a16z Podcast: The Future Of Decision-Making—3 Startup Opportunities

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0:00:22 a16z.com/disclosures. Hi, welcome to the A16Z podcast. This is
0:00:27 Frank Chen. Today’s episode is called “The Future of Decision-Making, Three Startup Ideas.”
0:00:33 It’s a conversation I had with Chad Nouse, originally as a YouTube video. You can watch
0:00:39 all of our YouTube videos at youtube.com/a16zvideos. Now on to the episode.
0:00:45 Hi, welcome to the A16Z YouTube channel. I am Frank Chen, and today I am here with
0:00:51 Chad Nouse. Chad is part of the enterprise investing team, and he’s noticed something,
0:00:55 and so let’s just get right into it. So you’ve noticed something around the way that big
0:01:00 companies are trying to do digital transformation. So why don’t we start there. What are the
0:01:04 big companies doing? What is digital transformation?
0:01:10 Yeah, digital transformation is something that gets thrown around quite a bit. I think
0:01:16 there’s a big shift now. We’re starting to see a lot of industries actually starting
0:01:20 to go through digital transformation, and I would bucket the things that people do in
0:01:27 digital transformation into two areas. The first one is around moving from these manual
0:01:34 paper processes to more digital ones that are easy to change, faster to modify, more
0:01:38 agile. The second thing that people tend to do when they’re doing digital transformation
0:01:46 is move from these manual processes to more automated processes, and so automation. And
0:01:53 I think that this shift is now starting to happen in earnest, and we’re going to start
0:01:59 seeing three things pop out. The first one is people’s roles and functions to a certain
0:02:06 degree are going to start shifting around. The second one is we’re going to start seeing
0:02:12 new demand for new technology and new tools as these new functions and roles actually emerge
0:02:18 and start to change. And third, that’s going to also lead eventually to a change in market
0:02:24 dynamics and how companies run, who will become successful, who wins in certain spaces.
0:02:30 Interesting. So anywhere there is a fax machine or a clipboard or sort of a big bundle of
0:02:34 papers, there’s opportunity. We’re going to go from analog to digital, and then we’re
0:02:38 going to automate whatever business process was behind that piece of paper that you had
0:02:43 fill out in triplicate. So why don’t we talk about a couple of examples of these? What
0:02:45 are good examples?
0:02:52 So I’ll talk a little bit about product management. So earlier on, people, the way they used to
0:03:00 decide what products to build, how to prioritize features or bugs to fix, is they’d go and
0:03:06 they’d run these surveys that are manually, and they send them out to people, or the product
0:03:10 managers go and talk to people. They spend a ton of time doing these, collecting all
0:03:14 the data and figuring out, okay, these are, this is the segment of people I care about
0:03:17 the most. Here’s the issues that they care about. Let me figure out what the problem
0:03:19 is and so on.
0:03:22 As an old product manager, I went on those calls.
0:03:26 You flew to a customer and you dutifully listened to what they wanted, and you’d sort of come
0:03:28 back and try to sort them all.
0:03:33 And that, I mean, it’s a huge time sink. A lot of the product manager’s time used to
0:03:39 be that. What’s happening now is we have a new generation of tools that actually allow
0:03:44 the automation of data collection from the product. What’s actually happening? What
0:03:51 features are people using? Where are they getting stuck? And so where the product manager
0:03:57 now, instead of having to go and do all these surveys, would look at a dashboard that describes
0:04:02 what people are doing in their product. And then they would be able to analyze it and figure
0:04:08 out from that what features, what areas of the product are they getting stuck in, and
0:04:12 be able to communicate with engineers. Here’s the things that we need to do.
0:04:17 And then once they fix some of these, they can actually roll them out gradually and do
0:04:22 A/B tests to figure out did this actually fix the problem or did it not fix the problem.
0:04:25 And decide that if something actually did fix the problem, then continue to roll out
0:04:27 to the rest of the population.
0:04:30 So that’s on the product management side.
