In this episode of the AI Innovators Interview Series, Brennan Woodruff, Director of Strategic Partnerships at mHUB, joined the AI Leaders Council to discuss how artificial intelligence is reshaping manufacturing, hard tech innovation, venture-backed commercialization, and the future of physical AI. Drawing on experience spanning SoftBank’s Vision Funds, Uber, and his own generative AI startup, GoCharlie, Brennan offered a grounded, practitioner-focused perspective on where AI is creating real value and where organizations often get stuck.
Below are the highlights from our conversation.
Building a Hard Tech Innovation Engine at mHUB
Brennan began by explaining what makes mHUB different from traditional incubators and co-working spaces. He described mHUB as a purpose-built environment for physical product innovation.
“The way we talk about it, it’s a hard tech innovation center, but really it’s a bunch of different things in one. If you were a mechanical or electrical engineer and you had an idea for a product, wouldn’t it be great if you went to a WeWork that was specifically designed for you to be able to go from idea to prototype as fast as humanly possible for as cheap as humanly possible.”
He explained that mHUB supports hundreds of companies while also connecting founders to capital, customers, and commercialization pathways.
“We have around 300 companies that work out of here, and 1,500 members. But then on top of that, we said these members need connection to capital, need connection to opportunity, connection to customers. So we built a product development firm… and then if that wasn’t enough, we set up a venture fund.”
According to Brennan, mHUB’s mission is to bring emerging technologies and industry together to create practical impact.
“We’re really this spark in bringing together emerging technologies and industry to ultimately elevate and commercialize new solutions that help benefit humanity.”
Physical AI, Robotics, and the Next Frontier of Industry
A major theme of the conversation centered on what Brennan calls “physical AI” and embodied intelligence. He described how AI is extending beyond software into real-world systems such as robotics, energy infrastructure, and manufacturing operations.
“Physical AI is a term that Nvidia keeps using for this… but really bringing AI into the physical world. So the concept of physical AI is becoming all the rage in 2026.”
He highlighted data centers and energy systems as emerging opportunities.
“How do you make them more flexible? How do you reuse the heat? How do you more efficiently power? How do you more efficiently operate these things so that it’s not taxing of the environment around it?”
In manufacturing, Brennan sees autonomous operations and adaptive production systems as key areas of innovation.
“What I think about is more autonomous operations. So how can you have systems that learn and adapt on the fly… What if the assembly line could adapt to different things, enabling basically large or small batch customization at the economics of scale of large batch, low customization?”
He also emphasized the importance of modernizing legacy industrial infrastructure.
“Many factories don’t even have the data feeds that they need to ultimately deploy more intelligent AI and digital twin software on top of it. We’re dealing with a very aged industrial base and we need to get them up to speed.”
Looking further ahead, Brennan described a future where AI augments human labor through advanced hardware.
“How do we enhance existing human labor using exoskeletons and really smart hardware… enabling humans, not so dissimilar to what we’re doing with ChatGPT for white collar work, but doing that for blue collar work where we can effectively have super humans.”
Lessons from Early Generative AI with GoCharlie
Brennan reflected on founding GoCharlie, his generative AI startup launched before ChatGPT entered the mainstream.
“Back in 2021, GPT-3 was available via API… so we were early in the industry. Our plan was to develop models specifically for enterprise. But what we found is that enterprise didn’t really understand the value of language models yet. And I would argue that they still don’t.”
He described how the company initially focused on programmatic SEO and long-form content generation.
“Killer use case, revenue driving, huge time savings.”
However, the market changed rapidly once consumer AI tools became widely available.
“When ChatGPT got launched, that market probably wasn’t gonna be as fruitful as we initially thought because ChatGPT was free. It’s hard to compete with free if you’re not well capitalized.”
Despite ultimately shutting down the company, Brennan said the experience provided valuable insight into how quickly the industry evolves.
“Really great place to be early, early, early in this gen AI craze… but also see a lot of the same problems we were facing back in 2022 are still very present in 2026.”
How AI Compares to Past Tech Revolutions
Drawing on his time at Uber and SoftBank, Brennan compared today’s AI wave to previous technology cycles.
“I think this AI revolution, we’re just getting started. It’s already everywhere, but I really think that we’re in the very early days.”
He explained how falling costs and expanding accessibility will drive widespread adoption.
“By making intelligence available via an API or via an app, you actually expand the market for software… people are building more software than they ever have before. They’re building it quicker than they ever have before.”
Brennan believes the scale of impact is still underestimated.
“I actually think that we are under appreciating the midst of what we are in right now.”
Why Many Organizations Struggle with AI ROI
Brennan offered a candid take on why some companies fail to realize AI returns.
“I would argue that not getting ROI is a matter of failing to have a better sense of what key priorities are and then breaking that down into tasks and then breaking that down into what’s manual and highest value.”
He emphasized the importance of grounding AI strategy in real business problems.
“There’s too many smart people that are coming at AI from a ‘need AI’ perspective instead of saying, this is my business. How can AI make me better at that business, more efficient at that business or create a new business for me.”
He also cautioned against vague AI strategies.
“People were like, yeah, AI, it’ll just improve itself, right? And like, what’s your innovation strategy? It’s AI. I’m like, that’s not a strategy.”
How Brennan Uses AI in His Own Work
Brennan shared how his personal AI usage has evolved over time, especially with the emergence of deep research tools.
“When deep research came out, I started to see the light and started using it a lot more… for me, it was a learning curve accelerant.”
He described using AI to identify companies, partners, and accelerator opportunities.
“It would give me 125 companies. And then I’d use that to go find individuals at those companies… and then use it as a way to get out the door to those people.”
One of his most surprising discoveries has been voice-based AI interfaces.
“Voice mode is wild. It’s the thing that will make you feel like Tony Stark… It’s almost like you’re asking your smartest friend in that space all the stupid questions you don’t want to ask everybody else.”
A Framework for Organizational AI Adoption
When asked what advice he would give leaders, Brennan emphasized the importance of experimentation combined with structure.
“You have to give people the space to experiment and you have to give a framework for people to operate within.”
He cited Zapier as an example of structured AI adoption.
“They took two weeks as a company… had everybody use this tool… applied it to their day-to-day work… It came up with a list of like a hundred different use cases.”
Brennan stressed that simply giving employees AI tools is not enough.
“If you’re just giving people ChatGPT and saying like, that’s it, figure it out, you might be a little bit too restrictive… It really depends on what problem are you trying to solve.”
Watch the Full Interview
To hear Brennan Woodruff’s full conversation on physical AI, manufacturing innovation, venture investing, enterprise adoption strategies, and the future of applied intelligence, watch the complete interview below.
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