It’s always nice and valuable to listen to these interviews/calls. Dave — as always — exudes confidence in what the team does and what they are working on. Whenever I listen to these calls, I feel even more confident with my overweight allocation. Here are my top 5 Q&As along with my comments.
Q: [00:02:54] The topic to begin with is this idea of speed as a habit in business. And this is very popular notion in technology, the move fast and break things, probably the most famous example of a phrase that exempt flies this idea. But I think you’ve really thought about the various levels of building speed into a habit and why that’s good and maybe ways in which it isn’t good, or it can be dangerous, and I’d love to just begin with this concept.
A: [00:03:35] Basically I came down to the idea that most of what we do in business is number one, making decisions and number two, acting on those decisions and then really as a sort of third thing, a lot of times you have to sort of get others, other companies, other people on board with those decisions. And if you could do all three of those things faster habitually, you’re going to just have more trips to the plate, more swings, whatever you want to describe it as. And that was the idea that went down to how a company makes decisions. I was just really into the effort of how you make decisions and which ones should take a long time or short time and just how you get things done.
My belief was, particularly for founders, but really for any type of executive, is you can begin to reinforce behaviours that will make your company move more quickly. And that was the heart of that. It was really thinking about, and maybe one of the most foundational ideas is the most important thing about a decision is to decide upfront how long you’re going to take to make that decision and who needs to be involved in it, which sounds like an obvious thing, but most people don’t really think that way. And if you do that upfront, you’ll find you may not always make the right decision, but you make an expedient decision and that’s almost always better.
[My thoughts: I really like the fact that Dave as the CEO wants to move things as fast as possible. He wants to make speed a habit as he calls it — especially for founders/executives —by reinforcing get-things-done-fast behaviours. And by focusing on how much time you will spend on a decision upfront you avoid wasting time which is even better than waiting for the “right” decision. This tells me that if there is a way to roll out new products he and the team will do so as fast as possible. Their goals are aligned with ours. As investors we want them to move into other verticals as soon as possible.]
Q: [00:05:38] So if there’s room for improvement or faster speed at three levels, making the decisions, and maybe there’s even one before that, which is knowing which decisions to spend any time on, but making the decisions once you know you have to make one and executing on them and sort of convincing or getting others that matter outside the business to move at the same speed. I’d love to just pick apart each of those, I’ll call it four stages, and just hear what you’ve learned. So I guess the first one is how do you know that something is deserving of a decision in the first place? That seems like a triaging that has to happen before we get to the other three. What have you learned just in selecting your decisions in the first place?
A: [00:06:12] As a CEO of a company your goal is not to make more decisions than you need to. You want to build a company capable of operating where important things are done far below you in the org.> And so that’s the first thing is, if I’m involved in every decision the company is making, that’s a bad sign. I should be breaking ties or helping to work some of the most important decisions that I have a unique perspective on. So that’s the first thing. And then beyond that, it really comes down to, we’re all making decisions all day long. We just have to discern which ones are worth two minutes and which ones are worth two days and who should have input? And the trick as a leader, I think generally is to give people a voice but don’t try to create consensus because consensus is almost impossible if the decisions are even, we’re thinking about.
[My thoughts: I like the fact that he doesn’t want to be involved in every single minor thing in the organization by micromanaging every single person. Micromanagers usually have the exact opposite result of what they initially thought. Dave doesn’t just want to appear as a forward-thinking leader. But he actually wants to be one. This was also evident when he made the digital-first announcement in the earnings call a month or so ago.]
Q: [00:06:59] So group decision-making doesn’t exactly work well. So if the first thing is making sure you’re not the bottleneck and the second is sort of identifying how much time you’re going to spend on a given decision. What have you learned about the execution layer of all this? Once you’ve made a decision, how does speed then translate into what the teams actually do?
A: [00:07:31] Execution often starts with meetings and discussions as well. As much as we all hate the concept of meetings and people love to bash meetings, it frankly is how a lot of things get done. But in meetings you have to organize a plan and execute a plan and oftentimes there’s not enough consideration to time. How many times I’ve sat in meetings where somebody would say, “Okay, here’s the next two things or three things we’re going to do. Thanks. Let’s go.” And no one ever said, “When are we going to be done with those? How fast can we get that done so we can move on to the next thing?” And having a habit of asking, when will this be done? Why can’t it be done sooner?
