AI + You | 5 steps for impactful experimentation

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On the final Thursday. Of every november, many homes across the US burst with the sounds of a parade on the TV. Spirited family debates. You wanna talk politics? Let's talk politics. And the squeak of an old oven door. For some people.

Preparation for the holiday begins months in advance. While traditions may stay the same, the menu often evolves. It's like what's a new way to make turkey? What's a new way to make pie? It can be so frustrating. That's Priya Krishna. Priya is a best selling cookbook author and food reporter at the New York Times.

Every Thanksgiving, Priya feels the pressure to conjure new and creative recipes. So Priya decided to welcome an unlikely sous chef into her kitchen. We thought What if we just asked? AI to generate our Thanksgiving menu for us.

Creo wondered. rather than bend over backwards to invent the next reduction. What if AI was the secret weapon every recipe writer didn't know they needed? Priya and her team. Film their experiment.

She committed to cooking the AI generated recipes word for word. We gave Chat GPT a bunch of different prompts. Show us a Thanksgiving dessert. That is

A spin on pumpkin pie. Show me an unconventional take on stuffing. Show me an indie inspired version of Turkey. And that's where things got really interesting. The more specific we made the prompts, the more creative.

Chat GBT would get. Tapri's surprise, GPD's recipe for stuffing. Called for Indian nonbread. None is not the Most intuitive bread for a stuffing.

you think you would want something that's super absorbent. But in my mind I was like Maybe there's something too non While Priya cooked through each of the AI recipes, she battled against her instincts. desperate to amend peculiar directions and measurements.

The first dish pre-finished and plated was the Nan stuffing. It looked a little gnarly coming out of the oven. It sort of looked like a cinnamon raisin bread pudding. Thankfully. Other dishes looked more appetizing.

The cake looked Delightful. It was like a pumpkin spice cake with cream cheese frosting. To judge AI's Thanksgiving dishes, pre-invited four New York Times cooking columnists. The green beans were cooked to that perfect crisp tender texture.

Bright green, they've got a little crunch. And then on the other hand you had this turkey that was just dry as a bone. The general consensus was If I went to someone's Thanksgiving dinner and they served this, I'd be ordering pizza afterwards. Despite GPT's lack of culinary success.

Priya came away with some optimism. There are plenty of uses for AI and cooking. you could tell AI, I've got mushrooms and chicken broth and green beans in my fridge. What are some things I could make? And AI could give you ideas. When cooking chicken thighs, what temperature should they be at to be considered fully cooked? What is the roasting temperature for sweet potatoes that are cut into cubes?

AI can be very good as sort of a kitchen assistant. Ране по the kitchen leader. Creo was only able to make this useful discovery through experimentation. No chef expects to create a flawless dish on their first attempt.

They know from experience that commitment and patience are key in finding the perfect balance of flavors. Textures and spells. Although AI lacks the distinctly human senses and emotions needed to enjoy a turkey with all the trimmings, It can still help us come together and experiment and collaborate to make cooking creative, exciting, and accessible. Even though the AI's integration was far from a triumph.

It encouraged Priya to step outside of her comfort zone and open her eyes to new ideas. Every business leader should take inspiration from Priya's willingness to invite AI into the kitchen and experiment. With patience and an open mind, the potential is boundless. That's why I believe that AI won't revolutionize your business overnight. To unleas AI's true power of scale, you must dive headfirst into an era of ongoing experimentation. Well we can talk about AI. We can talk about it. When most people think about AI, they think about killer robots. Things are changing every couple of months. AI is at an infection point. It is a switch. It's like we have power tools now. And we'll just keep figuring it out. Generative AI is the most excited I've been about anything in software since the internet.

Mm-hmm. It's definitely a super exciting time to be in this space. All of these things are possible. Let's try to figure out whether this is actually useful or not. The tech just shouldn't replace human beings. Is it the worst thing in the world or is it the greatest thing in the world? This is masters of scale.

Mm. Oh. I'm Reid Hoffman. Co-founder of LinkedIn, partner at Graylock. And your host.

And I believe that AI won't revolutionize your business overnight. To unleas AI's true power of scale, you must dive headfirst into an era of ongoing experimentation. Normally. On Master the Scale, we dissect the story of an iconic entrepreneur. to prove a theory of scale.

