2023 Big Ideas in Technology (Part 1)

At the end of 2022, our team at a16z asked dozens of partners across the firm to spotlight one big idea that startups in their fields could tackle in 2023. Emerging from this exercise came literally 40+ builder-worthy pursuits for the year, and that ranged from entertainment franchise games to precision delivery of medicines, small modular reactors, and of course, loads of AI applications. And in this two-part series, we'll be covering 12 of those big ideas. In today's part 1, we'll cover ideas spanning consumer, AI, games, and enterprise, featuring the voices and big ideas of Connie Chan, Anne Lee Skates, Jack Soslow, Doug McCracken, Sarah Wang, and Sumit Singh. In these conversations, you'll hear several important themes, including how physical transfers to digital, what the next wave of AI might look like, and specifically what role fault tolerance might play in this equation, what we can learn about social commerce from the East, and what role the growing number of bots in our lives might play. As a reminder, the content here is for informational purposes only. It should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a16z fund. For more details, please see a16z.com/disclosures. Let's start with Connie.

Hi, I'm Connie Chan, uh, I'm Anne Lee Skates, General Partner on the Consumer Team, and this is my big idea for 2023. The big idea is around social commerce. So social commerce, discovery commerce, video commerce, these trends are inevitable, and 2023 will be the year it all becomes obvious. Social platforms will become a natural place for product discovery, and platforms will seek to ease the friction between inspiration to purchase intent and finally the completed purchase transaction. Whether it's short form, long form, or a livestream, video is going to be a fantastic way to sell things and teach consumers about a product's value proposition. This will birth an entirely new ecosystem where anyone can become a seller and new companies can help everyday creators curate and sell products and services.

All right, Connie, so I like your prediction, but I think some people could argue that some of these aspects of social video and discovery commerce that you're describing are already in certain applications. I'm just gonna use Instagram as an example here. So what gaps do you see in terms of how social implements discovery or changes its involvement with e-commerce and how perhaps does this maybe compare to other parts of the world that have a different state?

So I think you're right in the sense that on Instagram, we already all shop from Instagram. Most of the time we get lots of product inspiration from the ads, but whether or not we actually complete the transaction right there and then, that's still a TBD. And I think it's because a lot of the platforms today, the Western platforms, still have way too much friction. Between inspiration to discovering more, learning about the product, to finally doing the actual checkout, the actual payment completion. Today, you might find inspiration on Instagram. You might find a video of an influencer talking about a product, but you might not click and complete the purchase right there and then. You might instead open a browser and go to Sephora.com and go search for that item or go to Nordstrom.com and go search for that outfit. The, the transaction right now is still kind of being broken up, and that's because there's a lot of friction in the actual implementation of it's not great. But I think the other thing about video that's still very underdeveloped is the idea of something like live video. And live video is a fantastic way to sell things. And it is something that I'd say we haven't really explored out in the West yet, outside of these other platforms like Whatnot that does live shopping for collectibles and other categories. And the reason why live is so powerful is a bunch of things. One, there's community. The people who are all buying at the same time are talking to each other through comments in the chat. They're talking to the seller. You can ask the seller a question directly. Say you are buying a used luxury purse. You can say, hey, turn it inside out. Can you flip it over? I see a little stain. Can you put it up closer to the screen? I want to look at it more closely. So basically, as a consumer, you get more confidence to buy. And shopping is all about giving consumers confidence that this is the thing that's going to change their life and make their lives better, right? So live video just gives you more ways to answer questions and get that information to the consumer. But also live video allows you to do these kind of limited time discounts or these window duration promos in the same sense that you have Black Friday sales. Why not have a sale for a few hours where the price is just different and then drive people to do volume purchasing, to do group purchasing. There's just a lot more stuff that gets unlocked when it's live. So whether it's again, short video, prerecorded video, long video, I still think there's more to explore in the same sense that you and I, when we grew up, we had these like as seen on TV infomercials and they worked. They worked for so many of us. And for a lot of it, it was because it had entertainment value. Right now, the video that we see on Instagram today, most of them are still ads or maybe an influencer kind of campaign sharing a product with you, sharing their endorsement, but they're not entertainment yet. And there's still much ways to go before video shopping in the West looks like entertainment.

Yeah, so I think you're painting a pretty compelling picture, but I still am trying to understand why we haven't seen some of these buying behaviors. I mean, to your point, the infomercial has existed in our Western culture for a long time, and you said it worked. And so what is stopping us from having these platforms or having the habits really of a consumer to go seek out that kind of shopping experience? Does it just really not exist yet? Has the right company not sprung up? Or why do you think we maybe see some of these behaviors in other parts of the world, but not really in the West?

We see them more exemplified in a place like China where live shopping is very big. I think it's because the platforms implemented it, quite frankly, years ago. And there's also a huge ecosystem built out where you have people who are just curating products, handling logistics, customer service, and so forth. But there is no reason that that user behavior, that customer behavior, is only going to work in Asia. In fact, in the US with Whatnot, you already see lots of people loving the community of buyers and sellers and finding things that they're buying that they didn't think to go buy. There's already an element of discovery that happens with life. But you start that question asking about infomercials. And, and let me ask you, when you saw those infomercials as a kid, it wasn't just that the person was demonstrating the product. It was because if you buy it now, In that like 10-minute window, you had all these freebies, or if you bought it right then in that 10-minute window, it was discounted. But once that infomercial ended, it might not be that price. That's why infomercials worked. And that kind of time element is very possible with something like live video, but not what we're seeing today with kind of these prerecorded Instagram videos with celebrities because quite frankly the platforms haven't built out the tools to allow them to do something like that yet.

I love that you brought that up because I think through digital scarcity and how in an infomercial you're like, if I don't buy this now, it's not— I'm not going to have access to this discount.

I won't know where to get it either.

Yeah, exactly. And I feel like there's almost like the opposite dynamic that has become a habit, at least for me, on Instagram, using that example again, where I will literally see an ad for something and if I don't click it right then and there, I'm like, I know I'm I know I'm going to get this ad again. And so I don't feel this urgency, this scarcity. But another aspect of kind of my shopping experience from when I was younger that I don't see in the digital experience is I used to think shopping was fun. It was a social activity. I'd go with my friends. We'd talk about, you know, what we wanted to get. I'd look at what they're buying. And I think what you're pointing at is also some of these other habits in, in other parts of the world. And, and I think in the East are it's a social experience, it's entertainment to actually shop, which I don't think really exists in—

I mean, for years now in, in China, when you were shopping, you could put 4 or 5 dresses in a shopping cart, send that link to several friends, and they could comment on every single one. And then you could see under every dress the comments from each of your friends. And that feature has been there for years. Right. So the idea of unlocking social commerce or product discovery excites me a lot. Like when I walk into Target or Costco, I might have like one thing I need to get and I always end up with a lot more stuff in my shopping cart.

Yeah, when I—

Totally inevitable, right?

Had a cart from Costco less than $200 even when I went there for one thing.

Exactly.

But does that happen when you shop on Amazon?

It doesn't. No.

It doesn't. Because the West shopping is still search-based and we are not focused on pushing kind of this discovery-based full 100% recommendation engine shopping experience to the end user. I say Shein is doing it. Temu, the new player in e-commerce pushed by Pinduoduo, they're, they're pushing that too. But we have still yet to see more commerce companies take on this product discovery focus as opposed to search and filter. And let me just help that person navigate all the SKUs I have. The reality is consumers, when you went shopping with your friends, you might have picked out coats or dresses that you would you wouldn't have gone to search for if you were just searching by, you know, neck type or sleeve length or bestseller, right? It's not going to surface the same things to you as if you were just walking in a store.

That's a great analogy. Something else I want to ask you about is I've heard you talk about how social apps in particular, which you could say are becoming commerce-focused apps if they aren't already, a lot of them have kind of devolved into copying one another. Maybe the most prominent example is when Instagram copied Snap Stories. We also saw a version of this last year when Clubhouse was copied by just about every social app out there in terms of having live audio. And so why does it at least seem like social platforms today are just copying the same features from one another and not testing some of these more creative features, especially when we've seen some of these creative features work in other parts of the world?

