David Pinn, CEO of Brain Corp – Interview Series

David Pinn is CEO of Brain Corp, where he has spent nearly nine years helping shape the company’s strategy, financial operations, and transition into a software-driven robotics business. He joined Brain Corp in 2017 as VP of Strategy and progressed through senior strategy and finance roles before becoming CEO in 2022. During his tenure, Pinn has helped lead major capital raises, the company’s shift from a hardware-centric model toward recurring software revenue, its expansion into robotic data capture and analytics, and strategic partnerships with global original equipment manufacturers (OEMs). Earlier in his career, he held finance and strategy positions at Qualcomm, advised venture-backed technology startups, worked with L Catterton portfolio companies, and began his career at Teradyne in engineering and technical sales, giving him a background spanning robotics, semiconductors, finance, corporate development, and technology commercialization.

Brain Corp is a robotics and physical AI company that develops BrainOS®, an enterprise autonomy platform designed to enable manufacturers and businesses to deploy and manage autonomous mobile robots at scale. BrainOS powers more than 50,000 robots across six continents and has accumulated more than 25 million hours of real-world operation, supporting applications including autonomous floor cleaning, inventory and shelf-data collection, and material movement in environments such as retail stores, warehouses, airports, healthcare facilities, and commercial buildings. The platform combines AI-driven navigation, semantic 3D mapping, safety systems, cloud-based fleet management, analytics, and integrations with enterprise systems, with data gathered across its deployed fleet helping improve navigation and reliability over time. Recent developments include BrainOS Clean 2.0 and SelfPath™ AI, which enable cleaning robots to automatically generate and adapt their routes rather than relying on manually trained paths.

Your career has moved from engineering work in radio frequency and wireless systems to strategic finance, SaaS transformation, and now CEO leadership at Brain Corp. How has that mix of technical and financial discipline shaped your view that businesses are not buying “robots,” but measurable operational outcomes?

My early career started in technical sales, selling semiconductor manufacturing equipment for Teradyne. Through a lot of trial and error, I learned an important lesson early on: there was a big gap between what I thought was important—usually the technical specs—and what the buyer actually cared about. I learned that the buyer really cares about the business outcome, and whether they trust me and my team to actually deliver it.

When I later transitioned into robotics, that lesson was amplified. Every technology investment competes for capital, and a system doesn’t get credit just for being technically impressive. In robotics, it’s so easy to get captivated by the machine itself. But a retailer doesn’t wake up wanting a robot. They want cleaner floors, highly accurate inventory, and more time for their associates to provide customer service. The robot is just the delivery vehicle for that outcome.

At Brain Corp, we start with the customer’s problem, define the business result, and work backwards into the autonomy layer and data required to deliver it. My engineering background helps me appreciate how deeply complex the underlying technical problem is, but my sales and finance disciplines keep me focused on whether solving it creates a scalable, commercially viable business. You need both. A robot that works but can’t produce an economic return is a science project, not a product, and a financial model built around unreliable technology is not a business.

With so much attention going toward humanoids and general-purpose robots, what do you think the market misunderstands about what enterprises actually need from robotics today?

Hot take: the industry’s obsession with humanoids in commercial and industrial settings is a distraction.

The standard argument you hear from humanoid enthusiasts is, ‘The world was built for humans, therefore robots need to be shaped like humans.’ That might be true in residential settings, but for enterprise settings, it is fundamentally false.

Look at a warehouse. It isn’t built for humans; it’s built for forklifts, pallet jacks, and heavy machinery. Look at retail stores, schools, hotels, hospitals, and airports. Wheelchair accessibility requirements mean these spaces have flat floors, ramps, wide aisles, and elevators. Commercial and industrial buildings are designed for wheeled movement.

When you realize that, you quickly see that putting legs on a commercial robot is an unnecessary liability and a massive waste of money. Why engineer an expensive, fragile bipedal system to navigate a space that is already suited for wheels? Wheels are cheaper, more reliable, and handle payloads better.

