Intelligent Money

Hi, everyone. Welcome to Grey Matter, the podcast from Greylock, where we share stories from company builders and business leaders. I'm Heather Mack, head of editorial at Greylock. Today, Greylock investor Seth Rosenberg leads a discussion on opportunities bringing AI into fintech. Seth spoke with fellow Greylock investor Reid Hoffman and Eric Gleiman, who is the CEO and founder of fintech startup Ramp. While machine learning has been around for decades, the recent advancements in large language models and the launch of ChatGPT has created a Cambrian explosion of applications and investor interest. AI is quickly becoming the enabling technology of our time and it's impacting nearly every industry Greylock invests in. 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 flow of a business can have dramatic economic impact. But so far, fintech has been left out of the conversation, partly because of the low margin for error in a regulated space. But these problems are being solved quickly. Just recently, Bloomberg announced Bloomberg GPT, a large language model trained on clean financial data. The upside of automated, intelligent, personalized and more secure financial services with the help of AI is in reach, and RAMP has become a leader in the space. Seth spoke with Reid and Eric to better understand how AI is impacting every profession today and how it could further impact financial services. This conversation was recorded in front of a live audience of founders, investors, developers and technologists. You can watch the video of this interview on our YouTube channel and you can read a transcript on our website. Both are linked in the show Notes. Now here's Seth Rosenberg, Reid Hoffman and Eric Glymon.

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 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 mean, I don't think either of you really need an intro, obviously, Reid Hoffman, Eric Lyman, CEO and founder of Ramp. But let's get into it. So read priority. I wanted to start off by 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 give us a kind of state of play.

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 earlier 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 the large language models. And then people started showing OpenAI more specifically there was some Google brain, other kinds of things part of some of our investments started showing that you could do out of training out of like call it 1 to 2 trillion tokens of language data. This creates a amazing kind of artifact that doesn't just do the kind of like oh look, the Declaration of Independence is as a sonnet or 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 5 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 there will be continued to be an amazing set of things coming from AI. Another of Seth's and my partners Sam Motum and I wrote an article last fall that said every professional will have a copilot that is between useful and essential within two to five years and define professional as you process information and do something on it. That's every everybody in this room plus doctors plus small business owners plus legal plus plus plus plus developers et cetera. 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 going to because you can think of what industry impact would be. That's true of every professional. Every industry hires professionals what that transformation looks like. But. 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 OK here's 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 and and I were looking at this, we thought, ooh, because we saw this in August of last year. It's always easy to predict the future when you're seeing it with your own ads. And we said, okay, let's get ready and start building stuff for it. 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 a big thing. This is like that's like if you were saying, you know, back in 1992, 1993. Oh yeah, the Internet's really hyped right now. Oh yeah. Anyway, so that's AI in a nutshell.

And 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 around the corner. And I think we're all seeing that happen.

Right. And then ChatGPT came out.

Yeah, exactly.

Yes.

So Eric, so yeah, maybe just give us an overview of what Ramp does, how everything that Reid just described is affecting your business and fintech more broadly.

Absolutely. And wonderful to be here. So Ramp is a finance automation platform. We're focused on functionally workflow and productivity related to movement of money. We're known for operating the fastest growing corporate card in the US Bill payment software, expense management, accounting automation and the like. And all of our products are designed with the intent of helping customers spend less money. Most venture products are designed with the same or more and spend less time. And so really what we're focused on is workflows that companies need to run in order to disperse funds, close their books and everything in between. I think the effect on AI from the business has been frankly profound from even the founding year of the company. Where there's simple things in expense management like text to receipt, match it to the proper transaction is very simple machine learning to today when you think about accounting. Ultimately these are generally accepted accounting principles. These are rules fundamentally of how transactions should be categorized and coded. And as you think about what are the patterns and how can you learn from the 10,000 plus businesses, hundreds of thousands folks learning using automating, both keeping of records, risk management and assessment to even go to market even in the way that Ramp has been able to grow so efficiently has come down to embracing AI in our sales characteristics and lead routing and mapping the like and so happy to go deep, but there's probably 10 core work streams all throughout the business that are leveraging in some way from pattern matching to generative use cases as well.

