How AI can help small businesses

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 [email protected] 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

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:06Sarah Guo?

  Hi listeners and welcome to another episode of No Priors. This week, we're joined by Alyssa Henry, who recently retired from being longtime 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 Alyssa's leadership. Welcome, Alyssa.

• 0:35Speaker 01

  Great to be here.

• 0:36Sarah Guo?

  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?

• 0:46Speaker 01

  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. Title wasn't even a thought. Crypto wasn't a thing. The Square business transformed from that little white reader into much larger business serving businesses of many, many different sizes, from still the smallest to also 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 IPO'd.

• 1:28Speaker 01

  And what was so exciting about that was, 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, "The neighborhood is going public." And I think that really kind of sums up just the mission-driven, purpose-driven organization that Square and Block is and was, and just the impact that the company has had on just countless small businesses, helping give them the tools and the technology that in many ways had only been previously available to the likes of Amazon or Walmarts of the world.

• 2:05Speaker 02

  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, which was always sort of functionally oriented or, you know, often functionally oriented. How did, 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.

• 2:47Speaker 01

  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, in tech. You know, I started as an engineer and then moved into product manage, 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 general manager role with P and L, and then back into a functional role when I joined Square. So it's, I've gone back and forth. I do like the general manager, the end, and, you know, multi-discipline, multi-functional leadership roles.

• 3:32Speaker 01

  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 edge, like all that creativity and execution threshold. And the kind of those, those problems in terms of either generating or resolving conflict are just more interesting when they're multifunctional, multi-dimensional in nature. It, for me.

• 4:02Speaker 02

  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'd be things like fraud detection or other areas like that. Could you tell us a little bit about how that evolution occurred at Square? And what areas you, you find most intriguing going forward?

• 4:24Speaker 01

  Well, as you state, in financial services, application of machine learning has.

• 4:30Speaker 01

  You know, has been key for a long period of time. And particularly if you look at the, you know, a business like Square, where you've got millions of small customers and it's, it's not a what you're, 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 like 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, 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.

• 5:11Speaker 01

  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, we were using GPT-2 as part of us, the Square Messages product, being a, you know, a virtual assistant to help customers, you know, answer responses to customer inquiries and variety of things. But what, what's so exciting to me about kind of really how the landscape has changed and the, the technology advances in the last year

• 5:52Speaker 01

  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 should be doing marketing, right? Like I, I know if I was more effective in doing that and reaching out to my customers, you know, I could drive more business, but I gotta tell you, you know, I work all day and then I come home at night and I gotta take care, you know, take care of my family.

• 6:32Speaker 01

  And then it's 8:00 PM and I'm starting to think about, gosh, you know, do I just need to chill for a minute or, you know, or 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 Well, I'll tell you, it's like, I know I should be doing this stuff, but 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? Um, and so what's exciting about all this technology, yeah, that's one example, but there's so many of these kind of different things where um, just the, the ease of use and the accessibility opens up what previously was effectively just massive white space, right?

• 7:11Speaker 01

  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 was, 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, blah. So I think this, this, you know, the evolution that's occurring right now is, is exciting in part just because of really the, you know, previously unaddressed demand, um, that it's unlocking.

• 7:45Speaker 02

  You've mentioned some really compelling ways that, um, 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 in 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 GPT two 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.

• 8:25Speaker 02

  Are, 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?

• 8:31Speaker 01

  Well, I think almost every aspect of kind of a small business, you can find applications and, you know, some of it, the technology is not quite there yet, um, but is rapidly getting there in terms of some of the finance and numbers and quant pieces. Um, you know, some of the, the quantitative hallucinations have been a little bit more than, um, than the others, but, but it's all rapidly going. And I think it's, you know, if you look at the largest e-commerce players. You know, the Amazons and the Walmarts, right? Like, they've been investing heavily in this area. So, you know, the, 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,

• 9:16Speaker 01

  basically, everything's already digitized. So, one of the differences between in-store commerce and particularly local commerce and, and e-commerce is the fact that, you know, just to basically to operate in e-commerce, you have to, you have to digitize, you know, you have to have images to, you know, show what your product is. You have to show, you, you have to have, you know, compelling description of it. You have to track inventory, you know. So, there's a bunch of stuff you have to have, which may, this is, again, some of the white space for small businesses, local business in particular, is the rate of digitization in-store significantly lags the rate of digitization online. And that goes back to, it goes back to the fundamental problem

