960: In Case You Missed It in December 2025

0:00Jon Krohn:This is episode number 960, our In Case You Missed It in December episode.

0:08Jon Krohn:Welcome back to the Super Data Science Podcast. I'm your host, Jon Krohn. This is an In Case You Missed It episode that highlights the best parts of conversations we had on the show over the past month. Let's start off with something from a widely reported study from last year from MIT's Nanda Lab, which is somewhat contentious this figure, but the headline figure was that they reported a 95 % failure rate for enterprise AI solutions. In episode number 953, I talked to Dell's global CTO and global chief AI officer John Rose about how businesses can make sure they're in that rare 5%. For people who are unfamiliar with that MIT study that 95 % of Gen AI projects fail, I've got episode number 924 for you listeners dedicated to that.

0:56But even more important than that reference to a back episode is John Rose's previous episode on the show, which is 887.

1:05Jon Krohn:and in 887, he provides tons of information on what he just gave an overview of where you can be basically guaranteed to have successful projects within your enterprise, how to select the projects that are gonna have the best ROI and you just gave some citations there, which I don't think you did in the previous episode of kind of 10 to one, 30 to one ROI ratios, which is amazing. And as soon as you have a few of these projects, Then you start this AI ROI flywheel spinning, which is a term that I learned from you and I have used in countless business meetings since we did that episode 887 together.

1:45It seemed to work. By the way, it's been a while. But, you know, look, the big impact that we had at the end of that process once we got the four big areas running is, you know, in our fiscal results last fiscal year, which we reported in March. That was the first year that we'd kind of got all this stuff up and running. Something happened at Dell that hadn't happened in 41 years as a company. our revenue grew at$10 billion and our absolute cost declined by 4%. We have never decoupled revenue growth from cost. Every other time the revenue grew, the cost grew with it. It didn't happen last year.

2:15And it was a combination of people, process, and technology. But it was really a positioning and redesigning the company to actually get better productivity as we did work. And if you do that, it's a pretty powerful tool when your revenue grows and your costs don't grow with it. That results in margin expansion. That results in just a better performing company. And so we now have a lot of empirical data, those ROI metrics, right? How can measure? We measure everything. And so I feel good that, hey, not only was it good theory and not only was it discipline, but it actually seems to work in terms of producing really strong ROI, which, hey, that flywheel works better if the ratios are higher.

2:50Jon Krohn:For sure. A hundred percent. You get a lot more attention when you have a 30 to one ROI ratio, for sure. And it's great to hear those kinds of stats around revenue increasing without costs going up or costs even decreasing because I'll keep this completely, you know, this isn't a comment on anything related to Google necessarily, but a friend of mine, a good friend of mine, just the other day at the time of us recording said to me, do you really think you're getting better productivity from this AI stuff? And yeah, it's, it's, it's, it's fantastic for you to be able to cite these hard numbers on how, if you do it right, you absolutely can be getting a great return on investment in your organization.

3:30Yeah. I remember one of the first principles was have a hard ROI metric. There is no, it's hard to measure goodwill and happiness. You know, it's a, it's a lot easier to measure things like, I don't know, cost or margin or revenue or, you know, things that you actually can measure and understand. And so by, by focusing your AI efforts on things that are empirically measurable, you, you can actually get some level of confidence that one, you're either achieving success or two, you're not. If the ROI is nothing, if there's actually a term, I won't use the full language, but I think it was a McKinsey report, and it described what they call BS work.

4:09And they basically said generative AI is a BS work detector because it turns out that if you use a whole bunch of AI tools on a piece of work that you currently do without them, and after you use them, even if you feel good about it, nothing changed. Your revenue didn't go up, your cost structure didn't go down, nothing really changed. What it's telling you is maybe that work isn't worth doing. Maybe it's BS work. And it turns out that there's probably a lot of that out there where just doing AI to something doesn't necessarily result in an impact. And I'm pretty, let's call it, focused on you want an impact from this stuff.

4:45You're investing heavily in the technology. It's costly, it's complex. And if you're not getting an impact that disrupts your business in some way in a positive sense, then why are you doing it? And trust me, there's a lot of targets to go after. So spend the time there.

5:00Jon Krohn:Right. So what you're saying is, in a lot of cases, the reason why AI projects are unfruitful or don't appear to move the needle internally is because it's automating something that was BS in the first place when humans were doing it. Yeah, exactly. Like I said, I think it was McKinsey, one of the consulting firms put this article out. You can go Google BS work in AI, and it makes a lot of sense. I mean, look, hey, you can apply AI to anything you want. Great. That might make it better, might not, but you probably pick the things that if you make them better, your business will improve. Something material will happen.

