#147 The Past, Present & Future of Generative AI—With Joanne Chen, General Partner at Foundation Capital

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Welcome to Data Framed. This is Richie. AI companies are sprouting like weeds and it's difficult to keep track of all the players that causes a problem when trying to implement an AI strategy. For your organization to make any sensible decisions about a technology stack, you need to know what tools are available and how they might fit together. Fortunately, having an encyclopedic knowledge of what new startups are doing is part of the job description for a venture capitalist, so I've invited one on our show. Joanne Chen is a general partner at Foundation Capital. She's been involved in a wide variety of seed stage investments in AI startups, targeting infrastructure through to consumer facing products. I'm keen to hear her views on the AI ecosystem, how she thinks these AI startups are going to affect everyone else, and how businesses can get the best value out of these new AI toys. Hi Joanne, thank you for joining us on the show.

Thank you for having me. Super excited to be here.

Brilliant. So I guess to begin with, one thing I noticed is that there are so many new AI companies around, and particularly generative AI companies. Do you have a sense of how you might go about categorizing them? Are there some important areas that are developing?

Sure. So from our standpoint, we've been investing in applied AI for the last almost 15 years now. There's three areas that we typically look at broadly. One is the application of AI. So what we call intelligent automation, meaning automation of workflows, tasks, decision making. Think of AI applied to marketing or sales or to an industry like legal or logistics, that's one big bucket. Or fintech, that's one big bucket. The second is machine learning infrastructure, which is all the tooling and all the infrastructure necessary in order for companies to capture data, clean data, process data and use that to leverage machine learning and AI. And I think the third bucket, which I think is super interesting that we are spending a lot of time in, is looking at cybersecurity because as the tools get more powerful, as AI gets more powerful, tools that are misused similarly are getting more powerful. And therefore how do you counter add things like fake content that's easier to generate and less costly to generate today? How do you counteract hackers are now trying to do model poisoning or data poisoning. How are you defending against phishing attacks that are now sounding more realistic? Right. You get an email saying please click this link and et cetera. So those are. There's another really big bucket that we're excited about.

Okay, so automation and infrastructure and also cybersecurity, these all seem like really important topics. Maybe we can get into the details of some of these later on. But are there any particularly important business use cases that you've seen of just emerged over the last year or so?

Business use cases. So maybe I'll comment on the application side, what I've seen companies pursue from a strategy standpoint, there are two main things that stand out to me. One is around a time to market strategy, meaning when you are competing in, let's say marketing, you're building this new AI solution where you can offer something that hasn't existed before. If you're competing in like a really noisy sector like marketing or sales, the companies that are faster market or first to market and are going about the land grab strategy, I think have an opportunity to get those customers and build deeper. And what they'll do is they'll build workflows, etc. Nothing. One broad area. And there's some other opportunities today that's touched very lightly by AI. So for example, procurement is one where there hasn't been that much AI innovation or innovation in general. And so I think from a competitive standpoint, you can still go and build a better product and compete there. Land grab and then build deeper. That's like one broad strategy that I've seen in the app side. The second is like rethinking business models. So for example, a legal firm right now charges an ungodly amount per hour for lawyer services and their revenues are scaled by the number of lawyers they have. But with I think generative AI, what you can imagine is a firm that just has 20 lawyers that are making a billion dollars in revenues because 80% of what they're doing is using machines. And the 20% is all about customer experience and customer service. And you can change the dollar per hour billing model to something entirely different. And I think so. There's opportunities to reinvent certain industries by completely changing the business model thanks to this technology.

That's absolutely fascinating. I think anyone who's ever been charged hundreds of dollars just to be sent some kind of standard legal form is going to be very happy. If there's innovation in the legal industry. Are there any other industries or sectors that you think have been particularly affected by AI or by generative AI in the last year?

But we're seeing innovation up and down the stack in all the three areas that I mentioned. There is also a big bucket of engineering, automation or augmentation that I haven't mentioned. And I think this especially applies to the engineers, or maybe not the 10x engineers or even like the Silicon Valley engineers, but the engineers are doing more mundane stuff like integration building or kind of sre. So, like thinking about site reliability, like the engineers are doing all the other stuff and there's a ton of them. I think there's a lot of opportunities to augment and automate some of the work that they're doing. And companies are more willing to buy software to augment or automate those pieces because they're less core to the company.

