OpenAI CEO Sam Altman | AI for the Next Era

Hi everyone. Welcome to Grey Matter, the podcast from Greylock where we share stories from company builders and business leaders. I'm Heather Mack, head of editorial at Greylock. Today, Greylock general partner Reid Hoffman interviews Sam Altman. Sam is the CEO of OpenAI, an AI research and deployment company whose primary mission is to develop and promote AI technology that benefits humanity. This interview took place during Greylock's Intelligent Future event, a day long summit featuring experts and entrepreneurs from some of today's leading artificial intelligence organizations. You can watch the video of this interview on our YouTube channel under our Intelligent Future playlist and you can listen to other interviews from that summit on the Gray Matter podcast. You can find that on your preferred streaming platform or on the content section of our website greylog.com now here's Reid Hoffman with Sam Altman.

Sam close friend Many, many things. I think actually we probably first met I think on the street on El Camino bumping into when you're doing looped and have done a number of things including a good portion of my nuclear investments are with you because you call me and say hey this is really cool and I agree. All right, let's start a little bit more pragmatic but then we'll branch out. So one of the things I think a lot of folks here are are interested in is based off the APIs that very large models will create. What are the real business opportunities, what are the ways to look forward and then how given the APIs will be available to multiple players, how do you create distinctive businesses on it?

Yeah, so I think so far we've been in the realm where it's you can do like an incredible copywriting business or you can do like a sort of like education service or whatever. But I don't think we've yet seen the kind of people go after the trillion dollar take on Google's and I think that's about to happen. Maybe it'll be successful, maybe Google will do it themselves. But I would guess that with the quality of language models we'll see in the coming years, there will be a serious challenge to Google for the first time for a search product. And I think people are really starting to think about how do the fundamental things change and that's going to be really powerful. I think that a human level chatbot interface that actually works this time around. I think many of these trends that we all made fun of were just too early. The chatbot thing was good, it was just too early. Now it can work and I think having new medical services that are done through that, where you get great advice or new education services, these are going to be very large companies. I think we'll get multimodal models and not that much longer and that will open up new things. I think people are doing amazing work with sort of agents that can use computers to do things for you, use programs and this idea of like a language interface where you say in natural language what you want in this kind of dialogue back and forth, you can iterate and refine it and the computer just does it for you. You see some of this with like Dall E and Copilot in very early ways. But I think this is going to be a massive trend and you know, very large businesses will get built with this as the interface and more generally that like these very powerful models will be one of the genuine new technological platforms which we haven't really had since mobile. And there's always like an explosion of new companies right after. So that'll be cool.

And what do you think the key things are? Given that the large language model we provided as an API service, what are the things that you think that folks who are thinking about these kind of AI businesses should think about as how do you create an enduring differentiated business?

So, you know, I think there will be a small handful of like fundamental large models out there that other people build on. But right now what happens is, you know, company makes large language model API, other people build on top of it. And I think there will be a middle layer that becomes really important where I'm like skeptical of all of the startups that are trying to sort of train their own models. I don't think that's going to keep going. But what I think will happen is there will be a whole new set of startups that take an existing very large model of the future and tune it, which is not just fine tuning, like all the things you can do. I think there will be a lot of access provided to create the model for medicine or using a computer or like the kind of like friend or whatever. And then those companies will create a lot of enduring value because they will have like a special version of. They won't have to have created the base model, but they will have created something they can use just for themselves or share with others that has this unique data flywheel going that sort of improves over time and all of that. So I think there will be a lot of value created in that middle layer.

And what do you think some of the most surprising ones will be? It's a little bit like, for example, a Surprise from a couple years ago and we talked a little bit to Kevin Scott about this this morning as we opened up which is train on the Internet, do code. Right. So what do you think some of the surprises will be of you didn't realize it reached that far.

I think the biggest systemic mistake in thinking people are making right now is they're like all right, maybe I was skeptical but this language model thing is really going to sure like images, video too but it's not going to be generating net new knowledge for humanity. It's just going to do what other people have done and that's still great. That still brings the marginal cost of intelligence very low. But it's not going to go create fundamentally new, it's not going to go cure cancer, it's not going to add to the sum total of human scientific knowledge. And that is what I think will turn out to be wrong that most surprises the current experts in the field.

Yep. So let's go to science then as the next thing. So talk the general tooling that really enhances science. What are some of the things whether it's building on the APIs, use of APIs by scientists, what are some of the places where science will get accelerated and how.