0:04:37 You see another example actually happening on the marketing side. I’m sure you’ve heard
0:04:45 of growth hacking. So for a long time, marketers used to be this madman kind of thing where
0:04:49 you spend a lot of time figuring out the creative aspect of what you do, you spend a lot of
0:04:54 time on a lot of money on advertising campaigns and you kind of spray and pray for the most
0:05:03 part. What has happened over the past few years is the rise of this marketing engineering
0:05:11 role to a certain degree. This is one where a marketer who understands numbers, who understands
0:05:18 engineering systems, who understands pipelines would work with these data systems and actually
0:05:25 try to figure out ways that are low cost that would actually increase growth in a certain
0:05:32 segment of the population. And that requires a lot of data instrumentation, a lot of understanding
0:05:38 people and a lot of creativity in figuring out how to spur growth or how to get traction
0:05:40 in a certain area.
0:05:47 So Don Draper’s tools were typewriters and stories, right? And so the tool set around
0:05:52 this is going to change dramatically if we make this transition from sort of the old
0:05:56 world analog un-automated to the new world. And by the way, I think you have a name for
0:05:57 the new world.
0:06:04 Yeah. So let me first say like what’s actually going to happen. So as people’s jobs become
0:06:10 more and more automated, a lot of the things that they get, a lot of the things that they
0:06:16 used to do that are work will go away. And what’s actually left in their jobs is mainly
0:06:22 decision making, figuring out like what am I going to do? What am I going to focus on?
0:06:27 How should I do it? And communication or other things that are actually related to their
0:06:35 job like creative work, human aspects that can be automated, buy in, alignment, et cetera.
0:06:40 But the road work goes away. And so that means that there’s a ton of decisions, a lot more
0:06:46 decisions that they’re doing more frequently on a daily basis that they have to go through.
0:06:51 So what that means is that to a certain extent, everybody is going to end up becoming more
0:06:56 of an analyst in that sense in the enterprise. When I say everybody, I kind of mean like
0:07:05 the middle of the enterprise. And what that really means is they’re going to have these
0:07:10 questions that they’re going to need to ask on a daily basis, but with no tools to actually
0:07:16 help them do these. So you might say, well, you know, people used to do this for a very
0:07:19 long time. They used to use BI tools to actually answer questions.
0:07:24 Yeah, business intelligence, right? So you build the data warehouse, you build the tables
0:07:27 on top of it, right? Then you build your reports. So.
0:07:33 Exactly. And so I think that BI tools are not going to be enough in this world. And I’ve
0:07:36 come up with a term for like the type of tools that we need that I’m calling operational
0:07:42 intelligence, because it’s actually targeting the operational people. It’s the type, it’s
0:07:47 questions that people need to answer on a daily basis and they have to answer them immediately.
0:07:54 Questions like, where is the bottleneck in my funnel right now? And how do I eliminate
0:08:03 it? Or I have my competitor is having a flash sale. How do I figure out how much of my revenue
0:08:09 is impacted? Which customer segment should I target? And what should I, what should I
0:08:13 put on sale? And those are things that you’re going to have to answer in the moment. You
0:08:19 can’t have, so for BI, you would need this army of analysts, where you would just ask
0:08:23 a question and then they would go off into your enterprise and rummage through all the
0:08:29 data sources, try to understand kind of like what the question that you’re asking is kind
0:08:33 of try to understand what the business context is and then show you, build you a dashboard
0:08:37 and hope that that’s the one that you want. Yeah. Well, there’s the old joke about BI,
0:08:42 right? Which is it’s $10 million to your first report. And then you realize, oh, I didn’t
0:08:47 want this question answered anyway. Wrong question. Exactly. And so the solution there
0:08:52 is kind of what I’m calling operational intelligence. And there’s three pieces to it. The first
0:09:01 one is that it has to be, it has to be immediate. It can’t be eventual like BI. You can’t just
0:09:06 say, oh, I need to answer this question and then get an answer like three months later.
0:09:10 It has to be answered in the moment. And that involves a few things. Like first, that you
0:09:15 have to actually be able to do it yourself. Like you have to actually get the data in
0:09:21 real time as opposed to it being late. The second piece is that it has to be kind of
0:09:28 continuous. It has to be real time. You can’t have your data being sent into these systems
0:09:35 on a batch basis every day or every week or whatever. The data that you actually see to
0:09:41 make your decisions has to be what’s happening at this point. Right now. So the classic example
0:09:44 of this would be sort of social listening on Twitter, right? Which is that’s got to
0:09:49 be an ongoing process because things can blow up with your brand either in a good way or
0:09:53 a bad way at any time. So you can’t say, hey, I’m done analyzing Twitter for the quarter.