As I mentioned earlier, a robot without an economic return is just a science project. Building a highly complex humanoid just to walk across a flat, wheelchair-accessible floor is the ultimate science project. It’s solving a locomotion problem that doesn’t exist. Enterprises don’t need human-mimicking machines; they need tools that reliably deliver a specific business outcome. And for commercial use cases, wheels win every time.

Brain Corp operates in messy, public, high-traffic environments like stores, airports, warehouses, hospitals, and campuses. What are the hardest real-world problems that don’t show up in robotics demos?

The autonomous vehicle industry learned the hard way how deceptive demos can be. We saw self-driving car demos over a decade ago, but it took another ten to fifteen years of intense engineering to actually launch commercial driverless taxis. Why? Because of what the industry calls the ‘march of the nines.’

Getting a system to be 90% reliable makes for a great demo. Getting to 99% is a massive engineering achievement. But when you are operating a fleet of tens of thousands of robots across thousands of public locations, 99% reliability is insufficient.

In a live commercial environment, people change direction without warning. Shopping carts are left in aisles. Displays move overnight. Pallets block routes. Floors can be reflective, wet, uneven, or covered with debris. Doors open and close, lighting changes, and employees develop their own ways of interacting with the machine. A robot may also encounter children, people using mobility aids, distracted shoppers, forklifts, or someone who simply decides to stand in its path.

The hardest problem is not navigating when everything behaves as expected. It is adapting gracefully when something unexpected happens. Can the robot understand why its route is blocked, find a safe alternative, and complete the task? Can it do that without creating more work for employees and at scale across millions of hours? 

There is also a social dimension that demos often miss. People need to understand a robot’s intent. Its movements should be predictable, its signals should be clear, and it should fit into existing human workflows rather than forcing everyone around it to adapt.

To us, real-world AI means “it just works”. The edge cases are not at the edge, they are the environment. Building for those conditions requires years of operational learning, thoughtful safety architecture, and a relentless focus on the details that are invisible in a simulated video.

When a company evaluates robotics ROI, what metrics matter most: labor hours saved, task consistency, uptime, data capture, safety, customer experience, or something else?

It’s all of the above.

Labor hours saved is useful, but it does not capture the full picture. In many industries, the underlying issue is not simply that labor is expensive; it is that recruiting qualified people can prove challenging, turnover is high, and important tasks are completed inconsistently because teams are stretched. In that situation, task completion, frequency, and consistency may be more valuable than a theoretical headcount reduction.

The next layer is operational performance: uptime, autonomous utilization, successful task completion, coverage, and the amount of supervision required. A robot that technically operates for many hours but frequently needs to be rescued is not delivering meaningful autonomy.

Improvements to safety and compliance should not be overlooked within an ROI model either. A cleaning robot reduces workplace strain and injuries, and gives employees more time to focus on customers and higher-value work. An inventory robot improves on-shelf availability, pricing accuracy, and execution consistency. Robots also create a new layer of operational visibility by capturing reliable, recurring data about what is happening across the physical environment.

The key is to establish a real baseline before deployment and measure performance across the entire lifecycle, including implementation, support, maintenance, training, and integration.

Brain Corp is unique in the robotics space in that it operates as a recurring SaaS robotics platform. Why are software, data, and fleet orchestration becoming more important than the robot form factor itself?

Hardware defines what a robot is physically capable of doing. Software determines how reliably it does it, how easily it can be deployed, how securely it integrates into an enterprise and whether it becomes more useful over time.

Without a unified software and cloud layer, a robot is essentially an isolated machine. It may perform one task in one facility, but it cannot easily be monitored, updated, integrated with enterprise systems, or coordinated with a broader fleet. That creates fragmentation as companies add different robot types, each with its own interface, support model, data, and operating procedures.

A platform changes that equation. With our autonomy platform, BrainOS®, we provide a common autonomy, safety, data, and fleet-management foundation across different machines and applications. Our partners can focus on their hardware expertise and the customer problem they understand deeply, rather than rebuilding the entire autonomy and cloud infrastructure around every new product.