So I think there's two things happening now and then a bigger third one later. One is there are these science dedicated products, whatever like AlphaFold and those are adding huge amounts of value and you're going to seeing this like way more and way more. Like I think I if I were like, you know, had time to do something else I would be so excited to like go after a bio company right now. Like I think you can just do amazing things there the anyway but there's like another thing that's happening which is like tools that just make us all much more productive, that help us think of new research directions that sort of write a bunch of our code so you know we can be twice as productive. And that impact on like the net output of one engineer or scientist I think will be the surprising way that AI contributes to science that is like outside of the obvious models. But even just seeing now like what I think these tools are capable of doing, Copilot is an example. You know, be much cooler stuff than that. That will be a significant change to the way that technological development, scientific development happens. But then so those are the two that I think are like huge now and lead to just an acceleration of progress. But then the big thing that I think people are starting to explore is I hesitate to use this word because I Think there's one, one way it's used, which is fine, and one that is more scary. But like AI that can start to be like an AI scientist and self improve. And so when like can we automate, like can we automate our own jobs as AI developers? Very first, the very first thing we do, can that help us like solve the really hard alignment problems that we don't know how to solve? Like that, honestly I think is how it's going to happen. The scary version of self improvement, like the one from the science fiction books is like editing your own code and changing your optimization algorithm and whatever else. But there's a less scary version of self improvement which is kind of what humans do, which is if we try to go off and discover new science that's like we come up with explanations, we test them, we think whatever process we do that is special to humans. Teaching AI to do that, I'm very excited to see what that does for the total. Like I'm a big believer that the only real driver of human progress and economic growth over the long term is the structure, the societal structure that enables scientific progress and then scientific progress itself. And like I think we're going to make a lot more of that.

Well, especially science that's deployed in technology. Say a little bit about how what I think probably most people understand what the alignment problem is, but it's probably worth four sentences on the alignment problem.

Yeah, so the alignment problem is like we're going to make this incredibly powerful system and like be really bad if it doesn't do what we want or if it sort of has, you know, goals that are either in conflict with ours. Many sci fi movies about what happens there or goals where it just like doesn't care about us that much. And so the alignment problem is how do we build AGI that does what is in the best interest of humanity. How do we make sure that humanity gets to determine the future of humanity? And how do we avoid both accidental misuse where something goes wrong that we didn't intend. Intentional misuse where a bad person is using an AGI for great harm even if that's what other person wants. And then the kind of inner alignment problems where what if this thing just be becomes a creature that views us as a threat. The way that I think the self improving systems help us is not necessarily by the nature of self improving, but we have some ideas about how to solve the alignment problem at small scale. And we've been able to align OpenAI's biggest models better than we thought. We would at this point. So that's good. We have some ideas about what to do next. But we cannot honestly look anyone in the eye and say we see out 100 years how we're going to solve this problem, but once the AI is good enough that we can ask it to like, hey, can you help us do alignment research? I think that's going to be a new tool in the toolbox.

Yeah. Like for example, one of the conversations you and I had is could we tell the agent don't be racist? Right. As opposed to trying to figure out all the different things where the weird correlative data that exists on all of the training set and everything else may lead to racist outcomes. It could actually in fact do a self cleansing.

Totally. Once the model gets smart enough that you can, that it really understands what racism looks like and how complex that is, you can say don't be racist.

Yeah, exactly. What do you think are the kind of moonshots that in terms of evolution of the next couple years that people should be looking out for in terms.

Of evolution of where AI will go? Yeah, I'll start with the higher certainty things. I think language models are going to go just much, much further than people think and we're very excited to see what happens there. I think it's like what a lot of people say about running out of compute, running out of data. That's all true, but I think there's so much algorithmic progress to come that we're going to have a very exciting time. Another thing is I think we will get true multimodal models working. Not just text and images, but every modality you'd like in one model able to easily, fluidly move between things. I think we will have models that continuously learn. So right now if you use GPT, whatever, it's sort of like stuck in time that it was trained and the more you use it, it doesn't get any better. And all of that, I think we'll get that changed. So very excited about all of that. And if you just think about what that alone is going to unlock and the sort of applications people will be able to build with that, that would be like a huge victory for all of us and just like a massive step forward and a genuine technological revolution if that were all that happened. But I think we're likely to keep making research progress into new paradigms as well. We've been pleasantly surprised on the upside about what seems to be happening and I think all these questions about new knowledge generation, how do we really advance humanity I think there will be systems that can help us with that.