Yeah, I think there's, There's both logic to it if you think about it, but also opportunity, right? To, to change the status quo. The logical part is that, hey, this is already a proven idea. I already have distribution on my app. Why don't I just copy this feature? It's gonna work on my app too. And that's actually a very, I, I call it super app, the phrase super app, almost a super app mentality, which is that I have distribution, my users will want the same thing. I should stick that same feature or that same product in my app too, and it's gonna work. And so there's logic for why they're doing it. But to your point, there is missed opportunity because there are all these other business models that they're not exploring yet. It's almost like they have to wait till some startup gets traction with it or some other company gets traction with it before they try and implement it. And if they don't do it correctly, it doesn't always work. But, you know, some of these Eastern to Western translations, they take time. This has been part of my investment these for the last 10 years. I take ideas that work in Asia and other parts of the world. I try and figure out what can be copied in the US, and then I try and teach it to as many people as possible so we can learn from companies outside of just the US. My learning is that there's usually a time lag of at least a year and generally 3 to 5 years before some of those concepts that are already mainstream in Asia become very popular here.

And is that just a reality that there's just friction, there's inertia in the way we do things, which sometimes is different to other parts of the world? Or is there some sort of misnomers or some sort of misunderstanding where people think, oh, there's like a different culture and therefore it won't apply here? I'm just trying to understand why it takes so long for us to absorb these learnings.

It requires a very open mind because you have to go at it or, or think about product With the assumption that, you know, just like Ratatouille, anyone can be a cook. Like anyone can come up with a great product idea. And if you come up with that mentality, you become much more open-minded and you actually want to see as many examples of success or failure as possible. And I think another big reason though, why some of this stuff takes a while to implement is twofold. One, it requires huge organizational changes. This is really a top-down decision. It's very difficult to change this type of mentality bottoms up. And the reality is if you are changing your business model of, say, like an Instagram newsfeed and you start pushing all these live shoppable videos, that's taking up space that otherwise would've gone to an ad that generates money, right? So your P&L takes a near-term hit. You are using up pixel space that could have gone to something else. And so this is a very high-level decision or high-level framework that needs to really come from the top and have really strong buy-in from the top because there are near-term consequences— not consequences, there are near-term impacts when you try and make these big shifts, right? The second reason I'd say is as much as we use our phones and are addicted to our phones in the US, I think we are still mobile but also PC-oriented. We are not mobile only. Maybe we're mobile first, maybe younger generations, but we're not mobile only, right? Whereas I say in China and a lot of developing countries, they are mobile only. There are people who literally do not have a computer and everything is done on their phone. And therefore they're experimenting much, much faster on mobile product experiences and mobile business models. And in the US, because a lot of platforms try and be backwards compatible, they have to make sure that whatever they have on their app is also supported on the computer. It limits their creativity or it limits the rate at which they can innovate. I'll give you an example to kind of solidify like how mobile-only China is versus the US. In the US, if I'm buying a plane ticket to another country, to another state, whatever, I might research that plane ticket on my phone, but I might still buy it on my computer. And that's the data that actually comes back from the travel companies, that those transactions, the high-ticket transactions, are still happening on your computer.

And some might even phone in, right? Yes.

Some people might even phone in to have that assurance. Right. And whereas like something like that, it would all be on the phone, 100% of that kind of transaction would all happen on the phone in these developing countries. And so this kind of disconnect between are you mobile first, are you mobile only, is another big reason why the US moves slower.

I like that frame. And I saw the same kind of leapfrogging from living in Indonesia for a while where truly, as you're saying, it's, it's not mobile first, it's mobile only. I like that frame a lot. And something that you articulated in your idea for the year is that these trends are, in your own words, inevitable. So what do you see specifically changing in 2023, this upcoming year, as we look to some of these perhaps long-term shifts? But what do you see happening very soon?

I see platforms taking— I don't know if more risk is the right word, but platforms are taking more action. They are going— they are going to experiment more, specifically with things related to commerce or things related to creators. There's just going to be more experimentation happening on that front. And because I'm so bullish on, on the efficacy of it, because I know I, I have like an Abrol or an OxiClean and like all this random stuff that I bought from infomercials, they just work, right? Um, and I think it's going to work on the phone too. There's no reason why it wouldn't. And because platforms are waking up to this and also brands are waking up to I think that combination is the important catalyst. Now that we're still missing ingredients, we still need products to sell. We still need creators who are really good at selling. Now the creators who are big on Instagram might not be the best sellers. You know, just go back to your infomercial days. Like there are some people really good at selling. They're not necessarily going to win Academy Awards cuz they're not necessarily great actresses, but they're really good at selling. And in the same sense, I also think even if we see an increase in commerce for video, you might also see a new category of creators emerge.

Next up, we have Anne.

Hi, I'm Anne Lee Skates, and I'm a consumer partner at a16z. My big idea for 2023 is unlocking the third space. Ray Oldenburg coined the term third space, places that host regular, voluntary, informal, and happily anticipated gatherings. Outside of home or work. These used to be, in real life, community gathering spots, such as bars, coffee shops, churches, gyms, and clubs. But remote and hybrid work, as well as this generation's natively digital habits, have given way to an online-first era. So many interactions and first encounters occur online, whether it's over Zoom or meeting a like-minded friend with similar interests over Twitter or TikTok, or in a Discord group or a game? What are the next generation of tools and platforms that are built to serve consumers, community builders, and creators in the post-COVID hybrid world? And how can new technology such as generative AI create user experiences that facilitate deeper discussions and relationship building between real humans and not bots?

All right, Anne, I am so excited to talk about this topic because we've been seeing this theme across several of our big ideas, this transition from physical to digital. And before we get into that, I just want to hear from you. I mean, we heard Ray Oldenburg's definition, but how would you define this idea of a third space?

I think of third spaces as physical locations that are not your home and not your work, uh, where you can explore different but important parts of your identity. To me, there's sort of two dimensions. One is obviously the actual physical location, but the other is a person's mental and psychological state. Third spaces are places that enable freedom and safety outside of our workplaces that are homes. I think in terms of the physical location dimension, there are places you congregate socially and you're able to explore different parts of your, uh, identity freely and safely. Psychologically, to me, these are spaces where you don't have a defined role responsibility. For instance, at home you might be a parent, right? Or you might be a breadwinner, um, or head of household, or both. But at work you have a specific job duty, uh, and associated responsibilities. Ability. Neither of these places, uh, offer true freedom. Colleges to me are really good earlier life examples of good third spaces. So there's lots of communities and spaces where you're free to, uh, explore and free to interact, uh, with whoever you want. For adults, um, this could be religion and churches, uh, it could be games and gaming bars, cafes, arcades, For fitness enthusiasts, this could be gyms, and for art lovers, you know, this could be a museum or a particular museum or even space within a museum. But there's been definitely a dwindling of these types of public spaces, and especially with COVID Actually, there's an interesting stat from The Atlantic that in 2019, two-thirds of Americans said they had a favorite local place they went to regularly, but that two-thirds has actually dropped to just a little more than half.

That's really interesting. And I like the frame of having a place in society or feeling like you can almost shape or mold how you show up, because as you said, there's kind of like predefined notions in the way that you show up to work or at home. But these third spaces are really important. You mentioned college, like that was such a formative time of so many people's lives because they could kind of reshape themselves and say, hey, I actually want to show up differently here. We're also starting to see, in addition to the dwindling of these physical spaces, maybe some of these digital spaces arise, which is really interesting. And something that I talk about a lot on the podcast is how we tend to copy and paste what exists in the physical world straight to the digital. So you gave examples of like bars or churches. During the pandemic, we saw the equivalent of those again kind of being copied and pasted, like the same routines, the same ways of interacting, just, you know, on a web page or in a Zoom application. But I think when things get really interesting, it's when we start to rethink these spaces with the tools that we have online. So have you seen any examples of where people have kind of, as people say, from first principles thought about how do I use this technology to actually facilitate some of these human connections that we previously formed in physical third spaces?

