Seven GTM Lessons from Scaling Robotics to 100 Factories

In a recent episode of Category Visionaries, Saman Farid, CEO and Founder of Formic, shared how his company deployed robots across 100 US factories by breaking every conventional rule in the robotics playbook. These aren’t theoretical frameworks. They’re battle-tested lessons from actually getting hard tech adopted at scale.

Lesson 1: Solve the Accessibility Problem, Not the Technology Problem

Most hard tech founders assume their challenge is making the technology work. Saman discovered the opposite after 15 years investing in robotics. “Despite the fact that the robotics technology works, there’s still this missing step in terms of getting it to a place where it’s useful for the people who need it the most,” he explains.

Before AWS, companies needed $50,000+ in servers just to run email. The technology worked fine, but only companies with massive technical capabilities could access it. “It wasn’t until hosted services or cloud services became much more prominent that we saw adoption at scale,” Saman notes.

For hard tech founders: your primary competition isn’t other startups with better technology. It’s the accessibility gap preventing customers from adopting any solution at all.

Lesson 2: Optimize for Reliability, Not Innovation

Most technical founders chase the newest technology. Saman made the opposite bet. “Let’s not necessarily go and find the newest technology out there for every kind of robot in the world. Let’s go find the things that are the most reliable and choose the path that leads to the highest robustness for our customers,” he explains.

This is driven by brutal economics. “With robotics, you don’t really get the chance to iterate quickly once you deploy something, if it doesn’t work, then you’ve just lost that customer,” Saman says. If your robot goes down for ten minutes, that customer is gone forever.

The performance bar is unforgiving. Academics pitch 98% accuracy algorithms. But the math tells a different story: “If you’re doing, let’s say, 10,000 operations per day and you’re only 98% accurate, that means you’re dropping hundreds of parts per day, making hundreds of mistakes per day, which is just completely unacceptable in a manufacturing environment,” Saman explains. Formic targets 99.98% or higher.

Lesson 3: Rebuild Your Business Model Around Customer Risk

Traditional robotics creates a predictable failure pattern. Buy a $100,000 robot. It fails. Pay $50,000 to reprogram. It fails again. Eventually it ends up in a “robot graveyard.” “Because there’s all these failures, people are not adopting new technology, and as a result, we’re ending with much lower productivity in our factories,” Saman explains.

Formic flipped the model. Try-before-you-buy. Hourly payment. Guaranteed performance. “We are providing the only solution out there that actually guarantees performance and guarantees throughput,” Saman says. “That means that we are able to provide robots to a whole class of customers that previously never thought they would adopt robotics.”

This required building unusual capabilities: $250 million in debt facilities, nationwide maintenance, predictive monitoring, and productized robots. But this infrastructure enables Formic to absorb customer risk, removing the primary adoption barrier.

Lesson 4: Fire Your Technical Salespeople

In robotics, everyone assumes you need technical salespeople. Formic discovered this was killing their sales. “I think one of the most important go to market decisions we’ve realized is that hiring technical salespeople is a bad idea,” Saman explains. “In robotics and automation, that’s quite controversial.”

The insight: supplement salespeople with technical solutions engineers. But you cannot fix salespeople who don’t understand customer pain. “We really pivoted towards hiring much less technical sales people, but instead focus on salespeople that really understand our customers and where they’re coming from,” he says. “And it’s made a world of difference.”

Formic doesn’t sell robots. They sell labor. Their customer doesn’t want robotics education. They want someone who understands their staffing crisis.

Lesson 5: Stop Iterating in Production

Software founders bring a “ship fast and iterate” mentality everywhere. Saman identifies this as a primary reason robotics companies fail. “In software, there’s this mentality that you can kind of iterate really quickly. In academia, there’s this mentality that the coolest technology wins,” he explains. “I think in the real world, what we found is neither of those are true.”

Manufacturing doesn’t tolerate iteration. “If your robot is down even 2% of the time, what that means is you’re dropping hundreds of boxes a day or hundreds of parts a day,” Saman notes. Your first deployment must work at 99.98%+ reliability immediately.

Lesson 6: Find Investors Who Already Believe

Having been a VC himself, Saman brought unique perspective to deep tech fundraising. “The more deep tech your product is, the less people there are that are gonna understand it,” he explains. You’re not convincing skeptics. You’re finding believers.

“If they believe it, you’ll have them quickly and early. And if they don’t, no amount of convincing is going to get them to the finish line,” Saman says. For hard tech founders, fundraising success depends more on investor selection than pitch quality.

Lesson 7: Severe Pain Lowers Adoption Barriers

Labor shortages in manufacturing are so severe that performance bars have dropped. “Because there’s such a shortage of labor, even if a robot performs at 50% of the speed of a human or 50% of the quality of a human, I think there’s still a lot of people that would adopt robots,” Saman explains.

“The labor shortages, it’s hard for people to grasp, if they don’t spend time in manufacturing, how dire the labor shortage is today,” he notes. This desperation means manufacturers will accept solutions that wouldn’t have been viable five years ago. Timing matters.

The Through Line

These seven lessons share a common thread: successful GTM in hard tech requires rejecting software assumptions about technology adoption. The winners optimize for accessibility over innovation, reliability over features, customer risk over technology risk, and customer understanding over technical expertise.

As Saman puts it: “Manufacturing is upstream of every other industry, whether it’s agriculture, whether it’s construction, whether it’s the military, whether it’s healthcare, none of those industries exist if you can’t build the parts and components that they all need.”

Most competitors won’t make these counterintuitive choices. Which means founders who do have a clear path to building category-defining companies.