YouSaid · the spoken record

Ash Fontana

lines on the record: 81
first: 2018-06-05
most recent: 2018-06-05
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Yeah, well, it's a starting point, right? And it's all relative and we keep a log like when we look at companies and research companies of how they sort of rank in each dimension. The really tough work is how do you assess how predictive that data is of something and how does that generate a competitive advantage. So this gets to how do you assess the models and this is tough because it basically requires you to go deep into the machine learning experiments these companies are running or they're modeling experiments they're running. So we think about a few things. So four things. Firstly is the model that you've built accurate at all. So have you used techniques that are able to predict something that at least has a path to getting better than a human is a typical bar you have not in every industry you're not looking to fully automate stuff in all industries but”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a really good question. It depends on how you're collecting it. If you have a direct pipe into something that's super perishable, that's really valuable because if anyone else even gets access to that for a second, that doesn't matter. So if you have exclusive access to a highly perishable dataset, that can be really useful. But if you have one-time access to it, you better hope it's not very perishable. So it just depends on what it is. So that's in terms of assessing the value of data sets. That's awesome.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, exactly. How many variables? And then there's breadth. Is it across a large swath of a population sticking with the consumer example or a segment of that population? And therefore, is it like representative of something that you're going to predict across a population or not? And the final one is perishability. So how quickly will it go out of date? So financial data goes out of date really quickly. Contact data doesn't really go out of date, like personal contact data, but data on the size of companies goes out of date really quickly.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Sticking with the consumer example, if you have the dimensions of age, gender, household income, whatever else, whatever else, if you have 10 of those things, you can predict way more than if you just have age. So how dimensional is the data?”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely not. So we have some sort of ways to analyze the value of a data set, like prospectively, like if I'm going to buy this thing, what should I look at? But then also once you've built these models, how do you know they're valuable as well? So on the data set side, the sort of five basic things we look at. The first is, is it unique? Was it hard to get? Can anyone just go and get it as well? Download it from the SAC or whatever. The second thing is fungibility. Okay, even if it was hard to get, could you effectively have the same sort of thing that can predict the same sort of thing with another data set that looks very similar? So like one set of credit card data on one set of consumers versus another set of consumers. Yeah, technically they're different data sets. They might have been hard to get, but they can sort of predict the same things. The third is dimensionality. So how many different points do you have to correlate? Because machine learning is really a correlative system. And so if you have different dimensions, like if you...”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“How you collect data, how you negotiate contracts, how you build technology, what people you hire, how you go to market, what your positioning is in the market, how you sell products, how you service products as well, and all sorts of things. So it changes everything, but AI first companies put those models in place from day one. And it's hard to go back if you don't do that.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Look, we're not going to see your data, we're not going to keep your data, we're not going to touch your data. Now, fast forward 20 years when data is so valuable and you need it to develop these predictive models, they're sort of struggling and they have to go back to their customers and renegotiate stuff and whatever else. And now they've done a good job at that. But that was quite a transition for them. An AI first company, on the other hand, from day one, just doesn't work with customers that aren't willing to be part of the data co op or the data coalition, so to speak. And so the first is interface, the second is being really strategic about that. And the third is really marketing that, telling customers from day one, we're all in this together, we're all trying to improve all of our own businesses, and if you can benefit from everyone else's data. And you say that's like a very positive way to position the company from day one. If you don't do that, like customers get scared and whatever else. But they're just three examples. It really changes how you operate every single part of your business.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean one of them is the product design which is what you've already brought up and that is from day one you've got to think about what data you can collect on a consistent basis from your customers through the interface so how do we design this thing so that it is not just click this to get that It's click this to give us data so that next time we present you with something to click it's less likely you're going to have to click it it's more automated so one is product design two is being really strategic about data rights from day one so this is why a lot of these incumbents are struggling to move into the AI era in a sales force is a phenomenal company however when Salesforce went to market if people can remember which most won't they were trying to convince people to put data into the cloud and that was really scary for a lot of customers and so one way they got customers comfortable with that was they said”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“We know that we've seen that before. And the end result of this is in real time you have a complete picture of what's on the shelf. And so the business value of that, of course, is you can quickly restock shells.