Deep Tech Companies Are Built Different - by Leo Polovets

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Deep Tech Companies Are Built Different<br>A summary of the structural differences between software and deep tech companies.

Leo Polovets<br>May 22, 2026

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For the last decade, capital crowded into software because bits scaled faster than atoms. But across the economy, the bottlenecks are becoming physical again. The next set of generational companies will be the ones that use frontier technologies to remove these bottlenecks in huge markets where demand is urgent, incumbents are brittle, and durable moats can actually form. Some will create entirely new categories; others will drag multibillion-dollar industries out of the last century.<br>You can already see early signals of this. Data centers are straining the grid and creating urgent demand for new power generation, cooling, transmission, and storage. Defense budgets are shifting toward autonomy, munitions, sensors, shipbuilding, and resilient supply chains. Manufacturing capacity is becoming a strategic asset again, not just a cost center to be outsourced. Biology is moving from lab science into programmable therapeutics and industrial-scale production. Robotics is leaving the demo floor and entering warehouses, factories, farms, hospitals, and battlefields.<br>But deep tech is not just “harder software.” It is a different game, and founders and investors who understand its rules early will have a real advantage. Companies that remove physical bottlenecks will not be built, financed, or evaluated like software companies, and the key differences show up in three layers:<br>People and path dependence

Risk and reversibility

Capital and value creation

While many of the constraints below make it harder to start, those same constraints make the winners more defensible, better financed, more valuable, and harder to copy.<br>Stylistic note: I use ‘deep tech’ and ‘hardware’ somewhat interchangeably below, even though the fit is imperfect. The common thread is that these are atoms-heavy companies: startups where the hard part involves some combination of physical systems, specialized talent, regulation, and manufacturing.<br>People and Path Dependence

Deep tech pivots are far more constrained . A software company can pivot from dating to video (YouTube) or from games to productivity software (Slack). But a robotics company can’t pivot to nuclear energy or pharmaceuticals without resetting its entire team. That makes it critical to get the early direction right, but it also creates focus. The company is not trying ten products to see what sticks; it is compounding specialized knowledge, technical progress, and customer relationships around one hard problem.

Early design decisions can make or break a hardware company . Because pivots are hard, the initial idea has to be directionally correct, and the early design has to be thoughtful. Changing something like the length of a robot arm can affect motors, actuators, batteries, manufacturing processes, and supply-chain decisions. The cost of being wrong can be months or years instead of hours with software. But the reverse is also true: good early decisions compound, and a strong architecture can make the product easier to manufacture, cheaper to service, safer to deploy, and harder for competitors to replicate.

Founder-market fit is essential for deep tech businesses . Many software products can be built with good software generalists. But deep tech products require specialized talent: electrical engineers, mechanical engineers, turbomachinery experts, etc. Missing a key role can slow things down dramatically. These teams are harder to assemble, but once assembled they become part of the moat. This is a recurring pattern in deep tech: the things that make a company harder to start can also make it harder to compete with later.

Being in the same room is critical for physical products. Software products can be developed remotely if you have well-defined interfaces between components. After all, as soon as someone in New York updates their code, someone in LA can build on top of the new code immediately. Hardware is different. Components have to be developed and tested together, which requires physical proximity — otherwise your iteration speed declines dramatically.

This is why we have clusters like space startups in LA and biotech startups in Boston: these cities have enough local talent density to make building in-person easier in specific categories. These clusters are an advantage because they concentrate talent, suppliers, advisors, customers, and repeat founders in ways that accelerate company progress.

Deep tech talent is scarcer but more mission-driven. There are approximately four million software engineers in the US, but only 20k RF engineers, 10k nuclear engineers, and 2500 turbomachinery engineers. If you want to hire a top 5% engineer, it’s much harder in hardware because you’re often looking at pools of hundreds or low...