My Journey into the Heart of Technology
From Wall Street Algorithms to AI Architectures
Earlier this year our viewership surged 5x because Naval linked to
on X/Twitter.Since then, we’ve had a few other posts “get picked up” and now our audience is much bigger than I expected to write to every week! If you’re here, you’re likely fascinated by the same things I am: the relentless march of technology, the elegant power of data, and the art of building businesses that last.
As result of this, and to make sure the BEST content possible is being created, we are moving in a premium direction with occasional free articles and 3 to 5 paid articles every month. The price is increasing to $18/month or $180 for the annual at the end of this month.
You can get a whole year for $129 right now. Lock-in that rate.
Why: Our article quality keeps improving but time is getting limited. I’m appearing on podcasts with 8-figure audiences, speaking at conferences and investing in tech start-ups. I just took over as COO of one of them. All of this is also competing with the 3 publications I write.
is focused on wealth building. is focused on technology. is focused on revenue engines.For years, I’ve operated at the intersection of finance, strategy, and deep tech, and this blog is my platform to share what I’ve learned, what I’m building, and where I believe we’re headed.
But before we dive deeper the intricacies of gradient boosting, revenue engines, and go-to-market strategies, I believe it’s important you know who’s behind the keyboard.
My story isn’t a typical one. I didn’t start in a garage in Palo Alto or emerge from a computer science Ph.D. program.
The Crucible: Forging a Foundation on Wall Street
My career began in investment banking.
It was a world of intense pressure, 100-hour workweeks, and an unyielding focus on numbers. We lived and died by spreadsheets (and lots of caffeine), financial models, and the ability to dissect a company’s health from its public filings. It was a phenomenal education in the mechanics of business. I learned what makes a company valuable, how capital flows, and how strategic decisions, good and bad, manifest on a balance sheet.
However, I also saw the limitations. The process was incredibly manual. An army of analysts would spend weeks digesting 10-Ks and 10-Qs, building intricate models that were, despite their complexity, static snapshots in time. We were searching for an edge. Some insight that the rest of the market had missed. But we were all using the same tools and reading the same documents. The competitive advantage often came down to who could work harder or faster, not necessarily smarter.
This nagging feeling that there had to be a better way led me to my first entrepreneurial venture: co-founding a hedge fund.
Our core thesis was simple but, at the time, technologically ambitious. We asked: What if we could stop reading financial statements and start processing them? What if a machine could ingest every quarterly report, every press release, and every footnote from thousands of companies simultaneously and identify patterns imperceptible to the human eye?
This was 2010-era… no LLMs in sight.
This was my first real foray into what we now casually call machine learning. We built systems to parse unstructured text from SEC filings, perform sentiment analysis on management’s discussion and analysis sections, and flag esoteric accounting changes that might signal future trouble or opportunity. We were essentially building a primitive form of Natural Language Processing aimed at a single, high-stakes goal: generating investment ideas.
The experience was transformative. Watching our systems ingest terabytes of data and surface non-obvious correlations was a revelation. It wasn’t just about speed; it was about a new kind of perception.
The machines weren’t just doing what humans did faster.. they were seeing the market in a way humans couldn’t.
That was the moment the spark was lit. I realized the most powerful force in the world wasn’t capital, but scalable intelligence. The hedge fund was a success, but more importantly, it had rewired my brain.
I was no longer just a finance guy. I was becoming a technologist.
From Investor to Operator: Building the Strategic Toolkit
The year 2011 marked a pivotal shift. Armed with a new perspective on the power of technology, I began investing in technology companies and even companies that were just tech-enabled.
My focus was clear: find amazing people building powerful, paradigm-shifting technologies and give them the capital and strategic support to succeed. This wasn’t just about writing checks; it was about understanding the fundamental drivers of a business and helping founders navigate the treacherous path to scale.
Over the next 14 years, I immersed myself in the core disciplines of company-building: Corporate Strategy, Business Development, Operations, and Finance. I moved from the investor’s seat to the operator’s chair, working inside companies to help them grow. This period was my extended MBA in the real world. I wasn’t just analyzing growth from the outside. I was in the trenches, responsible for making it happen.