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean another one is focal systems again a company we've worked with because I'm bringing these companies up because they're the ones I know yeah of course they have figured out a way to do real-time inventory in grocery stores now it seems a bit funny to think that grocery stores don't really know what's on their shelves but they don't most of the time they only half know what's on their shelves and so what Focal do is they have cameras that are just sitting on shopping carts and they're constantly being pushed around the store and whatever else and in the first pass the first time a shopping cart goes through the store There's a human labeling all the products but then in the second pass they just look for the differences is the product there has it moved is the price change whatever and then they have a system that has learnt from the first pass of all those humans labelling all those products what's meant to be there and what's changed and then if it's changed what is it has there been a product that we've already seen been put there oh cool we've already got that label so”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“When you go through it the first time, you'll make a heap of corrections. But next time you're doing some work for Dow Chemical, it's going to be a lot easier because you've seen those words before and you've told it what those words are and it's incorporated into the model. And then next time someone else at your organization does it and sees a word that you don't know but they know, then that'll be incorporated into your model next time. And so it builds all this fractal.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“chemical manual, and there are all these words that the translation system has never seen before, like all these weird chemical names and product names and whatnot.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so a fairly ambitious company that we partner with is called Lilt. And they're taking on language translation, enterprise grade language translation, which seems a little bit crazy because Google, Microsoft, Amazon, they've all got big dogs in the fight. Seems pretty good, yeah. Yeah, and it is pretty good. However, what Lilt invented, what one of the founders invented during his PhD, is this exact system, which is once you give someone a translation, they're able to correct it, and then the whole translation model recomputes itself. So, for example, on Google, if you see a translation, it's not very good. One, it's not very easy to figure out how to correct it. And when you do correct it, it's not actually reincorporated back. So next time if you put the same words in, it'll spit out the same result. It's a very statistical one-to-one sort of system. So let's sort of invented this interface that allows the model to get better and better over time. And so when you think about this in the corporate context, if you're like translating a dead”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“And it comes back to questions of product design. How do you design a product where customers every time you give them a prediction have the opportunity to correct it? And how much data do you collect from them when they correct it? Do they just say it's wrong or it's wrong because the edges of this image actually indicate that this is something else or this is the wrong color or this is the wrong ingredient in this product or whatever else? So it's very important to collect data from your customers so that you're constantly improving your data set and also improving the value of the model. And almost all the companies we partner with have really thought a lot about the interface so that it sort of interactive machine learning. The human is really in the loop. And that's hugely important. It's also important so that people feel like they're part of the system. Because if this AI is just able to run by itself, they are less likely to trust it.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Crucial, absolutely crucial. Because if you think about it, if you just have a data set and you build a model, a single data set and a single model that predicts something, it's probably not going to be a sustainable form of competitive advantage because the data you used might be perishable, as in it might go out of date, or it might be fungible, as in there might be another data set that's slightly different, but actually is able to predict the same thing. Or it might have low dimensionality. So when you try to predict something else, there's nothing else to correlate it to, or nothing else to correlate new variables to, so you can't predict something else. So if you just have one data set, one model, someone's eventually going to come along and beat it. And so what we look for is what we call a virtuous loop. And so it goes beyond the concept of a moat, which is like, I've got valuable data and a valuable model. It goes beyond it because it's constantly self-reinforcing loop. And this goes to what you were asking about, which is how important is it to have that feedback loop? It's incredibly important.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, similar, you mean in the crypto sense, not in the 40 year old database company sense. Yeah, correct. Yes, it's very much like that. And so your crypto token accrues value by contributing data to that system. Or you can do it the other way around where the data is held by the company. So for example, this company I've supported since the start that's called Numeri, they have a bunch of financial data and then people build models on that data and they contribute their predictions. And if those predictions are right, they trade on them, they make money, and then that accrues back to the value of the token which you get by submitting good predictions.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Sitting in a filing cabinet data sets are about company databases or people databases or whatever. They get data sets from releasing a consumer app that ostensibly does one thing but is actually collecting data for another thing. So ostensibly is like cool, find my friends app or weather app or whatever, but it's really collecting heap allocation data that's selling the ads ad companies. Companies that are building these micro labor platforms so you can do a little task like label something in an image is this cat is this not a cat a car not a car whatever and they build up a set of label images and then use that to solve another problem like what product is in this picture and build a retail inventory system that's more accurate than the current ones which is low bar there are companies that are building I mean on the really cutting edge token based incentive systems to contribute your data to a huge data pool and whenever companies buy data from that pool your token accrues