20VC: OpenAI's Sam Altman, Mistral's Arthur Mensch and more discuss: Will Foundation Models Be Commoditised | Which Startups Are Threatened vs Enabled by OpenAI | Is the Value in the Infrastructure or Application Layer?

This episode delves into the potential commoditization of foundation models in AI, their impact on startups, and where the true value lies—infrastructure or application layer.

1. Foundation models in AI are becoming commoditized, making differentiation challenging.
2. Value may increasingly reside not in the AI models themselves but in the applications they enable.
3. Startups need to focus on specialized applications and deep integration within industries to remain competitive.
4. Large investments are funneling into AI, but the rapid pace of innovation may limit the lifespan of competitive advantages.
5. Strategic partnerships and a clear focus on end-user needs are crucial for capturing and sustaining value in the AI sector.

1: The Future of Foundation Models

Discusses the rapid commoditization of foundation models and their implications for the AI industry. Key insights on investment and innovation dynamics. Sam Altman: "We're going to steamroll you." Arthur Mensch: "The application layer is going to grow thinner and thinner."

2: Investment Perspectives in AI

Explores investment strategies in the AI space, focusing on whether to invest in infrastructure or application layers. Tom Hume: "The technology is commoditizing incredibly quickly." Brad Lightcap: "I am a huge believer that the application layer is going to drive most of the value."

3: Strategic Focus for Startups

Analyzes strategies that startups can adopt to leverage AI effectively and avoid being overshadowed by larger entities. Des Traynor: "You're never gonna make money filling in the gaps in a platform." Sarah Tavel: "Focus on the application layer because that's where tremendous value ends up being captured."

1. Focus on User Ownership: Startups should concentrate on owning the end-user relationship to continuously provide value and capture it effectively.
2. Invest in Application Layers: As foundation models commoditize, the application layer offers greater opportunities for differentiation and value capture.
3. Leverage AI for Competitive Advantage: Use AI not just for cost reduction but as a strategic asset to enhance product offerings and user experiences.
4. Stay Agile with Technology Adoption: Embrace new advancements quickly to maintain a competitive edge in a fast-evolving market.
5. Understand Your Industry Deeply: Tailor AI solutions to fit industry-specific needs and regulations to ensure deeper integration and relevance.

Sam Altman is the CEO @ OpenAI, the company on a mission is to ensure that artificial general intelligence benefits all of humanity. OpenAI is one of the fastest-scaling companies in history with a valuation of $90BN and $2BN+ in revenue.

Brad Lightcap is the COO @ OpenAI and the man responsible for the incredible scaling of sales, GTM, partnerships and business to today being over $2BN in revenue.

Arthur Mensch is the Co-Founder and CEO of Mistral AI. Since its inception in May 2023, Mistral has raised over $520M in funding from investors like Andreeseen Horowitz, General Catalyst, Lightspeed Venture Partners, and Microsoft with a current valuation of $2 billion.

Des Traynor is a Co-Founder of Intercom, and has built and led many teams within the company, including Product, Marketing, and Customer Support. Today Des leads all of Intercom’s R&D efforts, and parts of Intercom’s marketing.

Tom Hulme is a Managing Partner of GV (Google Ventures), and leads the European team. Today, GV has over $10BN in AUM and Tom has led investments in Lemonade.com (IPO), Snyk, Secret Escapes, Blockchain.com, GoCardless, and Currency Cloud (exited to Visa).

Tomasz Tunguz is the Founder and General Partner @ Theory Ventures, just announced last week, Theory is a $230M fund that invests $1-25m in early-stage companies that leverage technology discontinuities into go-to-market advantages.

Sarah Tavel is a General Partner @ Benchmark, one of the most successful and renowned venture firms in the world. At Benchmark, Sarah has led rounds in Chainalysis, Hipcamp, Medely, Rekki, Glide, Cambly and more.

People

Sam Altman, Arthur Mensch, Brad Lightcap, Tom Hume, Des Traynor, Sarah Tavel

Companies

OpenAI, Mistral, GV (Google Ventures), Intercom

Books

None

Guest Name(s):

None

Content Warnings:

None

Sam Altman
When we just do our fundamental job, we're going to steamroll you. Ask the company whether 100 x improvement in the model is something they're excited about. The technology is commoditizing incredibly quickly. I likened it to investing a few hundred million into a power station. But you basically got to depreciate that asset in these foundation models over a few months.

