How to make your customers stay when anyone can copy your product | with Rob Ferguson (VP, Fireworks AI)

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1:09Rob Ferguson: People are going to copy you, right? And so your product doesn't stop at your features. So you have to be thinking about the entire experience and you have to also be thinking about how are we going to be distinguished with our data? But who are the AI companies who are getting the most funding right now? It's actually all the ones for data focused.

1:34Randy Silver: Hey, everyone. Welcome back to the product experience. I'm Randy Silver. And the thing that's come up time and time again over the past week is people talking about moats. Not moats around castles, not dragons, not hordes of gold, although corporate hordes of gold do come up in this. So we brought in someone who's an expert on the subject, Rob Ferguson. And Rob, do you mind doing a quick introduction to yourself? Where are you now? What are you doing? And how did you get into this game in the first place?

2:05Rob Ferguson: So I'm Rob Ferguson. I'm the Vice President of Technology and Strategy at Fireworks. I used to be the global head of AI for AWS, and I was a CTO for Microsoft for Startups. As well, I was a startup CTO and, you know, early at many startups, which have had three exits. And so I've kind of been in the technology game for a while now and definitely have done a ton of startup coaching, as well as helping people kind of understand how it is product strategy and, you know, your go-to-market actually affect your moat going into this new age of AI.

2:45Randy Silver: So let's start at the very basic of this before we go into the complicated, interesting stuff of what's changing in the world. But how do you actually define a moat? And is it purely one thing or are there multiple approaches to building a moat around your business?

3:04Rob Ferguson: well so you know a lot of times i think people worry about modes a little bit more than they should because ultimately it comes down to competition and competition is usually a good thing i remember when i was the cco of a place called automatic uh we were building an adapter we plug into your car and it was the first of its kind we're really excited but of course immediately a bunch of other people saw what we're doing we saw all of these little carbon copies on it and we would see these carbon copies they would have our exact icons in their app um same thing happened when it works for audio which was a music service we would see our exact same icon but it would just be rotated a little bit and so i think that people are always thinking you know how how do i deal with competition how do I make sure that I build something that is a durable business and that it has that sort of sustained value?

4:05Rob Ferguson: So a mode is competition and value and understanding how to keep building and make sure that it doesn't erode over time if somebody doesn't come in and take your lunch.

4:16Randy Silver: So there's lots of ways you can build a mode, right? So you can be about reach and distribution. It can be about customer service. It can be about locking in any number of ways. is there something that you think is the most defensible approach or something that you, as you said, you've been an advisor to a bunch of startups. You've done this a number of times yourself. What's the primary thing that you advise people on when they're thinking about what their mode is and how to approach it?

4:42Rob Ferguson: You know, I do actually think a lot about customer service. I do think a lot about understanding who your customers are and having a direct relationship with those customers because there's always that that energy that it takes to go out and really do things right really be present with people that you're working with that is not a small burden for a competition to come in of course they're going to try to go in and you get a lot of spray and pray kind of competition you know people who are just like well i wonder if there's business here i'm going to put a feeler out a lot of times i see folks who relentlessly worried about competition.

5:24Rob Ferguson: And the good news is that competition means that other, like, people think this is interesting. People think it's valuable. I'm always welcoming good competitions because it just, it's going to help me be differentiated out in marketplace because I'm going to, I'm just going to go out there and do a better job than anybody else who's there. Cause I'm going to care more. I'm going to put the investment into it and I'm going to really have to get through it. But these days, AI does make things a little bit more complicated. You know, it used to be there was a barrier of entry to like put up a SaaS site.

6:00Rob Ferguson: And now like, you know, the other day I was looking at a Korean barbecue site that looked exactly like your typical SaaS site, right? You know, somebody just went into, I don't know, Lovable or Copilot or something and said, give me a professional looking website. And here it was looking exactly like a traditional SaaS website probably took them five minutes. And so I do think that the story of how you go to market has evolved a little bit. And I do think the story of what makes you defensible has evolved a little bit. But at the end of the day, it is definitely gonna be your ability to understand your customer and your ability to go and have a direct line of communication with that customer and know them better than anybody else.

6:45Rob Ferguson: So it's not enough to just throw something over the border.

6:49Randy Silver: That works when you've got something that's kind of established already, but we're seeing things change. So, you know, the most recent example that came up was Jev launched, what, about two weeks ago now? And within days, I mean, that was something that took about two years, I think, of hard scrabble development and a lot of money, a lot of time, a lot of effort. And within days, maybe a week, there were competitors that were at 95 to 97 percent parity on it. In a world where features used to be a bit of a moat, they would take time, months to years for people to copy. And these are things are now showing up in days.

