Andrew is an Investor with Prosperity7 Ventures, specializing in deep tech and AI infrastructure. Some of his notable investments include Groq, Together.ai, Gimlet Labs and Upscale AI. He joined Prosperity7 in 2024 after 2 years with Barclays’ Technology Investment Banking team in Menlo Park, CA. A BYU finance graduate, Andrew enjoys golfing, weightlifting, motorcycle riding, snowboarding, and is a Washington Commanders season ticket holder.

What is a value, principle, or philosophy that has meaningfully shaped your journey? How does it continue to influence how you invest, lead, or approach challenges?

One principle that has defined my journey more than any other is First-principles thinking — refusing to accept a narrative simply because it's widely held, and instead rebuilding understanding from the ground up until you arrive at your own conclusions.

In venture, and particularly in deep tech and AI infrastructure, this matters more than almost anywhere else. The space moves fast, the terminology is dense, and there is no shortage of confident, consensus-driven narratives about what the next platform shift will be. First-principles thinking is the antidote — it forces you to ask "what is actually true, and why?" when everyone else is asking "what is everyone saying?".

That rigor is also what gives you the courage to be early. The most important investments in AI infrastructure rarely look obvious at the time of commitment. They require you to understand the technical architecture deeply enough, the market structure clearly enough, and the competitive dynamics honestly enough to see what others haven't yet.

For me, being early is never about contrarianism for its own sake — it is a natural byproduct of doing the work at a level of depth that produces genuine conviction before the broader market catches up. First-principles thinking doesn't just sharpen analysis, it earns the right to act before the crowd arrives.

Is there a technology, idea, or movement that most people overlook (or dismiss) which you believe will define the next era of innovation? What draws you to this perspective?

I am most bullish on photonic networking solutions going forward. AI today is achieving maybe 15% of a GPU’s full capacity under the current constraints, due to existing computer architectures that inefficiently shuttle data in and out across copper wire interconnects.

Copper interconnects send data via electrons across copper cables. They can run at high speeds within short distances, but they consume massive amounts of power and generate massive amounts of heat. When data centers generate too much heat and consume too much power, the world will run out of available power supply before next-level AI can be unlocked.

This problem not only exists connecting GPUs to other GPUs (scale-in networking) but also between GPUs and their memory sources, GPUs and CPUs, different trays within a rack (scale-up networking), and different racks within a data center (scale-out networking).

Photonic networking, on the other hand, sends data via light waves across fiber optic cables. This makes computing more efficient so that less power is generated, less heat is emitted, and AI runs faster, achieving closer to its full potential.

Jevons' paradox suggests that photonic networking's efficiency gains wouldn't reduce total power consumption — instead, cheaper compute per watt would drive greater overall compute demand, simply shifting the bottleneck to the next constraint in the stack (Theory of Constraints). I wrote an article exploring how the Theory of Constraints could potentially play out in our current environment.