Brad Lightcap
I just am a huge believer that the application layer is going to drive most of the value because what you have to imagine is who owns the user over time. If you own the end user, you're able to provide more and more value to them over time and capture that value. Our foundation models commoditizing, is there more value in the application layer or in the infrastructure layer? Which application layer? Startups will get steamrolled by OpenAI there are so many core questions in AI today that are simply unanswered.

And so today I compiled eight of the best experts in the world to share their thoughts on the future of foundation models and the application layer. Answering your core questions. But before we dive into the show today, when I invest in software today, I think software is valuable when it saves time and makes something easier. But when it enables the user to do something they simply could not do without it, then it's insanely powerful and valuable. That's the case with Tegus.

Tegus I literally could not live without. When I'm investing in a company, Tegus lets me see the most incredible reference calls with experts in a space who work at competitive companies or incumbents. These calls and data provide the most incredible insight that really informs our opinions and investment decisions. I can then share snippets from calls with team members. This is a tool that will change how you work.

And honestly, you cannot imagine life without it. Once you've experienced how powerful it is, you can tell how much I love Tegus. So simply head over to tegus.com. that's tegus.com to check them out. You have now arrived at your destination.

Sam, we have to start with you. How do you answer the question of are we seeing the commoditization of foundation models? There was a time when there were like more than 100 car companies in the US, I believe, or at least close to that. And if you go like, look at some of the old media at the time, it was like, no, there's this better car, now there's this better one, and now there's this better one. I think that same thing holds true for most new industries.

Sam Altman
I think it's fine. I mean, it's probably good, but I don't think that's where the enduring value will be. I think eventually it will shake out. There will be a small number of providers, just dozens, something like that, doing models at big scale. And it'll be extremely complex, extremely expensive.

And I hope we all continue to push each other to make the models better, cheaper, faster, and commoditize in that sense. And the long term differentiation will not be, I don't think the base model like that's just, you know, intelligence is just like some emergent property of matter or something. The long term differentiation will be the model that's most personalized to you, that has your whole life context, that plugs into everything else you want to do, that's like well integrated into your life. But for now, the curve is just so steep that the right thing for us to focus on is just make that base model better and better. Arthur at Mister Owl, I'm so intrigued to hear your thoughts.

Brad Lightcap
How do you think about the focus on just model improvement, where value accrues? And ultimately are the foundation models commoditizing in themselves? There's two opposing directions. The first is that the models are getting better and better. So it means that creating a verticalized application, as long as you have the data for it, and a good understanding of the use case you're facing, is going to be easier and easier if you have access to the tools that facilitate it.

Arthur Mensch
So that's the first aspect which would make me think that the application layer is going to grow thinner and thinner. But then there's also the fact that the molars are getting cheaper and cheaper because we managed to compress them, because we make a lot of improvement on their efficiency. And so that means that effectively, this plus the competitive pressure there is on the model layer, means that the price around the model, the dollar per intelligence unit, let's say, is definitely going to reduce. So there's these two aspects of growing ability, compressed price, which on one side says that the application layer is going to grow thin, and on the other side says that the model part is going to grow thin. So for us, the approach that we are taking is that the model part is still going to be big enough and that we need to build this platform on top of that, because that's where we are going to enable all of the vertical applications that will be interesting for humanity.

Brad Lightcap
Tom Hume at GV Id love to hear your thoughts. How do you think about that, the commoditization of the models and whether theres money to be made investing in the foundation model themselves, or actually in the application layer beneath it. My first observation would be the technology is commoditizing incredibly quickly, which worries me a lot. So I think I likened when we talked the other day it to investing a few hundred million into a power station. That's the training time, and then you can turn it on and you've got inference coming out the side.

That's your power. Now, the problem is, this is an industry where it's going to take you a few months to build your power station. And everyone else is building similar power stations next door with relatively little edge. They're all using the same GPU's, they're marginal improvements. But you basically got to depreciate that asset in these foundation models over a few months.

I just can't see it happening. And then now we've got meta coming into the market. I mean, Zuckerberg's done an amazing job. He'll have 350,000 h 100s by the end of this year. That is 14% of the world's h.

He's going to open source. The result, llama three, released last week, is already incredible. He's pledged that he's going to invest another $100 billion or so. He's already started to train llama for that team, and they're world class, they're formidable competitors. So to invest now in an asset that you think you're going to have to depreciate over the space of weeks or months is very difficult to do.

Now, we have made investments in Genai, but more in infrastructure, more in the application layer, more in the sort of picks and shovels to support. But we've not thus far invested in foundation models. Is there money to be made investing in foundation models? When you look at the quantum of capital that is required to go in, there's obviously rumors of Miss Jarl's new funding around. You see the amount of cash that's gone into OpenAI and everyone else, there's a dilution inherent within that.

