Career Management Archives - How to be Awesome at Your Job

Pete Mockaitis
Jon, welcome!

Jon McNeill
Thanks. Nice to be here, Pete.

Pete Mockaitis
Well, I’m excited to discuss The Algorithm and I’d love it if you could kick us off with a story you shared, which I found riveting, in which when you were just getting started at Tesla, even actually before you had the job, you took it on yourself to tackle a project. Could you tell us the story?

Jon McNeill
Yeah, I was talking with Elon about joining Tesla and I kept saying to him, “I think you need a big company guy to come in.” And he said, “No, that’s exactly what I don’t need. I need a fellow entrepreneur.” And I said, “Well, I want to make sure I can be helpful to you. So, like, what’s your biggest problem right now?”

And he said, “We have a demand problem.” And I said, “Okay, like frame that for me. They’re public companies. So what’s your commitment to the street this quarter?” He said, “Roughly 12,000 cars.” And I said, “How are you doing so far?”

It was about a month into the quarter. He says, “It’s a month into the quarter. We’ve sold like 2,500 cars, short of 3,000 cars.” I’m like, “Oh, now I understand your demand problem. You’re going to miss your quarter. Let me go to work on that and see if I can help.”

So I went to, just in the travels with my own business at the time, I went to eight different cities over the course of about 10 days. And I went into eight different Tesla stores and did what I was told was the pinnacle of the sales process and sales funnel, which was taking a test drive.

And the whole idea was that people would come in, they would get in an electric car for the first time, hit the accelerator. And the accelerator in an electric car is way different than a gas car. It’s instant torque to the wheels, and so you take off like a rocket. And so a lot of people experience that. And then they can’t stop thinking about it until they’ve got a car.

So I went in eight stores, I did a test drive. And then, oddly, like a few days later, I hadn’t heard from any of them. So Elon had put me in touch with his head of sales ops. And I called him and said, “Like, am I blacklisted in the system or something? I used eight different email addresses so people wouldn’t know who I was. And people don’t know who I am anyway. And I’m not getting any callbacks. Why?”

And he looked in the system, he’s like, “No, you’re not flagged or anything.” I said, “Can you do me a favor? Can you tell me how many test drives you’ve given in the last 30 days that haven’t been followed up?” And he’s like, “Sure, give me, like, an hour. I can go run that.”

So he calls me back in an hour, and he said, “Nine thousand, 9,000 test drives, no callbacks.” I said, “Well, congratulations. You’re going to miss your quarter because you haven’t called anybody back. And no wonder you’re so far short of orders.”

So then I said, “Can you shut off a store rep’s ability to take any new leads until they’ve called all of their previous test drives back?” He said, “Yeah, I could do that.” I said, “How fast?” He said, “Globally? I could do that in a few hours.” I said, “Awesome. Do it, because we got to force-change, like, super fast or, otherwise, you’re missing your quarter.”

So he does it. Calls me back the next day, he’s like, “You won’t believe the orders are flowing in like crazy.” And I said, “Yeah, because you’re just calling people back. It’s the easiest thing ever. You’re asking for the order.” And then it dawned on me that I didn’t work for Tesla yet. And so I kind of gulped, and I said, “Hey, I got to call you back, because I got to call Elon and beg for forgiveness.”

So I called Elon and said, “Hey, look. Here’s the situation. You’re demand-challenged. Here’s what I found out. You’ve done 9,000 test drives with no callback. And so here’s what I did about it. I was on the phone with your head of sales ops and we shut down any new leads into the system until everybody called their previous test drives back.”

And I said, “But I got to ask your forgiveness. I was acting like this was my company because I’m a CEO in my day job. But this is not the company I’m the CEO of. You are. This is your company. And I did something and I didn’t even ask your permission. And I’m really sorry. I got to apologize.”

And I didn’t know it because we were just getting to know each other. But I had this long period of silence on the other end of the phone line, and I thought, “Oh, my God, what have I done?” And it seemed like forever. It was probably 60 or 90 seconds of just dead silence.

What I’ve learned since is that’s how Elon processes. He, like, shuts off all other sensory input and just thinks. And so I was about to ask him if he was still there, and he hopped on and he said, “You know what? That’s exactly a rational decision, and I’m so glad you made it. I think you’re going to fit in here just fine.”

And that was the last hurdle. I said, “Well, I think I’ve proven myself I can be useful. So I think I’m ready to sign on, too.

Pete Mockaitis
That’s a lovely story on many dimensions. But when I reflect on it, what I’m stuck with is, “All right, Tesla, great, great brand, tremendous company, tremendous leaders, lot of smarts there.” How is it that we find ourselves in a situation where one of the most fundamental things one can do in a sales operation – call people back – isn’t happening?

Jon McNeill
I think I see this over and over again. People look at a lot of data and miss the obvious. And when you just go and see a process with your eyes – this is going to sound completely old-fashioned – but it’s actually the case.

Like, I believe that the strongest analytical instrument you have as a leader are your eyes and ears. And if you go to the front line and eat your own dog food and experience the product or the process for yourself, you’ll often see exactly what’s wrong. But unless you know the exact piece of data to look for that would give you the clue, you don’t know that.

And so this is a complete hack I’ve found in leadership is just go to the front line, go experience the process yourself. You’ll understand really rapidly exactly what’s wrong. And it doesn’t always work, but it works a lot of the time. And this was an example of that.

So a bunch of really smart people looking at data missed it because they didn’t leave their offices. And my encouragement to people, and those people, as I then became their leader was, “You’re going to leave your office at least one day a week, and you’re going to go to the front lines because you’re going to see stuff way faster than the data will show you. And, hopefully, we’ll miss a lot of these potential big divots in our plan if you do.”

Pete Mockaitis
Yeah, that’s tremendous. We had Marina Nitze on the show, who was the CTO for the United States VA, and that was one of her core recommendations, is follow the thing all the way through the process and then you’ll say, “Oh, that’s why it’s taking forever because so-and-so is waiting for something to be faxed. Well, let’s stop that. All right, now we know. Understood.”

All right. Well, so then zooming out a bit, your book, The Algorithm, any particularly surprising or fascinating discoveries you’ve made in your career or while putting together the book that you’re putting forward here?

Jon McNeill
I think this is, like, I had gone into Tesla having been a student of the Toyota Production System and Lean, and what I found was those frameworks were awesome for incremental improvement and optimization, but not awesome for quantum growth.

And so what we tried to distill in The Algorithm, this was the whole team at Tesla and, really, the leadership team that came up with this framework, was to distill a framework for quantum growth into some digestible steps that we could push to the edge of the organization.

And the edge could start to innovate super fast, because that’s what doubling a business every eight months at scale requires. And that’s exactly what the team was pulling off. They were doubling a business every eight months.

Pete Mockaitis
All right. So the classic Toyota manufacturing, Lean, Kaizen, Kanbans, etc., is great for your small incremental growth and you’ve got a whole another thing for hyper growth. And can you unpack sort of basically what is the algorithm?

Jon McNeill
It’s a five-step process that we used on a weekly basis to drive innovation. And this is used at Tesla. It’s used at SpaceX. And I think, as leadership experts or academics go to study Elon 20 or 30 years from now, and ask the question, like, “What made this guy such a successful entrepreneur and industrialist? Like, what was it?” I think they’ll come down to this, like, weekly cadence of deploying this framework, because that’s exactly what he does.

He determines the one or two issues that are existential to the business. In other words, the two things that could kill you if they don’t come true. And then he just devotes his time to that. He delegates everything else to the team. And so you have great agencies as a leader because you’re running the business and he’s really working on two existential issues, but he’s doing it on a weekly basis.

