How fast can you upskill in AI? We did a sprint to find out.

Note: Transcripts are automatically generated from episode audio, and are not fully corrected for spelling, grammar, and formatting.

TARYN FIXEL: Every company has to ask itself how it will be disrupted. Every company will eventually be disrupted.

RANA EL KALIOUBY: That’s Taryn Fixel. She’s COO and president of WaitWhat — the production company behind this podcast, as well as Masters of Scale and Rapid Response.

FIXEL: So we had to ask ourselves: How is AI set to disrupt our work? And what can we do to get ahead of that so that ultimately we can be successful in this new environment?

EL KALIOUBY: Countless companies, large and small, are at a similar crossroads. We could keep working the same way we have been. But we know AI can supercharge an enterprise — take on tedious tasks, streamline operations, and boost output. But how do you actually do that? Where do you start? Today, come along with this one small production company — ours — as we plunge into AI to transform our work.

And your guide will be Pioneers of AI Senior Producer Rachel Ishikawa. Hi, Rachel!

RACHEL ISHIKAWA: Hey, Rana!

EL KALIOUBY: This is a very meta episode.

ISHIKAWA: It is. So let me tell you the story. Our company took a very specific approach to leveling up our AI game. For three days, we paused all operations, and every single one of us tested how to use AI for our work. And for me, someone who works on an AI podcast, this was actually a lot of fun.

EL KALIOUBY: So what did that look like?

ISHIKAWA: We experimented with out-of-the-box products, wrote code — some of us for the first time, like me — developed apps, scrapped apps, and then started all over. We called it an AI Sprint.

EL KALIOUBY: And did it work? Is the company running at full AI speed now?

ISHIKAWA: That’s what we’re here to find out — and to show our audience what we’ve learned. I’m going to bring you along from the start to the finish line, and all the hurdles in between. I’m Rachel Ishikawa —

EL KALIOUBY: And I’m Rana el Kaliouby, and this is Pioneers of AI.

Copy LinkWhy a small media company hit pause to get AI ready

Okay, Rachel, let’s start with some background about the company WaitWhat. Give us the rundown. What does it do? How big is it?

ISHIKAWA: Definitely. We’re a media company founded about nine years ago. Right now, we make three podcasts — this one and two others. We publish about 200 episodes a year, on video and audio. Plus our website, social media, newsletters …

EL KALIOUBY: And there’s Masters of Scale Summit, which I love.

ISHIKAWA: Yeah, we also hold this big three-day event, called Masters of Scale Summit, in October in San Francisco. It’s dozens of in-person speakers and performers. There’s all the ticketing and logistics that go into it. And we also pop up with other one-off events. So I’ve got to say, for a company with fewer than 40 people, we definitely punch above our weight.

Copy LinkThe urgency to become AI native before the market does it for you

EL KALIOUBY: Yeah, that’s a lot. So how has the company been using AI up till now?

ISHIKAWA: To speak for myself, I’ve been using AI for research, which makes sense since we produce a podcast about AI. As a company, our AI use has been haphazard. And for a company that talks a lot about AI with leaders in the field on our shows, it feels like we should have better systems for it ourselves — because we acutely understand the stakes.

FIXEL: There is no industry that this will not impact.

ISHIKAWA: Again, Taryn — COO and president.

FIXEL: I want to, number one, make sure that the organization is nimble and has the ability to thrive in the future media landscape that we live in. And number two, I really do feel that we have a responsibility to every member of our team to give them access to these tools and help them feel empowered to use them.

ISHIKAWA: Taryn knew that we needed to retool around AI, but the “how” was still up in the air. So she phoned a friend — a new friend.

PARTH PATIL: My name is Partha Patil, and I’m an AI engineer, and I work with the office of Reid Hoffman. I spend most of my days working with generative AI and advising startups and entrepreneurs. In short, I’d say I spend 14 hours a day talking to language models.

ISHIKAWA: He talks to LLMs all day — not just chat tools, but coding platforms like Claude Code — to stay up to date on how to use them, how they’re changing, and how to teach others about these tools.

PATIL: If you don’t become AI native, anyone on your team with high potential is not likely to stick around long term. If they do stick around, it might be because they’re like, “Oh, I automated my job and no one knows.” For a lot of people, they may go build your competitor and beat you because they’re able to go deeper and further and faster because of these tools. What’s at stake is that you’re going to be competing with other new entities that are AI native and move in ways that you didn’t think were possible.