7:35Randy Silver: What's changing? How can you be comfortable distinguishing yourself? And yeah, I don't even know where to go with this. I mean, features used to be something you executed really well. You're really proud of it. And now it's just not something you can distinguish yourself on, is it?

7:53Rob Ferguson: You know, I'll push back a little bit and I will use Jeff as the example. So it's funny that you mentioned Jeff, because our team a year ago published a blog and you could go find it. And it's basically like, I think the title is something like how you could train a classifier to do this for$11. And not that many people really engaged with that blog at that time, even though like our research team had spent like a lot of time thinking about it. The barrier to entry there wasn't just building the classifier, which is effectively what Gem is. The team there has done such a phenomenal job. You go and you can look.

8:33Rob Ferguson: They launched with all of these cookbooks. books that launched with all this information on how and where to use it, a strong community effort, really kind of going through and taking an idea that I would say was in the periphery. And now it's something that like you're in the discourse around. And that's hard. It's rising above the noise. And the AI era is a huge feat. It's incredibly difficult. So all these people who are going off and they're saying, oh, we've got to climate this thing. Do you expect that they're going to update it the next time that it's not kind of newsworthy for them? Or are you going to go back to Java and see what they're doing, right?

9:11Rob Ferguson: It's rough. It's a little brutal out there that getting above the noise is much more difficult than it used to be because there's noise everywhere. One of the startups that I advise, they said that the previous cold open rate on emails was 4%. And do you know what the cold open rate on emails is now? Oh, God, it's going to be fractions of a percent, I'm guessing.

9:35Randy Silver: It's just zero.

9:38Rob Ferguson: People just don't open these cold emails anymore because it's all this AI slumps on the other side of it. And so what used to be a hard but useful channel, 4%, is now not a channel, right? So the ability to move above the noise is hard. The GEM team, I think, did a phenomenal job. It is so complete. They did such a good job of taking their time to market, understanding who their audience was, building a point of view, delivering it across many different resources, engaging with the people to not only come above the noise, but also give them space to think about building other products on top of that.

10:16Rob Ferguson: I think, you know, it's like it is so difficult. And the fact that it came to you so quickly, even though there's like that, like it existed. Other people were trying these things, didn't make it there. But they understood who their audience was. And they built something custom that made a difference for the people like me who were always trying to think, how can I do more? And they made it kind of personal. Okay, so it's an interesting one.

10:42Randy Silver: I come out of music journalism in my past. And the way I tend to think about this is the means of distribution. The people used to literally own shelf space in record stores. And those were the major labels. and that's why exactly and that's why bands would sign with them is same thing with grocery stores in anything with physical retail but now there's infinite shelf space and anyone can theoretically break through but you still it takes a lot of money it takes a lot of effort to launch and to market yourself well so that's what jeff has done and then a bunch of competitors came along and were following in their wake and drafting off of them and got some real traction very quickly.

11:28Randy Silver: And what I'm hearing you say is that initial launch is important, that drafting is very opportunistic, but then it's the long-term following up sustainability, that continuing to innovate, to expand, to deliver. That's the real secret.

11:46Rob Ferguson: Yeah, I think so. And I think you can't stay still, of course. And this is the hard part about this era for me. I could just speak to it very personally, which is at Fireworks, we are helping people not only build custom intelligence, but we're also always curating the latest and greatest in open AI. And what that means is that we have to stay on top of so many launches. We have to develop a point of view of like, is our audience going to care about this? Are they going to use it? Can we optimize it and make it work better for them? We're always trying to really push the frontier for people. But that means staying on top of one of the most active, busy markets you can imagine.

12:37Rob Ferguson: And so operationally, it can just simply be exhausting to try to figure out with everything going on, what should break through the noise. And I think curation, this is always a problem, but now it's in my industry. It used to be just in music and art and things. And you would have how many people make art. Everyone in my family makes a little art. But I guarantee you, a lot of that art is not stuff that needs to be making it to your magazines. No offense to my sister.

13:11Rob Ferguson: But it's very difficult to rise above there. And to be sustained, like, you know, relationships start to matter, right? The understanding what connects with people, there's a commercial element. You don't want art to be commercial potentially, but of course it comes into the fray. Like, you know, people think, can I put this in my living room? You know, do you have a point of view? And all of those things that maybe felt a little bit less tangible, like it just felt like all you had to do was be technically good. It's not enough. You can't just be technically good anymore. You actually have to be a tastemaker.

