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

Summary

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Masters of Scale: Pioneers of AI – How Fast Can You Upskill in AI? We Did a Sprint to Find Out

Release Date: May 23, 2026
Host/Participants:

• Rana El Kaliubi: Co-host, AI scientist, entrepreneur
• Rachel Ishikawa: Senior Producer, “Pioneers of AI”
• Taryn Fixel: COO and President, WaitWhat
• Parth Patil: AI Engineer, Advisor at Reid Hoffman's Office
• MG: Video Editor
• d'Angela Napier: Special Events Project Manager
• Stephanie Stern: Senior Talent Executive

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Episode Theme & Purpose

This episode of “Pioneers of AI” (Masters of Scale) dives deep into the challenge of upskilling an entire organization in artificial intelligence—fast. WaitWhat, the company behind Masters of Scale, decided to hit pause on all normal operations for three days and run a company-wide “AI Sprint.” The episode follows their journey: why they did it, what they learned, and how a small media company (less than 40 people) is adapting to rapid AI changes to remain competitive and empower its team.

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Key Discussion Points & Insights

1. Why Upskill in AI? (02:13–03:59)

• Disruption Is Inevitable:
  • Taryn Fixel underscores, “Every company has to ask themselves how they'll be disrupted. Every company will eventually be disrupted.” (02:13)
• The pressing question: how does AI change the day-to-day workflow on a practical level?

2. Launching the AI Sprint: The What and Why (03:46–08:31)

• WaitWhat recognizes their fragmented AI adoption despite covering AI in their content.

• Leadership calls on Parth Patil—AI engineer/advisor—for guidance and develops a plan: three days off normal business, everyone tries AI tools (Claude, code platforms like Replit).

• Groups formed to tackle specific business problems with AI (e.g., faster video production, better guest selection, event logistics).

  • Quote:
    “If you don't become AI native, anyone on your team with high potential is not likely to stick around long term ... 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.” – Parth Patil (07:04)

3. Getting Started: Initial Uncertainties and Technical Hurdles (09:27–12:31)

• The sprint begins remotely on Zoom; mixed feelings and uncertainties among staff, especially from creative roles like video editing.
• AI tools’ collaborative limitations (e.g., Claude is “single player”); workarounds include screen-sharing.
• Technical snags (e.g., Claude desktop issues) and questions about tool integration were frequent.

4. Three Core Lessons from the Sprint (12:41–26:58)

Lesson 1: Engage AI as a Colleague (12:43)

• Effective use entails dialogue—treating the AI as an active conversational partner:
  • Use back-and-forth prompts, let AI ask clarifying questions.
  • Parth Patil analogy: “It reminds me of like Harry Potter and like spellcasting ... If you know the right combination of words, things start happening.” (14:38)
• Real example: Building a guest-speaker suggestion engine with AI; letting it “interview” staff to improve recommendations.

Lesson 2: Offload Tedious Work to AI (15:30)

• Look for repetitive, low-value areas ripe for automation (spreadsheets, data gathering, status tracking).
• d'Angela Napier’s hotel management dashboard as a showcase: “Keeping track of everyone's travel information, their hotel and preferences ... this information isn't static. Travel plans change a lot.” (16:25)
• Recognition of “invisible work”—AI makes these processes observable, explainable, and shareable. (17:22)
  • Advice:
    “Don’t be scared. Just try. Then start thinking about how you can apply that in your career.” – d'Angela Napier (18:05)

Lesson 3: Be Strategic – What to Delegate & Whether to Build or Buy (26:39–28:41)

• Deliberate decision-making about where human judgment remains essential.
• Evaluation: build custom AI solutions or use off-the-shelf tools?
  • Many tools promising but not perfect—often lacked crucial podcast-related features (e.g., accurate transcription).
• AI can help you “get to the creative part faster” but doesn’t replace high-end creative or technical work—yet.
  • Reflection:
    “It was not to, like, take away the parts of our jobs that we love the most ... but rather to get us to those aspects more quickly.” – MG (26:23)

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Notable Quotes & Memorable Moments

• On Rapid Change:
  “The AI that we have today is the worst it will ever be.” – Rana El Kaliubi (11:43)

• On AI & Job Insecurity:
  “It is uncomfortable as a leader to ask your team to train on technology that they fear will replace them ... but 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 Fixel (23:33, 23:42)

• On ‘Ministry of Hateful Tasks’:
  “Using AI to minimize your time spent at the Ministry of Hateful Tasks. Great. I love it.” – MG (31:52)
  (MG and their mother’s term for menial, soul-draining work—AI as a liberating force.)

