Here is the full transcript of the conversation between Fund/Build/Scale podcast host Walter Thompson and Managing Partner Navin Chaddha:
Walter Thompson 00:01
Welcome to Fund/Build/Scale. I’m Walter Thompson.
I worked in tech before becoming a journalist. Now I’m talking to a diverse range of established and emerging founders and investors to learn more about the tactics, strategies and frameworks they’re using to create early stage startups. We’re discussing challenges like funding a co-founder, developing a value proposition, fundraising, going to market, and other essential topics for tech entrepreneurs.
In the first 10 episodes, I’m focusing on AI, mainly generative AI startups. I’ll be right back after the short break. Thanks for listening.
Fund/Build/Scale is sponsored by Mayfield, the early-stage venture capital firm that takes a people first approach to helping founders build iconic companies. The podcast is also sponsored by Securiti pioneer of the Data Command Center, a centralized platform that enables the safe use of data and Gen AI.
I’m here today talking to Naveen Chaddha, managing partner of Mayfield Fund. Thanks very much for making the time today. I appreciate it.
Navin Chaddha 01:07
Absolutely. Walter, it’s a pleasure to be with you.
Walter Thompson 01:12
And thank you also for sponsoring this podcast, it’s been a real opportunity for me, and I’ve learned a lot and hopefully, the audience is going to learn with me.
Navin Chaddha 01:17
Thank you, it’s a delight to be amplifying the work you want to do.
Walter Thompson 01:22
So my first question, why did you launch the AI Start seed fund? And what are your long-term goals? If you’re successful, how would the world look different in 10 years or so?.
Navin Chaddha 01:30
Absolutely. So I think first and foremost, as everybody realizes and is saying, AI is a tectonic shift in the technology industry. It’s going to change the way we as humans work, live and play. And then it will go beyond humans to go understand and do things for nature, if you will. So why are we bullish on AI? That’s the reason. And our primary belief is AI plus humans is going to be “human squared.”
The purpose of our seed fund is in our history of investing over the past 50 years, we have primarily been an inception-stage investor. Ten years back, inception-stage investing used to be Series A, then it became mango seeds. I don’t know what it is, but our focus is very simple: how do we partner with entrepreneurs at the inception stage, sometimes it will be called pre-seed. Sometimes it will be called the seed, sometimes a mango seed, sometimes a watermelon, sometimes a Series A. We want to just partner with entrepreneurs and help them build great companies, from their inception to iconic. And in order to do that, we have created a dedicated vehicle where we can start with smaller checks, which could vary from two to three million, four five million because in the past, Series As have been 8,10, 12, 14. So we want to meet the entrepreneurs where they are, and hopefully with what Mayfield has done, other established venture firms do that too. So the pie becomes bigger. We want to just foster innovation.
Walter Thompson 03:18
Mayfield talks about having a people-first framework: what does that mean in terms of evaluating new opportunities, talking to founders, how does that manifest itself?
Navin Chaddha 03:27
Yeah, so I think it’s fairly simple. Again, we have a point of view. And it could be different, or the same as others, our point of view is focused on human-centered AI, which elevates humans rather than displaces them. And for building those kinds of companies. First and foremost, we believe mission and values are critical. You need to think of doing good for the world at large, besides making money. Yes, it’s a capitalistic world, we’ll all make money. And in order to do that, you need to have a very strong mission and values. You need to be GenAI-native, you need to think from day one about trust and safety. And also keep in mind how you can take care of data privacy and security, and then focus on figuring out, can you really elevate humans and make them better? And that’s what our view on what a people-first framework for AI means.
Walter Thompson 04:36
So how far along does someone need to be in their journey as an entrepreneur before they can pitch Mayfield AI Start seed fund? What do they need to show you as far as validation, or social proof?
Navin Chaddha 04:47
Yeah, I think it’s very simple. As I mentioned earlier, we’re looking for entrepreneurs at the inception stage. Ideas can be half-baked. We have helped build companies over the last 50 years, where they were at the napkin stage with paper-and-pencil ideas. And we want to help them, partner with them and figure out what their journey looks like. So my call to action for the entrepreneurs, who are going to be listening to your podcast is jump in, call us, find how to reach us. And we are here to help evaluate what you’re doing. And if the stars align: you like us, we like you. Let’s go.