13:47Rob Ferguson: And you need to be able to bring this point of view, which means building your own intelligence, having the point of view, understanding who your audience is and connecting with them. That is a lot harder for technologists who sometimes want to be introverts and just solve difficult problems.

14:07Randy Silver: I really want to talk to you about where this is all going as well. So the cost of compute is something that comes up a lot. You know, there's the CEO's point of view for the last couple of years. Let's get rid of lots of people. We can just throw AI at this. It'll be much cheaper. But then the cost of tokens comes in and that can be quite expensive. How well you use the tokens can be quite expensive. The open weight models are catching up to frontier models pretty quickly. there's the question of can you do this on your own tin or your own data centers versus doing it buying it from somebody else where do you see this all going what do you where do you think the trends are going ask you to you know look into your crystal ball over the next couple years

14:55Rob Ferguson: where do you see this going so fireworks we're dealing with the biggest of the big bottles you're not going to put this on your mac mini at home so we take super clusters which on their own are hard to procure. I think a lot of people, and they think, oh, a GPU is a GPU. No, like we need physically proximate GPUs because the information that's going through is so dense, right? And we buy on 20 different clouds where we go and we find the best deals in market that we can. And kind of like Costco, we pass that value along to the people that we're working with because these GPU costs, I mean, we're talking real dollars.

15:36Rob Ferguson: You know, as a back of the neck an estimate, say like 10 ,000 GPUs represents a billion dollars. And then, you know, these models you're looking at, you know, on the small side, it might be like 80 GPUs or more. And then you want to have some replication of this because you want to have service quality on the other side. So it's not enough to just have the GPUs, but you have to keep them busy because idle time on GPU drives up costs for everybody. So if you don't see lots of people who want to go ahead and just be like hosting their own GPU infrastructures and models direct, we provide a serverless infrastructure so that just like OpenAI and Anthropoc do as well, that's the standard at the very high end of AI.

16:22Rob Ferguson: So those GPUs stay busy because otherwise those costs get passed down to other people. So we're out there really being very innovative in terms of performance and keeping the intelligence that people want locked and loaded in a way that they can use it wherever they need to. Whether it be, oh, I need it to be US only or I need it to be able to host some custom weights. All of these things are things that FireWish provides. And it's a tremendous amount of technology, physical and human resources to put all that together. So from our perspective, at the high end, the very large reasoning models that do the most work where you want to be really at the frontier, you need to work with a partner who can go and manage all those things.

17:07Rob Ferguson: Because otherwise, the GPU hosting cost, you're going to be trounced, right? You're going to experience what it's like to have idle time or what it's like if you have to figure out moving these large models in between resources because you're keeping 100 plus GPUs occupied that whole time, which is, I don't care if you're Microsoft, own character AWS. That is a real cost. And so being operationally excellent and handling that is like the bottom layer to be able to do this. In other parts of AI, we are going to see more and more. We're going to see multiple models. You're going to see a dub through kinds of intelligence delivery.

17:50Rob Ferguson: You're going to have some local models. You're going to have some models that are made to be really quick, like an audio. You're going to have this whole distribution of intelligence. We're all going to learn that graph kind of together over the next few years. But no one wants to have to go through and maintain this completely on their own. You need really excellent people to go through and take some of the burden out of the intelligence. That's the first layer on like your journey to AI is kind of figuring out kind of who your partners are and who like, you know, what kind of intelligence you need to bring to the table.

18:26Rob Ferguson: and all of that is going to come back to your data and your customers. So if you can tell me a little bit about the kind of data you have and a little bit about who your customer is, then we can figure out how to map that intelligence really well. But I think a lot of people are still kind of early in their adoption where they're what I would call model dependent. They're just hoping that the next model that comes out just happens to work for them. And so usually I tell people, Well, fireworks is your first step on your journey to model independence, right? That's when you start to know how and why do you want to use intelligence, where are the trade-offs that are there, and how do you get the best value for yourself and your customers?

19:05Randy Silver: Okay, I definitely want to talk to you about using models well, because I see lots of companies not using AI or models very well. But it sounds like what fireworks does, the historical analog I would use is during the gold rush, There were the people who were the prospectors who were going out for the gold. And then there was Levi's and other companies like that that were selling the tools to the people going out and prospecting. Is that a reasonable analog for a fireworks? You're selling the infrastructure to enable people to dig for gold?

