The Exchanges

Q Totally with you there. It reminds me of a conversation I had with Keith Raboy where he said exactly the same. The second experience though that I do want to touch on is your time with AngelList. And Andy McLaughlin asked this one specifically. He said, what did you learn from your time at AngelList and how have you applied that to your venture investing with Zeta?

A Well, I mean, firstly, I just learned a lot about investing in startups from Naval. He has hundreds of hard earned heuristics about what it takes to make a successful startup. Secondly, I guess I learned the importance of finding signals before anyone else through semi-automated high volume screening, which is obviously something we had to do at AngelList because there are millions of companies on the platform and you've got to sort of sort through them to help the investors find great opportunities. But that's something we also do at Zeta today in our own way. And at AngelList, we found companies like Ubers, Imogen, and Opendoor before anyone else, or before many others. So, I think finally, I just learned the nuts and bolts of how to run a venture fund as a business at scale. And to enable syndicates, we set up vehicles there that now manage billions of dollars of investors' capital, but importantly, at a very low cost to those investors with a really high degree of legal compliance. So, all that structuring was really important too, and obviously important today at Setup.

AI assessment note: “that's something we also do at Zeta today in our own way”

Q Can I ask, in terms of the data side, if we go back to the early cities of evaluating AI-first companies, what open banking in the UK has meant that we've seen real democratization of financial data, and my question to you is, for me analyzing fintech companies, and broadly AI-first companies, how crucial is it for the company to have proprietary data sets?

A Yeah, you know, I don't think it's crucial to have a proprietary data set, It's just mostly the case that you need it. You need proprietary data to build a model that generates results that are far better than what someone else can generate by throwing commodity data into, you know, an openly available model. Lots of research published on archive every day, lots of open source tools and whatever else. So, you know, if the playing field is level in that regard, and then the data is the same, then you'll probably end up with very similar result. But if the playing field is level on the model side, but the data is proprietary, you might get a different and sometimes So I think data modes are indeed real, and we evaluate those data modes very carefully at the diligence stage, and we look at things like accessibility, fungibility, dimensionality, breadth, and perishability. What we mean by that is, you know, basically, how hard is it to get? Can it be substituted by similar data that's easier to get? Can you use it to feed a model and generate some sort of real outcome? Do you have enough of it to represent reality? Is it broad enough? And then is it fresh enough to be close to reality? And so we sort of look at that on a qualitative basis, but also have different ways to quantify that. And that's how we value the data.

AI assessment note: “You need proprietary data to build a model that generates results that are far better”

Q Totally with you there. It reminds me of a conversation I had with Keith Raboy where he said exactly the same. The second experience though that I do want to touch on is your time with AngelList. And Andy McLaughlin asked this one specifically. He said, what did you learn from your time at AngelList and how have you applied that to your venture investing with Zeta?

A Well, I mean, firstly, I just learned a lot about investing in startups from Naval. He has hundreds of hard earned heuristics about what it takes to make a successful startup. Secondly, I guess I learned the importance of finding signals before anyone else through semi-automated high volume screening, which is obviously something we had to do at AngelList because there are millions of companies on the platform and you've got to sort of sort through them to help the investors find great opportunities. But that's something we also do at Zeta today in our own way. And at AngelList, we found companies like Ubers, Imogen, and Opendoor before anyone else, or before many others. So, I think finally, I just learned the nuts and bolts of how to run a venture fund as a business at scale. And to enable syndicates, we set up vehicles there that now manage billions of dollars of investors' capital, but importantly, at a very low cost to those investors with a really high degree of legal compliance. So, all that structuring was really important too, and obviously important today at Setup.

AI assessment note: “firstly, I just learned a lot about investing in startups from Naval.”

Q before we dive into the really nitty gritty on business models, I spoke to Carl at General Tappos before, and he said that you have a unique sense of business model quality, and it can articulately frame out why companies get big, and word for word, that's a great quote there. What do you think he meant here, and can you unpack your thinking on what drives business model quality?

