Inside AWS At 20: Werner Vogels On The Moment Everything Changed

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This one is one of those incredible origin stories I'm always excited to share on here because I'm gonna be joined by one of the most influential minds in modern technology. His name is Werner Vogels. He's the chief technology officer at Amazon and one of the driving forces behind the rise of Amazon Web Services. And the reason I'm excited to get him on today is AWS is marking its twentieth anniversary, and Werner brings a rare perspective that stretches right back from them earliest days of Amazon's internal engineering challenges to the creation of services like s three, e c two, Dynamo, and the pay as you go cloud model that changed the economics of IT forever. And here's someone that has seen firsthand exactly how Amazon moved from solving its own impossible scale problems to building an infrastructure that now powers businesses all around the world.

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In this conversation today, we're gonna talk about that near missed opportunity when he almost didn't take Amazon's call, but we'll also talk about what he saw inside Amazon's technology kitchen and why commercial software simply just couldn't keep up with the company's scale and learn more about how customer obsessions shaped AWS and why pay as you go changed everything and how AI is creating what Werner describes as a new developer renaissance. And at a time where so much of the AI conversation is dominated by fear and replacement, my guest will offer something far more practical and optimistic, a vision where AI becomes a better tool for builders rather than a replacement for them. And he'll also share why curiosity, communication, and human collaboration may matter even more in the next generation of software development. I wanna give a quick thank you that partners like NordLayer make it possible for me to attend events, speak with industry leaders, and bring those insights right back to you here on every podcast, every episode on the Tech Talks Network. And I was recently at a tech conference where Mark Templeton said that the browser is now the computer.

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It was a modern take on the old idea from Sun Microsystems where they said many years ago that the network is the computer. And I think this perfectly captures where we are today. The browser is now the computer, and work has shifted into the browser, which means security has to follow. And this is exactly what NordLayer is doing with its new business browser. Instead of protecting the edges and hoping for the best, it secures the place where people actually do most of their work, and it also gives their company a better visibility, stronger control, and a more practical way to manage risk.

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It's one of those ideas that feels so obvious once you hear it. But if you wanna know exactly what that shift looks like in practice, please pop over to nordlayer.com/browser to find all the information you need. And please come back to me. Let me know your thoughts on it. So with AWS celebrating twenty years and the next era of builders already taking shape, what does the future really look like for developers, businesses, and the people creating what comes next?

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Well, let's find out as I introduce you to my guest now. So thank you for joining me on the podcast today. For people just hearing about you for the first time, you know, you are a man that needs no introduction, but just tell everyone listening who you are and what you do.

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I'm Werner Vogels. I'm the Chief Technology Officer of amazon.com. And the role of a Chief Technology Officer can have different ways, you can look at that in different ways, you know, some CTOs are just a data centre manager, yeah. When I joined Amazon, I came out of academia, when the role of CTO was really as sort of the big thinker, Yeah, until 2004, more or less when I joined, Amazon engineers were really good at scaling, but from a practical point of view. They had gotten their hands dirty.

[00:04:53] - [Speaker 1]
And by getting an academic on board, they helped to sort of more structure the way that we were building our systems, because we were building systems at a scale that nobody else had done before. And absolutely not. And you know, no commercial software operated at the scale of Amazon. And so, but when we started AWS, the role as a CTO changes. Yeah, it changes much more as what I think Scott Dietrich called it an external facing technologist.

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Someone that talks to your customers, understands your customers, and takes that information back, and what are the next set of tools that we need to build? Yeah, what are the kind of things that we're doing well or not? Or like in my last keynote at re:Invent, I didn't talk about tools at all, but how do we change as developers? Yeah, and so, a Chief Technology Officer is not only the one that actually runs technology. There's a lot of other parts to that, that are in my eyes, really fun.

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And I'd love to take you right back to the beginning of that journey, because before you joined me on the podcast today, I was reading that you said you almost didn't take the call from Amazon because, hey, it was just an online bookstore. So take me back to that moment and what changed your mind when you finally looked under the hood because it it feels like almost a crossroads moment then.