But In the spirit of experimentation, today we're bringing you something a little different. We'll hear from an array of experts who have played a major role in scaling AI over the past decade. They'll share stories and lessons from the time of pioneering AI developers like OpenAI, Google DeepMind. And Microsoft.

Their experiences with AI. provide a perfect lens for any leader to better understand and navigate this new era, even in industries far from the leading edge of tech trailblazers. With a burgeoning AI revolution, it's helpful to remember that this isn't the first time modern humanity has transitioned into an intimidating new technology. When the world was first introduced to Netscape in nineteen ninety four. Many predicted that the internet would revolutionize the world of media and retail.

Of course, this came true. But it took well over a decade. to fully come to fruition. During that time. the technology gradually improved and the cost of entry lowered.

Computers became faster to manufacture. And the resolution of videos featuring sneezing pandas or cats and fedoras streamed in a higher and higher quality. However, it took years for many businesses to take advantage of the opportunities afforded by the internet. Today, we're experiencing change in a more compressed timeframe. AI is evolving at an exponential pace.

So the way we enact Patient experimentation must mirror that speed. As AI can be overwhelming, we want to cut through the noise and offer five steps to experiment impactfully with AI. By the end of the episode, I hope you're inspired by AI to forge a new creative and collaborative relationship. I also wrote a book earlier this year on this subject called Impromptu.

The first book on AI that is co-written with AI. Some of these experiments will be brand new to leaders, while others may be recognizable experiments. But with an added twist. Here's your five steps to experiment impactfully. With AI.

Step one Lear what's out there. Yeah. Uh Thorough preparation is essential for any experiment.

So let's begin by exploring some of the preliminary questions that leaders must consider before weaving AI into their business. Our first AI thought leader is Mael Gabe. Mael is the CEO of Techstars, a global investment firm that focuses on early stage startups. She currently mentors countless entrepreneurs who are transitioning to AI. I think there's a a lot of people right now where like, Oh my God, there's this

New wave of innovation coming at me. I'm not an engineer. I'm really worried that I'm gonna be left behind. I don't have the resource. I don't have the skills like what do I do? If you can relate, Mael offers some sage advice. It's a marathon, not a sprint. You do not have to run at full speed because right now, right here, it's only a hundred meters.

You need to save your energy and your resources to get to the finish line. It's important to go through the finish line, but you don't have to be the first one. That's okay too. Mael's right. Any reliable experiment. Takes time.

And many experiments fail. But that's exactly why we experiment. When we discuss the race with AI. It's important to remember that everyone's race is different. It could be a race for market share.

A race for capital. Or even a race for business survival. Regardless, if you lack confidence in your current ability to harness AI, that's entirely understandable. Despite which race you're running in.

We're still near the starting line. Considering when it is the right time to incorporate AI into your business. This is not a one-off process, like you're not gonna decide today whether you do ai or not. Do a whatever that means. AI will certainly align with some of your business needs. AI amplifies language and communication, which is relevant to every business.

Whether it's in meetings, sales, marketing, or customer service, there is a touch point for AI. However, today's available AI models may not immediately reshape your business. But the question isn't if you should begin experimenting with AI, but how much you should incorporate AI in your business. Following the experiment. Mael poses some questions for you to determine your need for AI integration.

At this moment in time. Does that answer your customer need? Does that help bring your business forward? And if it does, great. And if you don't know yet. In my world, I think it's okay to wait Another month, another quarter. I'm not saying ten years, but just a little bit, and then revisit on a regular basis. Whether or not there's new application of AI, new development that would make sense into your business.

En so experiment. Try things and see what sticks. I wanna echo my L here. Just because AI might not integrate correctly into your business today. Doesn't mean

That it won't six months from now. As humanity's partnership with AI has only just begun. It's vital. To keep your finger on the pulse. An experiment with all the new use cases that develop.

Step two Focus on the pain points. Thank you. Oh the big one. After you decided to begin your journey with AI, it's wise to choose a specific area of your business to focus your experiment.

Note. The AI discussed in this section refers to traditional AI. Rather than generative AI. In short, traditional AI can analyze data and tell you what it sees. It relies on explicit programming.