• Final Reflection:
  “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 could do anything you want to do.” – d'Angela Napier (36:46)

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Important Segments & Timestamps

• [02:13] Framing the urgency for AI upskilling in business
• [03:46–05:07] Company background and operational scale
• [07:04] Parth Patil on the existential race for AI adoption
• [08:38] How the AI Sprint was organized and goals defined
• [09:41] Sprint kickoff and initial reactions from staff
• [12:41] The three main lessons from the AI Sprint
  • [12:43] Lesson 1: Engage in dialogue with AI
  • [15:30] Lesson 2: Use AI for tedium
  • [26:39] Lesson 3: Strategic delegation and build vs. buy
• [23:33–24:12] Addressing team anxieties and agency
• [31:52] Changing perceptions among skeptics
• [32:44–36:09] Integration challenges after a successful sprint
• [36:38] Signature “What does it mean to thrive in the age of AI?” reflection

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Post-Sprint Reflections & Integration Challenges

• Idea Generation vs. Implementation:
  Plenty of exciting prototypes and new AI-driven tools, but turning these into daily workflows is its own marathon.
  • “The sprint is over, but here comes the marathon.” – Rachel Ishikawa (33:20)
• Security, Cost, and ROI:
  • Security is paramount—team develops an “AI warden” agent. (34:17)
  • True financial cost of widespread AI adoption remains unpredictable; budgeting is challenging. (35:12)
• On Slow, Systematic Culture Change:
  Leaders will use regular meetings for ongoing training—systemic, not just headline-grabbing experiments.

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Conclusion: Thriving in the Age of AI (36:38–37:15)

The episode closes with an uplifting exchange on continual learning and experimentation as the truest path to thriving alongside AI. The core message: “Experiment, try things, let curiosity be your driver, and don’t fear the technology—harness it for human flourishing.”

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Flow and Tone

• Candid, transparent, and practical. The team openly discusses anxieties and aspirations, and there’s a balance of optimism, realism, and humor—especially around the messiness and imperfection of new technology, and about finding joy in small, liberating AI wins.

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Useful For:

• Organization leaders considering company-wide upskilling in AI
• Employees anxious about, or skeptical of, AI's professional impact
• Anyone interested in practical, real-world stories of upskilling quickly and inclusively

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Episode in a Sentence:
You may not know exactly what the future of AI holds for your job or your company, but if you dive in, experiment, and treat AI as an empowering colleague, you’ll be ready for the ongoing race—and maybe even have more time for the work you love.

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Transcript

[Jeff Berman]

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 and maybe even your life forward. It's an experience that can change literally everything. Apply [e-mail] apply 26. That's mastersofscale.com apply 26.

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[Bob Safian]

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 CEO to NASA 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.

[Taryn Fixel]

Every company has to ask themselves how they'll be disrupted. Every company will eventually be disrupted.

[Rana El Kaliubi]

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.

[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?

[Rana El Kaliubi]

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.

[Rachel Ishikawa]

Hey, Rana.

[Rana El Kaliubi]

So this is a very meta episode.

[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.

[Rana El Kaliubi]

So what did that look like?

[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.

[Rana El Kaliubi]

That sounds pretty awesome.

[Rachel Ishikawa]

Did it work?

[Rana El Kaliubi]

I mean, is the company now running at full AI speed?

[Rachel Ishikawa]

Well, that's what we're here to find out and to show our audience what, 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.

[Rana El Kaliubi]

And I'm Rana El Kaliubi. 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.

[Rachel Ishikawa]

Definitely. We're a media company. We've been around for about nine years. We make three podcasts, this one and two others.

[Jeff Berman]

This is Masters of Scale. I'm Jeff Berman, your host.

[Bob Safian]

I'm Bob Safian. And this is Rapid Response.

[Rachel Ishikawa]

And we publish about 200 episodes a year on video and audio, plus our website, social media, newsletters.

[Rana El Kaliubi]

And there's of course the Masters of Scale Summit, which I love.

[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 40 people, we definitely punch above our weight.

[Rana El Kaliubi]

Yeah, that's a lot. So how has the company been using AI up till now?

[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.

[Taryn Fixel]

There is no industry that this will not impact again.

[Rachel Ishikawa]

Taryn COO and President, I want to,

[Taryn Fixel]

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.

