# Wait What's AI Sprint: Blueprint for Enterprise Integration

**Podcast:** Masters of Scale
**Published:** 2026-05-23

## Transcript

Hey folks, Jeff Berman here.
Exciting news.
Applications are now open for the Masters of Scale Summit.
It's happening October 20th through October 22nd in San Francisco, and it is a really special event.
Please join our curated community of founders, innovators, and leaders shaping the future.
Expect ideas that challenge your assumptions and connections that move your business and maybe even your life forward.
It's an experience that can change literally everything.
Apply now at mastersofscale.com slash apply26.
That's mastersofscale.com slash apply26.
Hey, listeners, Bob here.
If you listen to Rapid Response on Masters of Scale, you may be missing half the show because every Friday we release a second Rapid Response exclusively in the Rapid Response feed.
The guests and topics are just as compelling and timely from Ford's CEO to NASA's administrator to the lessons from the Devil Wears Prada.
It takes about 10 seconds to find.
Just search Rapid Response wherever you listen to podcasts.
and hit follow to make sure you never miss an episode.
I hope to see you there.
Humans will never be more intelligent than AI.
There's going to be two types of companies.
Those are great at AI and those that went out of business because they weren't.
How do we build a future that is human-centered?
I'm Rana Elkawiyubi, and on my podcast, Pioneers of AI, we answer that question and so many more.
As an AI scientist, entrepreneur, and investor, I know what it takes to build AI that works for everyone.
Every week, I sit down with the pioneers shaping our future, and we take you behind the scenes of the AI that's transforming our lives.
Find Pioneers of AI wherever you tune in.
Every company has to ask themselves how they'll be disrupted.
Every company will eventually be disrupted.
That's Taryn Fixel.
She's COO and president of Wait What, the production company behind this podcast, as well as masters of scale and rapid response.
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?
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.
Hi, Rachel.
Hey, Rana.
So this is a very meta episode.
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.
So what did that look like?
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.
That sounds pretty awesome.
Did it work?
I mean, is the company now running at full AI speed?
Well, that's what we're here to find out.
And to show our audience what we've learned along the way, I'm going to bring you from start to finish lines and all the hurdles in between.
I'm Rachel Ishikawa.
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 Wait What.
What does the company do and how big is the team?
Give us a rundown.
Definitely.
We're a media company.
We've been around for about nine years.
We make three podcasts, this one and two others.
This is Masters of Scale.
I'm Jeff Berman, your host.
I'm Bob Safian, and this is Rapid Response.
And we publish about 200 episodes a year on video and audio, plus our website, social media, newsletters.
And there's, of course, the Masters of Scale Summit, which I love.
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 40 people, we definitely punch above our weight.
Yeah, that's a lot.
So how has the company been using AI up till now?
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.
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 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.
Tara 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.
My name is Parth Patil, and I'm an AI engineer, and I work with the office of Reid Hoffman.
And I spend most of my days working with generative AI and advising startups, entrepreneurs.
In short form, I'd say I spend 14 hours a day talking to language models.
He talks to LLMs all day, not just chat tools, but coding platforms like Cloud Code, to stay up to date on how to use them.
how they're changing, and how to teach others about these tools.
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 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 that move in ways that you didn't think was possible.
And so Taryn made the call.
She just wanted to learn from me how I used some of 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?
Two days, let's make it three.
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.
And honestly, I was very skeptical.
I mean, I know for me, it's like if I sit next to a friend for four hours, I can definitely AI pill them, and then they'll be changed moving forward.
But I was like, how do you do this for a team?
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.
Okay, so the motivation was there, but how do you actually pull off a three-day AI sprint?
Were people even on board?
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?
How can we use AI to surface guest ideas?
And how can we make planning our three-day summit more efficient?
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.
Okay, and so the sprint begins.
Out of the starting blocks, how is it looking, Rachel?
Well, we're a fully remote company, so the sprint started the way all of our meetings do, which is on Zoom.
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.
After the kickoff, each group spent most of the day in their breakout groups.
We were working together, getting to know our new AI co-worker, Claude.
But we were also just catching up with our human co-workers who we don't get to see too much.
You know, talking about the important things in life.
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.
I think the first day I was like, oh, no, does this mean that I'm supposed to use AI to video edit entirely?
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.
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.
Okay, so the groups get started.
What platforms are they using?
Well, most of us were using Cloud, but Cloud, 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 Cloud on their own computer.
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 Cloud 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.
Yeah, 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, Rana, how quickly this is all iterating.
Totally.
The AI that we have today is the worst it will ever be.
And things are moving so fast.
But, you know, there were some other technical hiccups, too.
At one point, Cloud Desktop stopped working altogether.
Cloud Desktop failing to open for some users.
Oh, interesting.
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, since we're starting with Quad Code, is there a way that we should, like, sort of prep?
and quad first before we start in Repplit and give it like a good base of code to begin with.
So you can import projects into Repplit 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.
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.
Let's dig into it.
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, Littal, 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.
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 going to go away, right?
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.
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.
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.
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?
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.
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 Harry Potter and like spellcasting in Harry Potter.
It's like if you know the right combination of words, like things start happening.
Wingardium Leviosa.
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.
Exactly, which is why it's so important to treat your AI like you would a coworker.
So that's a good first lesson.
What's the second lesson, Rachel?
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 co-worker, D'Angela.
Hi, I am D'Angela Napier, and I am the Special Events Project Manager for the Masters of Scale Summit.
Again, Summit is the big three-day live event we produce.
Before we started the sprint, I really just thought about, of all the things that I do, what's something that I thought could be better.
Her work involves lots of details.
There's a couple of little pain points where you're like, but I wish this was better.
So I was thinking about the hotel management because that's a big part of what I do as it gets closer to Summit.
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.