And what that does is that keeps the organization innovating on a weekly basis. So if you’re doing that every week, you’ve got 52 opportunities to build advantage versus your competition. And that’s exactly what that delivers.

Pete Mockaitis
Okay. Well, so could you maybe walk us through a couple examples of, all right, grab existential issue and then work through the steps?

Jon McNeill
So I’ll take you through one to make this super simple. So the first step is question every requirement. Second step is you delete every step and you simplify. The third is you optimize. The fourth is you speed it up because no process can get faster and have high quality with speed unless it is like optimized, so that’s a test. And then you automate last, which sounds kind of nuts especially coming from a technical company or Silicon Valley company to say automate last, but I’ll kind of illustrate that.

So let me start with the first, the negative example. So we started to prepare for Model 3 production. And the idea of the teams was, “We’re going to have a fully automated line, the most automated manufacturing line ever in the history of automobiles.”

And so you might remember this phrase, “We’re going to build the alien dreadnought,” or, “the machine that makes the machine.” That was this whole era. And so teams went to design the Model 3 production line in digital simulation. Today they would call it a digital twin. And they designed all the machines, the flow, the conveyors, the people, everything. They didn’t lay it out manually and do it manually first.

And so when we were down to the wire in terms of, “We got to produce Model 3s because we’re going to be out of cash if we don’t,” they went to start that line, and the line wouldn’t work for a thousand reasons. There were major, major mistakes that were made in that digital SIM.

And digital SIMs usually don’t work because they can’t think of everything. And cars were falling off the conveyors, falling off the line. And that then led to a radical step, which was one of the leaders, Jerome Guillen, said, “I’m actually going to scrap this whole process and build a tent outside. And we’re going to do what we should have done from the start. We’re going to build cars by hand.”

And so we started, over the weekend, building Model 3s by hand. And we simplified the entire process because we didn’t have conveyors, we didn’t have a lot of the machines that were necessary, and we did the bare essentials to produce that Model 3.

So we simplified the process, we deleted all the unnecessary steps, we didn’t have the luxury, really, of having many steps. And then we started to optimize the process, and we started, we produced 50 cars a week, and then we produced 100, and then we produced 500. And the goal was to get to 5,000. And we kept creeping up, creeping up, creeping up by optimizing the process and applying speed to it.

Once we finally had the process nailed, then we automated at the very last step and moved from the tent back into the factory. We had rebuilt the factory production line by this time so that it could actually produce cars.

And when we went to do the postmortem, we said, “How would we have avoided this? Number one, we automated first, not last, and it almost killed us. We did not run the process manually first. We did not delete a bunch of steps. We did not optimize the process. We automated before we had done any of that work, and we almost killed the company.”

So it was, really, at the end of Model 3 production, that the algorithm came together, and we said, “Here are the steps we’re going to follow from here on out. When we go to launch a new product, when you go to invent a new product, we’re going to follow these steps.”

And so that’s an end-to-end example of how not to do it, but then it led us to a framework of how to do it.

Pete Mockaitis
Okay. So maybe digging into each of these in some detail, with “question every requirement.” I have a feeling many of us, it doesn’t even occur to us to question the requirements because the requirements, they’re just like the air we breathe or the water we swim in. It’s like, “Well, this is just how it is.”

So could you maybe surface for us some prompts, or some examples, or some perturbations so that we can identify some requirements that maybe need to get questioned?

Jon McNeill
So when we started to sell cars online, we figured out it took 64 clicks to sell a car, but 44 of those clicks were in the financing process. And that’s because a loan doc or a lease doc for a car has 12 pages of paragraph after paragraph after paragraph. You’ve got an initial sign, all this stuff.

And so a question on requirement step that we did was I took that loan doc to our general counsel at the time, his name’s Todd Maron. I said, “Todd, out of these 12 pages, I need to get rid of some clicks. Can you tell me how many of these paragraphs are a requirement of law or regulation?” He came back about 24 hours later and he said, “None.”

I said, “Well, how does a 12-page document exist then?” He said, “It’s the result of a bunch of well-meaning corporate lawyers at banks trying to protect their client. And so they insert paragraphs for all these uncertainties or these edge cases that could come up to protect the bank.”

But he said, “None of these are required by law or regulation. And, actually, the bank has case law on its side. Like, they don’t need all this stuff because if somebody cheats in a certain way, that’s already been decided by case law. So it’s not like you need to make somebody acknowledge something that the courts have already acknowledged as being out of bounds.”

So I said, “Todd, you’re telling me we could have a one-paragraph loan or lease that says, ‘Here’s the price of the car, here’s the interest rate, here’s the term, and here’s the monthly payment?’” He’s like, “I’m telling you, you can do that.”

Nobody in the industry had questioned whether or not the loan doc or the lease doc should exist. We were just crazy, silly enough to make that, to raise that question. We discovered something that none of our competitors had discovered, which is you didn’t need to put customers through a 44-click process. And as you know, if you had to buy anything on Amazon, and it took 44 clicks, you’d probably opt out a lot. And that’s true in e-comm, and it’s certainly true when people are buying a $100,000-car online.

So we went to talk to banks to see if we could get anybody to go along with us on this one-click loan release, and we got the door slammed in our face by everybody, even though they would intellectually acknowledge that they didn’t need all these paragraphs.

They said, “I would never take the risk of doing this. I would never take the career risk of going to my CEO and suggesting we do this.” Finally, we found a very enterprising CEO at US Bank, and he said, “We’ll do it. In fact, we’ll talk to a CrossTown digital bank called Ally. They’ll probably do it too. We’ll take your loans, they’ll probably take your leases, and we’re off to the races.”

All that started because we questioned why a 12-page loan doc had to exist. And that’s probably the clearest example or the best prompt I can give you on questioning requirements. There’s a lot of stuff that nobody ever questions and takes as a given. If you question that stuff and it turns out it’s not a given, now you’ve got an advantage.

Pete Mockaitis
Yeah, I like that a lot. And I’m thinking about, well, the phrase that comes to mind is checkbox marketing, or really checkbox anything. If your mindset is, “Well, that’s just what’s done. It’s just what needs to happen.” And I’m thinking about, well, I have a one-page guest release form.

And I think lawyers tend to want to put a lot of intense, you know, things in there. It’s like, “I own all of these forever, no matter what.” And I think, “Well, actually that’s not the vibe I’m trying to put out there when I’m just meeting somebody. I’m just sort of like, ‘Hey, you and I are both cool to do what we want to do with this thing, okay? So no pressure, it’s all good.’” And that’s kind of what I’m trying to convey.

So it was not the default. We had to shift and adjust and then it is, well, it’s much smoother because then I don’t have a lot of people say, “Whoa, whoa, hold on, buddy. What, what is this? I don’t know. Wait. Time out. I got to talk to my lawyer. I got to talk to my agent. I don’t know if we can do this, you know?”

No, that just about never happens because it’s very quick and it’s simple and it’s handy. And I liked that example because the law, in particular, feels like something that’s immovable, like, “Oh, ‘legal’ said, we just have to have that. And end of discussion. It was like, “Oh, well, maybe legal would be willing to have a follow-up conversation and see what can be done here.”

Jon McNeill
Totally. And it takes a certain mindset. So, like, Todd, as a leader of the legal teams was willing to come along on that journey with me and question requirements, and that’s pretty rare in that kind of a leader in that function, but he was super commercial and business-oriented.

