# How fast can you upskill in AI? We did a sprint to find out. - **Show:** Pioneers of AI - **Published:** 20 May 2026 - **Source:** https://prfx.byspotify.com/e/dts.podtrac.com/redirect.mp3/tracking.swap.fm/track/7G0M4p2bOLP0mP50O3CD/pscrb.fm/rss/p/rss.art19.com/episodes/f4427408-fde3-48d0-86d8-972b50d6ebf7.mp3?rss_browser=BAhJIhlwb2RjYXN0Lm9wZW5uYXNoLmNvbQY6BkVU--4dcea1dd70768d62b2466884c2f8513b5f848fbf - **Transcript:** https://podcasts.opennash.com/shows/pioneers-of-ai/how-fast-can-you-upskill-in-ai-we-did-a-sprint-to-find-out-31ffd3/ - **Provider:** OpenNash Podcasts (machine-generated) ## In brief Pioneers of AI argues that a company learns AI fastest by stopping normal work and building real things with it. WaitWhat paused operations for three days so every employee could try AI on their own tasks. The lessons: talk to AI like a colleague, aim it at tedious clicking work, and decide deliberately what humans keep. The central tension is excitement against anxiety. Staff built useful tools, such as a hotel dashboard, and a skeptical video editor warmed to AI. But leaders admit it is uncomfortable to ask people to train on technology they fear will replace them. The sprint also produced ideas faster than the company can adopt them. Security, unpredictable costs and unclear ROI now decide what survives. ## Key insights 1. **Treat AI like a colleague, not a search box** The guest-speaker team got better results by asking Claude to ask them clarifying questions before it built anything, and they spoke their answers by voice. More back-and-forth gave more specific output. 2. **The best targets are tedious, scattered data tasks** D'Angela Napier tracked traveler hotel details across many spreadsheets, pulling from email, Slack and texts. Her real-time hotel dashboard became the sprint's standout, showing AI fits work with lots of clicking and copy-pasting. 3. **Keep human judgment on creative and editorial decisions** The video team found no tool good enough to replace experienced editors. They concluded the goal was to reach the creative parts faster, not automate them. 4. **Three days of ideas don't equal adoption** The sprint yielded around thirty new AI ideas, but implementing them is the hard part. A task force now picks which to pursue, merges overlapping ones and plans weekly staff training. 5. **Costs and security are the hidden hurdles** Seats, token usage and learning time all cost money, and nobody yet knows what rollout will cost. A security agent called Warden was built to protect sensitive ticket-buyer data. ## Questions this episode answers ### What is an "AI sprint" and what did this company actually do? It is a short, focused period where a whole team drops normal work to learn AI by building things. WaitWhat paused operations for three days, gave everyone Claude accounts, and split into twelve teams that each built or tested a solution to a specific problem. ### Do you need to know how to code to build software with AI? Not necessarily. Rana El-Khalyubi says you can use plain language, in English, Arabic or Chinese, to prompt AI to build software for you, and several team members who were new to coding built apps. ### Is AI going to replace people's jobs at this company? The leaders say they don't know what any job, even theirs, will look like later, and they admit the tension. Rachel Ishikawa notes AI isn't why the show lacks an associate producer, though she now uses AI for much of that work. ### What were the biggest limits of tools like Claude during the sprint? Claude is a single-user tool, so teams had to share one person's screen on Zoom instead of collaborating in one document. Off-the-shelf video tools also kept missing something, such as audio transcription, and none was a clear winner. ### What does it cost to roll AI out across a company? The biggest current cost is paying for software seats like Claude and Replit, plus usage fees called tokens and staff time spent learning. Taryn Fixel says costs are hard to budget, citing a CFO whose team's spend jumped from $1,000 to $1,500 per person per day in a week. ## Facts - **Rachel Ishikawa** [0:04:28]: "We've been around for about nine years. We make three podcasts, this one and two others." - **Rachel Ishikawa** [0:04:43]: "we publish about 200 episodes a year on video and audio, plus our website, social media, newsletters." - **Rachel Ishikawa** [0:04:55]: "we hold this big three-day event called Masters of Scale Summit in October. It's in San Francisco." - **Rachel Ishikawa** [0:04:55]: "for a company that's less than forty people, we definitely punch above our weight." - **Parth Patel** [0:06:22]: "My name is Parth Patel, and I'm an AI engineer, and I work with, uh, the Office of Reed Hoffman." - **Parth Patel** [0:06:52]: "if you don't become AI native, anyone on your team with high potential is not likely to stick around long term." - **Rachel Ishikawa** [0:08:27]: "They cleared everybody's schedule for three days, signed us all up for Claude accounts, enough seats to cover the entire company" - **Rachel Ishikawa** [0:09:22]: "we're a