No Priors: Artificial Intelligence | Technology | Startups - How AI can help small businesses

Episode Date: December 14, 2023

AI tools are helping small business owners manage their businesses, so they can stay focused on the aspects of their business they love to do. This week on No Priors, Sarah and Elad are joined by Alyssa Henry, an executive at some of the most impactful companies from Microsoft to Amazon. Most recently she was the CEO of Square. She led Square’s team as they were very early adopters of a consumer-facing product that used GPT-2 and have continued to incorporate AI into their offerings. On today’s episode, they talk about the whitespace within e-commerce for AI and lessons from the prior generation of infrastructure. Alyssa recently retired from being longtime CEO of Square, within Block. Before that she was a vice president of AWS running, amongst other things, the storage products, or the digital storage bucket for the world. And before AWS, she ran order management software at Amazon Retail and started her tech career at Microsoft. She remains on the boards of Intel, Confluent and was previously on the board of Unity.  Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @alyssahhenry Show Notes:  (0:00) Alyssa’s experience and career trajectory (2:30) Transition from engineer to manager (4:09) AI implementation at Square (7:46) Small business AI applications  (12:14) Latent demand for content generation (15:04) The origin story of Square’s GPT-2 products (16:54) Consolidating ecommerce workflows (18:46) How will AI change cloud services (23:07) Hyperscaler foundation models and the AI land grab (25:16) Enterprise demand for open source models (28:08) Startups in the AI semiconductor space (31:02) Scale up architectures vs scaling out (34:32) What’s next for Alyssa (36:08) What Elad and Sarah are excited about in 2024

Hi, listeners, and welcome to another episode of No Pryors. This week, we're joined by Alyssa Henry, who recently retired from being long-time CEO of Square within Block. Before that, she was the vice president of AWS running, amongst other things, the storage products or the digital storage bucket for the world. And before AWS, she ran order management software at Amazon and started her tech career at Microsoft. She remains on the boards of Intel and Confluent and was previously on the board of Unity. I'm a huge admirer of Lisa's leadership. Welcome, Alyssa. Great to be here.

Let us start by talking about Square. You led there for almost a decade through enormous growth. Was there a single moment that most defined your experience? I don't know if there's a single moment that most defined, although there were a ton of different moments. Obviously, we were known as the Little White Reader Company and the Farmer Markets Payments, company when I joined, Cash App wasn't really a thing. You know, title wasn't even a thought. Crypto wasn't a thing. The Square business transformed from that little white reader into, you know, much larger business serving businesses of many, many different sizes from still the smallest to

also, you know, large stadiums and multinational companies. So the moment that really was, I don't know if it was most defining, but one of the, I still tear up a little bit about it, is actually the day we IPOed. And what was so exciting about that was, you know, if you looked at the sign that was outside the New York Stock Exchange, it was covered with just the logos of all these small businesses, right? And the note was, you know, the neighborhood is going public. And I think that really kind of sums up, you know, just the mission-driven, purpose-driven organization that's where Block is and was and just the impact that the company has had on just countless small businesses, helping them give them the tools and the technology that in many ways had only

been previously available to, you know, the likes of Amazon or Walmarts of the world. It's funny. I was an early investor in Square, a small one, and then I worked at Twitter. So I worked with Jack in two very different contexts. The way that he ran, the two businesses was radically different, right? I think it's everything from the org structure where Square was always more sort of GM slash business unit almost driven versus Twitter. I was. which was always sort of functionally oriented or, you know, often functionally oriented. How did your career shift over time? Because I think you started off more on sort of platform and technology and then you took over a big business area. And I'm a little bit curious about that

evolution and how you went from being somebody who's done a variety of things. They're more product and engineering driven to somebody who's really running a whole business area. Well, my career's kind of gone back and forth between product and engineering, between functional leadership and general management leadership a couple of times over, you know, my several decades in tech. You know, I started as an engineer and then moved into product management, engineering management, then back into IC as a product manager, and then into a general management type role at Microsoft, then back into a functional role when I moved to Amazon, then back into a general manager role with a P&L, and then back into a functional role when I joined Square with a

back. I've gone back and forth. I do like the general manager of the end and, you know, multidiscipline, multi-functional leadership roles. It just uses more parts of the brain. And as you get more senior and senior in leadership, you know, the job becomes more and more about how you both instigate and resolve conflict in order to kind of keep things on the, like on that creativity and execution threshold. And the kind of those problems in terms of either generating or resolving conflict are just more interesting when they're multifunctional, multidimensional in nature, for me. Yeah, that makes sense.

