How AI can help small businesses

0:06Hi, 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. Great to be here. Let us start by talking about Square.

0:38You 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, Tidal wasn't even a thought. Crypto wasn't a thing. And 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.

1:20So 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. 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 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 them give them the tools and the technology that in many ways had only been previously available to the likes of Amazon and Walmarts of the world.

2:05It'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. 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 often functionally oriented. um 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 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.

3:00You know, I started as an engineer and then moved into product manage and 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 P &L, and then back into a functional role when I joined Square. So I've gone back and forth. I do like the general manager, the end to end, you know, multi-discipline, 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 edge, like on that creativity and execution threshold.

3:51And 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'd be things like fraud detection or other areas like that. Can you tell us a little bit about 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 of time.

4:32And particularly if you look at a business like Square, where you've got millions of small customers and 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 who's a good actor and who's a bad actor to who do you target for a specific product, who's going to be most likely to find 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, manage fraud, as well as to cross-sell and grow the business.

5:19And then more recently, even kind of prior to this kind of latest big shift in the AI landscape, we were using GPT-2 as part of the Square Messages product, being a virtual assistant to help customers answer responses to customer inquiries and a variety of things. But what's so exciting to me about kind of really how the landscape has changed and 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 a much larger audience, right? But it effectively unlocked the consumer and started to then show what this technology could do when then further integrated into domain-specific areas.

6:13You go talk to small business owners, most of them will tell you, gosh, I know I should be doing marketing. I know if I was more effective in doing that and reaching out to my customers, 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 chill for a minute or, you know, am I going to spend the next three hours trying to create an image and write text for the campaign and everything like that? And what I'll tell you is like, I know I should be doing this stuff.

6:47It'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, that's 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 huge for me. But it wasn't accessible.

7:23It 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 the evolution that's occurring right now is exciting in part just because of really the 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.

8:03Are there other areas that you think nobody's really addressed yet or that are big opportunities in this space? Because, I mean, adopting GPT-2 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.

8:36And, 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, the Amazons and the Walmarts, right, like they've been investing heavily in this area. So 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.

9:13Because one of the things is if you're in e-commerce, basically everything's already digitized. So one of the differences between in-store commerce and particularly local commerce and e-commerce is the fact that, you know, 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 have compelling description of it. You have to track inventory. So there's a bunch of stuff you have to have, which 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.

9:56And 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? We'd launched, Squared launched a product a couple 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. And we had a 360 camera and people would ship us their products.

10:36Right. 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 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 and the revenue generation side.

11:17But I think there's 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, you know, 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.

11:55And so I think that 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 um how much latent demand there is for content generation of different forms right in like every business context i remember um 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.

12:42And 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 one one hundredth and one one thousandth the cost is quite large, right?

13:24And 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. Yeah, I mean, 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? You know, originally, who was the market for a word processor? Well, it's all the secretaries, right?

13:57Like, They're the only ones that are going to. 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 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.

14:31And 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 is 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 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 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.

15:21Like 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 Vinod Khosla 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 on their team.

16:08And so we acquired that company in, I want to say, 2018 or something and put them to work on kind of stitching together some of these customer-facing experiences, leveraging some of the early work that they'd done. And 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's 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.

16:46I feel I could 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? 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 pasting from one tool into another tool, or they're downloading from this, you know, and then they're emailing it to that.

17:32And like the, um, I think the workflows, even the ones that are digitized are 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, many industries and many parts of tech. what you see is kind of best of 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.

18:11And 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 So I think we're going to see, from an image perspective, kind of more aggregation and more bundling because we've been through – 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.

18:48So 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 to responsible for a huge number of innovations and 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? I mean, certainly there's a bunch of new aspects to it. You know, cloud computing historically, you know, very, very CPU intensive, some GPU as well, too, various use cases.

19:30But 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 SquareBlock, 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, And, you know, so it's never a tall moment.

20:15And 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. Like, 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.

20:54Right. 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? um it's probably both um the you know if you go back aws at the beginning right um 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 um like you know four rest operators or something what was compelling about it is it was so easy to use right um and then And obviously a whole bunch of stuff sprang up around it.

21:34You know, S3 became not just, you know, a first, well, it was a first-class service done, right, with 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'd go trace kind of 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 OpenAI. I started relatively simple, but adding to stuff, in fact, making them 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.

22:15And 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. 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, some additional model. I mean, you're already seeing some of this today, right? If you look at, you know, like the Amazon Bedrock, you know, the aggregate service at some level, right, where, you know, you can 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.

22:55So, you know, we'll see. It's usually some combination of the 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 the underlying foundation model or foundation model companies. So, for example, Google's, you know, very publicly building out Gemini as 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?

23:34Like, 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 grab and lots of people are trying to figure out what's going on but amazon has the anthropic um alignment and investment as well too and so you know they're pretty close although i think google just announced recently announced uh investment in anthropic as well um 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 part, you know, I think it's going to be built by partners, probably all do all three.

24:16From 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 So 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, you know, to get down your Azure AWS bill, you say I'm going to spend this many millions of dollars on your cloud.

24:55Some of it's going to be 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 how much I pledged. So I think all of these models, business models, all intermesh. 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.

25:37And you just looked 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 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 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.

26:16You theoretically could in-house it all if you wanted. So I think, you know, anytime 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, being a core committer or whatnot. You know, I think open source is going to continue to evolve. The question is, is, yeah, 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, you know, launching companies around it.

27:02I put Confluent with Kafka 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 quite got the commercial piece working well. And I think just time, not enough then 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 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 tweak and then help tweak it on your infrastructure.