ISHIKAWA: And so Taryn made the call.

PATIL: She just wanted to learn from me how I used some of my favorite tools. And basically, two hours in, she looked at me and said, “Do you think you could teach a whole team how to think like this in maybe two days?”

ISHIKAWA: Two days? Make it three …

FIXEL: I suggested that we pause company operations for three days in order to all align around how we’re going to use AI and what we can use it for.

PATIL: Honestly, I was very skeptical. I know for me, if I sit next to a friend for four hours, I can definitely AI-pill them, and they’ll be changed moving forward. But I was like, how do you do this for a team?

FIXEL: This is the Wild West. I think the most important thing that we can do for our staff, in terms of upskilling everybody, is give them the courage to start something new. You, too, can be an expert on AI if you just get started today.

Copy LinkHow to design an AI sprint that forces hands-on learning

EL KALIOUBY: So the motivation was there, but how do you actually pull off a three-day AI sprint? Were people even on board?

ISHIKAWA: Taryn, the rest of our leadership, along with Parth, put together a roadmap. They cleared everyone’s schedule for three days, signed up for paid Claude accounts — enough seats to cover every team member — and then split the company into groups tackling specific questions. Like: How can we use AI to help us make videos faster? How can we use it to surface guest ideas? And how can we make planning our three-day Summit more efficient? At the end of three days, each team would present its results to the whole company. Could AI solve the problem at hand? How? And don’t just talk about the answer — build an answer. Ready, set, go …

EL KALIOUBY: So the sprint begins — out of the starting blocks, how’s it looking?

ISHIKAWA: We’re a fully remote company, so the sprint started the way all of our meetings do: on Zoom.

JAI PUNJABI: Folks, welcome to our AI sprint. We made it. Today is a day about making things, about building things, about seeing things coming up that we have the opportunity to use and iterate, use and iterate.

ISHIKAWA: After the kickoff, each group spent most of the day in breakout groups. We were working together, getting to know our new AI co-worker — Claude — but also catching up with human co-workers, who we don’t get to see too much, talking about the important things in life …

MOLAD: I hate Gatorade. You know what?

JODINE DORCÉ: I hate it with a passion, and I only have it —

STERN: If you water it down, it’s better.

ISHIKAWA: Starting off, not everyone was feeling so confident about the exercise — or about using AI for certain parts of our work, including MG, who’s a video editor here.

MG FREDERICK: I think the first day I was like, “Oh no, does this mean that I’m supposed to use AI to video edit entirely?”

EL KALIOUBY: That’s understandable. It’s so important to draw boundaries around AI — what you want to outsource to it versus what you still do yourself.

ISHIKAWA: I agree. There are some tedious parts of my job that I would have no problem outsourcing to AI, but making editorial decisions, not so much.

Copy LinkEarly friction shows where today’s AI tools still fall short

EL KALIOUBY: OK, so the groups get started. What platforms are they using?

ISHIKAWA: Most of the teams used Claude. But Claude is a single-player tool — meaning there is no collaboration layer yet. So one team member had to screenshare over Zoom as they navigated Claude on their own computer.

EL KALIOUBY: Let’s dig into that for a second, because that’s an important point. This is one of the biggest limitations with tools like Claude today. It’s not like Google Docs, where multiple people can work off the same thing — it’s more like Microsoft Word, really.

ISHIKAWA: Exactly. Parth says that he expects this to change in the coming months. And this is something I’ve heard you say, Rana.

EL KALIOUBY: The AI of today is the worst it will ever be. It’s moving so fast.

ISHIKAWA: But there were other technical hiccups, too. At one point, Claude Desktop stopped working altogether.

MOLAD: Claude Desktop is failing to open for some users.

STERN: Oh, interesting.

ISHIKAWA: Every time we hit a snag, Parth was available to answer our questions. And remember, by and large, we’re AI novices at this point, so there were a lot of snags.

TAYLOR BRANCH: I was wondering, since we’re starting with Claude Code, is there a way that we should prep in Claude first before we start in Replit and give it a good base of code to begin with?

PATIL: You can import projects into Replit to work on something that you already have. GitHub is probably the best way to import, but you can also import a project as a folder of files.

ISHIKAWA: And with those snags came some important lessons we learned along the way. There’s so much to discuss, but I narrowed our big lessons down to three, because everyone loves threes.

Copy LinkTreat AI like a collaborator and ask it better questions

EL KALIOUBY: Let’s dig into it.

