Fintech’s AI Moment
While Machine Learning has been around for decades, the recent advancements in large language models and launch of ChatGPT have created a cambrian explosion of applications and investor interest. Artificial intelligence has become the enabling technology of our time, and it’s impacting every industry we invest in at Greylock.
At the same time, financial services represents 25% of the global economy, and has perhaps the most to gain from better prediction models. Even slight improvements in forecasting default rates on a loan or cash flows of a business can have a dramatic economic impact. But for the most part, fintech has been left out of the conversation, partly because there is low margin for error in a regulated space.
Encouragingly, this is starting to change. Recently, Bloomberg announced BloombergGPT, a large language model trained on cleaned financial data. Ramp, a fintech startup that is noted for being one of the fastest-growing companies ever to hit $100M in ARR, is among the fintech frontrunners using automation and machine learning to help customers with expense management, payments, reporting, and more. The upside of automated, intelligent, personalized, and more secure financial services with the help of AI is in reach.
So what does this mean for fintech entrepreneurs?
I asked Ramp CEO and co-founder Eric Glyman what type of company he would start if he wasn’t running Ramp. He said he’d look for manual workflows with lots of data, and find ways to automate them and own the transaction. For starters, accounting is fundamentally pattern-based, involves networks of proprietary data, and requires a significant amount of repetitive manual analysis. Glyman says.
“Most AI companies today are focused on productivity in the workplace – anywhere you have a lot of knowledge work fundamentally based in data, where by default almost all of it is digital,” says Glyman. “If you can start to get involved in those workflows in both the movement of funds, and the reduction of work, and the augmentation of all the folks involved, there are a lot of interesting things that we could do. If there is an open field that allows innovation that has happened in the rest of the world, it should no longer miss financial services.”
Glyman joined me and my fellow Greylock partner Reid Hoffman for a wide-ranging discussion on how AI is impacting every profession today – and how there is considerable room for it to impact financial services in the future.
This conversation was recorded in front of a live audience of founders, investors, developers, and technologists. You can watch the video or listen to the interview at the links below, or wherever you get your podcasts.
Hey, everyone. Welcome. Thank you so much for taking some time out of your Friday evening to spend some time together.
The joke is that every fintech investor is now an AI investor. But, you know, obviously at Greylock we’re investing in fintech and AI, and we’re also investing in the New York community. And so I thought this would be a good opportunity to bring everyone together.
The [AI] space is moving so quickly, right? And we’re lucky to have people like Reid and Eric and Kevin and everyone else in this room who are kind of in the middle of this.
So with that, I don’t think either of you really need an intro, obviously, this is Reid Hoffman and Eric Glyman, CEO and founder of Ramp.
Let’s get into it.
So, Reid, I wanted to start off for you to just kind of introduce the topic. You’ve been investing in AI for a long time, but there seems to be this explosion over the last six months of investor interest, applications. So what’s going on?
So, all right, look, macro frame is what’s really going on here is the application of scale-compute to create interesting computational artifacts.
We began to see that in the earliest stages with things like AlphaGo/Alphazero and the GO results – by the way, the protein folding stuff comes more out of that lineage than it does from the large language models. And then people started showing OpenAI, more specifically, there was some stuff from Google Brain and other kinds of things that are part of our investments started showing that you could do – out of training out of like 1 to 2 trillion tokens of language data –you could make things that create an amazing kind of artifact that doesn’t just do the kind of like, “Oh, look, write the Declaration of Independence as a sonnet,” or you know, “Translate my poem into Chinese,” all that kind of stuff – which of course it does. But also does coding, also does legal, also does medical, also can get a five on the biology AP exam and all the rest. And this is the path that we’re on with this.
And so that’s why generally speaking, it’s under the term artificial intelligence, because most of these amazing things are things that we would previously have looked at as cognitive achievements. But part of the prediction is not just that there will continue to be an amazing set of things coming from AI.
So another of Seth’s and my partners, Saam Motamedi and I wrote an article last fall that said every professional will have a copilot that is between useful and essential within 2 to 5 years. And we define professional as you process information and do something on it. That’s everybody in this room, plus doctors, plus small business owners, plus legal plus, plus, plus, plus developers, etcetera. That’s just from the large language models.
Now, presume that what’s happening there (and finance and all the rest) presume that what’s happening there isn’t just because you can think of what industry impact will be. That’s true of every professional, every industry that hires professionals can think about what that transformation looks like.
But I think we will see, in addition to amazing things from large language models, I think we will see other techniques of the use of this kind of scale-compute to create things. We’ll see melds of them in various ways. You see some of that with Bing chat going “Okay, we’ve got scale-compute and server which has truth and identity and a bunch of other stuff, along with large language models.
And here’s what revolutionizes the search place. And part of when Kevin, who’s here and others in AI who were looking at this and going, “Oh..” Because we saw all this in August of last year. It’s always easy to predict the future when you’re seeing it with your own hands.
And we said,”Ok, let’s get ready and start building stuff.”
And that’s what’s going on across AI. And so it’s extremely substantive. And what’s more, we’re just dipping our toes into this.
Like this is not like, “Oh, it’s a hype moment. It’s the big thing.” This is like if we were saying, you know, back in 92, 1992, 1993, “Oh, yeah, the Internet, it’s really hyped right now.”
Anyway, that’s AI in a nutshell.
And you know, Reid is always an optimist, but in the last 12 months there have been many partner meetings at Greylock where he’s kind of rung the bell of “Pay attention to this! This is meaningful and it’s right around the corner.” And I think we’re all seeing that happen.
And then ChatGPT came out.