Pivoting to Retool  ## Metadata - Author: [[The Changelog: Software Development, Open Source]] - Full Title: Pivoting to Retool - Category: #podcasts - URL: https://share.snipd.com/episode/42ce145d-073d-488d-af34-22be7d5939ea ## Document Notes ## Highlights - **Humble Origins of Retool** - David Hsu and his co-founder were lazy engineers frustrated by repeatedly building internal tools from scratch. - Their dissatisfaction with this led to the creation of Retool to simplify internal tool development. Transcript: David Hsu Yeah, wow. That was a while ago. I think that was around probably 2017 or so when we first got started. So when we first started Retool, my co-founder and I, we were both pretty lazy engineers, you could say. And we were tired of rebuilding internal tools all the time. Both of us had started a previous company before this. That didn't really go very far. We spent so much time building internal tools at that company. We thought there's got to be a better way. Why are we sort of reinventing the wheel every single time? Why are we populating tables, getting data from our database, figuring out how to build CRUD forms on top of that table? It was just so much work to do something really pretty simple, just CRUD on top of our database. And so that's where the initial idea of a retool came from, is could we, as lazy engineers, build a better way, a faster way for engineers to build internal tools? So that's where the idea first came from. And so we went through YC in around 2017. And initially, it was pretty slow. When ([Time 0:04:12](https://share.snipd.com/snip/fcdda2b5-b84c-497f-b23e-1cf2a275ef95)) - **Most Software Is Internal** - Over half of all software worldwide is internal-facing rather than consumer-facing. - Non-software companies employ many engineers building these crucial internal business applications. Transcript: Adam Stacoviak What exactly is internal tooling? David Hsu Yeah, so a really surprising fact about internal tools is that more than half of the software in the world is internal facing. When people first hear that stat, they're generally pretty surprised because a lot of software engineers, we work in Silicon Valley. And Silicon Valley's job is to go build software for other people. So if you look at traditional Silicon Valley companies, Airbnb, Google, Netflix, et cetera, these are all Silicon Valley software companies. Google's a software company that builds a software platform that connects advertisers and consumers. Same with Meta, same with Facebook, same with Airbnb, same with Uber. And so pretty much most software engineers at these Silicon Valley companies are building external facing software, the core product for Airbnb, Netflix.com, Google the search Product, et cetera. But if you think about most companies in the world, most companies in the world actually aren't software companies. If you go look at the Fortune 500, for example, probably 10, 20 of them are software companies. 480 of them are not software. 96% of the Fortune 500 are not software companies. But they actually hire a lot of software engineers. And pretty much all these software engineers do day in and day out is build internal tools. And so we think of internal tools as basically business applications that help you run your business. That's not being used by ([Time 0:12:04](https://share.snipd.com/snip/62909ebc-9667-4436-8166-04a7c845cd50)) - **Tyco's Massive Internal Dev Spend** - A Fortune 500 company, Tyco Electronics, spends about $500 million yearly on internal software development. - This revealed to Retool the massive scale and opportunity in internal tools. Transcript: David Hsu Customer number three or number four is this company called Tyco Electronics. And Tyco gives you a sense of how large the market is, which is when we first launched Retool, Tyco Electronics, a customer of ours now, a person named Alessio came inbound and said, hey, I'm really interested in using Retool. And we were trying to understand what kind of internal software do you want to go and build? How much internal software do you actually build every day, every year? And we were shocked that a company like Tyco Electronics, I think it's number, it's fortune number 250 or something like that. So it's solely in the middle of the Fortune 500. So an average Fortune 500 company. They, every year, spend around half a billion dollars building internal software, which to us was shocking. We thought we were working on Retool and it was a cool thing for engineers to use, cool thing for us to use, to go build a few internal apps every now and then. But when we heard that Tyco spent half a billion dollars every year building internal software, we realized the opportunity here was ginormous. And specifically, we didn't believe half billion dollar number at first, but if you do the math, it actually makes sense. So Tyco as a company has, I think, around 120, 130,000 employees. And of those 130,000 employees, around about two to 3% of them are software engineers. So they have around about 2,000, 3,000 software engineers. And if you do the math based on the average software engineering salary of, let's say, 200K per year, 200K multiplied by 2,000 software engineers actually comes out to $400 million Every year that they're spending on software engineers. And Tyco has no external-facing software. Pretty much all they're building is internal tools day in and day out. And so that gives you a sense sort of how large the market is just for one single company. And so when we heard that, ([Time 0:15:12](https://share.snipd.com/snip/6eeeac8b-8734-4003-9373-7a52e28f98ad)) - **Retool as AWS of App Layer** - Retool is like AWS but for application layers: it commoditizes