Welcome everyone. Welcome Guillermo. You know Guillermo, he also goes by G, but I'm going to practice my Guillermos this whole time if you think that's okay with you.
All right, move fast without breaking trust. Not easy to do, but let's start with a story. One of your favorite movies is Pixar's film Ratatouille.
Yep. Yeah. Still to this day.
Why do you like that movie?
Oh my god, there's so many dimensions. First of all, I just love — especially in the age of AI, I heard in the previous fireside we talked about quality.
I think something that distinguishes some of the finest creations, some of the finest pieces of art, movies, everything is just quality. I think Pixar really embodies that. It's something we strongly believe at Vercel. We were just chatting a lot about how with Paul from Salesforce Ventures, we're talking about the killer app of AI in my view is coding, is code generation.
And one of the dark sides of AI is that it's so easy to go zero to one that you might just be doing it too much.
And that picking the right battles and choosing what to build, what to generate, and maintaining a high level of quality is going to become a differentiation. And I ultimately think quality begets trust. What is trust? Especially with a software business and software company like Salesforce or Vercel, just quality of customer obsession and service proven over time, that's what I think trust is. To me, Ratatouille embodies that. Pixar embodies that. And also, it has this hilarious part of the movie or theme, it's just this saying that anybody can cook and encouraging people to try things and build things.
And I think AI is helping us all do that, right? Obviously talking my book because Vercel helps everybody within the organization build applications and deploy them securely. But I truly believe that AI is giving us all superpowers and that is truly a killer.
Yeah, let's talk a little bit about quality. You know, I just hired someone to take my job running Slack engineering, but when I was running Slack engineering you gave us a very hard time. You had a lot of very specific feedback on very detailed things because you use Slack. You use Slack heavily. You're a big part of the Slack.
But you had a lot of detailed feedback and I would equate it related to quality. Your expectation, your bar for quality. I think Slack has a lot of quality, so it's a very high-quality product, but your bar was very high. So talk to me a little bit about that and your expectation of speed also.
Like you were like, "Why isn't it fixed? Why haven't we done this? We have to go fast." So maybe give me a little aspect of quality and speed.
You know, as a spokesperson for a lot of engineers in the world, the question that I get very frequently is, you know, as an engineer, am I going to have a job in 5 years, in 10 years, in 15 years?
Creating high-quality software is extremely tedious and expensive.
I actually give it — Slack is a great example of something that on the surface seems like it should be easy to generate something like Slack because you look at it and it's like, "Oh, it's a sidebar, channels, conversations. How hard can that be?" But the depth of quality of engineering around security, around scale.
I remember when you guys, Slack, went vertical during COVID, right?
Supporting the throughput of messages per second. So there's all of these dimensions of quality, scale, security, reliability.
And what we try to do at Vercel is your next incremental piece of software has to always be higher quality than the previous one. You know, we've been excited talking a lot about AI software factories because as code becomes free, the value or the differentiation of a firm shifts when they are up into the best practices, the institutional knowledge, the context engineering that you put into your factory to produce that next incremental piece of software.
For example, something that I care deeply about that you just called out is speed. And over the years they collected a lot of nuggets of wisdom about speed. Speed is not just, you know, I measure a transaction in number of milliseconds. The speed can also be the perception of speed.
A great example, I give you guys a lot of feedback because I travel so much. I'm on a plane all the time. Occasionally a plane will be Starlink enabled and my happiness level goes through the roof.
More often than not, it's Gogo in-flight or whatever. And what happens with these internet connections is that they drop a lot of packets. If they drop a lot of packets, it makes messaging your friends, family, and co-workers really, really hard. And so what do you do in those circumstances? You have to provide a pleasant experience to the user even though the network is unreliable and slow, there's things you can do to make that experience more pleasant.
Now, you know, I've been studying real-time systems and human-computer interaction for decades. Now, how do I transfer that to the next generation? How do I enable my co-workers to use AI plus these best practices? So, we work a lot on codifying this into skills, into frameworks, and into tools that make this factory work better.
Yeah. Well, it's kind of a — in our keynote, we talked about determinism and probabilism. Hard to pronounce. Salesforce is definitely, you know, really working on providing frameworks, you know, around the right context at the data layer, you know, the right application definitions of your business processes or semantic layer, the right definitions of AI. You know, because anyone can write code anything. These new interfaces, AI-first layer as we call it, it can look amazing. Like, "Wow, that's incredible." But it might be the wrong data, it might not be secure, it may not even be a very good interface.
So, how do you think about allowing — like the power of building is now in everyone's hands. You're helping make that happen at Vercel, which is incredible. But how do you think about giving that power but adding those guardrails to guide, to security, to quality of user experience, to all the things that I would call trust.
Something that has created or enhanced the trust that our customers have for Vercel is our care for design.
We care a lot about the craft of our interfaces. We care a lot about the consistency of our interfaces. And so, if everybody can cook, can they actually all design beautiful interfaces? Well, we run the experiment and the answer is no. You ask an agent, "Build me a website or build me a sales deck." And they all look the same. It started out with purple gradients.
And it doesn't look like Vercel.
Look like Vercel. Started out with purple gradients, then it became beige stuff with serif fonts. And you look at it and say, "Well, it's really cool that we can do this." By the way, I don't dismiss that at all. Like, it's amazing that anyone can transform prompt into artifact.
