Nicolas Genest Podcast Transcript
Nicolas Genest joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies, home of The Digital Executive podcast. Do you work in emerging tech, working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.coruzant.com/brand.
Welcome to The Digital Executive. Today’s guest is Nicolas Genest. Nicolas Genest is the founder and initial CEO of CodeBoxx Technology.
Nicolas is a two-times CEO, and five times CTO, multi-exit Technology operations and Digital products executive, who has successfully founded, built and operated leading businesses. Some of them generate over a billion dollars in annual revenue. Launched Goodwill fines.com in 2022 to transition Goodwill into the digital age.
Awarded the best AI chatbot solution of 2025 and has fought food waste at full harvest and founded CodeBoxx Technology Academy providing accelerated AI first technology, education, and employment to individuals from all walks of life. Previously led technology during the hypergrowth phase of Venti Privy.
Now VP was first CTO of the RealReal and transformed the commerce practice at ModCloth and later at Walmart after acquisition. Well, good afternoon, Nicolas. Welcome to the show.
Nicolas Genest: Well, thanks for having me, Brian.
Brian Thomas: Absolutely, my friend. I appreciate it. You’re in St. Petersburg, Florida. Currently I am in Kansas City.
So, uh. Only an hour apart, but luckily the weather’s a little bit in your favor typically. I know it’s getting chilly there too, so, but Nicholas, let’s jump into your first question. You founded scaled and exited multiple businesses, sometimes reaching 1 billion plus in annual revenue, and then turned your focus to founding CodeBoxx to train diverse talent.
What was the moment when you said, I need to move from building? Businesses to building people. How did your experience as the CTO or CEO shape the way you designed CodeBoxx?
Genest-Nicolas: Well, as you said, like I’ve been the CTO and technology leader on a, a lot of companies, like a lot of startups, like it started back in France at the inventor of Flash Sales vp, uh, then I got poached by the RealReal to be their first steel.
In 2013, moved on to ModCloth to rescue the brand we got acquired by Walmart and there’s, there’s a, there’s a common theme for all the teams I was building and, uh, the experiences I was living as a technologist throughout the build out of those companies. And it was you, you can achieve great things for these companies.
If you have the right people behind the technology, in, in, in other words, it’s not about the stack, it’s not about the choices of tech being made. Is it custom built, tailor made? Uh, is it best of breed? Uh, integration of systems. What really matters is, is the team behind it executing on the plan that has been chosen?
Are they business first? Are they results oriented? Are they in agreement with you that it is a fail fast environment, uh, it’s all in execution and not losing sight of what truly matters for the business and. It. When I, when I understood that, it, to me it felt like a discovery. And I realized that the places I struggled in, in my career and in my, uh, in my companies was when the wrong people were in charge or accountable for things that really mattered.
So what what triggered the foundation of CodeBoxx in 2018 was, we need more. Business first people in technology so that we can reach our goals and we can manipulate and and control technology so we can really put it at the service of the business and make it as an enabler and instead of having it technology centric all the time.
So that’s really what triggered the need for CodeBoxx to exist is find the right people. Business first with grit, with like experience, who care for the outcome and the customer and the users and the, the stakeholders of the company and make them do great thing by teaching them hard skills of tech.
Brian Thomas: It’s amazing. Thank you. And I, took, took away a lot here. Um, which is great. You’ve had lots of experience in startups as a technologist or CTO and, um, what you came out of this, you learned from your experience. Basically, having the wrong people in the wrong positions can really impact, uh, the business, especially startups.
But your company can do great things if you have the right people behind the technology. They’re business first results oriented, they can execute on things. So I really appreciate you sharing your insights with us. And Nicolas CodeBoxx emphasizes that smart is everywhere, and that background or formal credentials shouldn’t limit access to technology careers.
How do you identify and coach people who may not have traditional tech credentials, and what traits do you see that predict long-term success in those students?
Nicolas Genest: Well, that, that. Emphasis on smart is everywhere, uh, has evolved into new kind of smart with the emergence of generative AI three years ago.
So, uh, like November 30th, will, uh, 2023 will, will always be remembered as a turning point in the technology field because it kicked off a, a revolution where everybody got smarter. Everybody got equally knowledgeable and everybody got equally capable. So it, it literally leveled the field between 20-year-old senior coders and, and, and software engineers and newly arriving AI native technologists who actually can focus their time.