And so he would start that journey without having a bunch of hesitation. Like, he’d say, “Yeah, let’s go, like, look into this and see if this is really true and if we really need this.” I don’t experience many general counsels like Todd because they largely go into that career to mitigate risk. And so that’s where their position starts.

Pete Mockaitis
Well, and as a point of curiosity, I mean, I understand frictions and clicks are, you know, undesirable. They slow down the behavior you want, and this is cool and convenient and nice and pleasant to have fewer clicks.

But, you know, Jon, really, lay it on me, is a few dozen clicks for signing an initialing a hundred thousand dollar vehicle enough to move the needle so that folks are like, “You know what? I wanted that Tesla, but this is too much effort. I’m done with this document. I’m abandoning my cart”?

Jon McNeill
It happened a lot, and when we eliminated it, the opposite happened. People started to buy like crazy online, and our digital sales went through the roof, which is because it’s just science. In e-comm, clicks equal anti-conversion. So you get rid of clicks and your conversion rate goes up. It’s just math.

So this is why when you get to the cart in Amazon, the search bar disappears. They want to take away any potential click that you’re going to do other than hit order, because they know how hard it was to get to that point and how hard it is to get you to actually click the order button.

Pete Mockaitis
Yeah, and I believe they’ve litigated the one-click ordering.

Jon McNeill
Exactly.

Pete Mockaitis
It’s like, “No, this is ours, we own it, no one touch it.” And it’s like, “Very touchy,” and because the stakes are huge.

Jon McNeill
Because of that, because it’s powerful. Yeah.

Pete Mockaitis
All right. Well, can you chat with us about deleting the steps in the process?

Jon McNeill
Yeah, so here’s another example, and Elon actually cut a little video in this early on, but he had taken on this challenge in manufacturing where there was this separator between the battery and the car. And so it’s basically along the floor of the car.

And we could not produce this separator, big piece of plastic. We couldn’t produce it to save our lives. It was warping and it wouldn’t fit right, etc. And so this is one of the chapters where he’s literally sleeping in the factory trying to solve this problem.

And, eventually, he asked the team, “I got to talk to the engineer who spec’d this part. Who spec’d the part?” And the closest people around are the battery people, and they said, “Oh, it’s the auto dynamics team. They wanted a noise dampener in between the battery and the passenger compartment.”

So he grabs the guy that’s the head of the auto dynamics team, and said, “Why did you spec this part?” He said, “I didn’t spec the part. It was the battery team. The battery team told us they needed a heat shield between the battery and the passenger compartment.”

He’s like, “I was just with the battery team and they said it was you, not them.” He said, “Give me the name of the person who spec’d this part.” So they go look for the name. And it turns out the name of the person that spec’d the part was a summer intern that didn’t even work at Tesla anymore.

Elon had spent weeks in the factory trying to solve this problem, all for a part that didn’t need to exist. And so, at that point in time, “We said, nobody claims ownership of this ‘requirement.’ So we’re going to delete it out of the car. And, therefore, we’re solving a whole production problem that was holding us up.”

And you will find, over and over again, that there are steps you can delete from your sales process, from your delivery process. And the hack to finding those is, essentially, map your process on a wall with a bunch of sticky notes. Then have your team go circle those steps that the customer pays you for. It turns out there’s very few of those.

They don’t pay you for the order sheet. They don’t pay you for the PO. They don’t pay you for, in our case, the bill of lading. They don’t pay you for all this stuff. And the things they don’t pay you for are immediate candidates for deletion, because you’re doing those for internal reasons, and you’re creating cost for internal reasons.

Now some of the stuff is necessary to track dollars and cents. I totally get it. They don’t pay you for accounting. They don’t pay you for tax. I get that. But there are a bunch of steps you can cut out when you start to say, “Hey, the customer really doesn’t pay us for this. And we don’t really get anything out of it. So why are we doing this?” And those are good candidates to delete.

Pete Mockaitis
That’s good. Well, I was curious, did Elon get on the phone with the intern? Like, “Hey.”

Jon McNeill
The intern didn’t even work there anymore. Like, didn’t even work there.

Pete Mockaitis
Well, mean, hunt him down wherever he is.

Jon McNeill
He was like, “I don’t need to waste time talking to the intern, like, it’s gone.”

Pete Mockaitis
Okay. So then simplify and optimize.

Jon McNeill
Yeah, so once you delete steps, now you’ve got way fewer steps. So you’ve got a simplified process and now you can start to optimize that process and make it go faster and faster and faster and faster. It’s a little bit like starting any skill, whether that is playing a musical instrument or a sport, where you start and you’re like, “Man, I can’t go very fast.”

And then you practice and practice and practice, and it turns out you get faster and faster and faster because you get more and more efficient and optimized. And that’s the idea here is you keep speeding the process up and speeding it up.

Now you’re still in manual mode. So you’re learning a bunch about what’s causing, what’s getting in the way of speed, and the stuff getting in the way of speed is usually a quality issue. And so you eliminate these quality issues. And then once you’ve got the target speed achieved, now you know you’re optimized and you can then start on automation.

Pete Mockaitis
A quality issue in terms of something needs rework or is outside of the spec we’re looking for?

Jon McNeill
Exactly.

Pete Mockaitis
All right. And then with the acceleration, so there’s practice. What are the other drivers of accelerating?

Jon McNeill
Mainly, it’s putting speed goals that are double the current speed. So you say, “I want to either double the output at current speed or I want to double the speed at current output,” either one of those works in my book, just try to double.

And it starts to reveal, “Oh, here’s all the things that are in the way of doubling. So maybe we can delete more steps. Maybe there are more requirements that we should question. Or, maybe there’s a better method.”

And as you do those, then you start to identify, “Ah, there are a bunch of things that we didn’t see at first that we now see now, that we can either delete or simplify to help us speed this process up.”

Pete Mockaitis
So the doubling of the speed is not so much a, “Hey, it turns out we can just double the speed, it’s all good.” But rather, it’s sort of like a magnifying glass to identify, “Oh, that’s what’s holding down the speed doubling.”

Jon McNeill
Totally. There’s this great series of scenes in the Hulu series, “The Bear,” which is about this super high-end restaurant in Chicago. And it’s turned from a roast beef cafe with the same team in the back that is now trying to earn its Michelin star.

And there’s a woman that’s at the pasta station, and she has two minutes once an order comes in to cook pasta. And she starts at five minutes. And then she does things like she portions each serving of pasta in a little plastic container. So she’s pre-portioned.

And she starts to do multiple boiling water pots to drop the pasta into so she can do more than one at a time. And then she’s got a saucier step that she realizes, if she preheats the sauce, it meets the pasta at the right time, she can speed it up. And she gets the process down from five to two and a half minutes by just eliminating all these steps, but she’s still not at her two minute target.

And then the sous chef, who really runs the kitchen, comes over and is just helping her. They have a busy night. And so the sous chef comes over and does a few things that experienced chefs know to do. Bam! Bam! Bam! Order comes out in a minute and a half.

And the woman who is at the pasta station says, “I’ve been working this problem for like 90 days and I got it from five minutes to two and a half. And you walk over and you do it in a minute and a half.” And she’s like, “Yeah, I’ve done this before. So just watch me and you can see the extra steps you can take out of your process to make yourself faster.”

That’s what we’re talking about is just optimize, optimize, optimize until you get to a really different output than you’ve had before.

Pete Mockaitis
And then the final step is automation.

Jon McNeill
Yeah, and then you can automate it. Because automation is like wet cement. When you put it around a process, it sets up really hard and you got to get jackhammers to get rid of it. So you don’t want to automate until you’ve really got the process nailed. Otherwise, you’ll be suffering with the cement that’s already solidified the current process where it is.