ISHIKAWA: First lesson: Engage in conversation with AI. Treat it like a colleague. The more back-and-forth you have with your AI, the more specific you get, the better the results. Team Gatorade — aka Jodine, Leital and Stephanie — was working on a guest speaker engine, a way to help us find cool guests for our podcasts and live events.

EL KALIOUBY: Rachel, that’s such a great example. As you know, I’ve been thinking a lot about what kind of work we delegate to AI and what work we do ourselves. In this particular case, we have weekly meetings where we discuss various guest pitches and whatnot. That’s not going away, right?

ISHIKAWA: Yeah, I think that’s right. We’d still have our weekly meetings. We’d still be the ones figuring out who’s going to land on our shows. But we’d have this other tool that could come up with new ideas that maybe we wouldn’t think about.

STERN: We first started out by asking Claude how it would find guests for podcasts and live events, and then how it would organize the database.

ISHIKAWA: That’s Stephanie Stern, senior talent executive — she leads booking guests on all of our shows.

STERN: But from there, we actually backtracked, asking Claude to ask us clarifying questions before creating this comprehensive database.

ISHIKAWA: They used a voice-to-text tool, so it was easier to have a natural conversation with Claude.

MOLAD: Before we start building, can you ask us some questions so you can get a better idea of the mission of our company and how we typically select guests and speakers for our podcasts and events?

ISHIKAWA: So: ask lots of questions. Ask the AI to interview you. And when you reach a roadblock, you can ask the AI itself for help. This is something Parth recommended again and again throughout the sprint.

PATIL: This is the first time we have a computer that can use language and speak. The idea that you can wield a computer through natural language means that you kind of have a steam engine for knowledge work. It reminds me of Harry Potter and spell-casting. If you know the right combination of words, things start happening.

EL KALIOUBY: When I was first learning computer science, I programmed in C++. What Parth is saying is that now, you don’t need C++ or any other programming language, for that matter. You can basically create software on your behalf using plain English — or Arabic or Chinese.

ISHIKAWA: Exactly, which is why it’s so important to treat your AI like a co-worker.

Copy LinkUse AI to eliminate invisible work and repetitive admin

EL KALIOUBY: So that’s a good first lesson. What’s the second lesson, Rachel?

ISHIKAWA: Lesson two: Look for areas of your work where there’s too much clicking around — all those tedious tasks, like manually entering data into an endless field of spreadsheets. Instead, see if you can use AI. To help bring that lesson to life, let me introduce you to DeAngela.

NAPIER: Hi, I am DeAngela Napier, and I am the special events project manager for the Masters of Scale Summit.

ISHIKAWA: Again, Summit is the big three-day live event we produce.

NAPIER: Before we started this sprint, I really thought about all the things that I do and what’s something that could be better.

ISHIKAWA: Her work involves lots of details.

NAPIER: There are a couple of little pain points where you’re like, “I wish this was better.” So I was thinking about hotel management, because that’s a big part of what I do as it gets closer to Summit.

ISHIKAWA: This is important and tedious — keeping track of everyone’s travel information, their hotel, and preferences, like what floor they want. And this information isn’t static. Travel plans change — a lot.

EL KALIOUBY: I am totally guilty of that!

ISHIKAWA: Look, a lot of people are. And DeAngela is the one tracking it all on a bunch of different spreadsheets, which meant a lot of clicking around — taking information from email or Slack, even text messages, and then inputting it again and again.

EL KALIOUBY: This is the kind of work AI is really good at — pulling and organizing data, a lot of which is scattered all over the place.

ISHIKAWA: Right. So she started building a real-time dashboard. And DeAngela isn’t the only one of my co-workers hoping AI can reduce the clicks and the cut-and-paste of it all. There’s so much back-end detail work around registration, ticket codes and tracking responses. Taryn had a good description here.

FIXEL: There is so much invisible work in everyday roles. It is very easy from the outside to look at somebody’s role and go, “What does this person actually do all day?” And the job description does not fully capture the reality. I think this is where AI introduces a really meaningful shift in creating workflows that are observable, explainable and shareable.

NAPIER: The people in our department know the things that I do, but I always get the feeling that most people don’t. I come from a military family, and my dad was like, “It doesn’t matter what accolades you get, just do the job well.” So I’m going to do it well anyway. But it’s nice to have that recognition.