0:09:57 I’m done. Exactly. Exactly. Another example, I mean, I said
0:10:03 this earlier about the A/B testing. I mean, if you’re looking at, if you’re trying to
0:10:09 do A/B tests, you can’t just let it go and come back next week and see whether the thing
0:10:14 worked or not. You actually have to be continuously monitoring what’s actually happening in the
0:10:21 A/B test space and figure out did the B test work or did the A test work better? And am
0:10:25 I going to flip the switch now? Because if, I mean, you’re doing an A/B test on a segment
0:10:29 of the population, you don’t want them to completely fail in the end.
0:10:33 In fact, we’re seeing with some of the more sophisticated machine learning systems that
0:10:38 you actually have multiple models, machine learning models that are live at any given
0:10:43 time. And you’re actually doing nightly bake-offs against these models, right? Which is model
0:10:47 A will get 40% of the traffic, and then model B will get 20% of the traffic. And then we’ll
0:10:52 just sort of let them run and the best models get promoted to receive more of the traffic
0:10:57 over time. So that’s an example of what you’re talking about is this sort of continuous process.
0:11:04 It’s really interesting that like what we’ve seen is this kind of monitoring, this kind
0:11:08 of continuous monitoring, like what I’m calling operational intelligence, has actually been
0:11:12 kind of standard on the engineering side for a very long time. People have been monitoring
0:11:21 systems and engineering for a very long time. And they would kind of run A/B tests continuously
0:11:25 to try to improve performance. And now we’re actually seeing these kinds of engineering
0:11:29 disciplines kind of migrate into other functions of the org, right? Like marketing seems to
0:11:33 have been the first one to go after that and then product management. And we’re actually
0:11:39 seeing now people trying to do this for salespeople, trying to like look, okay, here are the things
0:11:45 that salespeople have done. And in order to close a deal, let’s actually learn from that
0:11:48 as a pattern and like figure out how to get everybody, every salesperson on the team to
0:11:51 get to the level of the top performer.
0:11:55 Yeah. Cresta.ai is a great example of this, right? So you’re chatting and you’re getting
0:11:59 real time advice about, hey, maybe this is the time to mention we have a product in this
0:12:01 space. Yeah, that’s a real time recommendation.
0:12:02 Exactly.
0:12:08 Yeah. So in the old days, engineering typically was first because like websites were coming
0:12:12 online and you needed to watch those things, right? Because everybody knows the statistics
0:12:16 that if like, you know, your webpage loads this much slower, you’re going to lose that
0:12:20 much more people through the conversion funnel. And so like you had to watch all these things
0:12:25 in real time. And now that’s getting outside of IT, right?
0:12:31 Yeah. It’s interesting also that, so I used to work at AppDynamics. I was there for a
0:12:38 few years and AppDynamics sells APM tools, application performance monitoring tools.
0:12:43 It’s probably one of the easiest things to sell because you go up to your customer and
0:12:48 you’re like, well, how much does it cost for your engineering systems to be down for, you
0:12:58 know, five minutes, 10 minutes, an hour? And then you say, hey, we prevent that from happening.
0:13:02 That same kind of sale hasn’t yet happened in these other works. It’s a little harder
0:13:06 to prove the ROI. But I think it’ll get there.
0:13:11 Right. So now this is about sales performance, marketing performance of those people.
0:13:12 Exactly.
0:13:17 And we’re going to sort of treat them as if they were websites, right? What’s the downtime?
0:13:22 What’s the dollars lost if you have a salesperson being non-optimal at this point in time?
0:13:23 Exactly.
0:13:30 Yeah. And so to recap sort of the tool change from business intelligence to operational intelligence,
0:13:36 sort of, I need it now. I don’t need it in three months. Three months is too late. That’s
0:13:41 one. Two is I need it ongoing. I don’t need a one time, hey, I’m done.
0:13:42 Right.
0:13:46 I need to, and then I think there was another aspect of the tools that you expect to change
0:13:47 and what is that?
0:13:49 It has to be self-service, not full service.
0:13:50 Oh, I see.
0:13:56 You can’t have somebody else going and doing all the work for you. Those tools have to
0:14:01 actually give you insights that are catered to you and you have to actually be able to
0:14:03 ask the questions yourself out of these tools.