Walter Thompson 05:32
In your mind, what does a fundable idea look like — the rough shape of it?
Navin Chaddha 05:37
So what I would say is, whether it’s AI, or non-AI, it starts with is the product you’re trying to build? A must have? Or a nice to, for a certain segment of the market? Because our belief is, painkillers sell, vitamins don’t. Everybody’s busy. You need to rise above the noise. Second, is this a niche opportunity where we just have one hundred customers in the world who need this? A thousand? Ten thousand? A hundred thousand Millions? Let’s get on the same page on what it is.
Asking for what your revenue potential is, what’s the size of the market? That’s too early at the inception stage, right? So is your product a painkiller? How many people in the world need it? And what’s [it] going to be [like] if everything works? What’s going to be your unfair advantage, or moat, which allows you to win? It could be IP, it could be people, it could be knowledge, it could be a business process. It could be a business model. It could be pricing, let’s figure it out. Every company, a lot of companies will have the same idea. But you need some defensible moat. And then, are you willing to put the company first and surround yourself with excellence? Then let’s go, let’s go. So that’s what I would say. It’s not like, “you need to do this, you need to be this.” it doesn’t matter. But those are some of the tenets we look for.
Walter Thompson 07:11
That’s interesting, because at this point in the hype cycle, I feel like a lot of people wonder how deep a moat they need to have dug before they can talk to an investor. It sounds like you’re saying the idea is key, but the founders still need to have dug a somewhat defensible moat before they approach you. Is that fair to say?
Navin Chaddha 07:27
Or, let’s figure it out around the way. I want to just make sure the problem you’re solving is a painkiller for somebody, and that somebody is not ten customers, it’s potentially many, many. And then if everything goes well, you don’t need to have figured it out. What do you think is going to be the moat? I’m just looking for a bet you want us to make, clearly, since you’re a paper-and-pencil idea, you’re not going to know what it is. Let’s engage and figure out.
Walter Thompson 07:55
This leads to my next question: how large is a typical seed round these days?
Navin Chaddha 08:00
It’s all over the place. There are even before the seeds, they are like pre-seeds then there are seeds, which could start at two, three, four million. And there are mango seeds, which have gone up four, five, six, eight million, but I’m seeing round seed rounds from 8-12 million now. And then, if you are “hard team to beat” entrepreneurs, the number is 20. The number is 30. The number is 40 million, 50 million. So I wouldn’t say these are seed-stage companies in the traditional sense that we have been using for the last five to seven years, it’s really inception-stage companies, what capital they need to be able to get to the next milestone, some of them will be capital-light, they need little money to get to the next phase. Some of them will require a lot of capital.
So I would say seed should be looked broadly, as it’s an inception-stage company, what’s the right amount of money? Right? Let me give you an example. If you’re taking a short flight from San Francisco, to LA, you need a plane which can hold a certain amount of fuel. You go from here to Denver fuel increases. You go to Texas, we need more. You go to Chicago, a little bit more, then you go to New York even more. Right? Because if that’s your destination, you need to make sure at least you have capital, which is the equivalent of the fuel. So there is no right answer.
Walter Thompson 09:30
Can you speak a little bit to specifically what kinds of opportunities you are looking for in 2024? And are there specific subfields or industries you’re interested in or not interested in?
Navin Chaddha 09:39
Absolutely. So I think having been in business myself for 20 years as a VC, and another 10 years as a startup entrepreneur before that, I think there are two approaches. One approach, that’s the pertinent question you’re having, is to have a prepared mind: where is the puck going? Where are the opportunities? But unfortunately, the mega-mega companies, most of the time, you need to just have an open mind. Because nobody knows how greatness gets created, right. And then many examples in consumer and in business, you just don’t know. So I would say, you need to be nimble, have a prepared mind. But keep your vision not tunnel and have an open mind.
As far as investing is concerned on the prepared mind-side, what I would say is, in particular, in enterprise and AI-enabled enterprise, we are looking at opportunities up and down the tech stack. So it starts from semiconductors, moves up to the infrastructure layer, which is as a service now, then goes to the data layer, then middleware and tools, and then applications, which are built on top of it. And the bottom four layers Mayfield is calling “cognitive plumbing.”