A Yeah, I think Kyle's talking about my obsession with both compounding competitive advantage through these virtuous loops, and then also my obsession with vertical integration. So I sort of talked about that virtuous loop concept before, but I'll talk about vertical integration now. And I just believe the best products in the world are made by vertically integrated businesses. Apple vertically integrates from hardware to software. Amazon vertically integrates from warehouses to websites. And going all the way back to like Andrew Carnegie, vertically integrated from mines to his own steel mills. And for an AI first company, this means doing everything from collecting your own proprietary data, building your own models, delivering those predictions to customers through your own products, then offering your own services to integrate with a customer's business process. And this is the hard path. You know, it's easier to just buy a bit of data or use an open source model or deliver it through some other third party product or platform. Yeah, I go back to something a partner at Benchmark said once, which is what can go right, and that is what is a company doing differently that could be valuable to a lot of people, and I start actually from bottom up, so I don't do these sort of top-down tan models. When we're making an investment, I have this little tool I've built, and what it does …

AI assessment note: “I think Kyle's talking about my obsession with both compounding competitive advantage”

Q I'm super interested, and I didn't expect to go off schedule this early, but you said there about kind of signal discovery, and I hear a lot of fans today talk about algorithmic sourcing and how they intentionally use technology and AI to discover stock or anyone else. Largely to me, it's really using LinkedIn sales navigator effectively. What do you think about this next gen of AI-driven sourcing adventure?

A I think like a lot of what people call AI, it's more about augmentation than automation, and it's a little bit of automation, and that is I think you can use a lot of data sources to help you figure out where to look and who to talk to. The programmatic analysis of early stage companies, I think, is a fool's errand, and I have seen some of the best data in the world of AngelList. I've talked to all sorts of people experimenting with that data when I was there and since then, and it doesn't really work. There's nothing to really systematize there, I guess, to use a word that's not a word, but I think it can be very, very helpful if you have many, many different data sets To sort of point you in the right direction. And yeah, using LinkedIn sales navigators fine, but at Zeta, we have about 50 something data streams that we bring in, and then we use humans to annotate them. And then we have some human screens, then we have some automatic screens. And anyways, it's a multi-step process that goes all the way through to finding companies. And, you know, we're finding a thousand companies a month through this system, and then taking a hundred of those through diligence and bringing one term sheet stage every month or two.

AI assessment note: “The programmatic analysis of early stage companies, I think, is a fool's errand”

Q Tell me, if that's the buzzword of kind of AI first, the other buzzword of the day that I love when investors say is, you know, it's a system of record because everything's a system of record today, but you've spoken before about systems of intelligence. What does that mean to you?

A I remember writing about this in, um, 2016 or something. So I guess the distinction is a system of record gets data in a structured form as it's manually entered by a Human sort of after the fact in like instruction fields, so like a CRM or an ERP or something like that. A system of intelligence gets data usually in unstructured form, and it's automatically collected by machines as data exhaust as the product is used. So the distinction being structured versus unstructured, manually entered versus automatically entered, and then post facto or after the fact as, as opposed to in real time, as it's just exhausted or comes out of the product. David Wilkinson, he's an entrepreneur recently backstarted a company called hash. He said to me, the system of record owns your assets and records what your assets are doing, but the system of intelligence owns your strategy as in it actually is part of your decision-making process. So system of record just sort of really is just a database, um, with a UI on it. A system of intelligence is something that is, you know, a core part of your decision-making process and make suggestions to you. As you're trying to make a decision.

AI assessment note: “system of intelligence owns your strategy as in it actually is part of your decision-making”

Q I'm super interested, and I didn't expect to go off schedule this early, but you said there about kind of signal discovery, and I hear a lot of fans today talk about algorithmic sourcing and how they intentionally use technology and AI to discover stock or anyone else. Largely to me, it's really using LinkedIn sales navigator effectively. What do you think about this next gen of AI-driven sourcing adventure?

A I think like a lot of what people call AI, it's more about augmentation than automation, and it's a little bit of automation, and that is I think you can use a lot of data sources to help you figure out where to look and who to talk to. The programmatic analysis of early stage companies, I think, is a fool's errand, and I have seen some of the best data in the world of AngelList. I've talked to all sorts of people experimenting with that data when I was there and since then, and it doesn't really work. There's nothing to really systematize there, I guess, to use a word that's not a word, but I think it can be very, very helpful if you have many, many different data sets To sort of point you in the right direction. And yeah, using LinkedIn sales navigators fine, but at Zeta, we have about 50 something data streams that we bring in, and then we use humans to annotate them. And then we have some human screens, then we have some automatic screens. And anyways, it's a multi-step process that goes all the way through to finding companies. And, you know, we're finding a thousand companies a month through this system, and then taking a hundred of those through diligence and bringing one term sheet stage every month or two.