I guess like in the context of Square and its foray into AI, there's a set of areas that traditionally have been areas where payments companies have sort of applied ML, you know, so that be things like fraud detection or other areas like that. Can that's a little bit of how that evolution occurred at Square and what areas you find most intriguing going forward? Well, as you state in financial services, application of machine learning has been key for a long period time. And particularly if you look at a business like Square, where you've got millions of small customers and it's not a what you don't really have a one-to-one relationship with the vast majority of your customers just because of the scale and the size of them, you really have to bring technology. to bear in terms of understanding a whole range of things from, you know, who's a good actor and who's a bad actor, to, you know, who do you target for a specific product who's going to be

most likely to find, you know, a marketing product useful or least likely to find it useful or that sort of thing. So there's lots of internal applications and have been for years in terms of machine learning, manage risk, you know, manage fraud, as well as to cross-sell and grow the business. And then more recently, even kind of prior to this kind of latest big shift in the AI landscape, we were using GPT2 as part of us, the Square Messages product, being a virtual assistant to help customers, you know, answer responses to customer inquiries and variety of things. But what's so exciting to me about kind of really how the landscape has changed the technology advances in the last year are how much better the tools have gotten

and how much more broadly applicable they are in terms of bringing kind of expert assistance to much larger audience, right? But it effectively unlocked the consumer and started to then show what this technology could do when then, you know, further integrated into domain-specific areas. You know, you go talk to small business owners. Most of them will tell you, gosh, I know, I know I should be doing marketing, right? Like I know if I was more effective in doing that and reaching out with my customers,

you know, I could drive more business. But I got to tell you, you know, I work all day. And then I come home at night and I got to take care, you know, take care of my family. And then it's 8 p.m. And I'm starting to think about, gosh, you know, do I just be chill for a minute? Or, you know, am I going to spend the next three hours trying to, you know, create an image and write text for the campaign and everything like that? And what I tell you is like, I know I should be doing this.

those stuff, it's just too hard and it takes too much time. And I'm not an expert. Like, I got into doing this because I love cupcakes, not because I like writing email marketing, right? And so what's exciting about all this technology is one example, but there's so many of these kind of different things where just the ease of use and the accessibility opens up what previously was effectively just massive white space, right? It was customers or people that if it was easy enough to use. If it was accessible enough, if it was cheap enough, they go, yeah, that would be, that would be huge for me. But it wasn't accessible. It was too expensive. It was too hard to go find and hire a marketing consultant to do it for me and the ROI wasn't there and blah, blah,

so I think this, this, the evolution that's occurring right now is, is exciting in part just because of really the, you know, previously unaddressed demand that it's unlocking. You've mentioned some really compelling ways that different SMBs can really use generative AI. And I think one of the things that is a little bit under discussed in the AI world is the impact of this technology, particularly generative AI to e-commerce or other forms of commerce and fintech and other areas. Are there other areas that you think nobody's really addressed yet or that are big opportunities in this space? Because, I mean, adopting GPT2 was super early, right? You all use this technology before most people were aware that this was a big deal. And then to your point, there's some really interesting things that you've been doing in terms of merchant coaching and other areas that, you know, I think are really fascinating.

Are there big areas of e-commerce that you just think are going to be swept up in this technology that people aren't talking about enough? Well, I think almost every aspect of kind of a small business, you can find applications. And, you know, some of the technology is not quite there yet, but is rapidly getting there in terms of some of the finance and numbers and quant pieces. You know, some of the quantitative hallucinations have been a little bit more than the others. But it's all rapidly going. And I think it's, you know, if you look at the largest e-commerce players, you know, Amazon's and the Walmars, right? Like, they've been investing heavily in this area. So, you know, the larger e-commerce companies have definitely embraced.

And frankly, in e-commerce in general, it's been for these retailers or for e-commerce retail platforms. because one of the things is if you're in e-commerce, basically everything's already digitized. So one of the difference between in-store commerce and particularly local commerce and e-commerce is the fact that just to basically to operate in e-commerce, you have to digitize, you have to have images to show what your product is.

You have to show, you have to have compelling description of it. You have to track inventory. So there's a bunch of stuff you have to have, which many, this is, again, some of the white space for small businesses, a local business in particular, is the rate of digitization in store significantly lags the rate of digitization online. And it goes back to, it goes back to the fundamental problem of it's too either hard or expensive to effectively digitize.