Before I go into an example, I think there's two particularly interesting things here about what digital native unlocks. For these third spaces. So the first is you can connect to more specialized people and have a much wider selection of people because literally now online you have a global pool to choose from. That's like everybody, billions and billions of people in the world. So let's say you're an anime fan. Instead of connecting with 800K people in San Francisco because you live in San Francisco who may or may not share your interest in anime, you can now actually We have a selection set of 100 million people globally who are true fans of anime. And, you know, more concretely, like taking Whatnot, which is one of our portfolio companies, that's a live shopping platform focused on collectibles and communities as an example. If you think about Whatnot, it might either look like just a marketplace or it might just look like a digital games, toy collectible store, but it's actually not a copy and paste of a physical game. Game store. There's gamification built into the product via just a live auction mechanism. So it's kind of like you're in this real-time auction with all these people, and they're digital goods and services that are transacted live. And you can jump from one stream to another in real time and actually have tens of thousands of choices of different rooms or even different streams, different events that you want to be a part of that you can literally immediately go to. Without physically changing your location. This relates to my second thought, which is in a physical event, your experience is really limited by presence. You can physically only be present in one place at one time. Your physical belongings and items can also only be in one place at one time. But in a digital world, this concept of presence is completely different. You can actually reimagine what the means you can be in a digital environment but also know what's happening or be somewhat immersed in a completely different environment without having to physically go there. And you can immediately change the experience of presence by changing the environment in a digital world. You can change the space, change the mood, change the furniture, change the background, what you're wearing, anything and everything. Games is an area that's probably the most advanced and digitally native. And I think in general, it's always a good place to look for the future of consumer technology. Dungeons and Dragons is a good example. So it's obvi— it's original OG RPG game. You know, there was sort of like a physical tabletop version. You had to physically gather around with other players and have enough people to fill in various roles that are stipulated by the games. But you're limited by the specific people you pick, their real life imagination, and actually having these people available. But today in RPGs, you're able to explore completely different worlds, play with AI or humans, but these humans can be from all over the world. And you can immerse in any, you know, visual and audio environment of your choosing. That's literally not possible when you're playing in real life.

It's exciting. I think about chess as something I played when I was younger, and I only had access to the chess coaches in my immediate area. Or the chess clubs in my immediate area and the people within those clubs and the skill sets that they were at. And so, yes, it does completely change who you have access to. But I also like this idea of different modalities of fun or engagement that you mentioned. And something that comes to mind there is I've been talking a lot to my friends recently about how all we ever do is go and eat or drink. And that's what you do. You just meet up with friends and you talk. But I remember when I was younger, you know, college comes to mind, we would go play tennis, we would go, I don't know, go join a dance class.

There's so many more fun activities.

Right. Things to do. Replit comes to mind here where digitally we've turned coding into a game. You also mentioned, you know, the gaming space overall. Fortnite is something that we see many kids and older folks playing together. And so I think that's encouraging to me. I think a lot of people looked at gaming in a negative way, but to me, we're seeing again these new modalities of how people are engaging with one another instead of just talking to one another. And so are there any other examples that come to mind there in terms of like how the internet or how the digital tools we have really change our ability to engage and connect?

There's quite a few that come to mind, and I definitely think gaming is the best example. But actually, for the purposes of this question, since we're looking at all sorts of activities, I'm hopeful that there will be a lot more activity, digital activities that you can do with your friends outside of gaming. I think if you look at new relationships you can build through tech, whatnot, TikTok, Reddit, Discord, these are all really great at facilitating interest-based and sometimes even stranger-first relationships and actually deepening those and giving people a place to outlet and nerd out about very specific niche hobbies and niche interests. For existing relationships, you know, there can be different forms of gaming ranging from really casual types of games like raising a virtual pet all the way through to more serious games like Fortnite. And I also think there's new forms of media creation with BeReal, TikTok being the obvious most recent example. One interesting one is Lensa AI, which is, you know, the sort of GPT generative AI created visual of yourself, um, based on a few photos of a person. They actually have this creative thing where you can now make GenAI art for your friends and family and even for your pets. So not just for yourself. And it's an interesting new way, uh, to use technology to engage.

Yeah, I love how, as you're, as you're speaking about, people are creating together. I, I can't think of other examples like this in the physical world other than going to maybe a random pottery class once a year with friends Like, I think it's really exciting that you actually see friends and family from far away connecting over the creation of these digital products. I'm curious though, I could see some pushback from people saying, you know, this is great, like you can foster your online pet, but we have to exist in the physical world. And of course there's merit to that. We eat in the physical world, we sleep in the physical world, and connection in that world is important too. But I'm curious to know if you've seen any trends in terms of some of these habits that are inevitably being formed by the time, the sheer time we're spending online, if those habits or those new modalities that we've talked about, are they reshaping any ways of interacting in the physical world? Have you seen any of the transfer in reverse? Because so far we've talked about how physical transfers to digital.

Yeah, yeah. And I, I also think it's interesting to see, uh, just the interplay. One interesting example, I, I think from a techno technological lens is VR. It is obviously by nature a completely immersive experience. And so, for instance, we have this portfolio, education portfolio company called Prisms VR that's teaching kids math in a spatial world. And what we see is actually physically in classrooms, kids are, you know, using these headsets and learning math in VR. The more advanced kids are actually more eager to help out kids who are struggling with a certain concept after being in this new novel digital environment, even though they were solving problems alone in these digital environments. So I, I find that really fascinating. I would say another underexplored area here is thanks to Zoom, COVID, and remote work, we're now really comfortable interacting with people online, whether it's strangers or colleagues or even, you know, your loved ones. But I think there's an opportunity in digital products, you know, for digital products and features that prompt or create in-person interactions. So Pokémon GO to me was last such mass market product, and now we're seeing some innovations around the creator economy of fandoms and creators and celebrities gathering online and translating those to the physical space. Imagine like you showed up for a physical concert and you're prompted to to meet other fans that you've already interacted with digitally before who maybe share the same favorite songs or share the same favorite experience with a celebrity or collect the same digital good.

I've been thinking a lot about how we have, in many cases, so much data from the digital world, but then we don't, as you're saying, translate that back to when there are physical gatherings. Like, I see so many podcasts or communities thrive in the digital world, and then they organize these in-person meetups, and they're just like, hey everyone, stand together, like, figure it out, meet people. And they have so much information about the types of topics people like talking about, even potentially demographic data where they can connect people who have the same problems or who are at the same phase of life.

I think you can facilitate such richer discussions and richer interactions once you use all those pieces of digital data that you're talking about.

Exactly. I love this idea of blending the two because there's positives to both the physical and digital. And again, I think When we go physical to digital, we often just copy and paste in strange ways. And then when we go the reverse way, we almost like forget all of the benefit that we got from the digital world that could be implemented in the physical. Next up, we have Jack.

Hi, I'm Jack Soslow. I'm a partner on the games team focused on AR/VR, games infrastructure, and AI. And this is my big idea. Games as a never-ending Turing test. It's not hard for a computer to deceive a human. From ELIZA to ChatGPT, computers have successfully masqueraded their biology. This phenomenon also occurs in games through the mass prevalence of bots. Bots have historically been scripted procedures, but are increasingly becoming true neural network-based AIs. The bots are all around you, you just don't realize. Look to the rumored concurrency bots in Words with Friends, the close-win bots in Call of Duty Mobile's onboarding, the prevalence of cheaters, bots, and chess, or the never-ending war of bots and platforms in games like RuneScape. The next generation of these bots will take human-like to a whole new level. Startups like Inworld AI, Conv AI, or Charisma.AI are making in-game agents that understand game state and have conversations, emotions, real objectives. Imagine walking through the wilderness, not knowing whether your clan's mage is a bot, building a town in A Township Tale with strangers, without knowing the humanity of the local farmer, playing a game of diplomacy, but not knowing if Turkey is an AI gunning for European domination. In the year ahead, you may not know who's who anymore, and you won't mind. Games are good alone, but better together, or so you think.

As you've said, it's not that hard for a computer to trick a human. It turns out the Turing test was probably too low of a bar, and we're seeing this play out everywhere. I think maybe a recent example of this is ChatGPT, which is throwing out a lot of correct answers, but also a lot of incorrect answers, and humans are just kind of taking it at face value. So maybe we don't even need human-like bots to trick humans. It seems like less evolved bots are already doing that. So what do you think about that? Have we already kind of crossed that chasm?