Yeah, that makes sense. So one interesting topic is, and Reed, I know Sam Altman talks a lot about this, where the original narrative of AI was that it was going to automate more basic tasks, like more solvable problems like accounting or law. And the original thesis was that the last things to be automated were poets and photographers and graphic designers. It turns out that that's actually been the reverse with at least this first wave of generative AI, where the creativity gives the models a little bit of leeway for making mistakes. And at least the current state of these generative models, they don't lead to 100% accuracy. But in fields like health care or like financial services, 100% accuracy is required in many cases. And so what's your take on how fields like finance are going to be able to leverage these models?

Well, first, promise the question is not 100% correct because there is no 100% human accuracy. I presume most of the people in this room would know that if you were asked to have an average radiologist read your X ray film or a trained AI, you should take the trained AI if it's an average one, if it's the best one or one of the best ones, great, take that. And better yet, take the two together. So there is no such thing as human infallibility anywhere, including in finance.

That's especially true.

And so part of what we have to do is we have to kind of figure out. Now, part of the thing is we know how to hold human systems accountable and what accountability law looks like and what the error rate within human and what's acceptable when it's person plus machine or when it's machine driven, those will be relevant variables. Obviously, we worry about things like, for example, AI applied to credit decisioning. Do you have systematically biased data? Now, one of the benefits is to say, well, that becomes our scientific problem, which you can then work, work on and fix. As opposed to the human judgment problem where you say, well, we have that human judgment problem in how we're allocating credit scores or credit worthiness or parole or everything else. Today in our human system, the machine system may start out looking like it's going to systematize the old one, but we can fix it so that we can move past that, especially in person plus machine. And that's, I think, one of the things you're going to see with AI across a number of fields including and finance and some of the early areas. I did publish a book last week called impromptu with GBD4 as a co author where the central thesis was it's not artificial intelligence, it's augmentation intelligence. And obviously in various ways it is also artificial intelligence. But it's the look at how you amplify people like the copilot thing that I was saying earlier. And as an exemplar of that now I did deploy my own personal team in helping me with this. We started writing the book in January. So with that we got it out and there are physical copies now. The physical copies are more print on demand from Amazon, but we got it out last week. Right. So that's the kind of thing of the proof in the pudding with like think about how this augments human activity. Again, augmentation amplification. That's the pattern to be thinking as how you go forward. And of course there's a whole bunch of stuff when you begin to think about fintech, fintech products, how you operate as a fintech company, where this touches.

Yep, that makes sense. And Eric, I think you have a pretty similar point of view around this topic. Yeah. Anything you'd add in terms of even just practically like you know, how you build guardrails when you're dealing with sensitive information and AI.

Yeah. On the fintech side of it, I mean I think it's just like a very unique industry where it's such a large sector of the economy which as most of the world and again I think it's accelerating, but went from no phones to flip phones to iPhones. People still have the same credit cards in their wallet, same bank accounts. And I think in a way folks in this room know the fintech industry blew open over the past five years and actually software first orientation business models that can also move funds and get involved in workflows have started to spring up in a major way. So there's a lot of opportunities around it. When I think about some of the guardrails and patterns, I agree with Reid of inherently there's never been perfect. When you think about underwriting best efforts. When you think about fraud, you take patterns to understand and prevent. And so I think perfection is actually quite rare. I do think the copilot model is a powerful one when you're thinking about for us, one of our core customers is accountants. Where they need to be going and pattern matching and taking the learnings and bringing that back to augment and speed up is a very quick pattern but also most of customers, most employees are not accountants, don't know it. And being able to simplify, just as in coding over the past set of months, it's gone from primary language of coding from Python to arguably in a year or two it may be English is the primary way. I think those same patterns you'll find in workplace productivity, expense management, accounting, vendor intelligence, that and the like. And so I think it's really thinking about where needs to be a high degree with risk and underwriting. You may not want in making all the decision and you want to see and back test and there's ways you can get at it heuristically. Whereas if you have operational loops in that copilot design pattern has been emerging from to deal with it.

Yeah.

And actually not to put him on the spot, but I think Kevin Scott, who's the CTO of Microsoft, who's here, who pointed out to me he's like, oh, what's the largest programming language in the world? English.

Yeah.

And Reid, I just want to double down on the point you made around AI is not perfect, but neither are humans. And in many cases AI actually performs. How do you think we deal with kind of the political, social, regulatory kind of mindset shift? One example is autonomous vehicles, where okay, autonomous vehicles are safer than humans, yet we're not necessarily socially comfortable with autonomous vehicles making those mistakes.

