Seeing AI software move from concept to real-world impact reinforced why we do this work in the first place.
Artificial Intelligence is transforming industries at a breakneck pace, and the entrepreneurs driving this innovation are at the forefront of this revolution. From groundbreaking applications to ethical considerations, these visionaries are shaping the future of AI. What does it take to innovate in such a rapidly evolving field, and how are these entrepreneurs using AI to solve real-world problems?
As a part of this series, I had the pleasure of interviewing Matthew Shaxted.
Matthew Shaxted is the Founder and CEO of Parallel Works, leading the company's mission to make high-performance and AI computing effortless and broadly accessible. Since co-founding the company in June 2015, Matthew has steered its evolution from a pure HPC platform into a hybrid orchestration system that converges AI, ML and simulation workloads across cloud, on-premises, and SaaS environments. With a background in civil engineering and computational design at firms such as SOM and PositivEnergy Practice, Matthew combines domain expertise with technical vision.
Thank you so much for joining us in this interview series. Before we dive in, our readers would love to learn a bit more about you. Can you tell us a bit about your childhood backstory and how you grew up?
I was a very focused kid and knew early on that I wanted to build things. Growing up, I was fascinated by architecture — my grandfather was an architect — and for a long time I thought that was the path I would take. When it came to look at colleges, I explored both architecture and engineering and ultimately found that civil engineering with an architectural focus was the best fit for how my brain worked.
I studied civil engineering at Northwestern with a concentration in architecture, which taught me to think of buildings not just as structures but as systems. During college, I also started coding, which opened a whole new dimension for me. After graduating, I worked at some of the world's leading architecture and engineering firms, including Skidmore, Owings & Merrill, where I focused on sustainability and simulation for major projects, including Chicago skyscrapers.
Running increasingly complex simulations on larger systems naturally drew me into the world of high-performance computing (HPC). That was about 15 years ago, and once I entered that space, there was no turning back. My work eventually led me to Argonne National Laboratory, where I collaborated with incredible people and ultimately met my co-founder, Mike. From there, everything else followed.
Can you share the most interesting story that happened to you since you began your career?
One of the most formative experiences was working with large organizations like Unilever and View Glass, and later navigating government agencies. Early on, it felt like finding your way through a dark maze without a flashlight — you're learning systems, cultures, and security requirements as you go. We secured a major government contract in 2022, and only now are we fully immersed in that ecosystem, including its rigorous compliance and security expectations.
In the first five or six years of building the company, we did mostly one-off, highly customized engagements. One example was working with Jacobs Engineering on a flood-warning platform. We customized everything for that customer and even traveled to the UK regularly to make sure we delivered exactly what they needed. At the time, we were essentially doing whatever our customers asked us to do.
Over time, those one-off projects started to converge. Patterns emerged, and eventually the software platform we have today took shape. We bootstrapped the entire company — no institutional financing, no board in the early days and figured things out as we went along. Looking back, that journey of evolving from bespoke projects to a scalable platform was one of the most defining chapters of my career.
None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?
Without question, my co-founder, Mike. We've been partners for more than a decade, and he was instrumental in introducing me to the HPC world and its community. He taught me not just the technical side, but also how to think entrepreneurially within a deeply technical domain.
I'm also incredibly grateful for our advisory board. Having experienced advisors to bounce ideas off of has made an enormous difference. One of those advisors is Andy Lombard from Tesoro Ventures, he's been a constant sounding board for strategy and growth decisions.
Beyond that, I've been fortunate to grow up in a family surrounded by technology. Having that kind of environment, both professionally and personally, has been invaluable.
Can you please give us your favorite "Life Lesson Quote"? Can you share how that was relevant to you in your life?
"Creation is a better means of self-expression than possession; it is through creating, not possessing, that life is revealed." Vida D. Scudder
This quote has been relevant to me because creating things has always been how I understand myself and stay engaged with life. Building companies, products, and teams has consistently been more fulfilling than any outcome tied to status, money, or ownership. When I focus on creation, the process itself provides energy and clarity. It keeps life interesting because you are always learning, iterating, and shaping something that did not exist before. Possession is static. Creation is dynamic, and for me, that difference has mattered at every stage of my career.
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
The first is persistence. Building a company — especially a bootstrapped one — requires not giving up, even when progress feels slow or uncertain.
The second is what I'd call unbridled optimism. You must believe deeply in what you're building, even before the market fully catches up. That optimism keeps you moving forward when there's no external validation yet.
The third is a people-first mindset. We've been incredibly intentional about surrounding ourselves with people who believe in the mission and are committed for the long haul. I genuinely love this space — the technology, the problem-solving, and the impact. I still enjoy testing the product, talking to customers, and understanding how our work can support outcomes that truly matter, whether that's advancing science or improving human health.
Ok super. Let's now shift to the main part of our discussion. Share the story of what inspired you to start working with AI. Was there a particular problem or opportunity that motivated you?