AI assessment note: “I think like a lot of what people call AI, it's more about augmentation”

Q Tell me, if that's the buzzword of kind of AI first, the other buzzword of the day that I love when investors say is, you know, it's a system of record because everything's a system of record today, but you've spoken before about systems of intelligence. What does that mean to you?

A I remember writing about this in, um, 2016 or something. So I guess the distinction is a system of record gets data in a structured form as it's manually entered by a Human sort of after the fact in like instruction fields, so like a CRM or an ERP or something like that. A system of intelligence gets data usually in unstructured form, and it's automatically collected by machines as data exhaust as the product is used. So the distinction being structured versus unstructured, manually entered versus automatically entered, and then post facto or after the fact as, as opposed to in real time, as it's just exhausted or comes out of the product. David Wilkinson, he's an entrepreneur recently backstarted a company called hash. He said to me, the system of record owns your assets and records what your assets are doing, but the system of intelligence owns your strategy as in it actually is part of your decision-making process. So system of record just sort of really is just a database, um, with a UI on it. A system of intelligence is something that is, you know, a core part of your decision-making process and make suggestions to you. As you're trying to make a decision.

AI assessment note: “A system of intelligence gets data usually in unstructured form, and it's automatically collected”

Q Can I ask also, you said fungibility, what does fungibility of data mean? And what does that look like in the good case?

A Ah, yeah. So it means like, can it be replaced by something very similar? And achieve essentially the same result. So, you know, you could spend all of your time trying to get this data set that's really hard to get on customer preferences, like proprietary survey data or something like that, and spend a lot of money on that. And then someone could come along and just go, well, we can also just derive those preferences based on like, um, what people are clicking on, or there's actually a free data set that's not exactly the same, but tells us the same thing about what these customers want. So fungibility is just very similar data going to get you to the same outcome as you feed it through a machine learning world.

AI assessment note: “can it be replaced by something very similar? And achieve essentially the same result.”

Q Yeah, can we unpack that? How long can you stomach low-margin AI companies until you get to that inflection point of high, and does that not just mean it's So much more capital intensive and then for you as the investor, so much more diluted on your shareholding.

A Yeah. Look, AI first companies are really expensive to build. As I sort of said before, which is you have to build the model and do the machine learning research. You have to build the product to deliver that. And then you have to often build services to label data and to integrate with customer data and that sort of thing. So they take a lot of money to build. And I think a lot of people don't really realize this. They're a little bit more expensive. However, the point to make is a lot of these initial costs to build a model, so the fundamental machine learning research, the data labeling, and whatever else, are relatively fixed, as in there's a certain critical mass of data you need to get the model to superhuman performance, and once you're there, it starts generating its own data, so you don't have to label data, and it's already at a degree of accuracy where it's providing a huge amount of value to a customer, so you don't need to spend that money anymore, so you tend to start at 40 or 50% gross margin, but then you get to 95, 98%, we're seeing in some companies we work for gross margin, and then you just stay there. As opposed to SaaS companies, we're constantly having to build another feature or deliver a service differently or whatever else, and so you sort of see those margins level out at about 70%.

AI assessment note: “you tend to start at 40 or 50% gross margin, but then you get to 95, 98%”

Q Can I ask also, you said fungibility, what does fungibility of data mean? And what does that look like in the good case?

A Ah, yeah. So it means like, can it be replaced by something very similar? And achieve essentially the same result. So, you know, you could spend all of your time trying to get this data set that's really hard to get on customer preferences, like proprietary survey data or something like that, and spend a lot of money on that. And then someone could come along and just go, well, we can also just derive those preferences based on like, um, what people are clicking on, or there's actually a free data set that's not exactly the same, but tells us the same thing about what these customers want. So fungibility is just very similar data going to get you to the same outcome as you feed it through a machine learning world.

AI assessment note: “it means like, can it be replaced by something very similar?”

Q of segment before we move into the quickfire, though, and it's, I spoke to, as we know, many of our mutual friends And it was Kyle at GC that said, you're a student of venture. And something that we actually have in common, we both love Howard Marks. I mean, what a God. Question from Kyle, why the love of Howard Marks? And what do you like specifically about him?