Software also allows the value of the machine to compound. We can deploy new capabilities through over-the-air updates, learn from how robots behave across the fleet, improve performance, and respond to new edge cases without replacing the hardware. Traditional industrial equipment generally depreciates from the moment it is deployed. A connected autonomous system becomes more capable throughout its lifecycle.

The form factor will continue to matter, because the physical design must fit the task. But form factors will evolve. The more durable value lies in the intelligence, operational infrastructure, and data layer that can extend across any application. That is why we describe BrainOS® as the autonomy platform for the real world, rather than an operating system for one particular robot.

BrainOS-powered robots are now being used not only for cleaning, but also for shelf analytics, inventory visibility, and material movement. How do you see robots evolving from task automation tools into mobile data platforms?

Our BrainOS® platform equips enterprises with the infrastructure required to build, deploy, and support autonomous solutions at an enterprise scale. We provide the underlying autonomy and intelligence layer, which allows our partners to focus on what they do best: solving specific, real-world business problems. This is a unique approach that has enabled us to quickly scale and allowed enterprises to build not just robots, but robotics businesses.

Robotic floor care was one of the first commercial applications to reach meaningful scale because it addressed a clear, repetitive operational need; every commercial environment has a dirty floor and many face labor challenges. We achieved this scale alongside our partner Tennant Company, a leader in floor care for over 150 years that has now evolved into one of the world’s largest manufacturers of commercial robots.

Then through our work with large enterprises across retail and logistics, we recognized a fundamental truth: while these operations know exactly what inventory enters and exits their facilities, they often lack visibility into what is actually happening on the store or warehouse floor. A mobile robot is one of the few systems that repeatedly moves through the physical environment, making it uniquely capable of capturing this ground truth at scale. To help with this challenge, we have partnered with Driveline Retail, a leading merchandiser, and Dane Technologies, a leader in warehouse equipment, to bring inventory tracking and shelf intelligence to market at enterprise scale.

When you expand into these applications around inventory visibility, collecting data is only part of the equation. Most warehouses and retailers want more than just millions of raw images; they need to know exactly which product is out of stock, which price is incorrect, where a missing pallet is and what specific action an associate should take next. Brain Corp provides the advanced computer vision and AI required to translate those raw observations into accurate, prioritized business decisions.

Regardless of the robot form factor, these systems are also unlocking the ability for enterprises to create continuously updated digital twins of their facilities. Because our robots safely and reliably navigate entire footprints, they act as mobile data platforms, capturing highly detailed facility intelligence. That can mean, for example, verifying a fire extinguisher’s location, identifying a malfunctioning freezer, or mapping areas with poor Wi-Fi connectivity.

What separates a robotics deployment that works in one pilot location from one that can scale across thousands of enterprise sites?

Pilots reward possibility. Scale rewards reliability.

A pilot can succeed because the best engineer is standing nearby, the environment has been carefully prepared, and someone is quietly handling exceptions behind the scenes. None of that works when you are deploying across hundreds or thousands of sites. At that point, every manual step becomes a source of cost and failure.

A scalable deployment needs repeatability from the beginning. The robot should be straightforward to configure, intuitive for frontline employees, remotely observable, and capable of receiving software updates without someone visiting every site. The organization also needs mature systems for cybersecurity, safety certification, support, training, maintenance, analytics, and integration with the customer’s existing technology.

Change management is equally important. The people working alongside the robot need to understand what it does, what its limitations are, and how it fits into their responsibilities. Deployments fail when the technology is technically sound but operational ownership is unclear.

Finally, the economics have to work for the customer and the robotics vendor. This requires a robotics infrastructure that becomes more economical and more robust with every deployment.

That collection of challenges is what we call the Autonomy Gap. Building a working prototype is only one part of creating a robotics business. The much harder work is turning that prototype into a secure, supported, repeatable product that continues to perform when the specialists leave the building.

Brain Corp’s recent work around contextual grounding and semantic mapping points to a more intelligent layer for Physical AI. Why is “understanding the environment” the next major frontier for autonomous robots?

Traditional navigation is largely a geometric problem: Where am I, where can I move, and what should I avoid? Contextual grounding adds a much richer set of questions: What is happening around me? What are these objects? How are people likely to interact with them? What behavior is appropriate in this particular situation?