And again, this is where, you know, really in kind of all aspects, I think the gen AI is, lowering the, you know, making 10x faster, 10x cheaper kind of thing, right? You know, we'd launched a, squared launched a product a couple years ago called photo studio because we heard from businesses that they wanted to sell in line, but they didn't have product photos, right? And so the first iteration of it is we actually had a photos, like a physical photo studio in Brooklyn. And we had a 360 camera and people would ship us their products, right? You know, and we take what would look like professional-grade photos for them,

which was because we were addressing this blocker that so many of them had to getting online, right? But you fast forward and we then evolved it into iPhone app that was using, you know, again, less mature versions of, you know, image detection and generation to use AI to remove backgrounds and things like that. Again, making it easier, you don't have to ship, reducing the cost. And now if you look at what you can achieve with some of the latest stuff, like the barriers come down even further. So it's incredible kind of all of these different things. You know, and then I'm talking about, you know, the kind of selling size of the revenue generation side.

But I think there's a lot of back office as well, too, you know, from employee management and tools and communication to finances, you know, predicting and understanding what your cash flow actually looks like and what is your top selling product and all these sorts of things where a lot of the data is accessible. But again, most of these owners, they're not MBAs, right? They didn't, the line is they got into business and they like working in their business but not on their business. And so the business side of it is not the interesting part. It's the craft or the, you know, or the customer interaction and the hospitality of all those things.

And so I think all of these advances that we're seeing are making it easier and will make it easier to operate with better expertise. and less time and effort on the business side of the business. There's so much there. I feel like one thing that's been a surprise to many tech people, business people, has been how much latent demand there is for content generation of different forms, right? In like every business context, I remember like a year ago with things like mid-journey or with stable diffusion, like, you know, progressively better diffusion models. In general, there was a vein of like, oh, like, that's cute and it's like a novelty, but really how many artists are there in the world and how many people are really interested in art for the sake of art?

And you see it apply to everything from like product photography to being able to generate like short form or spokesperson video. and, you know, the number of people, as you said, that want to avoid the camera, like, you know, the professional studio in Brooklyn, or don't want to be on camera at all, or just want to do, like, really attractive product photography or marketing and sales videos at 1,100th, and 1,000th the cost is quite large, right? And so I think it's like really interesting thing about the demand categories here for these new capabilities, which like a lot of them don't feel like traditional software businesses. Yeah, I mean, I think that's always been one of the amazing things about technology. One of the things that, you know, I find just so compelling about our industry is that when you can increase ease of use and accessibility, you just unlock all this latent demand, this white space that exists, right? And you see it over and over again, right? originally who is the market for a word processor well it's all the secretaries right like they're the only ones that are going to now everyone uses a word processor right you know the town just exploded

you know and even you take the square example how many people could accept a credit card and take a payment right like but there were all these really small businesses that were completely underserved because it was too expensive and too hard right and you know you make something better cheaper faster and all of a sudden you unleash you know all of this things you know all of this late in demand. And I think that's, we're in the process of that with this technology in in sort of multiple vectors. And so I think it is really going to reshape a lot of things. And it's not just people like to talk about, oh, well, I was going to take away jobs. It's like, yeah, well, maybe. But like, but a lot of it's actually work that's not getting done that could

be get that someone could get done, right? You know, it's like sending that marketing campaign or it's actually, you know, putting together, you know, a real logo or it's, you know, writing better copy that actually is compelling and descriptive, the things that just were never going to get done otherwise. And, and now they're getting done. Yeah. Can I ask you to tell us the story of how you guys ended up playing with GPT2 for, I think it was like customer, like merchant facing responses to begin with? Because that was, we have other, a lot and I, I think each have other portfolio companies that were experimenting, but it was quite early. Like, it didn't really work or it took a lot of work to get something useful out from a

just messaging perspective. I would like to say it was maybe more strategic than it was, but Vinog Kosla was on our board for years. And he had a portfolio company that was being courted by another company to acquire them. And Vinod called me up and said, Alyssa, you should go talk to these guys and see if maybe like Square might be an interesting place for them. So I met the two founders, Stanford, machine learning, PhD folks, and they walked me through kind of what they were doing.

