Building a scalable software product to serve an entire industry offers greater long-term potential than an AI roll-up model, where products are captive to only the businesses you acquire. The platform approach allows for compounding effects and a much larger market, aligning with a builder's skillset over an M&A specialist's.
While most AI tools focus on assisting users as a co-pilot, the next major unsolved problem is creating fully autonomous systems that execute mission-critical workflows. This represents a shift from AI as a productivity tool to AI as a core operational engine for businesses.
Large AI labs focus on solving problems in the most generalizable way, which can be an 'intellectually lazy' approach for specific enterprise needs. Startups can win by building the necessary last-mile components—harnesses, orchestration, and software—that labs are not structured or incentivized to create.
The fear of being made obsolete by AGI is creating a 'permanent underclass mentality' among some Gen Z workers. This manifests as an urgent need to acquire wealth or skills within a short timeframe (e.g., 18 months), fostering a 'shiny object seeker' mindset that is counterproductive to long-term, focused company building.
To truly assess a candidate's agency, look for a continuous pattern of initiative and follow-through throughout their entire life, not just a single impressive example. Whether in school, personal projects, or previous jobs, consistent agency is a stronger signal than one standout achievement.
The perception of industries like HVAC or roofing as slow to adopt technology is a misconception. These businesses are often 'primal' in their customer acquisition (e.g., door-knocking) but simultaneously tech-forward, using sophisticated tools like satellite data to optimize operations. They are value-focused and adopt technology rapidly when a clear ROI is demonstrated.
The traditional private equity playbook is evolving from finding undervalued 'gems' to creating tangible value in portfolio companies. While their first instinct with AI is often cost-cutting, they are increasingly open to using it for net new revenue generation. AI companies must lead this conversation by demonstrating clear, tangible ROI beyond simple cost reduction.