Okay. So I do certainly like the idea of automating, like, maybe the boring bits of engineering. That certainly seems a great thing to do. Like, if you don't have to do all the boilerplate code, then that's a definite win. Before we get into the details of particular roles, I'd like to talk a bit more about companies and how you go about finding good companies. Because I have to say, just looking around at all the new companies cropping up, it seems like some of them are just like, well, we put a website around the OpenAI API or something. It seems like it's not really like a very substantial company, but there are some very cool things happening at the same time. So do you have a sense of how you go about finding, what are the quality companies out there for us.

Which we invest at the very, very early stage, meaning primarily the seed stage, when companies don't have revenues. Right. Like 85% of our investments are in companies that don't have revenues, that haven't discovered product market fit. And so there's very little data the way that we approach, that is, we take a very founder first perspective. Like is this founder, they have some kind of secret around the problem they're trying to solve? Do they have the persistence and tenacity to go about it? Especially because the ecosystem is moving so quickly and what they built today could be irrelevant tomorrow. Do they have an initial team that is really high quality, that can work together really well to move really quickly? Those are some of the things that we really focus on. We do a lot of references, we talk to people that they worked with before, we talk to potential customers and prospects. We have a customer network that we rely on. And so that's like one big bucket of investing strategy. I think the other big bucket is we spend time on problem statements that we think broadly have the opportunity to be reinvented. For example, the law firm example is one such opportunity. And we do this across the stack and look at where this new technology can make a really fast impact and then understand like what is the strategy they're pursuing.

I guess at that early stage you really are just investing more in potential than what's actually there already.

It was pretty fascinating and I think that it's a hard mandate to match the founders and the founding team to an interesting problem. But oftentimes we will spend years and years digging into a problem area before we invest. For example, Jasper, which is one of the companies we let the seed in. We looked at marketing automation and marketing technology since 2014, try to invest in companies that were automating content creation in 2016 and 2017 unsuccessfully. And then when we met Jasper way before all this hype, it was just very obvious because we had been looking at that space for a long time.

And Jasper of course been a huge success story for marketing content creation. Are there any other companies that you've seen that are particularly promising?

In our portfolio we have companies including Anyscale, which is a unicorn company that is helping companies like OpenAI reduce their compute cost. With such large language models, there's a lot of costs associated with training them. This was the biggest innovation in my opinion since 2017 when transformers were available to use as an architecture framework. It's really the cost. And in 2017 we invest in companies that were trying to do some of this, but they did it themselves, right versus use someone else's off the shelf models. That was really, really difficult for a young company because you don't have that much resources. Right. So I think a lot of what OpenAI and others have done is like reduce the cost by doing their own Training and offering that off the shelf. Anyscale is helping OpenAI reduce that cost. That's one example. Cerebras is another one. In our portfolio they are taking a hardware approach, meaning they're offering GPUs the way that Nvidia does. And they're also offering models as a service for certain use cases. And in that case the high level problem is still that COMPUTE is really expensive and how do we reduce that cost as quickly as possible. Cerebras perspective is that a hardware approach is the right one, whereas Anyscales is more of a software approach. Those are two other examples, but we have 50 plus companies in our portfolio that are leveraging AI significantly or are enabling the infrastructure and yeah, so both.

Those companies you mentioned, so Anyscale and Cerberus is like really quite low level infrastructure stuff. So it does seem that AI is causing changes at lots of different levels of the market, like from that low level infrastructure all the way up to applications and so on. So for organizations who are considering buying AI products or services, are there any things that you think they need to consider before they buy?

At the end of the day, whether someone has AI or not, what really matters is like are you moving the meter on a business metric? Business metrics are typically revenues and cost. And so when I think about the purpose of AI technologies, it's really to serve a and business problem or end user problem. So I think that's like the most important thing to consider. I think the second is culturally the balance between making sure someone's comfortable using AI as an augmentation tool and how do you reduce the fear of full automation. I think that's something that's culturally talked about quite a bit. My experience is such that no one has been fired because of an AI solution, but organizations have gone more efficient or can do more with fewer people. And I think that's a great thing.