• 10:00Speaker 01

  of it's too either hard or expensive to, 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 it 10x faster, 10x cheaper kind of thing, right? You know, we'd launched a, Square launched a product a couple of years ago called Photo Studio, because we heard from businesses that they wanted to sell online, but they didn't have product photos, right? And so, the first iteration of it is, we actually had a photo, like a physical photo studio in Brooklyn.

• 10:33Speaker 01

  And we had a 360 camera, and people would ship us their products, right? You know? And, and we'd 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, the barriers come down even further. Like, so it's, it's incredible, kind of, all of these different things. You know, and then I'm talking about the kind of selling size of the revenue generation side.

• 11:18Speaker 01

  But I think there's a lot of back office as well, too. From employee management and tools and communication to finances, 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 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

• 12:01Speaker 01

  make it easier to operate with better expertise and less time and effort on the business side of the business.

• 12:12Sarah Guo?

  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 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?

• 12:50Sarah Guo?

  And you see it apply to everything from like product photography to being able to generate like short form or spokesperson video. And the number of people, as you said, that want to avoid the camera, like 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 one one hundredth and one one thousandth of cost is quite large, right? And so I think it's like really interesting to think about the demand categories here for these new capabilities, which like a lot of them don't feel like traditional software businesses.

• 13:34Speaker 01

  Yeah, 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 was the market for a word processor? Well, it was all the secretaries, right? Like, they're the only ones that are gonna... Now, everyone uses a word processor, right? You know, the TAM just exploded, you know? And even, you know, taking 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

• 14:15Speaker 01

  hard, right? And, you know, you make something better, cheaper, faster, and all of a sudden, you unleash, you know, all of this latent 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 gonna 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, 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

• 14:59Speaker 01

  just were never gonna get done otherwise, and, and now they're getting done.

• 15:03Sarah Guo?

  Yeah. Can I ask you to tell us a story of how you guys ended up playing with GPT-2 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.

• 15:27Speaker 01

  I would like to say it was maybe more strategic than it was, but Vinod Khosla was on our board for years, and he had a portfolio company that was being courted by another company to, to acquire them. And Vinod called me up and said, Alyssa, you, 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 and their team. And so we acquired

• 16:09Speaker 01

  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. And because again, we just knew that, you know, there was a real customer problem for Square merchants. We, what, you know, 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's still going strong and continue to, you know, expand the capabilities and obviously move further down the line in terms of models and whatnot.

• 16:45Sarah Guo?

  So one more thing on Square. I feel like I'd be remiss in not asking you after the almost decade you were working there. Like, what else, even AI aside, 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?

• 17:01Speaker 01

  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 in that you go watch a business owner or their team kind of work, and you're like, 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 are not integrated, and then of course, the ones that are that are manual and not digital.

• 17:43Speaker 01

  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 of breed early on, where different parts of the landscape are built out. Then ultimately, what you see, you know, it's this 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, you know, for less than the sum of the individual parts and that kind of thing. So I think we're, I think we're going to see, you know, from an industry perspective, kind of

• 18:28Speaker 01

  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.

• 18:43Sarah Guo?

  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, there's a lot of discussion of AI changing the cloud services landscape? Like, do you see this as a new wave of computing?

• 19:17Speaker 01

  I mean, certainly there's a bunch of new aspects to it.

• 19:21Speaker 01

  You know, cloud computing historically, you know, very, very CPU intensive, some GPU as well too, 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.

• 20:03Speaker 01

  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.

• 20:27Sarah Guo?

  One thing that's interesting about this wave is how monolithic the services are today. Like, and you, 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 lob in, you know, more and more complicated prompts to a single model?

• 21:05Speaker 01

  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, incredibly simple API, right? Like, you know, for REST operators or something. What was compelling about it is it was so easy to use, right? And then, oh, 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

• 21:50Speaker 01

  most AWS services, probably, I would argue, probably all of them, and that, you know, you can go find S3 somewhere as a component of it. Take OpenAI, 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 a 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 see, continue to see an evolution where we're going to get some more capabilities that extend some of the core foundational.