My core focus became the “revenue engine.” This is a concept I explore full time at
, but at its heart, it’s the interconnected system of people, processes, and technology that a company uses to generate revenue. It’s not just sales or marketing; it’s the entire go-to-market motion, from the first touchpoint with a potential customer to the final renewal and upsell.I learned that the most successful companies treat their revenue engine not as a collection of disparate functions, but as a single, cohesive machine. And like any machine, it can be engineered, optimized, and automated. I became obsessed with using data and systems to build these engines to be powerful, efficient, and my favorite as an investor: reliable. Predictable revenue is the lifeblood of any scalable business, and I made it my mission to master the art and science of building the systems that create it.
This operational experience was the crucial missing piece. My time on Wall Street taught me what creates value. My time as an operator taught me how to build the systems that deliver that value, day in and day out. But a third, even more powerful wave was coming, one that would synthesize everything I had learned.
Artificial Intelligence, Real Opportunity
While I was busy architecting revenue engines using the best practices of the day, a quiet revolution was taking place. The confluence of massive datasets, affordable cloud computing and breakthroughs in neural network research was causing the cost of intelligence to plummet. Machine Learning was no longer a niche academic field or the exclusive domain of tech giants. It was becoming a general-purpose technology, a new kind of electricity that could be infused into almost any process to make it smarter.
Seeing the explosive growth in ML’s capabilities, it was obvious that this was the future. It was the ultimate tool for building the powerful, efficient, and reliable systems I was so passionate about. I knew I couldn’t just be a manager who understood ML; I had to become a practitioner who could build with it.
So, I went back to school to get structured learning on python and object oriented programming and the right ways to build systems.
I dove headfirst into the deep end. Outside of my classwork I consumed textbooks on statistical learning, took e-learning courses from the world’s best professors, and spent countless nights wrestling with Python libraries like Scikit-learn, TensorFlow, and PyTorch. I realized that to truly master the craft, I had to build. Theory is one thing, but applying it to messy, real-world problems is another.
My training ground became Kaggle, the competitive platform for data scientists. I threw myself into competitions, building models to predict everything from customer churn to house prices. Took home some Silver Medals in competitions! Kaggle is a humbling and exhilarating experience. You aren’t just building a model that works; you’re competing against tens of thousands of the brightest minds in the world. It forces you to be creative, rigorous, and relentlessly innovative. You learn the subtle art of feature engineering, the importance of robust validation, and the painful-but-necessary process of debugging a model that refuses to converge.
I supplemented this with hackathons, collaborating with teams to build novel systems under immense time pressure. My next hackathon is next week!
This practical, hands-on experience was invaluable. It taught me how to move from a raw dataset and a business problem to a deployed, functioning ML system. I became obsessed, not just with the “how” but with the “why.” I started reading the latest research papers from conferences like NeurIPS and ICML every week, staying on the bleeding edge of what was possible. This continuous learning cycle of theory, practice, and competition allowed me to develop my own proprietary techniques in data engineering and ML for solving real commercial problems.
Engineering Computational Intelligence
This journey, from finance to operations to deep ML expertise, culminated in the founding of McDonagh Technologies. Our company is the synthesis of everything I’ve learned. We have a singular mission: to translate cutting-edge science into performant, commercially successful technology.
We don’t just consult and provide diagrams or AI generated white papers.
We build. We are a team of engineers and strategists who create computational intelligence and layer it directly into the operational fabric of our partner companies. We design and deploy novel technical solutions, primarily machine learning systems, that solve a company’s most critical and complex challenges. Whether it’s a sophisticated demand forecasting system for a global retailer, a real-time fraud detection engine for a fintech platform, or a dynamic pricing model for a SaaS company, we build the intelligent core that drives business outcomes.
Our philosophy is built on three pillars:
Powerful: Our systems must deliver a significant, measurable impact. We are not interested in “AI for AI’s sake.” We focus on solving problems that, if cracked, create a step-function improvement in performance and a durable competitive advantage. This means deeply understanding the business context before a single line of code is written.
Reliable: In a commercial environment, a model that is 95% accurate but fails unpredictably is 100% useless. We build systems for the real world. This means an intense focus on robustness, scalability, monitoring, and retraining. Our systems are designed to be trustworthy, transparent, and to perform consistently under pressure.