Our journey into AI began about four years ago when we won a Department of Energy grant to explore machine-learning-based modeling. At the time, our focus was on traditional modeling and simulation, and we worked closely with Argonne on those efforts.
Over the last two years, however, the world has changed dramatically. AI has exploded, and it's become clear that HPC is the underpinning infrastructure behind modern AI systems. The core requirements — networking, storage, and scheduling — are fundamentally similar. Our expertise in HPC positioned us perfectly for this transition.
As AI workloads evolved, we shifted toward Kubernetes, adapted to new hardware ecosystems, and leaned into AI-focused use cases such as private LLMs, air-gapped environments, and GPU-intensive workloads that require specialized drivers and rapid scaling. What we're seeing now is a convergence: HPC and AI are no longer separate worlds as they're colliding, and our platform has naturally evolved to support that reality.
Can you describe a moment when AI achieved something you once thought impossible. What was the breakthrough, and how did it impact your approach going forward?
For me, the breakthrough wasn't a single technical milestone, it was market validation. We're a relatively small company, and while you can build advanced technology, having customers pay for it and deploy it in real-world scenarios is something else entirely.
Recently, we've seen a growing number of customers adopt our platform specifically for AI workloads — capabilities we've been building over the past 2 years. That validation confirmed that the vision we outlined wasn't just technically sound, but genuinely valuable.
Our platform is now being used in high-impact applications: next-generation hurricane risk modeling that helps save lives and reduce property damage, radiology and genomics workloads supporting cancer research, and even hypersonic missile detection. Seeing AI software move from concept to real-world impact reinforced why we do this work in the first place.
Please talk about a challenge you faced when working with AI. How did you overcome it, and what was the outcome?
AI is one of the fastest-moving technology landscapes I've ever seen. New hardware, storage systems, network topologies, and software frameworks seem to emerge every week. Orchestration and cluster management are constantly evolving, and staying relevant requires relentless adaptation.
We've addressed this by being extremely diligent about monitoring ecosystem changes and adjusting quickly. Rather than locking ourselves into a single approach, we've built flexibility into our platform so we can adapt as technology evolves. That mindset — continuous learning and rapid evolution — has been critical to staying competitive.
Here is the main question for our discussion. Based on your experience and success, can you please share "Five Things You Need To Know To Help Shape The Future of AI"? (Please share a story or an example, for each.)
1. Infrastructure matters as much as algorithms
Early in my career, I saw teams with strong models fail because the underlying systems could not scale, interoperate, or adapt. AI progress depends on compute, data movement, orchestration, and reliability. Ignoring infrastructure leads to fragile systems that cannot survive outside a lab.
2. AI must be designed for real-world constraints
In practice, AI systems operate with latency limits, cost ceilings, regulatory boundaries, and imperfect data. I have seen projects stall because they optimized benchmark performance instead of operational reality. The future of AI belongs to systems that work under constraints, not ideal conditions.
3. Human judgment remains essential
In high-consequence environments, fully autonomous systems are often less trustworthy than human-in-the-loop designs. I have worked with users who needed transparency and control, not black boxes. AI should augment decision-making, not obscure it.
4. Interoperability beats lock-in
Ecosystems evolve faster than any single vendor or model. I have seen organizations struggle because they committed too early to closed systems. Open, composable architectures allow AI solutions to evolve as technology and requirements change.
5. Responsibility cannot be bolted on later
Ethics, governance, and security need to be part of the initial design. I have seen how difficult and costly it is to retrofit controls after deployment. The teams that will shape the future of AI treat responsibility as a core engineering requirement, not a policy afterthought.
What advice would you give to other entrepreneurs who want to innovate in AI? Can you share a story from your experience that illustrates your advice?
My biggest advice is to focus relentlessly on delivering deep, immediate value. Solve a real problem that people have today — not a hypothetical problem five years down the line.
This is especially important for bootstrapped companies without institutional funding. You don't have the luxury of chasing hype. You need to clearly understand what pain you're solving, why it matters, and why customers are willing to pay for it now. That focus has been essential to our survival and growth.
Is there a person in the world, or in the US with whom you would like to have a private breakfast or lunch, and why? He or she might just see this, especially if we tag them. :-)
Satya Nadella — He has overseen one of the most successful cultural and strategic transformations in modern tech. I would be interested in how he thinks about leadership humility, platform thinking, and the responsible deployment of AI at massive scale.
How can our readers further follow your work online?
https://www.linkedin.com/in/matthew-shaxted/
Thank you so much for joining us. This was very inspirational, and we wish you continued success in your important work.
About The Interviewer: Dr. Sangani ("Doc") is a thriving cardiologist, business owner, husband, father and friend. His latest venture — LifeRx — is a community committed to helping growth-minded professionals create happiness through the pillars of health, wealth and connection.