Absolutely. And so that's. You mentioned the idea that a lot of organizations don't necessarily want a full AI solution, but they actually just want to make things more efficient, reduce the number of humans involved. So can you maybe talk about the trade offs between having a full AI solution versus this hybrid approach with humans and AI together?

Oh, I don't think there's full AI solutions, period. I think most of the solutions are hybrid in nature because there's very few instances where you don't need a human interface layer, whether that is a law firm. That AI can generate a contract for sure to like some high accuracy today, but you still need a Human to present that and provide the customer service. Similarly, if you have a sales team that's using AI to write emails and reach out, you still need a person to think through. Am I prioritizing the right things? How do I make sure the customer is having a good human experience on top of some of the automation? So I don't actually see that many areas where there is full automation in the company. In fact, as the AI automation piece is commoditized, the role of the person becomes even more important.

This seems similar to your point about how you need to start with thinking about revenue business metrics rather than starting with think about AI. So do you want to expand on what you were saying about how this customer experience is very important. So do you start with trying to have goals around customer experience and then figure out how AI can be built into that, or do you have any thoughts on how you approach this managing a good customer experience with AI?

I mean I think that one of the things to explore like for a startup, a startup has relatively limited funding, it can only hire so many people and so how do you make sure everyone is ultra productive and creating more opportunities? And they usually can. So one of the things they could do is build a stack of solutions where if you're on the marketing team, use AI to create content, AI to do some of the lead generation and also help you understand where the channels that you should focus on from US marketing spend standpoint. Right, like that, that can all be done by machines. But then the person still has to come and think about the brand, think about like the interface between marketing and sales, think about the community building and all the other aspects of it. So it actually frees up their time to do that plus all the marketing specific things they had to do before. Right, that's like when what I mean by this kind of augmentation. But I do think one important question that founders are now asking is hey, let's do we're going to adopt all these AI tools to help you be more productive. Let's think through what is the importance of each person's role from a human standpoint. How do we start to quantify and qualify and how do we improve what we're uniquely best at?

So you mentioned that cybersecurity is one of the the areas that you've been investing heavily in. And I think when it comes to security and data privacy, these are big concerns when it comes to AI. So do you have any advice on how organizations can go about mitigating security or privacy risks when they're using AI.

So we have a number of companies that are helping with privacy as a big problem. I think it's going to be a problem for every single enterprise to solve. There's lots of different ways they can go about it. Everything from understanding who has access to what data within organization to maybe translating that data to something else that mimics the original data set, to thinking about collection methods, interfacing, whether consumers and a whole set of issues around it. But what that means is that organizations will need a tooling layer to manage privacy concerns and privacy issues. And I think that's a huge opportunity for startups.

Some of the companies you've been investing in, infrastructure companies that are going to help it make it easier for you to build your own AI tools. But then there are also companies available that will sell you AI services. So like Jasper is going to sell you an AI service, but any scale is going to help you build it. How do you go about deciding whether you build AI or whether you buy it?

Do you have good engineers? Do you have good engineers? Do you have the resources, do you have like the organizational capabilities to prioritize this? It's the same way that a lot of Silicon Valley large companies prefer to build in house because they have very strong engineers and the rest of the world oftentimes prefers to buy because maybe they have fewer engineering resources. Right. Like engineering resources is still a massive bottleneck and the degree of that is different.

Okay, so really just got to decide, are you going to be playing to your strengths in one direction or the other?

If your strength of the company is something else, like you're selling insurance products and like maybe your strength is your go to market team, then perhaps you will want to just buy solutions for, for that to play to your strength.

In terms of if you are going to buy in some products or you're going to buy in some services, then I think a lot of these AI startups are pretty new and maybe there's a high failure rate with new companies. So do you have any recommendations on how you can judge their financial health or like the longevity of some of these startups that you might be wanting to work with?

Well for us we in the business of funding these startups that have no money. So that is, that is what we do. We have a on the enterprise side over a 75% graduation rate from nothing took something seem to see reset. So we help our companies increase the chance of success by helping them recruit, helping them get customers, shaping product market fit strategy or discovering product market strategy, helping Them with go to market and all that stuff. So we try to increase the chance as a venture firm, as a large company trying to buy solutions in this space. I mean one of the things to look at is who are the investors behind these companies?