Whereas generative AI Can use that same data. To create something entirely new. Like generating text. Images.

Music. or even human like conversation. The next voice you'll hear is Faye Fei Lee. Fayfe is co-director of the Stanford Human-centered AI Institute. She also served as Vice President of Google and chief scientist of AI at Google Cloud.

Healthcare has been a deep concern for Fei Fei from a young age. Reed, as you know, I come from a immigrant family. And I think as a friend you also know my mother. She's

Chronically sick with a very severe cardiovascular illnesses. So as far as I can remember I was like taking care of my mom. Over her decades of caretaking. Fey Fei has experienced.

Every facet of the US healthcare system. From ICUs. Ambulances. Operating rooms. to home care.

Her main takeaway? Healthcare. Is complex. Around twenty twelve.

There was a very exciting technological Revolution happening in Silicon Valley with a self-driving car. It really dawned on me, my God, what we're learning from self-driving car, which is smart sensors, deep learning algorithms, a holistic understanding of the environment, will also be applied to healthcare. Delivery. Inspired, Fei Fey tapped her Stanford network.

We immediately start talking to staff our hospitals. Our very first project I think started in twenty thirteen was Hen Hygiene. Hand hygiene in patient rooms is so important to reduce the Hospital acquire infection which kills Three times more Americans every year than car accidents.

Before they could make a difference in hand hygiene, they needed more data. The only way to collect this data was by having a healthcare worker monitor how regularly and extensively colleagues wash their hands. But Fey Fe saw the importance of reliably collecting the data to make healthcare workers aware of the current state of hygiene and potential risks. In a single hospital unit, Faye Fay and her team installed computer vision technology by every hand hygiene station. A machine learning algorithm captured

Constant images and was trained to detect sanitizer dispenser use. The algorithm's accuracy was compared with an in-person human observer fulfilling the same task. And this is pre-Covid. It sounded like a boring project, even though we knew it was so important. But after Covid pandemic came.

It made so much sense. Yep, even not being a doctor because it's actually the application of technology to a Very important human centered thing, which is you know, health and well being and the system of things and This the little thing of making sure the hands are washed.

Massive improvement. Exactly. Okay. When the results of Fei Fei's experiment were analyzed, her team came to the conclusion that AI integration Has the potential to surpass the current gold standard of hygiene in hospitals.

Another AI pioneer who dedicated years to AI's application to healthcare is Mustafa Sullivan. Mustafa co founded one of the most influential AI businesses in the world. Deep mind. After Google acquired DeepMind, they launched. Deep mind health.

We started Deep Mind Health to work on predicting Electronic health records. to try to detect when a patient was about to experience some kind of deterioration. Through meetings with doctors and nurses, Deep Mind Health learned that the detection of acute kidney injury or AKI. Was a consistent pain point.

Spotting the early signs of AKI was notoriously difficult. Often. Slow and unreliable. And with AKI contributing to the deaths of around a hundred thousand patients every year in the UK, the stakes were high. Experts believed that up to thirty percent of AKI cases could be prevented if

A doctor intervened early enough. We developed an algorithm that could read the electronic health record and try to predict when a patient look like they're about to go into kidney failure and experience either sepsis or some other kind of serious deterioration. Once the team saw success in their algorithm, they wove the technology into a new app called Streams. The first users were healthcare workers in the NHS.

Now doctors could simply feed the patient's charts and vitals into the streams app and it would alert them of a potential AKI risk. The uh Reduce the cost of Treating acute kidney injury by twenty percent. And sped up the time to detect

acute kidney injury from on average four hours to Fiftees. I was very proud of that work because It actually showed that you could simultaneously reduce costs and improve the quality of care. And that's the goal in health systems, is that you want to make things cheaper and higher quality. And that's the promise of AI. Over the course of four years, Google Deep Minds AI also became equally proficient at detecting eye diseases and breast cancer.

In twenty twenty one. Google took streams offline. Due to privacy concerns. But it's helpful to remember. A large part of the stream's app's original success.

was due to addressing pain points in healthcare. So for AI to make improvements to your business, explore where it's most needed. Getting the most out of human teammates may be one of your most critical pain points. I would say every employee, every knowledge worker should now learn how to be good at delegating. That's David Luan.

