YouSaid · the spoken record

Emily Glassberg Sands

lines on the record: 39
first: 2024-02-08
most recent: 2024-02-08
sittings or episodes: 1
sources: podcast

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“It's happening so fast. I mean, we saw a massive spike in the number of generative AI companies on Stripe over the last year, you know, and a bunch of them were two-person teams. You've likely never heard of all the way, well, maybe you've heard of, but most people haven't all the way to kind of hyperscaling startups with millions of users like Otter AI and Midjourney. We're looking at the list of top 50 AI companies put out by Forbes last year and noticed over half were using Stripe. And, you know, oftentimes you have top startups and a bunch of them aren't even really monetizing yet. And it's striking what share of these AI companies are monetizing and monetizing early and monetizing fast. At the foundation layer, yes, open AI and Mistral, but also a bunch of companies at the application layer, Moonbeam for writing assistant or runway for video editing, which is pretty remarkable.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Straight out of the gate, right? They're making digital art or music or all sorts of borderless things and they want to get that across borders from day one. Third, I would say is a lot of subscription businesses. And obviously we see subscription businesses in a bunch of different contexts, but especially sort of the AI startups that are consumer facing heavily skew towards subscription business models. And then I just say fourth, as a core layer of the first, maybe obvious because these startups are generally monetizing at a much earlier stage. They're in an interesting spot where with very lean teams, they need to operate financially like very real businesses, right? They need to grow up a little faster than their sometimes ready. And so we're seeing a bunch of adoption of our revenue and financial automation suite to deal with those differences.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“It's a great question. I mean, we've worked hand in hand with the builders of a bunch of different technology waves to make sure they have the financial infrastructure they need. Some of the earliest waves were marketplaces, infra-platforms, social media. I think kind of the young DoorDash or Instacart or Postmates or Twilio. And those were up to become some of the largest companies today. We've grown up with them. There's also, as you noted, kind of the SaaS wave. And the current wave is AI. And in terms of the unique needs of AI startups, probably four notable differences versus the prior waves. The first is just at a basic level, unlike a bunch of the past generations of software startups, we're seeing AI startups have substantial compute costs right out of the gate and that that's putting a bunch of pressure to build monetization engines faster. The second thing we're seeing is a lot of these startups are seeing global demand for their products.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Too, not instead of or as a substitute for using it in a learning experience, but just recognizing that so much of what individuals need from education is that signaling, is that credential? And I think the best way, most equitable, fairest way to do that is through skill management.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“School students learn and make that learning really customized to them and make sure that there is high quality instruction in lots of pockets of the world that wouldn't otherwise have it. But I also think there's this important pull through to the labor market. And I'm a labor economist by training. People get education for two reasons. They get it to develop skills, but they also get it to be rewarded for those skills in the labor market. And that first piece is like how you develop skills, the learning. And that's really important. And AI can definitely help. But the second piece is like how you signaled out learning out in the market, how you build a credential. And, you know, I was on some world economic forums and we were working a bunch on, hey, could we make with data skills more the currency of the labor market? And Coursera substantially moved that direction, including in their enterprise product. And I hope many others will move that direction.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“I was at Coursera for about eight years. I grew from an IC to leading the end to end data team. And through that journey, I was increasingly motivated by building products that were only possible because of the data. And the first places we started were in the obvious places. Oh, you personalized discovery of content. You personalize the learning experience. You do more to scale the teaching experience. But where we moved to relatively quickly was what you would think of as less education and more labor market, which is how can we use education data to help learners and companies measure and close skills gaps and get folks into the jobs that best fit their skill profiles. And so, you know, that's not at all to downplay the opportunity that we have in AI to make meaningful advances in how elementary.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“No, no, no, no, no. I mean, I think you know John and Patrick pretty well. Vinny, as yeah, no, that would be whiplash. Tripe is in a very fortunate position to really be in charge of our own destiny and be able to take a very long-sighted view in choosing where and how to invest in the business. And so no, from the perspective of like, do we add 10 people or 100 people or a thousand people, we're not micromanaging at that level? That's much less driven, honestly, by the macro on average and much more driven by where do we see opportunities to serve users given what's happening. And I mean, we can even talk about AI users, right? AI users actually have, there's this whole wave of AI startups and they have fundamentally different needs than a bunch of the waves of startups before them. And so that actually begs the question of where do we invest more now to get ahead of those needs.