Billions of Small Minds
We overestimate what technology can do this year and underestimate what discipline can compound over ten.
I sat in on a private session with the founder of a fast-growing AI company applying intelligence in the physical world. The kind of work that helps people see, move, and decide in real time.
At one point he said, “The world doesn’t work on if/then statements.”
He meant that the real world is never binary. A sensor can tell you if something is hot or cold. But intelligence, human or artificial, needs to understand context. The weather, the surface, the motion, the pattern. What is happening around the thing, not just to it.
That simple point hit me as profound. Because it is the same trap many leaders fall into when deciding what to build or where to focus. They think in if/then statements. If the market changes, then we pivot. If the technology works, then we scale.
But the world does not cooperate with conditional logic. The variables are too many, the timing too messy.
What this founder understood, maybe without saying it outright, is that clarity comes from seeing what is truly happening now. Focusing where the need is real and measurable. That is how you earn the right to build what comes next.
It reminded me of lessons I learned years ago about timing, complexity, and when to go all in.
In 2007 my company began running conversion tests for eCommerce sites. Early A/B and multivariate testing. I was convinced it would change everything.
We could measure what made customers buy, refine design choices in real time, and build a flywheel of growth that competitors could not copy. The logic was perfect. The results were tangible and incredible.
And still, almost nobody cared.
Some did not trust it. Others found it too complex. Many preferred intuition. Most preferred to simply copy what others were doing. We were right, but early.
A few years later we built price optimization systems that used real-time data to find the profit sweet spot. Again, the math worked. But organizations were not ready. Pricing touched supply chains, partners, brand perception, and too much friction followed.
Those experiences taught me that technology rarely fails on capability. It fails on timing.
Markets move when systems are ready to move. Technical readiness, organizational readiness, emotional readiness.
Today everyone is talking about AI
Markets have priced the S&P 500 as if productivity transformation is already here. But the numbers tell a different story.
Only 3.8 percent of U.S. firms currently use AI to produce goods or services, according to the U.S. Census Bureau. A 2024 BCG study found that 74 percent of companies have yet to show tangible value from their AI investments.
Even among leaders, most of the thinking still happens in pilot programs and slide decks, not in the real economy.
Meanwhile, infrastructure is still being built. McKinsey estimates that 70 percent of global data-center demand by 2030 will support advanced AI workloads, a number that shows how much plumbing remains unfinished.
Every technology curve looks flat for a long time. Then, suddenly, it bends upward.
Most leaders misread this curve. They either rush in too early, burning resources before the system is ready, or wait too long and miss the compounding when it starts.
The best operators know the difference. They look for alignment between readiness and return. They invest when the signal is clear enough to move the system, not just their curiosity.
Meta’s “mobile first” move came at exactly the right moment. Amazon’s patience with AWS was the opposite side of the same skill, knowing when the world is about to catch up to your conviction.
AI’s curve will bend, but it will not bend the same way for everyone.
In some industries the use cases are already obvious. Being able to see into buried pipes with context, for example, where basic cameras fail because the footage once required human analysis. That capability now exists in the field, creating real operational value.
In many other areas, the return on AI still depends on experimentation, infrastructure, and organizational change. The signal is weaker and the cost of chasing it too early is high.
Leaders cannot rely on a single market curve to guide them. They have to localize it to their own business and environment. The question is not whether AI transforms the economy, but whether the conditions around their specific use case are ready for it.
That decision is a down payment on validated learning. Go slow enough to learn. Move fast when the learning is clear.
In a world that rewards fast learning, the real constraint is not effort. It is speed of impact.
A simple way to use this in planning is to rank initiatives by three variables…
Impact measures the potential business result, such as revenue, margin, or risk reduction.
Speed measures how quickly the impact becomes real.
Scope measures how complex or disruptive it will be.
Score each initiative on a 1–10 scale, debate the rankings with your team, and decide which opportunities are ready for meaningful investment and which should remain in exploration mode.
When you do this, you stop chasing the broad AI curve and start shaping your own pace of impact.
If you want a simple tool to put this into practice, I built the Speed-to-Value Matrix. It’s a clear way to see which ideas create value fast and which can wait.
Scott
PS: Notes from the research
• 74 percent of companies still struggle to achieve measurable value from AI (Boston Consulting Group, AI Adoption in 2024).
• Only 3.8 percent of U.S. firms currently use AI to produce goods or services (U.S. Census Bureau, Business Trends & Outlook Survey).
• 70 percent of global data-center demand by 2030 will support advanced AI workloads (McKinsey, AI Power and Infrastructure Report, 2024).
• 75 percent of enterprise data is expected to be created and processed outside traditional data centers by 2025 (Gartner, via Otava, 2024).
• The Edge AI market is projected to exceed $160 billion by 2030, growing about 24 percent annually (NGP Capital, Edge AI Market Outlook, 2024).
Couldn't agree more. That "world doesn't work on if/then" insight is so spot on. It's like in Pilates, you can't just think "if I lift my leg, then it's correct." You need context – core engagement, breath, alignment. You really nailed how complex reality is. Great read!