The highest-leverage opportunity over the next 12–24 months is applying engineering, automation, and systems thinking directly to revenue functions to reduce manual work and increase throughput by 10x. I sit at the intersection of revenue ownership and engineering leverage: I understand CAC, AOV, contribution margin, P&L, creative testing, ecommerce operations, and growth decision-making, while also developing the technical ability to build tools and systems that automate those workflows.

AI, APIs, Supabase, Vercel, and Claude Code have dramatically reduced the cost of building internal software. What previously required a software team can now be prototyped by a business operator. This creates a rare window where someone with deep revenue context can build tools faster than a traditional engineering team could scope them.

Revenue is one of the most important functions in a business because it directly affects growth, profitability, valuation, and survival. Yet many revenue workflows are still manual: reporting, creative testing, budget allocation, product validation, P&L modeling, and go/no-go decisions. If these workflows can be converted into repeatable decision engines, the output per person can increase dramatically.

The leverage comes from replacing manual human loops with systems. Instead of manually checking dashboards, creating reports, reviewing tests, and making one-off decisions, the system should ingest data, identify patterns, recommend actions, and create decision gates. The goal is not just automation for efficiency; the goal is faster learning per dollar spent.

Product may be more important than revenue because great products create organic demand and long-term defensibility. However, product teams already naturally leverage engineering. Revenue teams usually understand the use cases but lack technical skill, while engineering teams have technical skill but lack revenue context and tight feedback loops. The opportunity is to bridge that gap.

In 12–24 months, this could evolve from a personal skill stack into a true Growth Engineering capability: dashboards that make decisions, AI coaches that pressure test P&Ls, creative testing systems, product validation systems, and automated revenue workflows. This could make me more valuable than a traditional ecommerce or growth director because I would not just operate the revenue function — I would build the systems that scale it.

This Big Rock is successful if I ship at least 2–3 production systems that replace meaningful manual work. Examples include: a CPG product research coach, a Big Rocks operating system, an ecommerce decision dashboard, a creative testing engine, or a revenue workflow automation at Greenhouse. The strongest proof would be moving one recurring manual revenue workflow into an automated or semi-automated decision engine.

This Big Rock is justified as a Q2 priority because it compounds across my career, Greenhouse, future businesses, and potential founder paths. It is not just learning software. It is building the capability to turn revenue judgment into scalable systems. This is likely one of the highest-EV skill stacks I can build over the next 12–24 months.