Pete Mockaitis
Okay. So now, as we were talking about this with the restaurant example and manufacturing examples, I’d love to change gears a little bit and hear about, let’s say there’s someone who’s in the middle of an organization and their output is not physical, but, nonetheless, there is a process that’s cranking out something on the other end. Could you give us an example in that domain of working through those steps?

Jon McNeill
Yeah, so, like, whether you’re producing software or whether you’re producing marketing or you’re producing financials, I think the same steps apply. If you step back and say like, “Hey, look, I produce financial statements for the business,” or, “I produce a budget for the business.”

If you step back and say, “Okay, let me just map the process that we use today to do that. And maybe I should question some base assumptions here. And do we have to do all these steps? Like, are all these things really required?”

And so an example of that is Tesla, when I joined, was doing standard annual budgets. And the senior management team and the CEO would set the target for sales, and the target for gross margin, the target for cash flow. And so these annual budgets were being done in the way they’re done at almost every company.

The challenge was we were doubling the business every eight months. And so, like, if you’re trying to project out a budget for a year, it was horribly inaccurate by, like, month three, because things were changing so fast. So all that was essentially wasted effort, all that planning and budgeting.

So we asked ourselves a question, like, “Time is super, super valuable in this company. How could we improve this process? And rather than going through a big planning cycle every year, what actually needs to get done?”

And where we ended up was, we questioned the requirement of having a year budget. It turned out what we did was we had quarterly budgets because the business was moving on a quarter basis, not a year basis, and we would have these rolling four quarter budgets.

We would spend less than two days setting targets in the budget because it was only going be good for a quarter. So you didn’t want to spend a week of the quarter, a huge chunk of the quarter doing it. And that finance team evolved a whole different way to do financial planning and budgeting.

And they did it, not on an annual basis, but a quarterly basis. They did it on a rolling basis, and they just used the key inputs that everybody was looking at in the business anyway, “What’s the sales rate? What’s the production rate? What’s the margin?”

And we can build a budget off of that, and rather than taking the whole company’s time planning, we’re just going to keep rolling this and rolling this, and we’re absorbing everything that we’re learning from the market as we double.

And so you can apply this whether you’re sitting in a finance department or a marketing department or a sales department. You can apply these principles to your advantage, too.

Pete Mockaitis
Okay. Well, Jon, tell me, any final top tips, do’s or don’ts, for folks looking to implement this stuff?

Jon McNeill
As we talked about, like one of the principles that is a complete hack is eat your own dog food. I learned this from Sam Walton in his book, Made in America, where he would tour stores on a weekly basis to find out what the customer wanted that they didn’t have, what they had too much of that the customer didn’t want, and what were the things that store managers were doing that were super good that he could spread across the company.

And I learned from that and started to see that, “Oh, he’s onto a complete hack.” Like, if you use your own product, if you experience the customer experience, you’re going to see all kinds of areas of improvement. And if you teach your people to see it before you, they’re going to move even faster than you can.

And so I would say, like, the secret hack is go experience your own product and use it. We even went so far at Tesla to have a rule that you couldn’t present product in a PowerPoint. We didn’t want to see, like, some rendering or rendition. We wanted to see the real thing. So if you’re presenting product, you had to do a live screencast onto the screen so we could see the product, we could play with the product, and we could see how it actually worked.

I was with a group of bank executives a few months ago. I asked them to raise their hand if they actually use their own bank’s consumer app. No hands went up. And I said, “I could have guessed that because I’m a consumer of two of your banks, and your app sucks.”

“It’s so bad that if you used it, you wouldn’t live with yourself for another day without fixing it. You would call up the head of engineering and you say, ‘We got to fix this, this, and this.’ But you don’t use it.” So the organization gets the sense that nobody cares. And if you can live with it, they can live with it.

And eating your own dog food changes all that. It creates an immediate feedback loop to the top and it allows you to set the bar of acceptability with your organization.

Pete Mockaitis
Okay. Thank you. Well, now could you share a favorite quote, something you find inspiring?

Jon McNeill
I think this whole approach is about simplicity, and so I love the Mark Twain quote, “I would have written you a shorter letter if I would have taken the time.” It speaks to how hard it is to simplify. Humans, I think, we are naturally complicators. We’re not natural simplifiers, and it actually takes work to simplify.

And I love that quote, because it reminds me, each time I read it, of the fact that simplification is work. It’s super valuable and rewarding work, but it’s work.

Pete Mockaitis
All right. And can you point to a favorite study or experimental or bit of research?

Jon McNeill
I think, if you want to understand AI and the current version of AI that we have with LLMs, the best piece to read is the original DeepMind paper.

Pete Mockaitis
“Attention Is All You Need”?

Jon McNeill
Yup.

Pete Mockaitis
All right. And a favorite book?

Jon McNeill
Favorite book would be, I’ve got two right now, one is The Goal, which taught me a completely different way of looking at business, by Eliyahu Goldratt. And the second is Unreasonable Hospitality by Will Guidara. I think it applies to every business. It’s so good.

Pete Mockaitis
Okay. And a favorite tool?

Jon McNeill
Reading. Like, if I could answer the question that way, I’d start every day reading for an hour and a half, and I read variety of things. I read books, I read, obviously, the news. I read Twitter, I read Reddit, Hacker News sometimes. Reading for me is a tool.

Pete Mockaitis
Okay. And is there a key nugget you share with teammates, audiences, that they quote back to you often, a Jon original?

Jon McNeill
We had the standard for service at Tesla that gets quoted back to me, it got quoted back to me today actually, and that is, “Make them talk about you at dinner tonight.” Do something that is so awesome that they’re going to talk about you at dinner tonight.

Pete Mockaitis
Okay. And if folks want to learn more or get in touch, where would you point them?

Jon McNeill
I’d point them, you can find me at DVX.ventures.

Pete Mockaitis
All right. And do you have a final challenge or call to action for folks looking to be awesome at their jobs?

Jon McNeill
Become a simplifier and you’ll stand out.

Pete Mockaitis
All right. Okay, Jon, thank you.

Jon McNeill
You bet. Thanks, Pete.

Pete Mockaitis
Aneesh, welcome!

Aneesh Raman
Thanks for having me.

Pete Mockaitis
Well, I’m excited to hear your insights about AI and economic opportunity. Could you maybe kick us off by sharing what’s the most surprising or counterintuitive thing you’ve discovered about us career professional folks and AI?

Aneesh Raman
Ooh, it’s a big one. It’s an existential one that we have internalized this diminished sense of self as humans at work across the industrial age. When AI broke out, what now three, four years ago, immediately, there was this great fear.

Immediately, the conversation was, “We’re done. We’re at the end of the road for humans at work. We’ve got this thing that can beat us at these things. It’s going to beat us at those things next. It’s going to beat us at everything soon.”

And I just sort of, like, intrinsically, didn’t believe that. I was starting from a place where, for a long time, I had thought the labor market did a horrible job matching talent and opportunity, indexed on pedigree signals like your college degree, “Where did you work?”

So I just knew there was so much human potential out there that had been blocked out or locked out of economic opportunity all over the world. And so I just didn’t believe that humans were done. I kind of felt like we hadn’t even begun yet.

And the more I sort of thought about it, the more our CEO and I talked about it, what led to the book was this realization that humans are more than we’ve been at work for a couple hundred years now. For a couple hundred years, we have been about one thing above all else – efficiency.