And the application layer for AI will enable itself as applications, services, co-pilots, autopilots, agents — you just don’t even know what the possibility is going to be. So I think huge opportunities like there have been in the tech industry, similar to the PC and client server era, leading to the internet, leading to mobile, leading to cloud. This is just a continuum, the whole stack is being reimagined. And that track is going to enable net new things which weren’t even possible before. So that’s the way one has to look at it.
Walter Thompson 11:59
Any thoughts on how it might impact I guess what we think of typically as consumer internet or consumer products?
Navin Chaddha 12:04
Yeah, absolutely. So I think the first phase is this cognitive plumbing layer needs to get built. And OpenAI is a good example. What it’s showing is, you use AI at the back-end, you build cognition, you build reasoning, you build context, but the way you interact with your product and services, human language, it’s natural language. I think we should assume that interface is going to redefine every consumer, every business application, and humans will interact with machines and machines will interact with humans in human language. So no longer do you have to script, then there was a graphical user interface, then it was mobile, this is the new new thing. So it is a combination of back-end cognition. But the front end is machines that understand human language. So humans will converse with machines, as if it’s another human, and machines will converse back to us in the language we understand. And then as a result, what will happen is AI is really going to be my teammate, it could be my coach, it could be my genie. It doesn’t matter, right? Like, it’s gonna be a companion with you forever, either you support it, or it supports you, remains to be seen.
I think it’s going to just disrupt the way we interact with computers with mobile phones. So it’s very, very exciting for entrepreneurs. But what it will be, this is where you need to have an open mind, open mind. And just imagine what could be done and don’t look back in the rearview mirror. That’s the wrong thing. If this platform is enabled and is available, what can you do? Don’t just think incremental in the consumer space, think bigger. Imagine the world, make it what you want it to be.
Walter Thompson
Exciting.
Navin Chaddha
Yeah, very exciting.
Walter Thompson 14:10
I mean, this is very different from B2B SaaS. So what are some of the success metrics you’re looking for a year after that initial inception investment?
Navin Chaddha 14:19
So I think you’re absolutely right. Getting SaaS companies funded at the inception stage used to be the hardest, because people used to say, “hey, how many customers do you have? Have you built a product? What do you have? I don’t know this market, are the big companies going to do it?” So people used to bootstrap most of them. Make some traction, show some customers, and then get some money.
This time is different, and the reason is the cognitive plumbing layer. Essentially, you need money. It’s capital-intensive, maybe the apps you can do with a lower amount of money. So what’s different this time is you need to lean in both as an entrepreneur and a VC be willing to take the risk and roll the dice. But what should one be looking at as the OKRs and metrics once you get this company funded is hey, “is your product that pain killer? Have you talked to 40-50 potential customers? Have you defined your minimal viable product, launched the product and start seeing is it a must-have or a nice-to?”
Rinse and repeat till you get some people who love you, and then raise more money after the seed to see if you can establish product-market-fit. And if you have that, the rest will be history. So learn, crawl, walk, run, and then we’ll fly. Seed stage? Let’s just crawl. To walk, you’ll need more money. To run, even more money. Then we’ll worry about flying.
Walter Thompson 16:07
We’ll be right back after this word from our sponsors.
Are you thinking about launching an AI startup? Mayfield’s $250 million AI Start seed fund is actively searching for idea-stage entrepreneurs who are working at the cognitive plumbing layer. That’s models, middleware and tools, data, infrastructure, and semiconductors and systems. Mayfield has a long track record: since its founding, the firm has been an early investor in more than 550 companies, which has led to 120 IPOs and over 225 mergers and acquisitions. If you have a fundable idea for an AI-first startup, email aistart@mayfield.com.
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And now, back to the conversation with Naveen Chaddha.
Walter Thompson
What are some of the biggest challenges in investing in AI startups? We’re seeing what keeps you up at night?
Navin Chaddha 17:27
I think what gets me excited first, is the massive opportunity. The biggest challenges today as a VC of investing in AI startups is that there is just way too much noise. Way too much hype. Raises are again, getting out of whack, valuations are getting out of whack. Most of the people just add “AI” as they used to add “dot-com.” Then they’re mobile, then they’re cloud. There’s no AI DNA. In most of the companies, they’re whitewashing the value prop as more of a vitamin, it’s not a painkiller. So you have to spend a lot and a lot of time to figure out what is real, and what is not real.
