Every technology pitch sounds compelling. The demo works. The architecture diagram looks clean. The team is credible. But somewhere between the pitch deck and production deployment, things get complicated.
I started noticing this pattern while building my own systems. The hard problems were rarely where I expected them to be.
What Building Taught Me
When I deployed a RAG-based AI system into production, I learned that the model was never the bottleneck. The real challenges were data quality, integration complexity, and the gap between "it works in a demo" and "it works reliably at scale." Access to the underlying technology wasn't the differentiator—execution was.
When I built an autonomous drone platform, I discovered that the core technology is essentially open source. PX4 autopilot, MAVSDK, ROS 2—if I can build a programmable drone in my garage in Texas using freely available tools, then the barrier to entry clearly isn't the technology. The differentiation has to come from execution around the edges: reliability at scale, deployment operations, regulatory navigation, and unit economics. Most robotics ventures don't fail because the robot doesn't work. They fail because the business model doesn't survive contact with reality.
When I built a marketplace on Ethereum, I saw how easily technical architecture claims can obscure economic and governance realities. "Decentralized" often isn't. "Trustless" usually requires trust somewhere. The whitepaper and the deployed system are often different things.
The Questions That Matter
These experiences shaped how I think about evaluating technology claims:
For AI: Is the differentiation in the model, or in the data and workflow integration? What happens when the foundation models commoditize further? Where does the moat actually come from?
For robotics: Can this scale beyond pilot programs? What's the deployment ops burden? Does the unit economics work at the 1000th installation, not just the 10th?
For blockchain: Where are the actual centralization vectors? How do governance and upgrade mechanisms affect the trust assumptions? Does the token economics create sustainable incentives or just early-adopter extraction?
For any technology: What breaks at scale? What's the gap between the demo and production? Where do integration costs hide?
Why This Matters
Technology increasingly drives strategic outcomes—whether a product bet pays off, whether an acquisition integrates successfully, whether a partnership delivers value. The ability to evaluate what's actually there, not just what's presented, becomes essential.
The ability to separate what's real from what's marketing translates directly into sharper investment theses, tighter diligence, and strategic recommendations that hold up.
That context is hard to build from the outside. It comes from seeing how systems actually behave—where they break, what scales, what's still hype dressed up as product. I've been building that context deliberately, system by system.
The pitch is always clean. The implementation is where the truth lives.