It was slightly different context, but just got super excited about the potential there, as well as the two people in their team. And so we acquired that company in, I want to say, 2018 or something, and put them to work on kind of stitching together about some of these customer-facing, experiences, leveraging some of the early work that they'd done. Because again, we just knew that there was a real customer problem for Square merchants. The goal was to apply technology to make our merchant jobs easier and give them time back to focus on things that matter. So that team still going strong and continue to expand the capabilities and obviously move further down the line

in terms of models and whatnot. So one more thing on Square, I feel like it would be remiss. and not asking you after the almost decade you were working there. Like, what else even AI side do people not understand is changing in e-commerce right now? More of the same in digitization, anything else you think like trends people should understand. Digitization is a big one. And I'd say integration. It's even more so true in in-person commerce versus e-commerce, but it's true in both places. And that you go watch a business owner or their team kind of work.

And you watch their workflows, right? And even the stuff that's digitized in many cases, what you see is they've got, you know, they've got multiple browser windows open and they're cutting and pacing from one tool into another tool or they're downloading from this, you know, and then they're emailing it to that. And like the, I think the workflows, even the ones that are digitized or not integrated, and then, of course, the ones that are manual and not digital, you know, the integration is. worse or nonexistent. And so I think there's just a huge opportunity. And I think we'll see the next phase kind of evolve, you know, in the same way that, you know, in many industries and in many parts of tech, what you see is kind of best a breed early on, where different parts of the landscape are built out. But then ultimately what you see, you know, it's the classic bundling and unbundling. Right now, a lot of the stuff is unbundled. And I think we're going to go into

a bundling phase because it addresses a number of things. It addresses integration. And it also you can typically offer a bundle for less than the sum of the individual parts and that kind of thing. So I think we're going to see, you know, from an Indy's perspective, kind of more aggregation and more bundling because we've been through when we went to when we went from in-person to e-commerce, a bunch of categories kind of got created and we're going to see the consolidation and the bundling of categories and the blurring of lines between them. I want to go back a little bit in your history. So you were previously at the forefront of the cloud revolution for a long time as the first GM for AWS storage.

And, you know, just beyond storage, too, responsible for a huge number of innovations in computing that we all use now, S3, Glacier, Lambda, EBS. I'm missing some. how do you think about like there are a lot of uh there's a lot of discussion of AI changing the cloud services landscape like do you see this as a new wave of computing it I mean certainly there's a bunch of new aspects to it um you know cloud computing historically you know very very CPU intensive some GPU as well too um various use cases but obviously you know AI just seeing an explosion in GPU based compute. Obviously, there's tons of demand right now just for compute capacity for training.

It's not replacing existing workloads. It's adding new workloads as people are figuring out how to then expand, you know, companies like square block figuring out then how to, you know, how are we going to apply these technologies and then where are we going to go do, you know, go do our training and whatnot. So I think it's an exciting time. You know, One of the fun parts about being in technology is, like, you just, you get these big shifts that happen. You know, so it's never a tall moment. And I think the race is definitely heating up. And, you know, it's in many ways, I think it's a land grab.

And, you know, lots of different players are figuring out how they go grab land. One thing that's interesting about this wave is how monolithic the services are today. I think you can think of this as essentially year one or year two of having access to these large foundation model services, right? But the interfaces are really simple. It's not even you're just talking about bundling. It's like a single natural language call versus if you contrast that to like what, you know, the joke is you can't even keep track of like the Amazon services released, right? Like, do you think we get a wealth of services over time the way many services have emerged in cloud or it's just you, you love? in, you know, more and more complicated prompts to a single model.

It's probably both. The, you know, if you go back, AWS at the beginning, right, you know, S3 was sort of the, you know, the first, SQS was actually technically the first, but S3 was really kind of the first service and incredible simple, incredibly simple API, right? Like, you know, four rest operators or something. What was compelling about it is it was so easy to use, right? And then obviously a whole bunch of stuff sprang up around it, you know, S3 became not just, you know, a first, well, was first class service done right with your direct customer relationships, but it became foundational as well for many of the other services that were built on top of it, right? So you go, you go trace kind of the, you know, the call stack, if you will, within most AWS services, probably, I would argue probably all of them.

And, you know, you can go find S3 somewhere as a component of it. Take Open AI started, you know, relatively simple, but, and, you know, adding to stuff, in fact, making them, you know, ease of use, actually, even though adding new capabilities in some ways, adding functionality that makes call patterns even simpler, right, with threads and messages and some of these other things. And so I suspect we'll continue to see an evolution where we're going to get some more capabilities that extend some of the core foundational services. we're going to see work that makes using them continue to simplify things that can be simplified.