So one thing I think would be useful to share because folks don't realize that you're actually making these strong predictions from a fairly critical point of view, not just a we can take that hill. Say a little bit about some of the areas that you think are current kind of illusionally talked about, like, for example, AI and fusion.

Oh yeah. So like one of the unfortunate things that's happened is, you know, AI has become like the mega buzzword, which is usually a really bad sign. I hope it doesn't mean like the field is about to fall apart, but historically that's like a very bad sign for new startup creation or whatever if everybody is like, I'm this with AI. And that's definitely happening now. So like a lot of the, you know, we were talking about, like, there are all these people saying like, I'm doing like these, you know, RL models for fusion or whatever. And as far as we can tell, they're all like, much worse than what, like, you know, smart physicists have figured out. I think it is just an area where people are going to say everything is now this +AI. Many things will be true. I do think this will be the biggest technological platform of the generation. But I think it's like we like to make predictions where we can be on the frontier, understand predictably what the scaling laws look like or already have done the research where we can say, all right, this new thing is going to work and make predictions out from that way. And that's sort of like how we try to run OpenAI, which is do the next thing in front of us when we have high confidence and take 10% of the company to just totally go off and explore, which has led to huge wins. And there will be like, oh, I feel bad to say this. I doubt we'll still be using the Transformers in five years. I hope we're not. I hope we find something way better. But the Transformers obviously been remarkable. So I think it's important to always look for like, you know, where am I going to find the next sort of the next totally new paradigm and, but, but I think like, that's the way to make predictions. Don't, don't pay attention to the, like, AI for everything. Like, you know, can I see something working and can I see how it predictably gets better? And then of course leave room open for like the. You can't plan the greatness, but sometimes the research breakthrough happens.

Yep. So I'm going to ask two more Questions and then open it up because I want to make sure that people have a chance to do this broader discussion. Although I'm trying to paint the broad pictures so you can get the crazy ass questions as part of this. What do you think is going to happen vis a vis the application of AI to these very important systems, like for example, financial markets? Because the very natural thing would be to say, well, let's do a high frequency quant trading system on top of this and other kinds of things. Is it just kind of be a neutral arms race? What's your thought in like, it's almost like the Life 3.0 Omega's point of view.

Yeah, I mean I think it is going to just seep in everywhere. My basic model of the next decade is that the cost of intelligence, the marginal cost of intelligence and the marginal cost of energy are going to trend rapidly towards zero, like surprisingly far. And those I think are two of the major inputs into the cost of everything else except the cost of things. We want to be expensive, the status goods, whatever. And I think you have to assume that's going to touch almost everything because these like seismic shifts that happen when like the whole cost structure of society change, which happened many times before, like the temptation is always to underestimate those. So I wouldn't like make a high confidence prediction about anything that doesn't change a lot or that where it doesn't get to be applied. But one of the things that is important is it's not like the thing trends either trends all the way to zero, they just trend towards there. And so it's like someone will still be willing to spend a huge amount of money on compute and energy. They will just get like unimaginable intelligence energy. They'll just get unimaginable amounts about that. And so like who's going to do that and where is it going to get the weirdest? Not because the cost comes way down, but the amount spent actually goes way up.

Yes. The intersection of the two curves.

Yeah. You know, the thing got 10 or thing got 100 times cheaper in the cost of energy, 100 million times cheaper in the cost of intelligence. And. And I was still willing to spend a thousand times more in today's dollars. Like what happens then?

Yep. And then last of the buzzword bingo. Part of the future questions, Metaverse and AI, what do you see coming in this?

You know, I think they're like both independently cool things. It's not like totally clear to me other than like how AI will impact all computing.

Yeah, well, Obviously computing simulation environments, agents, possibly, possibly entertainment, certainly education, like an AI tutor and so forth. Those would be baseline. But the question is, is there anything that's occurred to you that's I would.

Bet that the metaverse turns out in the upside case, which I think has a reasonable chance of happening in the upside case. The metaverse turns out to be more like something on the order of the iPhone, like a new, a new container for software and you know, a new way, a new computer interaction thing. And AI turns out to be something on the order of like a legitimate technological revolution. And so I think it's more like how the metaverse is going to fit into this like new world of AI than AI fit into the metaverse. But low confidence, but dvd.