ISHIKAWA: I asked DeAngela what advice she had for others getting started with AI …

NAPIER: I would just say, don’t be scared. Just try. Then start thinking about how you can apply that in your career. Because a lot of people think that AI’s here and I’m going to be replaced. And that doesn’t have to be the case.

ISHIKAWA: I’m not sure that’s the case. In a minute: the elephant in the room. Are we learning how to use these models, or are we training them to replace us? That’s after a short break. [AD BREAK]

Copy LinkFacing the fear that learning AI could also train your replacement

EL KALIOUBY: We are back, and let’s call it the second lap of three in this AI sprint. How’s it all going?

ISHIKAWA: The 12 teams are making headway on their projects. Some have even built prototypes …

TAYLOR BRANCH: We are working on a web-based app that can help us review applications for Summit much quicker. It has a queue for our applications. It allows us to score them.

ISHIKAWA: Some are testing off-the-shelf products …

ERIC PURCELL: Some of the Descript tools that we poked around in and discovered were actually pretty useful.

ISHIKAWA: And others are still in the exploration phase …

DROBNY: I feel like we might be a little behind, but this particular process is really wrapped up in a lot of other things that are going on both in the sprint and at the company.

EL KALIOUBY: Got it. And what’s all this costing?

ISHIKAWA: Leadership said that the highest cost is paying for seats to use tools like Claude and Replit. There’s also the cost of tokens, though most of the team members here haven’t maxed out on that. You also have to consider the cost of people spending time learning. It means they’re not doing the other parts of their jobs.

EL KALIOUBY: Absolutely. There are a lot of hidden costs we don’t talk about. OK, so far you’ve shared two of the three lessons you want to highlight. First, engage with your AI — treat it like a colleague. And second, make AI do the repetitive, tedious tasks. What’s the third lesson?

ISHIKAWA: We’re going to get to that in a minute, but first I want to address something: the big question. Is the sprint about upskilling us, or is it about replacing us? We all see the headlines about companies cutting thousands of jobs and saying it’s because AI can do them.

EL KALIOUBY: But some of that may be AI-washing, right? AI will disrupt some jobs, but it’s also my fundamental belief that AI, in the long run, will add more jobs to the economy.

ISHIKAWA: Right. But speaking personally, as someone who was on this sprint journey, Claude can do a lot of the things I do as a producer — for example, research, which is a big part of my job. We used to have an associate producer for this podcast. Currently, we don’t. AI is not the reason we don’t have an AP right now, but I will say that I’ve been using AI to do some of the work an AP would do. WaitWhat and its leadership are very transparent about this tension.

FIXEL: It is uncomfortable as a leader to ask your team to train on technology that they fear will replace them.

ISHIKAWA: Here’s Taryn again.

FIXEL: Also, I feel a sense of responsibility to make sure that everybody on our team understands how to use these tools so that they’re well-positioned for a long career.

ISHIKAWA: Taryn is upfront that she doesn’t know what any of our jobs — even hers — will look like in the future. But she made it a point to address that uncertainty head-on.

FIXEL: I felt that by co-creating a space where we were doing this collectively, it countered some of those fears and made it more productive.

EL KALIOUBY: Right — so having the sprint be a co-created project rather than something coming from the top down gives people agency in how AI is impacting their work.

ISHIKAWA: And I will say, that’s how it felt to a lot of us, too — even to MG, who is a video editor here and one of the team members who started out pretty staunchly against AI.

FREDERICK: I did not use AI before the sprint. I think there are a few different reasons. Number one, as an artist — I’m also a writer and a performer — I feel bad for artists whose work has been scraped from the internet, copied, and pushed through this sausage-maker AI thing. And also, environmental concerns are a big one for me, just worrying about all of the processing power and how that is affecting our planet.

EL KALIOUBY: Yeah, these are valid concerns we’ve talked about at length on this podcast. So how do you bring people like MG onboard?

ISHIKAWA: MG is also the kind of person who approaches skepticism with deep curiosity. MG was part of a team during the sprint trying to find ways to use AI to get to rough video cuts faster, which is no small order.

FREDERICK: There’s importing all of the files, organizing everything in Premiere, creating the multicam sequence, syncing the premixed audio. Then you get to, “OK, great, now let me pick the shot and do the rough pass.”

ISHIKAWA: A video editor’s role is creative and technical, like our audio engineers and design team, too. These are all areas where AI is advancing fast — it can make cuts to video, do graphic layouts, and manipulate audio files to improve the sound.