And then I do think we're going to see some specialization as well, some additional model. I mean, you're already seeing some list today, right? If you look at, you know, like the aggregate, I call, you know, Amazon Bedrock, you right? You know, the aggregate service at some level, right? Where, you know, you've been host and run all of these other different models. You know, it's a single service, but it's kind of a bundle, if you will, of a variety. of models. So we'll see. It's usually some combination of both. And I agree with you. We're super early on right now. If you look at the major cloud providers today, two of the three have

major alignment with an underlying foundation model or foundation model company. So for example, Google's very publicly building out Gemini is sort of its next generation foundation model. open AI has close alignment with Microsoft. Do you think it matters whether or not Amazon has its own close paired foundation model, or do you think it's just there'll be a lot of open source models, will be integration with multiple third-party vendors? Does that alignment at all matter as we think about the future oligopoly world of the cloud providers? Like I said, I think it's a land grabbing lots of people are trying to figure out what's going on.

But Amazon has the anthropic alignment and investment as well, too. And so, you know, they're pretty close. Although I think Google just recently announced investment in Anthropic as well. I believe Microsoft is also, you know, they've got the open AI, but they've also said, hey, we're doing some of our own stuff. So I think it's unclear where, how this is all going to shake out and who the winners are going to be. So I think everyone seems to be placing multiple bets from combination of, you know, I think it's going to be built by partner.

It's probably all do all three. From an end customer perspective, that makes a lot of sense because if you have an enterprise that's using your compute, there may be a variety of different models and approaches they want to take. And so it does seem like the monolithic world seems reasonably unlikely unless you're a model company worried about some competitive dynamic with the underlying cloud provider. But the flip side of it is all the cloud providers are also funding a lot of the different model companies. So that makes sense. It's not just the cloud providers are funding them. But it's also, if you look at the marketplaces on the cloud providers, so like they have partnerships with all these people where to get

down your Azure EWS bill, you say, I'm going to spend this many millions of dollars on your cloud. Some of it's going to be, I'm using, I'm using raw computer storage or whatever, but a good chunk of it is also, like, I'm going to buy this third party partner through your app marketplace and use that to satisfy my quota of, you know, how much I pledged it. So I think all of these models, business models all intermash. Alisa, I don't think you will remember this conversation or I'd be surprised if you did, but I ask. you, I came to like ask you a question, maybe in your first year at Square, about, I think it was like some Hadoop related thing. So this really dates this conversation now. But I was asking you about it, but it was some sort of like data infrastructure, open source thing. And you would just look at me and you said, Sarah, Amazon loves open source. We make more money off open source than any of the open source companies do. Right. And I think it's like, I think first of all, that was like the one of the most terrifying business conversations. I've ever had. You were perfectly nice about it, but I was just like, oh, my God, what am I doing, doing these open source companies? She's right. This is terrible. What do you, what do you think happens in that landscape of like the open source models? Well, there's certainly demand, like, there's strong customer demand for open source models, right? For enterprise demand for it, right? Because it's not, quote, black box. You theoretically could in-house it all if you want to,

So I think, you know, anytime there's demand, you know, the products will find a way into the marketplace. And any time there's a passionate developer community who, you know, is interested both in sort of giving to the community, but also it's a way to make your name as a, you know, as an engineer too by participating these projects and, you know, being a core committer or whatnot. You know, I think open source is going to continue to evolve. The question is, is, yeah, where. Where and how do you make money off of it? Obviously, you know, there are some companies that, you know, have done well, taking open source or some of the founders or, you know, the project or whatever, and then still, you know, launching companies around it. I put confluent with Kafka in, in that bucket. But, yeah, there have been some others that have struggled, you know, who dupe was there for a while, but kind of never, never quite got the commercial piece working well. And I think just time, not enough than differentiation relative to what the cloud providers could do, pick up and go out. And I think one of the things with the cloud providers, too, is because you have sort of default customer demand, you launch one of these services using the open source.

