The Exchanges

Q These are actually really interesting products and really interesting stories. Zing, for example, again, sorry for, like, going off schedule. I'm just too interested. It's such a crowded market. And I'm always pushed all of these different products, and you know, I'm pushed ladder to the extreme and 50 others. How do you succeed in a market where there's seemingly complete commoditization and no differentiation?

A So it comes to the way how we basically launch products and how we run the whole play. The way we operate. First, we're gonna UXR the area and see, like, if there is something there worth our attention. Then we're gonna do some fake door tests. We're gonna sense CAC willingness to pay. How the unit economics might look like at scale. If it's there, we green light. We bring a tiny team of two founders, commonly a tech founder and business or product founder, We're gonna put a lean crew behind them. We're gonna launch an MVP and build the core use case. This is where Palta growth comes in. We're gonna literally scale very fast the first use case. How we do that? We start with paid user acquisition. Commonly one channel. It's gonna be meta. We scale to three, five K daily. This is where the algorithms start to optimize, and this is where you see true Unit economics. Once it's there, and we nailed it, then we're going to expand to the next use cases. We commonly start with web onboardings, because you avoid 30% fee. There is attribution there, which makes user acquisition more efficient, but there are things that people commonly don't talk about. The nature of credit card payments drive higher retention, and on web, you can charge more. This is how consumers perceive pricing on web versus mobile. Now there is a catch around funnels. If you over invest into the funnels and start obs…

AI assessment note: “So it comes to the way how we basically launch products and how we run”

Q How do you do great product review sessions? Who's invited? We said it's weekly. How is the agenda set? What does that good look like?

A So the way I do it, and this is true across a lot of Revolut, is we will have the product owner along with the designer, the ops manager who are involved in certain projects, and then some of the engineering team. And then we'll potentially have a kind of a head of design who's more of Horizontal role who oversees some of the design aspects, same on product. Then it will be a review of metrics. So the goals that we talked about, how are we doing? Are we going the right way or not? Review of the roadmap. Are we on track to deliver what we said we'd deliver at the start of the quarter? And then what we spend most of the time doing is looking at designs. So the designer will literally present Figma and say, okay, we've got a new problem we're looking to solve. This is how we're going to solve it. And we go through screen by screen. We do that on a kind of team level once a week. And then once we're really confident in them, they get bubbled up to, we do a overall business one once a week, which is, this is the final output. This is going to be built. And that's the one then that Nick or Vlad occasionally join, and they'll then have their views at that point.

AI assessment note: “we will have the product owner along with the designer, the ops manager”

Q How do you think about navigating secondary markets? You know, when we look at, you know, there's a very strong chance that we have a trillion dollar private company in an open AI of the world. How do you think about navigating secondary markets when public market, uh, is sometimes not there?

A Well, look, I think for the very best companies, private markets behave like public markets. There's a secondary market, you can liquidate, so your shareholders can take liquidity, employees can take liquidity, you have access to credit, you can do M&A, your stocks value, you know, your valuations believed. I'm talking about the Stripes, the SpaceX's, I think OpenAI is going to get there, uh, and Tropic and so on, right? So the very bad, the Databricks is getting there slowly. So the very best, that's what happened. Then there's the very good companies, but not the, let's say the top 10 or 15, uh, uh, Private ones, not, not the magnificent, magnificent private 10, if you will. For them, uh, going public and, and getting validated actually could be more helpful. It may be that the secondary market, uh, isn't behaving as well, or they can't do M&A as effectively, or they need to access a lot more capital than they can just being in the private markets. And I think those decisions are what ultimately then push you to go public. And then there is the, as I said, this bloated, Set of companies that are good companies, that'll compound at 20, 25%, maybe, maybe forever, that have no access to public market because they're too small. They're not a billion dollar company growing 30% a year that the public markets, you know, would be excited about. And, and they're too slow, uh, for vent…

AI assessment note: “for the very best companies, private markets behave like public markets. There's a secondary market”

Q I think mothers are the most important things in the world. And Victor told me that I had to start with your ability to sell early and why your mother was nervous about it. Can we just start there?

