Every significant technology shift follows the same pattern: real capability arrives, a larger wave of hype follows, and somewhere in the middle most organisations make expensive mistakes. AI is not different - it is a more compressed version of transitions that experienced operators have seen before. The executives who navigate it well are rarely the ones who understand the technology best. They are the ones who know what questions to ask, and what to ignore.
Those principles have a track record. Constellation Software did not build 30%+ annualised returns by chasing trends. It applied a small set of rigorous principles across hundreds of businesses: pick the right niche, measure what actually matters, grow talent from within, hold your best assets indefinitely. Those principles are not about software. They apply to any serious business decision - including the ones AI is now forcing onto every leadership team.
That is what Rule 30 is about.
The Principles
Not frameworks. Operating habits tested across hundreds of businesses.
- Niche dominance over market share. Own a small market completely rather than chase a large one poorly. Scale follows focus.
- Measure the right things. Vanity metrics hide problems. The numbers that matter are rarely the easiest to collect.
- Grow talent from within. People who understand your customers and culture outperform imported talent. The exceptions prove the rule.
- Acquire carefully, hold indefinitely. Overpaying for growth is not growth. The best businesses are rarely for sale at the right price.
- Decentralize accountability. Decisions belong with the people closest to the customer, not head office.
Applied to AI
Apply the same criteria you would use for any major capital or operational decision.
- Where AI creates real leverage. Not every process benefits. Focus on the few where AI changes unit economics - and ignore the rest.
- How to evaluate an AI investment. Apply the same criteria as any capital decision: what problem does it solve, what does success look like, and how will you know if it is working.
- What your board needs to ask. The right questions are not technical. They are about risk, dependency, and measurement. Most boards are asking the wrong ones.
- How to build without getting burned. Most AI failures are not technology failures. They are clarity failures - teams that did not define what they were trying to achieve.
Most AI advisors have never run a P&L through a technology transition.
Most operators who have don't understand AI well enough to advise on it.
Rule 30 exists because that gap is real - and expensive.
We work with executive and leadership teams at established organisations, public and private, facing real AI decisions who need clear thinking, not another vendor pitch.
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