AI: The New Platform for SaaS

Good morning.

Hope everybody enjoyed that first strategy stage. My name is David Apple. I'm the head of the software and SaaS vertical at Intact, the world's largest independent PureCloud ERP provider. We're thrilled to be sponsors for the tactical stage Today. We got a great first session. It's going to be on AI, the next platform for SaaS. It's going to be moderated by Ludo Ulrich and Tomas Tungass. Ludo is the head of startup relations@salesforce.com, and Tomas is a very prominent venture capitalist at Redpoint Capital. There's three things we hope you guys to learn today. Machine learning is real, it's important, and some practical ideas that you can take on in order to apply it to your business. As the three of us were prepping, it dawned on me this old adage, tomorrow's crazy idea becomes today's dogma, which turns into tomorrow's kitsch. And we're right on the cusp of this, all of us on transitioning from crazy idea to dogma to put in a plug for both guys. Tomas is looking for fantastic firms to invest in and Ludo and team are looking for great firms that want to build and accelerate their development with salesforce.com so without further ado, Tomas and Ludo.

Thank you so much, David. Yeah. Oh, that takes the nerve, the edge off the nerves. Thanks. Hi, Everybody. Welcome to AI's new platform for SaaS. It's a pleasure to be here. My name is Tom Tungoos. I'm a venture capitalist at Redpoint. Redpoint is a venture capital firm based in Menlo Park. We invest in early seed, early and growth stage companies. We've funded about 434 companies. We've about 4 billion in assets under management and we've been successful to have. Sorry. We've been lucky to have 136ipo and M& A. The most recent of which you heard from earlier this morning is Jeff Lawson. These are some of the.

Let's see.