Okay, I suppose if your company's invested, then it's a good sign.

All right, we're biased.

All right, so I'd like to but like the different kinds of generative AI that have propped up. So I think ChatGPT has obviously been like the, the most high profile hyped AI recently, but lots of different AI media types, things going on. So do you see GPT being dominant for a long time or you seeing the adoption of other types of generative AI?

I think that ChatGPT was a significant moment for the ecosystem. We've been investing in all sorts of applications of different types of machine learning methods for a very long time. And the reason why ChatGPT was still very, very significant is because it was a moment where the mass market sell the powers of AI. People who never could imagine manipulating in AI systems who couldn't spell the word AI can use it. So it democratized this technology which was only accessed by very, very few people before. People who worked at Google, who graduated from Stanford, who had to study computer science. That was the cohort who leveraged it. And now everyone can. That was like a significant moment. And that moment led to a lot of entrepreneurs, a lot of people, a lot of companies thinking about now what do we do? How are we going to make our businesses and our lives better by using this technology? I think that was culturally a very significant moment from a market share standpoint. I think that there is going to be a larger fragmentation of available AI technologies to use, whether that's produced by OpenAI, which is still one of the leaders. But compared to two years ago when we first invested in Jasper, for example, OpenAI has lost market share simply because a lot of other players have come in and offered a bunch of stuff, Google or Amazon or startups. And I suspect that trend will continue because there's going to be foundation models that are more and more commoditized, there's going to be smaller models that are cheaper to use and optimize for different use cases. There's going to be proprietary models, that enterprise was a build because the compute costs will have gone down even more and that'll be used for their businesses. So I think we're going to see a diverse set of models created by different people and I think that's a positive thing?

Absolutely. This huge increase in competition has got to be good for everyone except maybe the people who were previously market leaders. Okay. So one thing I noticed is that a lot of companies seem to be focusing on either maybe text generation aspects, or they focus on image or they focus on video. And there seems to be this separation of different media that companies are interested in. Do you think that's going to continue or do you think there's going to be some crossover in the future?

Well, some of the techniques are different for the different types of data, but I do think when enterprises are adopting AI solutions, especially if they deal with different data types, they will want a multimodal or multi type of asset solution. What is still a bottleneck, I think on the text or not on the text, image and video side, is that the compute cost for those is much more significant in text. These are much richer and more expensive data types. And I think we're still at the forefront of some of the technology innovation for video especially. So we'll see likely the cost of creating videos come down, especially automatically. And I think the chatgpt point of that is still to come.

Okay, so maybe like the good video stuff is still in the future. So maybe we'll look forward to that then.

I mean, I haven't seen anything that can just generate a short clip on demand. That looks great. Not yet.

Fingers crossed for the future then. All right, so the other thing that's had quite a bit of hype in the last few months is the idea of AI agents. This is going a little bit beyond the traditional chatbot. So AutoGPT, for example, got a huge amount of hype around sort of April and May. What do you think the future of AI agents is going to be?

It's a super interesting technology. I think there's a number of really early stage teams that are exploring use cases. Like I spoke with one yesterday and they were thinking about what is the vertical to apply this to. And I think that's still an exploration to be determined. My sense though is to offer an enterprise solution that accounts for a large part of automation. They probably have to use agents plus machine learning, classical machine learning plus workflow building and just integration building and all other types of technologies combine to really help, let's say a real estate agent do his or her job holistically. Right. It's not a shock. An agent can do something, maybe he can do some things better. Prioritizing tasks, executing on tasks, thinking about the sequence of tasks, stuff like that. I think that's like really, really powerful with agents, but there's a whole host of other things including what is the interface, what is like the integration to other systems that a startup needs to think through.

Okay, so yeah, I guess just the ability to automatically send an email is a nice prototype if you actually want to really automate someone's full job or maybe tasks. That's like, that's a pretty serious challenge. So I guess we might be wasting some time.

It's not that like, I think the misconception is that this technology is going to magically give you a product in a very short period of time. I think it can give you a really interesting demo. But to have an enterprise ready product that accounts for a lot of other things that people care about in the enterprise, there's still a lot of billing to do on top of what this technology is enabling. Right. The same idea that hey, if you're just a wrapper on top of OpenAI, you're probably not providing that much value. If you just wrapper on top of some agent technologies, you're probably not providing enough value, even though the agents are a little bit newer than what OpenAI has been doing.