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It is, because it's really true. I almost didn't take that call. So in The US, it's pretty normal for academics to actually do some consulting for companies and give them advice and things like that, and I've done things for certain microsystems, and Microsoft, and Deck, and others. And when Amazon called to actually say like, Hey, can you come and give a talk about your work? And I go like, Really?

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Amazon? It's a bookshop. A web server in a database, how hard can it be? But I was curious, so I did go. Bleeds?

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One bleeds in their technology kitchen, and I was completely blown away. Commercial software doesn't work at their scale. Yeah? And so they had to build everything themselves. That's an opportunity I couldn't get go.

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Yeah?

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So when you walked into the Amazon technology kitchen, what did you see that made you realize that there was something fundamentally different here from anything happening in academia, or anything you'd seen anywhere else at the time?

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Well, first and foremost, the scale at which they needed to operate at. I didn't maybe I didn't really understand, because I'd always think about Amazon as you push this button, I want to buy this, and then the package arrives. What sits in between there is just massive. And whether that is recommendations or forecasting or how to run our fulfilment centres, and all these kinds of things were at a scale that I had never thought about in academia at all. And scale was really important because they couldn't use commercial technology.

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Because commercial technology wasn't remember, this is when Amazon was created, this is 1994. The word e commerce doesn't exist. Yeah. And almost everything that has happened in e commerce has been pioneered by Amazon. Think about recommendations.

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Yeah. Every site these days, and whether it's a e commerce site or anything else, has a recommendation engine. But Amazon needed to build those first themselves. They needed to come up with the idea. We've been using AI technology, what we well, in the past we called AI, from day one.

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Why? Because the datasets that Amazon needs to operate at are so large that no human can actually hold this in their head. And a great example, I think of really building things yourself to solve your own hard problems. For example, when we built Dynamo, this is the first key value store. So what had happened is in the early days of Amazon, there was something called $25 free shipping.

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That meant that if it was more than $25 we would ship it for free. But there was a cutoff date before Christmas, which is December 12. If you bought before that more than $25 we would ship it to you before Christmas for free. So December 12, biggest day of the year, and our customer management database runs on a cluster of a very well known commercial database vendor. There's a bug in there that only shows up on the massive scale.

[00:09:55] - [Speaker 1]
Of course, we're pushing that. Hit the bug December 12, most busiest day of the year with that in order. Of course, representative of this company all the way up to the C Suite comes and visit us because we were not happy. And they looked at me and said, You should have tested better. Now, that wasn't the right answer for me, of course, but they were right.

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Because we were using commercial technology completely out of bounds of what it was designed for. So one of the first things I did after that is actually send an intern into that data head, say, go figure out how are we actually using relational databases. By the way, that intern is Swami Subhrani, who now runs our AI group. Yeah. But he came back to me and said 70% of database operations are key value.

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They're not relational at all. And so we go like, oh, wait, but we know how to build that. The predecessor of DynamoDB was really our desire to not use this one hammer, which is a relational database, for a problem that was absolutely not built for. Look at the shopping cart. Nobody goes to the shopping cart services and says, Give me all shopping carts that have Harry Potter in it.

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No. It is you go to the shopping cart service with this customer ID, and you want the shopper card. That's all you want. And then you get a block. So this was a good example of sort of the kind of things how we do it at Amazon.

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We invent ourselves out of a tight spot. Yeah. And instead of going to look for a vendor that has this, because we know already that commercial operations don't work at the level of Amazon. And so we also went through a number of architectural changes over time, where at the same time, if you think about going and switching over to AWS from Amazon retail to AWS, something that was really popular in the early 2000s was to put an API on some of your internal processes. So in the case of Amazon retail, there was a catalogue, the shopping carts, and lots of young businesses were being built around that.