Here we go. These are some of the companies we've been lucky enough to partner with in the as a service space. Today's talk is on it on a topic that I'm really passionate about. When I was in college and grad school, I studied machine learning. And when I was at Google, we also did a lot of machine learning. And the world of machine learning has changed tremendously in 2016. And let me tell you about why I think it's going to be so impactful for SAS in 2016. It was hard to avoid machine learning. We saw Lee Sedol, the best go player in the world, be defeated by AlphaGo, a Google algorithm. We saw the advent of the first self driving car. And then we also saw more than 10 million Amazon Alexis put into consumers homes. And that's just in the consumer world. The challenge with machine learning in 2016 is that every startup that comes in to pitch at Redpoint now says, hey, we're an AI company, hey we're an ML company. What does it really mean to be an AI or an ML company? And I'm only going to use ML because I don't really believe in the term AI. Machine learning is really simple. All it does is it teaches a machine to find patterns in data. And there are four things that you can teach a machine to do. If you used Waze today, you ran through an optimization algorithm. What's the fastest way to get to the Pearl, which is a nightclub? The second thing you can do is you can do object identification. If you take a photograph of a cat with your telephone, how quickly can the computer tell you whether or not it's a cat? The third way you can use machine learning is anomaly detection. This is most prevalent in security and also in anti fraud cases. So I have a $10,000 charge on my credit card for buying a piano yesterday. It's clearly I don't play the piano. It's clearly a fraudulent charge. And then the fourth thing that you can do with machine learning is segmentation. So if you're a very avid user on FarmVille, you might segment the user base and decide to treat the whales differently. You could do the same thing for a SaaS company. Given that all these technologies are more than 30 or 40 years old, why is there so much recent buzz about machine learning? What's happening is a convergence of three different major trends that have been in the works for those three decades. Really cheap compute, more data storage than we've ever seen before, and advances in algorithms. And the key trend is something that everybody is calling deep learning. So neural nets, I studied them in college, they've been around since the late 1970s. And the real, you know, neural nets do something that we've never been able to do before and they're a consequence of those that convergence that I just talked about to break down machine learning really simply, there are two different steps inside of it. The first is feature selection and then the second is model tuning. When I was at Google, the data engineers working on machine learning algorithms, they were doing the first part by hand. The feature Selection that's picking the columns in the Excel spreadsheet that you are going to use in order to create an equation to predict something, optimize something, segment something, whatever it is. The second part is the model tuning. That's taking the huge amounts of data, putting it through the algorithm, and figuring out which of the algorithms that you're trying are the most effective at getting to the result that you want. So feature selection for this cat that might be like the angles of the ears or the shapes of the eyes, that kind of thing. The big advance in deep learning basically is that deep learning automates both processes. Now you can do everything in a computer, and the results that have come out as a consequence of these advances are fundamental. Let me walk you through three. Google has a product called WaveNet. It's a computer that speaks so well that no human can tell it's a computer. Microsoft released Some research in 2016 that a computer can understand human speech as well as another human. And Google released a machine translation algorithm that will translate from English to any other language, even if it's never seen that language before. Those are fundamental machine learning advances, and this is not the kind of simple linear regression stuff that we've been doing for decades. And so this kind and this scale of innovation is really going to impact the startup world. Let me walk you through a few ways that we're thinking about it. The first, we were lucky enough to partner and announce an investment in a company called Chorus yesterday, which is leveraging a lot of these speech recognition and natural language processing advantages to analyze conversations for salespeople and help them sell more effectively. In the insurance world, we saw a Japanese company lay off 30% of its claim staff because it's being replaced by an algorithm. And there's a British company that allows you to take a photograph of the accident that you just had in your car. And the machine learning algorithm will tell you exactly how much damage you've done to the car and the insurance company will pay you. That instance. In the world of logistics, everybody knows this example. There's a truck that drove across Europe entirely by itself. We're seeing self driving Ubers. In the world of construction, there's a robot that lays brick three times faster than a human. In the world of medicine, algorithms are looking at radiological scans and detecting Parkinson's and cancer at far greater accuracies than a human ever could. In the world of agriculture, a young farmer's son is using TensorFlow in order to mechanize his dad's cucumber farm. And categorize them for less than $1,000. So the big theme about machine learning is it's going to change the world of SaaS. Because all of a sudden it's not just the machine learning innovations will happen in the core categories that we've seen in the past, those horizontal companies selling to customer support, customer service, sales, products. But it's also going to broaden out SaaS. We're going to see an expansion in the world of vertical SaaS. And these are just some of the examples. All of a sudden, all these offline industries, the key processes are going to be revolutionized and that's a huge potential for innovation. The scale of which is similar to the mobile phone and cloud. That's how big of a wave we at Redpoint think it's going to be. And in order to prosecute this strategy, we have five different precepts about the kinds of companies we want to invest in. The first is we're looking for companies with proprietary access to data. The algorithms themselves are not that innovative, they're not that close source. So the company that's going to really innovate is going to need its own data set to be able to train its own models. The second is we are not interested in platforms. Google, Microsoft, Amazon, they will be releasing the APIs that lots of companies will be building on, competing with them. You may want to compete with them, but we will likely not invest in that because it's a very challenging go to market strategy. What we're more interested in are machine learning applications that solve an end to end problem where a customer can say this is exactly, this is generating me more revenue, this is reducing my cost. The third is a machine learning innovation in and of itself is not enough to generate substantial customer demand. It has to be able to change the go to market. The trope that we use inside of Redpoint is we're looking for a fundamental technology innovation that enables a go to market advantage. The fourth is we're looking for experts in the field. You can take a generic NLP or speech learning model and you can get 80% of the way there. But in order to really have a fundamentally new experience, you need to get it 95% of the way there. And you can't do that with just off the shelf algorithms. You're going to need special talent in order to deliver that magical experience. And the fifth is we will occasionally invest in potential algorithmic advances that are closed source for us. Machine learning isn't new. We've been investing in lots of machine Learning startups, startups that are not necessarily exclusively focused on machine learning, but use machine learning in order to create a competitive advantage in the market. And because these advances in machine learning are so, they're of such great magnitude, we are going to continue to invest in more. Great. So that's an overview of the way that we think about machine learning. And, and so now we'll have a conversation, Ludo and I, about how we're seeing machine learning. So Ludo, you work with a startup incubator at Salesforce. What kinds of trends are you seeing in the world of machine learning?

Yeah, thanks Thomas. So by the way, amazing presentation. Congratulations. Of course I don't know about you, but I think there's nothing like human being intelligence versus like artificial in this particular case. So anyway, you have to take a step back.