common building blocks so developers focus on unique app parts. - Internal software often reuses patterns like tables, forms, and dropdowns over databases or APIs. Transcript: Adam Stacoviak Here and there for Postgres or some database calls, things like that. When did you personally feel like, oh, my gosh, this is the next big thing? David Hsu It was probably a few separate moments. One moment was actually when Alessio, that customer from TE, described Retool as similar to BI, but a much larger opportunity. And what he meant by that was, if you think about BI, which is actually a parallel space to Retool, to internal software, today, if you ask any engineer, you ask anyone really to go build You a BI dashboard, BI is business intelligence. So maybe you say, hey, give me a chart of signups over time, for example. If anyone asks me that, my first reaction is, oh, I'll go use a BI tool. I'll go use Tableau. I'll use Metabase. I'll use Power BI. Whatever it is, I'll go use that. Because when I use a product like that, to build that chart, all I have to do is to go write a few lines of SQL, drag a chart on, and I'm done. It's so simple. I wouldn't even consider building that from scratch. To build a BI dashboard from scratch, I would probably have to go build or go import some database drivers. I have to go build an authentication authorization framework. I have to go find a bunch of front-end charting libraries and try to debug them and see which one is best. Then I'll find out, oh, it's a little bit slow, so I'm going to go build a caching layer. It's so much work to go build BI dashboards from scratch, and no one does it today. And so when Alessio said, hey, actually, this is just like BI, except there's 10x, honestly, more use cases than BI, that's when the idea really popped up in our heads. Wow, this is a product that is like Tableau, but five, ten times larger, actually, in terms of opportunity. And the fact that half all software is internal means that one day half of all software could be built in this way, which is really exciting. So that was one moment. Another moment was when another customer actually made an analogy to AWS, which she said that they had gone all in. This company had gone all in on AWS. This was maybe in 2016 or something like that, so a little bit earlier. And the way that she viewed it was AWS saved her a lot of time and her company a lot of time, which is instead of building your own data center, trying to figure out how to go manage racks of Servers. When a power supply blows out, you have to go figure out how to go replace the power supply. When a hard drive spindle fails, you have to go replace a hard drive. Now all they do, now we all do today, is we use AWS. We press a few buttons and servers magically appear. And if we want to go launch into a new region, we'll press a few more buttons and we'll launch into another region. And all this sort of commoditized or this undifferentiated heavy lifting, I think is what AWS calls it, has been commoditized. And we as Retool and that company just don't have to worry about having to go worry about all this heavy lifting anymore. We just go move a lot faster, actually, because AWS is handling all this stuff for us. And the analogy she gave is Retool is kind of like AWS, but the AWS of the application layer, which is there's so much stuff in the application layer that is so bespoke right now that you Have to build from scratch. So when you're building a kernel tool today, for example, you have to worry about what table library should I go and use? Okay, let me go use this table library. Oh, that's not quite right. Okay, let me go find this other table library. Oh, this table library doesn't come with virtualization. So when I have load big data into it, it's actually quite slow. Okay, now that I have that built, okay, now I have to go worry about authentication authorization. Who can actually go use my application? How do I go hook in with single sign-on, for example? Oh, now I need audit logs because I need to go verify when something bad happens. I want to know who did what, for example. There's so much crap that you have to deal with just to get a simple CRUD dashboard going. And it's so much work. And so idea that Retool can sort of commoditize all that away such that the engineer can focus truly on what is unique or differentiated about what they're building, I think is so powerful. And so that was maybe a second moment too. So I think it's a few moments like this when you see customer use cases, the customers go build things that you could not have imagined. And they actually go pitch ([Time 0:18:10](https://share.snipd.com/snip/b9c69fd8-2f18-42fe-8132-91f327e66aba)) - **Agentic Software Empowers AI Action** - LLMs are smart but lack action capability without tools to execute in a business context. - Providing LLMs with deterministic tools rather than allowing freeform web browsing improves accuracy and utility. Transcript: David Hsu Three to six months, honestly. So just a few months ago, a month and a half ago, we launched this product called Retool Agents, which really builds on top of a lot of the platform that we've built that has enabled customers To go do some pretty incredible things. So in particular, so in Retool, to use Retool, to go build a front-end application, basically you can build the front-end, but then you write a bunch of queries to connect it to your databases, To your APIs, stuff like that. And one idea we had around six months ago, and this is just really an idea at that point in time, was what if we could expose all the tools that you have built in your company and Retool and Expose them to an LLM. That could be really powerful because then