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Yes, certainly. We do get rich insight into where the economy is headed and we use it to guide our internal decision making. I think there's an interesting question we're exploring on is there a version of this that we can actually be providing to our users to help them make decisions and help them grow. The example of like the CPI or small business index and like can we get that in users hands six months earlier so that it's way more actionable is a really interesting question and honestly we're early days there but I think as part of thinking about how might we become more of the economic operating system for our users it's not just the micro components of how do you price or how do you personalize it is also the macro components of how do you think about the ecosystem that you're operating in”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Help by guiding them, and sometimes we help by actually just building the product for them, but that's been kind of a through line in my journey and a lot of what I love about Stripe.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Time change, and today half of productions on US stages are written by women. And I think that early experience showed me how powerful data, especially when you use kind of robust econometrics and causal inference and actually are getting to the root of the drivers can be in understanding and improving decision making. And it's why I pursued a PhD in economics. It's what took me out of academia to Coursera. Coursera at the time had only 40 people, but it already showed the potential to dramatically expand access to world-class learning and done right also downstream labor market opportunities. And there's also a lot of what led me to stripe, you know, well before me. Stripe was operating as kind of a beneficent player in the ecosystem and has been very interested in genuinely helping businesses on Stripe grow and using data to do that. And sometimes we”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“The through line in my career, both in academia and then in industry, has been using data to understand how individuals and firms make decisions, and in particular to help those decisions be higher quality. And so, you know, you mentioned labor economics. I've long been fascinated by who gets access to opportunity and why. So it started all the way back in college. I met this playwright in New York. She told me less than a fifth of productions on US stages were written by women and asked if I could help figure out why. And as part of that, I ran an audit study. you know some excellent playwrights donated four never before seen scripts and i sent them out to hundreds of theaters and asked them um whether they wanted to put it on stage why not and i just varied the pen name like is this written by mary walker or michael walker um and briefly you know basically i found that when purportedly written by a woman the exact same script was less likely to be produced but more importantly the theater community cared like they wanted the best plays in production and so the study spurred awareness and over”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“About 10%, so billions of dollars globally. And there are very similar applications across a range of our products. So for example, for recurring charges in our billing product, we use smart dunning to reduce declines. It actually reduces declines by about 30%. You basically identify the optimal day and time to retry a payment for transactions that are declined, for example, due to insufficient funds. It's really easy to know at what day and time sufficient funds will pop in. And the list goes on, you know, stripe radar, which you mentioned considers a thousand characteristics of a transaction and figures out in less than 100 milliseconds if each of the billions of legitimate payments made on Stripe can go through. And so those are all payments or payments adjacent optimizations, but conversion, auth, fraud. Normally talk about cost optimization. That's another one, are all places where having that scale of data allows us to create a”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so radar is a great example. I think you also see it throughout our payments product. So maybe the most salient to a consumer, like an end user, not our customer, but our customer's customer would be something like the optimized checkout suite. So it's this bundle of front-end payments optimizations. And it's a lot of little things, honestly, like dynamically presenting payment methods in the order that are most relevant for the customer that really add up in terms of driving efficient checkout experiences for end users and in turn driving up revenue for our customers and growing the internet economy. And less salient to the end user is this whole host of back-end payments optimization. So for example, we use ML to optimize authorization requests for issuers basically identifying the optimized retry messaging and routing combinations to recover a big chunk of false decon.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“To really deeply understand and improve the business is pretty fascinating. And you'd have to think about like, is it a startup that does that or is it an incumbent that does that? And what's the business model? What's the business model there? But if I think about the case of Stripe, Sort of Stripe has the opportunity to be beneficent, right? Incentives are super aligned. The more Stripe can help its users, businesses grow, the more Stripe grows, and the more the economy grows. And so whether it's Stripe or someone else using financial data to help businesses be more successful, to grow the pie, to grow the GDP, I think is really powerful.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Whole suite of no-code products, so you can use payment links or no code invoicing. But how does one actually build a really robust specific to the user integration without needing a substantial number of payments engineers or any complicated developer work? LLMs are proving that they can be very good at writing code. We have a couple cases actually where we're already seeing it work. But as the decisions get more and more complicated, I think there's still a lot of work to do to build the right integration and to build it well in an automated way. And then I think, as I mentioned before, some of this layer on top of the payments data of like, okay, you could build solutions that make payments work better, but payments actually allows you.