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Comparison shopping, complete new visual interfaces, or bookshelves, and things like that. As soon as one became popular, they all started to stutter in the execution. Because now suddenly, they needed to get investment, because they needed to buy hardware, because they became popular, they needed to hire IT people, because they needed to babysit the hardware and things like that. All of those companies failed because of that. And we were looking at that going like, but we solved this for ourselves.

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You know, can't we start taking can't we start building technology based on what we've experienced within Amazon, and actually offer this to companies, and that was how we were looking at it in the early days, that also need to reach internet scale, just like Amazon? And were we only originally focused probably more on younger businesses? Yeah, I think so. Because the internet was young in 2000. Yeah, we'd just gone through the bubble.

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Yeah. And so that was how things how we were thinking about it. Can we build technology that any other company that would like to be like Amazon can actually do this without having to make massive investments into hardware and related things. And there was one other thing. And if I think about the technology that we built, S3 and EC2, I'm immensely proud of that.

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But what I'm most proud of is something else that we did at the same time. Namely, when I was the CTO of Amazon, I have written some big checks to vendors. Yeah. And the only way to get your costs down was to make a very long term commitment. At Amazon scale, have no idea how many databases I need five years from now.

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So you massively overscale and overprovision, then you write his check, and then the vendor doesn't care anymore, because he's been paid. I hated that. I hated the fact that I felt I was being sort of the vendor was always in charge. I was never in charge. Yeah.

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So when we started AWS, we also decided to radically change the economic model, namely going from a prepaid model to a pay as you go model. And remember, this is what we do in everywhere in our lives. I don't go to the gas station on the beginning of the month and deposit, what is it, euros 500, and say like, and now I'm gonna come and get gas for the rest of the month whenever I need it? No, you pay for what you've used, yeah, or what you acquired. Or electricity, I mean, our electricity bill at home is from which you've used.

[00:15:00] - [Speaker 1]
And so it is a natural way, but it's not how all technology companies every technology company requires you to pay upfront. And so I think our switch to actually to base a Go model completely revolutionised the whole IT industry, because now suddenly customers knew that they could get actually bang for their buck. But they could also experiment, you know, and because they would know exactly with, oh, if I do it this way, this is how much this is going to cost me. And bringing that to today, you know, where AI, of course, is on the forefront of everybody's mind, Bedrock is one of those environments where we have all of these models, all sizes of each of these models as well. But with that comes a cost picture.

[00:15:52] - [Speaker 1]
Yeah, if you use this particular model, you know, it's going to cost you 5¢ per million, what is it, tokens? And maybe if you use that model, it costs you $5 per million tokens. Is that answer so much better? Is the quality better? You know, as such, Bedrock is an amazing environment, I think, for most of people that are building things with AI, to first of all, experiment to figure out, maybe this deep sea small model is just good enough for what we're trying to do here at this particular price point.

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But maybe, you know, you're in a heavily regulated industry. And, you know, your requirements are different, and you need a different quality answer, for which you may be willing to pay more. But all of these things are suddenly up in the air, the customer decides, I don't force my customers how much they need to use and what they need to use.

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And if we fast forward to present day there, just celebrate AWS is now celebrating its twentieth anniversary. So many big changes, so many big technology waves have changed there, and you often talk about the idea that the road map, though, has always been written by the customers, and you mentioned it there. I mean, in a world now driven by AI and Agentic AI and everything in between, is that still true? Or are we starting to seeing technology lead the conversation instead? Has that changed?

[00:17:18] - [Speaker 1]
First of all the stuff that people were doing in the past, the enterprise software and things like that, is not going to change. It's not the world's not changing overnight suddenly, where everything has suddenly been is based on AI. If you, you know, will it play a very important role in areas where, for example, there's a lot of paperwork? Yeah, absolutely. I just heard this story.

[00:17:44] - [Speaker 1]
This is the Department of Work and Pensions here in The UK. Take 25,000 letters a day, act what? Yeah. How many humans do you need to actually do this? So they build an AI system, where they scan in all these letters, how they say are them, and then figure out which should have the highest priority in being addressed.

