RevSystems

On the surface, here’s my story: - Launched a hedge fund - Fund failed... pivoted to tech investing - Sold cybersecurity company to Facebook - Built the premier AI consultancy RevSystems - Now invested in 25+ companies with an amazing network of brilliant passionate people When you dig deep into the details you realize: - Dealt with incredible imposter syndrome launching a fund as a 25yr old - Fund failing multiplied my fear of being a fake by a million - First 3 companies I invested in went to $0 - peak fear at this point - Almost got ripped off the cap table of that Facebook deal There is an incredible power in adversity. There is a small window between stimulus and response. You must be brave in that window and act with consistency and resilience. Be thankful for everything you overcome. Growth requires struggle.

You can have transformational AI without the catastrophic trade-offs. We know that successful AI doesn't start with buying the trendiest tool. It starts with analyzing the work. We align your leadership team to: 1) Pinpoint exactly where AI drives impact in your operating model. 2) Execute the necessary change management around it. 3) Establish strict metrics for true leverage. We help you build, integrate, and scale AI workflows that protect your IP, preserve your margins, and keep you in total control. Every company wants the massive leverage that AI promises. But in the rush to adopt, too many firms are blindly handing over their most valuable asset (their proprietary data) to massive tech monopolies, bleeding margin along the way. Don't rent your company's brain to a frontier model. Compound your cognitive capital with RevSystems.

Most corporate AI strategies are just expensive science experiments. Why? Because they start by handing out software licenses and hoping for the best. AI adoption fails when it starts with tools. It compounds when it starts with work. If you want real leverage, your leadership team needs to answer three questions: 1) Where does AI actually fit into our operating model? 2) How does the business need to change to support it? 3) How do we mathematically prove this is working? At RevSystems, we guide firms through this. More importantly, we do it safely. You shouldn't have to surrender your company's intellectual property to train someone else's frontier model 😅 and you definitely shouldn't be giving away your margins just to stay competitive. Let's build AI leverage that you actually own.

Abundant cognitive labor does not remove bottlenecks. It moves them. When output becomes cheap, coordination becomes expensive. When drafts become abundant, taste becomes scarce. When research becomes cheap, synthesis becomes scarce. When code becomes cheap, architecture becomes scarce. When content becomes cheap, distribution becomes scarce. When agents can work in parallel, the bottleneck becomes knowing which workstreams should exist, how they should connect, and what standard they must meet. This is why management does not disappear. It becomes more technical. The manager of the future is not just assigning work to humans. The manager is designing a production environment where humans and agents can generate, inspect, correct, and ship work reliably. That requires new operating muscles.

AI leverage starts with work design. We need less AI theater. More business leverage. I know a veeeeeeeeeeeeeery nerdy guy who's perfect to help. Align people, processes, and platforms for the AI era with RevSystems.

AI adoption fails when it starts with tools. It compounds when it starts with work. Your AI strategy must align leadership around where AI should enter the operating model, what has to change around it, and how the company will know whether leverage is real. That's what I focus on at RevSystems. Helping firms unlock the full value of AI without signing away their IP to the frontier models (and sharing WAY too much of their margin!).

McDonagh Technologies turns 14 next month. I'm feeling sentimental and want to share a few stories. TLDR: an ounce of success requires a ton of failure, it turns out! My first invention was a unique method to efficiently ingest financial statements and mechanically analyze them. I needed a Hold Co for the IP to license it to the hedge fund we launched... so McDonagh Technologies was born. Throne Capital failed (what a name.. no hint of ego at all!) but my love for technology was just getting started. I began investing into promising machine learning companies. So promising in fact that 3 of my first 3 angel investments went to $0 🙃 Eventually, one of our investments into cybersecurity was acquired by Facebook. This led to my second invention: a heartbeat pattern that listens to the CRM and other business systems, and then sends proactive signals to revenue leaders and executives. RevSystems is built to design, deliver and help operate high-performance revenue systems. I've seen 2,000+ companies in 10-years between angel investing and building these systems. As I saw all of the incredible things people were building, it dawned on me how much of our lives are now digital.. and how much of our wealth going forward is going to be made (and stored) online. That lead to my third invention: an autonomous cybersecurity platform that continuously studies authorized surface areas, identifies weak evidence, proposes attack hypotheses, tests exploitability under scope, verifies remediation, and preserves an auditable trail. It took a failed hedge fund, a few zero-dollar investments, and a decade of analyzing 2,000+ companies to realize my next focus: security. You can build the greatest AI, the most seamless CRM, or the highest-performing revenue engine in the world... but if your digital surface area is exposed, none of it lasts. 14 years of McDonagh Technologies has taught me that the best ideas usually come from solving the problems you face along the way. We are moving away from reactive cybersecurity and building an autonomous, continuous testing engine that thinks like an attacker so you don't have to. If you’re securing digital infrastructure and are tired of point-in-time assessments that are outdated the minute they're finished, my DMs are open. Let’s show you what proactive, autonomous defense actually looks like. 🛡️ Here’s to the next 14 years of building, iterating, and protecting what matters most!