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Supportability decisions and understanding where the business does or does not meet the requirements of a given car network or a given bin sponsor. And so I think that identity piece, like who is this merchant? Are they who they say they are, but also what's their business? What are they selling? And how does that map to this pretty complicated regulatory environment? is a really interesting and hard problem that lots of folks are solving in their own ways, but is likely an opportunity. I think there's almost certainly an opportunity to, you know, whether Stripe does it or somebody else does it, to make sort of financial integrations way more seamless. Stripe has”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“It's a great question. I don't know exactly what others will do. I think Having a really robust understanding of identity, who businesses are, what they're selling has always been important. And, you know, I think often in industry we think it's important for marketing or sales or sort of go-to-market motions. But it's also super important in fintech. Yeah.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so, you know, I think it's always a combination of buy and build. And we recognize that there are a lot of great companies building a lot of great ML infrastructure and experimentation solutions. And some of them are very plaint and some of them are very general. And we stitch together where there's a clear external solution and we build internally where we feel our need is more unique or somehow very important and not currently satisfied by the market. Our experimentation platform is one that we've built internally. We run a lot of charge level experiments and Latency and reliability requirements for charge level experiments are very, very high. And so building and running that internally has been worthwhile, but there are lots of cases, flight, weights and biases. There's lots of third-party solutions that we lean on as well.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“There's obviously depending on the application, different performance requirements. And then there also is this very real question of cost. So, you know, we're running this infrastructure centrally, but we found that for the most expensive applications, you know, we do bill them to the local teams. And so we work with them very closely to understand what makes sense given the uneconomics of product, the importance of quality at this stage, how they're thinking about scaling, what the latency requirements are, and so on. We have heard that previously, it was a little bit overwhelming for individual teams to figure out what model to use, but also to go through the enterprise agreement and get the infrastructure up and running. And so centralizing a lot of that, I do think has sort of economies of scale. But again, we're not prescriptive and we do leave agency to the individual applications.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“So we do have a proliferation of models, but we are not centrally, for example, like within our ML infrastructure group, super prescriptive about what model individual applications need to use. So I talked about LLM Explorer and the presets and sort of that was back in March and we very quickly turned that into building an internal API for more programmatic use of LLMs, right? We wanted it to be equally easy and safer stripes to build production grade systems and services. There are 60 applications built on that now, a bunch internal, but also several external. And I'm happy to talk about a couple of them. That's what planted the seeds for a lot of the product initiatives. We're now investing in more heavily. We have default models based on the use cases, but we also give individual teams agency to choose based on cost considerations, latency considerations.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“LLMs can automate the writing of code and accelerate information retrieval. And we're building those both out of the existing vertical teams and out of the accelerators.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, it's a great question. And I don't think we 100% have the answer of what's the right operating model, but we've been very conscious and iterative as we're learning. And so far, the answer is both. Like, you know, not one piece of team or two pizza teams. It's four of them today. And should it be six or should it be eight or should it be ten? And then in parallel, where can we really support the vertical team sort of the core product organization in adopting LLMs or generative AI more broadly directly? There are a couple of examples, but at Stripe, we're very focused on leveraging AI so that non-technical folks that are users can do things that they couldn't do before. And then also so that technical folks can move an order of magnitude faster. And there are some pretty obvious industry standard ways that we're finding.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Sorts of economic signals, CPI for tracking inflation or small business index, for tracking the health of the sector. And those are very useful for steering business decisions based on macro trends, but they tend to be quite lagging. And so this question of can real-time data speed the time to insight and thus response, I think is interesting. So, you know, those are all more sort of future looking, but I'm very bullish on a world where we're able to really holistically help users grow their businesses well beyond payments, but built on payments data.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Don't know 100 bips, 200 bips. So I think just in payments optimization alone, we can ask the question of what might foundation model look like in that context. And then I think where it gets really interesting with generative AI on all this payments data is can we actually become more of the economic operating system for our users? And you can imagine all sorts of ways this could be productized, everything from a dashboard of insights and recommendations to like an API you hit to get customer level predictions to like, you know, turning important business model and personalization decisions. So pricing, recommendations, discounting, kind of on autopilot with Stripe. We know we can abstract away a bunch of the need for our users to worry about payments and refunds and disputes, but you could imagine also starting to tackle those sort of higher order tasks, understanding the value of users and setting the right price and determining the geostrategy. And then this is more at a macro level, but businesses rely on all”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“When I step back and ask, Where should we be in kind of three years, five years, I think the vision, the opportunity is much bigger than what we could do in a year with a fintech lens in general. I think the current sort of Gen AI advances beg the question of what does it actually mean to apply generative AI to the economy at large? You could start with payments optimization. I think folks know that we do a bunch of back end and front end optimizations for payments. Is there some actually new foundation model built on financial data that would blow the existing conversion and auth and fraud and cost optimization models out of the water? You know, we can do incremental model improvements today. And quarter over quarter, they drive meaningful bips of uplift. But it doesn't feel crazy to think that a good foundation model could outperform more traditional.