Venezuela to attend the first-ever Venezuela Tech Week in Caracas and spend time understanding the current state of the country from a technology, business, and infrastructure perspective.

The trip was partly personal, partly professional, but mostly exploratory. After spending the last 18 years building my career in the United States, working across software, cloud, AI, and digital transformation, I wanted to better understand how I could align my new business with the long-term recovery and modernization of Venezuela.

And after spending time there, one thing became very clear:

Venezuela is entering a new era.
An era filled with enormous challenges, but also enormous opportunity.

Starting With Family, Friends, and Perspective

The trip started in the best possible way: reconnecting with old friends and family.

For the first time in probably 18 years, I got to spend Mother’s Day in Venezuela with my mom and family. That alone made the trip worth it. There’s something grounding about coming back home; hearing familiar accents, seeing old friends, revisiting places that shaped you, and realizing how much has changed while somehow feeling exactly the same.

Those first few days were less about business and more about perspective. Conversations with friends quickly shifted into discussions about the country, the economy, technology, entrepreneurship, and what the future could realistically look like.

And the recurring theme was clear: people are cautiously optimistic again.

Venezuela Tech Week: Imperfect, But Important

The main reason for the trip was attending Venezuela Tech Week, the country’s first large-scale technology and entrepreneurship conference focused on innovation, startups, investment, and economic development.

The idea behind the event was genuinely great.

The conference combined Venezuela Tech Week with the International Entrepreneurship Congress, bringing together entrepreneurs, investors, operators, policymakers, and technologists from across Latin America. There were attendees from Colombia, Argentina, and other regional ecosystems, which created an interesting mix of perspectives and networking opportunities.

That said, the event itself still felt early.

The organization lacked some structure, and many of the talks remained relatively high level without going very deep technically. I also felt the conference leaned too heavily into politics at times, when there was an opportunity to focus more aggressively on technology, venture capital, infrastructure, and practical innovation.

I believe a more execution-focused and less political approach could attract significantly more international VCs, operators, and technology partners in future editions.

But despite those shortcomings, the energy around the event mattered.

Because for the first time in a long time, there was a real attempt to position Venezuela as part of the broader Latin American technology conversation. And that is important.

The networking was probably the strongest part of the conference. I had conversations with founders, operators, engineers, consultants, and executives all trying to solve similar problems:

• How do we modernize legacy companies?

• How do we move organizations to the cloud?

• How do we modernize outdated tech stacks?

• How do we prepare companies for AI and automation?

• How do we create systems that can scale in a country with infrastructure instability?

The reality is that many private companies in Venezuela are still extremely early in their digital transformation journey. In some sectors, companies are years behind modern cloud and data practices.

And while that creates challenges, it also creates massive opportunity.

The leapfrog potential is real.

AI, Modernization, and the Opportunity Ahead

One thing that became obvious during many of these conversations is that Venezuela may actually have an opportunity to skip certain stages of technological evolution entirely.

A lot of companies are still modernizing foundational systems, but at the same time, the global AI wave is happening now. That creates a unique moment where organizations can rethink workflows from scratch instead of layering AI on top of decades of technical debt.

There is enormous opportunity in:

• AI-assisted operations

• Automation workflows

• Modern cloud infrastructure

• Data platforms

• Fintech modernization

• Government and public-sector digitalization

• Energy technology and grid innovation

Especially in the public domain, the amount of operational inefficiency and manual process still present means the upside for technology adoption is massive.

Conversations About the Economy

One of the most interesting parts of the trip was meeting with the vice president of a major Venezuelan bank.

We spent hours discussing the economy from both a macro and micro perspective: the evolution of the private sector, the realities of operating businesses in the country today, currency exchange dynamics, investment appetite, and the short- and long-term outlook for Venezuela.

The overall sentiment I encountered across many conversations was nuanced:

• The country still faces enormous structural challenges.

• Infrastructure remains fragile.

• Access to capital is limited.

• Institutional trust still needs rebuilding.

But at the same time, there is growing entrepreneurial activity, increasing private-sector participation, and a clear desire for modernization.

That said, it’s impossible to ignore that long-term investment and large-scale rebuilding will ultimately depend on stronger legal and institutional frameworks. Serious capital needs predictability, protections for investors, and policies that encourage entrepreneurship instead of penalizing it.

While there are still no definitive structural changes that fully guarantee those conditions, there are early signals of movement in certain sectors. The oil and gas industry, for example, has already started to see regulatory and investment shifts aimed at reopening the country to outside participation and capital.