0:14:04 Right.
0:14:06 They have to enable you to do all these things by yourself.
0:14:12 Yeah. So basically the tools need to be easy enough to use such that the average business
0:14:17 analyst can basically just poke at the data and then any answer comes out as opposed to
0:14:22 you think of a question some team later, six weeks later, turns that into a very complicated
0:14:24 SQL query and then the report comes back.
0:14:30 Yeah. I wouldn’t even say it’s an analyst that actually is doing this. These are tools
0:14:36 for the actual operational people as opposed to the, as opposed to, I call them meta-operational
0:14:40 because they’re like analysts. They’re about the business. They’re not the business.
0:14:41 I see.
0:14:45 So what a good example of somebody who now needs to consume these tools directly, which
0:14:47 is different, Brent, a marketer.
0:14:54 The growth hacker, the product manager, the customer support manager, the sales person,
0:15:00 these are all the actual functional operational people that need to consume this data.
0:15:04 Got it. So that would be a big change, right? Because in the past it was sort of a very
0:15:09 sophisticated technical consumer, right, who would be the interface between the business
0:15:12 person and the system and now you’re saying the business person needs direct access to
0:15:13 the system.
0:15:14 Exactly.
0:15:20 So it can be easy, right? So if we think about the entire stack of how it came to be that
0:15:26 you’ve got a BI answer, right, there was ETL, there was storage, there were data cubes,
0:15:35 there were analytics, right? So do you think each layer of the stack is going to need to
0:15:40 change or do you think these are just features that the incumbents can add?
0:15:41 Yeah. Good question.
0:15:49 So I think that the breakdown of the stages of data pipeline is a functional breakdown,
0:15:54 not really so much legacy. Like you’ve got ETL at the top, you’ve got, well, maybe at
0:15:59 the bottom depending on how you like to draw your pancake from the left to the right.
0:16:05 You’ve got ETL at the, right after your data sources, you’ve got storage where all the
0:16:10 data that you’ve processed goes in, like these are your data warehouses, your databases,
0:16:15 data lakes, et cetera. You’ve got processing that happens to extract the data from the
0:16:21 storage layer and turn it into insights or whatever. You’ve got analytics that’s actually
0:16:32 used to turn a question into actual execution. You’ve got the access layer which controls
0:16:37 and governs who is allowed to access what. And then you’ve got processing at the end,
0:16:40 I’m sorry, a presentation at the end that actually.
0:16:41 That’s where your answer comes out.
0:16:43 This is the dashboard.
0:16:49 I think every layer, functionally each layer is going to remain the same, like at the core
0:16:54 it’s going to be doing the same things. But each layer is going to have new non-functional
0:17:00 requirements. Each layer is going to have to be usable by a non-technical person who
0:17:08 is trying to ask their own questions. And we see that happen in large companies. These
0:17:14 large companies have already built these stacks. So Airbnb, for example, built SuperSet and
0:17:22 they luckily open sourced it to the world. And now it’s used by hundreds of companies.
0:17:28 It’s a presentation layer product that’s focused toward more technical engineers or data scientists
0:17:34 to be able to get ad hoc access to their data and answer questions immediately.
0:17:42 One of our investments imply is doing this for the analytics and the processing layer.
0:17:49 So they’re able to store streaming data directly into their database and allow you to do OLAP
0:17:54 types of queries and analytics on top. And they provide a presentation layer that allows
0:18:00 you to slice some dice on problems. Databricks is another one. They’re focused on the processing
0:18:09 layer. So we’re seeing a bunch of things happening in each of these layers. And I think probably
0:18:15 the layer that hasn’t yet seen the most changes is the ETL layer.
0:18:20 And what do you think that is? Is that the hardest layer? Is it just, well, that’s going
0:18:24 to be the hardest to turn a business user into a direct customer of? Because traditionally
0:18:26 that’s been very wonky.
0:18:34 Yeah. I think two reasons why ETL has been so hard. The first one is it actually requires
0:18:43 domain specificity. ETL for healthcare is not going to look the same as ETL for financials.
0:18:44 Ridesharing.
0:18:49 For ridesharing, for whatever. The ontologies, the things that they care about are different.