Yeah, I'm totally guilty of that.
Look, a lot of people are.
And D'Angelo is 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.
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.
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 backend 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, look, what does this person actually do all day?
I think this is where AI introduces a really meaningful shift in creating workflows that are observable, explainable, and shareable.
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.
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.
I would just say, don't be scared.
Just try.
Just try.
Then start thinking about how you can apply that 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.
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.
The very best founders I know are brilliant at building systems.
They connect teams, they remove bottlenecks, and they eliminate single points of failure.
And yet, when it comes to their own wealth, most are running a disconnected stack.
A tax accountant here, an estate attorney there, a wealth manager who doesn't talk to either one of them.
Creative planning was built to fix exactly that.
One integrated team of tax professionals, estate planners, investment specialists, all coordinated by a dedicated wealth manager who sees your full financial picture and keeps every piece working together.
Proactive tax efficiency, estate strategy, investments all under one roof.
Creative planning where wealth works together.
Learn more at creativeplanning.com slash mastersofscale.
All right, we are back.
Let's call it the second lap of three in this AI sprint.
How's it going?
Well, the 12 teams are making headway in their projects.
Some have even built prototypes.
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.
Some are testing off-the-shelf products.
Some of the Descript tools that we hooked around and discovered were actually pretty useful.
And others are still in the exploration phase.
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.
Got it.
What is this all costing, though?
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.
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 want to 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?
We're going to 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.
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.
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.
Wait What and its leadership is very transparent about this tension.
It is uncomfortable as a leader to ask your team to train on technology that they fear will replace them.
Here's Taryn again.
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.
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.
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.
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.
Yeah, and that's how it felt for us, too, as we were doing the 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 the sprint.
I think like there's a few different reasons.
Number one, as an artist, I'm also 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 all of the processing power and how that is affecting our planet.
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?
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.
There's like importing all of the files, organizing everything in Premiere, and then creating the multicam sequence, syncing the premixed audio.
And then you get to like, OK, great.
Now let me pick the shot, you know, and do sort of like the rough pass.
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.
But, like, it became clear that that was not the purpose of our AI sprint.
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.
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 aspects more quickly.
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.
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?
And what's the cost comparison in terms of time, money, and resources?
Yeah, and the team's instinct in this scenario was to test off-the-shelf products for video.
So I was looking at different products, so many of which were like brand new and having new versions like every single day.
And did any of them rate as, oh my God, we have to have this?
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.
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.
It's a tall order with or without AI.
Yeah, so maybe wait, what can consider building their own AI agents to solve this issue in the future?
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 we'll find out.
So what happens as we wrap up day two of the sprint?
Was there any clear winner?
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 their findings.
Team Gatorade, a.k.a.
Team Guest Speaker Engine, made some real headway on their database.
So what do we build?
We build a standalone web page.
Ooh, this is sexy, right?
It is.
It has some really good information on here.
Picture a dashboard with different tags and guest suggestions.
There's a little context about each guest, too.
Okay, but how good were these suggestions?
Well, there are some suggestions that were almost too on the nose.
Like, who doesn't love Oprah?
Yeah, Oprah, you're welcome on the show anytime.
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.
What about the other projects?
MG's group presented, and no surprise, they didn't find any perfect AI tool that they were confident in.
Okay, so maybe Team MG didn't win the race, but who did?
Look, it 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 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.
Okay, so this is the Summit Hotel Operations Reimagined.
I bet that if D'Angela 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.
Folks, please get it on.
We were all clapping.
On mute.
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 then, you know, as people were talking to me about it, I just thought, yeah, you know, I could really find other ways to apply this to other people's roles that could help them because, you know, once you have one system, you can repeat it.
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?
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.
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, 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.
Wow, I really love that.
So it sounds like we saw a real change in people's mindset.
But what's next?
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.
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.
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?
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 30 of them.
But I imagine there were some hurdles.
Yeah, there are.
Security is a big one.
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.
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.
What are the other hurdles?
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.
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.
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, 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.
Once the true cost becomes clearer, 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?
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.
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.
Well, I'm happy to bring part of that story to our audiences.
But, you know, it looks like, Rana, 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.
Signature question?
Yep.
What does it mean to thrive in the age of AI?
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 can do anything you want to do.
I love that.
AI opens up doors to what's possible.
And I think that's a really good note to end on, Rachel.
DeAngelo is great.
The whole team is great.
And, you know, maybe one day Rana will make a part two when we have all of these ideas from the sprint figured out.
And until then, you'll catch me on the track.
Still chugging along.
Yeah, Rachel, I know this is messy work, but also so fun.
You got this.
Thanks, Rana.
Yeah, I hope so.
Pioneers of AI is a Wait What original production.
Our executive producer is Eve Trow.
Our producer is Rachel Ishikawa.
Our senior talent executive is Stephanie Stern.
Mixing and mastering by Brian Pugh.
Video editing by Eric Purcell.
Original music by Ryan Holiday.
Our head of podcasts is Lital Moulad.
You can join the conversation across social media platforms.
Just look for us at Pioneers of AI.
Thanks so much for listening.