A Absolutely. So I had a dozen different side hustles when I was growing up selling things in, in one form or another, but one of my favorites is that in eighth grade I loved selling donuts, where I saw that Safeway was selling donuts for five dollars a dozen, and so I would buy Safeway donuts, I would bike to my middle school, and sell them for two dollars each. And I saw it was working, so I wanted to scale it up. So I asked my mom to drive me to Safeway. She said that she didn't want any giveaways, so she would charge me 20 dollars to drive me in her minivan to Safeway, buy 10 dozen donuts, go to my middle school, sell them for two dollars each. I had all sorts of things happen where competition popped up selling Chuck's donuts, which if people aren't familiar has like a one dollar cost basis, and so, but they're higher quality donuts, and so I dropped my prices to one dollar For two weeks to run them out of business, because I knew that middle schoolers would care more about, uh, price as the comparative advantage. I had my, uh, principal called me into their office to try to shut down my donut stand, saying that, uh, you know, I wasn't allowed to sell food on school campus, and so I moved my donut stand 20 feet over off of school campus so that they couldn't police me, so to speak. And tying back to your question, Harry, After my mom saw all of this when I was in eighth grad…

AI assessment note: “she was very nervous that I would start selling drugs, right?”

Q Do you buy sovereignty as a reason why a model provider wins? You know, we've got Mr. Allen Europe. You have cohere in Canada. Is sovereignty a reason why a model provider wins?

A Maybe wins in a scoped part of the market. Like, I could see why, for example, uh, there would be a lot of benefits to having Mistral be an expert in European law that might have nuances from other kinds of law, and they've just invested far more in having the best Model there where it doesn't make sense to use other models, but I don't think that The largest companies per se are going to be those that invest in a specific geography. I think it's going to be a broader set of capabilities and the general purpose models that people use every day to code or to, uh, build products, uh, or do their day to day work.

AI assessment note: “Maybe wins in a scoped part of the market.”

Q Do you buy sovereignty as a reason why a model provider wins? You know, we've got Mr. Allen Europe. You have cohere in Canada. Is sovereignty a reason why a model provider wins?

A Maybe wins in a scoped part of the market. Like, I could see why, for example, uh, there would be a lot of benefits to having Mistral be an expert in European law that might have nuances from other kinds of law, and they've just invested far more in having the best Model there where it doesn't make sense to use other models, but I don't think that The largest companies per se are going to be those that invest in a specific geography. I think it's going to be a broader set of capabilities and the general purpose models that people use every day to code or to, uh, build products, uh, or do their day to day work.

AI assessment note: “Maybe wins in a scoped part of the market.”

Q Rory, should they be valued like traditional financial services businesses, or should they be valued like a new technology-first provider?

A I always think that's kind of a bullshit question, because in the end, everything should be valued on a basis of risk-adjusted free cash flows. So let's start with that. But what you're really saying is what's the best, in the absence of free cash flows, what you're really saying is what's the best rule of thumb to value those things? And I think the truth is they have the margin profile and core dynamics of a financial services company, but they have the growth rate of a software company. So you, you have to adjust and come somewhere in the middle with the expectation that this is the key sentence. Once the growth rate slows, they will be valued. Just like if they're growing the same as Amex, they will be valued the same as Amex. The growth is what's saving them.

AI assessment note: “come somewhere in the middle with the expectation that this is the key sentence”

Q phase of like, actually, we can get glitzy people who don't really relate so much to our core, which is startups Venture, growth, and we had kind of famous people on, and the North Star misled us, and it was the wrong North Star. If I say to you, when do you think you had the wrong North Star or wrong goal structure, and what did you learn from that?

A I'll be honest, I think Mark is extraordinarily consistent. The biggest place, which I talk about in the book, is before I joined, it felt like the North Star wasn't clear within the leadership of the company. And that was the famous time when Mark nearly sold the company to Yahoo because it felt like the North star to a lot of the executives was it's a startup. We need to make it big and exit and become millionaires. And for Mark, obviously the North star was connect the world online, do what he felt would make the world, you know, a better place. And that was at odds. And I think that led to a lot of the executive team saying, I want to sell to Yahoo and Mark saying, I want to stay independent. And we turned over the entire executive team. And then out of that fire, which I think really forged the company, we came to much more clarity on what the North Star was, so everyone knew what they were signing up for.