• 22:24Speaker 01

  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, you know, some specialization as well as some additional model. I mean, you're already seeing some of this today, right? If you look at, you know, like that, you know, Amazon Bedrock, you know, the aggregate service at some level, right? Where, you know, you've been hosted and run all of these other different models. You know, it's a single service, but it's kind of a, it's kind of a bundle, if you will, of a variety of models. So, you know, we'll see. It's usually some combination of the both. And I agree with you, we're, we're super early on right now.

• 23:03Speaker 02

  If you look at the major cloud providers today, two of the three have major alignment with a underlying foundation model or foundation model company. So for example, Google's, you know, very publicly building out Gemini is sort of its next generation foundation model. OpenAI 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? Like, does that, does that alignment at all matter as we think about the future oligopoly world of the cloud providers?

• 23:40Speaker 01

  Like I said, I think it's a land grab and 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 announced, recently announced investment in Anthropic as well. I believe Microsoft is also, you know, they've got the OpenAI, 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 partners, probably all do all three.

• 24:16Speaker 02

  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 a, 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.

• 24:39Speaker 02

  So that makes sense.

• 24:40Speaker 01

  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 you'll to to get down your Azure AWS 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 it's I'm using raw compute or 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 intermesh.

• 25:14Sarah Guo?

  Alyssa, I don't think you will remember this conversation. I'd be surprised if you did, but I asked 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 listen to 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, 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.

• 25:57Sarah Guo?

  What do you, what do you think happens in that landscape of like the open source models?

• 26:04Speaker 01

  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, it's not quote, black box. You theoretically could in house it all if you wanted. So I think, you know, anytime there's, there's demand, you know, the products will find a way into the marketplace. And anytime 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 in these projects and, you know, be a core commit or whatnot. You know, I think open source is going to continue to evolve.

• 26:47Speaker 01

  The question is, is yeah, where, where and how do you make money off of it? Obviously, you know, there, there are some companies that, you know, have done well, taking open source or some of the founders or the, the project or whatever. And then still, you know, launching companies around it. Confluent with Kafka in that bucket. But yeah, there've been some others that have struggled. You know, who do was there for a while, but kind of never, never quite got the commercial piece working well. And I think just timing, not enough than differentiation relative to what the cloud providers could do, pick up and go. And I think one of the things with the cloud providers too, is because you have sort of

• 27:32Speaker 01

  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 infrastructure. And so I think that's the, you know, that's the cat and mouse. But yeah, 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.

• 28:02Speaker 02

  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 in the AI semiconductor space? You know, other specific areas or paths that you think are most interesting or intriguing

• 28:47Speaker 02

  relative to those?

• 28:48Speaker 01

  I think your observation is right about each kind of wave there being.

• 28:54Speaker 01

  You know, a 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 the, you know, a kind of a standard, you know, number one, number two, um, 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 Nvidia is selling the most GPUs, that's fun, yeah, that's driving, you know, a lot of AI, but, um, but I think there's still room. I don't think it's going to be a monopoly on the area. Um, and, you know, I, I do think there will be a clear number two. Um, and right now, there's opportunities

• 29:38Speaker 01

  like from, you know, different companies that are, that I think are running towards it and trying to, um, trying to take that position and 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, you start to then, you know, the, it shifts to, 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.

• 30:05Speaker 02

  That makes sense. Yeah. A lot of people talk about the defensibility of Nvidia in part being due to CUDA and then some aspects of interconnect and things like that. Um, the other thing that I hear sometimes people talk about is just as there was sort of this very positive dynamic in the Windel 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 virtuous cycle through that, um, on a relative basis. It's sort of a second driver on top of what you're saying in terms of that software stack being really relevant or important.

• 30:43Speaker 01

  Yeah.

• 30:45Speaker 02

  So it'll be interesting for sure.

• 30:46Speaker 01

  The two pieces have always kind of gone together, right? And, um, you know, and it's a push and a pull on both sides. Certainly, you know, having worked at Microsoft, you know, the, in the nineties, right? Even very close with, uh, with Intel at the time.

