My AI Story

1. 2010

   The first pitch deck (early uCondition)

   “When we first started, it was all about big data: machine learning, predictive analytics, and prescriptive analytics. If I could distill all those early pitch decks into that.”

   Keep reading in Ryan’s words

   “I know at one point, we pitched it to a prominent angel investor, and he even slid his rough term sheet across the table. It was one page with a bunch of bullet points, and it was an agreement. We turned him down because we thought we could build something bigger and better. We were wrong, but he was all interested in the machine learning and how it worked. He was very familiar with it and aware of it, it wasn’t new to him.”

   “I think that’s the important thing to note. These were not bizarre, weird, futuristic things we were talking about.”

2. 2010s

   The acquirers who couldn’t believe it

   “I’ll never forget my co-founder and I were sitting in a room together with a bunch of executives from this software company that had, goodness, I’d say four or five floors in a humongous office building overlooking a lake in an office park.”

   Keep reading in Ryan’s words

   “They were sitting there in this conference room, and they were not believing how few hours we put into it to make it.”

   “It was profitable. It was making money. It was great. And we had many acquirers interested in it.”

3. 2013–2014

   Beer Starts Here: the app, and the data they turned off

   “It was all about using machine learning and predictive analytics to basically predict what beer consumers were most likely to drink in a given zip code. It weighed brands and it weighed styles of beer, ratings, and rankings.”

   Keep reading in Ryan’s words

   “When they came to buy it out, the craziest thing about it was they shut off the user side of it, which was collecting data.”

   “We were successful because we had users continually inputting data.”

4. 2016–2018

   Anutra Innovations: named for the initials

   “Anutra was the parent company, the medical device company, and we were the spinout, but we named it Anutra Innovations because the initials were AI, and that was intentional from the beginning.”

   Keep reading in Ryan’s words

   “We had algorithms written out on whiteboards, and we could tweak them on the backend and look at how people answered questions, then display certain videos or learning objectives or written text. We could see if people responded better to video, diagrams, or writing, and self-service modules.”

   “The big thing was we weren’t too concerned about rote learning and memorization. We wanted to understand that the way someone learned could actually change the way in which they behaved.”

   “We even worked with a Virginia Tech instructional design master’s student to analyze outcomes of behavioral change in his thesis.”

5. Same era

   Predicting trade show sales

   “We had crazy data inputs: what the weather was in the place where the trade show was, what day of the week it was, who the person selling was, what outfit we were wearing (company outfit or people wore their own stuff), company logo, polo tee, who else was in the booth, where the booth was. I mean, we had 30 or 40 data points, and then even more on the salespeople.”

   Keep reading in Ryan’s words

   “If you want to call it machine learning, you could call it that, it was algorithms that were predictive and updated based on real-time data.”

6. 2018–2020

   Boon: matching people in seconds

   “I made the developers show me on the backend the algorithm weights and how they were self-adjusting and what modifications it was. I had manual overrides. We could see these things unfolding in real time.”

   Keep reading in Ryan’s words

   “…we got those connections and matches to happen in seconds, at the most under 60 seconds, but usually within 10 seconds or so.”

   “But basically, someone could say, “Hey, I got a ticket.” And it would automatically route them to the attorney that was most likely to get the positive outcome based on the jurisdiction, the type of ticket, and all of that.”

   “We were deciding what weights were important. I mean, there’s ethical decisions in all of that.”

7. 2021–2023

   Peoplelogic: predicting who leaves

   “…at a key point in time where they were trying to determine the direction of the platform, and we really leaned into AI prediction.”

   Keep reading in Ryan’s words

   “We used Amazon Lex there to produce answers in a conversation-like format. This was before ChatGPT was widely available.”

8. Summer 2022

   The demo, months before ChatGPT

   “I’ll never forget, I was at Elon University in the summer of 2022. This was before ChatGPT was released.”

   Keep reading in Ryan’s words

   “He’s like, “No, talk to it like a conversation.” So I started conversing. I just remember my mind being blown.”

   “Just a couple months later, it was available for everyone, and generative AI took on a whole new form.”

9. Today

   Teaching, researching, reading

   “But my overall role in AI today is to stay aware of the trends and to think about the implications it has on human formation in the next generation.”

   Keep reading in Ryan’s words

   “I’m teaching it as a professor. I’m researching it daily. I’m reading about it daily. I’m in conversations with experts daily. And that’s my role.”

10. Spring 2027

    Humans and Machines

    Beginning in January 2027, Ryan teaches a course he designed at William Peace University.

    Course

    BSA-395-01, Special Topics: Humans and Machines: The Past, Present, and Imagined Future of AI

    Credits

    2

    Meets

    Mondays and Wednesdays, 9:25 to 10:20 a.m.

    Term

    January 13, 2027 to May 4, 2027

    Instructor

    Ryan Vet

    An interdisciplinary introduction to artificial intelligence for students in any major. Students trace the history of computing and AI from Babbage and Lovelace to today's chatbots and AI agents; examine how machines make decisions and how that differs from human judgment; weigh the ethical and social questions AI raises about work, privacy, fairness, and power; and explore what separates humans from machines through psychology, philosophy, and the debate over transhumanism. Through speculative and dystopian literature like 1984, students write their own vision of the future: a story told by an AI observing humanity. No technical background required.

    Course description from Ryan’s proposal (draft catalog text). The official schedule

Receipts

The public record, with dates

Outlet names and dates rather than a logo wall, because a logo proves someone wrote the name and a dated link proves what it said.

• Kingscrowd, June 1, 2019 Boon used "a sophisticated AI algorithm" to match providers to practices; Ryan founded Boon in 2018.
• PR Newswire, June 3, 2019 Boon launch; matching algorithm based on technology use, experience, and personality traits; credential verification.
• Ideamensch (undated) Ryan: "Imagine Uber meets eHarmony meets healthcare staffing."
• WRAL TechWire, June 7 and Aug. 26, 2019 Boon launch; $245,000 raised.
• GrepBeat, Sept. 26, 2019 Boon profile.
• USA TODAY, Sept. 10, 2019; Financial Times, Jan. 8, 2020 Ryan quoted as a gig economy expert on AB5. The FT piece is about Uber and does not name Boon.
• Crowdfund Insider, Jan. 5, 2021 Boon acquisition, December 2020.
• Elon University news, 2024 Mustafa Akben named Elon’s first Director of AI Integration (context for the 2022 demo).
• Newsweek, Sept. 30, 2026 Quoted as an "author and generational futurist" on Gen Z and AI; Newsweek attributes the Velocity Gap to Ryan by name.
• Fast Company, Sept. 18, 2026 Quoted on consumer AI (AI toothbrushes and oral health). Identified as managing member of Speaking Consulting Network.

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