David is the co-founder and CEO of Adept. Developing models that actively fulfill digital tasks for you. Learning software. to become an active teammate. For knowledge workers everywhere. David shared with me the many layers that go into thoughtful delegation.

Delegation itself is a whole skill. How do you frame what the goals are? How do you uh convey the desired outcome you want to whether that's your human teammate or your AI teammate in the fewest number of words possible where you actually get that outcome. Prompting is a way to just get the right textual output back. So how do you delegate that whole thing to the model? They actually think.

Everybody should be thinking about How they use AIs. To make themselves more effective. And they should be trying to limber up their skills, doing a little bit of Flexibility stretching and bench pressing and other kinds of things. Even if delegation isn't currently a leadership pain point for you.

The new specificity in delegating to AI is something you'll need to master. Working with AI doesn't immediately mean it'll generate the perfect results. Delegating. AI is achieved through asking the model questions. Often described as prompts.

But framing a smart prompt. To generate the best result is its own skill. To show you what I mean. I want to take a moment to ask Pye a couple of questions. Pi is the large language model I help build at Inflection AI.

Hey there, great to meet you. I'm Pai, your personal AI. A pie. Give me a title idea for an episode of Masters of Scale. About AI. Here goes.

How about the automated workforce or robots, humans, and the future of work? These titles suggest that the episode will explore how AI and automation. Hm. That's not quite what I want. Let me reframe the question.

Can you give me a title for a Masters of Scale episode about AI that's pithier? and tells the audience that they'll learn tangible lessons about incorporating AI into their business? Absolutely. How about ten steps to becoming an AI powered business or the AI Playbook? These titles are short and sweet and they

Hint at the practical focus of the episode. I nailed it, didn't I? That's great. I might use that. I'm your AI at your service.

Good prompts. Are dependent on the subject. And the best prompt. Doesn't mean It's ultra specific.

I'd like to think of it more as prompt directing. Rather than Prompt engineering. Think of yourself like a movie director. Rather than telling the actor, say the line exactly this way.

Leave breathing room. For creativity by the actor. You might say, try it more in the style of this or inject. A little bit of that. The conceptual lenses you add into your prompts will lead you to the most satisfying surprises.

While delegation is far from a new entrepreneurial skill. These core lessons are more important than ever. As leaders enter an unpredictable and fast paced era. So far, we've explored some of the preliminary questions you should consider while diving into the great AI experiment. Next, we'll navigate how leaders should continue the experimental mindset once AI integration is off and running.

That's after the break. So stick around. When you've built substantial wealth through your business, it's often tied up in a single equity position. The upside is real, but so is the risk, and knowing when to act isn't always obvious. Creative planning works with business owners to build a strategy around concentrated equity.

When to diversify, how to manage tax risk, and how to protect what you've spent years building. Creative planning where wealth works together. Lear more at creative plating dot com slash masters of scale. 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'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. Hey there, it's Jill Slessenger. I'm launching a new show. It's called Money Moves, and your money is going to move. We're gonna help you make better financial decisions. We're gonna call out the BS you're finding all over social media. We're gonna give you actionable guidance to make your financial life clearer, less stressful. We're gonna answer your financial questions and take the mystery out of your financial life. Follow and listen to Money Moves with Jill Schlesinger wherever you get your podcasts.

We're back with your five steps. To experiment impactfully with AI. To see exclusive clips from my interviews with some of our AI thought leaders. Featured in this episode, head to the Masters of Scale YouTube channel. Before the break, we heard how AI won't revolutionize your business overnight.

And to unleash AI's true power to scale your business, you must dive headfirst into an era of ongoing experimentation. But now It's time to level up. I want to explore the ways in which you should continue experimenting. Wow.

begin to actively integrate AI into your business. So let's jump into the next step. For experimenting impactfully with AI. Step three. Don't preach the power of AI.

Illustrate it. Step three. Don't reach the power. While you might be eagerly anticipating all the ways in which AI can evolve your business.

There will be other members of your team. That are weary. I'm Awkwardly. Place across this triangle of technology, design and business.