fully remote company, so the sprint started the way all of our meetings do, which is on Zoom." - **Rachel Ishikawa** [0:10:48]: "there's no collaboration layer. So one team member had to screen share over Zoom as they navigated Claude on their own computer." - **Rana el Kaliouby** [0:11:03]: "it's basically not like Google Docs where multiple people can be in the document collaborating. It's kind of more like Microsoft Word, really." - **Rachel Ishikawa** [0:12:31]: "they were working on a guest speaker engine, a way to help us find cool guests for our podcasts and for live events." - **Rachel Ishikawa** [0:13:41]: "They asked using a voice-to-text tool, so it's easier to have a natural conversation with Claude." - **Rana el Kaliouby** [0:14:51]: "You can basically use just plain English or Arabic or Chinese to prompt the AI to build software on your behalf." - **Rachel Ishikawa** [0:19:53]: "the highest cost at this point is paying for seats to use tools like Claude and Replit. There's also the cost of tokens" - **Rana el Kaliouby** [0:20:50]: "AI will disrupt some jobs, but it is my fundamental belief that AI will also add a lot of new jobs to the economy." - **Rachel Ishikawa** [0:21:00]: "we used to have an associate producer for this podcast, and currently we don't. AI is not the reason why we don't have an AP right now" - **Rachel Ishikawa** [0:25:53]: "A lot showed promise, but there was always, like, one issue. So maybe the program didn't offer, you know, an audio transcription, which we really need when you're working with podcasting" - **Rachel Ishikawa** [0:26:49]: "At the end of day three of the sprint, each of the twelve groups presented their findings." - **Rachel Ishikawa** [0:28:16]: "One of my coworkers, Taylor, built this really cool tool to monitor all of the incoming application and ticket sales for Masters of Scale Summit." - **Rachel Ishikawa** [0:31:23]: "the guest speaker engine from Team Gatorade is joining forces with this larger speaker discovery app. And there are even more AI ideas in the pipeline since the sprint ended. There's around thirty of them." - **Rachel Ishikawa** [0:32:20]: "One thing the task force developed is this security AI agent. They call it Warden, as in the warden of a prison, and it helps keep everything secure." - **Taryn Fixel** [0:32:53]: "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 a span of a week." ## Transcript **[0:00:01] Ad read:** Stripe is built for whatever comes next because AI isn't just changing business, it's evolving fraud. Stripe Radar detects fraud with the fast scale of Stripe's 1.9 trillion dollar annual transaction volume. In fact, when a card is used, there's 92% chance the Stripe network has seen it before. That means emerging fraud patterns are detected sooner, blocking bad actors, welcoming good customers. Learn more about fighting fraud with Stripe at stripe dot com slash radar. **[0:00:35] Ad read:** The business world is moving faster than ever. And when change hits, we need to learn in real time. On Rapid Response, you'll hear candid conversations with CEOs and leaders making tough calls about AI, human talent, responsibility, and the bottom line. How they navigate uncertainty, pressure, and high-stakes moments. I'm Bob Safian, former editor-in-chief of Fast Company, and I'll be your host as each episode breaks down what you need to know right now. You can find Rapid Response wherever you get your podcasts. **[0:01:11] Ad read:** On Masters of Scale, iconic leaders reveal how they've beaten the odds. **[0:01:24] Speaker 8:** Asking really strong questions is a superpower. **[0:01:27] Ad read:** You want to show up with something radically different and how they've grown companies to incredible heights. **[0:01:33] Speaker 6:** The greatest rewards always come from the greatest risks. **[0:01:36] Parth Patel:** Let's hit the gas. **[0:01:37] Ad read:** Airbnb, Zillow, Microsoft, Liquid Death, and more. Hear from the founders who've changed the game. It's anything but business as usual. Find Masters of Scale on Apple Podcasts, Spotify, YouTube, or wherever else you get podcasts. **[0:02:02] Taryn Fixel:** Every company has to ask themselves how they'll be disrupted. Every company will eventually be disrupted. **[0:02:11] 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. **[0:02:21] Taryn 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 could be successful in this new environment? **[0:02:32] Rana el Kaliouby:** Countless companies, large and small, are at a similar crossroads. We could keep doing work the same way we've been, or we can really harness AI to reimagine how we do work. We know that AI can supercharge an enterprise, like automate tedious work or streamline operations, boost output. But how do you actually do that? How do you start? Today, come along with this one small production company, ours, as we plunge into AI to transform work. And your guide will be Pioneers of AI senior producer Rachel Ishikawa. **[0:03:08] Rana el Kaliouby:** Hi Rachel. **[0:03:09] Rachel Ishikawa:** Hey Ronna. **[0:03:10] Rana el