AI assessment note: “out of that fire, which I think really forged the company, we came to much more clarity”

Q We saw Monday got hit because a lot of that growth relies on SEO. You're seeing SEO really reduces the customer acquisition channel for a lot of companies. I know you've only got 10%, which is paid Cliff, but given 10% being paid, are you moving forward with the assumption that SEO is going to be a much smaller part of your customer acquisition funnel moving forwards?

A I think SEO is about 50, 15% of the 90% organic to date, so it used to be our number one channel, but now our, uh, our sort of like user flywheel, word of mouth, and people sharing designs are our biggest. No, I mean, we, we're seeing a lot of, uh, some of it, SEO is growing for us, uh, but also we're the number one productivity app on ChatGPT, and we're the fifth Highest domain that ChatGPT refers to. So out of all the websites, it's like Google, Meta, we're number five in regards to, essentially it's SEO for LLM. So we're, because we've invested a lot over the years, they're obviously taking a lot of the same signals that Google's taking, and anything we're losing on the SEO front is translating to LLM SEO, which is a huge tailwind for us. So to give you an example, a year and a half ago, .2%, I think it was, of the images uploaded to Canva were from ChatGPT. That's now over five percent. So the fuel and content being generated in these LLMs are being propagated into Canva for that editing, for how they're using it in designs, and for that collaboration, storage, deployment, that whole visual communication workflow that we're, we excel at.

AI assessment note: “anything we're losing on the SEO front is translating to LLM SEO”

Q We saw Monday got hit because a lot of that growth relies on SEO. You're seeing SEO really reduces the customer acquisition channel for a lot of companies. I know you've only got 10%, which is paid Cliff, but given 10% being paid, are you moving forward with the assumption that SEO is going to be a much smaller part of your customer acquisition funnel moving forwards?

A I think SEO is about 50, 15% of the 90% organic to date, so it used to be our number one channel, but now our, uh, our sort of like user flywheel, word of mouth, and people sharing designs are our biggest. No, I mean, we, we're seeing a lot of, uh, some of it, SEO is growing for us, uh, but also we're the number one productivity app on ChatGPT, and we're the fifth Highest domain that ChatGPT refers to. So out of all the websites, it's like Google, Meta, we're number five in regards to, essentially it's SEO for LLM. So we're, because we've invested a lot over the years, they're obviously taking a lot of the same signals that Google's taking, and anything we're losing on the SEO front is translating to LLM SEO, which is a huge tailwind for us. So to give you an example, a year and a half ago, .2%, I think it was, of the images uploaded to Canva were from ChatGPT. That's now over five percent. So the fuel and content being generated in these LLMs are being propagated into Canva for that editing, for how they're using it in designs, and for that collaboration, storage, deployment, that whole visual communication workflow that we're, we excel at.

AI assessment note: “anything we're losing on the SEO front is translating to LLM SEO”

Q Now, I want to start a little bit before Tezzy, and I want to start With, you've been at some of the most incredible organizations from your Instacart to your thumbtacks. If we start breaking them apart, actually, with Instacart, when we think about big lessons learned, what was your biggest product lesson learned from the days at Instacart?

A Biggest product lesson learned from Instacart was as much as A-B testing and like high velocity optimizations feel like progress, and to some extent they will be, When you're at a business of that scale, uh, where we're talking like 30 to forty billion in gross transaction volume, the only thing that really moves the needle is, like, new capabilities. So we would try and test a lot throughout the customer experience to optimize conversion, make things easier, make search better, the results more relevant, and it would deliver wins, but the sleeper, huge win, Was when we launched Snap EBT, which is like food stamps, um, to be able to pay with those on Instacart. It became a billion dollar business line in less than a year and then grew up to be four billion. So it changed how we plan and how we think about placing bets from what are the quick win optimizations on the current experience to What do we need to do to unlock the next billion to five billion?