So part of the reason why I would say that we need to own it as a society is every year that we delay shifting to basically autonomous vehicles on the roads. We're killing 40,000 people, blood's on our hands. So the important thing is to say it's actually worth saving and it won't be. Look, you still won't get below 1000 or something. I don't know what the number is, it'll be a lot, but you'll save a ton of lives. And so you say, look, we have to solve these accountability issues, we have to solve these kinds of things. And by the way, it's very similar when you get to, you know, obviously when you look at the press and government, there's all this buzz about oh my God, AI and it's going to have some job impacts and we should slow it down, all the rest. And obviously there's all this discussion about competition with China and so forth, which is important. But here's a way of making a tangible current China GBD4 looking at it line of sight to a AI doctor and an AI tutor on every cell phone deliverable cheaply enough that anyone who has a Smartphone can have access every month. You delay that. Think about what the human cost of that delay is. That's when I talk to government folks and say, think about it this way. I'm not saying don't ignore data, don't large companies and ecosystems, not to say those are all irrelevant variables, but one of the classic ways that democracy fails a la climate and everything else is what about children and grandchildren and all these other people? How do we help them? And this is part of it literally is buildable today with the technology. It's just a question of how soon and how do we get there.

Yeah. And yeah, doubling down on that kind of optimism.

Right. I'm happy to be optimist. That's not optimism, that's truth.

Yeah, I mean, yeah, I don't think you have to convince the people in the room. There's a bunch of founders in this room and also people who want to start companies. Maybe this would be starting with Eric. Let's say you hadn't started ramp. You're not the current CEO of ramp. You see this kind of Cambrian explosion in AI and you see these advancements that Reid's describing. What are the most interesting opportunities? What type of startup would you build?

Yeah, I mean I think in some sense that many of the companies behind AI often focus on productivity in the workplace. I think in some sense reveals where many of these interesting use cases are put differently. I think when you look at a lot of knowledge work fundamentally based in data, where by default almost all of it is digital, if you can start to get involved in those workflows, in both the movement of funds and, and reduction of work and augmentation of those folks, I think there's a lot of interesting things. And so look, I think one of the most interesting areas is frankly around accounting there's a lot, it is fundamentally pattern based. There's a large set of folks who need to look at repeated data. They're both within a company and you can learn across. So there's data network, there's proprietary data, there's some network effects, there's clear patterns and some personalization that needs to take place. And I think when you start to combine those efforts, I think accounting is a very interesting space. I do think in fintech particularly, I think there's both the ability to have better risk assessment and fraud fighting as well as probably a great opportunity for fraudsters too. I wouldn't recommend it as a venture funded business, but when you think about.

A lot of it's not a US domiciled one but yes, it turns out.

These are big businesses just not based here.

Russian VCs might be the. I think that's true.

It's a lot of opportunity when you can generate someone's look, face, sound predict information about them. And so I think both sides around that's going to be very significant opportunities. And last, just secularly, even outside of AI, the notion that core financial service products which once were locked, if you were a bank you could store money and move money. Now those requirements, well, you need to be thoughtful about regulations and the effect it has. It's a much more competitive and I think in a good way open field that allows innovation that has happened in the rest of the world should no longer miss financial services. And so I think it's quite exciting.

Yep. And Reid, just along these lines, there's a debate on whether, on whether startups or incumbents are kind of better positioned to take advantage of these advancements in large models. What's your framework in terms of which incumbents are best positioned to take advantage of this wave and which opportunities are more available for new entrants?

Well, the short answer is there's such a tsunami of stuff here. There's massive opportunity all around. So the usual kind of false dichotomy question is it only going to be Microsoft, OpenAI, Google and too bad for everything else? No. Are there going to be things that these companies are going to dominate and do? Yes, but there's tons of room for other things among them like last year with Mustafa Silliman, former co founder of DeepMind, venture partner at Greylock, we co founded Inflection. Unfortunately, I won't be able to talk much about Inflection. We will talk more about it in a month. Maybe we'll come back through town and that's a startup opportunity. So you're putting our money where our mouth is and doing this. And we have obviously a variety of great companies adept with David Cresta, Snorkel, et cetera. We have a whole stack of of AI companies that are invested in going so now to the more broad thing is there's going to be a combination of, I think two broad trends. One broad trend is the mega models which are super valuable and important. And there's a bunch of ways it'll turn out to be super valuable and important. But if you take interesting areas like medicine, law, coding and you say okay, we're going to spend $500 million to make the larger, better model of this and it's going to be 20% better, well in those you're going to do it. We have Internet distribution, major distribution ends and you're like okay, the 20% better product will just naturally have with nothing else some network effects because everyone can get it through the Internet as a way of doing it. And so the people doing the really big models and putting that in which there'll be a small N number of and I don't think Only Microsoft, Google, OpenAI I think there will be one to five others.