19:39Rob Ferguson: Yeah, it's a funny infrastructure. I usually say we're your favorite AI company's favorite AI company. so whether you're like cursor or notion or you know heidi health or any of these really phenomenal ai natives i'm thinking like the way that they became an ai native is that they found fireworks because before you're this ai dependent right you're just like hoping the big labs are going to provide what you have but how are you going to find your moat on the other side if you're at the point where you're spending, I don't know,$15 ,000 a month on inference, you would have already seen a return if you start thinking about building and operating your own intelligence.

20:26Rob Ferguson: But AI labs kind of obfuscate that from you. They don't really give you a lot of options, right? They're like, okay, you can choose the expensive model and the slightly less expensive model. Those are kind of your options there. But we help people customize intelligence for their application. So for example, Cursor, which is an incredibly popular AI coding platform, you know, those models didn't need to be able to, I don't know, tell you all about your health. So what they were able to do is they're able to distill a model that didn't really focus on things that were not AI coding. And as a result, able to offer a way better cost per intelligence for the customer at way better speed.

21:10Rob Ferguson: And so the way I think about it is, you know, all the startups who come to me, they're like, hey, Ron, I want to start a startup. I want to figure out AI. What should I do? I'm like, well, there's so many subsidies in the market right now. Go use up all your Anthropic, all your OpenAI, AWS, whatever is out there. Figure out kind of how you're thinking about your intelligence. And then when you're ready to actually like build a business on top of that, come to me because we're going to figure out how to take that intelligence. We're going to make it work for your costs and scale and for your customers in a way that instead of having, you know, I would call it exponential AI costs, you have costs that make sense for your business.

21:54Randy Silver: So we kind of went through something similar with the launch of cloud in that people were choosing stuff and then they got locked into a specific provider, whether it was AWS or anybody else. And it was very expensive to switch, but switching costs are next to nothing now. And what you're talking about with, with fireworks, you're helping people pick the right model for the right context at the right time. You'd think that to a certain degree, this is a feature. Anthropic or chat GPT, they could be offering this amongst their own models, but I'm not seeing them do that, or at least not very well right now.

22:35Randy Silver: Why not?

22:37Rob Ferguson: You know, at one point, OpenAI did try to allow you to customize models. Yes.

22:44Randy Silver: Didn't work for that well, as far as I could tell.

22:47Rob Ferguson: Yeah. You know, I don't think that they even, you map your business to where the opportunity is, which makes sense. I also just think that it's harder when you're dealing with closed weights. Fireworks, we really take this time to let people use the very best in open intelligence so that they can inspect everything. If you want to train a custom model on Fireworks, you can take that and then use it somewhere else. It just happens that we're the best in training and the best in inference on one platform. Most people don't realize that most AI training, building your models requires also excellent inference because as part of the training process, you actually want to go through and use your examples where you are inferencing across the best open models.

23:35Rob Ferguson: And having it all in one platform means that you get really like you need your inference costs to be reasonable, but also they need to be lightning fast. And we're performance experts. So we make sure and we figure out all the ways to optimize and some of the internals of that are incredibly complicated we've spent a lot of time focusing on things like numerics it turns out that if you have a tiny tiny fraction of a decimal point that's slightly off for example because these these models have multiple layers you went to the wrong layer and it got a tiny fraction of a decimal point off that will show up in the final results you'll actually see it on the other side and so we're the team behind PyTorch, which is the framework that, I don't know, probably 95 % or more of these models are trained on.

24:28Rob Ferguson: And so we understand the very, very internals of the whole system. And so we've long been considered the best option for AI performance, but then we also added the ability for customers to then go and specialize on intelligence with them. The end result is that the lock-in is that it works really super well.

24:52Rob Ferguson: I know that that's a funny piece of lock-in, but we also provide access to researchers with Fireworks Labs. We have a forward deploy team. We'll go in and we'll help you figure it out where we just really focus on excellence throughout the system. It's not that the other AI labs couldn't do this. It's just that they benefit from things being closed. They benefit if every time you have to upgrade to the next model. So they don't have to show their receipts for everything. They kind of want it to be a little secret because that's their opportunity. We're the opposite. Everything's out in the open.

25:26Rob Ferguson: You have the optionality to move between things. But, you know, even Kim Kardashian shops at Costco, like it's really good to be able to have like, you know, really excellent olive oil that you can go and get where you're like, you know, it's going to be really high quality. kind of know it's not going to be every single thing that's out there, but what's there is great. And then since we offer that customization, it makes all the difference.