What keeps me up at night is the AI startup market gets over-capitalized. And too many “me too” startups get funded, and essentially, the customers get confused and don’t buy and wait for the winner to emerge. And then the big companies get there. So I would like fewer companies, more focused, built for the long term, built in the right way, if you will.
Walter Thompson 18:54
I do want to talk about what you look for on a founder team. But going back to what we just talked about, how important is it to you as an investor to see a deep bench of AI expertise on the founding team? Are you happy to invest with academics and researchers? Do you want people who have business experience? What does your dream team look like for an AI startup founder team?
Navin Chaddha 19:12
So what I would say is, every team is not going to have everything. But if you’re going to play in the AI space, first and foremost, you need to have some technical chops, and some domain experience. Either you’re worked in a company, you’re worked as a PhD student, as a grad student, you need to have some experience to be able to figure out how to use this technology, how to innovate on it.
Second, it will be great if you have some team members who understand the domain you’re going after. The good news is if you are playing the cognitive plumbing layer, it’s about the tech, it’s about the product. But if you’re working at the applications layer, then if you’re solving a problem for salespeople, you need to have somebody on the team who understands how salespeople work. If you’re selling picks and shovels, it’s easier. If you’re in the cognitive plumbing layer, it’s easier, you don’t need to understand the domain specific stuff of how sales works, how marketing works, how legal works, how finance works, how business development works. So there is no right solution. And most of these companies will be founded by first-time founders. So it’s the same story again.
Walter Thompson 20:35
Warm intros are hard to come by unless you’re already inside a Silicon Valley network. Can you share any advice for founders who are working outside major tech hubs when it comes to building investor relationships?
Navin Chaddha 20:46
So I would say, you can try everything, you can do cold emails, you can reach out on LinkedIn, the response might be low, because the spam filters, not that we don’t want to respond, have gotten good. So what I would recommend is really try to get a warm referral on somebody you see the VC is connected to. And you know, to get a referral into them. And maybe they’re speaking at a conference, go meet them. Because most of the VCs are approachable and they want to help founders. And if nothing works, cold-call their office, if that doesn’t work, to show up at the office. Just be persistent. Don’t, don’t back off, just keep going. But the easiest one is warm referrals. Look on LinkedIn. Who does this VC work with, which boards they are on, look at through your network [to see] who knows those companies, somehow get to that network and most of the time, you will realize there are only two or max, three degrees of separation from these people. Reach out that way, that will be the best warm referrals.
Walter Thompson 22:11
I’m sure no VC wants to be seen as just an ATM machine. Cash on demand. So what are some of the ways that early-stage founders can actually leverage their inception investors? And where do you expect them to make mistakes and ask for help?
Navin Chaddha 22:23
First of all, as I said, AI, you should view it as being your teammate, I think we see, you should assume that they will be your teammate, they will be your coach, they will be your mentor. So essentially, the first step has to be before you call for help, build a zone of trust with your venture capital investor, get to know them. Spend time with them. be secure in your skin, share bad news earlier than good news. And just establish the rules of engagement, and use them as a safety net. And then be shameless in asking for help and say, “I’m stuck here. I’m thinking about strategy. What do you think?” Engage them rather than evaluating them. “I’m hiring this person. This is the spec. Do you know people? Are you willing to interview them? Hey, I’m trying to get into this account. Can you make an intro? I need help with PR, I need to get placed in Wall Street Journal, I need to get to know Walter.”
So essentially, ask for those things. Right? And ask questions. No question is bad. Right? Learn along the way, make VC or partner and go and go. Don’t hold back! The mistake can be thinking of them not as a team. That doesn’t work! It doesn’t work.
Walter Thompson 23:52
Actually, I want to skip ahead to a question. So what are some of the top questions AI first founders should be asking potential investors in 2024.