And we sort of told ourselves a story that the knowledge economy moved us out of the industrial age, of people working on factory floors on assembly lines. But it didn’t. Even if you were with a laptop in an office, you were doing more, better, faster, more, better, faster, more, better, faster. Everything was about efficiency and productivity.

And we had derided almost these skills that make us, us. We called them soft skills. We said they were nice to have, not must have. And all the math worked in terms of the industrial age and what the economy valued in terms of technical and analytic abilities.

But I think what’s been most surprising to me is that AI is forcing us to reassert ourselves because it is going to out-machine us, it’s going to out-efficiency us. And yet, our human brain, which is I think still the most incredible object in the known universe, it’s been around for tens of thousands of years, long before the steam engine arrived, and the industrial age descended upon us.

And so we’re going to pull from these strengths that we’ve had for millennia, that we talked about in the book, and it’s going to be a moment to reassert ourselves and our capability, I think.

Pete Mockaitis
Oh, wow! These are big ideas.

Aneesh Raman
Welcome to my brain.

Pete Mockaitis
“Internalize the diminished sense of self.” My goodness. You know, it’s funny, I was just chatting with Claude last night because I think the First Lady was with a robot doing an education event. And I was like, “Oh, can that robot finally load my dishwasher for me? And when can I buy one?”

And so, I was looking at that, and it said, “Oh, this robot is impressive because it could figure out how to fold a towel with just 80 hours of video.” I was like, “Oh, well, my five-year-old figured out how to fold a towel in about four minutes of instruction with a hug.”

So, yeah, I’m with you, our brains are spectacular. But, yeah, there are some domains in which we are being out-efficienced by the AI.

Aneesh Raman
Yeah, and I think that’s okay. It’s not okay right now because it’s causing a massive disruption to work. It’s changing the rules of the game, which are hard to manage if you’ve been playing the game the old way and you’ve done everything right and, suddenly, you know, old math won’t solve new equations. But I think it’s an opening for people that see it.

We were never meant to be machine-like. We were never meant to spend our days doing the work of efficiency and more better faster, more better faster, more better faster. I mean, at one point, as we were reporting in the book, you know, thinking about those people on assembly lines who are fastening that one widget over and over again, or the knocker uppers that we talk about, the people who their job was to shoot peas at windows to wake people up as this sort of human alarm clock.

I looked up and they were just at this coffee shop, four people sitting next to each other, on their laptops, just slamming through emails. And it just connected for me that this is all still ever been efficiency work. And so, I think the real opportunity we talk about in the book is to see this as a big change to everything, to be understanding of the fear that that’s going to cause, because the human brain is wired to fear change. It is not wired to get exponential change. We are in a moment of exponential change.

But then act despite that fear, push past it, because while it’s understandable, it’s also unhelpful. And if you start using these tools and using them not in ways that then just take all the work out of your day, but start to take away the stuff that’s mundane, routinized, efficiency work, free up time to do more cool things with these tools, to learn new things in new ways, to build new things in new ways.

And then to open up space to do the things we uniquely do, to give yourself time to think critically, to think about ethical implications of what you’re building if you’re an engineer, to spend time brainstorming or partnering with other humans.

The turning test started us down this path of AI versus human, and then it beat us here, it beats us there. But that’s not where the story goes. There’s no innovation. There’s no growth if it’s just all about AI. Humans are illogical. We’re unpredictable. What works for us requires someone to be us, to be human, to in-tune that.

So, really, the test we should be doing is human versus human with AI, versus humans with AI, because we always do the coolest stuff together. And that means think about yourself five years ago, think about yourself today, “What are you doing that’s new and better and energizing that you couldn’t do without these tools five years ago?”

And in the book, we talk about an easy way to start this. Put your job title aside. It doesn’t matter if you’re a CEO. It doesn’t matter if you’re a senior director. It doesn’t matter if you’re the newest hire at a company. Your job title is irrelevant for the purposes of this exercise.

Every week, you do about a dozen tasks, list them down, and then you’re going to put those tasks into three buckets. The first bucket are tasks that AI can now do or can soon do. So coding is in there, quick summary, quick analysis, first draft of content, meeting notes, like all that stuff’s in bucket one.

Bucket two is stuff you’re doing with AI that’s new. It’s not just additional stuff that you’re putting in bucket one. It’s, “Are you learning something new with AI?” “I’m on marketing, I got to talk to the sales team. They speak a different language. How do I close that expertise gap in what I’m trying to pitch them?”

Are you building something new? “I have to present. What’s a cool image I could associate with this idea? A video? How do I make this land in a more visceral way to the people I’m trying to convince to back this project?” So that’s bucket two.

And then bucket three is, with the time you’re saving in bucket one, with the cool new stuff you’re doing in bucket two, what are gyou doing that’s uniquely you? And that starts with what’s uniquely you as a human, your ability. We have the five Cs in the book, but with those, think critically, “What’s an ethical implication of what we’re doing?”

But then are you spending new time with people to try new things, to test something out as a partnership, to get advice on how to pitch something. And as you sort of move the tasks of your job away from bucket one towards bucket two and bucket three, you’re adapting your job. You’re redefining your job.

We have a statistic at LinkedIn, 70% of the skills for the average job will have changed by 2030. Now, the old way that disruptions hit meant that, at some point, in six months or a year, your boss, your boss’s boss would come to you and say, “Here’s how your job has changed by 70%.” Because old disruptions from the steam engine to electricity to the internet, they played out over years and they came top down.

This one is different. Your boss actually doesn’t really know how your job is supposed to change. Their boss doesn’t know how their job is supposed to change. CEOs are trying to figure this out at an organizational level. So we get to change our jobs now. We get to start figuring out where these tools come in and then what that opens up in bucket two and three, not just because we’re human, but because we’re us.

We have a chapter in the book, “No One Beats You at Being You.” That’s where it’s going, is that you’re going to shift your job and then re-center your career around your unique curiosities and capabilities.

Pete Mockaitis
No one beats you at being you. And it’s funny, I’m thinking about, you know, so me here now, here we are having a podcast about professional skills development, and there are a lot of places where you can get such things.

And it’s interesting how, if I talk to an AI about such a matter, I might get the answer, and it might be quick. And yet, it is not as satisfying, complete, thorough, giving rise to new ideas and connections, the way hearing a full blown conversation between two folks on a matter is. And I think that that really resonates.

Aneesh Raman
And I think that it’s really important. Yeah, I’m glad you’re saying, because right now there’s this sort of idea to AI or not AI, as if it’s binary. And if you AI, you’re doing everything AI. If you’re not, you’re rejecting it for righteous reasons, and that’s where you’re at.

And in the book, we have the story of Neil Pretty, whom really, he embodies this idea that you need to use AI, but you cannot misuse it, nor should you overuse it. And Neil starts using this tool to help him prepare for different presentations, and he overuses it and realize it doesn’t sound like him and it’s not going to distinguish him. And he dials it back and he uses it differently.

Instead of asking it to tell him what to say, it says, “What would this CEO say about what I said, or this academic? Give me 12 reactions to what I think.” And then he would use that to even get better at what he was going to say that would have taken much conversation with many people ahead of a meeting.

So you can overuse AI. And MIT has done cognitive scans and come up with this term cognitive debt. Like, if you’re sitting at work and your boss asks you something, you copy and paste it in the tool, then you copy and paste the answer back and send it to your boss, you might be doing efficiency work more efficiently.

But if you run into your boss a week later, and they’re like, “Hey, that was a great idea. How did you think of that?” you might not even remember that exchange because your brain is not tracking it, and you will have brought no critical thinking to it. So that muscle is atrophying. So you got to make sure that you are using this tool to do better things yourself. You cannot outsource to it.