25:50Randy Silver: Quick one for the product folks listening. AI can draft the PRD, prototype the idea, and help you build almost anything. But knowing what to build, that's the hard part. Jira Product Discovery helps product teams bring customer insights and ideas together, prioritize what's worth building as a team, and create living roadmaps that connect directly to delivery in Jira. So when more ideas are possible than ever, your team can focus on the ones that matter most. Try Jira product discovery for free today at Atlassian.com slash MTP. So I'm seeing lots of companies and lots of developers go down one avenue.

26:34Randy Silver: Because the tools are easy to use, because LLMs are the thing that everyone's focusing on, and it's got a great interface and almost an addictive interface. That's what people are using for everything. But there's a lot of other things out there that are under the hood of AI, ML and RAG and a thousand other things that are probably better for lots of these things than using LLMs. What are you seeing the best companies do? Are you seeing people trapped in this loop of just using a chat interface, or are you seeing people actually use this intelligently? So the state of the art right now, the best model in the world, it's not table.

27:15Rob Ferguson: It's not Astra, right? It is always a mixture of models. You can always, if you want to talk about the very frontier of intelligence, the search area of intelligence, where you're trying to do brand new and interesting things. Well, it turns out that the models underneath, they have affinities. You know, some of them are better at JavaScript. Some are better at Python. Some of them, you know, they have different pieces of intelligence that's there. And we can reliably reproduce. And if we do what's called Oracle modeling, you go through, you do a set of tasks. And then afterwards, you run every permutation of combinations.

27:51Rob Ferguson: You literally go and just check, what if we used every single thing? What would be best? The answer is always a mixture of models. And so the very best companies, they all sign the same place. They learn to measure what matters. And it turns out user interfaces absolutely make the hugest difference when it comes to intelligence. When you think about HCI, it affects not only how the models perceive what you're doing, but also how the users perceive how you do it. And so harnesses, the things that are running the loops, the things that you're building agents on, they have to understand your context really well.

28:31Rob Ferguson: And we see the difference between if you switch out one harness to another, it affects the quality of the intelligence. the way that we tell customers who are working with fireworks labs how to think about this is you have to start with kind of what sort of data you have and just sort of understand is it sort of like hunch data like humans working on it kind of needs to go like well i couldn't tell you exactly why this works it's a little non-deterministic but like my gut tells me it's this Well, that's a great place to be using models. It's a great place to be using AI. But that's going to be a different kind of set of techniques and things versus the things where you have a for sure deterministic result.

29:14Rob Ferguson: So that's your jabs to the world. Those are the things where it's like, hey, all my receipts, they always look this way. I need it to look this way. And so that's a different kind of... Really solid evals and things like that. Yeah. If you can measure it, you can improve it. That is like the great secret to AI. And like learning to find what I call the seams of the work that you're doing is how you get there. Don't think of it as one problem. AI teaches us to break it down. And it's lots of problems. And then to understand what's measurable and what's not. Where do I need my hunches? And where do I need my facts?

29:49Rob Ferguson: And of course, the more facts you have, the better you can measure it, the better you can do. My favorite example is in the field of video confession. because it used to be that video compression, like there were so many people who got their like doctorate in electrical engineering on video compression. And for a while, it kept getting better and better and better, but it finally petered off. Nobody thought there could be any more intelligence that was there. Nobody was like signing up to get the next PhD in video compression. But with reinforcement learning, they were able to find 4 % to 6 % more video compression that was on the table than the best people could find simply because it was so innately measurable.

30:31Rob Ferguson: And so I want you to think about for whatever field you're working on, if you can start understanding your data at that level, if you can break it apart into small parts that can be verified, if you have something that you can score, then you can do better no matter what it is. I don't care if it's customer service. I don't care if it is generative medicine, all of these things, if you can measure it and you can start building that data practice, you can get it better.

30:58Randy Silver: One of the things I keep seeing and hearing and certainly seeing in my own practice, the way I use these tools, the way I'm seeing other people use these tools, it absolutely amplifies the best parts of your practice, but it also amplifies the worst parts of your practice. It reveals some of the cobwebs. Absolutely. And I'm seeing a lot of things when you're talking about improving, Finding measurable improvement. I find people absolutely realizing that, but finding it at such local points that it doesn't add up to anything. You're creating a tangled spaghetti of a bunch of little improvements that don't actually work together.

31:37Randy Silver: So what's the best practice that you're seeing out there? What's the secret for teams and companies that are executing well? Is it they had really solid best practice in the first place or they're adopting it as part of this? Where do you see people doing this well? So it used to be during the design process that, you know, we would have like wireframes and we would have these other things.