FREDERICK: But it became clear that that was not the purpose of our AI sprint.

ISHIKAWA: Because these tools can’t perform at the level of experienced, talented humans. The end-product quality is just not the same. We are not looking to fully automate these areas — at least not yet.

FREDERICK: It was not to take away the parts of our jobs that we love the most, or that are creative, human, fulfilling and artistic, but rather to get us to those aspects more quickly.

Copy LinkDeciding what humans should keep and what AI should handle

ISHIKAWA: Which brings me to the third and final lesson I want to share with you: how to make decisions. As you bring AI into your work, you’ll need to think critically about what to delegate to it and what to keep in your own human hands. And you’ll need to decide what tools to build yourself versus buy.

EL KALIOUBY: Yeah, you want to build a framework where human judgment is irreplaceable. So, for example, for this podcast, we still get to decide who’s on the show and what questions we’re asking. And then there’s the age-old question when it comes to new technology: build or buy? Again, say you want to use AI to generate social clips from a podcast episode. Do you buy an off-the-shelf product? And what’s the cost comparison in terms of money, time and resources?

ISHIKAWA: Yeah. And the team’s instinct was to test off-the-shelf solutions when it came to video.

FREDERICK: So I was looking at different products, so many of which were brand new and getting new versions every single day.

EL KALIOUBY: And did any of them rate as, “Oh, we have to have this”?

ISHIKAWA: Not really. A lot showed promise, but there was always one issue. Maybe the program didn’t offer a solid transcription, which you really need if you’re working in podcasting, or maybe the plug-in would use the wrong kind of file.

FREDERICK: So much of it is brand new, and I would be super curious to see where these companies and their products are now. I feel like they’ve probably developed radically. But it was cool to try things out and see different things that worked. It’s a tall order, with or without AI.

EL KALIOUBY: Yeah, so maybe WaitWhat can look to build our own AI agents down the line to solve this issue in the future.

ISHIKAWA: Like MG said, it’s a tall order, and it may make sense to wait and see what these off-the-shelf products look like in the future. So we’ll find out.

Copy LinkWhat the teams built and which experiments showed real promise

EL KALIOUBY: So what happens next as we wrap Day Two? What are the results of this sprint? Does someone come in first place?

ISHIKAWA: Well, Rana, we’ll get to that in a minute after a short break.

At the end of Day Three of the sprint, each of the 12 groups presented its findings. Team Gatorade, aka Team Guest Speaker Engine, made some real headway on its database …

DORCÉ: So what did we build? We built a standalone webpage. Ooh, this is sexy, right? It has some really good information on here.

ISHIKAWA: Picture a dashboard with different tags and guest suggestions. There’s a little context about each guest, too.

EL KALIOUBY: But how good were these suggestions?

ISHIKAWA: Some of the guest suggestions were almost too obvious. Who doesn’t love Oprah? But there were also some lesser-known names that the tool identified, which was promising.

EL KALIOUBY: What about the other projects?

ISHIKAWA: MG’s group presented, and no surprise, they didn’t find any perfect AI tool they were confident in.

EL KALIOUBY: So maybe Team MG didn’t win the race. So who did?

ISHIKAWA: Look, this wasn’t a competition. And there were so many cool projects that came out of the sprint. One of my co-workers, Taylor, built this incredible tool to monitor all incoming applications and ticket sales for Masters of Scale Summit. But if I had to pick one top contender, there’s one person who really stood out.

NAPIER: OK. So this is the Summit Hotel Operations Reimagined.

ISHIKAWA: I bet that if DeAngela had demoed her hotel management dashboard in person, the whole team would have given her a standing ovation. But on Zoom, it sounded a little different.

JAI PUNJABI: Folks, please give it up.

ISHIKAWA: We were all clapping — on mute.

NAPIER: I have to say that I was actually surprised that people thought it was so amazing, because I just thought, “Well, I’m just helping my role.” But as people were talking to me about it, I thought, yeah, I could really find other ways to apply this to other people’s roles that could help them. Because once you have one system, you can repeat it.

EL KALIOUBY: OK, so I’m picturing every team crossing that finish line — maybe some stumbling. The crowd is cheering. And now everyone is all in on AI?

ISHIKAWA: I wouldn’t say that, but people are crossing that line with a very different mindset than they started with. After everyone presented, we reflected as a group. MG, former AI hater, saw things differently.