AI assessment note: “Biggest product lesson learned from Instacart was as much as A-B testing”

Q What's the biggest non-consensus product decision you made that didn't work, and what did you learn from it?

A I think the biggest non-consensus product decision that I made that didn't work is how we built TASY, like, the V-one. We took a breath-first approach. The consensus view is, you know, start narrow, Point solution, land and expand. It's the wedge, that sort of thing. What we set out to build is the equivalent of a full life cycle recruiter. And so that necessitated a ton of surface area across the entire recruiting journey from opening an offer all the way up and opening a role all the way up to the point where you're ready to extend an offer. Um, that was the like product scope. So very broad. What was right about that is it's a very differentiated positioning and has helped us get a ton of meetings, um, and customer interest because companies are tired of point solutions and they don't want to integrate another one. What we've struggled with is it's a lot of surface area and it's hard to get, you know, 12 things to a sufficient quality level. The thing that I learned from it is You can't do an MVP for a breadth first approach. And I think Ben, the CEO at equals, uh, has a post on this where it's basically MVP versus full build. And so they have a spreadsheet product and you had to take like a full build approach. We took an MVP approach to a full build breadth product when really we should have Delayed going to market by another six months to get it all to like a higher quali…

AI assessment note: “I think the biggest non-consensus product decision that I made that didn't work is”

Q In terms of everyone being in every meeting, it seems like we've seen this kind of intensification of work culture over the last six months to a year with the rise of AI, bluntly. Have you seen work cultures change when it comes to intensity over the last five years? If so, how?

A I haven't found that work cultures have changed In my observation, based on AI in terms of their intensity, I think of Instacart as effectively pre AI and super intense. A good week was. 60, 70 hours. A bad week is a hundred to a 110. And maybe this is just that like VP level or something like that. But no, at 11 PM, 12 PM, I could ping anyone on Slack and they were already online. Like, and they're immediately responsive. And so it was like an around the clock culture. And I think we're still pretty intense at Daisy. We're not that extreme, but maybe what's changed is I feel like the glorification of like nine, nine, six by certain companies has been like coincidentally timed with also the AI wave. And maybe that's what makes it feel More intense.

AI assessment note: “I haven't found that work cultures have changed In my observation, based on AI”

Q Now, I want to start a little bit before Tezzy, and I want to start With, you've been at some of the most incredible organizations from your Instacart to your thumbtacks. If we start breaking them apart, actually, with Instacart, when we think about big lessons learned, what was your biggest product lesson learned from the days at Instacart?

A Biggest product lesson learned from Instacart was as much as A-B testing and like high velocity optimizations feel like progress, and to some extent they will be, When you're at a business of that scale, uh, where we're talking like 30 to forty billion in gross transaction volume, the only thing that really moves the needle is, like, new capabilities. So we would try and test a lot throughout the customer experience to optimize conversion, make things easier, make search better, the results more relevant, and it would deliver wins, but the sleeper, huge win, Was when we launched Snap EBT, which is like food stamps, um, to be able to pay with those on Instacart. It became a billion dollar business line in less than a year and then grew up to be four billion. So it changed how we plan and how we think about placing bets from what are the quick win optimizations on the current experience to What do we need to do to unlock the next billion to five billion?

AI assessment note: “Biggest product lesson learned from Instacart was as much as A-B testing”

Q Did they have the idea for Lagor or Lea at the time then?

A The idea for the business was not a product. It was a problem space. It was the fact that AI and legal is going to be a thing. And they had been playing around with the early BERT models that came up from Google, and also a, a version of them called SWE BERT, They were so incredibly bad, frankly. I mean, you couldn't really solve anything back then, and so they had built their own document management system, they had built their own editor, they had built, like, a legal research platform, they had all this code, and when I came in, I sort of said, let's just delete all of it, and let's build on GPT, and let's, like, build a version of it that, at the end of the day, lawyers are benefiting from.

AI assessment note: “The idea for the business was not a product. It was a problem space.”

Q What did you learn about pricing from RS bars?