Indeed.

In my team we engage with a lot of startups and so it's separate from the M and A efforts. I've seen Relate iq, it started there. We invested as a company, as Salesforce in many, many companies and acquired a lot of companies, companies like Relate iq with a product as well as technologies who have more like kind of almost lab technology like metamind, like Prediction IO, like beyond Core implicit. So we've been really active, my colleagues have been very active on this front on the ecosystem development side with activities like the incubator Salesforce for startup. All those activities we really try and we really agree with what you put on the slide. Like it's all about the go to market and basically revolutionizing like the scenarios and the use case for customers. Like you know, we're on the tactical st, basically trying to make something like, you know, improve the productivity. A lot of people talk about AI as the new ui, so how we make that experience better. So personally, you know, and we actually just reviewed like the next batch, we'll announce very quickly the next batch of the incubator focused on AI. I've seen a lot of vertical, I've seen a lot of new, new businesses like you know, identifying unconscious bias within a company, optimizing the composition of a team based on AI. All those kinds of things that we haven't seen before. So I think you have clearly those trends and so we're trying to help them basically go to market, access to the Salesforce customers who are eager to get more AI basically.

Yeah, that makes sense. Yeah.

And so Tomas, I want to ask you because one of the things we see as well beyond the trends in like as we engage with startups, specifically my team more generally, Salesforce see a lot of demand from our customers. Right. I think the whole industry. He's talking about AI. I mean you probably came to that session saying, wow, okay, let's try to go beyond the buzzwords and beyond all that stuff. Right? But it's real, right? Beyond like the end users, the consumers who are using Facebook, Uber and all those AI powered applications every day and beyond. The VC community wants AI. We heard loud and clear. I said your investment thesis is clear. I think the top, you know, the C suite, the CXOs are there. Like Accenture is like evangelizing all their customers about the era of a new enterprise, intelligent enterprise. I think Salesforce have been going big in the last year with the brand Einstein, which basically the umbrella brand under everything that we do AI wise to tell our customers that they should be more demanding with us and get all those features. So how do you see some of your portfolio companies play with that phenomenon?

Yeah, I think the most important thing. So maybe I'll just tell it in the form of a story. So I've loved speech recognition for a long time and about three years ago I bought Dragon's Nuance and I started dictating all my emails and I started noticing that I was two to three times faster dictating than I was typing. And then I saw this movie her and I watched it. I think that's a brilliant movie, but not for the romance. It's for the human computer interaction. You watch the way that the main character just dictates a letter says print, the thing prints. Or the way he interacts with his email where he says, next email, next email, he replies. And he's only really looking at the telephone when he wants to see an image. And that's a great analogy for how machine learning succeeds. And what I mean by that is it's not in your face, it's hidden in the background. It's doing the work behind the scenes. And so the great. And so that passion for voice recognition technology was what led us to spend a year looking at different companies taking advantage of voice recognition and natural language processing. And that's ultimately why we invested in cores, because they can analyze conversations in real time and help salespeople sell better. And that's why we're so excited. And that's, for me, that's a great example of how, because it's a seamless experience, you just dial into the call and then all of a sudden you get value. You don't see the machine learning, you don't feel the machine learning. It doesn't feel like a different product, but what's happening is the product is giving you that magical moment. It's telling you, here's the battle card, here's the case study, here's how I handle that. Objection. He should be talking slower, you should be talking less. And I. So that's what we're looking for when we're looking for companies, we're not looking for companies where machine people are even beating you over the head. In fact, one of the things that we were talking about getting ready for this was a really great point that you made, Ludo, which is can you pitch your startup without saying machine learning? And what that means is you're not focused on the technology, you're focused on the value proposition. You're focused on why the customer is going to care about it, why the buyer is going to be promoted. And you know, that's what, that's the key. And like, yes, the technology's there and yes, it's going to be a fundamental transformation, but you can't lose sight of the fact that you have to deliver ultimate value to the buyer. Yeah, yeah, indeed.

