Innovating Leadership:
Co-Creating Our Future

Resources:

Learn more about Srini’s firm, Right Brain Labs, at https://www.rightbrainlabs.ai/.

Our host Maureen Metcalf posts a newsletter every week on LinkedIn. You can subscribe here.

Maureen’s latest book is Innovative Leadership & Followership in the Age of AI. You’ll find details about it at https://bit.ly/LeaderInAI, or check out the Kindle version at https://amzn.to/44buVz8. The audiobook version is now available at  https://amzn.to/4dTCleZ.

Her other 10 books are available on Amazon here.

Other episodes you’ll enjoy:

– Tech with Purpose: Protecting People with Innovation at Amazon with Aaron Parness & Beryl Tomay

– What’s the Point? Why Your Leadership Needs Purpose with Ryan Gottfredson

– To Stop a Tyrant: The Power of Followers with Ira Chaleff

Guest(s):

Guest(s) Bio:

Srini Koushik is a 3-time Fortune 500 CIO who has led technology at IBM, Nationwide, and Magellan. Srini isn’t just a technologist; he’s a practitioner-coach building a “Legacy Project” to correct the failures in how companies adopt AI. He believes in “teaching people to fish” and ensuring technology serves the human spirit.

Transcript
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Maureen: [00:00:00] Welcome to Innovating Leadership Co-Creating Our Future. I’m your host, Maureen Metcalf, founder and CEO of the Innovative Leadership Institute, where we help leaders become future ready. Today I am thrilled to welcome Srini Koushik, co-founder and CIO Hall of Fame inductee at Right Brain Labs. Srini, it’s wonderful to have you with us.

And today we’ll be discussing Srini’s take on AI and the state of AI literacy and fluency in business.

Srini: Maureen, thank you. It’s a pleasure to be on this Innovative Leadership Institute podcast. When you start thinking about what should leaders be ready for in the future, very much aligned with what we’re gonna talk about today.

Maureen: You’ve spent over 30 years driving transformation from your time as an IBM distinguished engineer to being CIO at Nationwide to leading the Foundry of AI at Rackspace, known as [00:01:00] FAIR. You’ve got deep AI expertise. What was the moment that made you pivot to found right Brain Labs, the Human Judgment Company and away from large ,corporate?

Srini: Yeah. At the core I’m a technologist. I’ve always enjoyed technology. I’ve loved working with technology and that love started at IBM. they’re not flashy, but they’re steady and they get stuff done, right? And you pick up a lot working in a company like that and especially the love for technology, but also the breadth of technology, not just becoming deep in one subject and that breadth is pretty important to get. So fast forward to about four or five years ago when I started this thing called Fair Rackspace. Initially I looked at AI as, okay, this is the next wave of where technology is headed. But it only took about two months before I realized that if people are looking at this as a technology, they’re missing the boat.

They’re fundamentally getting it wrong. They’re looking at it [00:02:00] exactly the same way they looked at prior technology revolutions, right?

AI is different . This is gonna be controversial for some people listening, the fundamental difference I see is in the 40 years I’ve been in technology, computers are dumb.

However, they’ve been smart enough to teach us how to talk to them. ’cause they can only understand zeros and ones. So in order to talk to them, to get them to do anything, we had to learn their language. It was Fortran, Pascal, like Cobal, Python, whatever. I wanna talk to a piece of hardware, I gotta know the drivers, I gotta know the APIs to make the calls and everything else since the machines are dumb.

They were smart enough to treat humans how to talk to them. AI is different. ’cause for the first time you have a partner on the other side that can understand what you’re saying and change that.

So the implications and why I say this is not a technology, [00:03:00] the implications are very different. Think about someone who’s driving business transformation for claims in an insurance company, right? They want to transform claims. Prior to this, the only way they could do it is to be handed over to a requirements engineer. They’re the middlemen who are converting what people want to do into what the computers can do for them. I can now talk to the computer and it can actually understand what I’m saying.

Everything changes. It unlocks significant potential. And those folks who don’t get this and start thinking about it as, here’s another tech, I just need to go through the same playbook are putting their company’s future at risk, and their individual careers as well, right? I actually think if humans can talk to computers, now we’re talking about something that’s a language, right? I use this example with everyone. When I go to Mexico, I am barely functional because I [00:04:00] understand the grammar of Spanish and some of the key words and others.