A In American market, at least in the bar category, there's a real threshold Getting to a 99 cent or a dollar 49 price point really unlocks the mass market. So like a lot of Americans are not willing to pay anywhere north of a dollar 99 for a bar. The real, real massive market is in the 99 cent and below. Um, you think Nature Valley, like the big, big, big brands all have an offering that's pretty low. And another interesting thing about price is with like kids products. So in the survey data, If you asked a mom just pricing questions, it would show up as like, yeah, I'm willing to pay for my kid. But in the actual data, they are not. And I thought that was really interesting because no one wants to admit that they're, they don't want to spend too much money on their kids. Right. But in the, in the actual behavior, they absolutely do not want to spend a dollar 50 for a bar for their kid. They want to spend 49 cents. So that, that's one of the things with like, partially I don't like survey data because you can get bad data.

AI assessment note: “Getting to a 99 cent or a dollar 49 price point really unlocks the mass market.”

Q What made you say that? Sorry, it's interesting because I see it everywhere now.

A The TAM in food and beverage is pretty clearly defined. One 61, I was like, we could probably get to 300 or two hundred fifty million in sales, but I didn't see a very clear path to like five hundred million. The product portfolio that we could do was defined by quantifiable whole food ingredients. So our design, that famous design of our X-Bar, really was a constraint on where we could innovate. Because you had to formulate stuff that could be communicated on the front of PAC. That, to me, lowered the TAM. So as a CEO, I'm responsible for the vision. I didn't see a path to like five hundred million or a billion in revenue. So therefore, it sort of made sense to get it to the big guys.

AI assessment note: “I didn't see a path to like five hundred million or a billion in revenue.”

Q place also for Dylan, which is obviously kind of this kind of two billion dollar moonshot, like grant kind of Elon Musk style. And Brian, we were saying that we don't talk enough about CEO comp. I wanted to, why don't we start with you, Brian, on this, given you're the best person here to speak about it. How do you think about this and how do you analyze that?

A Now, I think CEO comp is pretty broken, uh, at the moment, and there's two things that I think are pretty broken about it. The first is just everyone really relies heavily on RSUs. And when I grew up in the industry, I hate to be like, I'd be like, I'm back in the old days. Um, it was mostly ISOs. It was optioned until 2006 and regulations changed and the expensing of that changed. So the world kind of moved to RSUs. It just creates sort of a risk averse behavior in the CEO. Like it's basically cash comp goes up and down a little bit, let's say, but an ISO you're swinging for the fences. Like you've got a strong incentive to swing. And so it's really had a dampening effect on the risk-seeking behavior of a CEO that I think more companies should want, and it's kind of pervasive across the industry. So I don't like this RSU comp thing. That's the first problem I see with all this stuff. The other problem with comp is almost every company looks to CO comp, and the way it works behind the scenes is, you know, HubSpot's got a compensation committee. Everyone's got a compensation committee. And HubSpot wants to pay the CEO, let's say, at the 75th percentile of what her peers make. And so we look at 20 different peers of similar sized companies, the last thing, blah, blah, blah, blah. And we peg her at that 75th percentile, which in her case is, you know, it's twenty million bucks, a …

AI assessment note: “I think CEO comp is pretty broken, uh, at the moment”

Q I mean, it's a pretty smooth transition from me. Give me credit. I do want to start with just laying the kind of landscape framework for how CMU operates is structured today. If you think about like a construction, that's easy for everyone to understand. How does that portfolio construction look like for CMU today from a top down?

A From a, from a top down perspective, we manage four billion on behalf of the university. And so starting at the highest level, we think of equity and fixed income as, as kind of the two parts of the endowment. 85% of the endowment is equity. 15% is fixed income. That is our allocation. And, and we manage to that on a, on a quarterly basis. One step below that then is the sub-asset classes within that. And so our target is 50% of the portfolio Is in privates. That's a mixture of venture capital, private equity, real estate, natural resources, private credit. Um, the other 50% is hedge funds and liquids, which the liquids are public equities and fixed income. Uh, and so that is the, the top down management of the portfolio within that private bucket. We have, um, free reign into the underlying allocations within that. So we call it a best athlete portfolio. So how do we find the best risk-adjusted returns globally? Across all of those different private asset classes so we can have the best risk-adjusted return for the portfolio.