[My thoughts: The CEO is laser-focused on getting things done. I remember in an early interview of his when he described himself as a results-oriented CEO and not an Elon Musk kind of CEO. If you’re looking for a crazy, visionary founder, I don’t think anyone describes me that way. I look like the CEO you bring in when the founder goes off the rails." You can read that interview here (if you haven’t already) review.firstround.com/fresh-off-ipo-upstarts-ceo-shares-why-…
Q: [00:18:40] Can you describe credit score and I’ve realized I’ve never really thought about credit score before. What drives it? Why is it suboptimal as the sort of predictor of why someone will pay or won’t pay repay a loan? And then I really want to hear the full story of building an AI function like this or a data model function like this into a core product for business, because I suspect we’ll see this applied in a million other industries over the next couple of decades.
A: [00:19:16] So the credit score was invented about 30 years ago, 1989 ish and before that, I mean, there was nothing. If you were going to try to get a loan, you would sit down across the table from somebody, a loan officer at a bank, and they’d ask you a bunch of questions, they’d have a bunch of rules, they’d want to know where you work and what you paid for your mortgage, et cetera. And that obviously was a very bespoke process with all sorts of problems, it had problems of fairness, problems of accuracy, of performance. And when Fair, Isaac came out with the FICO score in 1989, suddenly there was this universal three digit number that gave a sense of how credit worthy you are. So at the time it was a radical leap forward for any particular type of bank or lender trying to make a credit decision to actually have a number that means something based on your prior use of credit.
30 years later, it still is the centerpiece of how credit decisions for consumers are made, whether that’s a credit card, a mortgage, the government uses it to decide which mortgages can be sold to the government sponsored entities, Fannie Mae and Freddie Mac, and it just became encrusted into how the world of credit works. But when you think about it for a moment, a three digit number is never going to capture all the subtlety of a person and whether they would pay back a loan and when, and what size of loan, on what type of loan. So we had a huge pallet of opportunity to improve on that.
[My thoughts: If 30 years ago FICO score was a radical leap forward, then what Upstart is doing now is surely logical and the natural move of things considering what technology we have available. As the first mover, Upstart has a tremendous opportunity to change this forever (or at least for the next 30 years or so). Speed is critical here too as they want to capture as much opportunity as possible.]
Q: [00:33:07] Is it fair to say that if I think about the compounding asset that you are trying to foster or build at Upstart is this core data model and data set that you’re just every year getting better than the next relevant competitor at knowing something about the consumer in question here and that with that knowledge you become almost like a platform. If you’ve got that insight on a customer, that insight can be used right now to make loans but potentially for lots of other financial services too. So do I have that right? And if it is right, how do you think about intentionally compounding that value because I assume that’s where a lot of your competitive advantage comes from?
A: [00:33:47] They are centralized artificial intelligence models. So every consumer that gets a loan, every bank that works with us is both contributing to and benefiting from these centralized AI models. So we do get this advantage because the model with more data, more experience is the best one. Now the other part is we have to teach it new tricks. We started with unsecured lending, which is a very simple form of credit. Now we’ve moved into auto lending, which is a secured form of credit, right? You have the consumer to underwrite, but you’re also going to have this asset that’s backing the loan, which in this case is the car. So now we’ve sort of taught the model new tricks and instead of being backed by an automobile, it might be a home. It might be the cash flows of a small business. It might be a piece of heavy equipment.
So our idea is generally centralized models that are learning as quickly as possible and then taking sidesteps to learn new tricks where suddenly almost any type of lending in the world and potentially domains beyond lending, we have significant advantages in. And I think that’s the heart of what we’re building is an AI model that can properly price almost any flavor of credit, it just gets a little bit better at doing that every month, every week, every month and that’s enormous opportunity. Not easy to navigate, a lot of things you could do wrong, but I think with strong execution it’s almost unlimited addressable opportunity there.
[My thoughts: The thing I like about Upstart is that they are a technology company solely. They do not underwrite the loan or carry it in their books they simply make it easier and fairer to acquire. This has almost no limitations compared to let’s say Affirm that needs the funds to back it up. Also, by teaching their models “new tricks” they essentially prepare them to be ready to be applied to a new flavor of credit. Just like compound interest, the models learn more and more as time goes by and as more tricks are thrown into the mix. Secured, unsecured, whatever it might be. So all these decisions today, help to form the AI model of tomorrow which can work almost on any flavor of credit. That is an enormous advantage over potential competitors who will need both fresh data and adequate time for their models to learn and improve. There’s simply no shortcut.]