So if somebody’s talking to me in Spanish, I can maybe get around, I can tell if they’re screaming at me or giving me a compliment. But beyond that, i’m not functional, but when I went to India two weeks ago, I was in four different states. I knew all of the languages. I was fluent in that language. I could get things done by talking to the other person.

Extend that same construct into what I’m talking about, the AI and AI fluency. A lot of people are barely literate and these guys will challenge me. That’s okay. They’re barely literate with with ai, even though they’ve been using chat GPT for two years. I’m saying that’s fantastic that you’re literate with ai, but if you truly want to be that differentiator, you gotta know how to use AI as if it’s your natural language. I think in English . I’m getting closer and closer to thinking in ai.

And so , not just the current generation of workforce, but also the future [00:05:00] coming in, we gotta make them AI fluent because they can’t avoid it.

Maureen: So let me pick up on a couple of things that you’ve mentioned. One is the importance of the idea that how we move forward with AI is different, and you illustrated this in a way that I haven’t heard anyone do, and I find it very beneficial.

You’ve talked about the Brain lab manifesto and the greatest risk of the AI era isn’t that machines will think like humans, but that humans will start to think like machines. How does your co think methodology and AI fluence operating system counter this risk?

Srini: It’s a great question and it is fundamentally going back to how you, reverse engineer the thought process, right? And try to figure out how to do this stuff. I use examples for a lot of things that, [00:06:00] growing up, like in the communications class, we were always taught Hey Martian’s just landed in your city and they absolutely loved the peanut butter and jelly sandwiches that you guys make.

They need for you guys in the room to kinda write down instructions step by step for them to be able to create a peanut butter jelly sandwich. And they would make us write the instructions down English, no programming, nothing, just English. And it was very, very difficult because you have to go through, and apply critical thinking.

Well, would these people know what a slice of bread is if I didn’t explain it to them? That level of understanding where you can unpack a problem through critical thinking, problem solving, and apply what’s uniquely human is where that methodology comes from. If you can reverse engineer that and put it back into the system you can now train people to think with AI instead of think like ai.

Maureen: Can you give an example where I’m [00:07:00] prompting, how would I think with ai?

Srini: very,

Very common example for everyone. You’re asking aI to pull together your itinerary for your next three day vacation.

You do the rudimentary prompting. It’s gonna come back and give you a pretty good agenda these days, right? However it doesn’t know your preferences. You may have put a little bit of those into prompts, but the prompt could become pretty long when you start thinking about everything else that you as a human experience.

If you get the first answer, then start unpacking it. Ask the five W, what, who, where, all of those things. And see how it responds. Actually, try these things like with ai when you’re not sure, or when you think you have the answer, ask what am I missing?

And see what it’s gonna come back with. The simplest way I put it is these things have got capabilities right now where they can mimic human roles. The way you gotta think about it is [00:08:00] AI is an employee makes a lot of things easier because we know how to deal with employees, we do background checks on employees.

Why wouldn’t you do a background check on what AI you’re using and what language model is it using? We know when we put a person into a team, there are things that we do for making sure that the team chemistry stays intact. Why not that, you’re adding a different thinker to the team so the team dynamics changes.

Stop thinking about this as a tech and thinking about it as a very capable thought partner. And also use the skepticism; just because somebody who’s got the right title comes in, tells you that this is what you should do, doesn’t make it the right answer.

Go verify, validate. Those are things that we should continue to do as humans, but not get lazy and start using Chat GPT as Google. And by the way, we had the same challenge with Google search. you use Google search properly, most of the best answers existed on [00:09:00] page two and page three. But most people click the first two links that come back from Google because that’s what everybody else wants us to see.

That’s what people are paying us to see. And you miss the forest for the trees, right? The danger of that in AI is exponentially higher.

Maureen: It’s enticing when I type something in and it comes back with an answer that’s far more detailed than I would have done.

It’s easy to think that’s complete rather than stepping back and saying, what’s missing? What’s inaccurate? Even though it is more precise and detailed, it may not be more accurate .

Srini: Remember that what generative AI and large language models are doing is not intelligence.

It is just probabilistic pattern matching and since they’ve seen a lot more of the data, if you basically say Humpty Dumpty sat on a wall, it [00:10:00] can finish the sentence, Humpty Dumpty had a great fall. That’s the pattern, right?

That’s how the vector embeddings and everything else work. And the reality could be the Humpty Dumpty could have been sitting on a motorcycle, and it’s a completely different story that I’m telling. And unless you’re able to go in and understand that this may appear to be intelligent and it is probably more intelligent than everybody else because if I’ve read 10 papers, it’s read a million papers, so it’s gonna know more than I do. But it doesn’t mean that how it’s interpreting equates true intelligence.