Oh, absolutely. Um, and I think the question here is like, at what point do you cross the chasm in whatever domain to deceive humans? So for example, it's like, You used to read the words like, don't believe everything you read on the internet. Now I think this applies to AI, like don't believe everything you read from AI. Um, the Turing test, as you say, is like a flawed metric. And the reason for this is in part because it is too low of a bar. It is a binary bar. So it's either intelligent or not intelligent. And it tests for deception, not real understanding. What these AIs do is they try to understand the underlying rules that generate the dataset that they're trained on, um, and then generate an approximation of what they believe is the next token. But it doesn't mean that it's right. Like, in the case of ChatGPT, it communicates with a lot of confidence. A great example of this might be, say, asking ChatGPT to multiply 153 times 257. Like, it might get an answer that's close to correct, and it communicates it like it is correct, but it's not correct. And this is a deterministic domain where you know what the right answer is. There are a lot of domains where there isn't actually a right answer. The right answer is much more amorphous. Say, like, you ask about China's— what should China's AI strategy be? Like, this is a very complex question, and it is dictated by, say, like, your bias underlying what data you've seen in the past, what you believe about China at large, and then also, like, what you believe the importance of AI should be at large too. This is a complicated question with a lot of different answers. OpenAI and ChatGPT might have one answer and then say maybe a Chinese AI might have another. Because these things are information sources, how much can you really trust them? In the case of like Google, you ascribe some trust to the outputs, but you don't really know like the underlying model that contributed to generating the sources that you see. You might trust your mother, but what went into her like reasoning process? You probably don't trust the Onion, but I'm sure some people's grandmothers do. And the question of like, should you trust ChatGPT? The answer is like, probably for most things, but it's like not always that clear. We don't know exactly what went into their training data, which is the underlying rule set that they're generating their model upon. And then additionally, there's like a second layer of filtering. So like, what do they consider sensitive and then not give you a gen— generation for. With these two layers of filtering, it's hard to understand exactly what is or what isn't going on in the model. But for the most part, like, when people have been testing the outputs, they've been fairly trustworthy. So, in my opinion, my faith is like, I trust the AIs for the most part, and then like, we'll validate their results on like, Google or other pieces of information online that I, I like, have been more properly validated. And what makes me positive and optimistic that trusting AIs is going to work out well is because there's been a large transition more towards open source and openness. Um, so you understand the data that's going into the models and you also understand like the weights and biases. So you know exactly what you're getting. And then additionally, these models get better and better over time. So where 153 times 257 may have been incorrect in the past. It might be right in the future with GPT-4. And then because it's been trained on the entirety of the internet, it actually does understand certain things much better than a human would. So like, I do trust it, uh, as an information source for the most part.

I like that you mentioned trust is really a sliding scale, and that's not just with bots, but that's with humans as well. And maybe one differentiating aspect of bots, or in the case of ChatGPT as an example, is that when there are falsehoods, they can be corrected at scale, as in, like, to use your example, if, if an answer is incorrect and you can retrain the AI to no longer do that, it will no longer do that, not just in the single instance, but globally. So that's interesting. But I think another interesting aspect of your big idea for the year is that we are really experiencing this exponential age as it relates to software, and that includes bots, includes AI. And by nature of that exponential technology, I think we can expect to see a lot more bots and these bots becoming a lot more convincing, which is exciting, maybe scary. There's all sorts of implications of this where we're not just talking math, true or false. It's, it's like, do I know if this person's real or is it really their video, right? Is it a deepfake? And so I think there's going to be all sorts of implications, but focusing it back on games specifically, how do you think this is going to impact the gaming world? Is this going to make certain games not even worth playing because you're just really encountering a bunch of bots instead of humans?

A game is a structured form of play, and the keyword there is structure. Like, there are rules involved in a game. So like, when I go and play a game, I enter, say, like Call of Duty under the auspices that everybody is going to be a human and they're playing by the rules. When there's a bot involved, say someone has aimbot or something like that, then I feel like I've been treated unjustly, and then the game becomes less fun. The question then becomes, like, should you design rules for bots? If you're going through, like, the RuneScape never-ending war of, like, banning bots, then the bots come up with a different strategy to get around the ban, and then you have to ban those, and new strategy, ban those. But it's like, is that worth the effort for just maybe designing the rules for bots in the first place? Maybe you lower the resource constraints or make it particularly hard for bots to configure in this world or make it so that The humans have to interact with another human in order to get the rune ore to smith whatever sword that you want. These are the types of questions that you have to ask. If it is a structured form of play, perhaps the structure should be built around bots in the first place if you can't really defend against them. Then additionally, it is like there are sometimes in these games you are interacting with bots and you just don't know it. Especially when they are non-complex games. It may be a good analogy to think about about this is imagine a bot versus a human who played tic-tac-toe. Like, tic-tac-toe is a solved game. It would be pretty much impossible to distinguish between the two. Now think about chess. Now think about chatbots. There's a sliding scale to the complexity of tasks and like what you can distinguish to be a bot and not. In an exponential age, we're looking at, say, chatbots that pass the Turing test. But what does this mean for, say, like in Skyrim, say, like the local guard, like In that case, they might eventually get to the point where they are purely human-like. Maybe the types of interactions that you have with them aren't scripted chatbot lines, but really the types of interactions that you might have with somebody that you build a relationship with. Perhaps the first time you enter a town, the guard is pretty guarded. They give you a little sass and they ask for money. But after you beat the werewolf that has been haranguing the town for years and you put them back in their cave, The guard treats you with the love and respect that you deserve, and you go to the local bar and people celebrate you. These are the types of things that, like, over time, we'll get more and more believable worlds with more and more believable entities. The reason why games like Minecraft have, like, added pets or villagers and things like that is because these single-player games, like survival Minecraft, is a pretty lonely experience. You want to have, like, real companionship, or at least the feeling of pseudo-companionship throughout your journey. So if you have like really believable villagers in Minecraft, that would be a nice add. Or say, for example, if Navi in like Link or Ocarina of Time, like there's literally someone who is with you the entirety of the game who kind of tutorializes the experience and gives you the sense of companionship throughout. Like what happens when that's an AI that can understand all the different mistakes that you might be making ahead of time or is actually building a relationship with you that's personalized for your experience. There, there are a million different ways where this can kind of bubble up. The ones that I'm most excited about are with like single-player games. And then also with multiplayer games where the multiplayer games become personalized, where you actually don't play with other humans anymore. You're playing with perfectly catered AIs for the experience that you want. A good example of this is Call of Duty Mobile, where when you onboard, you're playing against really bad bots. And then they give you an initial win that helps you learn the actual skills that go into being good at this game. And then slowly but surely, you introduce more and more humans into the equation to level up the skill cap. This is the type of experience that could be done at scale with games like Fortnite, say, for example, groups of different AIs that give you a close shootout. These types of close wins are really good factors for predicting the likelihood that you come back in the future. And there's this chart that game makers try to bring you on, which is this kind of like flow state, which is like over time, like the dopamine spikes hit and then they go down and then they go up a little bit more and then they go down a little bit more and then so on and so forth all the way through. And they try to cater the game experience for this exact feeling. But in the case of multiplayer games, you can't actually control what other people do. But if you had AI bots that were believable, you could. So those are the kinds of things that I'm looking forward to.

Yeah, I'm hearing a couple important things there. One is it does seem unrealistic to try to void games of all bots. It doesn't seem like that's possible. And so again, re-curating the way that we think about how games are designed is important. And the second part is how do we make these bots additive instead of reductive, right? There are the kinds of bots where actually you're describing they can make the game experience better. And then we all know of bots where it just like completely destroys the game. It makes it seem like it's not even worth playing because I can never achieve the skill level of a particular bot, as an example.

The important distinction there is like, a bot is something that is made by the game creator, and then a hack is made by a game player to bypass the rules. So like, a bot is supposed to improve the game, and then a hack is detracting from the game from everyone else.

Right. But at the same time, we can't prevent the hacks, most likely, either, right? That's going to have to be part of the game design as well.

Yes, yes.