Efficient: Brilliance without efficiency doesn’t scale. We design systems that are operationally efficient. This means elegant code, optimized algorithms, and a design that minimizes technical debt and maintenance overhead. An efficient system is one that can grow with the business without requiring a complete re-architecture.
Our team has had the privilege of applying this philosophy to a wide range of partners, from Fortune 500 firms looking to innovate within their vast organizations to scrappy, bootstrapped start-ups where every decision is a matter of survival.
This experience has given us a unique perspective on how to tailor state-of-the-art technology to the specific needs, constraints, and culture of any organization.
The Flywheel
My work doesn’t stop with a technology deployment or an investment. In fact, that’s often just the beginning. The second core pillar of my work is what happens after I invest in a company. I roll up my sleeves and partner with the founding team to power up their revenue engine using a discipline known as Revenue Operations, or RevOps. Working on creating the #1 publication in RevOps:
For me, RevOps is the ultimate synthesis of my skills. It’s a dynamic mix of high-level strategy, deep data analysis, and hands-on systems engineering. The goal is to build a powerful, efficient, and reliable go-to-market machine that is greater than the sum of its parts.
My approach focuses on meshing three critical layers:
Go-to-Market Best Practices: This is the strategic foundation. It involves defining the ICP, crafting a compelling value proposition, mapping the customer journey, and designing the sales and marketing processes that will guide a prospect from awareness to advocacy. This is the art of business.
Master Data Management: This is the unglamorous but absolutely essential bedrock of any intelligent system. You cannot have effective AI without clean, unified, and trustworthy data. I work with companies to establish a “single source of truth” for all customer and revenue-related data. This means breaking down data silos between CRM, marketing automation, and finance systems to create a holistic view of the business. Without a solid data foundation, any AI or automation initiative is doomed to fail.
AI and Automation: This is the force multiplier. Once the strategy is clear and the data is clean, we can layer in cognitive leverage. This is where the magic happens. We build predictive lead scoring models that allow sales teams to focus on the opportunities most likely to close. We deploy churn prediction models that give customer success teams a chance to intervene before a valuable client leaves. We automate routine tasks, freeing up talented humans to focus on high-value activities like building relationships and solving complex customer problems. This is the science of business.
By weaving these three layers together, we create a revenue engine that is not just effective, but also learns and improves over time. It’s a flywheel that spins faster and more efficiently with every new customer and every new piece of data it collects.
The Human Element: My North Star
I’ve spent a lot of time talking about data, systems, and algorithms. But I want to close with what is, by far, the most important element of all: people.
Technology is a tool. It is an incredible, powerful, world-changing tool, but it is nothing without human ingenuity, creativity, and passion to wield it.
The single greatest joy of my professional life is the opportunity to work with brilliant, driven, and creative problem-solvers. Whether they are founders with a world-changing vision, engineers wrestling with a complex technical challenge, or sales leaders motivating their teams, these are the people who truly build the future.
Building companies is the ultimate team sport. It is a messy, difficult, and often frustrating endeavor, but doing it alongside people who inspire you and challenge you is, in my opinion, the best way a person can spend their time. I truly love it.
This place is an extension of that passion. It’s a place for me to share my playbook, to explore the frontiers of technology, and most importantly, to connect with a community of fellow builders, thinkers, and innovators.
Thanks for being here. I hope you’ll join the conversation. The road ahead is more exciting than ever, and I can’t wait to explore it with you.
Friends: in addition to the 17% discount for becoming annual paid members, we are excited to announce an additional 10% discount when paying with Bitcoin. Reach out to me, these discounts stack on top of each other!
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I started Life in the Singularity in May 2023 to track all the accelerating changes in AI/ML, robotics, quantum computing and the rest of the technologies accelerating humanity forward into the future. I’m an investor in over a dozen technology companies and I needed a canvas to unfold and examine all the acceleration and breakthroughs across science and technology.
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Thanks for writing this, it clarifys a lot; your shift to premium content totally aligns with valuing expertise at the intersection of deep tech and strategy.