[00:18:09] - [Speaker 1]
Yeah, because there are certain some of these letters are just pensioners having a beef about something, but some may be very vulnerable people needing immediate assistance. So using AI to sort of reprioritize sort of the answering of these letters and addressing the issue is important. Now, is our development still driven purely by customers? Absolutely. But not necessarily always what your customer tells you.

[00:18:38] - [Speaker 1]
You need to observe it, and you need to look at multiple customers. Now often, a good example is Amazon Workplaces, which is our virtual desktop environment. We would never have built that if it wasn't that when the twentieth CIO I talked to also said, Don't you know, isn't there a better solution than the virtual desktops that we have at this moment because everybody wants to bring their own laptops to work and things like that? Yeah. So sometimes it is also observing the kind of things that are happening in the industry, or where the big, big open gaps are, and then filling them.

[00:19:18] - [Speaker 1]
I want to make a distinction between the things that we do by customer feedback, and whether that is direct or indirect feedback, and the things that we do under the covers, which I call invisible engineering. So if you think about cost, reliability, availability, security, all these things under the covers that we do under the covers is continuously innovating there. And you'll never see a press release about that. Suddenly, customers realise that their Lambda functions don't take three hundred milliseconds to start. Actually, it's now only two hundred microseconds.

[00:19:54] - [Speaker 1]
And so there's a lot of engineering that we do under the covers to continuously improve over these six different pillars that we have. Sustainability, for example, being one of them. Well, there are kind of innovations that we can do in our data centre to actually be a better steward. And for example, it's very important for us in the innovation in our data centres, to for example, to manage water responsibly, or to manage energy responsibly. By the way, all the energy, the 100% of the energy that we're using, we are matching that with investments in renewable energy.

[00:20:33] - [Speaker 1]
Do we do that purely for our shareholders? No, because we do not because it's better for the world, yeah, and better for our kids. And so, also now with the dashboards that we give our customers, where exactly milligramme CO2 for your particular application is being shown, it allows our customers to actually start taking that into account when they design their applications. Oh, we want to reduce the amount of CO2 that we're using, yeah, or that we that we create. So there are so many different areas that we are continuously innovating in.

[00:21:09] - [Speaker 1]
Is it only the customer? Yeah. For a very large part, after all, we are building products for customers, but we also continuously improve the quality of those products under the covers, without people seeing it. When we launched Lambda, which is our serverless service, nobody else had done this before, we didn't really know how to implement this, but we did know that customers wanted this. Customers wanted not to run a whole battery of EC2 instances just in case some work came by.

[00:21:43] - [Speaker 1]
A good company, a good example there is a company called WeTransfer. So WeTransfer, if you have very large files, basically what they allow you to do is upload it to S3, and then someone else can download them. But what they also do is check for virus and compress it. For that, they had to run a whole battery of EC2 instances in case a file got uploaded. Now, imagine you would not have to worry about those EC2 instances, but you know, you get an event when this new file arrives, and then this particular code gets executed.

[00:22:19] - [Speaker 1]
And many of our customers had these kinds of problems where serverless was actually the right answer to that. But when we started that off, we had no idea how to implement this. We knew what we wanted to do. So we had taken our smallest EC2 instance, the T2, as being something underneath the covers. It costed us a lot of money, not the customers.

[00:22:42] - [Speaker 1]
But we, at the same time, were developing something called Firecracker, which is a micro VM management system that would be able to allow us to actually run servers at maximum scale, and at minimum cost for our customers. So under the covers, we do a lot of innovation as well, that the customer never sees. And so there's many different areas that drive our level of innovation.

[00:23:12] - [Speaker 0]
And looking ahead, you've spoken about a developer renaissance of sorts, a shift towards AI working with humans rather than replacing them. And it's such a refreshing narrative after seeing so much doom and gloom out there. So what does that actually look like in practice for builders and businesses over the next years, what excites you about that Maybe

[00:23:29] - [Speaker 1]
because I'm a bit older, I've seen many programming languages come by as they demote du jour. When I went to school, I learned Pascal and COBOL, yeah, now probably kids that come out of university probably are all proficient in Python or something like that, all of us. And so, over time, we see continuously evolution of tools. And so, my first programming was done in VI, and then you started getting all these different IDEs and visual code is actually one of the most important ones now. And then Cursor and Kiwo come along.