That's actually a good tip. So I really recommend, again, it's a tactical stage. Ask yourself if you can do that without saying AI and surfing that wave. But yeah, and so one of the company, for instance in Salesforce Incubator is called kw. And so they also have a massive team around the world. They're highly distributed and they invest in a lot of the technologies ML and everything that you discussed. But they also have these very approachable voice activated interface. So again, coming back to AI being the new ui, how do you help coach the companies in your portfolio, the one you talk to, to basically keep it super simple, approachable, etc. In the way they build their pitch, whether you know whether they're in the portfolio or they're pitching to you like what's what, what could be a tip that you give them outside of the one.

Yeah, we have this fear that's created by Hollywood about AI. Yeah, yeah. And we have an annual investor meeting every year and one of our partners is Andy Rubin. And Andy Rubin created Android and he, he brought one of the Boston dynamic dog robots. Have you guys seen this thing? You know, it's like runs like 40 miles an hour. It's exactly what you'd see in a post apocalyptic film. And there's this video where they're testing it and there's a human that's hitting it with a hockey stick as hard as he can and he Starts kicking it and hip checking it. And Andy gets up and he says, this is why they will rise up. They never forget. Right. It's like the whole Westworld thing. Good storytelling. Yeah, that's, that's whether, like, that's a completely irrational fear. And whenever anybody interacts with AI, they have that fear somewhere inside of them. And so now, like when I was interviewing PMs at Google, like, we would constantly see people who had studied human computer interaction. And now there's a new field. It's no longer human computer interaction, it's about human robot interaction. How do you get a machine learning algorithm to interact with somebody so that they're not afraid of it?

Yeah, not being afraid of it. So it's funny you say that, because in a lot of the companies I interact with, another one is Novado AI. And basically it's as simple as they help you draft, like a sales email. And the clear impact is that you have 5x, the open rate. So it's pretty down to hers. It's more like productivity, basically, doing simple things. So do you have example of that again, to try to dumb it down and try to say, okay, it makes it approachable. It's not the robots, it's actually just a core component of what you're doing.

Well, so the key thing in human robot interaction is you have to set the expectation. You can't say, hey, this is a chatbot that is going to be able to answer any question that you could possibly imagine in an intelligent way. And if you do that, what's going to end up happening is the user's going to use it once, figure out its limitations, and then say, hey, this doesn't work with my mental model of the world or how a robot should work. And so they'll stop using it. And so this is particularly relevant in the chatbot world. The reality is there's a limited syntax that you can use to interact with the chatbot for it to be effective. And you have to train people. People are becoming more sophisticated. We're all getting to the point where we use Google more effectively. We're more comfortable with command line interfaces. We're using text message on a daily basis. And so I think what it really comes down to in order to get people comfortable with ui, particularly interactive ui, is being able to set the expectation of the user appropriately. And then. And that means much lower than you think it typically ought to be. And then the second thing that you really don't want to have happen is if you're wrong, you lose the Trust of the user, whether it's a recommendation about, hey, you should bring up UCLA in the sales email, or how we both went to that college, or whether it's a slightly incorrect answer. The second somebody, if they think it's a human and then it acts like a robot, it breaks down people. I was talking to one entrepreneur this morning. He said, we're very explicit that this is a robot. These are the limitations, and we're going to escalate to a human when and if necessary. So we're starting from a place where whenever you're using machine learning with humans, you need to build trust. You need to build that relationship just like you or I would. We're starting to build a rapport and we can't forget that when we're building robots.

Yeah, understood. Cool. And so another thing is, like, we keep hearing about AI from all the big players in the industry, the Google, the Facebook, Microsoft salesforce. We do a lot of moves. Right. So what's your suggestion to the founders you work with in terms of buying, building, et cetera, and building team? Because talent is scarce as well. So maybe some tactical, practical advice to the founders here.

Yeah, I think it's awesome that these monoclouds are building all these AI algorithms, they're publishing all this research, they are offering it to startups for pennies on the dollar. You can start with an NLP algorithm, you know, for free. And what's going to end up happening is that layer is increasingly become commoditized. And so for a startup, the way to think about it is what value can I add on top? And that's, you know, in the four and five investment parts of our strategy, one is experts in the field, like, you need to continue to be able to add value on top of the mono cloud platforms in order to have a sustainable competitive advantage. So you have to assume like generic NLP or generic automated speech recognition is commodity and everybody has it. And so the question is, okay, given that, what is it that I'm going to do next?