Pete Mockaitis
So in the universe of more, better, faster efficiency work, how would you suggest is the contrast? If I’m thinking efficiency work AI stuff is in the land of more, better, faster, what is the land of our humanity?

Aneesh Raman
This is the first time someone’s asked me that. Because what’s crazy is we had to define what makes us us. We had to define what makes humans unique in the arena of work.

It turns out not much work had been done around that. As you started talking to neuroscientists, behavioral psychologists, behavioral economists even, when I’d ask like, “Who from companies or from the world of work comes and talks to you?”

One neuroscientist said, “Not many. Like, athletes will come because they want to push their brain to the limit. The military will come because they can’t hire from another military. Hedge funds will come because they want a cool like summit speaker. But you were not getting incoming from everyday practitioners of work.”

But now the mind is going to the center of work, not the machine. And so what does it all lead to? You know, in the book, we identify the five Cs that we think are at the core of human capability – curiosity, compassion, creativity, courage and communication. Those are what we offer up at the intersection of our IQ and EQ of our consciousness and conscience.

Then we say there’s some habits we all need – resilience, adaptability, handling hard well, failing fast, learning quick. And we sort of bundle it all into this idea of being entrepreneurial in our habits and in our thinking. And we know that in saying that, we lose a lot of folks who think being entrepreneurial means starting a business, and that is not for them.

Paul Cheek from MIT, who’s a professor on entrepreneurship there, has an amazing definition for it. “Entrepreneurialism,” he says in the book, “is doing more than is reasonable with the resources you have.” So every one of us every day has a task, has a project, has something we’re doing at work, what’s more than is reasonable that we could do with these tools and with others with the resources we have?

And then what that leads to is a flip. Instead of more, better, faster, I think we’re doing new, bigger, bolder. We are creating a whole bunch of new ideas, a whole bunch of new ways for businesses to grow, a whole bunch of new businesses to go after, a whole bunch of new areas from climate to healthcare to all sorts of stuff that we could create technology for businesses to address, at the local level for just a community, or at the country level, or at the global level.

So, to me, “more, better, faster” becomes “new, bolder, better” and not just in terms of work. Like, I think if we do this right, and it isn’t just us as individuals. Institutions need to completely redesign the systems of work from employment to education, entrepreneurship.

But, ultimately, we get to better work for each of us, better work for all of us, better defined by just more human work, more fulfilling, more high value, more impactful. But that all leads to greater prosperity and progress because of this sort of innovation explosion, this entrepreneurialism that can take root.

Pete Mockaitis
And to these notions of efficiency work versus newer, bolder stuff, I’d love your hot take on these AI layoffs. I mean, some say, “Oh, well, that’s just AI washing. They over-hired, interest rates are worse. It makes a better story for Wall Street to say, ‘Oh, it’s because of AI efficiency.’” But others say, “Oh, no, no, no. Sure enough, one person can now do what things that previously require two, three, or four, and thus it makes sense to shed those jobs.” You’ve got an interesting vantage point. What’s your take on this?

Aneesh Raman
I mean, we’re seeing jobs get added, one million plus jobs around AI. That’s not just the sort of like hardcore engineering jobs. That’s also the data centers. We know sectors are hiring, like healthcare. Look, there’s these two truths we have to hold at once that are somewhat inconsistent.

The first truth is we know at the other end of all of these moments of disruption from technology, we generally see an increase in employment, and the Fed is out there sort of repeatedly with that. Jobs change, new jobs emerge. MIT has a stat, 60% of employment in 2018 didn’t exist in 1940. Creator wasn’t a career 10 years ago. Data scientist wasn’t a job 20 years ago. So we, generally, do see employment go up.

That happens after a messy middle where a lot of lives get upended and it’s really hard for people. But we do see employment go up. That sort of thing one, truth one. Thing two, truth two that contradicts that is we’ve never been here before in terms of this technology. It is fundamentally different in terms of what it’s able to do.

And I think predictions are unhelpful right now. Anyone with absolute certainty about what’s happening to a job category or to all jobs or to jobs in this sector, let’s see how it turns out in five or 10 years. Like, I think it’s just impossible for anyone to know with certainty anything absolute.

The one thing we do know, to contradict myself a little, is the only thing that matters right now is what we believe, and what we choose to do, and what decisions we make, and what steps we take. And that is true for us as individuals. It’s true for us as organizations. It’s true for us as societies. It’s true for us as humanity.

If we decide that worse is more likely, and we make decisions that make worse more likely, worse is more likely. If we decide that better is more likely, and we make decisions that make better more likely, better is more likely.

So in terms of where employment goes, there’s so much in the air. I mean, we’re going from an old world to a new world of work. There’s so much macroeconomic muckup that’s going on on interest rates and geopolitics. Every company is going through its own moment of business transformation.

Some over-hired and now they’re managing that. Some are built with an org chart for stability, order, predictability. That isn’t going to help you innovate, be agile, and grow. They’ve got to manage that. There’s no one truth to everything that’s happening.

And so I think for folks who are looking for a single answer, who are looking for someone to tell them what to do, who are looking for an off-the-shelf playbook as an individual or as a company, like, that’s not this moment. It’s not like back when it was like everyone get a CS degree or bootcamp certificate in coding if you can find one, because that’s the ticket.

There is no “it” right now, but that’s it. We’ve all got to start to figure this out on our own, use these tools, and with these tools start to understand our own unique interest, capabilities, where those could go, where we would pitch ourselves across a broader set of job opportunities that we might, otherwise, have looked at. And I think that’s what we’ve got to do is look. It’s a metacognition sort of moment.

Pete Mockaitis
Understood. But you do have some interesting data. 85% of people are in jobs where AI can automate at least a quarter of routine tasks. Can you tell us a little bit about that data and what sorts of things we’re seeing automated now with AI?

Aneesh Raman
A big thing we’ve been trying to push people on is to look at jobs as a set of tasks, not as titles. A good example of how not to do it, I would say, is looking at software engineers. And for a bit coming out of the gate with AI, everyone said that title describes all software engineers in one way, and that one way is that they code. And as AI got better and better at coding, it became, in that line of argument, intuitive that software engineers were done.

We’ve actually seen recent data where the hiring for software engineers is going up because as AI is able to do more coding, more companies want to build tech, so they’re hiring. And the job of software engineer was never limited only ever to coding.

Some of those jobs, entry level jobs, even middle, they were, but the job doesn’t have to be just that. In fact, as AI is able to do coding, it shifts to reviewing the code, or talking to customers, or having those conversations about ethical implications of what’s being built. It’s the same three buckets and things move.

ATMs are a good example, we talk about in the book. When ATMs hit in the late ‘70s, everyone thought bank tellers were done. We have a quote from the New York Times. It basically says bank tellers are done. Bank teller jobs, like, doubled between that moment and 2010, and they doubled because you had more banks opening so you needed more tellers because ATMs allowed it. The job of the bank teller shifted to being more about relationship banking.

Now, there were some debate over whether the quality of the job went up, but in an absolute term, you know, the jobs went up. They came down after that because of the iPhone and the smartphone and all the banking we’re now doing. But you couldn’t have predicted that in the ‘70s.

So that’s all to say our data doesn’t look at job titles. It looks at jobs. It looks at the tasks people are doing. And it says, if you are in a job where a lot of your job is that bucket one, that’s worth knowing. That doesn’t mean that entire job is going away. And it doesn’t mean you are done at work. It means you’ve got AI coming for well over 50% of the jobs in that task. So you want to start on your own moving tasks in your day to day to bucket two and bucket three.