My personal belief is that these types of changes will gradually expand into other industries over time. And even before that happens at scale, Venezuelans abroad can already begin contributing by building businesses, creating jobs with strong salaries, transferring knowledge, and helping improve quality of life for people inside the country.

Recovery will not happen overnight, and it will not come from any one sector alone. It will come from thousands of people deciding to build again.

Energy: The Country’s Biggest Problem, and Biggest Opportunity

If there was one topic that consistently overshadowed everything else, it was energy.

The electricity situation remains extremely difficult. Blackouts continue across many parts of the country, in some places lasting for long periods of time. Reliable infrastructure remains one of the largest bottlenecks to growth.

But it is also where I see one of the biggest long-term opportunities.

Toward the end of the trip, I met with an expert deeply involved in the Venezuelan energy sector. We discussed both the tactical needs of today and the long-term vision for the future: modernization, grid resilience, renewable energy, operational technology, and the role software and AI can play in transforming infrastructure.

Energy has been a major focus of my work over the last three years, particularly around digital transformation and AI initiatives in the sector, so these conversations were especially meaningful to me.

The challenges are enormous.
But so is the potential impact.

A Country That Will Need All of Us

I left Venezuela with mixed emotions: hope, excitement, concern, nostalgia, and motivation.

What became clear to me is that the country’s recovery, if it continues moving in the right direction, will require the participation of Venezuelans everywhere: entrepreneurs, engineers, operators, investors, builders, and professionals across every industry.

Not because anyone is coming to “save” the country, but because rebuilding systems, institutions, infrastructure, and businesses at scale takes collective effort.

There is still a very long road ahead.

But for the first time in a while, it feels like there is movement.
Like the country is beginning to reopen itself to the future.

I spent the last few days of the trip visiting the beautiful Morrocoy National Park and the kitesurfing town of Adícora, two places that reminded me yet again of the incredible natural beauty Venezuela possesses. But during those travels, I also experienced the country’s infrastructure challenges firsthand. Recurring blackouts remain a part of daily life in many areas, sometimes lasting for hours at a time. And on one stretch of the trip, we traveled nearly five hours without finding a single gas station that actually had fuel available. It was a stark reminder of how deep the infrastructure crisis still runs across the country.

It became impossible to ignore how foundational infrastructure truly is for everything else the country hopes to rebuild. Tourism, technology, industry, entrepreneurship, and investment all depend on reliable energy, transportation, connectivity, and public services.

At the same time, those experiences reinforced my optimism. Because if Venezuela can begin solving these core infrastructure problems, the upside across every sector of the economy could be enormous. Beyond technology, energy, and infrastructure, it’s impossible not to see the massive long-term opportunity in tourism as well. From beaches and mountains to culture and hospitality, Venezuela still has all the ingredients to become one of the most unique destinations in the region again.

And while the path will not be linear, I genuinely believe Venezuela has the talent, resilience, and creativity to build something remarkable again.

We are entering a new era, one filled with both opportunities and challenges, and I’m optimistic that, over time, we will once again see a beautiful, thriving, and prosperous Venezuela.

So let me do what practitioners rarely do publicly: be honest about what AI actually is, what it isn’t, and why most people talking about it have never actually touched it.

Let’s start at the beginning

Artificial Intelligence is the broad field of making machines do things that would normally require human intelligence. It has been around since the 1950s. It is not new. Fun fact, I have a minor in Artificial Intelligence. My capstone project was building an algorithm that could play Pac-Man on its own and win.

Inside AI lives Machine Learning, which is about teaching machines to learn from data instead of explicit rules. The two big flavors are supervised learning, where you train on labeled examples (spam or not spam, fraud or not fraud), and unsupervised learning, where the model finds structure on its own: clustering customers, detecting anomalies, compressing patterns.

Then came Generative AI, which trained models not just to classify but to create: text, images, code, audio. The architecture that unlocked it was the Transformer, from Google’s 2017 paper. But most people didn’t notice until late 2022. Generative AI is what most people are referring to as AI

On November 30, 2022, OpenAI released ChatGPT. One million users in five days. One hundred million in two months. The fastest consumer adoption in history. Not because the technology changed overnight, but because for the first time, anyone could just talk to it.