So can we talk a bit about the business models of some of these new AI companies? Can you maybe talk me through how these AI companies make their money?

I don't think they're making money any differently. The foundational layer, they're charging on a per API basis for a lot of them infrastructure, if it's a developer tool, maybe they're building some enterprise on top of it and charging as a subscription or some kind of usage dependent thing. If you're like an application, most of them are charging on a monthly or annual basis. So that hasn't really changed. We haven't seen companies that have evolved significantly where they can say, hey, I'm going to change the economics of a law firm. Instead of charging on a per hour basis, we're going to charge on a per product basis. We haven't seen that mature yet to the point where that is actually happening.

Okay, so a lot of it is simple API subscription things and maybe do you think there's going to be any innovation in this space in terms of how these AI companies are going to change business practices or change how they're monetizing things?

I think if the business model innovation is happening, then yes, I think the business can have a different pricing structure. But again, I don't think AI businesses versus non AI businesses. That's not really a distinction in my mind. The AI businesses are using a cooler Tool that maybe can reduce product development cycles, that can reduce go to market costs, that can make things more efficient. But it's not a business. It's just one small but very important part of it's like, hey, you're going to war and you invented gunpowder, you have an advantage. Or the ones are still doing hand to hand combat. But does that mean your war tactics don't matter anymore? Probably not, especially if the gunpowder is now open source and everyone can use it.

Open source gunpowder. So I'd like to ask a little bit about economics as well, because my sort of view of this is like since maybe 2008 or so, we've had low interest rates and low inflation and that meant that a lot of tech companies could delay monetization in favor of growth. And that sort of changed last year. So now there's this big pressure to make money. I'm wondering how this is going to play out in the AI space.

Well, I think the high inflation and high interest rates affect the investment market first and foremost and also everyone across the board. Right. So I think venture capital, for example, is scarcer today. And I'm not sure that's my guess is not because there's not dry powder, but because there is just more hesitation to invest until people feel more comfortable that this company is going to be an enduring company. Whereas in 2021 everyone just invested very quickly because they knew the next round was going to get marked up because of a low interest rate environment. So that's trickling down across the board. I think venture funds are taking longer to raise. Venture fundraising cycles are also taking longer. So all these transactions are slowing down, which puts the onus on quality. And I think that's a good thing because now companies need to think about like what is it that they're going to do to differentiate and make money and have a much better product way beyond than just saying, hey, I'm an AI company, right. I don't think an AI company is any worst efficient to get you funded.

Okay, so that's interesting that just saying, hey, I'm AI isn't just going to get you that, that instant sort of cash. All right, interesting to know. So I'm also curious about the partnerships between different companies. So Microsoft have developed quite an interesting relationship with OpenAI as perhaps maybe the most high profile example. Are there any other partnerships between companies that you've seen that are interesting?

Well, I think the hyperscalers like Microsoft and Amazon and Google are all going to have their own research labs, if you will, like OpenAI's Microsoft. I'm being a little bit more edgy here, but Amazon will have the same. Google have the same. Google has more of an investment there internally. Amazon is partnering with a bunch of organizations to do some of this in addition to investing in house. So the folks that are selling cloud services that have a lot of budget, that have the ability to absorb compute cost, they're all going to be foundation model providers. And whether they're monetizing significantly on these foundation models or they are offering it because they want to sell other stuff in their suite of things they're selling is tbd. But I would not be surprised to see more partnerships with any of these large players.

Okay, so watch this space and see what cost comes out of it. I suppose. So, for people who are interested in getting jobs in the AI space, do you have a sense of what sort of roles these AI companies are hiring for?

There's a couple of different categories. One is like folk that have actually trained large language models. There's very few of them and that's why there's so much demand for alumni coming out of OpenAI and others, because they actually have that capability. There's also just like what everyone was looking for before, which are like engineers or data. Data engineers, manipulating data, processing data, understanding how to use data at scale, I think that's still a very height in demand job. And then there's other types of engineering work which is more like, oh, how do you put some of the outputs of AI in context? This can be in the form of data analysts or even business folks that are using some of these AI systems. So I don't think it has changed all that much from before with the exception of a higher demand for folks that have trained large language models, which is a very small fit of okay.