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Sigma Assistant is similar in that it generates code from natural language, but it's in a pretty different context. It's actually applied to generating business insights. So sigma is our SQL-based reporting product. It lets businesses analyze and get insights directly from their Stripe data. And Stripe data is, as we've talked about, pretty interesting for most of our users. all of their revenue data. So which customers where are buying what for how much, who's retaining, who's churning pretty central to a bunch of different decisions the firm has to make. And Sigma Assistant is all about making sure our customers employees don't have to speak SQL to get access to those business insights. They can just use natural language to ask questions of the stripe data. Some of the folks in the beta are asking really interesting questions and getting them answered from the very basic, how much revenue did we generate in December to what types of customers tend to be most.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Totally. And, you know, I think for some of our customers, and this is opening the aperture in terms of which employees can use solutions like custom radar rules. But for a lot of our customers, it's allowing them to use these solutions for the first time, right? So think about the non-technical small businesses on Stripe, a bunch of them. You don't have to be technical to get started on Stripe. You can use our no-code integrations. You need payment links. You can use hosted invoices. These are companies who wouldn't dream of coding up custom fraud rules. And so not having to have, they just don't have the developer skills on hand. And so just not needing to being able to use these tools with just plain English, I think is really powerful. And more broadly, I really love that democratizing power of generative AI. And it's very much aligned with our Founding Ethos.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“From IP addresses in that geo. To generate these roles, employees that are users used to have to code up the roles themselves, but radar assistant lets them use natural language to write those rules. So it's a little thing, but speed matters a bunch in fighting fraud. You have to work faster than the fraudsters. And with radar assistant, a whole range of people in an organization from fraud analysts all the way to less technical folks can implement rules quickly and directly without having to work through a developer.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“The primary ways we're finding LLMs useful. Today at Stripe in user facing applications is first automating the writing of code. And then second, accelerating information retrieval. And both are proving really powerful for our user. So on automating code, radar assistant and sigma assistant are two new products that are in beta and rolling out to all users soon. Radar assistant is really about generating custom fraud rules from natural language. Most folks listening probably have heard of Stripe Radar. It was one of our first non-payments products. It's an ML powered product. It helps identify and block fraudulent transactions. But then in addition to the core radar product, which works generally under the hood without any user provided direction, we have radar for fraud teams, which is about letting users write custom rules. So maybe you know you don't have any customers in a given country and you want to block any transaction.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Of an output from the accelerator. We have other accelerators that are working on the applied side. So, hey, we know that given the advances in LLMs in particular, there's way more we can do for our support experience, both user facing and also internally for our ops agents. And to your comment earlier, Lod, for sure, there's third party solutions we can buy, but is there some homegrown solution that can actually be used across a variety of internal applications? And can we just go build that? So that's another example of the kind of thing that our applied accelerators build. And I think the applied accelerators aren't, you staff of one, you know, you fund a one pizza team and you go hire these people. They're actually opportunities for growth and development, for internal talent. So the vast, vast majority of folks who join the accelerator have been a strike for many years. They're doing a rotation onto the accelerator.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“We can talk about it here and we can talk about it later. So, the idea of accelerators is basically ring fencing one to two pizza teams and multiple of them to get new AI bets seated. And one of the accelerators was actually what produced this LLM Explorer. So it's very hard to just pull three engineers off of, you know, their work building radar to build an LLM explorer. So we have this sort of experimental bets funding internally. It's run out of, you know, by David Singleton, so out of our CTO's office. And it'll basically be like, hey, we'd like to build a one pizza team and we want to fund it for six months. So relatively durable. And here's roughly the charter and here's roughly the milestones, but we're going to learn and iterate as we go. And so actually this infrastructure is an example.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Sales development rep, writing a cold email, or like, you're an exec who's preparing for a meeting, you run your copy or talk track or whatever through this style guide, and it returns back to you the same content in Stripe Tone. The enthusiasm was palpable and we had to figure out ways to harness it and build more of a community around it. The weekly active user count of this LLM explorer is still at almost 3,000, which is just shy of half the company using it every single week. And yeah, for sure engineered, but also a ton of salespeople. Internal prototyping to actually like enabling also more production grade solutions.