AI assessment note: “From a top down perspective, we manage four billion on behalf of the university.”

Q I mean, it's a pretty smooth transition from me. Give me credit. I do want to start with just laying the kind of landscape framework for how CMU operates is structured today. If you think about like a construction, that's easy for everyone to understand. How does that portfolio construction look like for CMU today from a top down?

A From a, from a top down perspective, we manage four billion on behalf of the university. And so starting at the highest level, we think of equity and fixed income as, as kind of the two parts of the endowment. 85% of the endowment is equity. 15% is fixed income. That is our allocation. And, and we manage to that on a, on a quarterly basis. One step below that then is the sub-asset classes within that. And so our target is 50% of the portfolio Is in privates. That's a mixture of venture capital, private equity, real estate, natural resources, private credit. Um, the other 50% is hedge funds and liquids, which the liquids are public equities and fixed income. Uh, and so that is the, the top down management of the portfolio within that private bucket. We have, um, free reign into the underlying allocations within that. So we call it a best athlete portfolio. So how do we find the best risk-adjusted returns globally? Across all of those different private asset classes so we can have the best risk-adjusted return for the portfolio.

AI assessment note: “85% of the endowment is equity. 15% is fixed income. That is our allocation.”

Q When we think about though, like, absolutely not, you're not getting paid for the risks that you're taking. And then a statement that you said to me before, which is, 90% of LPs shouldn't be investing in venture. Who should and who shouldn't then?

A That's, that's the million dollar question. I think you need to have a frank conversation with yourself. Say you're, you're a new endowment or a new family office and you say, we want technology exposure. Right? You've got two options. You could do that through the public markets. You could do that through the private markets. And so my, my question to any new allocator or an investor is, do you think you're going to have access to top decile managers? Cause at, at that point, top decile, you are achieving returns above the PME consistently, but below that, even top quartile, you're not. And so that is the question. And, and I think most people clearly buy the data. Especially as a new entrant to a mature asset class are not going to have top decile access.

AI assessment note: “do you think you're going to have access to top decile managers?”

Q give a shit about it. It's like a coffee. They put three and a half billion dollars into Ray-Ban at the same time, and no one paid any attention. My point being, we have these kind of opposing worlds of liquidity, starvation, or drought, and then the glut of these public markets players who just are playing with market caps that are two trillion. How do you think about that?

A I think, uh, if Wiz gets approved, I think Every other large Mac seven company is going to see a green light in regards to making big, splashy acquisitions again, which is a good thing. Um, I mean, you look at Google, Microsoft, Amazon, and Meta combined, I mean, they're doing six hundred billion of operating cash flow, just cash coming off the company every single year, and I think they would much rather make very strategic acquisitions than buy back 50 basis points of the company, right? Um, I, I think that the big worry that I think those companies see today from our purview is that the AI landscape is changing so rapidly that The, the, the 12 month time period it could take to go through a review to get that acquisition done, that company could be obsolete in 12 months.

AI assessment note: “Every other large Mac seven company is going to see a green light”

Q building or schmoozing, but I, you know, there's a lot of the best founders, you know, they just want the same guys that are in Figma, the same funds that are in Figma. They do. Right. And And 20 VCs above the line, but it's not, it doesn't have the storied history of, uh, of benchmark. What do you think, Harry? That's why I'm not sure I would leave benchmark.

A I think people greatly over-exaggerate the impact that this has on Benchmark, and I don't mean that rudely to Victor, but I don't think any firm has played AI in the last 18 months as well as Benchmark have done, despite the team churn that they've had. Let's just go through this. McCaw over a hundred million in ARR. Hey Jen hitting a hundred million in ARR. Fireworks a hundred and forty million in ARR. Sierra with Brett Taylor over a hundred million in ARR. Manus AI, the best Chinese AI team there is. Lagoor are the best European AI team there is in many respects, all with double digits ownership in one single fund. Hell, how do I be an LP in that fund?