All right, questions?

Thanks. Hey there. How do you see technologies, foundational technologies like GPT3 affecting the pace of life science research specifically you can group in medical research there and sort of just quickening the iteration cycles and then what do you see as the rate limiter in life science research and sort of where we won't be able to get past because they're just like laws of nature, something like that. So I think the currently available models are kind of not good enough to have made a big impact on the field. At least that's what most life sciences researchers have told me. They've all looked at it and they're like it's a little helpful in some cases. There's been some promising work in genomics, but stuff on a benchtop hasn't really impacted it. I think that's going to change and I think this is one of these areas where there will be these new hundred billion to trillion dollar companies started and those areas are rare, but when you can really change the way that if you can really make a future of pharma company that is just hundreds of times better than what's out there today, that's going to be really different. As you mentioned, there still will be. The rate limit of bio has to run at its own thing and human trials take however long they take. And that's so I think an interesting cut of this is like where can you avoid that? Like where are the synthetic bio companies that I've seen that have been most interesting are the ones that find a way to make the cycle time super fast and that benefits an AI that's giving you a lot of good ideas but you've still got to test them, which is where things are right now. I'm a huge believer for startups that the thing you want is low costs and fast cycle times. And if you have those, you can then compete as a startup against the big incumbents. And so like I wouldn't go pick like cardiac disease as my first thing to go after right now with like this kind of new kind of company. But you know, using bio to manufacture something, that sounds great. I think the other thing is the simulators are still so bad and if I were an a, if I were a bio meets AI startup, I would certainly try to work on that somehow.

When do you think the AI tech will help create itself? It's almost like the self improvement will help make the simulator significantly better.

People are working on that now. I don't know quite how it's going, but there's very smart people are very optimistic about that.

Yep. Other questions and I can keep going on questions. I just want to make sure you guys had a chance this here. Yes. Great. Mike is coming.

Awesome, thank you. I was curious, what aspects of life do you think won't be changed by AI? Sort of all of the deep biological things I think we will still really care about. Interaction with our other people will still have fun and the reward systems of our brain are still going to work the same way. We're still going to have the same drives to create new things and compete for silly status and form families and whatever. So I think the stuff that people cared about 50,000 years ago is more likely to be the stuff that people care about, you know, 100 years from now than 100 years ago.

As the amplifier on that. Before we get to the next whatever, the next question is, what do you think are the best utopian science fiction universes so far?

Good question. Star Trek is pretty good. Honestly, like I do like all of the ones that are sort of like, you know, we turn our focus to like exploring and understanding the universe as much as we can. This is not a utopian one. Well, maybe. I think the last question is like an incredible short story. Yeah, that came to mind.

Yep. I was expecting you to say Ian Banks on the culture.

Those are great. I think science fiction is like there's not like one. There's not like one sci fi universe that I could point to and say, I think all of this is great, but like the optimistic corner of sci fi, which is like a smallish corner, I'm excited about it actually. I took a few days off to write a sci fi story and I had so much fun doing it, just about sort of like the optimistic case of AGI that it made me want to go like Read a bunch more. So I'm looking for recommendations of more to read now, like the sort of less known stuff, if you have anything.

I will get you some recommendations.

So in a similar vein, one of my favorite sci fi books is called Childhood's End by Arthur Clarke from like the 60s, I think. And I guess the one sentence summary is aliens come to the Earth to try to save us and they just take our kids and leave everything else. So, you know, they're slightly more optimistic.

Than that, but yes. I mean, this ascension into the overmind is meant to be more utopian, but.

Yes, okay, you may not read it.

That way, but yes, well.

So in our current universe, our current situation, you know, a lot of people think about family building and fertility and like, some of us have different people have different ways of approaching this. But from where you stand, what do you see as like, the most promising solutions? It might not be a technological solution, but I'm curious what you think other than everyone having 10 kids, you know, like, how do we.

Of everyone having 10 kids?

Yeah, how do you populate? How do you, like, how do you see family building coexisting with, you know, AGI high tech?