5:34And that level of discipline is something that people have to spend a little time on upfront. And if you do, yeah, it seems to result in things that matter.

5:41Jon Krohn:To quote John, as AI professionals, we should always be preparing to make something material happen. My next guest put that very idea into action. In this clip from episode number 945, the software engineer and podcast host Joel Beasley talks about using Gen.AI as a writing partner, coach, and analyst to help him get his feet wet in the standup comedy scene. And so tell us about the AI tools that you use to support your comedy development. Yeah. So I started in December of this past year. So it's been about 10, 11 months or so. and I realized really quickly, I was like, what's the difference between good comedians and great comedians?

6:22And why are some people more popular than others? And comedy is very subjective, right? Like what's funny to me and my group may not be funny to you and your group. So I was looking for the common data point that would, you know, transcend groups. And I found that that common data point was laughs per minute. And so you can look at these comedians, the Nate Bargettskis, the John Chris, you can take their, you can do it old school and just watch their special and jot down the laughs per minute, or you can run it through Gemini and have it calculate the laughs per minute. And so I found that four to five laughs per minute is the standard for a professional full-time comedian.

7:02And so I said, okay, all I have to do is write material that elicits four to five laughs per minute. And I will be on par with a professional comedian. And I've pretty much done that. So what I do is I record every set. I run it through the AI after the set. Nothing special, just Gemini prompt. Why Gemini? It was the only one through the browser UI that would consume video. GPT and Grok as of the recording date today will not consume video. And it was able to do it. And so I said, okay, that's super easy. And I just started, I have a spreadsheet, John, I have a spreadsheet, like a nerd does. And it lists every performance with a recording of the performance, the laughs per minute, what worked, didn't work, notes, and how many people were in the audience, how much money was made, all of that stuff.

7:53Jon Krohn:I love that. I can't wait to see if it gets more sophisticated over time. Like as your comedy career goes on, you get more and more sophisticated metrics. So right now, it's kind of like laugh velocity. I wonder if we can take the derivative of that and get laugh acceleration. Yes. Well, also, all laughs are not equal, which is actually a harder thing. So some people built software for this, by the way, called Laugh Evaluator Pro. But the duration of the laugh is important. The intensity of the laugh in relation to other laughs are important. So it can get pretty nerdy. I'm trying to keep it basic.

8:28So use AI in the evaluation of it. But I also use it in the writing process. So for example, what I like to do is I write the joke, like I write it and practice it. And I'm like, that's a good joke. And then I put it through AI to ask for different perspectives, how they would see that joke, different potential areas, like directions that joke could go in, maybe words near this joke that have a double meaning, things like that. And I've got a saved prompt that I use to act like a, a writing coach. Now, the way I figured this out, John, was I was going to a writer's group, a comedy writer's group, where there would be like eight comedians in a circle.

9:08And we would all do like, we'd do like a minute of our jokes for that week. And then they would all give feedback. And so I went to like 10 of those modeled how those professional comedians gave each other feedback, turn that into a prompt. And now I just get to do it with, with the crock.

9:23Jon Krohn:From counting laughs per minute, we turn to the more serious subject of burnout. In episode number 952, I asked the fit data scientist, Penelope Lafoy, how she used AI to help her recover from work fatigue and put her on the path to a much more rewarding career. Tell us about how you were a burnt out scientist and how you turned things around. Yes, I was working in finance in New York right after my master's degree. And even if I was good at what I was doing, I think that the fact that I was burnt out came from two different aspects. The first one, I was working too much. And the second one, what I was doing was not really aligned with the direction I wanted my career to grow.

10:09And so when I was waking up every morning, thinking about how do I see myself in five years, there was a huge disconnect between what I was and what I wanted to be doing in five years. And that's when I decided to change careers and to switch from finance. I was still doing data science for finance, but doing data science more in the life science industry because this is the industry I'm genuinely passionate about, just like studying the human body and using this technical knowledge in order to create new drugs, develop clinical trials, and so on. That was the first point. And then the second point was about, I'm not going to be successful at being a data scientist working in the life science industry if I am not taking care of my body myself.

10:59So that's when I hired a lifestyle and fitness coach. And when I started working out pretty regularly, taking care of my nutrition, as well as my recovery with like sleeping recovery practices and so on. And that's how I was able to very clearly do a 180 in both my career and my personal life at the same time.

