I Started Kansas City’s First Artificial Intelligence Lab in 2016. Here’s What I Learned About Building Community Before the Hype.

A personal reflection on pioneering AI education in the Midwest — years before ChatGPT made it mainstream

My journey into AI started the way many do — building basic neural networks, experimenting with machine learning to solve marketing challenges at Fortune 100 companies. But somewhere along the way, I became convinced that AI could do more than optimize ad spend. It could fundamentally reshape how we think about work, wealth, and opportunity.

That conviction led me to start a small meetup group in Kansas City in 2015. What began as a handful of curious professionals gathering to discuss machine learning would grow into something I never anticipated: a 900-member talent cooperative, a research partnership with Kansas State University, and what I believe was Kansas City’s first real AI community.

This was years before ChatGPT. Years before every company had an “AI strategy.” Years before venture capitalists started throwing money at anything with “generative” in the pitch deck. We were building something in the Midwest when most people still thought machine learning was science fiction.

Looking back now, I want to share what that experience taught me — about community building, about the real potential of AI, and about why Kansas City might be better positioned for the AI era than anyone realizes.

Why I Started an AI Lab (Before It Was Cool)

In 2015, I was deep in the weeds of data science and analytics. I’d spent years at large organizations solving marketing and advanced analytics problems. But as I experimented with machine learning, I kept having this nagging feeling that we were only scratching the surface.

The technology wasn’t just useful — it was transformative. And not just for business. I became convinced that AI could change how we look at jobs, income, capitalism, poverty, wealth disparity, disease, and many other things we face in modern society. That might sound grandiose, but I genuinely believed it then, and I believe it even more now.

The problem was access. Machine learning expertise was concentrated in a handful of coastal tech companies and elite universities. If you lived in Kansas City and wanted to learn this stuff, your options were limited. Take an expensive graduate degree. Watch YouTube tutorials alone. Or move to San Francisco.

I thought there had to be another way.

From Meetup to Movement

I started with a simple meetup group. No grand vision, no business plan — just a gathering of people interested in machine learning. We’d meet, discuss papers, share what we were working on. The usual stuff.

But something interesting happened. The group kept growing. And the people showing up weren’t students or hobbyists — they were experienced professionals. Business intelligence developers. Data analysts at Sprint. Engineers at Cerner and Hallmark. People with real skills looking to add AI and machine learning to their toolkit.

By 2016, I realized we had something bigger than a meetup. We had the makings of a genuine talent cooperative. So I formalized it as the Kansas City AI Lab — KCAIL.

An Experiment in Disruption

I’ll be honest: I was frustrated with the traditional models. Higher education felt antiquated and expensive. The typical startup path — raise money, burn cash, hope for an exit — didn’t align with what I wanted to build. So I tried something different.

KCAIL was structured as a member-owned education and development cooperative. Members weren’t customers or students — they were co-owners building equity in something larger than themselves. We ran live workshops every Saturday morning. We took on real projects with real partners. We built skills together.

Our mission was explicit, and admittedly ambitious: advance the development of AI, Machine Learning, and Deep Learning while using these technologies to commoditize knowledge, automate labor, disrupt outdated capital flows, and transform the human experience for the better.

Some people thought that was naive. Maybe it was. But it attracted over 900 members — what I estimated at the time was roughly $3 million worth of collective talent in the Kansas City area.

What Made It Work

Looking back, I think several things made KCAIL different from a typical bootcamp or networking group.

First, we had practitioners teaching practitioners. Our instructors weren’t academics — they were people working in AI every day. When you learned from someone solving real problems at Cerner or analyzing data at a Fortune 500 company, you got practical knowledge you could use immediately.

Second, we worked on real problems. This wasn’t theoretical. We took on projects that mattered. Our partnership with Kansas State University on malaria drug discovery is the example I’m most proud of. Malaria kills hundreds of thousands of people every year. We used machine learning to help identify compounds worth investigating — work that could actually accelerate life-saving research.

Third, we built genuine community. Members supported each other. They taught each other. The Saturday workshops became something people looked forward to — not an obligation, but a highlight of their week.

One member put it better than I could:

“With the KC AI Lab, I have found a community of other Machine Learning enthusiasts who can help each other build our skills. This has given me a chance to practice my skills and further refine my knowledge through teaching others. This group has also given me the feeling that I can help create something greater than the sum of the parts.”

That sense of building something together — that’s what kept people coming back.

Voices from the Community

I’ve kept some of the feedback from those early days. Reading it now, years later, reminds me why this work mattered:

“It’s a novel approach to help the Kansas City community and groom data scientists. I am delighted to be in KCAIL group and be part of this entrepreneurial mindset approach in providing AI solutions to the businesses here. The highlight of this program is live workshop every Saturday morning.”

“While educational and industrial communities grapple with solutions to meet explosive growth in Machine Learning and AI needs, KCAIL is a trendsetter and worth a serious look. KCAIL initiative is a game changer with an innovative education/business model for the new era.”

“I have always wanted to deep dive into machine learning ever since I took Andrew Ng’s machine learning course on Coursera, but projects where I am the only participant don’t go very far. I need a group/team project to motivate me and that’s what KCAIL is providing at no cost. It’s really an amazing group of people!”