0:18:56 And so any company that does these has to really get deep into that domain. The second
0:19:04 one is it’s a lot of integration and a lot of kind of heavy manual work. And engineers
0:19:08 don’t really like to build these kinds of things. So they’re going for the lower hanging
0:19:09 fruit at this point.
0:19:14 Got it. But it seems like overall you’re arguing there are a lot of startup opportunities
0:19:19 here that the incumbents are going to have a hard time retrofitting their products, right?
0:19:24 So it’s pretty hard to change a product that was designed originally for a technical user
0:19:29 to turn that into a non-technical. Is that sort of a fair summary of where you’re going?
0:19:34 Yeah. So if you think about the opportunities in operational intelligence, I’d probably
0:19:41 break them into maybe three categories. The first one, actually the first two are maybe
0:19:46 like related to each other. It’s basically you want to become an operational intelligence
0:19:55 vendor. So you sell software and tools that enable existing incumbents to become operationally
0:20:02 more capable. You enable them to do operational intelligence. And within that category there’s
0:20:07 a breakdown. So you can either target a specific role. So I’m going to enable the salesperson
0:20:11 to become successful or I’m going to enable the product manager or I’m going to enable
0:20:20 the customer success manager. And so we see products in each of these categories today.
0:20:26 There hasn’t yet been complete breakout success in any of these, but it’s super crowded and
0:20:35 I think it’s probably the hardest one to win in at this point. The second category is within
0:20:49 that vendor superset is segment focused vendors. So companies that sell operational intelligence
0:20:59 tools to existing incumbents, for example, companies that sell sensors and analytics
0:21:07 for oil and gas companies. So these are people who will collect data from your wells, optimize
0:21:13 it and then collect that data from your wells, put it into dashboards, tell you how your
0:21:18 wells are doing and tell you how to optimize it in order to improve efficiency. So like
0:21:23 a vertical solution for oil and gas. For oil and gas. So those are those are still vendors
0:21:28 selling software, maybe some hardware into an existing industry. And then finally you
0:21:38 have the vertically integrated, you know, operationally intelligent company that competes
0:21:43 against the existing incumbents. And so we’ve got plenty of examples of that at this point.
0:21:48 So we’ve got Airbnb that’s in the hospitality business. We’ve got some Sara in the logistics
0:21:59 industry. We have Lyft and Uber in transportation. And I think that’s where the biggest value
0:22:06 is, but also one of the hardest to go into. Yeah, the classic full stack startup, right?
0:22:10 Which is I’m going to build these operational intelligence tools, but nobody else gets to
0:22:14 use them. I’m using it to serve my own business. And I’m going to win the market by winning
0:22:20 the customers directly. Yeah. And I think that the industries that are going to win
0:22:25 the most out of operational intelligence are going to be these kind of like traditionally
0:22:35 non-IT buyers. So oil and gas, groceries, construction, these are businesses that are
0:22:43 really, you know, trillion dollar industries, or trillions. But they have very low margins.
0:22:47 Like they’ve existed for such a long time that they’ve they’ve operationally become
0:22:58 really efficient. And at the same time, commoditized. So I’ll give you an example. The largest
0:23:05 construction group in the world is called the ACS group. The revenues are about like
0:23:12 34 billion per year, but their margins are about six and a half percent. And so a small
0:23:18 change in the gross margins for these businesses, a small change in how operationally efficient
0:23:26 they are translates into huge increases in their profit margins. Another example is Costco.
0:23:32 So in 2017, their revenues were about 12 and a half billion. And they were operating on
0:23:40 about 11% gross margin. Again, another another place where a change in operational efficiency
0:23:46 can lead to huge changes in revenues. The final example is a little different. This
0:23:50 one is less about gross margins, but more about capital deployed. And so the example
0:23:58 here is ExxonMobil, the mobile. If you were to guess what their like the value of the
0:24:01 capital that they have deployed around the world, what would you what would you guess?
0:24:07 Oh, ExxonMobil. Yeah. Hundreds of billions. Is it the order magnitude?
