Lessons

Martijn Verbove@verboveLessons · #3

I keep seeing the take: “AI will wipe out jobs.” What I think we’re actually watching is classic economics reassert itself. When a technology drives the price of a product/service down, the market for it expands. Demand grows, supply has to catch up, and labor reallocates to the new bottlenecks. AI is a productivity shock. That means: • Some work gets automated (and yes, some roles shrink) • Prices drop • More people get access • Entirely new categories become viable • Human labor shifts toward what stays scarce: trust, taste, responsibility, physical execution, context, relationships I’ve seen this dynamic up close before. During Covid hotels cut staff, then demand returned overnight. Many hotels couldn’t operate at full capacity because the physical rooms existed, but the labor to run them didn’t. You can see a similar pattern in job markets like the Netherlands. The constraint is not “there is no demand.” The constraint is “where can labor actually flow to meet it?” Important nuance: it doesn’t mean everyone’s current skills will be in demand. It means the economy will keep rebalancing around scarcity. Zoom out: there are far more people who would like to eat out at restaurants than the current system can serve. If the process becomes more productive (less waste, lower cost, higher throughput), quality of life goes up and it becomes the new standard. Many things that used to be financially impossible will become normal. Like previous technology waves, this plays out over years, not weeks. The question isn’t “will AI remove jobs?” It’s “which constraints become the new bottlenecks, and how fast can we retrain and reallocate labor to meet expanding demand?” Curious how you’re seeing this in your industry: where is the bottleneck moving to?

Labor reallocation mechanism with case study

Martijn Verbove@verboveLessons · #4

The future of education is not “learning facts”. It’s learning how to build skills as systems. University should look less like: • memorize -> test -> forget And more like: • build repeatable systems for 1-2 years • using the latest tech + tooling • with real constraints (security, data, stakeholders, budgets) • in environments that resemble actual enterprises Because in the AI era, the value isn’t knowing the steps. The value is designing a machine that produces the outcome reliably. If you can build skills this way, you don’t just become “employable”. You become a force multiplier. You can: • replace parts of a team • automate a department’s workflows • or remove entire outsourced processes This is why “skill building” matters more than “credential collecting”. And why AI automation isn’t optional anymore, it’s the new literacy. The winners won’t be the people with the most knowledge. They’ll be the ones with the best systems.

Future education framework for skill systems

Martijn Verbove@verboveLessons · #5

You can't change the startup that failed, but you can reframe it. Find the pattern in it. Find the edge it gave you. Extract the insight and pass it forward. You can't choose what broke, but you can choose what you build next because of it.

Extract insights from failures to inform next builds

Martijn Verbove@verboveLessons · #6

There is a lot of founder advice circulating from people who genuinely built something real Most of it doesn't transfer. They succeeded because of who they are, not just what they did Copying their path without their context, network, and timing is how you waste years Take the signal, ignore the blueprint. Your edge has to be yours.

Context matters more than copying founder playbooks

Martijn Verbove@verboveLessons · #7

There is a lot of startup advice floating around from people who have "figured it out" Most of it will quietly wreck you if you follow it too closely, because it was built for who they are, not who you are Borrow the principles, ignore the playbook Their path is not your path

Borrow principles from others; ignore exact playbooks

Martijn Verbove@verboveLessons · #8

In my first company, I thought it was about the product. In my second, I realized it was about the distribution. In my third now, I realize it's about the timing.

Condensed evolution: product → distribution → timing matters