Maureen: I was updating our website recently, so I was asking it specifically, go look at research from McKinsey, Korn Ferry World Economic Forum . Then I said, write me a quote to put on the webpage. It cited something referenced a McKinsey thing as a Gallup thing. It was clearly confused, [00:11:00] inaccurate,

Srini: And you’re going to see more and more of that. I’m not poo-pooing the management consultant. Most contents that’s being produced by all of them is through ai. So it’s like you have something training itself . So the reality is it moves the truth farther and farther away. I do think that there’s ways to counter that, and that’s what I want to keep raising awareness to within Right Brain Labs. Let’s be clear what we’re dealing with right now is not true intelligence, right? It is amazing capabilities that puts things in people’s fingertips that never existed even three years ago.

That’s great. What it’s gonna do is it’s gonna raise the average, right? On NPR, they had that show, right? Lake Wobegon where everybody is about average. Every time I hear AI I keep thinking like, that’s Garrison Keillor, that just shows my age.

But you know what I’m thinking about that i’m saying everybody’s average here, because it doesn’t matter whether you have a master’s degree or a PhD, or you didn’t go to high school, if you know how to ask AI the right question, [00:12:00] the output that you’ll produce may look like you’re intelligent.

That’s beneficial, that’s a good thing because it puts knowledge in everybody’s hands, right? But if that’s where you stop, then what makes you human goes away, right? That creativity, that thought process, the ability to tell a story, the ability to critically think about a problem.

And really when you run into a roadblock, how do you problem solve it? Before Google, we were good at going into the library system, looking at the Dewey Decimal system and pulling out the book, and doing the research and all of those other things, when something like Google came along, all those skills atrophied.

Very quickly go to Google and then drive that piece, i’m not saying it’s a bad thing, but if you lost that discipline of searching for the right information, knowing what keywords to search for, how do you sift through the information, which is what most people did when they used Google, right?

It’s going to get way worse when you get into AI. Because [00:13:00] it’s way more powerful and a lot more people are gonna get comfortable and give it two years, three years, those skills will atrophy and you start losing the cognitive capabilities of humans. And so I talk about the workforce in two sides of a dumbbell.

You got people who are experienced like 20 years, 25 years, they’ve got a different problem. They don’t want to trust AI because they’ve done their thing their way to for 25 years. For them, it’s a question of unlearn and relearn. And my worry on the other side for people, like kids who are six years and one and a half years is they’re growing up with ai.

If we don’t pay attention to that, they’ll grow up without those skills. And now you’re in a position where it’s like everybody can use ai. It’s all around them. They just need to ask a question, and it’s answering the question, whether it’s Siri or Google or Alexa or whoever it is, it’s answering the question, but they don’t know how to deal with the basics of work.

Problem solving, critical thinking, those types of things. And then as I said, like at the other end of the spectrum, it’s yeah I’ve done this way the same time [00:14:00] over and again, that just makes you dumb these days because you can do it way more efficiently if you learned how to use ai.

Maureen: Let’s talk about the five competencies that elevate a leader from reactive user of AI to a strategic co thinker.

Srini: first of all one of the valid excuses a lot of leaders had, I don’t care if you’re the head of hr, head of marketing, the CEO of a company, a valid excuse they had is, this is a complete science.

I need these guys, I need these engineers and nerds who can actually translate it for me so I don’t have to understand the thing. Because if I hire a good CIO or something like that, they’ll translate it for me. I said is what most leaders have done, that’s where most people have been.

This has been changing for the last decade or so as companies become more dependent on technology. You’re starting to see, the operations people more fluent in data, right? And how to use data to improve their operations and things like that. With ai, it [00:15:00] starts with aI is everybody’s business.

Everyone from the security guard to the CEO needs to be at least AI aware, if not AI fluent. They don’t have to know what a large language model is and what a context window is or any of those things. But if I’m a security guard looking at images coming from a security cameras and don’t know that can be manipulated by AI or or how I validate it, I can’t be a very good one.

’cause like these DeepFakes and everything else coming up is going to get that. Same way on the other side. Most CEOs wait till their financials get all finalized and everything for someone to come interpret it to them so that they can take action on it. I may not have the perfect information that my CFO is gonna give me in a week. I have 80% of what I need to do to make decisions much faster. All I need to know is how to use AI to go do that function for me. [00:16:00] You can see the speed of decision making picking up. So the first thing for me is all about do you have the skills right?