Well OpenAI is a good example of you wouldn't have predicted that five years ago or you actually would have because.

You did invest in it.

But most people wouldn't.

Yes, although the investment was from my foundation because it was like no actually in fact this project for AGI is a very good thing. We had a discussion around the partnership table of should we do this? And we're like okay, no revenue plan, no go to market plan. We have a responsibility or LPs to put something into that is doing that. So in retrospect if you could have said hey look at now then we would have said how much of it can we take? But that's always the easy part of investing is the ten year look back. But anyway so there will be this large, this large channel and then there also will be a whole bunch of smaller models for all kinds of reasons especially tune something in finance that does a specific kind of accounting fraud, other kinds of stuff, something that may run on your phone da da da. And there will be a whole bunch of those things. So for example GPT3 was very expensive to run the last compute run on. You know about a month ago I saw something was maybe this is a swag 80% of GPT3 that cost $3 million to make. And this is partially this other channel of stuff that will be happening images, text, other kinds of things and both of those will have great economic opportunities in them. And the thing that people a little bit too much mistake go to is just because I have an AI tech actually there's a lot of what's your go to market, what's your business model? How does that tune? Like how do you competitively position? How do you create a moat? Is the motor network effect or is it something else? Those will broadly still play into how you're thinking about how the tech disrupts things. So the short answer is that isn't only large incumbents, isn't only startups, it's massively both.

Yeah, that makes a lot of sense. And Eric, on this topic of large language models versus more fine tuned models I'm curious as it relates to ramp, obviously you have a huge workflow opportunity of just applying some of these large language models to your existing product, having better underwriting, et cetera. Are there any opportunities for you to also invest in some fine tuned models and some of your own AI?

I mean I think it's a super interesting and present question for a lot of practitioners, people building startups, do you bet on either A the mega models or B more fine tuned in house development models? And frankly I'd be curious for your view and opinion on seems to me.

I think the very unhelpful general answer is it depends.

It depends. Fair enough.

It's the more detailed conversation that we'll get to maybe X or Y.

Fair enough.

Yeah. Anyway, so I didn't mean to interrupt your answer. I just.

It's totally. We'll dig in but I think that's right ultimately. What do I think? So first I think that for a variety of use cases like relying on like I wouldn't bet against what's happening in the mega models themselves to for the vast and general use cases, empowering more experienced generalized use cases using that but there are a variety I would be thinking as folks building businesses of are there proprietary, is there proprietary data that's involved in the workflow of your business? Is there a data effect? Is there some level of personalization and as you run it through does the experience get better for every customer? And I think even back to some of the themes that we've been touching on, there may be broad based risk and underwriting that once you start getting data you can apply it and turn it on. But there may be even smaller use cases and loops from tagging transactions to understanding more about specific merchants and learning from the set and sharing that back out that you can tune to your model and I think makes sense for tuned models. I think one of the other questions specifically for finance, whereas most of these mega models have been trained primarily whether it's text or images, in some cases code, I think that there are larger models being built on numbers relationship to counting and so I think the answer will evolve over time. I think outside of the training set of the mega models I think the functional answer today is training more locally but being ready and thinking about is your stack prepared to make a switch and evaluate Because I think that the.

Just.

The core infrastructure and codevelopment is changing so rapidly and so building in a way where if you need to change things out I think is important in this style of architecture.

Yeah everything Eric said is exactly right. Here's how it also amplify, which is if your theory of the game is a thin layer around the AI model, it better be playing on the trend of the large model. So right, you better be anticipating Lex large model. If that's not your theory, then the small model or the self run model or whatever can itself go. But what happens is people go, well, I just put a thin model layer around it. You're like, well, the large model is going to blow you out of the water almost every single time. Unless you just happen to be the. I'm trading the next large. The large model is the back end. I'm just trading the next one happens. Fine. That can be in a strategy too. But anyway, that would be another principle to add to it.