32:05Rob Ferguson: Now I have my best designers and product managers. They will come with like a prototype. And that prototype, like you can actually click it. It looks like it might be doing the thing, but probably like one path is actually working. But then what happens is the quality of the conversation goes up so much from that experience of having that prototype step because it's like, oh, that's what you meant. And then you have the real conversation. It's like, what are all the ways that this could fall apart? How do we start talking about what matters in this thing? So it used to be the first step was a design that like it was a little shy.

32:48Rob Ferguson: Some people didn't quite understand. But now you have something that people can really react to and have real conversation around. The worst companies stop at that stage. Right. You know, and we see it. We see like done is the most seductive word, I think, in the English language.

33:08Randy Silver: It's like, no, no, that's just. Can't we just do this?

33:14Rob Ferguson: You know, you put out this little prototype and people are like, oh, this kind of works. It's like, no. This was vibe coding. dude that that that was the first step now it's time to ship it right the 50 or 60 iterations to get this from a prototype into something that's going to be meaningful and so i think about it as that gateway to these conversations now the tricky bit and it's always a tricky bit is you know it's very tempting to show a prototype to a customer right it is very tempting to go in and show something that it's like looking pretty done and there's a lot of expectations that's coming to to the fold and i think it's so much more important that you're actually talking about what done is before you're showing that prototype if you haven't had a conversation on like how are we going to know if this worked, then you skip the most important step.

34:18Rob Ferguson: And in AI, I think that's always the steps that it's not that fun to be like, how do we know what we're doing?

34:28Randy Silver: The conversation I keep having with people is that first 80 % of product development of getting to a prototype of knowing what it is, being able to describe it, not just a written PRD, but something that people actually understand that's got real definitions behind it. That's so much faster. It's so much easier because we can do it. We can feel it. We can have a much higher quality discussion. So discovery is definitely accelerated. The problem is that second 80 % of making it reliable and shippable and, and something that's going to deliver value consistently. That's still really, really hard.

35:04Randy Silver: How do we accelerate that? And that's the seams.

35:08Rob Ferguson: and that like i know it's a weird idea because there's always been seams in our work but sometimes like there's these these these kind of funny valley executives where they'll say things like oh if we're gonna we're gonna replace this whole function with this and then they don't realize that inside of that is you know a million different jobs but then like the person has kind And it's like, well, we were priced one of the 100 things that needed to happen there, but not realizing what was in the work. And so you have to understand the things that are within the work. But then also, as I said, I think that it was very easy to look at something that feels like it kind of got like, oh, it kind of worked.

36:02Rob Ferguson: And it's like, it's so seductive. like, well, maybe I could just put that in there. Of course, lots of things are going to kind of work, but that's not the work. And so actually, when I talk about this, I think of the history of software and how it got here. In the old days of software, what was software, like literally, it was not hardware. But it was the things that you were selling. Software is the things that you were selling that were soft and not hard. And then we had software as a service and software as a service really switched towards like, Hey, I'm promising to keep this up to date for you.

36:38Rob Ferguson: I'm promising to keep this information always lively. So you come to here, you're getting this service on the other side, but now we're at work and work is honestly like you have to actually understand the other person on the other side of it. You are now literally selling the work on the other side of this. And it's kind of the most human thing ever because we're always building these systems for other people. And like those relationships and understanding what's on the other side of it and the expectations of how to talk about quality and if this worked and setting those things up in the front are so much more important in this era of work than ever before, which is why we see forward deploy teams everywhere, right?

37:21Rob Ferguson: It used to be software was mostly like, okay, we're going to send this off into the think tank and we're going to write down this whole list of requirements and everything else. And then we're going to ship it to you in the end of the long period. Now we're actually getting in there and really trying to understand along those seams, what is the work? What's that hidden contract on the other side? What does the data look like? How does this go through together? And I don't think it ever goes away. I think we'll forever be in the era of workwear, but it's taken a long time to get to the point where that other piece of the, that craft the software, the barrier to entry wasn't so high that it can finally be about all the design behind it, understanding the other person on the other side of it, understanding the data on their thing, and not just kind of saying, here's the stuff and you're going to have to figure it out because this is the way our system works.

38:15Rob Ferguson: Our system actually has to work for you.

38:18Randy Silver: So for a while, all the AI CEOs and everyone, all the prognosticators were talking about how the world is going to change and we're going to be able to do everything with companies of one and things like that. And obviously that is not where we're going at the moment, but I'm also hearing from -

38:35Rob Ferguson: I'm hearing more engineers than ever. Yeah, yeah.