Yeah, cool. Specifically around data. Right. I mean, AI without data doesn't exist. So, you know, that's why a lot of companies want to deal with our customers data. So you mentioned in your investment thesis that, you know, you care about proprietary data. Can you elaborate a little bit? What, what kind of, you know, again, where do you get that as a founder? Where do you get. Unless you have that unfair advantage?

Yeah. We've probably seen two key strategies in creating proprietary data. The first is creating it yourself. So you build a Workflow application. And by virtue of people doing work within this application, they generate a data set that you can then mine that becomes proprietary. That's a really good strategy. The challenge in the short term is that data set needs to be really big in order for many of these algorithms to work. So you can't really offer a whole lot of ML features right out of the gate. You have to be patient. But it does make sure that the core value proposition of the workflow product is on the money. And so one really great example of that is like LinkedIn, right? That's an older example, but they had huge data network effects as a result of all the traffic and all the information that they have on their system. They can continue to build different products on top, like Sales Navigator or whatever it is. The second example that we're starting to see more and more of is it's particularly in enterprise, an early stage SaaS company will go to a large like Fortune 100, Fortune 1000 and say, hey, there's this really critical problem that you have that you don't have the expertise to solve, but we can solve it. So why don't you put in a little bit of money, why don't you give us proprietary access to your internal data and then we will build a system that isn't necessarily built to suit for you, but will definitely benefit you, but that we can scale across. And so this is happening a lot in the Internet of Things world, in the construction world. Caterpillar's made a substantial investment in a Chicago based company where they're doing analytics on top of sensors for all these equipment for predictive maintenance. So how do you repair a Caterpillar excavator, that kind of thing. Or we've seen a recruiting company that partnered with lots of Fortune 500s to get access to their hiring, recruiting and promotion information so that they can change the way that the applications, the job requisitions are written. And so if you can start with that kind of corpus out of the gate, then what you can do is you can offer those ML features right out of the start. Those are the two strategies. I'm sure we will see other strategies. One that we haven't seen yet is a cabal of startups getting together and sharing information across. But you know, who knows, right?

So not to get too much into a political thing, but I think AI can also help a lot. You know, as I said, create like a composition of a team or you know, identify unconscious bias, you know, maybe help us like do a better immigration, you know, identifying all those things. So hopefully AI will help us in many ways. Can you just predict we're running out of time? Like, okay, we have this panel in AI today. I think no startup here is probably defining themselves as a mobile cloud startup. So probably next year they won't say mobile cloud AI. So what do you think we're going to talk about tomorrow? Right.

I think a year from now we're still probably talking about machine learning.

Okay.

I think we're in the world of like there are very few SaaS companies that use machine learning at scale. And I think maybe next year we'll be talking about the first handful of them that are 10 or 15 million in ARR. And we're going to be trying to dissect how it is that they got access to their data set. What was the team composition? What was the kind of expertise they needed? How did they translate the technology advantage into a go to market advantage? Because that's the part that technology advantage into a go to market advantage. It's still not clear, like let's say you create the next version of Zendesk that's got a whole bunch of ML. It's not necessarily that technology, that intel inside technology is going to all of a sudden reduce your cost of customer acquisition relative to an incumbent. And so yeah, next year we'll be talking about those strategies, I think.

Great. So let's finish with a tip. I get a start to let you think about it again. I think we see all of customers trying to reinvent themselves. As I the C suite is being educated by all the key players about the fact that AI is going to transform their business. That's the new way they need to engage with their own customers with personalization at scale. So I think this is a great opportunity to come in. It's always hard for startups sometimes to sell to large accounts. You can probably build an ecosystem like Salesforce and try to leverage access marketplaces like the App Exchange. But I think this is the time trying to go beyond the buzzword, help those customers reinvent their own experiences. Again, I'm going to say one more time, AI being the new ui, how can can do all those big accounts basically have using that technology, a very disruptive kind of approach. So really take your chance. It's now and ever basically with large companies there is demand and we're evangelizing that on your behalf.

Yeah, great. So in closing, thank you very much to Jason for inviting us. Thank you for coming to the session.