Awesome, thank you.

One of the final topics here just I wanted to double click on. Obviously these models are very powerful. We talked about kind of the risk for using these for fraud phishing attacks. I guess I'll pass it to Reid. You know, obviously we're investors in abnormal securities. That's taken the other side of that, which is using AI to detect phishing attacks and protect people.

There's tons of security applications of this and unfortunately tons of offense applications too. Yeah. And so, yeah, Russian vc.

Exactly.

Yeah.

What are the vectors of offense defense? How do we put guardrails around this technology, you know, as a society and then also as a business? Like, how should you think about the risk factors here?

So one of the things I'll start with, paradoxically is a defense on some of the criticism that OpenAI gets, because people tend to say, oh, it's OpenAI, it should be open source, et cetera. And people go, oh, the people who are releasing open source models.

People say that?

Yes, people say that. I don't say that, but people say that. And academics want it because they want access to the open source models and entrepreneurs want it because they want to be able to build on it. By the way, I understand all this stuff. The problem is that an open source large language model of sufficient capability is a built in phishing tool. Just to be really clear, it's like here you want to do cyber phishing, we have it, we could do it right now. And so you have to be much more careful about open sourcing these things. I mean, for example, to be clear about something, last year Dall E from OpenAI was ready four months before it launched. Why? Because they took the extra four months and they said, well, it could be used for these kind of bad things. It could be Used for child sexual material, it could be used for revenge porn, it could be used. We don't want any of these use cases. We're going to spend the extra time to really make sure that these are very difficult to do with our tool. And by the way, part of the reason why they offer it through an API is we can be paying attention and we go, what's that? Let's fix that. Right? That is. And you get other companies that go, oh, we're heroes because we're releasing the open source models and the open source models cause a notable increase in this kind of garbage distributed on the Internet and terrible impact for the stuff. And by the way, it's not, there's financials, but a whole bunch of other stuff. So I'd say one kind of area is to say, well, we gotta be much more thoughtful about the good outcomes. Because by the way, Dr. Tutor, like hugely important outcomes. Fraud prevention, cyber prevention. And one of the other things is because it's being driven primarily by commercial entities, entities versus governmental entities. All of them are going, oh, we don't do weapons. Right. And by the way, a respectful, honorable position. But on the other hand, okay, there are going to be weapons coming out of this and you need to understand them because you can't defend against them if you don't understand them. So I've been going around to just about every lab that has a major effort and going, no, no, you should work on some weapons stuff too. You should learn advanced security procedures. You shouldn't do the weak ass stuff like the ns, which is a contractor can run out the door with them. That would be bad, right? But you should do, but you should figure that out because we should be in advance so that we're not vulnerable to them because there are real vulnerabilities. And of course when you see that as a venture firm like Greylock, we start going, oh, we should start investing in a lot of security companies and we should do this, this and this because we got to make this happen.

I think we say that every year regardless.

Well, but maybe 10x this year.

Exactly. I want to end this part of the discussion just on an optimistic note of.

I haven't been optimistic exactly.

This is just the realism, the realist read. So let's project five years out. What impact will this technology have on just regular people? And what are you most optimistic for?

I mean, I think it's funny, like many of my competitors, their founders were around in the 1800s, they wore top hats. You know, these organizations are not built thinking about how much time it takes for people. They have all the time in the world, tens of thousands of employees, and time is functionally free. But it's not for most people. Right. And when I think about most aspects of financial services, there's an incredible amount of busy work that's required. Whether it's in applications and reviews, in submitting expense reports and doing accounting and doing procurement, figuring out what things cost. And if you collapse the amount of work it takes in order to get at more data and understand what's happening in the world and have the world's knowledge given to you, more rapidly personalized to the problems you're solving, or have work done for you, it's incredibly freeing. I mean, for us. I mean, one of the most common things that customers would say about ramp is I don't think about expense reports anymore. I have time actually to do the interesting strategic work, not just to go and collect receipts and tag transactions. And I think that's a very small and early sign of things to come. And so in many ways allowing people to be more strategic, focused on higher level work and more interesting and profound questions I think is. I think that's the potential.