This is like a question that comes up at OpenAI a lot. Like, how do I think about, you know, how should one think about having kids? There's, I think, no consensus answer to this. There are people who say, yeah, I'm not. I was gonna. I thought. I always thought I was gonna have kids and now I'm not going to because of AGI. Like, there's just for all the obvious reasons, and I think some less obvious ones, there's people who say, like, well, it's gonna be the only thing for me to do. And, you know, 15, 20 years. So of course I'm gonna have a big family. Like, that's what I'm gonna spend my time doing. You know, I'll just like, raise great kids. And then I think that's what'll bring me fulfillment. I think, like, as always, it is a personal decision. I get very depressed when people are like, I'm not having kids because of AGI. The EA community is like, I'm not doing that because they're all gonna die. The kind of like techno optimists are like, well, it's just like, you know, I want to, like, merge into the AGI and go off exploring the universe and it's going to be so wonderful and I want total freedom. But I think all of those I find quite depressing. I think having a lot of Kids is great. I want to do that now more than I did, even more than I did when I was younger. And I'm excited for it. What do you think will be the way that most users interact with foundation models in 5 years? Do you think there'll be a number of verticalized AI startups that essentially have adapted and fine tuned foundation models to an industry? Or do you think prompt engineering will be something many organizations have as an in house function? I don't think we'll still be doing prompt engineering in five years. I think it'll just be like you and this will be integrated everywhere. But you will just like, you know, either with text or voice, depending on the context. You will just like interface in language and get the computer to do whatever you want. And that will, you know, apply to like generate an image where maybe we still do a little bit of prompt engineering but you know, it's kind of just going to get it to like go off and do this research for me and do this complicated thing or just like, you know, be my therapist and help me figure out how to make my life better or like, you know, go use my computer for me and do this thing or any number of other things. But I think the fundamental interface will be natural language.

Let me actually push on that a little bit before we get to the next question, which is, I mean to some degree just like we have a wide range of human talents right now. And taking a look for example at Dall? E, when you have like a great visual thinker, they can get a lot more out of Dall? E. They know how to think more, they know how to iterate the loop through the test. Don't you think that will be a general truth about most of these things? So it isn't that while it'll be natural language is the way you're doing will be. There will be almost an evolving set of human talents about going that extra mile.

100%. I just hope it's not figuring out how to hack the prompt by adding one magic word to the end that changes everything else. What will matter is the quality of ideas and the understanding of what you want. So the artist will still do the best with image generation, but not because they figured out to add this one magic word at the end of it because they were just able to articulate it with a creative eye that I.

Don'T have certain what they have is a vision and how they're visual thinking and iterating through it. Yeah, yeah, obviously there'll be that word or prompt now, but it'll iterate to better. All right, at least we have a question here.

Hey, thanks so much. I think the term AGI is used thrown around a lot, and sometimes I've noticed in my own discussions, like, the sources of confusion has just come from people having different definitions of AGI. And so it can kind of be the magic box where everyone just kind of projects their ideas onto it. And I just want to get a sense from you. How do you think. How would you define AGI and how do you think you'll know when we. I should have defined that earlier. It's a great point. I think there's like a lot of valid definitions to this, but for me, AGI is basically the equivalent of a median human that you could hire as a coworker, and then they could say, do anything that you'd be happy with a remote coworker doing just behind a computer, which includes learning how to go be a doctor, learning how to go be a very competent coder. There's a lot of stuff that a median human is capable of getting good at. I think one of the skills of an AGI is not any particular milestone, but the meta skill of learning to figure things out and that it can go decide to get good at whatever you need. So for me, that's kind of like AGI and then super intelligence is when it's like, smarter than all of humanity put together.

Did you have a question? Yep.

Great, thanks. Just what would you say in the next 20, 30 years are some of the main societal issues that will arise as AI continues to grow? And what can we do today to mitigate those issues? Obviously, the economic impacts are huge. And I think if it is as divergent as I think it could be for like, some people doing incredibly well and others not, I think society just won't tolerate it this time. And so figuring out when we're going to like, disrupt so much of economic activity and even if it's not all disrupted, by 20 or 30 years from now, I think it'll be clear that it's all going to be, what, like, what is the new social contract? Like, how. My guess is that the things that will have to figure out are how we think about fairly distributing wealth, access to AGI systems, which will be like kind of the commodity of the realm, and governance, like how we collectively decide what they can do, what they don't do, things like that. And I think figuring out the answer to those questions is going to just be huge. I'm optimistic that people will figure out how to spend their time and be very fulfilled. I think people worry about that in a little bit of a silly way. I'm sure what people do will be very different. But we always solve this problem. But I do think the concept of wealth and access and governance, those are all going to change and how we address those will be huge.