Well, I mean, to that point, it— my limited game experience is in the realm of chess, and it reminds me of some of the stuff we've been seeing in chess recently where we've actually known that bots are better than humans at chess for decades now, since 1997, the famous Deep Blue versus Kasparov game. But for decades since then, people have still willingly and enjoyingly played chess because there was still a sentiment of, I can improve at chess relative to other humans. I'm, I'm still not facing bots in this sense. More recently, there's obviously been some controversy because there is now this gray area of, can we control bots kind of coming into the human realm of chess? But even then, that's kind of skyrocketed chess into the zeitgeist and it's become more interesting or important than ever. And then you almost see this new spinoff. It's actually existed for a while, but it seemed to gain relevance, which is the Fischer Random Chess, which basically has the same fundamentals as chess, but the pieces are swapped around. And so it's almost like humans are trying to extend the game so that's still relevant, so that they can still kind of test their humanness, their human skill, while avoiding the influence of bots. So I'm interested to see how that might play out in other types of games. As you said, kind of designing games so that they are not so much bot-proof because the bots will play a role, but that they are still interesting and exciting with the influence of bots.

100%. And what I really appreciate about games like Fischer Random Chess is that it's about understanding chess versus, say, like memorizing, which a computer can do really well and was essentially what, like, Deep Blue was doing at, like, a greater extent. It was just, like, a really glorified search function. I'm excited for other games that kind of consider bots as first-class citizens within them. And then additionally, new game modes that are built specifically for humans, where instead of saying, hey, um, no bots, you can play on bots on these servers, but also these servers, these are for humans. And we'll let like Darwinism decide what is played more often.

That's it. That's a good way to put it. We'll let the consumers decide. Let's close this out by talking about 2023 in particular, because we're going to see games and the influence of bots change over the coming years, the coming decades. That's going to be really hard to predict. But what specifically in 2023 are you excited about in this space?

I'm excited for foundation models to come out in more domains. So we saw image pretty much be solved last year and then text continually get better and better. This year I'm excited about 3D foundation models, audio foundation models, video foundation models, those sorts of things. And then additionally, I'm excited about how they all come together under one roof. So how do the text models inform the 3D models inform the audio models. In the case of games, like, every game asset is not really just one 3D asset. It is an asset with sound attached to it, with code and scripts, and each of these informs the other aspects. Say, for example, changing an old wooden door. It might have a rickety, slow-moving, like, script as well as a sound attached to it. But if I were to change the door to be a metal door, how does that actually change the script and then also the sound And then if I were to change the code, how does that actually change the 3D asset? And so on and so forth. So all of these things are connected, and right now they've been primarily point solutions. And then additionally, there's like this kind of AI unity that is born that really helps creators and game creators kind of abstract away a lot of the things that are mostly wrote or at least have been solved by experts in their respective fields.

Yeah, I think you co-wrote an article with James Quartzman from our team and I thought the way you put it was really, really strong in the sense that if we look at how easy it is to publish online for so many creators to tweet or write up a blog post, that has happened in the last couple decades. And if we look back to the 1990s or early 2000s, that was not the case. It was not that easy to publish online. And I'm excited for kind of the same renaissance within games. It's kind of crazy actually to imagine that anyone, myself, who has very little gaming experience, could actually spin up a game potentially in the coming years.

I'm sure you've had games in your head that you've wanted to create, but you couldn't because it's too hard. In the same way that maybe like you wanted to make an image in the past, I don't know, or a meme, and it wasn't really possible until you saw Midjourney or DALL·E. Like this, this type of capability is right around the corner, and it's a bet on that derivative. AI is moving so quickly, um, it's— you have to to, like, kind of ride the wave and see where it's going to go. So excited about all of this. I think games is coming for a new renaissance. There's going to be tons and tons of games that are made, but the real skill is in understanding what a good game looks like.

Next up, we have Doug.

Hey, I'm Doug McCracken from the Games team. Here's my big idea for the year. The metaverse goes fashion forward. Gamers know that character skins in games like League of Legends and Fortnite are an important form of self-expression as they become part of a player's identity. That's why character skins are big business despite having no gameplay benefits. Digital natives Gen Z and Gen Alpha demand that brands enable self-expression in the metaverse. Of that group, 2 in 5 already believe that self-expression via fashion is more important in the digital world than the physical, and 3 in 4 say they will spend money on digital fashion. Brands that lean in, like Gucci, will be rewarded by the hearts and wallets, both physical and digital, of consumers. And as physical brands go digital, more digital brands will go physical, creating even stronger competition and broader adoption. Brands that don't go all in will be left behind. Consumers will demand interoperability across experiences in the metaverse, so over time brands will favor platforms that enable them to to wear their Nike shoes in different games and virtual worlds. Gen Z and Gen Alpha move seamlessly between the physical and digital worlds. The fashion brands that embrace this will win.

All right, Doug, so I feel like maybe as someone who's not a super active gamer, Fortnite comes to mind as a great example of where game players are spending money on digital assets that, as you said, don't offer specific gameplay benefits. Can you give some other examples for people who may not be familiar of this science? Same dynamic?

So first of all, for games, 90% of Gen Z play games, right? And Gen Alpha, it's even higher. I think it's 94% for Gen Alpha. So the idea, the definition of games is very broad at this point, and games are becoming increasingly social. I'm not sure if you've read the book Status and Culture, but it's, it's a really interesting look into what drives identity and culture. And it really, a lot of it is about status. So, you know, games that have a strong sense of identity tend to be social. Multiplayer games in particular have a very strong sense of identity. So, so Fortnite has really built an amazing game that essentially built for partnerships. Like, what other game can have DC superheroes, Ariana Grande, and Rick and Morty all in the same world, right? So they've just done an incredible job. But another example is League of Legends. The skins skins really started as kind of fully imagined alternate versions of the characters in alternate universes, but they've also done partnerships. They partnered with Louis Vuitton, for example, for their World Finals when it was in Paris, and they launched a set of skins. And there are other examples as well. So Minecraft, GTA Online both have really strong skin ecosystems, especially with UGC components, right? So, so people that are players actually creating skins for those game experiences themselves. And then two other examples are Roblox and Animal Crossing that focus more on, on virtual items.

Got it. And I love that you went through so many examples, but I do feel like we have a limited number of examples just by the nature of this being relatively nascent. Based on those examples, are there any trends, any playbooks that you've seen work particularly well? Is it just about partnering with these large brands like Louis Vuitton or artists like Ariana Grande? Is it about introducing some level of scarcity? Is it the UGC component? Have you seen any kind of indicators of what people have been starting to figure out in this space as, as it comes to what really works here?

Yeah, I mean, staying more focused on fashion for the moment, I think we're in this experimental phase where, you know, the brands seem to be thinking about their partnerships with the digital worlds more as marketing and And over time, I think that will build into a bigger business for them. And back on that book Status and Culture, one of the other hypotheses is that high-status individuals, and I will apply this to brands too, tend to deviate from the norm. So it's interesting to see the luxury brands seem to have embraced this trend first with the virtual worlds and the games and really digging into these partnerships. I'll give you an example. So Kim Kardashian wore a Balenciaga dress dress at New York Fashion Week. And then Fortnite players noticed that there was actually a skin that looked very similar to that dress that was locked. They couldn't see the full skin, but they started conversation. And then of course Fortnite comes out and reveals that they've done a partnership with Balenciaga and created skins in the game related to this New York Fashion Week event. So I think that's just an interesting way that the brands have been really building awareness with the player bases of these games. And excitement with those player bases. And that's one example, but creating scarcity is another example, right? And I think over time, as we look at Web3 and other types of technologies that enable ownership and resale, that actually amplifies this idea of scarcity and the value of scarcity. Another example is feature differentiation. So the digital world enables a lot more feature differentiation than the physical world. I'll give an example of League of Legends. There are different tiers of skins, and this is kind of OG for all the League of Legends players out there, but DJ Sona was the third ultimate skin, which is the highest tier to be released. And, you know, they charged, I think it was roughly $40, $50 at the time for that skin, and it was a complete rework of the character. She had 3 different versions of herself. She was a DJ, so there was music that was custom and you could share that music with your team and then all of the effects were redone. And so there are many different opportunities to really try new things and add really cool benefits into the experience with fashion or with skins or cosmetics. And so I think that there's a lot of experimentation happening, proven the business kind of in the core games like League of Legends and Fortnite, and what's gonna happen for the brands, how are they gonna participate in that business over the long term. That remains to be seen.