That last quote captures something important. Learning AI in isolation is hard. Having a community makes it possible. And we offered it at no cost — because democratizing access was the whole point.

The Bigger Vision: AI and Economic Justice

I should be transparent about something. KCAIL wasn’t just about career development or technical skills. I had — and still have — a bigger vision for what AI can accomplish.

Look at the global distribution of wealth. Most of it is concentrated among a small percentage of people — many of whom are inheritors, or founders and executives whose compensation has no rational relationship to their actual contribution. From my perspective, this is a broken and inefficient system.

I believe AI can help us see these inefficiencies more clearly. Machine learning can analyze capital flows, identify systemic dysfunction, reveal where value is actually created versus where it’s extracted. The illusion of meritocracy can be exposed through data.

That’s a controversial position, I know. But it’s honest. And it’s been the thread connecting everything I’ve worked on — from KCAIL to my current research at Vector1.

Why Kansas City (and Why It Still Matters)

People sometimes ask why I built this in Kansas City rather than moving to Silicon Valley or New York. The answer is simple: Kansas City has real advantages for this kind of work.

The cost of living is dramatically lower than coastal tech hubs. The talent base is strong — local universities produce excellent computer science and engineering graduates. The community is genuinely supportive in a way that’s harder to find in more competitive environments. And thanks to Google Fiber’s arrival in 2011, we had infrastructure that rivaled anywhere in the country.

There’s also something cultural. The Midwest has an orientation toward practical, revenue-generating businesses rather than the “growth at all costs” mentality that dominates coastal tech. When I said KCAIL would focus on real problems for real organizations, people here understood that instinctively.

Kansas City sits at the heart of what’s now called the “Silicon Prairie” — a stretch of tech-focused cities spanning Missouri, Kansas, Nebraska, and Iowa. Today, the Kansas City area has over 3,900 tech companies and more than 106,000 tech jobs. The city has invested over $7.8 million in AI development — more than double the national average. Meta and Lambda are building major data center infrastructure here.

None of that surprises me. We saw the potential years ago.

What’s Happened Since

The AI landscape has changed dramatically since I started that first meetup. ChatGPT made AI a household conversation. Suddenly everyone has an opinion about large language models and generative AI.

Kansas City’s AI community has grown too. New organizations have emerged. The ecosystem is more robust than ever.

I’ve continued my own work through Vector1 Research, an applied research studio focused on AI, machine learning, econometrics, and economic systems. I’m building tools like Papilon (an open-source framework for complex system simulation) and Daedalus (a platform for causal and economic analysis). The thread connecting it all remains the same: using AI to understand and improve the systems that shape human economic life.

Recently, I’ve been exploring topics like the economics of agentic AI, the mathematical properties of language models, and how AI might transform marketing attribution and economic modeling. I also created Memory-Node Encapsulation (MNE), a data structure for artificial episodic memory and cognitive AI reasoning. The work has evolved, but the core conviction hasn’t changed.

What I’d Tell Someone Starting Today

If you’re thinking about building an AI community, or learning AI, or applying it to problems you care about, here’s what I’d share from my experience:

Start before you’re ready. I didn’t have a perfect plan when I organized that first meetup. I just knew people were interested and I could help facilitate learning. The plan emerged from doing the work.

Focus on practitioners, not theory. The most valuable thing KCAIL offered wasn’t curriculum — it was access to people actually doing the work. Real problems, real solutions, real experience.

Build community, not audience. There’s a difference between people who consume your content and people who build alongside you. The latter is harder to cultivate and infinitely more valuable.

Connect AI to things that matter. Drug discovery for malaria. Services for nonprofits. Economic analysis that reveals systemic dysfunction. The technology is more meaningful when it serves purposes beyond optimization and profit.

Don’t wait for permission or validation from the coasts. We built something real in Kansas City years before mainstream attention arrived. The Midwest doesn’t need Silicon Valley’s approval to lead in AI.

Looking Forward

At a recent investor summit in Kansas City, Phil Reynolds of DevStride made an observation that stuck with me: “This is all moving faster than anyone expected. It’s a moment for builders.”

He’s right. AI tools now make it possible for smaller teams — even individuals — to build things that once required massive engineering organizations. The barriers to entry have never been lower. The potential impact has never been higher.

I started building in 2015 because I believed AI could transform society. That belief hasn’t wavered — if anything, it’s stronger. The question now isn’t whether AI will reshape our world. It’s who will shape how AI is used, and for whose benefit.

Kansas City gave me the community to explore those questions. KCAIL proved that you could build something meaningful outside the traditional tech centers. And the work continues — through Vector1, through research, through the ongoing effort to use AI not just for efficiency but for understanding and improving the systems we all live within.

If you’re reading this from somewhere in the middle of the country, wondering whether you can contribute to the AI revolution from where you are — you can. We proved it. And there’s never been a better time to start.

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Brian Curry is a Kansas City-based data scientist, AI researcher, and founder of Vector1 Research. He founded the Kansas City AI Lab (KCAIL) in 2016 and continues to explore the frontiers of AI, machine learning, econometrics, and economic systems. He is the creator of Papilon, Daedalus, and Memory-Node Encapsulation (MNE). Connect at brian@vector1.ai or visit vector1.ai.