On the requirements and the operationalization of the AI output. So since coding is no longer a barrier to entry to software development, what remains is the people behind AI and the new kind of smart means we’re doing great with waiters. Uh, tired nurses, uh, Uber drivers who remember everything, uh, Starbucks baristas who are like super customer oriented and outcome oriented and want things to be just right.
These kinds of people if given the right AI native skills and managed to cut through the noise of all the tools that are available and they’re not all good. They will be augmented in a way where they can be super impactful within a business because the, the, the grit and the, the business bias and the, the, uh, propensity to deliver that they show naturally as individuals and their personality and then the way they are, and through their, their human skills, it can be turned into very valuable assets for a company when they’re augmented and tied to artificial intelligence.
Brian Thomas: Thank you. And I would certainly say that would resonate with, with me obviously, but uh, with a lot of people, uh, you know that with the generative AI explosion in the last three years, um, there is really no longer a barrier to entry, uh, when it comes to coding.
And you’re right. Over my career in technology, uh, we’ve hired a lot of business people with no tech background into the tech space, and it brought a different. Uh, set of thinking. Um, and there were different ways that we looked at outcomes and, uh, it’s always, it’s been a positive thing, so I appreciate you sharing your insights as well.
And Nicolas, under your Leadership CodeBoxx, developed an award-winning chat bot solution Gem Chatbot for Goodwill Finds named the chatbot Solution of the year in 2025. What makes an AI or chat bot solution business first, in your view? And how did you build a bot that not only works, but drives measurable, measurable results rather than just being a novelty?
Nicolas Genest: Well, we, we were really surprised to be awarded Best chat bot with what we had delivered at Goodwill Finds. Um, it, it came out as a surprise. In, in such a way that we, we even felt the need to like document how we did it, and we deconstructed and we, we broke down, uh, what went right, uh, and, and delivering that solution to the world so that we, we can really understand what set us apart.
And it, it really is four things that we really landed and, and mastered. That, that led us to, to win that award. The, the first was, so we briefed the conversational agent perfectly on purpose. So we were able through like a, a very extensive, documented prompt, uh, very elaborated, um, what it, what this agent was meant to be.
Uh. What the guardrails were, what, what the purpose were was, uh, what, um, in what order things were meant to be done, what knowledge, uh, was supposed to be, um, top of mind as, uh, it was conducting conversations with customers. And that brief is really. What we call the, like, the consciousness behind the capabilities of ai.
So we, we, we collaborated with true customer service people and we broke down every single conversation that they had recorded and done in the past. Uh, to understand how the ideal conversational agent called GEM would behave. That was the first pillar. The second pillar was what knowledge does it need to be successful?
So, general knowledge. Knowledge, like frequently asked questions, statement of processes. Uh. Past, uh, anecdotes and past experiences like a, a, a knowledge base of things it needs to do and things that it, it shouldn’t be doing, uh, and it absolutely cannot do. So that’s the, the core knowledge that need to be fed systematically when a conversation is spun up.
The, the third pillar was around giving it arms and legs. So it’s one thing to. Know things. It’s, it’s another to be relevant and that that third pillar serves that purpose. So arms in the sense where you can send it everywhere, like legs, uh, in every corner of your databases, of your, uh, you can provide it with contextual information.
You can, um. Feed it with profile information. Once you know who the customer you’re talking to is, and it’s authenticated, uh, and then you give it things to do and things it can do. So the more arms and legs you give to a conversational agent, the more possibilities it has to satisfy the customers and, and close on the, the case, uh, itself.
So that’s, that, that was key. So like, is it, is it. A good case to refund? Is it a good case to make an escalation? Is it a, uh, is it a case where, um, I’m gonna recommend an alternative item for a broken one that was delivered? Uh, so in order to achieve all of this, you need to give it access to inventory.
You need to give it access to the profile information of the customer. You need to give it access to the last 10 orders, the tracking information of the parcels that were sent out on his behalf. So. All of this needs to happen. And that’s the third pillar that we nailed, like contextual information at the right time through retrieval, augmented generation, and um, the like very thorough, solid functions that really make a difference for the customer.