I love that you mentioned that because I think it's very natural for us as humans to just kind of replicate what we have in the physical world in the digital world. So to give an example that's parallel to your Kim Kardashian example, people have been doing this all the time. When they see a celebrity wear a particular outfit, they'll go and figure out what designer created that outfit, but then they'll also find a bunch of different brands who have created similar clothing and they'll say, hey, if you wanna be like Meghan Markle, you know, wear these jeans or wear this dress. And so it looks like we're again kind of copying and pasting some of the behaviors from the physical world into digital. But as you said, there's this opportunity to rethink it. It's like, what does fashion look like natively within games? As you're saying, like there's all types of signaling from music to different tiering of the specific assets that people are selling. And it just reminds me of Snow Crash from 30 years ago, where I'm reminded of when people went to the metaverse in that book, they could show up as a better version of themselves, as in like what they physically looked like in real life, or they could show up as like a duck or something that doesn't even exist in the quote unquote real world. So I think that's really exciting. But I want to ask you about something that I've been noticing, which is that we spend so much time in this digital world. Many of us. I can speak for myself, I probably spend more time realistically in the online world than the offline world, but my share of spend, you could call it, does not represent that. So people are still much more likely today to buy a suit in the physical world than, you know, buy maybe some sort of equivalent in the digital world. Are you seeing any indicators, any data, any trends that maybe show that this might be changing?

Yeah, so it's interesting. I— the global apparel and footwear business, I've seen stats that say it's a roughly $2 trillion business, right? And so that's massive. But I think it's hard to compare revenue. The cost structure is completely different. So I don't think digital will fully disrupt physical. We still need to wear our outfits and dress for daily life. But I do see that for maybe a specific example is Fortnite earned over $2 billion for multiple years, right? More than half of their revenue comes from cosmetics. And I also think the UGC component for digital is very different because if you're able to immediately download something that a creator created, maybe for free, maybe for a small amount of money, how do brands compete in that environment? And so the pricing structure may be different, and I do see that the features that we were talking about earlier could play a big role in that and improving the value in the digital world. I would have loved when I was a kid to have those, those those Nike shoes that Marty McFly put on in Back to the Future 2, and you know, they, they're self-lacing. That's a basic feature in the metaverse, right? Like what, what other cool features can we add? But maybe just one last, last point is that with so much content, people will likely change their outfits more depending on their experiences. And so the consumption of different types of, of content could be much, much broader than in the physical world.

I like that you also mentioned the pricing structure because I, it reminds me of apps in the early days. For whatever reason, people felt like it was too much to ask for people to spend $0.99 on an app, even though often it provided much more value than maybe its parallel in the physical world. And so it will be interesting to just to see all of these experiments come to be and see where the pricing really ends up, because this is, as we've been talking about, a nascent space, and perhaps it is also a generational thing. Whereas more digitally native people start interacting with these games, maybe they'll have less friction as, you know, people who maybe are older, who, you know, who question this idea of spending on this digital asset within a game without the game benefits. But something I want to get to that you spoke about in your big idea is the need for interoperability. So you said in your big idea that consumers will demand the ability to interoperate their assets between platforms. But I want to push you on this because we don't see that many examples of this today. Most games do kind of operate in their silos. And I want to ask you, is this due to maybe the technology we've had up until now, or is it due to consumers truly just enjoying the quote unquote best games? And by nature of that, these are the games where the creators of these games have decided to allocate engineering resources to just their games and not necessarily interoperating with other games. Do you have any thoughts on that and how we might progress there?

I do. I have two main points here. I think that the first is really related to— there are kind of standards and technical hurdles for implementing interoperability, of course. And then, you know, certain games have created their own moats, and a lot of the most popular games now have existed for 10+ years, right? And they continue to update and innovate. But what interested in is what are kind of the next generation of companies doing? And there's a company, for example, Ready Player Me, who is a cross-game avatar company for the metaverse, and they have 6,000 developers that have signed up to leverage their avatar technology, right? And so what happens when one of those developers becomes the next generation Roblox or League of Legends, you know, and so I think that it's the builders of the future who are leveraging this tech. It actually solves an engineering problem for these startups because they don't have to develop their own avatar tech, right? So it actually helps them solve that and then adds interoperability. And so I think there is tech that's being developed to solve this problem and there is adoption happening pretty rapidly. And I think the second point really is on the consumer side. You know, if you think about back to the Gen Z, Gen Alpha demographics, kids are growing up today playing games like Roblox, where there's an expectation that you can customize your avatar and you can go into different experiences with that avatar. You come out of the experience, you customize it again, and you go in a different experience, right? And so I think that that is really creating this environment where kids are going to expect that with the next generation of games and virtual worlds as well. So So, you know, we'll see how this all plays out. And I don't think it's, it's black or white, like there's an interoperable future or there's not an interoperable future. I think both can coexist, but I think interoperability will grow over time.

Yeah. Yeah. And we see parallels of that on the web. It's not binary where everything is interoperable or not, but there are layers of interoperability that do exist. So excited to see how that progresses. But a lot of the trends we've spoken to so far are maybe macro trends or trends that at least will play out over the next several years, if not decades. What specifically are you excited to see in this space in 2023?

With the user-generated content environment, I'm very excited to see more digital-focused or digital-only creators and businesses forming around digital fashion. We have examples like RTFKT, which was sold to Nike. We have a company that's focused on building fashion called DressX. You know, there are designers creators that are creating amazing content. I want to see this, this grow even more. I'm also interested in how digital content and trends will translate more and more back into the physical world as well. I think that that's going to continue to happen. Like I mentioned, kids move seamlessly between worlds. They're both the real world to them, right? And so how will these influence each other? I think it's gonna be really interesting to track that. And then finally, just continued growth of of avatar interoperability across platforms.

Next up, we have Sarah.

Hi everyone, I'm Sarah Wang, and I'm a general partner on the A16z Growth Fund focused on enterprise software. And this is my big idea for 2023. Generative AI advances beyond text-to-image to complex workflows. We're starting to see the next momentous platform shift in technology. AI is eating software. In 2022, a lot of this took the form of AI-generated images of dogs flying in outer space or AI avatars collecting the best version of ourselves, selfies. But in 2023 and beyond, the enterprise productivity implications will really start to shine. AI will drive 10 to 100 times performance improvements, showing companies that there is a new way to work, advancing from text-to-image to more complex workflows such as text-to-SQL queries, or eventually text-to-Excel modeling and more. We're tremendously excited for the next generation of AI-native infrastructure and application companies to emerge, along with the enduring existing companies that incorporate AI into their products. As AI becomes increasingly democratized and underlying models potentially commoditized, applications will need to differentiate on the basis of winning mission-critical workloads as they did in the last great platform shift and moving from on-prem to cloud.

Sarah, I'm so glad your prediction touched on this idea of practical applications of some of the generative AI tools that we've seen arise in the last year. I feel like 2020 was so exciting. We got to, you know, create avatar versions of ourselves, write up these like half-baked screenplays. It was really fun, but I'm excited to see the next step there in terms of how this is applied, maybe on a more fundamental level in applications that we use every day. And I think one of the reasons that we saw some of these more creative spheres take on AI first is because there's not really a correct answer, right? If you get an avatar, sure, I want it to look like me, but there's no perfect avatar that I'm looking for. In fact, I love the fact that I don't know what I'm going to get back, but in some other cases, there is perhaps a more correct solution to a problem, and there's a little less, what you might call, fault tolerance in the system. And so, as we think about maybe workflow applications or enterprise applications, are there any that come to mind with similar potential, maybe a little more tolerance in terms of getting wrong answers, or there not being maybe a specifically correct solution?