In many cases, you'll have existing customers that want to use it. And so then you're immediately getting customer feedback, you know, to help improve and then help tweak it on your information. infrastructure. And I think that's the, you know, that's the cat and mouse. But, you know, there's certainly customer demand for open source in general and certainly open source AI models right now. And so I think we'll see both. So the one other, you know, really impressive association you have is being on the board of Intel. And there's obviously been a variety of different computation waves that have occurred over time, you know, and it feels like every wave of technology has an underlying different sort of massive semiconductor company that emerges, right? And so we had

Intel and AMD for the microcomputer revolution. We had ARM and Qualcomm as part of the mobile revolution. Nvidia, I think, has really emerged as a big driver on the GPU side and sort of the AI revolution. How do you think about startups and the AI semiconductor? space and, you know, other specific areas or paths that you think are the most interesting or intriguing relative to those. I think your observation is right about each kind of wave there being, you know, kind of a clear one and two. Each wave, there is also a three and a four and a, you know, you can kind of go down there.

But I think there are, you know, in semis, as in many, many industries. I do think there's a, you know, kind of a standard, you know, number one, number two kind of market position and it's really hard to be number three and you're dead if you're number four. And so I do think right now, you know, there's still, I obviously, Envidio is selling the most GPUs. That's fun. Yeah, that's driving, you know, a lot of AI. But, but I think there's still room. I don't think it's going to be a monopoly on the area. And, you know, I do think there will be a clear number two. And right now there's opportunities like from, you know, different companies that are, I think, running towards it and trying to take that position and then perhaps over time

challenge number one. We're kind of going through the standard thing where you're as, as you progress in each of these, the tooling goes up the stack. So you start to see some things where maybe things were more coded to the metal, start to then, you know, it shifts to tools. And that's part of what then creates an opportunity, you know, for one and two and that kind of thing as well, too. That makes sense. Yeah. A lot of people talk about the defensibility of Nvidia in part being due to and then some aspects of interconnect and things like that. The other thing that I hear sometimes people talk about is just as there was sort of this very positive dynamic in the Wintel world, you know, Windows and Intel reinforcing each other,

maybe today that's kind of the GPU transformer world where GPUs are in part optimized now more and more for transformer-based workloads and transformers are obviously have been optimized by large armies of people relative to GPU. And so it may also have a virtual cycle through that on a relative basis as sort of a second driver on top of what you're saying in terms of that software stack being really relevant or important. Yeah. So it'll be interesting for sure to watch that.

The two pieces have always kind of gone together, right? And, you know, and it's a push and the poll on both sides. Certainly, you know, having worked at Microsoft, you know, in the 90s, right, even very close with Intel at the time. On the AI accelerator topic, it's really interesting. Like, I think the view of many of the, you know, the view of many of. the AI semi-startups is that actually GPUs, they're really good at matrix multiplication, but they're not specifically tuned to Transformers architectures. But it's very hard to make long-term

bets in AI right now with how quickly everything is changing. Like even from an architectural perspective, I think people are talking about the limitations of the attention mechanisms that we have and experimenting on the research side with different architectures in a way that they were not three months ago. And if you had asked me, I'd be curious if there's like interesting survey data around this, we can look for it or ask researchers. But if you'd ask me, how committed are people to transformers as the dominant architecture at the end of 2023? I'd say very committed and I feel less confident about that today. Yeah, I mean, anytime you're in a phase of kind of rapid change, right? You know, they love the Jeff Bezos quote.

Focus on things that, you know, you know, you know, will not change. Because, you know, there's going to be so many, like, trying to make a bed on things that, you know, are not just fundamentally true. You're gambling at some level. What I think is interesting, a lot of this stuff, though, is, again, technology often goes through these kind of cycles where what you see is, you see scale up, you know, scale up architectures to a point. And then you reach some sort of a tipping point where you just, you're not making as enough. as much process on sort of a scale-up architecture. And so then you start to break it down and you kind of scale out. And then it kind of reg got, anyway, so we've been through these curves multiple times.

I think what's sort of interesting in the, in the semi-space, and in many ways, it's been kind of a scale-up model for years. And I think part of what's happening is already underway, right? But you're starting to see, you know, more of these chiplet designs, more use of advanced packaging and like it's really starting to look more like in some ways it's almost like a you know a microservices architecture or whatever you draw the software analogy but but i think one of the things about one of the reasons you know kind of a system design architecture is interesting is because um in some ways it allows you to predict it smaller and like you you