value”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“These companies really resemble a product company but then have a few extra layers. So they resemble like any other technology company in that you have to have a well-designed product and you have to have a way to distribute that product cheaply like through the cloud or whatever and you have to have billing and you have to have all these things and you have to have sales and marketing. However, there are layers underneath as you put it and really that's where you start when you start building one of these companies. You start with the data set. You start by finding a dataset that is predictive of a problem or a decision. Problem people are trying to solve, decision people are trying to make every single day and you want to try to fully automate that or at least partially automate that or augment a human in making that decision. And so it always starts with finding a data set and you can do that in all sorts of weird and wonderful ways. Let's talk about that. Sure. We see companies do all sorts of things. So they will go into local councils or municipalities and get data sets that aren't even online. So they're either sitting on someone's computer or they're”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“The fourth phase, which is really exciting, and where I spend most of my time, is in this whole area where AI is able to do things that we just can't do as humans. Make decisions we can't make. And so these are particularly decisions in complex systems like energy grids or healthcare systems or biological systems. And at this point in time, the AI technology is not necessarily good enough to rely on to run a power grid. And we don't really trust it for that reason. And so the adoption of those technologies is sort of still probably a few years off. And so in a sense, we're spending a little bit less time there than the sort of the third phase, but in a sense we're starting to spend more time there because we're looking through five years up.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Recommending stuff to them. The next phase is what we call sort of AI enabled, which is completely changing a workflow in an industry to the point where the AI is essentially doing it for you with some supervision. So an example of this is the car insurance example I brought up before, where the AI is actually making the repair replace decision.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Apply AI to their products. And so they did that like more than 10 years ago. The next phase was bringing AI into the workplace and people were very skeptical of this and it started around 2010, 2012, and that was bringing AI into applications like a CRM for a salesperson in a way where it gives you a recommendation and if that recommendation is wrong, it's not very costly to you. But if it's right, it could be really cool. So a company that did this well was inside sales and we were first institutional investors there, my partner was. And what they do is they tell you, they look at your CRM and they look at all these signals and they have hundreds of billions of data points about when people answer the phone and what sports team did well in that city last night and what the weather is today. And they figure out, okay, who should you call next? Who's most likely to say yes to a deal at 1042 a.m. on Tuesday? And the cost of getting that wrong is not very high. You make it shouted out on the phone and you hang up and you move on. The cost of getting it right is cool. You get a new deal. So I think people are pretty comfortable with AI.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I guess the context for thinking about the stages of adoption of AI technology is our job is to time markets. If we fund a solution that the market's not ready for, it's not going to be a successful company. So we think a lot about how ready is the world to adopt machine learning-based AI-based technologies. And we thought, well, actually, the world adopted this stuff a long time ago. So there are four phases in our mind. The first was AI in consumer. And really, it's a risk paradigm all along these four stages. So the risk of Netflix giving you a recommendation is really low. There is in the risk of getting that wrong is really low. Like it might present something that your kids probably shouldn't watch. But on the upside, it might present you this amazing documentary that changes your life of or save you a lot of time from calling around all your friends and asking what to watch. So whether it's a Google search result, an Amazon product recommendation or a Netflix recommendation, that was a very low convex payoff for those companies to”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“As an LP, I mean, I think there are two things to consider. One is the obvious, which is are they good money managers? And I say money managers rather than good pickers or whatever else, because I think that's a clear differentiator in the market today. There are a lot of people starting funds that haven't managed money before. And that is a set of experiences that you need to make sure you don't lose money. It's somewhat obvious to someone coming from outside the VC industry, but it's not particularly obvious to some that are in the industry today. And I think the second thing is, from an LP perspective, and I'm not an LP, so it's not necessarily my place to say this, but the world is becoming a lot more dynamic and having a flexible model where you can co-invest, you can jump in and out of certain situations, special situations, you can do later sedge rounds for funds that are early stage. I think given that fees aren't going anywhere, they're not really budging, I think that's the way around the fee drag in the industry. If you assume returns again,”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“So the state of VC in general is its just really, really, really competitive. When my partner started in the industry thirty years ago, there were five funds and you saw everything because the only people the entrepreneurs could visit were the five of you. Today it's very competitive. It's very hard to see everything within a certain vertical. So for example, when we first started the fund and in 2015 at least, we saw over 95% of the companies that were within our area of focus. Today, I think that number is far lower. I didn't even bother to measure it last year. When we entered the industry, there were probably 100 machine learning papers a year coming out. Now there are 100 per week. It's actually a little bit higher now. Now, of course, not all peer-reviewed and whatever else, but anyway, the industry is super competitive. I think we can talk about differentiation in the industry perhaps as another topic. But to answer the second part of your question, what would you look?”