AI assessment note: “I think people greatly over-exaggerate the impact that this has on Benchmark”

Q When we look at it there, you started with, like, young first-time traders, added options in crypto, attracted maybe higher risk profiles, Then speculative traders, high volume spec traders, Robinhood Gold and Cash Management led to more. I guess my question is, how do you think about the different customer profile types you serve today, and how's that changed over time in your thinking, given the massive product expansion recently?

A I think that, uh, there's been one big change, which is recently we started thinking about not just the millennial and Gen Z customer, but also the active trader, like the digitally native active trader. And that, that's a very different type of profile. I mean, these are folks that you can almost think of them. They have some similarity with gamers. They've got like the screens, the headphones, the fancy keyboards, and they're just locked in. They're locked into their, to their system. And they have very specific needs. They're like, they need to be at the frontier of technology and innovation. They, they care about speed. They care about latency. And we really had to get into that customer's head to design products like our futures ladder, like Robinhood legend.

AI assessment note: “recently we started thinking about not just the millennial and Gen Z customer, but also”

Q considered the mag seven, what we're seeing is this kind of concentration of value in public markets. And I really wanted this to be a free flowing discussion just cause I bluntly respect your brain so much, but you see this concentration of value like we've almost never seen before. Does that worry you? And what does that mean for the future value dispersion of public markets in your mind?

A Oh, I think it's, I think it's hugely worrying because you have essentially, 34% of the S&P is represented by seven companies, and 50% of the equity value of the world, and 70% of the enterprise value, if included the debt, is wrapped up in the U.S. economy, which has seven companies, and if any of those seven companies sneeze, the whole economy, and potentially the whole global economy catches a cold. And in addition, it's very hard for small and medium-sized businesses To attract the kind of human and financial capital to be competitors. So when you have the most valuable parts of our economy, e-commerce, one company does between 50 and 70% of all e-commerce, depending on how you categorize e-commerce. When you have social media as being kind of the most powerful thing to happen in traditional, or, you know, what we refer to as media, and one company has 75% share of social. When you have search, 90%, 89%, and then you have AI, the biggest revolution of the last, you know, several decades, 90% of the processing has run through one company's GPUs, and 85% of the queries have gone through one company. I think a concentration of power and shareholder value makes a company or an economy less robust, less diverse. I think it's, it's, I think it's a, I think it's, yeah, I think it's bad.

AI assessment note: “Oh, I think it's, I think it's hugely worrying because”

Q What do you think is the core way that, you know, that they have weaponized government to work for you, and what would you say is the best way to reverse that?

A Well, the tax code, it's the boring shit that moves the needle. So I make most of my money buying and selling stocks and companies, and we've decided that people who can do that should pay a lower tax rate, and the people that can do that are old and rich. Old people make, old wealthy people make their money from buying and selling assets. Young people make their money from salary and working. Why am I paying 22%, and the top income earner on current income is paying 37%? The second biggest tax deduction is mortgage interest rate. Old people own homes, young people rent. Why is, why is the primary cost, the biggest cost for old people tax deductible, but the biggest cost for young people is not. So even the two biggest tax deductions are just to transfer wealth from the young to the old. So I think it starts with tax policy. Um, essentially, um, social security tax. I pay, I make a lot of money. I pay every year 9000 dollars in Social Security, because it taps out at six percent up until 160 grand. An analyst working for me that makes 160 pound also pays nine grand. So if you look at the tax code in the U.S., and I apologize for deferring to the U.S., but I just know more about it. The tax code's gone from 400 pages to 4000, and those incremental 3600 pages are basically full of loopholes for corporations and the wealthy to get wealthier and wealthier. So some solutions. I thin…

AI assessment note: “Well, the tax code, it's the boring shit that moves the needle.”

Q a PLG motion as efficient as Slack or Notion, do you choose the verticals that are the biggest in the PLG side of the business and go after them? And so what I mean by that is you look at Notion or Slack and you go, okay, financial services is a big component of our PLG users that also has budget. Let's go after them. Is that how it works?