My first “wait, that actually worked?” moment

Shortly after that launch, I was building a proof of concept for a major insurance company. The idea was straightforward: a user describes their symptoms, and the system recommends relevant medicines and coverage options. I gave the model a handful of examples, symptom inputs paired with the right responses, and it generalized immediately, working on cases I hadn’t shown it at all.

I didn’t know it at the time, but what I had stumbled into was few-shot prompting, one of the core techniques in working with LLMs. You show the model a few examples of what good looks like, and it figures out the pattern. No training runs, no labeled datasets, no months of ML pipeline work. Just a well-structured prompt and a weekend.

The barrier had moved. The question was no longer whether we could build something like this. It was how well we could build it, and how responsibly.

The evolution nobody talks about

Most people think using AI means asking ChatGPT a question. That is like saying you understand surgery because you have used a bandage. The real journey goes deeper.

Prompting is where it starts. You learn that models are brutally literal. Tone, structure, and examples reshape the output entirely. You become a translator between human intent and machine interpretation.

RAG (Retrieval-Augmented Generation) is the next step: connecting the model to real data. Instead of relying on what it was trained on, you retrieve relevant context at runtime and inject it. Now it can answer questions about your internal documents, your customers, your products.

Agents are where things get genuinely interesting. You give the model tools; it can search, write and run code, call APIs, read files. Now it doesn’t just answer, it acts. Coding agents in particular have compressed my development timelines by an embarrassing amount. Building alongside an agent that can write, test, and debug code is the closest thing to having a senior engineer available at all hours.

The buzzword graveyard

The sales cycle for AI has created a species I can only call the AI-fluent non-practitioner. They can hold a 40-minute conversation about large language models without ever having built a prompt chain or debugged a hallucination. They make architectural decisions and product commitments based on vibes.

I have been in rooms where a salesperson promised a client that their AI would understand context across all their systems, and the reality was a keyword search with a ChatGPT summary stapled on top. That is not transformation. That is expensive word search.

AI-native, AI-first, and other promises

I would estimate fewer than 10% of companies calling themselves AI-native are actually operating that way. The rest are rebranding existing software with an AI veneer. In the best case, AI-native means the model is the engine, not a button in the corner. In the most common case, it means they added an LLM API call and updated the marketing site.

Trust AI. Just don’t trust it blindly.

The more I have worked with these systems, the less I trust them unconditionally, and I think that is healthy. LLMs hallucinate, they confidently produce plausible-sounding falsehoods, they are inconsistent, they are not reasoning engines; they are extraordinarily sophisticated pattern-completion machines.

This matters enormously when AI enters governance. Saudi Arabia has announced AI initiatives for judicial decision-support. The UAE has explored AI in regulatory review. I understand the appeal: faster decisions, less corruption, consistent rules. But an LLM that hallucinates legal precedent or encodes historical bias into sentencing recommendations is not efficiency. It is institutionalized error at government scale, and the people most harmed will be the ones with the least recourse.

A note from Venezuela

I write from Caracas, and that gives me a particular vantage point. AI is absolutely a conversation here. People are aware of it, curious about it, excited about it. But when I look at most companies around me, the honest reality is that the AI conversation is getting ahead of where they actually are.

Most are still navigating digital transformation: getting their data in order, moving off paper-based processes, adopting cloud tools for the first time. These are not small things, and they are not finished. You cannot meaningfully layer AI on top of operations that have not yet been digitized. The foundation has to come first.

So when the world talks about AI adoption, I think about the gap. Not with pessimism, but with clarity. The companies here that will benefit most from AI are the ones investing right now in getting their digital house in order. The technology will be there when they are ready. The question is whether they are building toward it deliberately, or just talking about it because everyone else is.

So what am I actually saying?

Play with it. Not just asking ChatGPT trivia. Build something. Call an API. Write a system prompt. Watch a model fail. Feel what it is like when an agent does something autonomously that would have taken you three hours.

The people shaping how AI gets deployed in your industry, your government, your city: most of them have never done this. That gap between decision-makers and practitioners is the most dangerous thing about where we are right now. More dangerous than any model capability.

The noise will keep coming. But somewhere, someone is building a real thing, with nothing more than an API key, a clear problem, and the willingness to try.

Go be that person.

Subscribe now

That’s where I found myself this year. A lot changed in the world, in tech, in my own life. And instead of watching it happen, I decided to move.

A little background: I grew up in Caracas in a middle class family that was always building something. My family had a book publishing & teaching materials company, and my dad has always been a natural salesman at the center of it all. Business and strategy was the normal environment around me as I grew up.