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“And just make sure you construct your portfolio accordingly so that you can have the right level of exposure to companies in that market and get a decent return. So that's one reason why. The other thing is this view, and this is perhaps a tangent, but it leads to really lazy thinking, which is it leads to you using words like bets. It leads to you thinking that what you do is binary, and it's just not. very few things in the world are binary. And it leads you to ignoring what I think is rule one of investing, which is don't lose money. If you think in a binary way, you're often not like trying to protect against a zero. You're just trying to look at the one. And I think that's very dangerous as an investor to ignore rule number one.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“I just think that view of venture capital is fallacious. I think it's bizarre because one, it's not the only way to do things. And two, it's actually empirically not that successful a strategy. And to clarify that is raise a very big funds, try to be the best companies, the once in a generation companies that are worth ten, twenty billion dollars or so, and try to have a meaningful amount of ownership on this company so you can return your billion dollar fund. It's not that that is a bad business. It's sort of unproven deploying that much money in venture capital. We haven't been through full cycles with a lot of those funds that are that big yet. Early indications are not super positive, but they're certainly not definitively negative. So that's not a bad strategy. However, I think it's certainly not the only strategy. Like another strategy is raise smaller funds and just go into markets that are less competitive from an investment perspective, but also from the perspective of companies you're investing in.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it is just about picking a market. It really is about picking something that they have no strategic imperative to be in that is just in aggregate not big enough for them. It's not going to put a dent.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Because you've got to figure out what decision are we trying to make? What's it worth to people? How often do we make that decision? What are the costs of getting that decision wrong versus right? And what data do I need to teach it appropriately? Is it going to stay stable? Do I need to keep fitting it data, et cetera, et cetera, et cetera? So that's the shift. Bringing this back to like my job, my job is to invest on the 10-year horizon, not a three, four-year horizon. So, you know, if I was investing on a shorter horizon, yeah, I would be investing in SaaS. I'm not. And so I have to think about what companies are going to still be competitive in seven to ten years, which is the point at which we're likely to exit an investment. And so they have to be valued really nicely by the market then. And I don't think SaaS companies will be.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“And so the reason that SAS companies have high quality revenue streams is because people have constructed them that way. However, the reason SaaS companies have some degree of competitive advantage is because software was quite hard to build or has been quite hard to build. To get all the bits together, put them in the cloud, and make them reliable and whatever else was hard. But it's not hard anymore. It's really not. There are so many sort of Lego blocks that you can play with now. APIs, infrastructure services from Amazon, whatever else, that you're sort of just assembling those, and that is not, that doesn't require the degree of skill that an advanced computer scientist has, for example. So if the basis of competitive advantage is shifting away from that because it's easy, what is it shifting to? What's hard? And what's hard is taking technology from something that does something for you, like executes a workflow to something that makes a decision for you. And that's really hard.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and I think the keyword there is future. So right now, vertical market software is Constellation Call it or SaaS or whatever you want to call it is the opportunity of a lifetime. Particularly for entrepreneurs, I mean, if you're an entrepreneur that has some experience in some business and you notice the software is bad, just creating better software that's really sticky is an amazing business to have. It provides you essentially if done well with an annuity that can earn you some great cash for the rest of your life. Then obviously as an investor, that's a very high quality revenue stream. So to be clear, right now it is the opportunity for lifetime. That's not a mirage. It's actually generating cash on a repeating basis for people that they can compound very nicely year over year. But empirically, we're already seeing some of those rates of return start to shrink in constellation whatever if you look at their results. But the point is if I look out to the future, I see the basis of competitive advantage shifting.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source
“So essentially, we're investing in what we think is a fundamental shift in computing and therefore a shift in the technology industry and therefore a shift in how you invest in the technology industry. And that is investing in things that don't just give you calculations quickly or put things in and out of a database quickly, but investing in things that give you predictions. And those predictions are super relevant to your business or create real value for your business. So we invest in those sorts of companies. Now, what does that mean from the bottom up? It means they're collecting unique data, and then they're compounding the value of that data with some sort of intelligent system. Usually that's machine learning. Sometimes it's something more simple than that. So that's what we invest in in terms of stage pre-traction is when the company has for us, it's when we can see that the data is going to be predictive of something really valuable, and we can talk to customers and say, would you find that valuable? How valuable? And then we go from there, and we help them actually put it into market.”
2018-06-05 · Invest Like the Best · Ash Fontana – Investing in Artificial Intelligence - [Invest Like the Best, EP.90] · IDENTIFIED FROM THE TRANSCRIPT · source