Most agentic systems are still task runners with loose memory, weak provenance, and vague claims. It takes more than that to truly build leverage with agents. State, evidence, audit, replay, authority boundaries, and operator controls all need to be engineered into the design. Trust isn't magically granted because an LLM can parse a PDF. Trust is earned through deterministic reliability and transparent execution. By prioritizing robust architecture over flashy demos, we finally empower firms to safely scale. The next generation of AI won't be defined by who has the smartest foundational model, but by who builds the most resilient execution environments. We need to shift our focus from giving agents more autonomy to giving them better infrastructure. Only then will we transition from unpredictable chatbots to reliable digital colleagues that drive measurable impact. Reach out to me if you are on this journey!

Most companies are still structured around human bottlenecks. Meetings. Approvals. Hand-offs. Status updates. Permission layers. Fragile processes hidden inside people’s heads. These systems were designed for a world where labor was scarce, communication was slow, and execution required humans moving one task at a time. That world is ending. Friction drops when work reorganizes around agents. At OpenAI, Codex became dominant across functions, not just engineering. Legal, recruiting, research, product, communication, and data workflows all moved toward agentic execution. This is the pattern every serious organization should study. The question is not whether your employees will use AI. The question is whether your company can restructure work fast enough to absorb agentic labor. RevSystems is built to help firms get the most leverage from AI.

In late 2025, I went into a 6-week development sprint that rewired how I view productivity. I built a web-based OS designed to compound cognitive capital across the enterprise. It mapped complex workflows and automated manual steps wherever it was cost-effective and reliable. That core technology is now sitting inside a rapidly scaling, venture-backed company. Thought that was the ceiling of what one person could build. Then Summer 2026 arrived, and the ceiling disappeared. Agent harnesses, and the models flowing through them, have both reached new heights. Over the last month I've leveraged Codex from OpenAI to engineer a fully autonomous AI research lab. This digital facility operates around the clock to formulate hypotheses, test complex mathematical models, and synthesize groundbreaking engineering solutions without human intervention. We are funneling the raw output of this autonomous research lab directly into three companies, and RevSystems is building versions of this system for hedge funds and other private pools of capital. And very soon, we will be unveiling our first public-facing system: Apex. 👀 At RevSystems, we are redefining what a single human can achieve in 24 hours. Imagine opening your phone, reviewing 10 strategic plans, deciding where to deploy 100 hours of deep work, and letting a fleet of agents handle the execution. We are already seeing top-tier users squeeze 70+ hours of output into a single day. The 1000:1 leverage era is imminent. But to get there, we have to change how we build. Right now, the industry is distracted by AI experiments. Isolated scripts and party tricks. But experiments don't scale. Systems do. Systems provide the structure needed to turn raw intelligence into engines of exponential growth. The future belongs entirely to those who compound their time. We build those enduring systems at RevSystems. I'm also investing in companies building those systems personally and through our family office, McDonagh Family Office. The future belongs to those who compound their time. The market will soon fracture between operators who manually execute tasks, and architects who orchestrate autonomous labor. Brute-force hard work is obsolete. The future belongs to those who build smart, automated systems of leverage. I am dedicating my life to building the engines and backing the people that will drive this new reality. The goal is simple: Create continuously compounding leverage with AI.