At some point, like so many Venezuelan families, mine scattered. One by one, almost everyone we knew left the country. Everyone except my parents. They were there through some of the country’s hardest years and never considered leaving. That helped me keep a strong connection to Venezuela.

I left at 17 to study in the U.S. Not knowing exactly what I wanted to do with my life, I found myself getting into Software Engineering. Eighteen years later, what started as a one way trip turned into a career in tech, a life I'm proud of, and a home in Miami with my wife.

In between the big projects and the client engagements, I kept coming back to the same thought: I want to build something of my own. Growing up around my family's business probably planted that seed early. It just took eighteen years of learning, working, and building my expertise to feel ready to act on it.

Leaving corporate wasn’t a hard decision once I was honest with myself. I started ODS Tech Consulting, a company focused on helping businesses navigate AI: not with big promises and complicated strategies, but with real engineering and practical solutions. We work with elite LATAM talent and we focus on getting things done without overcomplicating them.

Working deeply in AI over the past few years genuinely changed how I think about building things. Things that used to require big teams and big budgets don't anymore. I saw this firsthand helping clients scope and solution projects. What used to take ten people can now take three with the right tools and approach. That fits how I've always thought: don't add complexity you don't need, just get the thing built. AI didn't teach me that, but it made it a lot more possible. And that's exactly why the vision for ODS goes beyond just a technology services company. I want to help build and operate businesses in Venezuela too, because those same tools make it possible to build something real there today in a way that simply wasn't feasible before.

What's Happening in Venezuela Right Now

There is a lot to unpack and you have probably seen some of it in the news, but I want to focus on the professional and technology side of things. A couple of weeks ago I went to the Startup Venezuela Summit in Miami. This is my second time attending and the event gets better and better. Personally, I knew more people this time, there was a sense of optimism and inspiration in a room full of successful Venezuelans from all over the world doing incredible things. It was good to be in that room with the diaspora, people who left but never stopped caring about where they came from.

In a few days I’m flying to Caracas on one of the first direct flights from Miami in seven years. I was actually on one of the last ones out in 2019, so there’s something that feels full circle about this. I’ll be attending the Venezuela Tech Week Conference: 494 international attendees, 48 countries, 6 world regions. That’s a clear signal that people are paying attention and that something real is moving there.

The country is going through a real moment of transition and the energy is hard to ignore. Investment is coming back, entrepreneurs are doing serious work, and a tech ecosystem is starting to take shape. The people I'm most excited to see are the ones who stayed through the hardest years and kept going anyway, just like my parents did. My goal is to show up and genuinely contribute, not just because there is opportunity, but because I feel a real responsibility to give back to the place I come from.

I’m not trying to tie this up too neatly because life doesn’t really work like that. But when I look at everything happening at once, starting ODS, going back to Venezuela, watching what AI is making possible, it feels like alignment. Like I’m finally building something that’s actually mine and pointed at things I actually care about. I always wanted to do this and the timing just finally made sense.

If you’re a business leader trying to figure out your AI strategy and want to talk to someone who’s been in the weeds on this and not just in theory, I’m happy to connect. And if you’re part of the Venezuela tech world or just curious about what’s happening there, reach out. I’ll be on the ground next week and glad to share what I see.

Subscribe now

Subscribe now

Most companies think their biggest AI challenge is technology. It’s not. It’s people; specifically, how they’re organizing, hiring, and thinking about the engineering teams that are supposed to deliver on all those AI investments.

I’ve watched this play out across organizations of all sizes, from Fortune 500 to growing mid-market companies, and the pattern is consistent: the tools are getting adopted, but the team structures haven’t changed. That gap is where transformations stall.

This is my first post on Substack, and I’m excited to finally have a place to share what I’ve been seeing in the field. I have a software engineering degree and spent eight years at Bank of America, moving from engineer to lead to senior, building data and analytics systems at a scale most people don’t get to touch early in their career. Then five years at Slalom, consulting across banking, energy, fintech, logistics, and travel. Data pipelines, software platforms, AI systems. Rooms full of smart people trying to solve hard problems with technology.

For the last three years, I’ve been living inside the GenAI wave: building RAG systems, agentic workflows, running AI enablement workshops, and helping engineering teams actually absorb these tools into the way they work. Not in theory. In production. These are the patterns I keep seeing, and what I think you should do about them.