[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.

[Parth Patil]

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.

[Rachel Ishikawa]

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.

[Parth 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. 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.

[Rachel Ishikawa]

And so Taryn made the call.

[Parth Patil]

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?

[Rachel Ishikawa]

Two days? Let's make it three.

[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?

[Parth Patil]

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?

[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.

[Rana El Kaliubi]

Okay, so the motivation was there, but how do you actually pull off a three day AI Sprint? Were people even on board?

[Rachel Ishikawa]

Well, Taran 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? 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.

[Rana El Kaliubi]

Okay, and so the sprint begins out of the starting blocks. How is it looking, Rachel?

[Rachel Ishikawa]

Well, we're a fully remote company, so the Sprint started the way all of our meetings do, which is on Zoom.

[Jeff Berman]

Folks, welcome to our AI Sprint.

[Rachel Ishikawa]

We made it.

[Parth Patil]

Today is a day about making things,

[Rachel Ishikawa]

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 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. Although I hate Gertie Rain, you know

[d'Angela Napier]

what, I hate it with a passion.

[Rachel Ishikawa]

And I only have water down. It's better. I find Vitamin Water 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 (Video Editor)]

I think the first day I was like, oh, no, does this mean that I'm supposed to use AI to video edit entirely?

[Rana El Kaliubi]

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.

[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.

[Rana El Kaliubi]

Okay, so the groups get started. What platforms are they using?

[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.

[Rana El Kaliubi]

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?

[Rachel Ishikawa]

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.

[Rana El Kaliubi]

The AI that we have today is the worst it will ever be. And things are moving so fast.

[Rachel Ishikawa]

But, you know, there were some other technical hiccups too. At one point, Claude Desktop stopped working altogether. Claude Desktop failing to open for some users.

[MG (Video Editor)]

Oh, interesting.

[Rachel Ishikawa]

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 snacks. I was wondering, 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 give it, like, a good base of code to begin with?

[Parth Patil]

So 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.

[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.

[Rana El Kaliubi]

Let's dig into it.

[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 podcast and for live events.

[Rana El Kaliubi]

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?

[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 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

[d'Angela Napier]

our company and how we typically select

[Rachel Ishikawa]

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.

[Parth Patil]

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.

[Rachel Ishikawa]

Wingardium Leviosa.

[Rana El Kaliubi]

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.

[Rachel Ishikawa]

Exactly, which is why it's so important to treat your AI like you would a coworker.

[Rana El Kaliubi]

So that's a good first lesson. What's the second lesson, Rachel?

[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.

[d'Angela Napier]

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

[Rachel Ishikawa]

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

[d'Angela Napier]

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?

[Rachel Ishikawa]

Her work involves lots of details.

[d'Angela Napier]

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

[Rachel Ishikawa]

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.

[Rana El Kaliubi]

Yeah, I'm totally guilty of that.

[Rachel Ishikawa]

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.

[Rana El Kaliubi]

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.

[Taryn Fixel]

Yeah.

[Rachel Ishikawa]

So she started building a real time dashboard. And d' Angela 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 backend detail work around registration, ticket codes, tracking responses. Taryn had a really good description here.

[Taryn 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, like, what is. 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.

[d'Angela 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. And so I'm going to do it well anyway. But it's nice to have that recognition.

[Rachel Ishikawa]

I asked d' Angela what advice she had for others getting started with AI.

[d'Angela Napier]

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.

[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.

[Jeff Berman]

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 [e-mail] Masters of scale

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[Rana El Kaliubi]

will never be more intelligent than AI.

[Parth Patil]

There's going to be two types of companies.

[Bob Safian]

Those who are great at AI and

[Parth Patil]

those that went out of business because they weren't.

[BILT Sponsor Voice]

How do we build a future that is human centered?

[Rana El Kaliubi]

I'm Rana El Kaliubi 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. All right, we are back. Let's call it the second lap of three in this AI sprint. How's it going?

[Rachel Ishikawa]

Well, the 12 teams are making headway in their projects. Some have even built prototypes.

[Taryn Fixel]

We are working on a web based

[Rachel Ishikawa]

app that can help us review applications for Summit much quicker.

[Taryn Fixel]

It has a queue for our applications.

[Rachel Ishikawa]

It allows us to score them. Some are testing off the shelf products. Some of the descript tools that we poked around and discovered were actually pretty useful and others are still in the exploration phase.

