Improving Your Thinking Skills with Judah Taub

george grombacher 0:02
Uh. Judah Tubb is the managing partner of Hetz ventures. They’re an international fund focusing on Israeli startups. He’s been named to Forbes 30, under 30. Welcome to the show, Judah.

Judah Taub 0:14
Hi. Thank you very much for having me.

george grombacher 0:17
Yeah, excited to have you on. Tell us a little about your personal lives, more about your work. Why you do what you do?

Judah Taub 0:23
So I do what my I do out of passion. I used to run data for a very large hedge fund. I now am at an early stage VC venture capital firm, where we invest in cutting edge tech startups. We’ve been doing this for the last six years, and it’s really fascinating just to see new innovation at a very, very early stage, and things that will probably hit the general market in years to come and potentially change the way we live. More recently, I’ve written a book which will come out on the 22nd of October, and the essence of the book is around the way AI thinks and how we as humans can potentially change the way we think. Learning from Ai,

george grombacher 1:12
a nice, light read, terrifying read, exciting read.

Judah Taub 1:16
So actually, I started the book when I when I originally wrote it, it was very technical. And then I was challenged. This was by a bunch of folks at HBS who challenged me to write it basically again, but in a very sort of friendly, I’d call it sort of Malcolm Gladwell style. So tons of stories, and then between the stories, there are learnings that individuals can take home and maybe sort of think about and ponder on in the evenings, and that’s what I did. So I rewrote nearly everything from scratch. It’s now not technical at all, but technical folks out there who will read this will say, Hang on a minute, some of the real cutting edge stuff that we are doing in artificial intelligence, in sort of some of the big tech firms, when they’re optimizing the software, are, in essence, the moral, the outcome, the takeaway from some of these sometimes funny, sometimes exciting stories that are Throughout the chapters.

george grombacher 2:20
So as somebody who has written books, the idea of finishing a book and then having people that I respect say you should consider rewriting this, I don’t think I would have liked that.

Judah Taub 2:33
Um, I think it depends what your goal is. For me, the goal was never just to have a book published. It was to have something published that I think many people will enjoy reading will sort of feel pretty light potentially so to answer your first question, light fun, but at the same time, as a result of it being sort of easier to digest, could potentially have a more impactful punch and really sort of make those folks who read the book think in a slightly different way afterwards.

george grombacher 3:12
Yeah, I appreciate that. I think that that’s probably where I would arrive at, but you didn’t have the initial Damn it.

Judah Taub 3:21
No. I mean, it’s a fair comment, but it wasn’t like the student who gives in his homework and the teacher says, No, you’ve got to do this again. There was never an assignment to give in, and therefore there wasn’t that type of feeling. But just to give you an idea of how I approach this book, because I really do think there is a unique take here, and that is, I have been sort of engineering, writing software, seeing cutting edge stuff now for quite a while, and if you look at the basics behind computing, it has always been humans teaching computers how to think so the very, very basics of computers comes back to if and not and some really, really basic sort of functions that for years and years, computer Scientists have built upon those. And what we really did was we told machines This is how we computers think. We are now telling you computer software algorithm to think this way. And what we’re now seeing with artificial intelligence is that computers are sometimes able to solve problems in a better way than humans are, and this is not because their computing power is so much vaster. I’m not talking about speed like a calculator. I’m saying they’re coming up with more creative, innovative, unique perspectives on matters that we care about, that we. Humans don’t always think about that way. And so what I’ve tried to do, but I think the first time, is say, Okay, what you what computing logic can we humans learn and maybe implement into the way we think about the world, into the way we make decisions? And I’ve done this around a very specific problem called local maximum, which I can explain in a moment. But I think it’s very unique, because it’s one of those challenges that without realizing most humans run into and really it affects their lives, their businesses, etc, humans are not very good at solving it, and computers are pretty damn good at it. So that was really the focus, to be really friendly to the user, to give stories and show humans how they could do this like a computer. But for me, it’s maybe the first book of what could be a future genre of here’s interesting insights for how computers solve problems, which is, by the way, what chess players are now doing and go players are doing. They’re watching what move would a computer make, and then learning themselves.

george grombacher 6:13
It’s really interesting, for sure, how did you were just going through the course of your work, working with AI, trying to understand it, and a light bulb turns on, it’s like, oh, wow, I need to there’s, there’s really something here.