Navin Chaddha 24:01
First and foremost, an entrepreneur should not be asking the VC, they should be figuring out, are these people really going to be backing me and my idea, or are they backing the market? Are they going to be there in tough times? Will they watch my back and support me through thick and thin? And rather than asking them, figure out where things didn’t work for them, and how did they act with those founders? And of course, you should ask them for on sheet references. You should ask, how do they add value? Do they have any experience in the AI space? Are they company builders, have they been entrepreneurs, but just spend time with them? Spend time with them like you spent recruiting or getting your co-founder. It’s very, very important. Read them, understand them, diligence them, backchannels on-sheet references, off-sheet references… don’t go wrong on this decision. It’s one of the most important decisions to get your first investor, as important as getting a co-founder, so do the work, whatever time it takes. Do the work.
Walter Thompson 25:25
What does it look like when someone’s rushing this process? What happens?
Navin Chaddha 25:30
I think they make a lot of mistakes. It’s not only just rushing, just going for the highest price sometime? It’s all paper and doesn’t lead to the best outcome. Just relax. Company-building is a marathon. It’s not a sprint, take your time. Opportunity will still be there. Right? It just takes time. This is a different game. This is a different game. You’re dealing with humans. As you said VCs, there are machines, they’re not ATM, you have to deal with them. They’ll show up at your company. They show up at the board meeting.
What are you going to do when they want to be on your board? You report to the board. Spend the time, make sure they’re the right people. Because most of the time, companies can lose because there’s no market. A lot of times, there’s a market but there’s in-fighting. Well, I don’t have time and neither should you to be part of that. Take your time. Slow down. This is one of the most important decisions. Take your time.
Walter Thompson 26:32
Going back to the pitch process for a second. And the idea of creating a painkiller not a vitamin. I’ve heard you say that before. But generative AI is still relatively new technology. So when you look at a pitch deck, how important is that TAM slide, or can I just leave it out entirely?
Navin Chaddha 26:47
Yes, I think at least for us, it doesn’t matter. Because whatever TAM you show us is going to be wrong. Majority of the time, the TAM will never get there. And for the companies which work, it will be 10x more than what you have thought. So at the inception stage, that’s where our AI Start fund is focused, I go back to the same thing. Don’t worry about revenue, don’t worry about market size. Focus on, “is this our top pain point for a certain segment of the market?” They have a headache man, they need Tylenol or Advil, you’re selling them vitamin C, they don’t even know why they need vitamin C. Second, right, like, think abstractly and engage with them. I have talked to 40 customers, 50 customers, I think they have this need and extrapolate it think — how many customers in the world need it?. But tell me once you know that, how are you going to get there? Talk about fast iteration, the Lean Startup, because dinosaurs never survive.
Tell me how you’re going to reach these customers? What do you think your business model will be? Let’s engage in those kinds of things. If you’re going to fail, fail fast, don’t worry about revenues at an inception stage, dollar amount of the TAM, dollar amount of the SAM, it’s irrelevant, irrelevant. Focus on basic principles of building a company. It’s a paper-and-pencil inception stage, you don’t even have a product. Right? It’s like you’re gonna build a tall, tall building, man, like, let’s figure out the architecture. Spend the time. Let’s build the blueprint. What will it look like? This is not good, that is not good. Let’s do it ahead of time, rather than putting the building and you keep adding more floors in the building tilts — just doesn’t work.
Walter Thompson 28:45
So there’s no building the plane in the air when it comes to AI, then.
Navin Chaddha 28:48
You can’t, right? Like, just take your time. Once you’re aligned, go, go, go, no looking back, go, go go. Because then you know, you’re prepared. You’re going to war, when you’re building a startup, come up with a game plan and a backup plan and go, don’t say “I’m gonna just land up in the field, and then I’ll figure it out.” It doesn’t work that way. The best teams in any sport, when they practice, they watch the videos of the other people they know their weaknesses. Think before you act, plan before you build,
Walter Thompson 29:21
But there’s still that Silicon Valley trope of building in public, you know, showing people what you’re working on and kind of doing it all very transparently. Does that still obtain for AI startups?
Navin Chaddha 29:30
Yeah, it does. But I think like, make sure you have something before you go talk about it. Because otherwise you’re gonna lose all credibility. If it doesn’t work. Take your time. Do it the right way. Do it the right way.