Actually one thing, I don't know what level of devs. You can share that, but one of the things I love about what OpenAI and you guys are doing is when you. They think about these questions a lot themselves and they initiate some research. So you've initiated some research on this stuff.

Yeah. So we run the largest UBI experiment in the world. I don't think that is we have a year and a half, a year and a quarter left in a five year project. I don't think that's like the only solution, but I think it's a great thing to be doing and I think we should have 10 more things like that that we try. We also try different ways to get input from a lot of the groups that we think will be most affected and see how we can do that early in the cycle. We've explored more recently how this technology can be used for reskilling people that are going to be impacted early. We'll try to do a lot more stuff like that too.

Yeah, so they are. The organization is actually, in fact, these are great questions addressing them and actually doing a bunch of interesting research on it. So next question.

Hi.

So creativity came up today in several.

Of the panels and it seems to me that the way it's being used, you have tools for human creators and go and expand human creativity. So where do you think the line is between these tools to allow a creator to be more productive and artificial creativity, sequential creativity itself, I think, and I think we're seeing this now, that tools for creatives, that is going to be like the great application of AI in the short term. People love it, it's really helpful and I think it is, at least in what we're seeing so far, not replacing. It is mostly enhancing. It's replacing in some cases. But for the majority of the kind of work that people in these fields want to be doing, it's enhancing, enhancing. And I think we'll see that trend continue for a long time eventually. Yeah, it probably is just like we look at 100 years. Okay. It can do the whole creative job. I think it's interesting that if you asked people 10 years ago about how AI was going to have an impact with a lot of confidence from almost most people. You would have heard first it's going to come for the blue collar jobs working in the factories, truck drivers, whatever. Then it will come for the kind of like the low skill white collar jobs, then the very high skill, like really high IQ white collar jobs like a programmer or whatever. And then very last of all and maybe never it's going to take the creative jobs and it's really gone exactly and is going exactly the other direction. And I think this like there's an interesting reminder in here generally about how hard predictions are, but more specifically about you know, we're not always very aware maybe even ourselves of like what's skills are hard and easy, like what uses most of our brain and what doesn't or how difficult bodies are to control or make or whatever. We have one more question over here.

Hey, thanks for being here. So you mentioned that you would be skeptical of any startup trying to train their own language model and would love to understand more. So what I have heard and which might be wrong is that large language models depend on data and compute and any startup can access the same amount of data because it's just like Internet data and compute. Like different companies might have different amount of compute, but I guess they're big players because they have compute. So how would a large language model startup differentiate from another?

How would the startup differentiate from another?

How would one large language model startup differentiate?

I think it'll be this middle layer. I think in some sense the startups will train their own models, just not from the beginning. They will take like you know, base models that are like hugely trained with a gigantic amount of compute and data and then they will train on top of those to create you know, the model for each vertical and that those startup. So in some sense they are training their own models, just not from scratch, but they're doing the 1% of training that really matters for whatever this use case is going to be. Those startups, I think they will be hugely successful and very differentiated startups there. But that'll be about the kind of like data flywheel that the startup is able to do. The kind of like all of the pieces on top and below. Like this could include prompt engineering for a while or whatever. The sort of the kind of like core base model. I think that's just going to get too complex and too expensive and the world also just doesn't make enough chips.

So Sam has a work thing he needs to get to and as you probably can tell with the very far ranging thing, Sam always expands my boundaries. And a little bit unlike when you're feeling depressed, whether it's kids or anything else, you're the person I always turn to for options.

I appreciate that.

Yes.

So, anyway, I think no one knows we're sitting on this precipice of AI and people are like, these are going to be really great or really terrible. You may as well. You got to plan for the worst. You certainly it's not a strategy to say it's all going to be okay, but you may as well emotionally feel like we're going to get to the great future and work as hard as you can to get there and play for it, rather than act from this place of fear and despair all the time.

Because if we acted from a place of fear and paranoia, we would not be where we are today. So. So let's thank Sam for spending dinner with us.

Thank you.

That concludes this episode of Grey Matter. You can read a transcript from this interview on the content section of our website greylock.com blog and you can watch a video from this interview on our YouTube channel under the Intelligent Future playlist. You can subscribe to Greymat on your preferred streaming platform and you can follow us on Twitter reylockvc.