Kaliouby:** So this is a very meta episode. **[0:03:12] Rachel 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, somebody who works on an AI podcast, this was actually a lot of fun. **[0:03:32] Rana el Kaliouby:** So what did that look like? **[0:03:34] Rachel Ishikawa:** Well, 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. **[0:03:48] Rana el Kaliouby:** That sounds pretty awesome. Did it work? I mean, is the company now running at full AI speed? **[0:03:54] Rachel Ishikawa:** Well, that's what we're here to find out, and to show our audience what we've learned along the way. I'm gonna bring you from start to finish lines and all the hurdles in between. I'm Rachel Ishikawa, **[0:04:05] Rana el Kaliouby:** And I'm Rana El-Khalyubi, and this is Pioneers of AI. Okay Rachel, so let's start with a little bit of background on WaitWhat. What does the company do and how big is the team? Give us the rundown. **[0:04:28] Rachel Ishikawa:** Definitely. Uh, we're a media company. We've been around for about nine years. We make three podcasts, this one and two others. **[0:04:36] Ad read:** This is Masters of Scale. I'm Jeff Burman, your host. **[0:04:39] Bob Safian:** I'm Bob Safian, and this is Rapid Response. **[0:04:43] Rachel Ishikawa:** And we publish about 200 episodes a year on video and audio, plus our website, social media, newsletters. **[0:04:52] Rana el Kaliouby:** And there's, of course, the Masters of Scale Summit, which I love. **[0:04:55] Rachel Ishikawa:** Yeah, we hold this big three-day event called Masters of Scale Summit in October. It's in San Francisco. It's dozens of in-person speakers and performers. There's all this ticketing and logistics that go into it, and we also pop up with other one-off events throughout the year. So I have to say, for a company that's less than forty people, we definitely punch above our weight. **[0:05:18] Rana el Kaliouby:** Yeah, that's a lot. So how has the company been using AI up till now? **[0:05:22] Rachel Ishikawa:** I'll speak for myself. I've been using AI a lot for research, which makes sense since I produce a podcast about AI. But 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 in place because we acutely understand the stakes. There is no industry that this will not impact. Again, Taryn, COO and President. **[0:05:52] Rachel Ishikawa:** I want to number one make sure that the organization is nimble and has the ability to thrive in the future **[0:05:59] Taryn Fixel:** Media landscape that we live in. And number two, I actually 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. **[0:06:13] Rachel 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. **[0:06:22] Parth Patel:** My name is Parth Patel, and I'm an AI engineer, and I work with, uh, the Office of Reed Hoffman. And I spend most of my days working with generative AI and advising startups, entrepreneurs. In short form, I'd say I spend **[0:06:35] Rachel Ishikawa:** And 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. **[0:06:52] Parth Patel:** 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. I mean, for a lot of people, they may go build your competitor and, like, beat you because they're able to go deeper and further and faster because of these tools. And what's at stake is that you're going to be competing with other new entities that are AI native that move in ways that you didn't think was possible. **[0:07:21] Rachel Ishikawa:** And so Taryn made the call. **[0:07:23] Parth Patel:** She just wanted to learn from me how I used some of the my favorite tools. And she basically looked at me after two hours in and she was just like, Do you think that you could teach a whole team how to think like this in maybe like two days? **[0:07:35] Rachel Ishikawa:** Two days, let's make it three. **[0:07:39] Taryn Fixel:** And I suggested that we pause company operations for three days in order to all align around how we're going to use AI, what can we use it for? **[0:07:49] Parth Patel:** And I honestly, I was very skeptical. I mean, I I know for me, it's like if I sit next to, like, a friend for four hours, I can definitely, like, AI pill them. And then, like, they'll they'll be they'll be changed moving forward. But I was like, how do you do this for a team? **[0:08:04] Taryn Fixel:** This is the wild west. I think that the most important thing that we can do for our staff in terms of upskilling everybody is giving them the courage to start something new. Like, you too can be an expert on AI if you just get started today. **[0:08:20] Rana el Kaliouby:** Okay, so the motivation was there, but how do you actually pull off a three-day AI sprint? Were people even on board? **[0:08:27] Rachel Ishikawa:** Well, Taryn and the rest of our leadership, along with Parth, put together this roadmap for us. They cleared everybody's schedule for three days, signed us all up for Claude accounts, enough seats to cover the entire company, and then they split the company into small groups that were tackling specific questions. Like, how can we use AI to help us make video faster? Or how can we