And the last, it’s a lengthy answer, I apologize, but like the last fourth pillar, uh, was constantly monitoring the conversations like, like you would. Supervise an employee in the early days of their jobs to know and, and understand how much they need can be left alone. It’s the same with an agent. You plug AI into monitoring the conversations that happen, the outcomes that are obtained, and you adjust constantly, like almost daily in the early days of the the release to make sure that you course correct the behavior.
And the next thing you know, a few months in, you’re not even, uh, you don’t even, you do no interventions anymore. And, and it’s completely autonomous and it, it sat more than 80% of the, the people, there was no escalation required and customer service signs up winning its own prize. So, uh, we were pretty proud of, uh, of that approach.
Brian Thomas: That’s amazing. I really get, get excited about, um, the technology that is getting better each year. And, uh, I think that’s amazing. And you broke out the pillars. Uh, I know you were surprised that you got the award, but. It made a lot of sense. And those four pillars, just real quick, is you really, uh, got focused, you elaborated on what the conversational agent should do, the end golds, guardrails, et cetera.
In that, that whole conversation, um, that knowledge that the agent needs to have to be successful, that that was important. Uh, giving it arms and legs, as you said, it’s important that the agent ha is relevant. Uh, more possibilities, giving it more, uh, again. Access those arms and legs to do more things, and then constantly monitoring and adjusting, you know, improving the call and the outcome.
I think that’s so, so important and, uh, we need to focus on that for sure. And Nicolas, the last question of the day, looking ahead, how do you see the intersection of tech education, AI adoption, and workforce development evolving by 2030? What role do you hope CodeBoxx will play in that future? And what challenge do you believe deserves more attention right now?
Nicolas Genest: Well, if you look at what CodeBoxx was created for in 2018 and like the, the, the first 300 graduates we’ve had, they were really taught the, like 60% coding and it was about making sense of the latest software as a service capabilities and making sense of the cloud. And it was, it was really about that.
But over the past. 18 months, I would say it, our purpose really evolved into no longer seeing coding as a barrier to entry, like I was saying, but, but making sense of the immense power that AI brings to the table and making sense of like, what are the best tools that are available to you today? To do a good software development job and, and to reach, uh, to reach type to market faster.
And how do you spend more time reaching what I call user experience fit, uh, on the journey to product market fit? You have this, this moment where what you propose in terms of software reaches and resonates with its users. AI can definitely help you get there faster and iterate more frequently. And there’s this whole trend around vibe coating that’s gaining, gaining steam.
Right now. We are at a very critical time of that trend where it’s either gonna deliver on its promises or it’s either gonna fall on its face and, uh, code, CodeBoxx’s role in, in this is to make sure that. CEOs, marketing leads, digital product owners, stakeholders of companies. They can actually express requirements in natural language.
And we, we managed to remove those interpretation layers that used to exist, like, like user stories and like sets of requirements and breakdowns in, into code. Like those interpretations are no longer required in ai. Can do a good job producing software, just straight OUTTA requirements. So teaching people what to ask for correctly in their prompts and help them make sense of the output, make it secure, scalable, compliant, uh, and pleasurable for, for the users so that adoption can happen.
Like those are the, the, the true. Focus, uh, that that’s the true focus where our, our profession is shifting to. And, uh, I think those who will be successful will understand that migration and will, will focus on these things instead of, uh, of coding.
Brian Thomas: That’s great. And you have the right mindset, in my opinion. You are looking forward, uh, that visionary, uh, type of leader, but uh, just cover a couple of things that you talked about here. Um, I know CodeBoxx has evolved, uh, and you said the last 18 months. Obviously, uh, is you look at things a lot differently, um, teaching students what is really making sense. Um, as far as the platforms today, that’s gonna allow you to build the, the best platform, the best product, and get it to market faster.
But AI is certainly getting, uh, good enough to do the business requirements interpretation, uh, leaving more of that critical thinking and a visionary type, uh, strategies, uh, for people so they can focus on really building a better outcome and a better product. So I appreciate your insights and what you’re doing with CodeBoxx.
And Nicolas, it was such a pleasure having you on today and I look forward to speaking to you real soon.
Nicolas Genest: Thank you Brian. I really enjoyed my time with you. Uh, uh, I hope there’s a next time.
Brian Thomas: Bye for now.
Nicolas Genest Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