It's just gonna be monstrous. Is there money to be made investing in foundation models, do you think? There definitely has been. Because if you were to invest in OpenAI in the $10 billion round, there's liquidity in the market. You could sell that for a five x now, and you could have done that over a year.

So if you've got a momentum strategy and you believe that firm that you're investing in is gonna be at the front of the pack and continue to be, I suspect there's money to be made. But if you're investing in fundamentals, it's very difficult to invest in something that actually is going to commoditize that quickly. In fact, I'd say the best teacher I ever had was Clay Christensen, just unbelievably smart human being. He wrote the innovative solution. We all know that.

And he will talk about, or he did talk about sustaining and disruptive innovations. I think one of the frustrations with Genai as the technology is commoditizing so quickly is it's a sustaining innovation. It's actually going to get sprinkled across all businesses to lower costs in call centers or to improve the product in personalization. It's not going to have a creative destruction effect like the Internet did on many industries. And so as an investor, that's frustrating because you want to invest in stuff that persists and completely rebuilds industries from scratch.

But I can't really see it. I mean, we found some targets and we've made quite a few investments, but it's not for me the sort of radical sort of shift or opportunity from an investment perspective that we perhaps saw with the Internet. What do you think the end state then is for models? I was with a friend who will remain nameless because he hates being publicly named anywhere. And he mentioned that bluntly.

Cloud providers will be the cash cow business and they will buy your Googles, your Amazons and your Microsofts will basically buy the foundation model companies, acquire them all out inflection, and then have cash cow businesses in the cloud providers and then give away the foundation models for free. Yeah, that's to date be my thesis as well. It will look more like a utility. And the cloud providers rationally are saying we want to provide that utility on our compute and they're going to charge on that basis. And they already are, whether you're on AWS, GCP, anywhere else.

Des trainer at intercom. I'd love to hear your thoughts. Use OpenAI to power fin in many respects. Where do you think that the value is going today? How do you think about the commoditization of these models?

I'd love to hear your thoughts. Right now, a lot of value is going straight into the infra. Like as in we're handing it all at the back door to open AI and we actually torture test all of the LLMs. It's not yet the case that they're all equal. I'd be wary of Amazon.

Des Traynor
So like I could see Amazon just like flat up, like buying anthropic and being like, let's just make this part of the EC two cluster. I think Apple will make massive, massive strides forward with AI. I feel like bard unfortunately, felt like we had to release this because chat GPT was getting a lot of traction. They need to have that JZ, like, allow me to reintroduce myself moment. But I think it's a really important attribute to be able to transition from one LLM to another, because if somebody does unlock new power, you'll want to be able to use it very quickly.

Winning involves more than just simply being agnostic about your LLM. Speaking of being agnostic about your LLM, would you invest in OpenAI at $90 billion then? Des? I don't think I would under reason. Whilst I think they'll pass 90, the areas I'd be wary of is Amazon.

Amazon played this game well. So like, I could see Amazon just like flat up, like buying anthropic and being like, let's just make this part of the EC two cluster. And that's just a very easy route to market. And I think if OpenAI run out of new vectors of differentiation and the commoditization starts to kick in, even for basic stuff, I think it'll just become easier. Why wouldn't you use Amazon?

It's already in the cloud, it's already virtually private. You can leapfrog a lot of other adoption concerns. So I think they're one risk. Tom Hume, my friend, what about you? Would you invest in OpenAI at $90 billion valuation?

Brad Lightcap
I would struggle to make that investment today, and it's not because I don't respect the team. My biggest concern at the moment, but if I observe the emergence of what meta is doing, if I look at the arms race of what the cloud providers are investing in, and the sort of Gemini, etcetera, any advantage is pretty ephemeral. And the consumer facing product that doesn't, that drives, I don't know, is it 50% of the revenue? Something like that is not sticky. So to invest in a foundation model, what would I want to be true?

I would want to believe that they had some unique approach that made them more defensible. So an obvious one is memory. Actually, none of these have cracked memory yet, but if you have a personal assistant, a chat gp two equivalent, and it remembers so that it can actually be applied probabilities as to what you want going forward, then it's interesting. If it's unique in its ability to take agency, then it might be interesting. There's other orthogonal approaches that might be interesting.