Walter Thompson 29:43
When it comes to first-time founders, how involved do you get when it comes to helping them define their value proposition and carve out that path to compete?
Navin Chaddha 29:50
I don’t think it’s about first-time founders or second-time or third-time founders. Our business is like that of a coach or a doctor, it depends upon what the need of the entrepreneur is. And where do they need help. So we don’t want to do backseat driving. They need to just tell us, “hey, I want to go from Menlo Park,” where you and I are to San Francisco, and engage in a brainstorm, “what do you think is the best route based on your experience?” That should be the discussion. If they don’t ask me that question, they have Google Maps, I know they can get there. So I think there’s no right answer, you sit down with the entrepreneur at the beginning, first, second, third time at every stage of the company and see, “hey, do you need help? Or you don’t need help.” If they don’t need help? Stay out of their way. Let them execute more power to them. I want more entrepreneurs like that. But if they ask for help, either jump in yourself, or have your broader team help, or you have a broad network, introduce them to the right people. They are the driver. Never as a VC, get into the driving seat, though — backseat driving doesn’t work, doesn’t work.
Walter Thompson 31:10
I mean, I think a lot of investors encourage founders to cultivate that mindset that they’re in this for the long haul. They’re building something, it’s going to take years. But it seems like a growing number of AI startups are finding the cost of developing their model so prohibitive, that they’ve started looking for a buyer trying to find an exit without access to compute and data. Is there any way to get a bootstrapped AI idea off the ground? And do you think any AI startups will be born in garages?,
Navin Chaddha 31:36
I think they’re already being born in garages, and I would say, don’t solve problems, which are already solved, solve new problems, raise the right amount of money. To get to the next milestone, maybe it’s four million, maybe it’s 10 million, maybe it’s 20 million, maybe it’s 30 million, find the right set of investors who can give you that money. But please, please, please, please, please don’t build a cloud. Don’t build an NVIDIA chip, don’t go compete with OpernAI, endorse them, use their stuff, and innovate around the edges or above them on the problems they are not solving. And the world needs help on or solve adjacent problems at the layer they’re playing.
NVIDIA chip, we have a very good company: When AI gets to the edge, they provide cooling, cooling and devices where fans can go, it’s great for the whole world solve an orthogonal problem, you go to the infrastructure layer, clouds are being used, there is need for trust, model evaluation, safety, security, go solve that problem. You don’t know whose model will get used, you don’t know whose cloud will get used. So solve problems. And all the cloud providers will be happy. NVIDIA will be happy, they can sell more chips, more cloud adoption, more model adoption, everybody wins.
Align yourself with the roads, the waves that are being created, become a parasite on the marketing they’re doing, ride their coattails. Once in a while you can attack them, but make sure you have enough money and a defensible moat. And not a “me too” proposition, because in AI, besides domain expertise, capital, capital capital is very, very important. It’s very, very important. Because of the questions you talked about model development, all trading, compute, just doesn’t stop. So be mindful. Right? Don’t reinvent the stack. Endorse things which have been done, leverage them, be adjacent to them, maybe disrupt them once in a while. But figure out how you play in the ecosystem. Don’t become an island, no need to become an island.
Walter Thompson 33:57
Returning to that comparison, between AI-first and B2B SaaS startups, most software as a service companies will start with small and medium-sized businesses and work their way up to the enterprise customers. That doesn’t seem like that’s the path for AI, you’ve really got to go straight to the enterprise. But that path is fraught with difficulty. So what challenges are you seeing with regard to AI adoption at the enterprise level? And how would you advise a smaller startup that’s trying to get buy-in with CxOs?
Navin Chaddha 34:26
So I would say the market is still early. You’re absolutely right on working with the impedance mismatch between a startup and a Fortune 500 company. God, man: it’s 10,000x, 100,000x, a million x. So choose your customer wisely. My advice would be: the size of the customer doesn’t matter. It’s more important, like they used to say, a tech-forward customer. A cloud-forward customer, a mobile-forward customer. Now you need an AI-forward customer size doesn’t matter.