Judah Taub 6:29
Excellent question. So what I do a lot of my time is I get to work with CEOs of startups and as a board member and typically, first investor you are not dealing with the day to day challenges that is sort of the CEO, the CEO and the rest of the team. You get the opportunity to speak with them, typically late at night, when they’re suddenly thinking, thinking about big problems, and you’re talking to them about things like, is our strategy the right one? Are we going to hit a glass ceiling if we pursue this in a year’s time? And I found myself having those types of conversations again and again and again, are we going to eventually get to a point where we’ve done everything right, every step along the way, but we ended up hitting a point called a local maximum, a point which is not the maximum, but it’s a point where we can only go down from so if you imagine many, many hills in, I don’t know, the Nevada desert, and your goal as a company is To get to the highest point, highest being the best, the most profitable, the most, whatever it is, by the way, the same thing would happen to an individual who’s trying to get to the happiest point in his life, the most, whatever it is. You’re trying to get to the highest point, but you ended up navigating to a top of not a very tall mountain. And the challenge you now have is you’re at the top of a mountain, so you can only go down from here. If you go one step forwards, backwards, left or right, you’re going to have to go down. But you got to the top, so you did everything right. You went step up. But now you realize there’s much higher mountains that you can’t reach without going the whole way down and going back up. And these conversations I was having with CEOs were happening again and again and again, and slowly I came across friends who were asking me questions like, I’ve been in this marketing job for many years, and I’m now paid really well, but I actually don’t like it. What should I do to maybe get to a different role, which I might have to take a cut for a few years, quote, unquote, going down off the mountain before I go back up. And I’m hearing this, and at the same time, I’m speaking to cutting edge engineers at places like Google, Nvidia, Amazon, Facebook, and I know that engineers are having these same challenges training their software to so at some point, the question to me was, can we write a book which is targeting these everyday individuals who are trying to navigate their own businesses, their own lives, their own that they don’t want to hit these glass ceilings. But maybe we can suggest a variety of techniques that AI uses today, techniques that AI has benefited from billions of dollars being invested by these major corporations to come up with, but rather than just help them to become more profitable. Help these individuals navigate whatever they’re trying to achieve in a more effective way. I

george grombacher 9:48
think that that’s fascinating. So the local maximum, it’s it’s siloed thinking, and I can maximize all the thinking within the with. Than the silo, but it’s not going to be taken into consideration the whole Yeah.

Judah Taub 10:04
So let me, let me, let me give an example. So each of the chapters, the first chapter, is literally just an introduction to what a local maximum is, and it gives the example of somebody in the field, like a desert trying to get to the highest point. And then we’ve got eight chapters, each teaching a different technique that AI now uses, or engineers that some of these big corporations use. So I’ll give you the first one, which I call rather than using AB testing, use ABX testing. So AB testing, you may have heard of this, or be familiar with this. And this is a way that we also try and shields where take two options, A and B, whichever is better. Like, if you’re trying a marketing campaign, take slogan a, take slogan B, see which one works better. Go with a and then take a new B and carry on. Refine in smaller and smaller and smaller increments until eventually you get a slogan that you can’t in a small increment improve anymore, and you know you’ve got to the best option. It’s very similar to saying to that Nevada climber, climb the mountain and every second stop check if you go left is pill. If you go right, is it uphill? And whichever way is uphill, take that step. The problem is that software programmers, especially complex ones like AI, have found out that if they do this, the algorithm will very quickly hit a local maximum. Just like the marketing guy, you might get a good slogan, but you’re unlikely to get the best slogan or maybe even a great slope. So one of the ways that AI now tries to overcome this, and this is with examples like Google search and Amazon Prime, and I don’t know, Netflix recommendations, they all use. This is every couple of AB tests, it will throw in an x, a absolutely random, sometimes not exactly random. And I detail how to pick your exes in the book, but a random result, a different slogan completely to the A, B ones that you’re doing. And what the algorithm sees is, by doing this, you sometimes find mountains that otherwise you wouldn’t have come across. And by the way, I think the best marketing slogan in the history, I think it’s Nike’s slogan of just do it was invented exactly this way. So this was many, many years ago. They were doing AB testing. And then this guy, which escaped my name, came along, but we could Google it afterwards. And he said, one second, do me a favor. Let’s try this x, this random idea, which actually wasn’t just do it. It was a slightly different version, which he then tweaked. It was a completely different idea left field, and they suddenly found out that this new mountain was so much vaster. So this is one example of eight different strategies we humans, can try and learn to implement to better ourselves, and it’s actually techniques that comes from Ai, I