38:38Randy Silver: And one of the things I'm hearing from CEOs and CTOs is people, individual people are being way more productive, but they're also running to stand still. They're just trying to keep up to date and keep things solid and running. We're not seeing huge advances in new features and great new experiences and amazing new products. We're seeing point solutions and some new models and things like that, but we're not seeing revolutionary new software coming out. Or am I just missing this? What do you think is happening?

39:09Rob Ferguson: So I do think there's a bunch of revolutionary new software, but of course, hedonic adaptation. The second something comes up, we've already decided it's not as amazing as you see. But, you know, my mom passed away from cancer a decade ago. And when she was losing her ability to type or to be able to see, I had to work my hardest to try to figure out how I could set up an iPad where she could send letters to people. And the technology was pretty painful, but we figured out a way that it could like slowly do this. Now I can operate my computer with things like Whisperflow. I can, I use voice throughout my day all day to do work.

39:59Rob Ferguson: I record myself while I'm on my drive on the way to work. I'll like set up AI agents to research things. So I'm like, okay, today I was talking to these people and you're talking about all these things I don't understand. I need you to go find first resources on all these things. Some rest are for me. And then on the way back from work, I'll listen to a generated podcast of all of the research that I was listening so I can kind of start learning this stuff. Yes, incredible, man. You have all of this technology where you have access and the ability to go in and make some masterful short scenes and work on many more modalities of experiences across all these things where you can be so much more expressive than anything that we've ever seen before.

40:47Rob Ferguson: And you can get people to be able to communicate and receive it in the way that they like it to be received. You mentioned that you used to be in the music industry. My undergrad was in music and computer science, and I studied music technology for AI. And what we hoped was that we were going to have so many more people who were going to build their own music because we were going to reduce the barrier for people to make music. is people have these incredible ideas with the, like they can hear these beautiful ideas of sound, but might not have the technical skills to be able to do so. I think it's so democratizing that now that we can have more people creating richer, more interesting experiences without having those incredible gaps to be able to do this.

41:44Rob Ferguson: And that's the AIG try, I think.

41:47Randy Silver: the interesting parallel though to me is as someone who was worked as a as an editor and in music the most important thing was not the ability to produce the music it was the ability to edit the outcome and curate and choose what to actually publish because the most incredible artists whether they're visual artists audio artists written artists whatever they produce way more way more than what you always do and the you know when you get access to their entire archive it is not always a good thing um the best stuff is incredible but there's a lot of other stuff that is nowhere near at that quality and you know just producing volumes and volumes and things isn't always the answer curating uh and finding the right stuff and And concentrating on that is absolutely a skill as well.

42:41Rob Ferguson: And it's going to be one of the most important skills going forward. You know, I think of all of the new, all these new fields that open up in this area. Now, of course, now we have AI ethicists and we have people who are themselves AI artists. They're going through, they're remixing, they're finding all these new modalities and everything else. But then you still have to have critics. You have to have the people who go through and help other people understand what is pushing the boundaries and what else is interesting that is up there. Unless you're Chopin or Bach, I think the full archive probably doesn't need to be opened.

43:20Rob Ferguson: You have to have a few practice runs in there.

43:23Randy Silver: Yeah, these are tools. They can be used really, really well. that can be used poorly, but we already went from changing with GarageBand itself, changed the way that we record and produce and distribute music itself. So Rob, this has been fantastic. I've got time for, I think, just a couple more questions. I wanted to go back to Moats very quickly and talk about from the perspective of a product team and also a management team. So a product team comes to you and says, we're terrified of being disrupted. You know, people can copy us so quickly. What's the one thing we should focus on this quarter to build a real mode?

44:02Randy Silver: What kind of response do you have to them? People are going to copy you,

44:08Rob Ferguson: right? And so your product doesn't stop at your features, right? So you have to be thinking about the entire experience. And you have to also be thinking about how are we going to be distinguished with our data? and in AI the most important thing you'll notice actually who are the AI companies who are getting the the most funding right now it's actually all the ones for data focused because whether it be you know even a very small set of very curated data can be used to specialize intelligence specialized experiences and so the very best companies are going to be going through and they're going to really understand their data they're going to really understand now like And at every single scene, you know, here's the way my customers expect data to me to be coming in on the other side.