Are they ready? Is the pain high enough? Will they jump in and make sure if they’re jumping in, you’re not becoming their development shop, you’re not building a custom product. Build a product that’s applicable to a broader set of the market. So that’s the way I would look at it: are they putting you in the lab, and will never see the stuff, or they’re ready to write the check and ready to deploy. And if you can’t match the impedance between them and yourself, get a big company like Accenture to work with you get somebody else. But make sure, right, this is a broad set of problems, you’re not their development shop, and choose the right company, the right company. Don’t jump into the first customer, it will bankrupt you. So I’m not sure your first customer should be a big company, find an AI-forward company. It could be a fast-growth SaaS company, which needs to leverage AI, or a lot of companies are looking at building on AI. Security is a huge issue. They don’t know what model trust they can do. Figure out who they are. get traction, get proof points, and go with customers who already?
Walter Thompson 36:21
You kind of touched on them briefly, but kind of go a little deeper: what are some of the downsides of working with a big company, and how can founders mitigate them? I’ve heard from CxOs that they don’t have a lot of time to work with startups, they only have time to work with maybe two or three at a time, because they require so much resources and overhead. And then also the issue of liability: it can it’s a make or break for the smaller startup, but let’s talk about the break aspect.,
Navin Chaddha 36:45
I have seen the way a big company works versus a startup. A startup has five employees, 10 employees, zero salespeople, and the founder is the one selling. And the enterprise brings 20 people 30 people, 40 people to their office, the office can’t accommodate them. The office was only for 5-10 people. So be careful, right, they might be wasting your time. So the way I would go about it, is figure out if the team you’re working with is nimble. They’re not at the customer. They’re not layers or decision makers. They drown you with one meeting, a second meeting, third meeting, three months pass, six months pass, nine months pass, you will be dead. You need early adopters, right? When you go through a tornado market, right? You’re not looking for majority or late-majority, find customers, if your product was built, can use it and use it very quickly. And want to die if they don’t use your product. If you have a product, go find customers. When they hear about you, they’re going to get to the top of their building, and jump up and down, up and down from the roof. Shouting, “man my day got made, I heard about this company, this is the problem I was trying to solve for 10 years, and I finally found it!” And that’s what you have to figure out, not “you come in, I’ll do this for you.” Like maybe you can, maybe you can’t, it doesn’t work. Alignment of interest.
Walter Thompson 38:22
Mayfield has a lot of in-house expertise when it comes to AI. But most investors, they don’t. They’re generalists. So do you have any thoughts about how that could affect the companies they’re investing in, and the sector at large as these companies mature?
Navin Chaddha 38:35
I think this is where first and foremost, I put the problem on the entrepreneur listeners: make sure you’re partnering with somebody who’s in for the long haul, believes in AI, is not going to run away, and has some expertise. And I think there are enough of us and Mayfield is one of them. So where are the entrepreneurs going to go wrong? If they don’t choose wisely who their investor is, they’re going to make mistakes. And nobody would have seen the movie before or have pattern recognition to keep them within the guardrails, right?
Second, a lot of money will be lost. The AI sector will get over-funded. As I mentioned earlier, lots of “me too” companies and what will it lead to? Customers will get confused, pause, established companies will catch up. And this is what is called “the trough of disillusionment,” disillusionment in the hype cycle. Let’s not do that please, solve real problems. Fewer companies, must-have, do it the right way. Otherwise, some will make it on the other end, but 90% will fail. I don’t want that. The industry doesn’t want that. Let’s play wisely. Let’s have muscle memory.
We made money in internet, lost a lot of money. We made money as an industry in mobile, lost a lot of money. That’s the nature of venture, it’s okay, let’s make new mistakes. That’s a muscle memory. Because if you’re not careful, the next tulips have arrived, this time they are the AI flavor. We saw it in the PC era when the tulips became.com, then they became social, then they became mobile, then cloud. Let’s be careful, huge opportunity. Make new mistakes, not the same mistakes. Let’s not create a bubble. When it bursts, it hurts. You know what I’m talking about? Right? There was a COVID bubble recently. Sure. Relax, sell. So if it’s such an important wave, let’s do it the right way, the right way, this time.
Walter Thompson 40:41
You touched on this a little bit earlier in the talk, but go a little deeper this time? How does Mayfield view the role of AI with regard to driving societal change? And how does that inform your investment decisions?