[00:24:12] - [Speaker 1]
And there will be other tools after that. So as a developer, yeah, you need to be curious. You need to be willing to learn. Because otherwise, this is not your job. Yeah, because our world is continuously in flux, continuously new.

[00:24:29] - [Speaker 1]
So, there are, depending on the newer tools that arrive, are different requirements on developers as well. For example, communication becomes way more important. Understanding, you know, we have a meeting with your customer, or whether that's internal or external, and how available does this need to be? Yeah, because four nines is more expensive than three nines, yeah, in a way that you implement. Now, your AI can't figure that one out for you.

[00:25:01] - [Speaker 1]
They can't have this understanding the bigger picture, really and owning the tools, realizing that AI is just a tool. It's a better tool than we had in the past. But it's still a tool. I'm still responsible. I'm still the owner.

[00:25:20] - [Speaker 1]
If I'm in a regulatory environment, let's say financial services, or healthcare, or things like that, and my AI tool has made a mistake, I'm on the hook for it. Not the AI Yeah. And as such, you know, you're still the owner of what the tools built for you. So you better understand what the tool has built. Now, we're getting more and more tools to help us with that.

[00:25:48] - [Speaker 1]
But it is that it's the quality. I mean, it's humans that have the ingenuity. And I think one of the things in as part of being a Renaissance developer, is not to be only be deep on your databases. But if you're a database expert, you also understand some of the what your colleagues are building, such that everything is a collaboration. And, you know, no matter what tools we build, collaboration is still a human interaction.

[00:26:20] - [Speaker 0]
Wow, and I think that is a powerful moment to end on. I will include links to everything that we've talked about and some of the things that I referenced, but I know how busy you are. So just thank you for taking a few moments to sit down with today. Really appreciate your time.

[00:26:32] - [Speaker 1]
Thank you.

[00:26:34] - [Speaker 0]
Wow. What an incredible story. And I think one one of the many things that stands out here is that the biggest breakthroughs rarely begin with perfect certainty. Sometimes they begin with curiosity, with looking under the hood, and and saying yes to something that initially looked like, hey. Just an online bookstore.

[00:26:53] - [Speaker 0]
But from there, building systems that commercial software simply could not support to creating pay as a creating that pay as you go model that changed enterprise tech forever. I think Werner's story is really about solving real problems rather than chasing tech trends, and that very same mindset continues today with AI, where the focus remains on giving developers better tools, not replacing people behind the work. And I particularly loved his perspective on the developer renaissance because in a world full of headlines that predicts the end of coding, he reminds us that ownership, communication, judgment, and collaboration, all of these things still belong to humans. Yep. AI might be able to help write code, but it cannot replace curiosity or the ability to understand the bigger picture, and that is a crucial message.

[00:27:46] - [Speaker 0]
And it's also a timely lesson for every business leaders listening today that technology moves fast, but it's the principles behind good decisions. They all remain the same. So what I'm gonna be taking away from this is listen to customers, stay curious, and never assume that the tool is smarter than the person using it. So as AWS enters the next chapter and AI continues to reshape how we build, maybe the real question is this. Are we ready to evolve with the tools, or are we still waiting for certainty before taking the call?

[00:28:23] - [Speaker 0]
But over to you. You've heard me. You've heard my guest. I wanna hear your stories, and if you've got a story you'd like to share, let me know. Techtalksnetwork.com.

[00:28:31] - [Speaker 0]
You'll find 4,000 interviews across all of my podcasts on the tech network there, and you can even send me an audio message. So remember, this is a dialogue, not a monologue. I encourage you to reach out to me, but that is it for today. Thanks for listening, everyone. Bye for now.