11:23Jon Krohn:Fantastic. Were you into fitness before getting this fitness and lifestyle coach or was that something new? So I was, but I was not doing the right things, meaning that I was also burnt out in my fitness. Right. Because outside of my job, I wanted to have something that I was looking forward to. So I was working out nearly every single day and I was very clearly overtrained. I was not feeling my body properly. So everything was completely out of whack, to be honest. Yeah, this probably happens to a lot of the kinds of people that listen to this podcast who are not only pursuing a career in data science or AI in some way, you're also listening to a podcast about it in your free time, probably.

12:10Jon Krohn:And so you're probably the kind of person that is trying to maximize everything all the time. And so I certainly, with my workouts, I very, very often tend to overdo it, training too many days in the week. too hard on a given day. Yeah. Not taking enough rest days. Yeah. And just kind of having the level of intensity too high on a regular basis. And so kind of, instead of fitness being something that rejuvenates me, it is often something that just wears me out even more. And then I'm kind of like, you know, on the worst days, it's like, I keep feeling like I just need to nap. And it's like, you know, just sitting at my desk feels like, you know, it's too hard.

12:53Yeah.

12:53Jon Krohn:So what kind of, how do you strike that balance? How do you design a program that is going to make you feel rejuvenated? How do you structure that? So working with my coach, we were looking at what's my work schedule and also what are the activities that I'm doing outside of the gym that also give me energy and that I don't want to sacrifice. So for me, for instance, I play a lot of pickleball going to play in tournaments and so on. So I still want to have this background. What is that game called in French? We don't have it. It's too lowbrow, too American. Le pickle. Yeah, that would be weird in French.

13:42Jon Krohn:I don't know. I saw, I actually, I recently saw, uh, there's a, I've, I've lately, I've been working out at this lifetime gym in, in central Manhattan called it's a Penn station location and they have tons of pickleball courts. And in fact, if you, you, it's impossible. You can't pickleball is so popular and there's so few nice places that you can do it in Manhattan that there's a waiting list. like I'm already a member and I pay a crazy membership fee already, but I can't access the pickleball courts. If I want to access them, I do want to access them. So I'm on a waiting list to hopefully someday get an invitation.

14:20Jon Krohn:And then if I get that, I have to pay a fee, a huge fee, hundreds of dollars. Plus my membership goes even higher, just have access to these pickleball courts. But the whole reason why I'm saying this is that the other day I saw a woman in there who was wearing a t-shirt. She was playing pickleball and it said, the pickle. So I don't know. Maybe that's what French people say. But yeah, so anyway, so pickleball, you do a lot of pickleball. That's nice. It is pretty rejuvenating. Yeah, absolutely. And it's also a great way to meet people, which is another point about rest and recovery, which is that it's also a lot about the people that you're surrounding yourself with.

14:59Because hanging out with people want to go out every night or every weekend, spoiler alert, there is a very high likelihood that you're not going to be able to recover and to sleep well. By all means, I do go out and I do drink, but it's all about striking that balance and surrounding yourself with people and activities that you enjoy doing together beyond just going to the bar or going to the restaurant, even if we are doing it afterwards, after playing pickleball.

15:27Jon Krohn:You basically, you set up a schedule, a fitness schedule that's based on work, that's based on your social schedule. It kind of sounds like that means that, does that mean that you need to be in a really rigid routine or is there some flexibility as well? Both actually, meaning that I still want to be able to go to the gym four times a week, just because I know that it's good, not only for my physical health, if I want to build or even to just maintain the muscle that I have, but also for my mental health. I typically go right after work. I work New York time and I'm on the West Coast. So it's typically in the middle of the afternoon, which is good because there are not that many people at the gym.

16:10And so it's a good break between my professional life and my personal life. Because then when I go out of the gym, my friends are also out of work. And so I can go hang out with them or even go play pickleball. So it's, I know that I'm going to be working out four times per week, usually Monday, Tuesday, Thursday, Friday, but it's not super rigid. I just want to get those four times per week in.

16:39Jon Krohn:Excellent. And so how does nutrition fit into this? Cause you mentioned that that is, that's part of the change that you made as well. So in addition to kind of having this, this balanced fitness structure, what are your key tips for refueling for your job? Yes, I would say the main two tips are around eating enough protein. So I try to have one gram of protein per pound of body weight, just because as I'm working out, I want my muscle to have the right amount of protein in order to be able to grow or just like maintain them and also for recovery. And the second one is about timing my carbs the right way, meaning that I don't want to have a huge glucose spike when I'm in the middle of my work session and then I just want to go nap.