it could allow the LLM to go do some pretty incredible things in your company. The thesis came from, the main idea here basically is we think that if you look at the power of LLMs today, LLMs are really quite smart. I think they've conclusively passed the Turing test. So I was a philosophy major in college. And back in the day, we would debate, how do you know if an AI is smart? What does it mean for an AI to be smart? And I would say a good amount of philosophers thought, to this point, still believe that if an AI could pass the Turing test, that we would consider that smart or intelligent. And the Turing test is basically, if you're talking to an AI, can you distinguish the AI from a human? And today with LLMs, you really can't. You talk to any state-of model, whether from open AI, from athropic, deep-sea, Kalama, honestly, they sound more human than humans actually a lot of the time, actually. And so they've conclusively passed. At this point, I think we can all say that AIs sound just as human, if not more human actually than even humans do. So that's great. But when we look at how much value are people getting out these LLMs today, it's really pretty limited. And it's really just one use case. It's really all just around chat. Can basically pass as a human, why is it that we've invested so much energy and money as an industry into AI, and yet there's just not that much ROI today? So to give you an example, I think over the past few years, the industry collectively has invested somewhere between a trillion and $2 trillion into AI, which is a lot of money. A lot of that has gone to GPUs and to training models, et cetera. But if you sum up all AI revenue in the market today, I think it's maybe around 20, $25 billion. So that's like a 2.5% ROI based on how much money we've invested, which is pretty surprising. It's interesting to think about it, yeah. Yeah, it's kind of shocking actually. And our thesis was, it's not actually about how smart the models are. The models are plenty smart. It's actually about what the models can actually go and do, which is when you use chat, the model can't really do anything. Google Docs, for example, what I'm doing is I'm just conversing within my ChatGPT app, and I have to copy and paste it into the Google Docs. Or if I'm just building a slide deck, for example, I have to go copy paste the data into a slide deck. And then, oh, actually, that's not quite right. So let me copy paste it back again. It's so much work going back and forth. And so the idea is LLMs today are plenty smart. Really, we just have to give it the arms and legs for it to actually go do things. And this is, I think, what's so powerful about something like cursor, about the idea like cursor, which is theoretically, if cursors didn't exist, you could theoretically do, or Copilot Didn't exist, you could do what Copilot does with just ChatGPT. You could say, hey, I'm going to go copy-paste this code from my IDE, paste it to ChatGPT, have it write it, and then copy-paste it back. Oh, but it doesn't work. Okay, let me copy-paste the error message. Okay, let me copy it back again. And theoretically, that would work, but you could see how cursor or Copilot is so much better. The fact that you could expose the IDE capabilities to an LLM, actually allow the LLM to go do everything, makes your life 10 or 100 times better, actually. And yet, if you look at how non-developers, most of the world is using ChatGPT today or using LLMs today, that's what they're doing. They're kind of pasting back and forth, actually. And so our idea was can we go expose uh all these uh at lm and allow it to actually do things in your business systems so it could actually go run queries your postgres database it can actually Go refund customers in stripe you actually go modify salesforce you actually go send emails on your behalf for example if we could do that that would almost be like a cursor for everything, Basically. And so we shipped that a month and a half ago, and the reception has been really positive. So we can talk more about that, but that's probably what we're most proud of shipping, I think, over the last few years is this product, Retool Agents, which, yeah, ([Time 0:24:40](https://share.snipd.com/snip/f8a411ed-8500-4e56-924b-e51be77d3016)) - **AI Agent as Sales Manager** - A customer built an AI agent managing sales team feedback by analyzing calls and providing real-time advice. - This agent outperforms human managers by processing far more data and delivering immediate insights. Transcript: Adam Stacoviak Any particular specific scenario that gives us some clarity? David Hsu Yeah, sure. So what pretty cool use cases we have a customer who has actually built an agent to go replace management, actually, inside their company, which is really cool. So they have a sales team, actually, that is doing a lot of cold calling. And for them, it's important for the salespeople to be getting feedback in a timely manner on, hey, you could have done better here. Here's where you did really well. Actually keep on doing that. And you can think of it, it's pretty interesting because as a sales manager, it's actually hard to go and listen to all the calls your salespeople are doing. Because if your salespeople are working, let's say 40 hours a week, it's actually hard for you as a sales manager to be supervising and to be listening to eight times 40. So 320 hours of calls happening every week. And so what this customer has actually done is they've actually built an agent actually to go manage this team of salespeople, where the agent actually, after every call finishes, Goes and analyzes that call and then thinks about and gives feedback, actually, on, hey, this could