[Parth Patil]

I feel like we we might be a little behind, but this particular process

[Rachel Ishikawa]

is really like wrapped up in a

[Parth Patil]

lot of other things that are going on both in the Sprint and at the company.

[Rana El Kaliubi]

Got it. What is this all costing though?

[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.

[Rana El Kaliubi]

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?

[Rachel Ishikawa]

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.

[Rana El Kaliubi]

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.

[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. Wait, what? And its leadership is very transparent about this tension.

[Taryn Fixel]

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

[Rachel Ishikawa]

Here's Taren again.

[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, positioned for a long career.

[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.

[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.

[Rana El Kaliubi]

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.

[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.

[MG (Video Editor)]

I did not use AI before the Sprint. I think like there's a few different reasons. Number one, as. 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.

[Rana El Kaliubi]

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?

[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.

[MG (Video Editor)]

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, okay, great, now let me pick the shot, you know, and do sort of like the rough pass.

[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.

[MG (Video Editor)]

But, like, it became clear that that was not the purpose of our AI sprint.

[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.

[MG (Video Editor)]

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.

[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.

[Rana El Kaliubi]

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?

[Rachel Ishikawa]

Yeah, and the team's instinct in this scenario was to test off the shelf products for video.

[MG (Video Editor)]

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

[Rana El Kaliubi]

And did any of them rate as, oh, my God, we have to have this?

[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 podcasts. Or maybe the plugin used the wrong kind of file.

[MG (Video Editor)]

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.

[Rana El Kaliubi]

Yes. So maybe Weiwut can consider building their own AI agents to solve this issue in the future.

[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.

[Rana El Kaliubi]

So what happens as we wrap up day two of this Sprint? Was there any clear winner?

[Rachel 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 their findings. Team Gatorade, aka team guest speaker Engine, made some real headway on their database.

[d'Angela Napier]

So what do we build? We build a standalone webpage.

[Rachel Ishikawa]

Ooh, this is sexy, right?

[d'Angela Napier]

It is.

[Rachel Ishikawa]

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.

[Rana El Kaliubi]

Okay, but how good were these suggestions?

[Rachel Ishikawa]

Well, there were some suggestions that were almost too on the nose. Like, who doesn't love Oprah?

[Rana El Kaliubi]

Yeah, Oprah, you're welcome on the show anytime.

[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. 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.

[Rana El Kaliubi]

Okay, so maybe Team MG didn't win the race, but who did?

[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.

[d'Angela Napier]

Okay, so this is the Summit Hotel operations reimagined.

[Rachel Ishikawa]

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 can help. We were all clapping on mute.

[d'Angela 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 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 roles that could help them. Because, you know, once you have one system, you can repeat it.

[Rana El Kaliubi]

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?

[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.

[MG (Video Editor)]

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.

[BILT Sponsor Voice]

Wow.

[Rana El Kaliubi]

I really love that. So it sounds like we saw a real change in people's mindset, but what's next?

[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. Taran put it like this.

[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.

[Rana El Kaliubi]

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?

[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 30 of them.

[Rana El Kaliubi]

But I imagine there were some hurdles.

[Rachel Ishikawa]

Yeah, there are. Security is a big one.

[Rana El Kaliubi]

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.

[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.

[Rana El Kaliubi]

What are the other hurdles?

[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.

[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 a thousand dollars a day to $1,500 a day per person in a

[Rachel Ishikawa]

span of a week right now. Wait, what is paying for every team member to have access to Claude and Replit? But it may not make sense to do that long term.

[Taryn Fixel]

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.

[Rachel Ishikawa]

Over time, once the true cost becomes

[Taryn Fixel]

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.

[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.

[Rana El Kaliubi]

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 successful systemizing AI into their workflows.

[Rachel Ishikawa]

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.

[Rana El Kaliubi]

Signature question.

[Rachel Ishikawa]

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

[d'Angela Napier]

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.

[Rana El Kaliubi]

I love that AI opens up doors to what's possible, and I think that's a really good note to end on.

[Rachel Ishikawa]

Rachel 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 the sprint figured out. And until then, you'll catch me on the track. Still. Still chugging along.

[Rana El Kaliubi]

Yeah. Rachel, I know this is messy work, but also so fun. You got this.

[Rachel Ishikawa]

Thanks, Ronna. Yeah, I.

[d'Angela Napier]

I.

[Rana El Kaliubi]

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 pioneersofai. Thanks so much for listening.