But if we're just talking about a foundation model where you've got to throw huge amounts of data, hundreds of millions of dollars of compute at h 100s like everyone else, it's very difficult to see a return on these investments. Tom Tungas, I know you've done some work around the analysis of where value was created in terms of application versus infrastructure layer for the cloud generation. I'd love to hear how you think about this moving forwards in the next few years with foundational models versus application layer, and what the analysis from the last generation could tell us about the next generation. I ran this analysis. So in web two, if you take the top three clouds and you look at their market cap, so AWS, GCP and Azure, it's about a $2.1 trillion market cap just for the cloud businesses.

And then if you take the top 100 publicly traded cloud companies, both on B, two C and B, two B sides, Netflix and ServiceNow, they have equivalent market cap, about 2.1 trillion for both. So once at the infrastructure layer, one's at the application layer, market cap is basically equivalent. The difference is the infrastructure layer there are three businesses, and at the application layer there are 100. If the analogy holds, as an investor, the odds of success are going to be significantly higher at the application layer, because the diversity of needs there is greater, imad, formally, of stability. What do you think?

How do we see the end state for foundational model companies? What does that look like? Are there going to be many, many? Is it going to be a concentrated set of fewer players? How do you think about that?

I think that there's only going to be five or six foundation model companies in the world in three years. Five years. I think it's going to be us. Nvidia, Google, Microsoft, OpenAI and Meta, and Apple probably are the ones that train these models. Is anthropic good?

Anthropic, great. But from a business model perspective, you have Claude on Google API and you have palm two. How are they going to keep up with palm two? They can raise billions. But Google spend $20 billion a year on AI DeepMind.

Salary budget is 1.2 billion a year. They technically make a billion a year from their internal counterpayments with Google as well. But again, Google, how much money do they have? $150 billion to win this? Okay, so we spent a good amount of time now trying to understand whether the model landscape will commoditize and where true value is built in that segment of the market.

When we think about the consumer or the application layer, so to speak, the application layer, that sits on top of these models. I'd love to start with Sam Altman at OpenAI. Who who better in understanding how do we think about where value lies in terms of infra versus application layer and the different strategies to build on top of AI right now, there are two. Strategies to build on AI right now. There's one strategy which is assume the model is not going to get better, and then you kind of like build all these little things on top of it.

Sam Altman
There's another strategy which is build, assuming that open air is going to stay on its same rate of trajectory and the models are going to keep getting better at the same pace. It would seem to me that 95% of the world should be betting on the latter category, but a lot of the startups have been built in the former category. When we just do our fundamental job, we're going to steamroll you. Brad, you're the CEO of OpenAI. What is a simple question that a company or a founder can ask to determine whether they will be steamrolled or not by OpenAI?

Ask the company whether 100 x improvement in the model is something they're excited about. It's actually, we can tell pretty well because we know the companies that come to us saying we want the next model. When is it coming out? When is it coming out? I want to be the first to try it.

It's going to be the best thing for my company. And then there's a lot of companies that we don't hear from in that regard. And I think that's like a pretty good delineation, is if there's a clear path to how better intelligence, better underlying intelligence, accelerates that product and that company. Most companies can tell that story really clearly. Des, you're a founder, obviously, of Intercom, and you build with OpenAI, stay integrated into your product.

Brad Lightcap
How do you think about a thin wrapper versus a thick wrapper, and whether OpenAI will or will not steamroll a potential startup. The thick wrapper is like when you've actually solved the user's problem end to end fully in a way that OpenAI never will. OpenAI will hit some sort of minimum viable, like kind of what's good enough for everyone. You're never gonna make money filling in the gaps in a platform. The thing they haven't gotten around yet.

Des Traynor
Like I described that as, like, it's like you're on a train tracks picking up, you know, euros or dollar coins or whatever. There's a train coming, it's gonna hit you at some stage, doesn't matter how rich you get. That thing's hitting you right. I think if you find an area where they're not gonna go that deep, OpenAI is never gonna put like five other engineers going hard on wealth management and like, you know, banking integrations. So that's the thing.

You pick an area and you say, let's do all of it. Not, let's just do a little tech demo science fair, let's nail the use case. Sarah Tavel, you're obviously an investor at benchmark today. I'd love to hear your thoughts about where you see enduring value being created, whether it's in the foundational model layer or whether it's actually in the application layer moving forwards. I just am a huge believer that the application layer is going to drive most of the value, because what you have to imagine is who owns the user over time.

Brad Lightcap
If you own the end user, you're able to provide more and more value to them over time and capture that value. There's, you know, and we could talk about what happens to the underlying models. There's certainly just incredible intense competition. Is it going to be an oligopoly? Is it going to be.