And if you do that, don’t worry about the job category or the job title you have. That’s all secondary now. It used to be job title mattered first because you’d reverse engineer from it. Job title matters last now because we’re going to be reshifting work.

And so depending on your number, and you don’t need us to tell you this data, you can do an evaluation of the tasks in your job and look at where you’re heavy. Anyone who is heavy in bucket one, okay, what do you got going in bucket two and three? How can you build out from that?

If you’re in a job category that feels really volatile, okay, what are the transferable skills you have across two and three that could take you into other job functions or job categories? But it’s not an overnight, everything is done end of day sort of thing. It’s a step-by-step incremental of, “How do I manage this change sort of thing?”

Pete Mockaitis
And we had a really good chat with Jeremy Utley, who suggested shifting perspective from AI is not the oracle with the answers, but rather like a collaborator, a teammate, and that’s been pretty helpful. I’d be curious to hear, for those whose AI use is limited to sort of an enhanced Google or enhanced Bing, where do you think are the most promising opportunities, like, “Hey, go do this right now with AI, and you’re going to see some cool career benefits”?

Aneesh Raman
This is another one where there’s no one answer for everyone. I’m sorry to tell folks. Like, you’re going to have to go to the gym. You’re going to have to try things out, test things out, and figure out what is the high value of AI for you.

It could be learning new things. It could be building new things. It could be coaching you on new things. You got to keep trying the tools. Like, to your point, I think too many folks either are afraid of AI and don’t want to touch it. Or, if they’re using it, they’re using it just for a better search.

We have five Cs I talked about, and curiosity matters most right now as individuals. And the first place to start is be super curious about these tools, because their capabilities are changing every day. It’s almost like a skill of tool dexterity.

I use multiple tools every day. Every once in a while, I shift to the dominant tool. I use them for all sorts of things. Every week, I’m trying to push a new task out to the tools so that I’m constantly testing what new things that I’m working on can it help with.

It helped with writing at first, and then that pushed me to realize, “Okay, I got to focus on how I elevate what I can get help with on the research or first draft side. But also how do I spend more time doing in-person communication?”

So I started studying, like, theater actors who have it down, who know how to command a room, command energy. What can I learn from them now that my bucket three is going to be more of this? Right now, I’m using the tools for a lot of coaching. I want to send this email. I’ve told the tools what I’m working on, which is sending less emails that are less lengthy with less ideas to people like Ryan, who I can bombard with ideas.

And so it’ll say like, “Hey, you just got yesterday, like take a day off,” you know, like stuff like that. So it’s not so much about the tool capability. It’s what is the human capability you’re working on in that moment? Because we’re all going to want to keep growing right now in new ways, which is fun. It’s hard, but it’s fun.

And then you’re going to use the tools differently based on what you’re working on. If you’re just doing search, that’s an issue. So the easiest way to do it is find the hardest part of your tomorrow. You got something at work tomorrow that you’re not looking forward to, that is either monotonous and drudgery or complicated and a hard conversation you got to have, or you’re suddenly going to have to come up with an idea and you’re bit freaked out about that.

Whatever is your hardest thing tomorrow, start going to the tools and asking them how they can help you with that. And they might not be able to help you. Okay, that’s like a good example of a tool that isn’t there yet, or a task that isn’t there yet, but they’ll give you something. And that’s the sort of rhythm you want to get in.

Pete Mockaitis
Okay. Well, could you give us some fun examples of folks who saw some really great tactical good things flowing from tools into what they’re up to tomorrow?

Aneesh Raman
My favorite from the book, probably, I mean, they’re all stories of individuals who are using these tools to get jobs, keep jobs, and change their career, build businesses, like, all of it. But Jonetta Gresham is in her 50s, and because of her age, she came to AI with, in her words, a “Hell, no to AI,” mindset, because she had seen Terminator and Terminator 2 and Terminator 3, and really felt like this was a robot apocalypse come to life.

Again, she had a task at one point, which was to get her resume ready. And she used a tool to just help her do that. And she was blown away at how the tool helped her articulate skills that she had and better organized experience that she had in a way that made her so much more employable for the job she was going after.

And so that sort of opened up her eyes to like, “Okay, maybe this can help me.” And a little bit later, she was taking an IT certification course. And she is someone who, in various moments of education, didn’t feel like she was being taught in the way that she would like to learn, in the way that her brain process information, and in the way that would feed her curiosity.

So she told the tools to help her learn all the stuff she had to learn in the ways that she likes to learn, with stories, with analogies. The tools did that, she passed the test, she got the certification.

It can personalize against your needs in a way that no technology has before. It can close learning gaps. It can close entrepreneurial gaps or building gaps. You just got to get in the rhythm with it.

Pete Mockaitis
Yeah, I like that a lot in terms of, if you’ve got a laser focus on, “This is the thing I need to go learn,” and then how can the tool facilitate that, I found that rather handy. Like, I found myself getting in the Mac OS Terminal bash command line, and I don’t know what I’m doing, but it’s like, “Hey, if you want to, you know, do this automatic downloading of stuff, you’re going to need this tool.”

So I was like, “Go to GitHub,” it’s like, “Okay, no, you’ve already lost me.” And so I say, “Explain it like I’m five.” And it’s really funny, it’s like asking the robot to go to a store. It literally explains it like I’m five. But that was very handy. And I’ve heard that theme from a number of people.

It’s, like, they know a little something about the domain that they’re after or looking to enter for perhaps the first time, but not nearly enough to actually achieve anything. But with this sort of mega-crutch, they’re saying, “Oh, okay, I can kind of fake my way through step by step.” And then afterwards, like, “Oh, hey, I guess I know how to do this now. How about that?”

Aneesh Raman
Yeah, we’ve got a great story of a guy named Diego, who’s in Texas and is trying to push for rural entrepreneurship with AI to really inspire more folks to realize they can build businesses. Often, folks who couldn’t afford to go to college for whom entrepreneurship is, as he calls it, this permissionless path, this ability to go build your career on your terms.

And he has a great line, which is, “We are no longer limited by what we know. We are only limited by what we can think to ask.” And just imagine what that means for all people all over the world who have access to these tools, who hopefully have electricity and AI infrastructure.

But you’re only limited by what you can think to ask. All of us as humans are innately curious about things, wildly different things. That’s what’s amazing. We all have different perspectives, different things that drive us. But imagine now having this tool that can sort of feed that curiosity and help us align it with the work we do and the impact we want to have and the purpose we want to bring to our jobs.

That’s where you start to get excited. I think we are collectively doing a horrible job telling that story and making it clear, that hiding in plain sight is this thing that is going to make all of our jobs more interesting and more fulfilling, and all of our efforts lead to greater impact for good in the world.

And that’s now on us to try and reset that story and that conversation, which is like the big reason we did the book.

Pete Mockaitis
I like that, “What you can think to ask.” Have there been some power questions that you found transformational?

Aneesh Raman
I think a lot of it is, right now, for folks around, “Where do I start?” or, “What do I do about? How do I think about AI? How should I use AI? When should I not use AI?” You can just dump that in as a voice memo into one of these tools, and it gives you a start. It gives you something to react to.

Again, it doesn’t give you the answer. It gives you an option. And it’s your job to push back on it or to pull from it, and then try and learn something here. Ask it to challenge your thinking over there. One of the things we talk about in the book is like the, I think, it’s a hundred to one rule.