That's doctor John Maeda. He's an artist, author, and VP of design and AI at Microsoft. As a student of many disciplines, John is in a unique position to monitor enthusiasm. For AI. I can hear the technology, folks. This is amazing. Let's do more of it.

The design social science part of it saying we have to ask questions. How does it impact everything? How did it lead to more unfairness? Humanity questions. Did I get here on the Business product side.

Well, how is this gonna lead to more profitable business? Of an experiment. has multiple conflicting aims. It's harder. To get everyone on board.

So for example. If your business development or marketing team is gungha with AI. But your creative team is resistant. Make sure to begin a process of clear communication.

To get everyone in sync with a strategy and roadmap moving forward. You can do this by starting a simple Frank discussion. Playing out. How you plan to mitigate the downsides.

And take advantage of the upsides. Navigating those frictions. Is so critical. For AI to make a true difference to business, to the culture. And to advance technology.

To understand more about how to align your team, we need to hear from Jared Spotero. Jared is the CVP of Modern Work and Business Applications at Microsoft. As an expert on the future of work, Jared has ideated on all the ways that AI could transform the workplace. Like Doctor Maeda. Jared knows that everyone across your team might differ in their enthusiasm for experimenting with AI.

It is very rare. that you're gonna have everyone in a group say, That's it. That's the solution. You have early adopters. They play an important role in evangelizing the rest. You have laggards. they play an important role in kind of anchoring the group to make sure

there is some thought that's being applied to the new technology. So My advice would be finesse it, don't force it. Don't feel like you have to convince everybody that this is the best thing. Some sliced bread. The best way to win over reluctant teammates is through experiments that have clearly defined outcomes. At Microsoft, Jared has witnessed firsthand the power of AI through the Microsoft Three Sixty Five Copilot Tool.

With large language models embedded in a Microsoft's productivity apps, users can collaborate with an AI assistant. For example. On Excel, you can prompt the copilot to create data visualizations in seconds, or on Outlook, it can declutter or organize your inbox. I want to shine a light on how Jared drummed up enthusiasm for using Microsoft's Copilot around the office. We would use them in meetings, we'd send meeting summaries that were created by the co-pilot. We'd in the middle of a meeting use it to kind of analyze what was happening.

And I'll tell you what, that drove adoption, diffusion, and curiosity more than anything we could have done. People were like, there's something going on over there. What's going on? It created more of a positive pull. Then some sort of feeling of like okay, gonna have a big town hall. Jared motivated his team through illustrating AI's direct use cases.

For our listeners, Jared devised some questions for you to pose once your AI experimentation is underway. This will allow you to establish clear metrics that can align your team behind AI. What types of Innovative, unique. uses are you finding for it.

How much time's been saved. What is the difference in overall metric output sales. That are higher. Steal rates.

Are those changing the close rates associated with deals. Is satisfaction higher. To Jared, the questions you ask move through three distinct phases. You start with just are you spending enough time to get to know it? And then are you finding unique and novel uses and then finally, Okay, don't worry, now you've

recognize it's valuable. Let's see the value in terms in language that is no different than the way we expressed it. Six months ago of like we're running a business. Let's go. Be more efficient. As the world of AI

can be overwhelming workers, it's important to be intentional with your aims. This is why metrics of success must be communicated clearly. And early. Once you've achieved this. You'll find that your team is aligned and motivated.

To tackle your business's next bold chapter. Step four. On board. And collaborate with AI. Like any new employee.

Yeah. Once you've aligned with your team. It's time to set your expectations for your incoming new hire. AI itself. Let's hear again from David Lewan.

For more on this. I usually don't like to anthropomorphized these models, but this is actually a case where I think we should. Let's say you hire someone new to your company. How do you know whether or not you trust them with something? Maybe

In the beginning it's like start with something simple like um Let's go prep the financial update for the next board meeting. But then you'd ratchet up the level of complexity of what you trust them with, and you consistently make sure that you're getting the results you want. When you don't see the results you want, you do what you would do for any underperforming teammate. You investigate.

By asking a few questions. You're like, well, why'd you do it this way? What did you think about over here? Did you consider this other possibility? This is a great example of the way leaders everywhere should consider onboarding AI into their workflow. It's natural to be wary of trusting AI with important tasks from the very beginning. But the more you experiment and patiently drip feed responsibility, the more AI will have the chance to prove itself and open the door for trust. It's important to commit to healthy collaboration with any new employee.