We can talk about those subjects, but like, I focus on the application layer because I do think that's where just a tremendous amount of value ends up being captured and created. Tom Blomfield at Y Combinator, you have this incredible perspective. You see so many thousands of companies that apply for YC every year. How do you think about the wrappers versus non rappers in AI? And then also bluntly where the excitement is, whether it's in application layer versus infrastructure layer, there are clearly some rappers.

You know, if you can build it in a weekend at a hackathon and make a bunch of money, probably not defensible for most businesses building on top of these models, you know, you can describe the last generation of startups as like MySQL wrappers or AWS wrappers or something like, you know, it's the same kind of logic applies. I think where the sustaining value lies is identifying an industry deeply understanding the regulation in that industry, the tooling, the language, the all of the training, how people sort of work and behave and act and tailor your software to fit into that industry in a way that's extremely deeply embedded. Most people building application layer stuff in AI say it's 80% to 90% traditional software with 10% AI. It's working in construction, figuring out how procore works, or how the Salesforce CRM works, or some oracle database. And I don't think OpenAI is going to come and steamroll the construction company AI companies, because they're not going to deeply integrate into the processes and software that exists in each of those industries.

So I really believe everyone who works with a computer will have an AI copilot assistant thing in the next two or three years, whether you're like an oncologist or a law professor. Tom, it's so interesting you say there, about the copilot strategy. It's the strategy that I see is the entry wedge for all startups today when I'm investing miles Grimshaw at thrive, I know you have some thoughts on the copilot strategy. So hit me. How do you think about the copilot strategy today for startups and how you feel about it?

E
I think Copilot is an incumbent's strategy. Incumbents own distribution, they own data, they own the UX, and they own a business model that all aligns to a copilot. Copilot as GitHub Copilot, right? Like inline suggestions. Think of it like how most go to go to any Microsoft product right now.

Every Microsoft product now is a copilot experience instead of a sidebar, an autofill, things like that, right? Where the UX, the core product is a layer on top of it, right? It's immediately added in, which is also totally incumbent strategy. And it's still about sort of supercharging that worker, but still where every user has a seed and every user is doing most of the work and it works probably. If you think about the evolution here, the models, most of what's rolled out might not be good enough for some of this yet, but that's what will come around the corner.

If you think back to Salesforce disrupting. Siebel Salesforce launched five years after Netscape launched. It might take a moment for that to happen, but the copilot, this idea of I'm still the pilot, I'm still the user controlling everything. And it's giving me assistive suggestions. Like GitHub Copilot fits into the UX of incumbents, it fits into the business model of incumbents, and they already control of that distribution.

The opportunity offered up to a startup being a copilot for something else like probably won't be that amazing, and there might be pockets of it where it can really work, but the opportunity to disrupt is to be orthogonal to the incumbents. It's so interesting to hear you say about kind of being orthogonal to the incumbents. My next question from that is, well, how does that then change the business model and the pricing model that SaaS providers use when thinking about selling to their customers. Sarah Tavel, I'd love to hear your thoughts on this because I know you have a different take on this that I love. So how do you think about that?

Brad Lightcap
What AI enables is actually a very different unit of work that you sell, which is doing the work. And so you're almost a software company that looks like a services business that is able to sell like the full work product. The outcome, as opposed to selling software that an employee has to learn to use and then gets a productivity boost from. And this is very disruptive to incumbents because incumbents are used to thinking about selling per seat and pricing per seat based on the cost of the headcount. But if instead you're selling something that doesn't require a seat, that is like a very disruptive opportunity for startups team, I want your feedback on that style of show.

They're compilation episodes. I really want to hear what you think. So let me know on Twitter. Harry Stebbings, you can check it out on YouTube by searching for 20 vc. That's 20 vc.

But before we leave you today, when I invest in software today, I think software is valuable when it saves time and makes something easier, but when it enables the user to do something they simply could not do without it, then it's insanely powerful and valuable. That's the case with tgus. Tegus I literally could not live without. When I'm investing in a company, Tegus lets me see the most incredible reference calls with experts in a space who work at competitive companies or incumbents. These calls and data provide the most incredible insight that really informs our opinions and investment decisions.

I can then share snippets from calls with team members. This is a tool that will change how you work and honestly, you cannot imagine life without it. Once you've experienced how powerful it is, you can tell how much I love Tegus. So simply head over to tegus.com, that's t Dash s.com to check them out. As always, I cannot tell you enough how much it means to me that you listen to the show.

We have an incredible episode with Jason Lemkin coming out on Monday. The review where we analyze his top three best deals and the lessons learned. And his top three worst and the lessons learned. It is incredible and not to be missed.