FREDERICK: I’m very impressed, and I’m very pro people using AI in ways that take away the hateful tasks. My mom and I say that if we have to do something like taxes, then we have to go to the Ministry of Hateful Tasks. So using AI to minimize your time spent at the Ministry of Hateful Tasks — great. I love it.

EL KALIOUBY: Wow, I love that. So we saw changes to the team’s mindset, but what happens next?

ISHIKAWA: Truthfully, a lot of that is still being worked out. Something that was really clear the moment the sprint ended is how incredible this experiment was at generating ideas. When you have a brainstorming session, writing ideas on the big notepad is easy. Actually implementing those ideas is a whole other thing. Taryn put it like this …

FIXEL: It is not enough to simply do a three-day pause if we’re not then taking the learnings and applying them to our day-to-day workflow. The point of doing a three-day pause is to apply it to your day-to-day workflow. We’re still figuring that out.

Copy LinkTurning a burst of experimentation into secure, measurable workflows

EL KALIOUBY: So how is this going to roll out? Because I see a lot of organizations do some version of this AI sprint, but then nothing happens. And I get it — to go from experimentation to integration is actually nontrivial. So this really isn’t the end of the race, right?

ISHIKAWA: Yeah, the sprint is over. Here comes the marathon. We have a task force dedicated to figuring out which projects to pursue, and then how to implement them. Some of the projects from the sprint are combining into super-projects. For example, the guest speaker engine from Team Gatorade is joining forces with a larger speaker discovery app. And there are even more AI ideas in the pipeline since the sprint ended. There are around 30 of them.

EL KALIOUBY: But I imagine there are some hurdles.

ISHIKAWA: There are. Security is a big one.

EL KALIOUBY: This is something we talk about all the time on Pioneers of AI. Any system needs to have guardrails against things like prompt injection. And when you are dealing with data — especially personal data — it’s critical to have systems in place to protect that information.

ISHIKAWA: Since our company does ticket sales, we have sensitive personal information, like people’s email addresses and some financial information, too. One thing the task force developed was a security AI agent named Warden — as in the warden of a prison — and it helps keep everything secure.

EL KALIOUBY: What are the other hurdles?

ISHIKAWA: Another big one: measuring ROI, which isn’t a clear-cut calculation because we don’t know yet how much rolling these projects out will cost.

FIXEL: New technology is hard to budget for, especially when nobody’s used it before. This isn’t a category that we’ve spent on previously, or that anybody has spent on previously. I actually know a CFO who just told me that her team went from spending $1,000 a day to $1,500 a day per person in the span of a week.

ISHIKAWA: Right now, WaitWhat is paying for every team member to have access to Claude and Replit, but it may not make sense to do that long term. Plus …

FIXEL: We don’t know how many tokens something’s going to take, right? Our security platform takes up quite a few tokens, but we have to run it for a period of time to figure out exactly how many tokens it’s going to use over time.

ISHIKAWA: Once the true cost becomes clearer …

FIXEL: We’re going to have to determine what the ROI is. Are some things better to do manually because the cost of having an agent do it is too expensive?

ISHIKAWA: In the coming weeks, these AI projects will roll out for the rest of the company. And leadership plans on using our weekly companywide meetings to train us on how to use them. This kind of slow, strategic work isn’t headline-catching, but it’s the kind of work where we can see meaningful impact.

EL KALIOUBY: I 100 percent agree. A lot of these use cases aren’t sexy, but they can really change how we do work. So we need to hear more examples of how teams are systemizing AI into their workflows.

ISHIKAWA: Well, Rana, we’re coming to an end here, and I wanted to leave on a note that we often end our Pioneers episodes on.

EL KALIOUBY: Signature question?

ISHIKAWA: Yep.

What does it mean to thrive in the age of AI?

NAPIER: What does it mean to thrive in the age of AI? I think that if you are in a state of experimenting and trying things, you’re going to feel motivated and inspired and more confident, and understand that our mind is limitless. As my parents taught me growing up, you can do anything you want to do.

EL KALIOUBY: I love that. AI opens up doors to what’s possible. It feels like a really good note to end on, Rachel.

ISHIKAWA: DeAngela’s awesome. The whole team is. Maybe we’ll make a Part 2 when all of the ideas from the sprint are figured out. Until then, you can catch me on the track, still chugging along.

EL KALIOUBY: It’s messy, but fun — you got this, Rachel!

ISHIKAWA: Thanks so much, Rana. I hope so.