Yeah, I think, by the way, excellently said. Because I think it can transform not just the productivity, but also the joy and meaningfulness of the stuff. That's actually one of the things that's frequently mistaken. I think you highlighted that very well. So with both my greylock and Microsoft hats on, I kind of thought about this through a lens of a company. You said, okay, say you gave everyone the power as being 10x and cause the classic press dilemma like oh my God, people are gonna be laid off and it'll be disaster. It's like, okay, so let's walk through the departments. We had 10x salespeople. Are we gonna lay off any salespeople? No, 10x sales would be great. Let's take it marketing people. Well, you might have different functions. Cause the person who's doing the data entry, we don't. Those functions we don't need as much. But by the way, do you, do you want less marketing? Do you now have a competitive bar about how your marketing is? You probably have the same number of marketing people. Product, yeah. Engineering, probably, blah blah blah, probably even accounting. Because now you can do all kinds of different kinds of accounting and analysis of the business and a bunch of other stuff. Again, marketing has changed now. It doesn't mean it's all roses and utopia, but we've walked through a huge number of departments looking at this and gone. No, this is going to just be helping these companies operate a whole lot better, is not going to create any kind of. There'll be some workforce transition in terms of what skills and what are you focusing on? Customer service. You might have less. Right. So it doesn't mean, again, zero. But if you look at the overall package, you go, actually, in fact, this is not a. This is a workforce transformation. And then, you know, when I'm talking to us folks, you go, well, what have we been doing for the last 20 years? We've been putting customer service jobs in India and Philippines and so forth. This is actually, in fact, not actually a big workforce problem. And so the optimistic thing is actually, in fact, I think this will be productive. And I think part of the reason I didn't start where I normally start, which is also the joy of work will be a lot better. And by the way, I presume, given the tech and everything else, that everyone here has played with ChatGPT, at least, like, if you didn't stay up at night kind of going, oh, my God, I kind of don't understand you. Like, it's fun. It's interesting. Right?

You should tell my girlfriend that.

I will. Maybe it's normal.

It's totally normal.

Yeah. Oh, that. Sure. That may cost you a bourbon. True.

Thank you, Mike.

Well said.

I was thinking maybe we'd open it up to the audience for maybe just a couple questions and then we can let everyone eat. Anyone have any questions for Reid and Eric?

So there was an art gallery open here that you know, about a few days ago at one of the most prestigious galleries, the Gorcia. And it was a creator who had made all of the art presented with Dal E2. And I think you. It would be fair to say that some of the most, you know, it was public art opening. Anyone could walk in and see it. And there were some of the most, you know, famous photographers and artists and film directors in the world there, and they were all depressed because they felt like, this is the. This is. Like this is it. This is the beginning of the end. I mean, one of the photos was, you could have easily thought, was a Diana, the twins photo, but, you know, a new work. So what do you have to say to those people?

So first, a bit of history on that Pacifica one, because it's kind of fun. And then what I would say, so the history is Bennett Miller, amazing film director, Moneyball, et cetera, et cetera, a friend of a number of ours, mine here happened to be in town having lunch with Mustafa Suleyman, my co founder at Inflection, and said, oh, come join us. You like this AI stuff? And so we were talking about what was happening, this was last year, about image stuff. And I looked at Bennett and I went, you know, I think OpenAI would give you early access to Dall E. Would you be interested in Bennett because he's invented. Yes. And I went, okay, great. And I could called Sam on my way to the airport and they're like, hey, I kind of promised Bennett open access. Are you cool with that? And he's like, yeah, okay, great. And a month later, Bennett shows me this amazing art stuff I couldn't have even thought that Dall E was capable of doing. And I was like, dude, this is really good. We got to show it to the OpenAI people. I don't think they have any cause. Part of what's happening with these hundreds of billion parameter models is turning computer science into natural science, right? Like, it's like, who knew you could do this stuff? And you unleash someone creative doing it. And so, you know, it was like, okay. And we did that and it was all great. And ends up at the negotiation. Now, given that I haven't. That we are on camera and I haven't said to this person. I was talking to a musician last July, and I said, we have a program at OpenAI that can either give me an original X, like a Celine Dion, et cetera, and create that song, or can give 15 seconds of it and create the rest. I know that right now you're feeling terrified, bad, right? You're like, holy shit, my job is done. Here is why I think it should excite your creativity, because you can go do that and then when it plays out your four minute bit, you can go, oh, the spit between second 25 and 35, that's really good. And the spit between a minute 30 and a minute 48, that's really good. And I'm going to take that and make something a lot better. This can be a tool for amplifying me, right? Yes. You have to learn the new tool. That's the transformation. So, yes, if you're like, I refuse. I'm really good. I'm just a expert now and I don't need any new tools. Okay. World's a little harder now, right, for that. But if you go, oh my God, I could use this tool. And I. Because, by the way, who are these people? They have amazing artistic sense. Like, for example, part of what Bennett taught me was what is Bennett's like Genius is that he thinks intensely visually as a director. Now we're giving him the tools. Like we'll type it all in here. And he can also then create art. Cause he does it well. It's because he's a genius at that stuff, right? It's an amplifier and that's the thing. So yes, they have to learn the tools and they go, well I don't want to learn the tools. Well, it's like look, I drive a horse and buggy and I don't want to learn the car. Ok, that's a choice. But go learn the car. Like do that. That's what the general answer is.