use AI to surface guest ideas? And how can we make planning our three-day summit more efficient? **[0:08:59] Rachel Ishikawa:** And at the end of the three days, each team would present their results to the whole company. Could AI solve the problem at hand? How? And don't just answer, build an answer. Ready, set, go. **[0:09:16] Rana el Kaliouby:** Okay, and so the sprint begins. Out of the starting blocks, how is it looking, Rachel? **[0:09:22] Rachel Ishikawa:** Well, we're a fully remote company, so the sprint started the way all of our meetings do, which is on Zoom. **[0:09:30] Speaker 8:** 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. **[0:09:41] Rachel Ishikawa:** After the kickoff, each group spent most of the day in their breakout groups. We were working together, getting to know our new AI coworker, Claude, but we were also just catching up with our human coworkers who we don't get to see too much, you know, talking about the important things in life. **[0:09:57] Speaker 6:** I hate it with a passion. And I only **[0:10:01] Rana el Kaliouby:** Clean water down is better. **[0:10:02] Speaker 6:** I've heard vitamin water a lot more. **[0:10:04] Rachel 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. **[0:10:15] Speaker 8:** I think the first day I was like, Oh, no, does this mean that I'm supposed to use AI to video edit entirely? **[0:10:25] Rana el Kaliouby:** That's understandable. It's so important to draw boundaries around AI and be intentional about what you want to delegate to AI versus what you want to do yourself. **[0:10:34] Rachel Ishikawa:** I agree. Like, there's so many parts of my job that feel really tedious, but there's parts that I don't want to outsource. For example, making editorial decisions. **[0:10:44] Rana el Kaliouby:** Okay, so the groups get started. What platforms are they using? **[0:10:48] Rachel Ishikawa:** Well, most of us were using Claude, but Claude, as you know, is a single player tool right now, so there's no collaboration layer. So one team member had to screen share over Zoom as they navigated Claude on their own computer. **[0:11:03] Rana el Kaliouby:** Let's dig into that for a second, because that's a very important point. That is one of the biggest limitations of tools like Claude today. It's changing very fast, but it's basically not like Google Docs where multiple people can be in the document collaborating. It's kind of more like Microsoft Word, really. **[0:11:20] Rachel Ishikawa:** Yeah, that's that's what Parth was saying, too, is that he expected this to change in the coming months. And I know this is something that you've talked about, Ronna, how quickly this is all iterating. **[0:11:31] Rana el Kaliouby:** Totally. The AI that we have today is the worst it will ever be, and things are moving so fast. **[0:11:37] Rachel Ishikawa:** But, you know, there were some other technical hiccups, too. At one point, Claude Desktop stopped working altogether. Every time we hit a snag, Parth was available to answer our questions. And remember, most of us were AI novices, so there were a lot of snags. I was wondering, we since we're starting with quad code, is there a way that we should, like, sort of prep in quad first before we start in Replit and get it like a good base of code to begin with? **[0:12:08] Parth Patel:** So you can 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, like, a folder of files. **[0:12:19] Rachel Ishikawa:** And with those snags, there were some important lessons that we learned along the way. There's so much to discuss, but I narrowed it down to three lessons because, you know, everybody loves threes. **[0:12:30] Rana el Kaliouby:** Let's dig into it. **[0:12:31] Rachel 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 are going to be. So, Team Gatorade, AKA Jodine, Lital, and Stephanie, they were working on a guest speaker engine, a way to help us find cool guests for our podcasts and for live events. **[0:12:57] Rana el Kaliouby:** Rachel, that's such a great example. As you know, I've been thinking a lot about, like, what kind of work do we want to delegate to AI versus do ourselves? In this particular case, we have our weekly meetings where we discuss various guest pitches and whatnot. That's not gonna go away, right? **[0:13:15] Rachel Ishikawa:** Yeah, I think that's right. We'd still have our weekly meetings, we'd still be the ones figuring out who's gonna 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. 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. That's Stephanie Stern, senior talent executive. She leads booking guests on all of our shows. **[0:13:41] Rachel Ishikawa:** But from there, we actually backtracked, asking Claude to ask us clarifying questions before creating this comprehensive database. They asked using a voice-to-text tool, so it's easier to have a natural conversation with Claude. 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? 