can tune and predict smaller components and you can rip out and replace components rather than having to kind of change the whole thing and so i think we're we're definitely moving towards more and more modular architectures, and I think that's going to get more and more flexibility, which then I think can help accelerate the innovation cycle as well. It also feels like at this point of any innovation cycle, there's always a ton of experimentation that happens. And, you know, that that happened, I think on everything from social products to more recently with crypto, where there was all these different L-1s that were invented to be sort of scalability alternatives to Ethereum, and then it moved in L2. And it just feels like

every way of computing, you suddenly have this burst of, while this thing is really working, let's try five other things that could work potentially better. And then, you know, it feels like 90% of the time it collapses back to the original thing that you just keep scaling it or whatever, but we'll see. I guess, you know, you've had this. Yeah, Darwin selects for the thing that keeps going. A lot of us to bring back the monolith. Yeah, I love code monoliths and I really love, no, I'm just kidding. You know, you've had, I think, one of the most impressive, of careers and technology in all sorts of different ways. You've been involved with some of the most important companies in the world. And literally every decade that you've operated, you've been

at the most, or one of the most important companies. What's next? Like, how do you think about the next couple years, the next decade? I don't know. It's a good question. It's one I'm working and figuring out. My husband retired from work two years ago. I have the last two years, And he'd be like, Alyssa, come out and play with me. Come play with me. I'm like, ha, ha, I'm working. I like, I didn't work in 78 hours a week. It's like, what are you doing, girl?

So, um, and I'm like, well. So, you know, I think, obviously I like, I love technology. I love deep technology. Square was super interesting. It was the highest up the stack I'd ever really worked. It was first time I were working on, you know, financial stuff. Intel and confluent and whatnot. You kind of help scratch the.

itch of I still like fundamental of technology like it's fundamental right I mean there's something there's something to it and so still reading stuff still like you know watch the you know watch the open AI dev day right you know tinkering around but he's my husband trying to keep me as busy as possible and running around with him so we'll see I don't know I don't know if it's going to be a permanent retirement or a you know or a sabbatical kind of thing don't know we'll see well I thought to hear like what are you guys most excited about in this face if you could pick kind of one thing what are you you know for the next year ahead what do you what do you hope to see or you know what do you hope to you know be involved with i'm gonna make a lot go i actually invested in all of the good

ideas this year so i'm gonna take the next year off she's also announcing her retirement at the same half decades in i'm done yeah she's just over with it you know if you basically look at the last year and it's only been a year since chatypd came out right and it's been a year and change since mid-journey and stable diffusion came out. And so I think really the last years has been everybody waking up to the opportunity of what could happen with generative AI. I think there's been an enormous amount of investment in the foundation model side, and I still think there's lots of open questions in terms of where does that all go.

But I think we know at least a handful of who the incumbents, at least in the next couple years will be, maybe not forever, but at least for the next three, four years. You're starting to see the infrastructure side start to get filled out in different ways. And for me, that area that is still wide open is just all the various applications, both on the B2B side as well as the consumer side. And there's an enormous amount of white space there and a lot of open things to do. And so I think that's just a huge transition that's coming, both in terms of incumbents adopting AI to their existing workflows,

as well as a huge chunk of the services economy being converted into code. And in this case, instead of traditional software being converted into AI. And so I think that's probably the story of the next two, three years. I'm excited about it. I'm not actually going to take the next year off. I think we're very early in the exploit. cycle. Like one of the things that has been even surprising over the last six months is when you are doing a lot of very new things with technology, there's a rush to try them once it has been

proven. My favorite example would be like eight by eight like diffusion model image generation and how far we have come in the last two years from when people said like, oh, look, this is possible, but the quality, the controllability, the usefulness of this is like not even close. And even a few months ago, like, you'd have leading researchers that say like, a video, like, who knows if, you know, that's a, that's a huge number of technical problems that feel unsolvable. And I think you increasingly see on the creative fronts, but many different applications, like how quickly you can get over some minimum quality that is useful, right? And it takes people who are playing with GPT2 or like an 8x8 image to picture what quality and scale improvement can happen.

But I think a lot of really smart engineers are paying attention to that now. And I think that I'll accelerate the exploit cycle a lot. So, you know, it's new UI. It's a lot of, as you were talking about latent demand. So it's not as obvious where you're not like, oh, like I'm just going to replace this existing software category. but I'm really excited that a lot of that experimentation is going to happen a more sophisticated way at the application layer next year. And you get to like ride the capability curve too. So no no no retirement this next year.

Alyssa, thank you so much for joining us. Thanks, Derek. Find us on Twitter at No Pryor's Pod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no dash priors.com.