AI is no different. After his time at OpenAI. David moved on to Google. Where he led their large models research. It was during an experiment using AI as an assistant on Google Software program.

That he experienced a revelation. In the process of solving a particular task. If a human gives the language model one piece of feedback. Like you're writing code and you forgot to import the Python OS library. then the solve rate for these problems would jump up like twenty, thirty percent.

From just one targeted piece of human feedback. The model's ability to learn improved dramatically. A core insight is We should not just train models and scale them up in a vacuum. The model should be the product and the end user should interact with the model directly and be able to directly give feedback to the model and for the model to be able to learn on it. And that loop, that human feedback loop, is gonna be the fastest way to progress.

Getting the best results from AI Will always require an ongoing collaborative relationship. Even when you've begun to implement AI across your business, the experiment. Isn't over. The models can always improve.

If you're willing to spend the time. To illustrate this idea in practice, I'm going back to my AI assistant. Hi. When I chatted with Pi earlier in the episode.

I reframe my prompt to encourage an answer. That align closer to my creative vision. Now though. I want to discover Pi's limitations and offer direct feedback to make the model one step smarter. Let's try it.

Hey pie. Hi, Reed, got another question for me? Who was the first guest ever on Masters of Scale? That's a great question. The first guest was Reed Hoffman. The founder of LinkedIn.

Actually, Reed Hoffman is the host of Masters of Scale. The first guest. was Brian Chesky. You're absolutely right, my apologies for the mix up. So let's see if you've retained that information.

Hi. Who was the first guest ever? On masters of scale. The first guest was Brian Chesky. The episode was released in twenty seventeen and it was titled

Do things that don't scale. Did I get it right that time? That's right. Thank you, Pi. Certainly.

Are we going to keep going with this? This is kinda fun. Pi improves. as it learns what makes a positive interaction. versus the negative interaction. It retains information like we just heard by generating memory that's specific to you.

Pi enters a data store that is only accessed during your interaction. If someone else were to ask Pi. Who the first guest on Master the Scale was, it might still get the answer wrong. That's because the model improves for you as an individual user without sharing your personal data and private prompts. The key.

Is having you. The human. In the loop to improve the model for yourself. As you'll hear in our episode on AI and Ethics. A human in a loop.

І знали crucial to AI experimentation. But also Moral responsibility. Which brings us to our final step. Step five.

Experiment with your eyes open. and be prepared for discomfort. While it's a vital time for leaders to begin experimenting with AI, it's equally important that you experiment safely. Sometimes

That means safeguarding your expectations too. Let's hear again. For Microsoft's Jared Spatara. When asked about the most common AI misconception. Here's what he said.

I would say misconception number one, it's gonna solve all my problems. And I would say no, not quite. That's right. In the same way that deploying AI Isn't an on off switch.

Neither. is its total impact on your business. But that's not the only safety concern. That Jared wants you to consider when experimenting with AI. A lot of people have questions about security and

Privacy and A lot of what we see today. that is most visible tends to be coming from a consumer service. And those consumer services have terms and conditions that essentially say, Hey, whatever you send us. We can use it to train and will use it to train our models. And so we certainly have cases where people are taking important information.

in an organizational context and unknowingly, unwittingly exposing it to the world. So be bold experiment, but make sure you do that in the context of a service that will Protect your data, your security, your privacy. You need to create an environment that you feel comfortable experimenting in. If you're distracted by security concerns throughout your testing,

you're less likely to be creative and agile. Before you give AI tools. Too much responsibility. Make sure you understand your relationship. With its developer.

This is especially true for users who are uploading information like customer data, client data. Or medical data. In Mustafa Sullivan's book, The Coming Wave, he wrestles with the need for AI experimentation. Without. Sacrificing responsible scale.

In twenty twenty. Mustafa left Google Deepmind to become Google's VP. Of AI product management and AI policy. I moved to work at Google full time. And I was lucky enough to be able to work on

an earlier version of Lambda. Lambda. is Google's large language model. First released. Understand.