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. **[0:14:22] Rachel Ishikawa:** This is something Parth recommended again and again throughout the sprint. **[0:14:27] Parth Patel:** This is like the first time we have a computer that can use language and that can 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, like, um, Harry Potter and, like, spell casting in Harry Potter. It's like, if you know the right combination of words, like, things start happening. **[0:14:46] Speaker 6:** Wingardium Leviosa. **[0:14:51] Rana el Kaliouby:** When I was first learning computer science, I programmed in C. But what Parth is saying is you basically don't need to learn C or any other programming language for that matter to build your own software. You can basically use just plain English or Arabic or Chinese to prompt the AI to build software on your behalf. **[0:15:10] Rachel Ishikawa:** Exactly, which is why it's so important to treat your AI like you would a coworker. **[0:15:15] Rana el Kaliouby:** So that's a good first lesson. What's the second lesson, Rachel? **[0:15:19] 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 my coworker, D'Angela. **[0:15:38] Rana el Kaliouby:** Hi, I'm D'Angela Napier, and I am the special events project manager for the Masters of Scale Summit. **[0:15:45] Rachel Ishikawa:** Again, Summit is the big three-day live event we produce. **[0:15:49] Rana el Kaliouby:** Before we started the sprint, I really just thought about, of all the things that I do, what what's something that I thought could be better? **[0:15:57] Rachel Ishikawa:** Her work involves lots of details. **[0:16:00] Rana el Kaliouby:** There's a couple of little pain points where you're like, but I wish this was better. So I was thinking about, um, the hotel management, because that's a big part of what I do as it gets closer to summit. **[0:16:13] Rachel 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. **[0:16:27] Rana el Kaliouby:** Yeah, I'm totally guilty of that. **[0:16:29] Rachel Ishikawa:** Look, a a lot of people are, and D'Angelo's the one tracking it all on a bunch of different spreadsheets, which means a lot of clicking around. Taking information from email or Slack, even text messages, and then inputting it again and again. **[0:16:45] Rana el Kaliouby:** Yeah, this is the kind of work that AI is really good at, pulling and organizing data that is often scattered all over the place. **[0:16:52] Rachel Ishikawa:** Yeah, so she started building a real-time dashboard. And D'Angela isn't the only one of my coworkers 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, tracking responses. Taryn had a really good description here. There is so much invisible work in everyday roles. It is very easy from the outside to look at somebody's role and go, like, what is what does this person actually do all day? **[0:17:21] Taryn Fixel:** I think this is where AI introduces a really meaningful shift in creating workflows that are observable, explainable, and shareable. **[0:17:32] Rachel Ishikawa:** 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 a military family, and my dad was like, it doesn't matter what accolades you get, just do the job well. And so I'm going to do it well anyway, but it's nice to have that recognition. I asked D'Angela what advice she had for others getting started with AI. **[0:17:53] Bob Safian:** I would just say don't be scared. Just try. Just try. Then start thinking about how you can apply that **[0:18:00] Speaker 6:** In your career, because a lot of people think that AI is here and I'm going to be replaced, and that doesn't have to be the case. **[0:18:07] Rachel Ishikawa:** The thing is, I'm not sure if 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. **[0:18:20] Rana el Kaliouby:** The companies winning right now aren't just building great AI. They're building the infrastructure to actually run a business on top of it, which is where Stripe comes in. Stripe is built for whatever comes next, with planetary scale financial infrastructure to help you power any business model. As a matter of fact, 86% of the Forbes AI fifty run on Stripe, along with industry leaders like Salesforce and Pepsi. Last year, Stripe users processed $1.9 1 trillion combined. **[0:18:55] Rana el Kaliouby:** See how Stripe can power what's next for your business at stripe dot com. All right, we are back. Let's call it the second lap of three in this AI sprint. How's it going? **[0:19:10] Rachel Ishikawa:** Well, the twelve teams are making headway in their projects. Some have even built prototypes. **[0:19:16] Speaker 6:** 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, like, score them. **[0:19:26] Rachel Ishikawa:** Some are testing off-the-shelf products. **[0:19:29] Bob Safian:** Some of the descript tools that we poked around and discovered are actually pretty useful. **[0:19:34] Rachel Ishikawa:** And others are still in the exploration phase. **[0:19:37] Bob Safian:** I feel like we we might be a little behind, but this particular process is really, like, wrapped up in a lot of other things that are going on, both in the sprint and at the company. **[0:19:49] Rana el Kaliouby:** Got it? What is this all