Yeah, first of all, I love how you put that, fault tolerant. You know, I think that's notably different from other areas where the bar is incredibly high. Autonomous vehicles would be a good example of that. The stakes are, you know, the highest, right? People die if the product isn't right. And as a result, the process of getting there may take a bit longer. On the other hand, I believe that the potential workflow applications of AI are enormous and incredibly near-term. And a lot of that is due to the again, more tolerance for, for fault. And right, I think a lot of these workflows are actually oriented around AI assist. So in other words, where AI is making the human more productive, but humans still stay in the loop. And as a result, they can be deployed immediately. I think coding has been pretty salient recently. I've even heard developers say that they can't work without it. You know, that's not to say that AI is, you know, developers are saying, hey, "Create this app, soup to nuts," right? That may be in the future, in the very near future. But today, what developers are doing are using it to code complete, right? And figuring out how they can make their process, especially the repetitive parts of their job, just way more efficient and faster. And, you know, we always talk about finding new solutions that are 10x+ better, and that's arrived in that arena. There's so many use cases we could touch on, but ones that we've seen lately that are that have been mind-blowingly good. Video editing with companies like Runway really come to mind. You know, this has the potential to replace software that's been around for, for 30 years. Pitch, pitch editing and pitch creation. The list really goes on, but I think the key theme here, to your point on immediate value, really centers around how AI is assisting the human and making them more productive and efficient. And then we'll slowly get to the idea that, hey, We can actually make a perfect pitch deck that reflects exactly what we want. We can code it out that is exactly what we want, and we can edit a video soup to nuts without humans in the loop. But until then, I think the intermediate step has already been pretty groundbreaking.

Yeah, I also like the idea of an AI pushing us to be more creative. So one concept that Mark spoke about in an interview he did with Vijay recently is Mad Men was a show that was known for always having these really, really creative plots. And one of the reasons behind that is they would sit in a room and they would talk about what are all the different plot lines that people are going to expect, and then let's avoid all of those. Let's make sure we don't give people what they think is naturally coming next. And I think, you know, they kind of pontificated this idea of you could do that same thing with AI. You could ask the AI to come up with, you know, 10 different plot lines and then say the same thing. And so it's, again, it's pushing you to think more creatively. And, you know, in this case, again, have the human in the loop. But another interesting part of AI that I think is, is maybe underrated is its ability to transition from not just text-to-text, but we've talked about text-to-image, you talked about text-to-code. And I think something you talked about in your big idea was us getting to those more complex models like text-to-SQL. As we do that, how do you see maybe the foundational layers changing? if at all? Because right now we are relying on a few models like GPT-3, which influence Copilot or some of these other layers. Do you see that changing where we then have many, many more kind of fine-tuned models to the use cases that people need? Or do you see it all kind of layering back to the few foundational models that we have today, like GPT?

Yeah, this is a really fascinating question and one that we've been discussing internally a lot, especially as to your point, a lot of the focus more recently is on these horizontal large language models and the sheer capabilities of these models has, has really been astounding. But to your other point on fine-tuning or, you know, the process of using additional data to further train a pre-trained language model has a really big impact on how accurate and useful these models are. Especially if you think about the B2B use cases where a lot of the truly useful data is actually proprietary enterprise data. And not available widely on the internet to train on. So if you put this together, what does it actually look like in the future? Honestly, the real answer is likely that it's use case dependent. You know, I think one of the exciting aspects of the LLMs is that they've really democratized access to large and expensive models. Via their API, you don't need tens or even hundreds of millions of dollars to deploy them in your product. And if companies are then further able to fine-tune these models to their particular industry, there's an argument that specific use cases don't need expensive models. And we're seeing some companies start to make the decision to own the model, which affords them more control, but also maybe more cost effective in the long run. And so it's, it's still an open question in my mind. And again, it will be likely use case dependent, given the capital demands of developing large language models. There is a likelihood that it points to a potential, you know, more oligopolistic structure the way that you have with the big clouds. And not everyone will go down the route of attempting to build a model of that size. But the real question is which use cases need what and how much will it cost? And how do you trade that off between control, having in-house talent and not relying on an independent vendor versus just being able to move really fast?

In addition to that, I also think about competition, right? If we think about GPT-3 as an example, there have been several products built off of that. Some that come to mind, Jasper, Lex, these writing tools that, again, are all built on the same foundation. And the question has come up repeatedly, and from my experience of how are they going to differentiate? And of course, there's differentiation through distribution, through product, through UX. But do you have any thoughts there on if there really are only a few models that so many products are built off of, how do you really differentiate in that environment?

You know, it goes back to, I touched on this a little bit in the big idea, but it really goes back to owning more of the workflows. And, um, you know, if we work in the idea of fine-tuning your model, right? How do you collect the data that enables you to do so and makes your product better? I think it goes back to product improvement cycles, if you will. This isn't a perfect parallel, but if you look at, the last great platform shift, which was the shift to cloud, and who the big application winners from that shift were. You could look at a Salesforce or a Workday, a ServiceNow, and the competitors that they faced, right? And all of their competitor— direct competitors, not sort of last gen, um, had also moved to cloud, right? And so the, the benefit of this platform shift that they were touting was not at all unique to them, um, but came down the ability for them to build out workflows for their end customer that were useful and sticky, and has really enabled them to dominate their continued mindshare in front of customers the decade-plus since then. And so I think in this next gen of applications built on OpenAI and OpenAI's competitors, the way to really differentiate yourself is to further build and develop workflows that are not just sort of a one-click button of add AI to XYZ, which, you know, is really neat over time and has that novelty wow factor, but is frankly not sticky and not something that captures the attention and differentiation in front of your customers long-term. But to really say, hey, you know, we're actually going to develop the entire video editing workflow or whatever workflow you've decided to tackle. And to that point, Steph, I actually think there's an interesting opportunity for companies that already exist and own these workflows to incorporate AI into their current products. A lot of our own portfolio companies are doing this. And in fact, I would say this is so important to Venture that if your company doesn't have an AI strategy, it should really be thinking about one yesterday. Because the fact that owning the workflow and maybe the data associated with it, the user is already living your product all day long to further enhance that with AI features just makes a very powerful loop that, you know, I think contributes to the point that you mentioned on, hey, how do you differentiate if you're built on the same infrastructure.

Absolutely. I've been thinking about that a lot. This idea where, again, 2022, I feel like was, maybe this sounds too negative, but a little bit of a gimmicky period as it related to AI, where it's like, look what we can do, and it's in your face. And when a company's using it, they need everyone to know. But upon reflection, there have been so many companies that have incorporated AI or machine learning into their products for years, and it hasn't been front and center. And one company that comes to mind there is Descript. Which we use for editing, and their overdub feature is using machine learning to train on your voice, and that has existed for quite some time. It's been called overdub. It's not like AI-assisted voice tuning system, right? And so it is, it is going to be interesting to see how companies differentiate in the technology they have, but also maybe the marketing, the branding, the positioning of it, whether they choose to put it out in front or whether it's just As you said, like existing companies, almost like improving, solidifying their existing features. But to that end, it feels like almost like every white-collar professional is going to be reshaped in some way by AI to differing degrees. But I'm curious to hear from you in 2023 specifically, because that's what we're talking about with these big ideas. Are there any kind of short-term? Near-term companies, products, industries that you think opportunity lies the most?

Yeah, I think there's a lot of talk or attention on some of the things that we mentioned, right? Copy editing, coding, um, a lot of the consumer use cases. Um, so maybe I'll throw out one that's a little bit less than news, but that I'm really excited for, and I think will be a 2023, uh, change. Um, and that's just the potential for AI and the data workflow. So, I'm no data scientist, but as an Excel junkie, you know, I know there's a lot of repetitive workflow involved in getting to the insight or the answer that AI can help you wade through. And from the demos that we've seen that have, frankly, I think the 10x potential that you see in a lot of these other arenas can be applied to data workflows. And that's incredibly exciting because data-driven decision-making has become the norm. It's the backbone of our data infrastructure investing thesis. But for AI to tackle that as an arena next, I think is incredibly exciting and a very, very large end market. And again, you know, I think a lot of the companies that own the workflows already will come out with some of these breakthroughs in AI features. They really do change the use case from novelty to something that's actually useful, that will save you hours of your day, that will take you to an outcome that is greater than you would've gotten to had you not used the AI. And I think that's what, that's what I'm most excited for.