17:29So what I do is like usually I have most of my carbs in the forms of either like rice, potatoes, fruits, sometimes ice cream, mostly like around my workouts before, during and after, because it's the prime time for your body to absorb those carbs the right way without feeling sluggish afterwards.

17:51Jon Krohn:Got it. So ice cream before, during, and after working out is the key to being a successful data scientist. I love that. And you also, you mentioned to me when I asked you before we started recording about kind of your top tips related to avoiding burnout as a data scientist, you said that rest is the most important thing. Is there anything you want to dive into on that? Absolutely. I would say more specifically around sleep. I try to have at least eight hours of sleep, which means actually more than eight hours in bed. It's more or less, I would say, 8.39, just because I love reading before bed to downregulate a little bit, because if you're swiping or on your computer, there is no way you're going to be able to fall asleep.

18:38sleep and it's also about the quality of the sleep that you have it's not only about eight hours but eight hours of quality sleep with deep sleep REM sleep to just for your brain and the best way to have it is also to be able to down regulate and not be stressed during the day and for me it comes in the forms of just going on walks outside even just a five minutes walk in between meetings to kind of shift my brain a little bit instead of being always in the go, go, go mode. And instead of swiping on Instagram, I just go on walks without my phone. Sometimes I forget my keys and I'm locked outside.

19:22And so just going on walks outside and helping me not only focus for my meetings afterwards on my work session, but also telling my body that you can downregulate in five minutes, which then is a good way to fall asleep faster and to avoid waking up in the middle of the night if you're too stressed during the day.

19:45Jon Krohn:A healthy body is a critical foundation for a healthy mind. And I personally believe that keeping active and well-rested are important and effective ways to avoid burnout. I'm taking my next clip from episode number 947. My conversation with Netflix senior data scientist, Jeff Lee was a popular one in that episode, not least because it gave listeners a little insight into getting a job at tech giants like Netflix, Spotify, and DoorDash. Here, Jeff explains the application and interview process behind his success, netting roles at these highly desirable companies. So we've talked about some of the approaches that you've been applying at top companies like Spotify, Netflix, DoorDash, some of the most competitive companies to get into for any kind of role, whether it's data science or software engineering or marketing or even probably people working in legal, HR.

20:39Jon Krohn:Those are probably some of the companies that people most want to get into. What kinds of tricks do you have for our listeners to get hired by these top firms? Yeah, I have so many. So I would say I'll kind of talk through all the tricks that I used. So when I was trying to get into DoorDash, there was a technique I learned back then called the briefcase technique which i think actually still works and i've talked about this on a number of podcasts but um basically the idea is that like the core principle of getting a job right it's not like interviewing it's actually can you add value and solve that company's problems right so when you an interview is just a way to kind of test your skill set for to see if you can actually solve their problems but in reality you can actually just circumvent this and actually see if you can solve their problems so an example is actually at doordash um like i what i did was i figured out what their biggest pain points were and then i like put together a doc outlining hey these are your problems this is how i would solve it um and i like it was pretty detailed i spent at least like a couple days on it and then you send it to the hiring manager and sometimes it'll hit it's not always going to hit but you have a reasonable shot for it to hit and if it does hit then they'll actually be much more sold on saying, hey, okay, this person has a skillset that could solve my problem.

22:02I'll bring them in again to have a conversation with them. So I found that technique to work pretty well for -

22:08Jon Krohn:Do you mind if I interrupt you for one quick sec on this one, on that first point there? Something that's interesting today is, so you started working at DoorDash like more than five years ago, I think if I remember. Yeah, it was a while ago, yeah. And so back then, obviously we didn't have generative models that could be creating. So if somebody gets an email from you that's like, these are your pain points. And you clearly spend a lot of time, a couple of days. This is quite an unusual email to get probably for a hiring manager. And that's part of what makes it such a great tool for getting hired.

22:39Jon Krohn:But I wonder if today a hiring manager might say, this is definitely Gen AI. You know, and it's almost, there's kind of like a, it's almost like the more effort you put into it, the more crisp and perfect it looks, they might say they maybe they're more likely to say this is something that was just created by gen ai yeah this person doesn't like they're just you know they've figured out some relatively simple agentic workflow to be like spamming tons of hiring managers with these like what look like very complex reports because i get that you know for the podcast for example or for my consulting business for y carrot i end up getting these like huge pdfs like 30 page pdfs with illustrations tons of detail.