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Dear Friends,
I’m back with a new piece in the 30 Lessons Learned series on Deep Tech.
After the recent pieces on execution and exit strategy, this naturally sits between those two points.
In Deep Tech, scale-up is one of the most difficult and fascinating parts of the venture-building journey.
It is the moment when a company stops proving that something can work once and starts proving that it can work repeatedly, economically, safely, and reliably.
It is where the lab meets the factory, where the pilot meets the customer’s operating reality, and where technical performance has to become throughput, yield, margin, quality, delivery, and trust.
Of course, scale-up is difficult to generalize.
It does not look the same in semiconductors, energy, advanced materials, robotics, mining, aerospace, or biomanufacturing.
Each sector has its own bottlenecks, qualification cycles, capital needs, regulatory paths, customer behaviors, and operating constraints.
But even if the details change, some questions, moves, and challenges keep coming back.
That is what pushed me to write this piece.
As always, the result is not a definitive map of every possible outcome.
It is a curated and distilled set of lessons learned along the way.
If you build, back, or study Deep Tech, I think these lessons will help you ask sharper questions about scale-up design — and maybe avoid discovering too late that the company you built is not as easy to scale as you once assumed.
Lesson 1
Repeatability is the first language of scale.
Scale-up begins when a company stops proving that the technology can work once and starts proving that the system can make it work repeatedly.
Can the company deliver the same result across batches, sites, operators, customers, and time? One successful build can still hide a fragile process. Scale requires documentation, supplier qualification, inspection, training, quality systems, and process control. The question is no longer whether the best engineer can make the product work once. It is whether the company has built a system that can make it work without extraordinary effort.
Lesson 2
The first plant should be a learning machine.
Every production run should make the next one smarter.
The first industrial plant needs to support early production, but its deeper job is to teach the company how production really behaves. It should reveal process windows, labor requirements, supplier performance, quality controls, customer specifications, working capital needs, and unit economics. If the first plant creates volume but not learning, it is underperforming its most important role.
Lesson 3
The techno-economic model should challenge the company before the market does.
The best models do not make the story look investable. They reveal whether the business can survive contact with scale.
Techno-economics does more than support fundraising. It forces the team to connect technical assumptions to CapEx, OpEx, pricing, revenue, and margin. It shows which variables matter most, which improvements actually change the business, and where the company is most exposed.
Lesson 4