But how can you infuse the brand and design principles of your company? So, the first thing we did is we took this standard that Google pioneered called design.md. It's basically a skill for design that brings some more determinism. So, I really like that framework that you just put out, combining the good parts of AI, which is the creative stochastic nature of it, with some determinism.
So, design.md, for example, can codify your color system. You know, obviously we like black at Vercel, but we like some other colors and we listed them out. And then you can actually use some more stochastic stuff. Like, you can write down in English, "Hey, if I need to produce a report for sales, avoid this and avoid that and use my tone of voice here and there." So, design.md helped us a lot, but we took the deterministic guardrails even further. Yesterday we announced a linter for enforcing design system constraints.
So, one of the — and this is just an example of like the cool things that you can do, but I think the future is going to be combining these two things. I call it verifiers and skills. Skills alone are just guidelines. And just like humans can sometimes mess up and not follow guidelines, you need a hard verification system. You need something to enforce constraints.
In engineering, you know, there's a programming language Rust that is growing in popularity because Rust imposes very harsh constraints around memory safety. And so combining the loose of AI, the loose vibes of AI with hard almost like mathematical proofs is I think the future of a lot of this where software engineering is headed.
Have you heard about the SaaS apocalypse?
You have?
Well, we've been talking about that, that it's not really true, but I've heard you talk about like you're not going to vibe code all of your systems in the enterprise.
100%. So first of all, I think that discipline to decide where you put your tokens. So I think the first wave of AI was CEOs. And for the record, I didn't do this, but CEOs were holding like leaderboards of like, "Please just spend as many tokens as you can."
Token maxing. I always thought it was the dumbest thing ever because fundamentally, tokens are compute. Would you ever wake up one day and say, "Hey everyone, go to the AWS console and create as many instances as you can." Like fire up as many computers as you can. Well, I would fire that engineer saying like my costs are going to go to the floor, right? And so with tokens, it's the same. It's just GPU compute.
But we were for a moment comparing token usage to productivity.
Yes, I think that's wrong. And I also think again like the question that we need to answer is there's some finite amount of tokens that you're going to use, right? Where do you put those tokens towards?
Is it towards hardening your cybersecurity? Great use case. We launched DeepSec, a cybersecurity harness. I believe that that's one of the best things you can do with AI today is strengthening the reliability and security of your systems. Quality assurance. Agents with computer use and browser use can literally navigate your interfaces and your programs and tell you where you're messing up. So all of the things that sort of increase quality I think are great uses of AI.
Now, going to SaaS apocalypse, I think what AI is showing is that systems of record are extremely valuable, but they have to have the right agent ergonomics. So, agent ergonomics or agent experience is the term that I use for if I have a bunch of data in my CRM, can an agent access that data? Do I have the MCP for it to access that data? Do I have the CLI, the SDK? Is the data fast? Is it scalable?
One of the things that we're going to have to confront is that agents will use compute far more than human beings. I'm a heavy user of Slack, but let's say I send 1,000 messages per day, right? Or maybe that's too much, but I send a handful of messages per day. Just imagine how much more agents can do because they never get tired. And so, they're going to query your systems of record like you've never seen before.
I think you guys were sharing some data on this. You launch an MCP, and all of a sudden the demand for it seems like nearly infinite. And so, I think the SaaS apocalypse is what it is about is customers are going to generate a lot more software. They're going to access a lot more data.
And those repositories of data are going to become extremely important, and they have to have the right interfaces for agents to be able to use them.
So, let's talk about interfaces. You know, we call it headless. That's our strategy.
And Claude for us with your AI for us is actually our brand. When we launched Claude for us, you saw that. And the amazing artifacts, those are incredible. Slack calls them surfaces. So, in the keynote, you saw that. How do you think about Vercel and Salesforce? 1 + 1 = 4. And what are use cases for?
Infinity, right? Because when I started Vercel, I realized very quickly that the value that we were to the world was in the customer experience side of things. So, a lot of our bigger customers were saying — great example, customers like Under Armour and Nintendo, they wanted to innovate on the customer experience side, but they wanted to keep using Salesforce as their source of truth and customer data repository. And so, they combined Salesforce and Vercel, and they launched amazing web applications and websites way faster than they could have done if they had to rebuild this thing independently from scratch.
And so, that's why the 1 + 1 = infinity. Now, the limiting factor back then was if you wanted to move fast and create the next underarmour.com, you needed a DevOps team. Vercel solved that. You needed to create frameworks like user interface frameworks, etc. Vercel solved that with Next.js. The other limiting factor was writing code. Now, that's not a limiting factor anymore. We can use coding agents to generate these amazing interfaces, to launch new products.
And so, like I mentioned, I think this is a match made in heaven. Also, Slack code — I mentioned I run the company on Slack. Slack code for me is sort of the — it was the missing piece.
Because so much of improving a product emerges from conversation. Conversation plus customer data plus feedback that we get, and then we digest it together. The best software has always been a byproduct of human collaboration, so now it's human plus agent collaboration in one place. So, really, really bullish on where things are headed there.
Yeah, glad you like that. So, for those of you who don't know, Slack code — it sounds like coding in Slack, and it is, but Slack's not offering the coding, so partners like Vercel can actually use the interface of Slack for multiplayer coding, and get all the value of Vercel plus the value of Slack.