costing, though? **[0:19:53] Rachel Ishikawa:** Leadership said that the highest cost at this point is paying for seats to use tools like Claude and Replit. There's also the cost of tokens, though most of the team members haven't maxed out on that yet. You also have to consider the cost of people spending time learning, which means they aren't doing the other parts of their jobs. **[0:20:12] Rana el Kaliouby:** Absolutely, Rachel. There's a lot of hidden costs here that we often don't talk about. So, so far, you've shared two of the three lessons you wanna highlight. Lesson one, engage with your AI and treat it like a colleague. And lesson two is make AI do the repetitive, tedious work. What's lesson three? **[0:20:30] Rachel Ishikawa:** We're gonna get to that in a minute, but before we do, I want to address something for a moment. This big question, is the sprint about upskilling us, or is it about replacing us? I mean, we've all seen the headlines at this point. Companies have been cutting thousands of jobs, and some are saying it's because of AI. **[0:20:50] Rana el Kaliouby:** Yeah, but some of that might be AI washing, right? AI will disrupt some jobs, but it is my fundamental belief that AI will also add a lot of new jobs to the economy. **[0:21:00] Rachel Ishikawa:** Yeah, I do agree with that, but at the same time, it is kind of weird to see Claude do aspects of my job and do it, I would say, almost as well or just as well. It's a little bit of an ego hit. And just to put a finer point on this, we used to have an associate producer for this podcast, and currently we don't. AI is not the reason why we don't have an AP right now, but I will say that I have been using AI a lot to do the job that an AP would do. **[0:21:30] Rachel Ishikawa:** Wait, what? And its leadership is very transparent about this tension. **[0:21:36] Taryn Fixel:** It is uncomfortable as a leader to ask your team to train on technology that they fear will replace them. **[0:21:43] Rachel Ishikawa:** Here's Taryn again. **[0:21:45] Taryn Fixel:** And 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. **[0:21:56] Rachel Ishikawa:** Taryn's 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. **[0:22:06] Taryn Fixel:** I felt that by creating a space where we were doing this collectively, it countered some of those fears and would make it more productive. **[0:22:15] Rana el Kaliouby:** Right, so having the sprint be a co-created project rather than something that is imposed on the team top-down is giving people agency to think about how AI is impacting their work. **[0:22:26] Rachel Ishikawa:** Yeah, and that's how it felt for us too as we were doing this sprint. It felt like that even to MG, who is a video editor here and one of the team members who started out pretty against AI. I did not use AI before this sprint. **[0:22:40] Taryn Fixel:** Before the sprint, I think, like, uh, there's a few different reasons. Number one, as as an artist, I'm also a a writer and a performer, and I feel bad for artists whose work has been scraped from the internet, copied, pushed through this, like, sausage maker AI thing. And then I think also, you know, environmental concerns are a big one for me, just sort of worrying about how that is affecting our planet. **[0:23:11] Rana el Kaliouby:** Yeah, these are valid concerns we've talked a lot about on this show. So how do you bring somebody like an MG on board? **[0:23:18] Rachel Ishikawa:** Well, MG, you know, is the kind of person who approaches skepticism with deep curiosity. They were part of a team that was working on ways to find how they can use AI to get to rough video cuts faster, which is no small order. **[0:23:34] Taryn Fixel:** There's like importing all of the files, organizing everything in Premiere, and then creating the multicam sequence, syncing the pre-mixed audio. And then you get to like, okay, great, now let me pick the shot, you know, and do sort of like the rough pass. **[0:23:48] Rachel Ishikawa:** A video editor's role is creative and technical. It's kind of similar to our audio engineers and design team, too. These are all areas where AI is advancing fast. They can make cuts to video, do graphic layouts, manipulate audio files to improve the sound. **[0:24:05] Parth Patel:** but like, the it became clear that that was not the purpose of our AI sprint. **[0:24:13] Rachel Ishikawa:** Because these tools can't perform at the level of experienced, talented humans, the end product quality is just not the same. So we're not looking to fully automate these areas, at least not yet. **[0:24:26] Parth Patel:** It was not to, like, take away the parts of our jobs that we love the most, or that are like creative, that are human, that are fulfilling, that are artistic, but rather to, like, get us to those **[0:24:38] Taryn Fixel:** Aspects more quickly. **[0:24:42] Rachel 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, what to keep in your own human hands, and then you'll have to decide whether to build or buy. **[0:25:01] Rana 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 gets to be