A Look, I guess Howard Marks is just constantly focused on what he calls the most important thing. And that is pricing a risk that others can't price. And it was sort of very intuitive or like my liking of his work is really just because it resonates with With my pathological allergy to competition. And I just think it's really important to focus on always pricing a risk that other people just seem to find too hard to price or haven't developed the tools to price or whatever else. And that's what he focuses on. So that's why it resonated with me. I think more broadly, it's really important to studying investing in all forms because it's fundamentally about that. It's about pricing risk, whether you're pricing risk in real estate and debt and whatever else. And there's so much to learn from pricing, all sorts of assets.

AI assessment note: “my liking of his work is really just because it resonates with With my pathological allergy”

Q Can I ask, how difficult is it for the incumbents to acquire AI first companies and then integrate, and then they kind of skip that challenging transition?

A That's a really good question. I think often it's actually relatively straightforward, because if you think about it, an AI first company has to build the models, the predictive models. They also have to build the product, and then they have to build a way to deliver that product. Through services or other sort of integration methods. And incumbent companies usually have the product part down pat and maybe the delivery or the services part down pat, but they don't have the model part. So it's all part of the same chain in the end. At the end of the day, you have to give the customer the full solution. And incumbent companies have at least part of that solution. They just don't have the really valuable part, which is the predictive part, the part that sort of helps you make a decision, not just calculate something for you. So we've seen incumbent companies very successfully acquire AI-first companies, if they spot the right one, it fits nicely into their product.

AI assessment note: “I think often it's actually relatively straightforward, because if you think about it”

Q Can I ask, in terms of the data side, if we go back to the early cities of evaluating AI-first companies, what open banking in the UK has meant that we've seen real democratization of financial data, and my question to you is, for me analyzing fintech companies, and broadly AI-first companies, how crucial is it for the company to have proprietary data sets?

A Yeah, you know, I don't think it's crucial to have a proprietary data set, It's just mostly the case that you need it. You need proprietary data to build a model that generates results that are far better than what someone else can generate by throwing commodity data into, you know, an openly available model. Lots of research published on archive every day, lots of open source tools and whatever else. So, you know, if the playing field is level in that regard, and then the data is the same, then you'll probably end up with very similar result. But if the playing field is level on the model side, but the data is proprietary, you might get a different and sometimes So I think data modes are indeed real, and we evaluate those data modes very carefully at the diligence stage, and we look at things like accessibility, fungibility, dimensionality, breadth, and perishability. What we mean by that is, you know, basically, how hard is it to get? Can it be substituted by similar data that's easier to get? Can you use it to feed a model and generate some sort of real outcome? Do you have enough of it to represent reality? Is it broad enough? And then is it fresh enough to be close to reality? And so we sort of look at that on a qualitative basis, but also have different ways to quantify that. And that's how we value the data.

AI assessment note: “I don't think it's crucial to have a proprietary data set”

Q Can I ask, in terms of that kind of runaway in value, you like to go in just pre that kind of value inflection point. How do you think about deciphering from signals? When is the moment just Pre-value inflection point. What does that look like in terms of model activity, flexibility?

A Yeah, absolutely. So look, no startup is perfect from day one. So what we try to do is help them figure out how to get from where they are today to the point where they're generating a lot of value for customers. And so, you know, this starts with looking at the minimum accuracy required to exceed human performance, because once you've exceeded human performance or Something like that. You're going to be able to sort of save something for a customer. And then we look at, okay, once you get to that minimum accuracy, how stable is the model is you throw different data at it. And then what's the fundamental payoff that this prediction will yield for the customer? Like, is it less errors on their production line? Is it harder inventory management? Is it something like that? And, you know, along the way, we're looking at the usual metrics and machine learning models, such as the F one score. And then once we've sort of figured out where the startup is today, um, By looking in detail at their machine learning experiments that they've run. And look, they could be running these experiments and not even have a product to deliver this yet. They could be running the machine learning model and delivering the results to customers in like an Excel sheet or PowerPoint presentation. But once we've looked at these machine learning experiments, we try to help them figure out what needs to be don…

AI assessment note: “starts with looking at the minimum accuracy required to exceed human performance”

Q Can I ask, in terms of that kind of runaway in value, you like to go in just pre that kind of value inflection point. How do you think about deciphering from signals? When is the moment just Pre-value inflection point. What does that look like in terms of model activity, flexibility?