The New Shape of the Engineer

The engineer who thrives in this era doesn’t just write code; they scope problems. They build working prototypes in days, not sprints. They put on a product hat, ask “what are we actually solving for,” and then reach for the right tool from an increasingly massive toolbox: the right model, the right orchestration layer, the right retrieval strategy, the right evaluation framework.

The best engineers I’ve worked with recently remind me less of the 10x coder archetype and more of a technical product owner, someone who can move from problem definition to working demo to architectural recommendation without needing six layers of handoffs.

That’s a different profile. And most job descriptions, whether at a 50-person company or a 5,000-person one, still haven’t caught up to it.

You Don’t Need a Large Team. You Need the Right One.

This is where things get genuinely exciting, especially if you’re running a small or mid-sized business.

The old model was simple: big projects required big teams, long timelines, and large budgets. That kept a lot of ambitious ideas on the shelf for companies that couldn’t compete with enterprise-level headcount.

That model is breaking down.

I’ve seen teams of three or four engineers ship what used to take fifteen, when they’re properly equipped and AI-augmented. More importantly, I’m seeing a new engagement model emerge: small, focused teams assembled for a specific problem, for a defined period of time, that come in, build, and deliver. No bloated retainers. No year-long projects that lose momentum halfway through. Just sharp execution against a clear outcome.

For small and mid-sized businesses, this is a massive unlock. You don’t need to hire a full engineering department to build something meaningful. You need the right three people, the right tools, and a clear problem to solve.

The right frame isn’t headcount. It’s leverage. What gets built when your best people are no longer blocked by the tedious 70%? What becomes possible when you can spin up a capable team for twelve weeks instead of twelve months?

That’s the conversation worth having.

Flatten the Hierarchy, But Keep the Adults in the Room

Something interesting is happening at the senior technical leadership level. Workday’s CTO left the role to join Anthropic, not as an executive, but as a member of technical staff doing hands-on reinforcement learning engineering. Instagram’s co-founder and CTO made a similar move, stepping away from a CPO title to get back into building. These aren’t people who got pushed out. They’re choosing proximity to the work over the title.

That’s a signal worth paying attention to at any company size. The leaders who understand what’s being built right now, because they’re building it, are the ones making good decisions. The ones who are purely in PowerPoint mode are flying blind.

At the same time, flatten doesn’t mean flatten everything.

You still need the grown-ups. Senior engineers and architects who have seen systems fail at scale, who understand what “this will become a compliance problem in six months” looks like, who can tell a junior engineer why the clever solution is actually the dangerous one. That pattern recognition doesn’t come from a model. It comes from scar tissue.

For smaller companies especially, this person is often the difference between a project that ships and one that quietly becomes technical debt.

The Unsung Hero: The Platform Engineer

If there’s one role I’d bet on being consistently in-demand for the next decade, at every company size, it’s the platform engineer.

DevOps, Cloud, SecOps: the people who build the rails that everyone else runs on. As AI-generated code increases in volume and velocity, the need for guardrails, observability, cost controls, and security policies doesn’t decrease; it multiplies.

Who governs what models the org can use? Who sets the infrastructure guardrails for agentic systems that are spinning up compute dynamically? Who owns the audit trail when a GenAI system touches sensitive data?

Platform engineers. And right now, there aren’t enough of them. For smaller businesses without a dedicated platform function, this is often the hidden risk in their AI ambitions. Building fast without guardrails is how you end up with systems nobody trusts.

What This Means for You

Whether you’re running a ten-person startup, a mid-sized company trying to move faster, or a larger org feeling the pressure to modernize, the question is the same: are you building the team shape that actually captures the AI opportunity?

That means investing in engineers who can think across the full problem stack. It means embracing the model of small, focused teams with clear mandates and short timelines. It means making sure someone senior is in the room to catch what the tools can’t. And it means protecting your platform and security function even as you accelerate everything else.

The org chart designed for 2015 won’t get you to where you need to go in 2026.

I’ve been in the trenches of this transformation, and now working on helping companies of all sizes navigate exactly this. The teams that move well aren’t the ones with the most AI tools. They’re the ones who’ve thought clearly about the humans around the tools.

That’s still the hard part. And it’s still worth getting right.

This is the first of many posts where I’ll be sharing what I’m seeing at the intersection of AI, engineering, and business transformation. If you’re thinking through what your engineering org needs to look like in the AI era, whether that’s team structure, tooling strategy, or enablement, I’d love to compare notes. Subscribe, reach out, or leave a comment below.