on the show and what questions to ask. And then, of course, there's the age-old question when it comes to new technology, build or buy. Again, for instance, say you want to use AI to generate social clips for the podcast. Do you build this yourself using agentic AI, or do you buy an off-the-shelf product? **[0:25:29] Rana el Kaliouby:** And what's the cost comparison in terms of time, money, and resources? **[0:25:34] Rachel Ishikawa:** Yeah, and the team's instinct in this scenario was to test off-the-shelf products for video. **[0:25:40] Taryn Fixel:** So I was looking at different products, um, so many of which were, like, brand new and having new versions, like, every single day. **[0:25:48] Rana el Kaliouby:** And did any of them read as, Oh my God, we have to have this? **[0:25:53] Rachel Ishikawa:** Not really. A lot showed promise, but there was always, like, one issue. So maybe the program didn't offer, you know, an audio transcription, which we really need when you're working with podcasting, or maybe the plugin used the wrong kind of file. **[0:26:09] Taryn Fixel:** So much of it is brand new, and I would be super curious to see where these companies and, like, their products are now. I feel like they're probably radically developed, but it was cool to sort of try out and see different things that worked. With or without AI. **[0:26:26] Rana el Kaliouby:** Yeah, so maybe WaitWhat can consider building their own AI agents to solve this issue in the future. **[0:26:33] Rachel Ishikawa:** Yeah, I mean, I would think so. I mean, like MG said, it's a very tall order. It may make sense to wait and see what the off-the-shelf products look like in the future. So, so we'll find out. **[0:26:44] Rana el Kaliouby:** So what happens as we wrap up day two of this sprint? Was there any clear winner? **[0:26:49] Rachel Ishikawa:** Well, Ronna, we'll get to that in a minute after a short break. At the end of day three of the sprint, each of the twelve groups presented their findings. Team Gatorade, AKA Team Guest Speaker Engine, made some real headway on their database. **[0:27:22] Speaker 6:** So, what do we build? We build a standalone webpage. Ooh, this is sexy, right? It it is. It has some really good information on here. **[0:27:33] Rachel Ishikawa:** Picture a dashboard with different tags and guest suggestions. There's a little context about each guest, too. **[0:27:39] Rana el Kaliouby:** Okay, but how good were these suggestions? **[0:27:42] Rachel Ishikawa:** Well, there were some suggestions that were almost too on the nose, like who doesn't love Oprah? **[0:27:48] Rana el Kaliouby:** Yeah, Oprah, you're welcome on the show anytime. **[0:27:51] Rachel Ishikawa:** Yes, Oprah, please come on the show. But, you know, in reality, we're also looking at other people who might be lesser-known names. And the app was pretty good at coming up with some of those people. **[0:28:03] Rana el Kaliouby:** What about the other projects? **[0:28:05] Rachel Ishikawa:** MG's group presented, and no surprise, they didn't find any perfect AI tool that they were confident in. **[0:28:12] Rana el Kaliouby:** Okay, so maybe Team MG didn't win the race, but who did? **[0:28:16] Rachel Ishikawa:** Look, it wasn't a competition, and there were so many cool projects that came out of the sprint. One of my coworkers, Taylor, built this really cool tool to monitor all of the incoming application 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. **[0:28:38] Taryn Fixel:** Okay, so this is the Summit Hotel operations reimagined. **[0:28:44] Rachel Ishikawa:** I bet that if D'Angela had demoed her hotel management dashboard in person, the whole team would've given her a standing ovation. But on Zoom, it sounded a little different. **[0:28:56] Bob Safian:** Um, folks, please. **[0:29:01] Rachel Ishikawa:** We were all clapping on mute. **[0:29:04] Parth Patel:** 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 what my role. But then, you know, as as people were talking to me about it, I just thought, Yeah, I, you know, I could really find other ways to apply this to other people's, uh, roles that could help them because, you know, once you have one system, you can repeat it. **[0:29:31] Rana el Kaliouby:** Okay, so I'm picturing every team crossing that finish line, maybe some are stumbling, the crowd is cheering. So now what? Everyone's all in on AI. **[0:29:40] Rachel Ishikawa:** I wouldn't say that, but people are crossing that line with a different mindset than they started. After everyone presented, we reflected as a group, and MG, former AI hater, saw things differently. **[0:29:53] Taryn Fixel:** I'm very impressed and I'm very pro people using AI in ways that, like, yeah, take away some of, you know, the hateful tasks. My mom and I say if we have to do something like taxes, it's then we have to go to the Ministry of Hateful Tasks. So, you know, using AI to minimize your time spent at the Ministry of Hateful Tasks. Great, I love it. **[0:30:18] Rana el Kaliouby:** Wow, I really love that. So it sounds like we saw a real change