0:24:16 So ExxonMobil is about 230 billion capital. And they’re the way they measure their performance
0:24:26 is on return on capital invested ROIC. It’s it’s it’s a it’s very different. It’s different
0:24:30 than how you know the grocery example I gave earlier, which was based mainly on the gross
0:24:37 margins. And their return is about nine and a half percent or so. So again, a small change
0:24:43 in the operational efficiency of the of the capital that they have deployed can translate
0:24:49 into huge additional gains. I mean, they’re deploying about like 23 billion dollars additional
0:24:55 capital this year. That’s a lot of spending. Yes. And that’s that’s that’s the I mean,
0:25:00 it’s really interesting, like helping these companies on the that that are capital heavy.
0:25:06 So it sounds like you’re excited about a whole sort of gamut of startups. One would be, hey,
0:25:12 look, I’m going to sell a particular technology to enable you to be more operationally intelligent.
0:25:17 Right. You’re also interested in the full stack startups, which is I can sell an entire
0:25:23 solution to a customer directly and nobody else gets my oh, I goodness, so to speak.
0:25:28 What are some examples of sort of startups that you are? What are some examples of things
0:25:36 that you’re personally excited about? I can give you some some examples on the on the
0:25:40 infrastructure side. So I’m excited about the SuperSep project. I’m excited about what
0:25:51 implies doing. I think I think there’s a lot of I think a lot of what’s actually happening
0:25:59 is people are now starting to see analytics and observability as as urgent, as necessary
0:26:05 to running their business. And so I think that there’s a really great opportunity in
0:26:15 that space. I’m also really interested in companies or vendors, software vendors, into
0:26:19 incumbents, into large existing industries, like into construction, companies that sell
0:26:26 into construction or companies that sell into groceries. We’ve seen a few startups in that
0:26:34 domain. The hardest, some of the hardest problems here is that these are startups that are going
0:26:41 to have very different economic profiles than the traditional, you know, Silicon Valley
0:26:43 startup that that we know.
0:26:54 So first off, these are you’re selling into markets that are stagnant, that are very low
0:26:58 margin. They don’t have a lot of margin to go around, right? They can’t afford to pay
0:27:04 a lot. Exactly. And they’re not used to buying new technology. They kind of understand one,
0:27:09 two, and three, and like they don’t really know about four, or they don’t know how to
0:27:15 digest it. And so a lot of the effort there is going to be around educating and the sales
0:27:22 cycles are going to be very long. The pie at the end of that, like the other, the flip
0:27:26 side of this is that these are huge businesses, right?
0:27:30 Yeah. Construction, oil gas, retail chains, right?
0:27:36 Once you’re in, you’re in. And so when you’re actually starting a company in this area,
0:27:42 there’s a few things that you want to keep in mind. One, you need to educate your investors.
0:27:45 Like these are usually investors are not going to understand these businesses really well.
0:27:51 And they might not know the difficulty of actually selling into them, like what it takes.
0:27:59 And so you need to prep your investors for this like long haul thing for the long term.
0:28:05 And they need to understand that this is at the end of this, there’s a really bright light.
0:28:11 The second piece is you need to get domain expertise. Like you need to become the expert
0:28:19 in that business. And you need to become a kind of trusted advisor to these companies.
0:28:24 And so when they say things like, oh, you know, we want to go through digital transformation,
0:28:27 you need to help them understand like, here’s what that means. We’re going to be here for
0:28:33 you. We’re going to guide you through it and actually help them with both a significant
0:28:37 amount of services as well as software on the back end.
0:28:42 So don’t shy away from the services. Don’t shy away from the services, especially in these industries.
0:28:49 Well, Jan, thank you so much for coming and sharing this idea. The good thing about this is that
0:28:55 the world really is changing fast. If you are a retailer, Amazon has scared the bejesus out of
0:29:02 you, right? And so what used to be a very long tedious sales cycle has gotten a little quicker
0:29:06 because Amazon’s in the rearview mirror. And so everybody sort of knows that they need to go
0:29:10 faster. They need to make decisions sort of lower in the organization. They need to make them in
0:29:16 real time. And so it’s exciting to see startups helping that transition to real time decision
0:29:22 making pushed lower in the organization. So thanks for joining the YouTube channel.
0:29:27 If you liked what you saw, go ahead and subscribe. Feel free to leave comments. Maybe the question
0:29:32 that I’ll use to prime the comments is, what are your favorite examples of decisions that now need
0:29:39 to be made in more real time? And look forward to joining the conversation there. See you next episode.