Maureen: Let’s go through the five. You’ve talked about I have access, and one of the most important things I do as a leader is make decisions. If I can make them closer to the time they’re required, that helps me and the business remove risk and accelerate progress.

Srini: And the other piece that it does it gives you the ability to put your own unique stamp on your decision making. I may have a CFO that I trust, but he or she is good at what they’re doing because they have perfected a discipline to of pull things together and do that.

I want to benefit from that, but my CFO may not be looking at some of the sources I’m looking at. Saying wait, I see this, I see some lateral approaches. I see this in a [00:17:00] different industry. If I applied it here, I wonder what’s gonna happen. I may have that idea, but today, because it’s so dependent on technology, I gotta translate it into requirements.

And then I gotta wait for a data scientist to go scrub the data and wait for another six months. By that time, that idea is useless. And that idea may have been useless from day one, but I could at least very quickly cycle through it and go, yeah, that doesn’t work. Let me move on to the next one.

As opposed to spending six months of 30 people’s time and half a million box to get to a place where we said, yeah, that didn’t work. Now that computers can understand human language, if we can make humans more fluent in that, they will make it uniquely themselves.

Like they can put their unique touch on it instead of becoming the average that we talked about. That’s one, right? But nobody has gone into the how. Everybody says critical thinking is important.

How do you break down critical thinking? How do I train critical thinking? Very quick example:, I’ve got two daughters, 34, and 26. [00:18:00] The 34-year-old grew up navigating with me, looking at atlases. The 26-year-old grew up when the iPhone was up and running and she’s never seen an atlas or a map.

Both of them have a Tesla. They get into a Tesla in Columbus, Ohio and tell it self-driving. Drive me to Cleveland. Now the Tesla gets onto I 71 South. We by default know that something’s wrong. My older one can tell that difference. My younger one can’t. She’ll be sitting in Cincinnati and looking at the lake and going, wait, I thought the lake is much bigger than this.

Not because she’s dumb. She’s very smart in what she does, but she’s lost that capability to understand directions and navigation because of being completely dependent on a technology. Critical thinking is the ability to go out and look at a problem and say, this works, this doesn’t work.

I need to explore two or three different angles to go figure out I can [00:19:00] solve this problem. ’cause when you start exploring the angles, you’re getting into problem solving, which is the third skill, which is same example. If same thing happened to me and I’m on I 71, first thing I’m thinking is maybe there’s a traffic accident that I’m getting routed through, right?

I look at the map, it’s not there. Next thing I go is, something’s wrong. Maybe I’m wrong. Let me get off the next exit and ask the gas station attendant. I’m just going through a problem solving set of steps that I am familiar with and I’m gradually unpacking the problem so that I can get to the solution and go

i’m not gonna trust the machine anymore. I’m gonna go north. Being able to understand the technology and its capabilities and limitations is the first skill critical thinking. And how do you keep reinforcing. Think about it this way guys critical thinking and problem solving is exponentially better with ai.

You just need to know that you should use AI to do those things as opposed to seeding control of critical thinking and problem solving to ai.

Maureen: And it is exponentially better.

Srini: It is [00:20:00] exponentially better if you know how to do it. Back to the AI fluency thing, right? The next thing you’ve gotta be able to do is the whole creativity, right?

Like you being able to connect the dots because like part of large language models is it connects the dots that have been most frequently connected. It reinforces those neural networks and you start getting average answers. Humans can start thinking, wait, the last time I read something, I I saw somebody else doing this here.

And I did this a couple of days ago, right? As part of a different project I was searching where the advanced AI research is coming in, and China, over the last three months has put out about 250 different academic papers. By default. That’s not where I’m thinking. I’m thinking MIT, I’m thinking Stanford, I’m thinking U Penn, those things, right?

Yes, I should still think that, but now I gotta go get this. What do you think large language models are gonna do? You go to Grok, it’s gonna take you [00:21:00] to Twitter as the knowledge source. You go to Sam Altman stuff. He’s bringing in everything from movies to porn, everything else he’s putting into the model, right?

And the reality is that’s not creativity. Creativity is like, for me to have unique perspectives. I need to bring in different sources and I have to curate that and bring that in. But if I don’t have the basic capability of doing that, it’s tough to do it with ai.

But just like with the other three skills, if you know how to do it, then it’s gonna be exponentially better with ai, it’s my fourth skill. And the last one is in this space, that resiliency and continuous adaptability: Being willing to continuously learn. Those five capabilities are important for AI fluency.