• 31:00Sarah Guo?

  On the AI accelerator, um, topic, it, it's really interesting. Like, I think the, the view of many of the AI semi startups is that actually GPUs.

• 31:15Sarah Guo?

  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 asked me, how committed are people to transformers as the dominant architecture at the end of 2023?

• 31:59Sarah Guo?

  I'd say very committed, and I feel less confident about that today.

• 32:04Speaker 01

  Yeah, I mean, anytime you're in a phase of kind of rapid change, right? You know, they love the Jeff Bezos quote, you know, focus on things that you know will not change. Because, you know, there's going to be so many, like trying to make a bet 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're not making as much process on sort of a scale up architecture. And so then you start to break it down, and you tend to scale out. And then it kind of rego... Anyway, so we've been through these curves multiple times.

• 32:49Speaker 01

  I think what's sort of interesting 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, and it's already underway, right? But you're starting to see, you know, more of these chiplet designs, more use of advanced packaging, and it's really starting to look more like, in some ways, almost like a microservices architecture, whatever you draw the software analogy. But I think one of the things about, one of the reasons, you know, kind of a system design architecture is interesting is because in some ways, it allows you to predict it smaller. Like, 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.

• 33:33Speaker 01

  And so I think we're definitely moving towards more and more modular architectures. And I think that's going to give more and more flexibility, which then I think can help accelerate the innovation cycle as well.

• 33:43Speaker 02

  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 L1s that were invented to be sort of scalability alternatives to Ethereum, and then it moved into L2. And it just feels like every way of computing, you suddenly have this burst of, well, 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.

• 34:19Speaker 01

  Darwinism.

• 34:20Speaker 02

  Yeah, Darwin selects for the thing that keeps going.

• 34:24Speaker 01

  A lot of us to bring back the monolith.

• 34:26Speaker 02

  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 careers in 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.

• 34:43Speaker 01

  Thank you.

• 34:44Speaker 02

  The most or one of the most important companies. What's next? Like, how do you think about the next couple of years, the next decade?

• 34:53Speaker 01

  I don't know. It's a good question. It's one I'm working on figuring out. My husband retired from work two years ago, and for the last two years, he'd been like, Alyssa, come out and play with me. Come play with me. You know, I'm like, haha, I'm working, I like it. I didn't work at 78 hours a week. You're an idiot. It's like, what are you doing, girl? So. Aww. 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 the first time I ever working on, you know, financial stuff, Intel and Confluent and whatnot, you kind of helped scratch the itch. I still like. Fundamental technology, like it's fundamental, right?

• 35:38Speaker 01

  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 OpenAI 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. Um, I don't know if this is going to be a permanent retirement or a, you know, or a sabbatical, you know, a sabbatical kind of thing. Don't know. We'll see. What I'd love to hear, like, what are you guys most excited about in this space? 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?

• 36:15Sarah Guo?

  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.

• 36:21Speaker 02

  She's also announcing her retirement at the same time.

• 36:23Sarah Guo?

  Half decade's in, I'm done.

• 36:25Speaker 02

  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 ChatGPT came out, right? And it's been a year and change since Midjourney and Stable Diffusion came out. And so I think really the last year has just 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 of 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, the area that is still wide open is just all the various applications, both on the B to B 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.

• 37:07Speaker 02

  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, into code. And in this case, instead of traditional software, being converted into, into AI. And so I think that's probably the story of the next two, three years.

• 37:25Sarah Guo?

  I'm, I'm excited about it. I'm not actually gonna take the next year off. I think, 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, um, uh, 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, ah, video,

• 38:08Sarah Guo?

  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, some minimum quality that is useful. Right? And it takes people who are playing with GPT-2 or like an eight by eight image to picture what quality and scale improvement can, can happen. But I think a lot of really smart engineers are paying attention to that now. And I think that'll accelerate the exploit cycle a lot. So, you know, it's, it's new UI. It's a lot of, as you were talking about, latent demand.

• 38:52Sarah Guo?

  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 in 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, Eric. Find us on Twitter at NoPriorsPod. 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-priors.com.