george grombacher 13:30
think that that’s really exciting. It really resonates. I’m somebody who is endeavoring to realize my highest potential and to get a little bit better every day than I was yesterday. What? What do we stand to gain? Are we? Are we splitting hairs? Are we letting perfect be the enemy of good?

Judah Taub 13:54
So I think what we stand to gain is quite enormous, and I I typically hear from folks one of two things, it’s either, wow, I never realized I was on such a small mountain and I could do So much more, and what one could gain from there is really just finding techniques that will allow them to really achieve their true potential. And the second one is more tragic, which are typically from slightly older folks who then say, I didn’t realize this. Would have realized this earlier on. I could have potentially done so much more, or been much happier, or whatever the they’re trying to achieve. And sometimes there’s still things you can do, but as you sometimes go up the wrong mountain, it becomes painful. Coming down. I do have a part in the book where I talk about shortening valleys and making it less painful to go down and to go back up and again. AI has a variety of techniques for this as well. Doesn’t mean it’s always easy, but it does have a number of techniques. But yeah, I would say you only live once and learning how to navigate your decisions along the way. I talk about sports and dating, and it’s not just career and business driven and nonprofits, but whatever it is you’re trying to achieve, if you can make it that little bit better, and I would say sometimes you can make it a lot better, that that’s a lot of potential to achieve.

george grombacher 15:43
Yeah, yeah, there’s no doubt about it. You essentially, you had climbed to the top of a moderately high mountain in the Nevada desert, and then your friends at HBS told you, no, you need to climb back down and find the actual highest one.

Judah Taub 16:02
Yeah, I mean to be blunt. They said, Look, some computer programmers might like this, and I literally had code on some of the pages. And I think the quote, some of the computer scientists might like this, it was not even like, they will definitely like this, but it was, it was, it was a great piece of advice. And, and now, like, yeah, it’s very much sort of stories. And at the end of each story, some insights that, at the end of each chapter, I say what this guy did when it was whether it was with a Nike slogan, or whether it was trying to sort of catch monkeys in Indonesia or any one of these examples, is actually something that computer scientists do at Amazon Prime when they’re trying to deliver it faster to your house, or at Google when they’re optimizing your search engine, or even in big farmers when they’re trying to sort of do protein folding and things like that. And so we literally have at the end of each chapter two pages which are not technical. It’s just what you learned here is also seen in technical environments such as and it gives one or two examples. So unfortunately, all my, my coding is out.

george grombacher 17:27
Bummer, right. Shoot. It makes it. It makes all the sense in the world. And I think, do you feel like? I feel like it takes somebody who is who is brave to be able to to dig into this and to say I am limiting myself just through my patterns of thinking. And I do think that I’m finding myself on one of those mid level mountains instead of the really high ones. Do I have the courage to be able to climb down and to set out on another one? The answer for me is yes. What kind of feedback are you getting there, if any at all? Yeah.