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Momentum within days, a third of stripes were using it. And so from there, we started to look at okay, what are they using it for? What do we see in the logs? And the answer was stripes were using it for all sorts of things, honestly. But there was this opportunity to create more community and sharing in the tool directly so that they could build on each other's work and weren't sort of doing that in SideFlack. A simple example, but shortly after we launched the original tool, we set up this little functionality called presets and basically just lets you save and share your prompt engineering. Maybe this exists, if not some startup should go build it for everybody. And then everyone else at Stripe can like search and upvote and you see what bubbles to the top. And basically overnight we had like 300 of these reusable LLM interaction patterns. And they ran the gamut, but just an example, like thousands of stripes still today use the stripe style guide, which basically don't care if you're a product marketer writing copy for the website or”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“I think you're spot on that a lot of companies, you know, the first place they go in their mind is how can this manifest in our product? How can we help our users? And then they realize, hey, any one person can actually answer that question. We need to be putting this in the hands of folks with a range of different backgrounds and expertise thinking about different parts of our product and business to really apply it. And that's exactly what kind of this built-in three weeks beta did within days, a third of stripes were using it and you can think of LLM Explorer basically as a front end that supports multiple models in the back end. So we started just with GPT 3.5 and GPT 4, but today we serve over a half dozen models through the tool. We knew it needed to have certain security features, stripping PII and rehydrating, et cetera, straight from the start. And we spun up, yes, a Slack channel and a hackathon and more to help kind of build momentum. We didn't actually need to do much to build.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Was palpable, you know, at Stripe as it was across industry. We knew the experimentation was going to happen. And so we really wanted to make sure that we enabled it to happen well and safely.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Ecosystem with awe, but also honestly a little bit of overwhelm, the sense of well, there's very clearly a real opportunity here to better serve our users, but what is it exactly and how do we get it off the ground quickly and safely? So it starts with a story of three engineers who hack together in three weeks an internal beta for an LLM explorer. And the basic idea of LLM Explorer was, hey, let's get a chat GPT-like interface in the hands of the 7,000 talented stripe employees and really let them figure out how to apply it to their work. Our leaders all the way up to John and Patrick have intentionally crafted this strong culture of kind of bottoms up experimentation. And we think a lot about sustaining it internally as we grow. And with LLMs, it was no different, right? And so where we started was let's quickly unlock internal experimentation, let's get LLMs safely in the hands of all employees at Stripe. The enthusiasm”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“I mean, I think it's fair to say that Stripe isn't first and foremost an AI company. As you know, fintechs, including Stripe, have long used traditional ML in many contexts, including sort of fraud and risk. But first and foremost, we're building financial infrastructure for the internet. So Stripe got started by enabling first really digitally native startups to accept online payments. And then over time, millions of companies started relying on Stripes, financial infrastructure for a bunch of different needs, whether that's reducing fraud or managing money flows or unifying online or offline commerce, all the way to launching embedded financial offerings. And so as not a kind of first and foremost AI company, we probably like a lot of people listening to this podcast had kind of our like, hey, what the heck are we going to do moment a year or so ago when LLMs really broke through the zeitgeist? And we were looking at the technical breakthroughs on the product launches all over the”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“But also in applications like seeding a bunch of new Gen AI bets and getting them out to our users. So that's kind of hat one. And then second, I'm accountable for our self-serve business. So a huge number of SMBs and startups come to Stripe directly to get started. They self-serve through the website. And we're really focused on understanding who those users are, getting them the right shape of integration efficiently, building product experiences that meet their needs, including as they grow and grow the portfolio of products they use. So for many of our users, it's not just payments, but invoicing or subscriptions or billing or tax or reveric, depending on what their business model demands.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT
“Yeah, so I joined Stripe back in 2021. Originally actually to lead data science. And David Singleton, Stripe's CTO, reached out. I didn't know a ton about Stripe, but I knew millions of businesses were using it to collect payments, which had demean really interesting data on those businesses and on a large swath of the economy. Stripes, clearly helping companies run more effectively and also in a position to learn from its data what kind of interventions significantly improve companies' long-term success. And in some cases to actually action those. Today I wear two hats. So the first is I support a bunch of different teams that are together tasked with enabling the effective use of data across Stripe and this includes from decision making internally to building data powered products. We've been investing a bunch in foundations, which includes building out our ML infrastructure and better organizing our data, you know, the really sexy stuff.”
2024-02-08 · No Priors · Build AI products at on-AI companies with Emily Glassberg Sands from Stripe · IDENTIFIED FROM THE TRANSCRIPT