A Yeah, absolutely. So look, no startup is perfect from day one. So what we try to do is help them figure out how to get from where they are today to the point where they're generating a lot of value for customers. And so, you know, this starts with looking at the minimum accuracy required to exceed human performance, because once you've exceeded human performance or Something like that. You're going to be able to sort of save something for a customer. And then we look at, okay, once you get to that minimum accuracy, how stable is the model is you throw different data at it. And then what's the fundamental payoff that this prediction will yield for the customer? Like, is it less errors on their production line? Is it harder inventory management? Is it something like that? And, you know, along the way, we're looking at the usual metrics and machine learning models, such as the F one score. And then once we've sort of figured out where the startup is today, um, By looking in detail at their machine learning experiments that they've run. And look, they could be running these experiments and not even have a product to deliver this yet. They could be running the machine learning model and delivering the results to customers in like an Excel sheet or PowerPoint presentation. But once we've looked at these machine learning experiments, we try to help them figure out what needs to be don…

AI assessment note: “starts with looking at the minimum accuracy required to exceed human performance”

Q Can I ask, how difficult is it for the incumbents to acquire AI first companies and then integrate, and then they kind of skip that challenging transition?

A That's a really good question. I think often it's actually relatively straightforward, because if you think about it, an AI first company has to build the models, the predictive models. They also have to build the product, and then they have to build a way to deliver that product. Through services or other sort of integration methods. And incumbent companies usually have the product part down pat and maybe the delivery or the services part down pat, but they don't have the model part. So it's all part of the same chain in the end. At the end of the day, you have to give the customer the full solution. And incumbent companies have at least part of that solution. They just don't have the really valuable part, which is the predictive part, the part that sort of helps you make a decision, not just calculate something for you. So we've seen incumbent companies very successfully acquire AI-first companies, if they spot the right one, it fits nicely into their product.

AI assessment note: “I think often it's actually relatively straightforward, because if you think about it”

Q of segment before we move into the quickfire, though, and it's, I spoke to, as we know, many of our mutual friends And it was Kyle at GC that said, you're a student of venture. And something that we actually have in common, we both love Howard Marks. I mean, what a God. Question from Kyle, why the love of Howard Marks? And what do you like specifically about him?

A Look, I guess Howard Marks is just constantly focused on what he calls the most important thing. And that is pricing a risk that others can't price. And it was sort of very intuitive or like my liking of his work is really just because it resonates with With my pathological allergy to competition. And I just think it's really important to focus on always pricing a risk that other people just seem to find too hard to price or haven't developed the tools to price or whatever else. And that's what he focuses on. So that's why it resonated with me. I think more broadly, it's really important to studying investing in all forms because it's fundamentally about that. It's about pricing risk, whether you're pricing risk in real estate and debt and whatever else. And there's so much to learn from pricing, all sorts of assets.

AI assessment note: “my liking of his work is really just because it resonates with”

Q That's super interesting, and totally screwing up my quick fire round, because I often ask The question to my mentors. Hey, I'm great at this, and I suck at all these things. What should I do? And they always say, Harry, cap tables, anyone can do them. Valuation models, basic ones, anyone can do them. Commodity skills. Only you can do X. Focus on that.

A I think that's really important early on. Like, you've got to make your mark and learn how to bring some value into the world that others can't bring, and that'll get you far very quickly. I think Once you've done that, and thinking about it more in an emotional sense rather than a functional sense, then start thinking about your weaknesses, because they're the things that are going to slow you down once you've built up the momentum. So I think it's just a sequencing thing. I think that advice that you're getting from your mentors is great, but then it's thinking about, well, once I've got that and established that and can deliver this value into the world, what else do I have to do to make sure I can broaden the audience for that value that I can deliver?

AI assessment note: “So I think it's just a sequencing thing.”

Q before we dive into the really nitty gritty on business models, I spoke to Carl at General Tappos before, and he said that you have a unique sense of business model quality, and it can articulately frame out why companies get big, and word for word, that's a great quote there. What do you think he meant here, and can you unpack your thinking on what drives business model quality?