That is pretty cool to see creative minds, these tools in the hands of creative minds.

I had no clue by the way. So it's at the Gagosian, go see it. I had no clue until Bennett went, oh, let me show you a few things. I was like, holy grolly.

Maybe we'll do how we're doing for time. Two more questions.

This question's for Reid. So as the founder of one of the foundation model companies, we're out like Stanford 2 weeks ago ago they took 50,000 input output pairs from a sub 175 billion parameter model from OpenAI and they fine tuned a 13 billion parameter model off of it. Cost like $600 and it's as good as the OpenAI model. How do you view moats evolving now that anyone can just replicate your state of the art model? Just having access to it, not the architecture or anything, just the inputs and outputs.

Very deep question and great question and the fundamental answer is it's unknown and we're discovering it. There is no simple answer to that question. There will be some answers to that question. It just this huge ocean and I got a ship and I'm going to launch it. Like where's Gleed and what does it do now? A little bit to the earlier point is that there are still a bunch of things that Stowe we have learned in many so software entrepreneurship about. Well actually in fact you get kind of certain kinds of system integration. You have a good go to market virality something else. You have a data set stuff that you were talking about earlier. Things that say, oh, that gives me it. Isn't that all of the old wisdom about entrepreneurship and software entrepreneurship has just been dumped off the side of the boat right now it's a technological platform platform. Change that. When I say it's bigger than any others, it's because it's the crescendo. You can't have it without the Internet, you can't have it without mobile, you can't have it without cloud. It takes all of those and amplifies them. And that's why it's completely changing the name of all these things. So yes, but that doesn't mean that the old business model, the old business wisdom of kind of things, all of those folks, all of those traits are still relevant but they're transforming and while figuring out which ones matter in which ways now. So it was a great question for everybody.

Yeah, thanks for that.

Just like further that question on this.

Idea of like will LLMs just be commoditized? Is First Mover advantage just so much more important now?

Because.

And when OpenAI released the plugins today, I thought that was brilliant because now it's like that's first ChatGPT was becoming commoditized anyway and now it's like, oh, that's interesting, now they have plugins. Now it's going to be hard for everyone else to get plug ins. So I agree with that idea of like traditional model still applies.

Exactly. And the short answer is some of it, this is the reason why it's a work in progress. Some of it will be completely commoditized, will be kind of like it's the cost of the compute, electricity, et cetera, et cetera. And you can get it from three or four different players and it's kind of like which one do you buy? Well, I like this soft drink versus that soft drink, you know, kind of thing. But which ways do you make it non commodity and how do you do that? Maybe some of it's in the tech, maybe some of it's in the business, Maybe some like classic is a network of developers doing plugins. That's the classic play. So of course. And you know, I have the privilege and honor of working with the OpenAI team. They're very smart.

All right, I think let's break there, but thank you, Reid. Thank you, Eric. I really appreciate you taking the time.

And thank all of you to coming to our dinner on Friday night.

Thank you so much for being here. I hope you have great conversations with the people here and yeah, enjoy the evening.

That concludes this episode of Gray Matter. You can watch a video of this interview on our YouTube channel and you can read a transcript on our website. Both are linked in the show notes and if you aren't already a subscriber to Gray Matter, you can sign up wherever you get your podcasts. I'm Heather Mack. Thanks for listening.