So it's that kind of extreme machine learning engineer kind of position that seems super in demand. But maybe there's only a small number of roles and beyond that it's the standard sort of data engineering, data science type stuff. So related to these idea of roles, are there any skills that you've seen are particularly important?

Well, as more and more automation happens in a company, I go back to what does the person bring? Right. So in my law firm example, if I were to build a law firm, I would hire 20 lawyers that are exceptional at customer service. And maybe that wasn't the criteria as much before because they had to be skilled at understanding all these documents and stuff like that. And they still do, don't get me wrong. But like what really, really matters from a competition standpoint is that the clients have to feel like they're amazing. Right. So relationships, relationship management relationships matter more than before. So I think that's a really important thing. I think engineers are always going to be a bucket of talent that's in high demand because everything in an organization is becoming somewhat technical. Whether you're using an AI powered business solution, it's great to understand how it works and the limitations and how do you manipulate that. And having that background I think is super, super useful across the board, whatever role you're taking.

I really like that idea that customer support skills are incredibly important. It goes back to this idea you were talking about earlier. This is, it's all about customer experience and that should be your big business goal. Okay, so because datacamp's audience, we have a lot of data scientists, data analysts, and I've had a lot of questions recently about, well, saying, okay, I've got this role in data, I want to get into AI. What do I do to transition from one area to another? Do you have any advice in this.

Area in transitioning from one role to the other? So someone who's non technical who maybe want to learn some of this stuff.

Oh, so actually these are a lot of people who do have this sort of technical data background, but they're now saying, okay, well I've done data science, maybe I want to try AI and what do I need to do to.

I think it's the same, exact same skill set. I don't think it really changes. I think what people need to do is be up to date on the latest innovations, read research papers or have ChatGPT help you read research papers and make sure that they understand what's going on. I think that's probably what's happening. I think the folks that have studied data science and are practitioners are very well equipped and well suited to be significant contributors in this new world. I mean, it's just the same transition. If you're a classical machine learning engineer and you're working with structured data to now deep learning is a capability you probably have to do some learning and reading around how to use deep learning and how do you manipulate those systems. But the fundamentals are still very similar. Like at the end of the day, an AI is a prediction machine and you're taking data, you're creating something to predict something else. And so the questions like the methods are different. In the case of text, you're predicting what is the next sentence. In the case of finance, you're predicting what could be the risk score. And so at the highest level, like nothing has really changed.

That's good to know. I like that. It's just maybe a little bit of a different Python code, but a lot of the sort of similar ideas. Before we wrap up, can you tell me what is your number one favorite AI company?

Ah, that is a hard question. I'm not sure I have a favorite. I am very excited for our portfolio companies, but I also think that I'm more excited, even more excited, that just the number of people and entrepreneurs who are looking to start a company who are working on something interesting has really exploded. And that's a net benefit to all of us and to this ecosystem. So that's probably what I'm most thrilled about.

All right, brilliant. So the kind of the global benefit rather than any individual companies. I like that there's a good, good answer. It's going to avoid picking favorites. Okay. Is there anything you're working on right now or anyone you're investing in that you're particularly excited about, though?

We are making a lot of investments. We have been investing in applied AI for the last 15 years and we continue to do that. So I think if folks are starting early stage companies, especially at the formation stage, we'd love to have.

Fantastic. And do you have any final advice for organizations wanting to adopt AI?

You gotta adopt automation and AI or you're going to be competing in a war with knives. That's going to be how it is. Right. And right now there's still this huge bifurcation of companies that leverage AI and those that don't. But 10 years from now, I think it'll be very rare to see anyone who still exists who's not leveraging that technology. My advice would be understand your own capabilities, understand your talent pool, understand where are the low hanging fruits that you can start working on and just know that it's either you evolve and adopt and figure out ways to compete or probably won't exist.

Nice. Okay, that's brilliant advice. Yes. Figure out where your strengths are and just figure out how to adopt things. All right, excellent. Thank you very much for your time. Joanne, it was great having you on the show.

Thank you, Richie. Talk to you soon.