have gone better, but you did a really good job here, keep on doing this, et cetera. And then, actually, in future calls, can actually provide in context while the salesperson does the call, can actually give them live feedback, tell them what to say, actually. So it's really pretty cool seeing the power and what you can do, actually. And I think a lot of jobs, actually, if you think about sort of an agent doing this, the agent's actually probably doing a better job than the manager was before, actually. Because the LLM is able to take in a lot more information, actually, than an ordinary human is, than I am actually able to. And so I think really leaning into what LLMs are good at, which is consuming information, summarizing information. But that's actually a large percentage of knowledge work, actually. A large percent of my job, actually, is doing those kinds of tasks. And so I think in the future, we're going to see a lot of, I think the labor market is really going to change quite a bit when agents become more and more powerful, have more tools, and actually Go do things in your business, like giving feedback, writing performance reviews, stuff like that. ([Time 0:45:00](https://share.snipd.com/snip/06ab54ce-da83-42ff-a606-cc715615bae8)) - **AI's Impact on Labor Market** - Knowledge workers will face major labor market changes as AI automates information processing tasks. - Embracing AI and building AI-based tools will be key to career success and adaptation in coming years. Transcript: Adam Stacoviak Be responsible for the labor market change. How do you feel about that? Has it always been just whatever it takes to innovate? How do you sit at that wheel, so to speak? David Hsu Yeah, I think there's going to be enormous change in the labor market, especially for knowledge workers, myself included, over the next few years, because like we're just talking About, honestly, LOMs are really quite smart at this point, and in many cases can do a better job than I can, actually at consuming information, summarizing information, even making Decisions at times. Maybe they're less biased than I am, for example. So I think there's so much potential there. And you add that to what you said, which is, I think ultimately, we're outcome oriented. I think businesses, business owners, we're outcome oriented. We don't actually care. Even all employees, I think we're all, as long as the work gets done, whether it's done by a human or a robot actually doesn't really matter that much if it's being done to a high quality. And oftentimes with robots can be done to a high quality faster than humans. So that I think is from a philosophical perspective, a little bit unsettling. But the way I think about it, and perhaps this gets to your original question, is I think it really is a... I think in the short term, it's going to be painful in the sense that we're going to have to adapt. I think the labor market will have to adapt. A lot of humans will have to go and adapt. But those that really embrace AI, I don't think it's something you can really hide from at this point. Those that embrace AI, the builders that are able to go build the building blocks such that their companies can really leverage AI, I think those are the ones that are going to do really Well over the next few years. Of this in our customer base today, where someone takes action and they're able to go write some SQL queries, they're able to go build some building blocks, build some tools for LMs, And they go automate a whole process, and they get promoted three times in a few years. I think those are the people, the sort of action-oriented people that are hands-on keyboard able to go do things. I think there's going to be a premium to that in the market over the next few years. But I also think in the long term, it will lead a lot more abundance. And so I think Democrats have been talking a lot about this in California around an abundance agenda. But that's ultimately what I think AI will do for us, is there will be enormous deflation where the cost of providing a lot of services will go down. And that will allow humans to go afford a lot more of those services, which would be really cool. So I think overall, it's going to shake out really well overall for humanity in the 5, 10, 20-year timeframe. But there's going to be a lot of, I think, pain and gain for those that can leverage AI. There's going to be a lot of gain for those that can really leverage AI for the next few years and a lot of pain for those that can't and end up having to retool themselves or to move to a different Job family. So yeah, ([Time 0:49:40](https://share.snipd.com/snip/fe44c545-6bf4-4e9c-9596-d8112b27e123)) - **Choose Retool for Custom Needs** - Retool suits companies needing custom internal software rather than those who are fine with off-the-shelf solutions. - Small businesses often should buy specialized software rather than build internal tools themselves. Transcript: Adam Stacoviak Yourself. You're probably going to use, you know, these four or five web services. Let us complement that with Retool. Is that something you do? David Hsu Yeah, ish. But oftentimes, no, actually. So the way we think about it is there are times when internal tools are really quite valuable. And I think for large companies, for example, Amazon, internal tools are, this is why Amazon is such a successful company, is due to the internal tools they've built. And so it's so important for you to, same with Brex, for example, same with Ramp, another one of our customers, same with Stripe, for example. Internal tools are the lifeblood of their business. To be honest, if you're a barbershop, you probably don't need the world's best internal