A Yeah, I think Kyle's talking about my obsession with both compounding competitive advantage through these virtuous loops, and then also my obsession with vertical integration. So I sort of talked about that virtuous loop concept before, but I'll talk about vertical integration now. And I just believe the best products in the world are made by vertically integrated businesses. Apple vertically integrates from hardware to software. Amazon vertically integrates from warehouses to websites. And going all the way back to like Andrew Carnegie, vertically integrated from mines to his own steel mills. And for an AI first company, this means doing everything from collecting your own proprietary data, building your own models, delivering those predictions to customers through your own products, then offering your own services to integrate with a customer's business process. And this is the hard path. You know, it's easier to just buy a bit of data or use an open source model or deliver it through some other third party product or platform. Yeah, I go back to something a partner at Benchmark said once, which is what can go right, and that is what is a company doing differently that could be valuable to a lot of people, and I start actually from bottom up, so I don't do these sort of top-down tan models. When we're making an investment, I have this little tool I've built, and what it does …

AI assessment note: “I think Kyle's talking about my obsession with both compounding competitive advantage”

Q Yeah, can we unpack that? How long can you stomach low-margin AI companies until you get to that inflection point of high, and does that not just mean it's So much more capital intensive and then for you as the investor, so much more diluted on your shareholding.

A Yeah. Look, AI first companies are really expensive to build. As I sort of said before, which is you have to build the model and do the machine learning research. You have to build the product to deliver that. And then you have to often build services to label data and to integrate with customer data and that sort of thing. So they take a lot of money to build. And I think a lot of people don't really realize this. They're a little bit more expensive. However, the point to make is a lot of these initial costs to build a model, so the fundamental machine learning research, the data labeling, and whatever else, are relatively fixed, as in there's a certain critical mass of data you need to get the model to superhuman performance, and once you're there, it starts generating its own data, so you don't have to label data, and it's already at a degree of accuracy where it's providing a huge amount of value to a customer, so you don't need to spend that money anymore, so you tend to start at 40 or 50% gross margin, but then you get to 95, 98%, we're seeing in some companies we work for gross margin, and then you just stay there. As opposed to SaaS companies, we're constantly having to build another feature or deliver a service differently or whatever else, and so you sort of see those margins level out at about 70%.

AI assessment note: “you tend to start at 40 or 50% gross margin, but then you get to 95, 98%”

Q That's super interesting, and totally screwing up my quick fire round, because I often ask The question to my mentors. Hey, I'm great at this, and I suck at all these things. What should I do? And they always say, Harry, cap tables, anyone can do them. Valuation models, basic ones, anyone can do them. Commodity skills. Only you can do X. Focus on that.

A I think that's really important early on. Like, you've got to make your mark and learn how to bring some value into the world that others can't bring, and that'll get you far very quickly. I think Once you've done that, and thinking about it more in an emotional sense rather than a functional sense, then start thinking about your weaknesses, because they're the things that are going to slow you down once you've built up the momentum. So I think it's just a sequencing thing. I think that advice that you're getting from your mentors is great, but then it's thinking about, well, once I've got that and established that and can deliver this value into the world, what else do I have to do to make sure I can broaden the audience for that value that I can deliver?

AI assessment note: “So I think it's just a sequencing thing.”

Q I love that weekly optimization. And then if that's the most recent, what's your favorite optimization or life hack that's maybe had the most profound impact?

A This is a tough one because I get asked this question a lot. I sort of reject this term life hack. Because it's sort of like an oxymoron, right? Like your life is long, but a hack is short. And really the things that work over time are those that are consistently done. If you just eat less, move more, and sleep well every day, you'll be, you're doing great. So I sort of have a real problem with like picking life hack. I mean, that said, if anyone's interested, my friend Oz and I wrote this post, a hundred ways to get more done. So there are 100 hacks in that post if anyone cares to look them up. And some of them are pretty quirky, but maybe a little bit different to what you've heard. So I'd encourage people to go there if they want a little hack or two.

AI assessment note: “I sort of have a real problem with like picking life hack”

Q I love that weekly optimization. And then if that's the most recent, what's your favorite optimization or life hack that's maybe had the most profound impact?

A This is a tough one because I get asked this question a lot. I sort of reject this term life hack. Because it's sort of like an oxymoron, right? Like your life is long, but a hack is short. And really the things that work over time are those that are consistently done. If you just eat less, move more, and sleep well every day, you'll be, you're doing great. So I sort of have a real problem with like picking life hack. I mean, that said, if anyone's interested, my friend Oz and I wrote this post, a hundred ways to get more done. So there are 100 hacks in that post if anyone cares to look them up. And some of them are pretty quirky, but maybe a little bit different to what you've heard. So I'd encourage people to go there if they want a little hack or two.

AI assessment note: “I sort of reject this term life hack. Because it's sort of like an oxymoron”