They’re gonna be even more important because most people who are using AI , they’re using AI as a crutch,

Maureen: That gets back to then less competent rather than more competent. We’re all driving to the average.

Srini: The paper [00:22:00] that came out from Upen about a week ago where they had two control groups. One that uses chat GPT, the other one that didn’t use chat GPT to write analytical articles . And on the other side, they had a group of people who read these articles, but they weren’t told how it was created.

Almost 90 plus percent people said the stuff that came from humans was more insightful more actionable. And in the same token, what came from ai… said it’s useless. It’s generic vanilla, blah. that’s what I mean when I say everybody’s average goes up but the top comes down. The top comes down if you don’t do that. And so I think if you think about it that way and say, okay, if the average all goes up, great, I can assume a certain average in the future.

How do I make everyone exceptional? Make them uniquely human?

Maureen: You’ve talked about critical thinking and how vital that is for ethical leadership and I think [00:23:00] bringing ethics in is crucial. Mm-hmm. To how we use AI on many levels. One, what we’re delivering as ethical, but how we think about framing the impact AI is gonna have on society.

How we look as a human species in a decade or two decades will be impacted significantly by our ethics.

Srini: Ethics and integrity are such loaded words, but for responsible ai, I’ve said look, it has to be aligned with your values, right? I’m not someone who’s gonna judge someone for their values, but at least put in AI that aligns with your values, right?

And what that really means is if I’m truly committed to the environment, then I wouldn’t be using ai to help with cryptocurrency or NFTs or whatever else. It’s there because if you knew the environmental impact of all of your queries, you could even justify the environmental [00:24:00] impact.

If the societal impact is very high, saying look, I’m using this to bring people’s capabilities up. I’m using it to improve the life in poor neighborhoods, all of those things. There’s good users of ai, but spending it on something like cryptocurrencies or creating a picture of four dogs playing cards with Elon Musk is a complete waste of resources .

So what’s the point? You have companies that we’ve talked about like IBM and Nationwide that are just absolutely values driven people.

I can probably recite all of the core values and performance values of Nationwide from the top of my head because like it’s being drilled in, it is a very decent, good company . I think for them to be able to do things ethically, you just have to translate those values into, I call them non-functional requirements for your ai.

So if you’re a company that’s committed to your people, like the use of AI for good and all of those things, personally, I would not go anywhere close to OpenAI. Because their [00:25:00] values don’t align with me.

It’s not that their technology is not good. So when you look at Right Brain Labs, which is the first time I’ve got to make that decision, I built everything on Anthropic. I did that primarily because as of not right now, open AI may be better. It doesn’t matter. It’s what do they stand for and are they like really helping bring down income inequality or not?

I have a choice as a consumer. So I think values based AI is important. Put your value system into the work you’re doing and the AI that you’re putting out there.

That’s one thing. And the second piece that helps guide that is I do think that most people, Maureen become much more responsible when they’re conscious about what’s going on, right? I may not be protesting like with Greenpeace, but as a conscious consumer, both from my wallet and from the things that I could do from a small standpoint, I’ve changed every bulb in this thing for LED, right?

I got switches put in so that when we leave on vacation, you flip the master switch and [00:26:00] everything’s down. There’s no ghost power being drawn. It’s an electricity example. It’s the same thing with ai. What do you want to use it for and what are you not going to use it for, and who do you choose to do business with?

And that’s why I full circle back to the AI as an employee. You’re not going to hire employees that don’t fit your culture. Why would I go select a tool just because it’s cheap or it’s the same vendor when you don’t know where they got the data from, they don’t know what they’re using your data for.

And they don’t really tell you any of that stuff. As an employee, I wouldn’t hire that person. Because the technology is mostly equal, I now have a chance to go and align what I’m doing with companies that match my values not just for the good of it; it’s better enterprise risk management. Why would I put myself in bed with someone if I don’t know how they treat open source software and embed that software [00:27:00] into my software, you wouldn’t do it. So just be conscious of that.

Maureen: For innovators and change makers, specifically, what’s the single most important action they can take today to model the human centered approach to working with ai?

Srini: First and foremost, practice makes perfect. If you’re gonna show up in front of AI every two weeks to try to get better with it, or more fluent with it, it’s not going to work. These days with AI being embedded into everything, it’s become more and more easy for you to do that.