Judah Taub 18:10
So I think, I think the truth is more nuanced, and one of the points I to make in the book is it’s not just no mountain, but there are aspects or ways you can treat yourself, or things you can do along the way that will make you as an individual more capable of going down mountains and climbing up other ones. So it’s not just that one sort of single, vertically, sort of thinking decision example. So one of the point I talk about is trading off between agility and muscle. And maybe to put it, in a more sort of pictures environment this you and in the gym, you’ve got sort of the guy who, all they do, all that guy does every day is goes in and bench presses, bench presses, bench presses. And the goal of bench pressing is just to increase, increase, increase, increase the weight. And you can imagine what this guy looks like, huge, huge sort of muscles, but at the same time, he’s good at this one type of task. Then there’s the other guy who’s going around the gym who is more agile, so he’s doing a lot of flexibility, and he’s doing a little bit of this and a little bit of that. And if there’s a bench pressing competition, there’s no doubt who will win between the two. But if it’s nearly anything else, any other sport, any other activity, the Agile guy will completely destroy the bench pressing guy. And so the question is, what type of whether it’s an organization or whether it’s as an individual, what type of person are we trying to create here, and what we see from places like AI and their. Seeing this today more and more. So if you compare GPT four to three to two and across all the big sort of llms coming out, we’re seeing that the Agile ones are increasingly out the muscular ones. And you can build algorithms that have more muscle, and you can build algorithms that have more flexibility, and continuously these software specialists are finding that the agility is more important than the muscle, mainly because that we find that the mountains are moving. One day we want the algorithm to do this. The next day we need the algorithm to do that. One day, this was the main goal, and one day that was the main goal. And the Agile ones are the ones that conflict between them. So when you think about what this means about us as individuals or organizations, there’s a clear sort of set of instructions here. It’s what are the elements that we are building that make us muscular in our organizations. It might be capex. It might big infrastructure that we’re investing in that might be really good at something narrow versus how do we keep our skill set, our mind frame, our organization, more agile, so that when things change, we’re able to more quickly adapt. So to your point, it’s not just about can you, when you get to the top of the mountain, make the decision to come back down it? Can you, along the way, build yourself as an individual, as an organ, profit, a movement, whatever it is, so that when these decisions occur, it’s actually not that painful, and you are better positioned to make these adjustments.

george grombacher 21:45
I love it makes a ton of sense, and I can already I can, I can see the book in my mind, and how the stories are are helping to bring these difficult concepts to life. So I think it’s great. How long did it take you to to write the book.

Judah Taub 22:06
Um, it didn’t take that long. It took a couple of months to actually write it. The big part for me was to find really, really, just good stories that emphasize the points that we are seeing and learning in the technical world, but in very, very different environments. So I’ll give an example. So there is a so for the agility and and muscular one, there’s a cute competition of sandhole digging, where the goal is, within seven minutes, which two individuals can dig a deeper hole in, sort of just the sand next to the beach. And there was two individuals who went to this competition who weren’t necessarily that good at and again, in the book, I detail it a bit more, but they weren’t that good at digging compared to the others. They weren’t as big or as muscular as strong, but unlike the others, they realized that the first few minutes of the competition they should dig a much wider hole, rather than what you would imagine everyone’s doing. It’s like sand flying everywhere, putting their hands in. These guys wider hole. That meant went on they could put more of their hands in the hole and Dig faster towards the end. So what most of them, and you could have, you could imagine this, I saw it, but, but most of them were doing at the very end is they were taking turns because only one of the individuals could get one of their hands into this very narrow hole that is getting skinny and skinny to the bottom. And these two individuals started with something pretty broad, so they didn’t go that deep at the beginning, but they had the agility as time went on, to carry on playing with the hole. And there’s even a story of one time where they were so close it was a tie, they gave an extra sort of, I think it was three minutes to see who can get even further. And the people who were basically getting as deep as they had their hand could go, was screwed. And then these two individuals, because they had a broad, agile approach, just went another foot deeper and won the competition. So to answer your question, for me, it was finding Everyday Stories that actually take a deep insight from artificial intelligence and finding a way to tell it and show it in day to day, just sandhole digging and then ideally at the end, give some of the practical takeaways, whether it’s for the individual or the institution, Corporation.

george grombacher 24:52
Well, it’s exciting. I can’t wait to read it. Thank you so much for coming on. Where can people learn more about you? And. Tell us where they can get the book and all that information.