44:58Rob Ferguson: And here's what makes that a good experience. Here's how they're going to verify it. But all the way at the actual HCI of the experience. You know, if you take a screenshot of a good, a well-designed piece of software and you put that in to your first prompt, you'll get better intelligence from then on out than if you do the same thing with a bad screenshot. They'll give you that exercise to do at home. But because there's context that is hidden in how the UI is arranged, whether it be the validation that has happened there. The AI can infer that, right? Whether it be understanding kind of what we think is important in that situation.

45:41i think a lot about uh the words that do work for your organization and so i my my kind of

45:49Rob Ferguson: canned example is i you know i play piano and a string on piano is a piece of wire so every time i'm talking about string i'm talking about a piece of wire but you know my intelligence needs to know i'm talking about wire and not yarn and makes sense right you have to be able to work with intelligence where you actually feel like it's getting smarter with you rather than stupider. The state in the art for most people is that they switch models and the next model that they use, like as they're using it, as there's more and more context, it's getting worse and worse and worse at its job. It's a reverse person, right?

46:26Rob Ferguson: A person, the more context I have, the better I get at doing the thing, the more curated, the more exact I become. These intelligences that people are not going through and building that context, they get worse over time.

46:43Randy Silver: So the basic skills of product development, being able to figure out an audience who you can deliver real value to, demonstrable value with something that you have proprietary access to and make it a good experience. That's really the key. Amazing.

47:01Rob Ferguson: Good data, good experience, understand who your customers are, ask great questions. And I think it's, you know, it's all these same fundamental product skills that you had to learn on the way. It's like kind of the golden era of the product manager, in a sense. But it's only the golden era of the product manager if they can avoid that really seductive word, which is done. Because all the pressure in the organization is entirely going to be around that word. And if you can't push that conversation to the forefront, okay, but how do we know when we're done? Because otherwise, we're going to have something that works for like, well, okay.

47:48Rob Ferguson: We're into one of the things, but you're not going to have done in a way that your customer wants it to be done. And I think I will caution folks that people really underestimate the value of their data. AI labs these days, especially the closed ones, really want to discount what your data is worth for them to absorb it. And I will say that we tried to do the math on how much of a discount would we have to give to our customers if we were to start trying their data. Because fireworks is zero data retention. We don't keep anything unless you're specifically using it to train a model. But then it's yours the whole time.

48:34Rob Ferguson: and we couldn't figure out the math because it was just, it was like, it's a lot. That's the answer. Your data is incredibly valuable. How you work is incredibly valuable. And I think that people greatly underestimate what they're giving away for free to make everyone else's intelligence a little better.

48:56Randy Silver: Sorry, just going back to what you said a moment ago, it's yeah, build, measure and learn hasn't changed all. how we do each of those steps has changed radically, but it still comes back to that core concept. But let me ask you the last question, Rob, at a management team layer, you know, as a senior management layer, what should they be thinking about with relation to moats and what kind of timelines should they be thinking about? Is this, you know, we used to talk about three to five year plans and 10 year visions and then thinking quarter and annual plans. What's changed at the management layer?

49:32gosh i wish i had a five-year plans that would be phenomenal um i think my boss probably does but but uh you know it i would say it's sort of there are certain things that you're going to be able

49:48Rob Ferguson: to count on in terms of the way that intelligence is working and those things that you can count on kind of help and a lot of businesses themselves have a seasonality to them and so a lot of the folks that I'm advising, the core basis of the seasonality of the business is just kind of what it is. If you're in education, they buy once a year, right? Like it's just going to be what it's going to be. I think you have to restructure your teams themselves. And those teams need to be composed of sort of different things where the trend here is really clear. You have to have a much more direct relationship with your customer on the other side.

50:29Rob Ferguson: You need to be talking about what the work is. And so I think the big difference between kind of old world and new world is that like our teams are much more forward deployed. And I think the end result is if you don't have teams that are actually directly engaging with customers and trying to build a superior product for those customers, then I think that's going to be the biggest fault that you're going to see on the other side. A lot of folks want to sit in the design pantheon and have it be entirely from themselves. And I think that's going to be a little challenging because it is far, far, far too easy to copy.

51:15Rob Ferguson: And the more durable thing is a relationship that's on the other side.

51:20Randy Silver: Rob, this has been a fantastic conversation. Thank you so much for taking the time out with us. Yeah, thank you so much for having me. The Product Experience hosts are me, Lily Smith, host by night and chief product officer by day. And me, Randy Silver, also host by night. And I spend my days working with product and leadership teams, helping their teams to do amazing work.

51:44Rob Ferguson: Luron Pratt is our producer and Luke Smith is our editor.