A Not quite. And this is the common trap that PLG companies fall into, because they'll typically look at their data and look at product usage signals, and they'll use that product signal to figure out where should they focus. And so at Slack, we saw that our biggest workspaces were actually communities. They were professional networks that were using Slack to trade best practices. There were student groups that were using Slack to work on projects. And the sales team was tempted to sell them because our sales team was using product-led growth signals and seeing what are the biggest workspaces because lots of active members translate translated to lots of licenses. And it was very important that we actually remained focused on the biggest customers that had the most value from the product. And so that wasn't necessarily communities because when you think of communities, It's a very different post sales motion. The values that you're seeking are different than if you were selling to a company. It would have led your product roadmap in a very different direction because you would have had to invest in features like inappropriate message flagging within a community. Those groups also don't have a lot of money. And so while they might have had a lot of licenses, there's not a lot of budget to unlock there. And so we made a very deliberate decision to not focus on communities.

AI assessment note: “Not quite. And this is the common trap that PLG companies fall into”

Q a PLG motion as efficient as Slack or Notion, do you choose the verticals that are the biggest in the PLG side of the business and go after them? And so what I mean by that is you look at Notion or Slack and you go, okay, financial services is a big component of our PLG users that also has budget. Let's go after them. Is that how it works?

A Not quite. And this is the common trap that PLG companies fall into, because they'll typically look at their data and look at product usage signals, and they'll use that product signal to figure out where should they focus. And so at Slack, we saw that our biggest workspaces were actually communities. They were professional networks that were using Slack to trade best practices. There were student groups that were using Slack to work on projects. And the sales team was tempted to sell them because our sales team was using product-led growth signals and seeing what are the biggest workspaces because lots of active members translate translated to lots of licenses. And it was very important that we actually remained focused on the biggest customers that had the most value from the product. And so that wasn't necessarily communities because when you think of communities, It's a very different post sales motion. The values that you're seeking are different than if you were selling to a company. It would have led your product roadmap in a very different direction because you would have had to invest in features like inappropriate message flagging within a community. Those groups also don't have a lot of money. And so while they might have had a lot of licenses, there's not a lot of budget to unlock there. And so we made a very deliberate decision to not focus on communities.

AI assessment note: “Not quite. And this is the common trap that PLG companies fall into”

Q How do you know if someone's a good problem solver? Without being direct to me like, Kim, are you a good problem solver? Yes, I'm very good. Very good at problems.

A This is where past behavior is the best predictor of future behavior. So asking for stories and examples of how they've solved problems in the past. Questions that I really like are, tell me about a time you weren't set up for success. Because then you can also start to hear, not only did they solve that problem, But did they take accountability, or were they a victim in that scenario? Another line of question that I really like is, tell me about a really big challenge that you had to overcome. Great. Tell me another challenge that was really hard, that you had to figure out the right path forward. And you'll start to see, like, is there a pattern of them taking an ambiguous problem and just finding a way to persevere and get to a great outcome?

AI assessment note: “asking for stories and examples of how they've solved problems in the past.”

Q requires storytelling. You're very good at storytelling. And we chatted before, and you said to me, and a lot of people throw around storytelling as this kind of, you know, oh, it's so important. And, but there's not really much substance to it when you actually dig deeper. You said to me before, there's a science to storytelling that all businesses need to understand. What do you think that is?

A Yeah, I think there's a science and then there's a fundamental principle. So the science is really simple, which is that our brain is constantly scanning for information that helps us survive or thrive. And so your brand has to very clearly communicate how you will do that for that customer. That can take many forms, right? Saving money is a form of survival to our brain, saving time, increasing your status so that you can be part of a tribe, whatever it is, but you've got to very clearly communicate that. And if you confuse You lose. The other thing our brain is doing is constantly trying to conserve calories. So if your message is not super clear, if the brain has to work really hard to figure out why, like how you're actually contributing to that, it will just tune you out. So that's, that's the very basic science of it. And I can talk through the principles of how you do that if it's helpful.

AI assessment note: “the science is really simple, which is that our brain is constantly scanning”