I mean, “You pick your number, but give me like 80 versions of something,” and then you react to which of those you like. And then you can start to think about why and build from that. Or, “Give me the five best arguments against the thing that I think is true about what I’m going to do tomorrow.”

That’s where I think people are really starting to get good results from it. Not, “Give me the answer,” but, “Give me a way to get to a better answer.”

Pete Mockaitis
And I like what you’re saying with regard to the challenging is because it seems like that’s the default setting is sycophancy in these AI tools. It shows me a study. I was like, “Wow, that’s very compelling.” I was like, “Do we think this is real?” It was like, “Actually it has all the hallmarks of being a fraudulent paper mill submission.” I was like, “It’d be nice if you proactively shared that.” But, yeah, to ask it to do the challenging is great.

And you said voice memo and I think that’s a brilliant hack right there in terms of not just the one sentence, but the five minutes of verbiage can make all the difference.

Aneesh Raman
And you can just dump it all in and it’ll organize it. I know someone who does a call with AI every day. They do the chat functionality because you can also, without having to type in or do a voice memo, you can literally converse with these tools.

And they go out for a walk, and they just talk about everything on their mind, “I’m like thinking about this, I had a hard conversation with this person.” And over time, what these tools start to have is longitudinal data on us. They aren’t another person. They are just a collection of insights and knowledge that’s out there. And then, increasingly, if you give it context and give it info, insights, and thoughts on us over longer and longer periods of time.

And so in these conversations, the tool will start calling out, “Hey, you’ve talked for a few times now about wanting to learn more about neuroscience, or how that’s going to relate to work. Here are some, like, podcasts you might want to listen to.” Or, “I’ve noticed, like, anytime you have to have a conversation that’s tough, and it is how you end your day, it really upends your day. Like, have you thought about making those conversations happen at the start of a day?”

It starts picking up on stuff for us that we might miss in the day-to-day of just life being busy. So the key thing is, like, this is a tool that is the easiest technology humans have created for humans to use. It is literally like just talking to someone else at the most basic level. You don’t have to go learn AI. You just have to, like, sign up for a free tool or watch a couple of free videos on it, and then just start using it.

Pick a thing. It can be an exercise plan, a meal plan, or something at work, a project, or a set of tasks you’ve got going, and just start using it. Like, it’s sitting there for any of us to use. So you got to just start using it and keep using it but don’t outsource to it. Use it to then start building you into a better person.

Pete Mockaitis
All right. Well, anything else you want to make sure to mention before we hear about some of your favorite things?

Aneesh Raman
I think it goes back to your opening question. Like, it has been surprising how we have internalized a diminished sense of self, and yet it is a reality. And so the biggest thing I would tell people is, like, what matters most right now is your belief in yourself.

You’re going to have to bet on yourself. That’s the future of work and then push your leaders, and we’re pushing them, to build systems that make that bet pay off. But the thing that starts it all is going to be that you believe in yourself and you’re going to bet on yourself. And that’s going to take some work because we are all coming out of an era for work that wasn’t about betting on yourself.

It was turning yourself into whatever the job description needed, whatever the job category needed. So it’s going to be a flip for your brain. The good news is, and there’s a great book, Tiny Experiments, a bunch of them on neuroplasticity and how we can rewire our brain to become a different person.

The good news is you got a human brain and it is able to be rewired and you can become a different person. You can get to belief. You can get to a place where you know exactly how and why you’re going to bet on yourself, but it’s going to take some work. But that’s where it starts. That’s what I would say.

Pete Mockaitis
All right. Well, now could you share a favorite quote, something you find inspiring?

Aneesh Raman
I like the, “Some men see things as they are and ask why. I dream of things that never were, and ask why not.” That drove me for a while. I think, right now, it’s more “Be curious, not judgmental.” I think it gets appropriated to Walt Whitman.

Pete Mockaitis
All right. And a favorite study or experimental or bit of research?

Aneesh Raman
Mine, I mentioned at the MIT one, 60% of jobs in 2018 didn’t exist in 1940. That’s just like a good number for us to keep in mind.

Pete Mockaitis
And a favorite book?

Aneesh Raman
A bunch of them. My favorite hits at a certain moment in life, that sort of like hits me deep as it relates to this conversation, Sapiens. I read that in August 2023, GPT had gone global the prior year, and it is a brief history of humankind.

And so it just helps you have a sense of, like, how incredible the human brain is, but just also how much has happened across millions of years when we all focused on a few hundred years. So that really widened my perspective.

Pete Mockaitis
And a favorite tool?

Aneesh Raman
Copilot, I’m using it a lot because I work at LinkedIn. My co-author is working on Copilot. So it’s kind of a default because I can go tell someone if there’s something we should make better out of it. But it can really give me good advice on, “Should I send this email?” or, “What am I overspending my time on?” because it’s got my calendar, it’s got my email, it’s got the team’s messages. So, for me, right now, because a lot of my growth is growth I want to do at work, it’s been helpful.

Pete Mockaitis
And a favorite habit?

Aneesh Raman
This is probably controversial, because I don’t know if people would say this. My favorite habit is small talk, actually. I love small talk. I think every human is like a documentary unto themselves. There’s this great word, sonder, that I won’t do it justice, so people should look it up. It’s a word someone made up, I think, five, 10 years ago. But it’s that every human around you is living a life as complex and interesting as your own.

And that’s true across human history, or at least since we’ve had the brain we have with the ability for complex thought that we have. So I find small talk just amazing. I love meeting new people and just, I’m sure I’m awkward about it because I ask like deep questions sometimes really quick, but I just, like, love learning about people.

Pete Mockaitis
And is there a key nugget you share that people really connect and resonate with and quote back to you a lot?

Aneesh Raman
Hard things are hard. That’s a good one. You know, at one point, someone asked me, “What’s been the hardest part of your career?” And I was like, “You know what? All of it.” And I did it kind of, like, begrudgingly. And then as stuff has stayed hard in just figuring this story out, I revisited, you know, when I worked for President Obama, he had a “Hard things are hard” plaque on his desk because at certain moments when legislative victories were hard fought, people would remind each other hard things are hard.

And that just has led me to become a real believer in the bigness and value of hard. I have, like, an ode to hard things. It’s got, you know, quote from a stoic Marcus Aurelius, who’s like, “The obstacle is the way,” to “Hard things are hard,” to Carl Lassen, the Duke women’s basketball coach who has a viral video that’s amazing about how the whole thing in life is, “How do you handle hard well?”

Roger Federer had a commencement speech a few years ago about the mastery of hard things. Nvidia CEO Jensen was at Stanford Business School and said, “I wish upon you pain and suffering,” because his point to these Stanford Business students was, “You got to build resilience and you got to go through hard for that.” So, yeah, hard things are hard. I think I’ll take that.

Pete Mockaitis
And if folks want to learn more or get in touch, where would you point them?

Aneesh Raman
LinkedIn. The sleeper functions on Linkedin, I think, that people don’t know enough about is you can follow people without connecting to them.

You can go follow folks and they’ll be in your feed. So I am, I’d say, regularly, every three months, unfollowing some people where my curiosities have moved somewhere else, following new people who are talking about the things that I’m newly curious about.

So follow me until you get bored of me. Follow others. All the people in the book are on LinkedIn. And then the book, Linkedin.com/opentowork, that’s where you can go find out about the book.

Pete Mockaitis
All right. And do have a final challenge or call to action for folks looking to be awesome at their jobs?

Aneesh Raman
Bet on yourself. Find your way to betting on yourself and you’ll be okay.

Pete Mockaitis
All right. Aneesh, thank you.

Aneesh Raman
Thanks for having me.