A basic navigation system may see that something is occupying space. A contextually aware system should understand exactly what the object is, why it’s there, what it’s doing, and ultimately, how to behave around it. Those distinctions matter because the correct response is different in each case.

This becomes particularly important as robotics begin to incorporate more generative AI, vision-language models, and other probabilistic systems. These models can bring tremendous intelligence and flexibility, but a plausible answer is not sufficient when software is controlling a physical machine. The action has to be grounded in the actual geometry, conditions, safety requirements, and social context of the environment.

We believe the answer is to combine advanced AI with a persistent contextual representation of the physical world and deterministic safety controls. That allows the robot to benefit from increasingly capable AI models without allowing probabilistic intelligence to operate without boundaries.

How should businesses think about trust when deploying robots around employees, customers, and the general public?

First, we have to acknowledge a fundamental truth: robots are different from other enterprise technology. A software bug might crash your laptop, but a bug in a 1,000-pound machine navigating a crowded retail aisle has entirely different stakes. Businesses cannot simply treat a robot like another piece of connected IT equipment.

Trust in robotics is earned every single day. At a time when the FCC is limiting approval of foreign-produced robotics due to security concerns, the bar for that trust has never been higher. As Physical AI scales into our hospitals, airports, warehouses, and grocery stores, businesses need to evaluate trust across a few distinct layers: physical safety, data privacy, and human integration.

Advanced AI is incredible for making a robot ‘smart’ enough to navigate complex environments, but intelligence and safety are not the same thing. A trustworthy robot separates the two. It must have dedicated, redundant hardware safety controls that can override the system. Safety must be hardcoded, not learned.

Additionally, robots navigating public spaces are essentially roving data platforms. They use cameras and sensors to see the world. Businesses must demand extreme clarity from their vendors: What is the robot looking at? Where does that data go? Who has access to it? Privacy and cybersecurity cannot be bolted on after deployment as a PR exercise; they must be built into the system’s architecture from day one.

The biggest mistake companies make is dropping a robot into a facility without any thought of change management. You cannot ask your employees or customers to take robotics on faith. Employees need to be brought in early, trained properly, and shown exactly how the machine makes their day-to-day jobs easier and safer. They need to know who is responsible if something goes wrong, and how to intervene if necessary.

Ultimately, certifications, audits, and security controls are just the baseline evidence. True trust is built through repeated experience. When a robot behaves safely, consistently respects a customer’s personal space, and takes the dull, dirty work off an employee’s plate day after day, that is when it simply becomes part of the team.

Looking ahead, where do you expect commercial robotics to create the most value over the next five years: retail, logistics, healthcare, manufacturing, airports, or another sector entirely?

The most value tends to come from environments that are large, distributed, and labor-constrained. They have repetitive tasks that are difficult to complete consistently.

The supply chain: from manufacturing to logistics to retail combines all of those conditions. Stores are complex public spaces, labor availability is a persistent challenge, and small operational issues (an empty shelf, an incorrect price, a delayed cleaning task) can directly affect the customer experience and financial performance. Logistics and manufacturing will also create enormous value, through inventory visibility, material movement, and coordination across increasingly automated facilities.

But the greatest value won’t be defined by one specific industry. It will be a shift in how enterprises buy robotics. Specifically, the next five years will bring the death of the isolated robotics project.

Right now, enterprises are running heavily fragmented initiatives. They buy one robot from company A to clean the floors, another from company B to scan inventory, and a third from company C to move materials. Each has its own local understanding of the world that isn’t shared with the others. It is an operational nightmare that doesn’t scale.

Over the next five years, value creation will come from shared context and execution. Enterprises are going to stop buying fragmented robots and start investing in a unified Physical AI foundation. They want one software platform, one autonomy layer, and one data pipeline that manages a diverse fleet of machines, shares context about the environment, and orchestrates centrally to automate entire facilities rather than individual tasks.

That is how AI moves beyond ambition to make the real world work better.

Thank you for the great interview, readers who wish to learn more should visit Brain Corp.