Mina. But when Mustafa first joined the team. It was nothing more than a small research project. With six employees. As their lambda model improved over time,

Mustafa discovered what set Lambda apart. from other large language models. So previously We had the prompt answer engine.

Whereas what we built with Lambda was an interactive back and forth agent. So in many ways there was chat GPT way before chat GPT. And we were completely blown away. with how good your seventh and tenth

turn of conversation was with the model. With an interactive agent. So when you sat down for your tenth conversation with Lambda. The model had retained the context.

From all of your prior interactions. Mustafa was convinced. That the encouraging signs of their interactive agent Would spark. the next wave of technology.

We tried really hard to get that launched at the time, but there just wasn't the appetite for Taking the kind of risk. was pretty clear to a lot of people at Google that this was potentially going to unseat Google's existing search business. It's super hard for a company to try to compete with itself and upend itself from within.

Google's search engine is ubiquitous. But the rigid nature of the search engine would suffer in comparison to the fluid collaboration of an interactive agent. As the research team's creation would undercut Google's bread and butter, Google was reluctant to help Lambda take center stage. In february twenty twenty three, Lambda was deprioritized. In favor of Google's

newer chat bot. Bard. Experimentation and discovery go hand in hand. But it can also lead to some discoveries that are hard to hear. When you begin experiencing

The capabilities of AI. Be prepared for AI to reveal limitations in your business. When that happens. You'll have the choice to stick with what got you where you are today. Or

Trust that what you're seeing is true. When shining a light on your limitations, you must be prepared to embrace change. As an early figure in the modern development of AI. Mustafa's witnessed the breakneck speed at which technology And its capabilities.

has scaled. Every year. The cutting edge of AI models. has used ten times more compute. Than we used back in two thousand thirteen.

So over the last ten years. The amount of compute used to train the best and the biggest models in the world. Has ten X. So that gives you a sense of the trajectory that we've been on over the last decade. It's kind of hard to comprehend. Looking back can also give us a good idea of where we're headed.

I was looking at a photograph. A few days ago. of a floppy disc. In nineteen fifty. And it was the size

Of a pallet. And it was being lifted into an aircraft. Buy a Falklift truck. Now we have literally billions of those floppy disc. sized units of computation in our smartphones.

And if that trajectory continues over the next decade and more, and we see no reason why it won't That is going to be the most incredible boost to creativity and productivity in the history of our species. Amid the noise. It's easy to mistake AI. For merely this year's trend.

However, this is your time. to adopt a new leadership mindset that embraces innovation for years to come. Even if we can't comprehend what that innovation looks like yet. This mindset will help navigate all future transformations. As leaders look to evolve and gain new skills.

Over the coming weeks, we'll continue our AI series, exploring AI's potential to unlock new opportunities, accelerate your personal scale. And revolutionized the business world. From Grand Strategy. Insider Tips will make sure to offer all the tools to take advantage of this moment. If you're still intimidated by AI.

Except. That you will make mistakes. But that's A key byproduct. Of experimentation.

Because those mistakes may lead to your greatest learnings. And as Einstein said Q AI generated Einstein.

No amount of experimentation. Can ever prove me right. A single experiment can prove me wrong. I'm Reid Hoffman. Thanks for listening.

Humans will never be more intelligent than AI. There's been 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 El Calyubi. 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. Masters of Scale is a wait what original. Our executive producer is Chris McLeod. Our producers are Chris Gartier. Adam Scus Alex Morris

Hogarski. And Marsha, Marco Trinina. Our editor at large is Bob Safian. Our music director is Ryan Holliday. Original music and sound design by Eduardo Rivera.

Ryan Holiday. Hayes Holiday and Nate Kinsella. Audio editing by Keith J. Nelson, Steven Davies, Steven Wells, Andrew Nult, and Liam Jenkins. Mixing and mastering. by Aaron Bastanelli and Bri.

Our CEO and chairman of the board. is Jeff Burman. Master the scale. was created by June Cohen. And Darren Drift.

Special thanks to Jodine Dorsey. Alfonso Bravo. Tim Cronin. Erica Flynn. Sarah Tartar.

Kitty blazing. Ariel Karecker. Allen Howard. Brendan Klein.

Send me your punter. Calcy saison. Louisa Valence. He Williams. And Justin Winsler.

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