in people's mindset. But what's next? **[0:30:25] Rachel Ishikawa:** Well, truthfully, a lot of that is still being worked out. Something that was really clear the moment that the sprint ended is how incredible this experiment was in generating ideas. Like, when you have a brainstorming session, writing ideas on the big notepad is easy, but actually implementing those ideas is a whole other thing. Taryn put it like this. **[0:30:47] Taryn Fixel:** It's not enough to simply do a three-day pause if we're not then taking the learnings and applying it 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. **[0:31:03] Rana el Kaliouby:** So how is this going to roll out? Because, you know, I see a lot of organizations doing some version of this AI sprint and then nothing happens. In reality, it's actually pretty hard to go from experimentation to integration of these AI workflows. So this really isn't the end of the race, right? **[0:31:23] Rachel Ishikawa:** Yeah, the sprint is over, but here comes the marathon, right? So we have this task force that's dedicated to figuring out which projects to pursue and how to implement them. Some of the projects from the sprint are actually combining to these super projects. For example, the guest speaker engine from Team Gatorade is joining forces with this larger speaker discovery app. And there are even more AI ideas in the pipeline since the sprint ended. There's around thirty of them. **[0:31:56] Rana el Kaliouby:** But I imagine there were some hurdles. **[0:31:58] Rachel Ishikawa:** Yeah, there there are. Security is a big one. **[0:32:03] Rana 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're dealing with data, especially personal information, it is so important and critical to have systems in place to protect that information. **[0:32:20] Rachel Ishikawa:** Yeah, and since our company does ticket sales, we have sensitive personal information like people's email addresses, some financial information. One thing the task force developed is this security AI agent. They call it Warden, as in the warden of a prison, and it helps keep everything secure. **[0:32:40] Rana el Kaliouby:** What are the other hurdles? **[0:32:42] Rachel Ishikawa:** Another big one measuring ROI, which isn't a clear-cut calculation because we don't know yet how much rolling these projects is actually going to cost. **[0:32:53] Taryn 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 a span of a week. **[0:33:16] Rachel 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, **[0:33:26] Taryn Fixel:** We don't know how many tokens something's going to take, right? Like, 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. **[0:33:39] Rachel Ishikawa:** once the true cost becomes clearer, **[0:33:42] Taryn 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? **[0:33:52] Rachel Ishikawa:** in the coming weeks, these AI projects will roll out for the rest of the company. And leadership plans to use our weekly company-wide 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 see really meaningful impact. **[0:34:12] Rana el Kaliouby:** I 100% agree. A lot of these use cases aren't sexy, but they actually really change the way people do work. And they can be powerful, and we need to hear more examples of how teams are systemizing AI into their workflows. **[0:34:26] Rachel Ishikawa:** Well, I'm happy to bring part of that story to our audiences. But, you know, it looks like, Ronna, we're coming to an end of our episode. And I wanted to leave on a note that we often end our Pioneers of AI episodes. **[0:34:40] Rana el Kaliouby:** Signature question. **[0:34:41] Rachel Ishikawa:** Yep. What does it mean to thrive in the age of AI? **[0:34:49] Taryn Fixel:** What does it mean to thrive in the age of AI? I think that if you are in a state of just experimenting and trying things, you're going to feel motivated and inspired and more confident and understand that our mind is limitless. And you can actually, as my parents taught me growing up, you could do anything you want to do. **[0:35:13] Rana el Kaliouby:** I love that. AI opens up doors to what's possible, and I think that's a really good note to end on, Rachel. **[0:35:18] Rachel Ishikawa:** D'Angela's great, the whole team is great, and, you know, maybe one day Ronna will make a part two when we have all of these ideas from this sprint figured out. And until then, you'll catch me on the track, still still chugging along. **[0:35:33] Rana el Kaliouby:** Yeah, Rachel, I know this is messy work, but also so fun. You got this. **[0:35:38] Rachel Ishikawa:** Ugh, thanks, Ronna. Yeah, I I hope so. **[0:35:54] Rana el Kaliouby:** Pioneers of AI is a WaitWhat original production. Our executive producer is Eve Tro. Our producer is Rachel Ishikawa. Our senior talent executive is Stephanie Stern. Mixing and mastering by Brian Pute. Video editing by Eric Purcell. Original music by Ryan Holliday. Our head of podcasts is Lital Malad. You can join the conversation across social media platforms. Just look for us at Pioneers of AI. Thanks so much for listening.