Navin Chaddha 40:52
So I think this is where you need balance as an investor. Mayfield pioneered the concept of conscious venture capital, where we believe beyond doing financially well, for the society, our limited partners, who are endowments, foundations, pension funds, people in need, who serve needs of other institutions who serve needs of people. Our job is not to just do financially well, but do good for the world. And that’s what we call conscious capital. So we look at our role as investing responsibly in the AI space. We’re looking for companies, which eventually Yes, there will be loss of jobs, those things in the short run.
But in the long run, when humans adopt AI, do they perform at human-squared? The GDP improves, the income per person improves. Think about the end goal. And are they building the company in the right way thinking about trust, thinking about safety, thinking about privacy and security? If they don’t, they’ll be shut down. Forget about compute costs and data costs, nobody’s going to use them. People wanted self driving cars to fail. They are just looking: lobbyists will come, unions will come, this will come. So just keep that in mind. Keep the big picture in mind. huge potential. But let’s put guardrails. Let’s make sure it’s done the right way.
Walter Thompson 42:33
My last question, when I started this podcast, I realized I had to educate myself about generative AI, just so I could ask the right questions. And it’s been a real journey for myself. But can you share some of the resources you’ve used to learn more about AI? What are you reading or listening to?
Navin Chaddha 42:49
My belief is very simple: entrepreneurs need to surround themselves with experts and excellence. That’s what I do. Hire people in your team, or AI-native. Go talk to entrepreneurs, go talk to university professors, PhD students, postdocs. That’s how I learned: go talk to established companies. Jensen [Huang] at NVIDIA, Satya [Nadella] at Azure, or Microsoft, right? Go talk to TK [Thomas Kurian] at Google. Talk to the model providers, understand what they’re doing, read technical papers. We have 3,000 customers in our network at Mayfield — understand their needs, do surveys, you have to be hungry.
But that’s the way I learned, which is essentially, talk to people, understand their needs, then go verify is this technology safe or not? And then sit back and connect the dots, and say, hmm, “I was an entrepreneur in both the PC and the internet era. Then I was an investor when social, mobile, cloud happened. Now it’s AI, can I connect the dots based on real data?” So I’m trying to learn the basic building blocks that are happening at universities, what engineers are thinking, what’s happening in labs, what are the big providers doing the customer pain points, and then figuring out how Mayfield should attack that market, and what kind of entrepreneurs we should be partnering with. So that’s my approach.
And then we want to work with people like you who will make not only us, but the rest of the world aware of what the possibilities are. I think the thirst for learning at Mayfield and also me has grown 10x. And this is an amazing opportunity. Even for a person like me who has been in the business for 20 years. I couldn’t have wished for better because you need to start from scratch and realize: same rules, new technology.
Use the past, learn from it. Have no shackles, apply some of those principles. But learn, adapt, and keep going.
Walter Thompson 45:13
Fantastic. I really appreciate the conversation today. Thanks for your time. Absolutely.
Navin Chaddha 45:17
It’s an honor to be here with you and to be partnering with you. Onwards and upwards. Let’s make the pie bigger.
Walter Thompson 45:23
Thanks, Navin.
Navin Chaddha 45:24
It’s a pleasure.
Walter Thompson 45:26
I’ll be right back with some show notes after a word from my sponsors.
Fund/Build/Scale is sponsored by Mayfield. If you have a fundable idea for an AI-first startup at the cognitive layer, email aistart@mayfield.com.
The podcast is also sponsored by Securiti, pioneer of the Data Command Center, a centralized platform that enables the safe use of data and GenAI. To learn more, visit Securiti.ai
I’d like to thank my guest Navin Chaddha, managing partner of Mayfield. Coming up in Episode Two, I spoke with Rodrigo Liang, CEO and co-founder of SambaNova, about digging a moat, customer discovery,and product led growth. He also shared some advice for anybody who’s thinking about joining an AI startup, and had some thoughts about fundraising in 2024.
Please subscribe to Fund/Build/Scale on your favorite platform, and if you liked this episode, I hope you’ll give me a great rating. For now, you can find the Fund/Build/Scale newsletter on Substack.
The podcast theme was written and performed by Michael Tritter and Carlos Chairez. Michael also edited the podcast and provided additional music, and I’m deeply grateful. Thanks again for listening.