tools. You can probably go use some barbershop management software, a CRM built specifically for barbershops, and you're probably fine, honestly. If you're a cafe, for example, probably should go buy Square. And with Square Terminal, you can manage your POS, you can go manage your inventory, and that works great for you. So we really think that, we don't think Retool is the right fit for everyone. We think Retool is the right fit if you truly need custom internal software. The good news is there's a lot of custom internal software being built across the world every day. It's half of all software. But it's also not 100%. And in many cases, you would actually be better served just by buying something off the shelf. So that's something we're pretty honest with ourselves about and pretty honest with our customers about too, which is if you're a cafe, probably just go buy Square instead of trying To build your own POS system. It's going to be a lot of ([Time 0:58:10](https://share.snipd.com/snip/fd3bdd6f-2e3d-4ed3-bcd8-cbc2f607bfb4)) - **AI-Generated Apps from Data** - Soon, Retool will allow generating full applications directly from existing databases using AI. - This enables fast, secure production-ready apps on top of complex data sources like Snowflake and Salesforce. Transcript: Adam Stacoviak Can I describe the problem today and it builds me the application today in Retool? Virtually. David Hsu We are launching that later in August. So today you can build large parts of your app. You can go write queries. You can go build some UIs. But it's not a full end-to experience yet. This is our next big launch coming in August. It's actually launching a full end-to way. It's like AppGen, basically. So it's something similar to Bolt or Lovable, where you can just say what you want and it builds the whole app for you. But what's going to be really incredible is the fact is two things. One is that we can actually build on top of your existing data sources. What we found is with many of our customers, like Amazon and others, Amazon has a lot of databases. They use Snowflake internally, they use RDS. They have so many places where data lives. And for them, they're not really interested in using something like Bolt because Bolt is really great for prototyping. It's great for generating a new application from scratch. But how often are you truly generating a new app on top of new data where you have no data, really? Not very often, actually. In most businesses, the data already exists, but you want to build an app on top of that. And that is where I think Retool is really going to shine. We're going to be the first product in the market that allows you to go generate applications on top of pre-existing data. Whether it's in Postgres, Snowflake, Databricks, Salesforce, or Stripe, you can actually go generate an app entirely based on that, which is going to be really cool. The second thing is that because we're so focused on internal software, we'll give you a lot of things right out of the box, like authentication, authorization, audit logs, stuff like That. And so ([Time 1:06:40](https://share.snipd.com/snip/cb0f88d3-fed5-491d-bc86-37848428b348)) - **Tomorrow's Developers Role Shift** - Future developers will shift from coding apps themselves to building architected building blocks for AI agents. - Line of business teams with SQL skills may build production-grade apps with AI assistance. Transcript: David Hsu So, for example, maybe you work on the API endpoints, maybe you work on the data schema, because that stuff is really important. But for the actual application building, you don't need to be doing that anymore. It's going to be either the AI or even lines of businesses actually building and generating applications based on your building blocks. And so we have this concept that we're internally calling tomorrow's developers. And the idea there is today's developers are the ones that have both the skill and the knowledge to go build applications. But tomorrow's developers may just have the skill and not the knowledge, actually. What I mean by that is, my wife is actually a good example of someone like this, which is, she was an econ major in college. And she wrote some MATLAB in college. And so she's able to code. She has, she's able to think in a coding-like way, if you will. But if you go ask her to go build a web application, she would be pretty overwhelmed because there's just so much going on. It's so complicated to go build a web app today. You have to worry about what front-end framework do I go and choose? What state management library do I use? What are the pros and cons of using something like Heroku or Vercel, for example, today versus AWS. Should I use a VM or should I go use an all-in platform? Should I use Supabase or should I go use Postgres? What's the difference? It's so complicated. There's so much knowledge that is required to go build an application. And so we think that these kinds of people with AI and with software architects, with software engineers becoming software architects, building the building blocks, if you give them The building blocks and you give them AI, they will be able to build really powerful applications directly that you can actually directly use. Vision is we think that software engineers, their role will change a little bit. And a lot of these line of business people that can learn SQL, can pick up SQL, will now be able to ship secure applications and actually build them themselves and actually use them in Production. So yeah, it's going to be a pretty exciting next few years, I think. It would be interesting to think about, to ([Time 1:14:00](https://share.snipd.com/snip/4aa8dda5-176d-4396-abbc-57a9d3d93e7d))