As an example, my search on my phone and my computer is Perplexity. My my code development partner on my machine is Claude. My marketing and communications partner that’s sitting on my desktop is Gemini, right? So first thing you have to do is you have to get familiar with the technology so that you know who you want to work with.

There’s [00:28:00] that saying in leadership, right? You’re as smart as the five people you spend your most time with. Then why would I to waste all those five people on chat GPT when Google’s good at doing something, Claude’s good at doing something, no surround yourself with that and you start interacting with these tools.

You’re always told build a diverse team that doesn’t think like you. And that’s the power of this thing. You build diversity of thoughts.

So using it every day is definitely one. Having that diverse team is definitely two. And then the third is actively practicing a lot of things that, that I’ve talked about, right? The critical thinking, problem solving, using it every single day, in what you’re doing. Don’t take the answer for good.

The only answers that I take as is that comes back is when I go to Perplexity, because these days I don’t use Google or Duck Duck go, I go to Perplexity and I ask for something. It’s a very narrow down search. It’s giving me different directions I could take my exploration in. [00:29:00] It’s giving me ideas as to how to go do that piece.

But don’t use it as a crutch. I’ve got these one-liners like you cripple your brain when you use AI as a crutch. Because that’s exactly what people are doing. You’re not taking advantage of what makes you human.

Maureen: What’s next for Right Brain Labs.

Srini: I’m a huge fan of Peter Thiel’s Zero- to- one. He always says, whenever you’re doing something try to have a contrarian point of view and try to build a monopoly wherever you can.

That’s the secret to success. There’s a lot more, but those are two of his key ideas. When I was thinking about brain Labs , first contrarian thinking. People are spending billions in training large language models. I’m saying, what if we spent that training humans?

that thing’s gonna happen. The tech guys are gonna do it, but I need to be able to spend time bringing humans up. This is not a training company. This is like a habit forming company. This is really more cognitive behavioral therapy and things. It’s not training, right? So because training doesn’t stick [00:30:00] unless you practice it.

So it’s cognitive behavioral therapy. That’s one part of it. That’s the contrarian part of Right Brain Labs. So everything we’re doing is gonna be human-centric, but we’re gonna be the best at using ai. So that’s one. The second is the space moves so quickly, I don’t have to invent anything in this space. So the monopolistic piece for me is really it’s a misnomer, but it’s the same concept: Where is it that I can go build something that I can scale pretty easily where I have an unfair advantage. And having spent 35 years in the roles I’ve been in, I can pick up the phone and call people and they will at least take my first call, maybe even my second call.

If you ask me to do the same thing in Pittsburgh, I can’t.

’cause I have a unique advantage here. So I’m trying to take advantage of that, I want to be in that top three when people talk about AI talent.

And that’s what you shoot for. I want use AI to make machines better machines so that humans can be better [00:31:00] humans. It’s as simple as that. If you know what you’re using, if you know why you’re using it and what you shouldn’t be using it for, you’ll be more responsible in your use of ai.

Maureen: Machines be better machines. Humans be better humans in service of our community and in service of the global population.

Srini: Yeah.

Maureen: There are a lot of challenges we’re facing from disease to climate to, fill in the blank and we are in a unique position to, as you’ve used the term, exponentially accelerate the solutions to some of these problems.

Srini: These problems become very, very difficult very quickly when you start expanding into cultures and geographies and everything else. But if you can do it and achieve a level of success, then you can figure out what made that work and try to replicate it. But the objective is we can make a difference here. Let’s go try to do it.

Maureen: Beautiful. Srini, thank you.

Srini: I’ll leave you with this. I think [00:32:00] every leader today who’s telling the market that this is why we’re not hiring people who are fresh outta college? It is not just shortsighted. They’re, yeah. I’ll say it like I’m not corporate anymore. They’re just dumb.

Because they’re shooting themselves in the foot. Because the reality is, sure there’s a short term period where like maybe the graduate that came out this year doesn’t understand everything with ai.

However, they’re also going to be adaptive learners because you put them in front of a computer, they’ll figure out a way to get things done and they invent new things like tiktoks and other types of things that we would’ve never thought about.

That’s huge potential.

Maureen: This has been an absolute delight and for our listeners, aI fluency is crucial whether you are entering the workforce or if you are 25 or 30 years in. If you plan to keep working for more than a year [00:33:00] building this fluency will make work different, will help you elevate your impact and it may end up being a lot more fun. So there is a challenge for everyone.

Srini: Yeah. You’ll be surprised how much personal free time you get to do the things you want to do when you become fluent in using AI for your daily tasks.