Next up we have Sumit.

Hey everyone, uh, this is Sumit Singh. I'm on the a16z fintech team, and this is my big idea for 2023. In 2023, fintech companies will need to strike a delicate balance between pushing the envelope by building with new technology rails such as large language models or LLMs, but also maintaining customer trust. While potential use cases within fintech are still emerging, LLMs like GPT-3 and the upcoming GPT-4 may help businesses train datasets much more quickly and cheaply. In addition, they may finally be able to automate data-heavy and manual tasks, such as insurance claims processing or loan origination, that have really only been semi-automated in the past. But while LLMs can address some low-hanging fruit, more complex use cases will require reserves of user trust. When dealing with, say, fully automated investment decisions or automatic, automatic financial reporting for businesses with complex money flows, companies will need to balance these new services and experiences with potential skepticism from consumers.

All right, Samit, so something I want to call out here is it seems like robustness to error really matters here. So to give an example, AI today being applied to potential drug candidates sounds like maybe a better option than actually prescribing a drug. To a patient definitively. And I say for now, today, the, the models that we have, because this may change, but what applications stand out to you again today as near-term opportunities that maybe have some of this fault tolerance?

I think what we're going to see is, you know, the initial tools that have this sort of level of fault tolerance being around customer engagement tools, for example, in fintech, where both banks and fintechs, you know, can use these sort of models to basically automate what might be a CX agent in the past. But today you see a sort of virtual assistants when you log into your bank. And I think there's a level of fault tolerance in there in that, you know, while it's obviously not great if the customer doesn't get what they want right away, but there still is going to be CX agents in the back, right? I don't think these people are going away anytime soon. And so, you know, they might be able to be plugged in should there be an issue versus something. That, you know, is just more sort of higher value, right? Like an automatic underwriting or automated accounting, where things need to be 100% correct. So for automated accounting, for example, if things are incorrect, there could be huge potential implications, like IRS fines.

I like the example of customer service, because something I wanted to ask you about is the transparency that you think is required here, or how founders should maybe be thinking about that. And Just to give a couple examples here, there are several applications today that use machine learning within their feature set, and some of them choose to be very, very transparent about it, and some of them don't. An example that I can think of is Descript has had this awesome feature within their application for years, I think, and they call it Overdub, and they're basically using machine learning to train your voice so that you can replicate it as you need. That's obviously using machine learning, but they're not branding it as such. But I could also see how different circumstances would be the opposite, where you'd want to vouch and say, hey, look, look at this awesome new technology that we're using. Um, and maybe a silly example is just when you use ChatGPT, you know, I'm using an LLM, you know that front and center. And so how do you think about that? How transparent should founders be when they're thinking about implementing this technology?

I think it's, you know, a bit of pontification at this point, because it's so early. But I think in some cases, there will be interfaces for consumers to, you know, consumers to, for example, do their taxes via ChatGPT-like interface, or for an earnings call to be automated, for where an equity research analyst can basically use a ChatGPT-like interface to, you know, search based on specifically tuned enterprise data for that company. But I think on sort of like more sort of consumer fintech products or other products that we haven't even thought of, these might just be sort of backend products or backend platforms where the consumer is never really seeing the interface, if you will. They're just beneficiaries of that technology shift.

Right. And if they're beneficiaries, I'm also curious to know if it's in the backend and maybe they're aware, maybe they're not. Do you think that there will be a requirement, for example, to disclose that something is being run through an LLM, or how do you see regulation maybe coming into play in this arena?

Yeah, no, I think it's a bit too early to tell today. But I'm sure there will be cases where, you know, let's say in a world where loan decisioning or tenant things like tenant screening are being fully automated by LLMs, and say there are consumer issues with that, or, you know, it turns out that the models that were being trained upon were using sort of discriminatory datasets, there could be huge implications from, you know, folks like the CFPB and other regulators, who crack down on these types of companies. But again, I think it's just something the founders are going to have to keep in mind, given it's— no one is doing that just yet. Right. So it's, it's kind of warning if you, if you are going to use these things to be aware of the potential regulatory implications.

Yeah, I think it's going to be a fascinating thing to watch because maybe less so in fintech, but in certain areas, knowing that something is being run through a computer instead of a human just changes your relationship with it. I think I saw a tweet recently. Where someone was using LLMs for therapy, and it kind of, as you'd imagine, got a bunch of blowback because just the sentiment of a human not being there in the process, people were not a fan of, even though in that example, I think actually humans were part of that process. But what else do you think founders should be thinking about here? We've kind of touched on regulation. We've touched, touched on the transparency that might be required. Is there anything else that you think founders should be really paying attention to as they're thinking about integrating this technology?

Yeah, for sure. I think, you know, on the product side, I think these product leaps will likely come from tuning existing large models with 4 specific sort of commercial use cases with specific data. However, I think the one thing that founders can't forget is that no matter the sort of platform shift, whether it's been internet, cloud, mobile, distribution is always key. And so even with this new sort of product leap where maybe, you know, you have consumers flocking to the product because you are advertising, say, hey, this automated tax product is powered by LLMs, and you're gaining a bunch of customers through that. In the event that this sort of product become, or technology becomes table stakes, well then we're sort of back at square one, which is distribution is still key. We often say that it's almost more important than product, but hopefully that's not the case given sort of a lot of the last generation of financial services, fintech products that were more financial engineering than software engineering. And so it'll be interesting to see if these new product leaps can account for distribution woes that folks have had in the past.

Yeah. So what I'm hearing is one, it'll be important for people to train their own datasets to a degree or, or kind of fine tune these models because if they don't, they're using essentially the same models that everyone else has. But then even if that's the case, as things advance, there will be kind of this baseline layer that most applications are using and some, and so from there, as you're saying, distribution really matters. How can you reach the customers that matter the most?

Right. And that's a bit of pattern matching from previous sort of technology shifts, right? And so, you know, there's a chance this time is different. However, you know, people more wise than me have always said it's not, it's never different. Just, you know, just maybe just a different flavor, but.

Yeah, I mean, we always think it's different. Um, but no, I think it's something that I've been thinking about a lot. How do you build a moat, especially if you're using some of the baseline LLMs? Like if everyone is using GPT-4 when it comes out, then it's all being built on the same foundation. So how do you stand out? How do you build a real moat around that? It'll be interesting to see. You mentioned insurance claims processing and loan origination as two potential avenues of this being applied. Are there other developments that you see specifically coming in 2023 or the near-term future?

I think in the near-term, it probably even won't be, you know, loan origination, for example. I think where we'll see the low-hanging fruit is likely around kind of back office automation of like workflows, if you will. And so, you know, we've been very excited on the fintech team around vertical SaaS businesses that help, for example, SMBs across industries manage their back office finances. And right now, this software or the software products that we've seen is really augmenting people and augmenting for the fact that there are basically less back office people to do this work. And so software can help make each employee even more powerful. I think what we might start seeing is this ability to fully replace that person. And the reality is, in a lot of cases, you're not even replacing anyone. It's just these people have left the workforce, this sort of like typical back office employee at an SMB. And so, you know, perhaps we'll see models being used by companies of all sorts, uh, to automate workflows fully. And I think something that we've, you know, said in the past on our team is it's better to automate 100% of 5% rather than trying to automate a little bit of the 100% of a workflow. And I think the big question is now like, okay, we have, we may have the potential to automate everything. And what are the implications of that? I think that's going to be really exciting to see.

Yeah, I think that'll be really interesting to watch. I mean, I think the canonical example is the ATM and how that replaced one aspect of what bank tellers did. In the arc of history, it seems like people have always managed to reapply their skills to a new, exciting, perhaps more interesting, more fulfilling area. So yeah, that'll be fun to watch.

Right. That's the hope.

All right. That's all for part 1, but look out for part 2 of our big ideas for 2023, where we cover fintech, American dynamism, and bio and health. And if you're looking for the full list of 40+ ideas, you can head over to a16z.com and you'll find it on the homepage. See you soon. Thanks for listening to the A16Z Podcast. If you like this episode, don't forget to subscribe here on YouTube to get our exclusive video content. We'll see you next time.