Judah Taub 25:02
So first of all, it’s been great being here. So thank you very much. Got a website, Tudor tab.com the name of the book, which is also on Amazon, is how to move up when the only way is down. And yeah, great if you come onto the website, order the book and please share comments, always trying to learn and hear of new ideas and stories. Thank you very much. Yeah, well, it

george grombacher 25:28
was my pleasure, and that is a wonderful name for the book as well. So if you enjoyed as much as I did, show Judah your appreciation and share today’s show with a friend who also appreciates good ideas. Pick up your copy of how to move up when the only way is down. And I know I’m excited to learn about some different ways to try to get my brain to process information, and the idea of some different ways to think, or different paradigms of thinking, and taking some cues from our friends in the machine and computer world, I think that that’s nothing but a good thing. And then go to Judah, tob, J, U, D, A, H, T, A, U, b.com, and check out all other things that Judah is working on. Thanks again, Judah, thanks so much. Till next time, remember, do your part by doing your best. You.

george grombacher 16:00
So if I want my iPhone, and my Tesla and my Bitcoin to work, we need to get the metal out of the ground.

Pierre Leveille 16:07
Absolutely. Without it, we cannot do it.

george grombacher 16:13
Why? Why is there a Why has production been going down.

Pierre Leveille 16:21
Because the large mines that are producing most of the copper in the world, the grades are going down slowly they’re going there, they’re arriving near the end of life. So and of life of mines in general means less production. And in the past, at least 15 years, the exploration expenditure for copper were pretty low, because the price of copper was low. And when the price is low, companies are tending to not invest more so much in exploration, which is what we see today. It’s it’s, it’s not the way to look at it. Because nobody 15 years ago was able to predict that there would be a so massive shortage, or it’s so massive demand coming. But in the past five years, or let’s say since the since 10 years, we have seen that more and more coming. And then the by the time you react start exploring and there’s more money than then ever that is putting in put it in expression at the moment for copper at least. And what we see is that the it takes time, it could take up to 2025 years between the time you find a deposit that it gets in production. So but but the year the time is counted. So it’s it’s very important to so you will see company reopening old mines, what it will push also, which is not bad, it will force to two, it will force to find a it will force to find ways of recalibrating customer, you know the metals, that will be more and more important.

george grombacher 18:07
So finding, okay, so for lack of a better term recycling metals that are just sitting around somewhere extremely important. Yeah. And then going and going back to historic minds that maybe for lack of technology, or just lack of will or reasons, but maybe now because there’s such a demand, there’s an appetite to go back to those.

Pierre Leveille 18:33
Yes, but there will be a lot of failures into that for many reasons. But the ones that will be in that will resume mining it’s just going to be a short term temporary solution. No it’s it’s not going to be you need to find deposit that will that will operate 50 years you know at least it’s 25 to 50 years at least and an old mind that you do in production in general it’s less than 10 years.

george grombacher 19:03
Got it. Oh there we go. Up here. People are ready for your difference making tip What do you have for them

Pierre Leveille 19:14
You mean an investment or

george grombacher 19:17
whatever you’re into, you’ve got so much life experience with raising a family and doing business all over the world and having your kids go to school in Africa so a tip on copper or whatever you’re into.

Pierre Leveille 19:34
But there’s two things I like to see and I was telling my children many times and I always said you know don’t focus on what will bring you specifically money don’t think of Getting Rich. Think of doing what you what you like, what you feel your your your your your, you know you have been born to do so use your most you skills, do what you like, do what you wet well, and good things will happen to you. And I can see them grow in their life. And I can tell you that this is what happens. And sometimes you have setback like I had recently. But if we do things properly, if we do things that we like, and we liked that project, we were very passionate about that project, not only me, all my team, and if we do things properly, if we do things correctly, good things will happen. And we will probably get the project back had to go forward or we will find another big project that will be the launch of a new era. So that’s my most important tip in life. Do what you like, do it with your best scale and do it well and good things will happen.

george grombacher 20:49
Pierre Leveille 21:03
Thank you. I was happy to be with you to today.

george grombacher 21:06
Damn, tell us the websites and where where people can connect and find you.

Pierre Leveille 21:13
The it’s Deep South resources.com. So pretty simple.

george grombacher 21:18
Perfect. Well, if you enjoyed this as much as I did show up here your appreciation and share today’s show with a friend who also appreciate good ideas, go to deep south resources, calm and learn all about what they’re working on and track their progress.

Pierre Leveille 21:32
Thanks. Thanks, have a nice day.

george grombacher 21:36
And until next time, keep fighting the good fight. We’re all in this together.