EVERY SPOKEN WORD
25 min read · 4,880 words- 0:00 – 0:24
Intro
- HTHarj Taggar
[on hold music] I'm excited to be joined here today by Aaron Cannon, co-founder and CEO of Outset. Outset is the leading AI customer research platform used by some of the biggest brands in the world, like Google, Microsoft, and Nestlé. Uh, thanks so much for being here, Aaron.
- ACAaron Cannon
Thank you for having me.
- HTHarj Taggar
Cool. Well, why don't we start a little bit by telling us exactly what Outset's product does?
- 0:24 – 1:18
What Outset Does
- ACAaron Cannon
Yeah. So I find it useful to kind of look at the world of research and, like, to, to put into context what we do. So, um, when you're trying to listen to your users or talk to them, uh, usually you've had to use things like surveys, which are very, very, uh, uh, kind of shallow, uh, capturing of data, or you actually do user interviews and you call them one by one. The problem is that's super expensive, slow, and time-consuming. And so what we invented back when we started was this idea of AI-leading interviews. That means AI is actually leading a conversation with a person, with a real person, having a back and forth and digging really deep, so you get this kind of the scale and speed of what we would, you know, do with a survey, but you're getting it at the depth of an actual interview.
- HTHarj Taggar
Cool.
- ACAaron Cannon
And so that's what we built.
- HTHarj Taggar
Cool. And can you give a sense of kind of what scale you operate at today?
- ACAaron Cannon
Yeah. So, so as you mentioned, we work with a lot of the biggest brands in the world, um, and we've now done millions of customer interviews all led by AI. So it's become a really key way that people are actually learning from their markets
- 1:18 – 6:31
Building a Category Before the Market Was Ready
- ACAaron Cannon
and their users.
- HTHarj Taggar
Cool. So as I remember it, when you went through YC in winter 2023, uh, this whole idea of AI doing customer interviews seemed, uh, non-obvious. It wasn't, like, a market that clearly existed. Uh, tell us what that felt like back then, and what was it like trying to get the first few users and, and people to believe in this?
- ACAaron Cannon
Yeah. So back in 2023, it was like ancient history at this point from the world of AI, the AI timeline. And so we, like, had this idea leaning very much on my own background. So I started my career doing research myself, uh, a lot of ethnographic research in people's homes and doing very one-on-one interviews. So I had a deep kind of care and passion for how we understand human beings. It's like what drives, you know, business forward e- e- everywhere. And so I cared a lot about it, so I thought, you know, at the time, uh, ChatGPT was really good at, you know, conversation, and so we thought, well, what if we put that into the position of a, of a interviewer, right? And ultimately, an interview is just using conversation to elicit good insights. But the problem was, like, the, the market was not there yet, right? Like, in summer '23, it was like people were barely absorbing the idea of AI in the first place. And so, you know, what we put forward was super novel, so, like, we got lots-- I think it was, like, our-- We launched the first week of the batch. We were the first one in our batch to launch-
- HTHarj Taggar
Mm-hmm
- ACAaron Cannon
... which we thought was, like, our special hack. And a lot-- We got a ton of demos booked during the batch, which is, like, an incredible position to be in. But honestly, very few of them converted. And so it's like, that's what it feels to be too early to the, a new category, right? Where it's like everybody wanted to come and learn about what was new and interesting and possible, but no one had internalized where exactly, how exactly to deploy this kind of thing.
- HTHarj Taggar
And do you remember exactly what that sort of the first version you launched with? Um-
- ACAaron Cannon
Mm-hmm
- HTHarj Taggar
... what did it do?
- ACAaron Cannon
So the first version, uh, it was, it was a, uh... Basically, it was taking the GPT models and it just, like, wrapped around a basically chat interface where it would ask questions and a hu- you know, a person would answer. The synthesis of that data, which is the other half of our product, is, like, AI leads interviews and then it synthesizes the results. The other half was literally a CSV export. [chuckles]
- HTHarj Taggar
[chuckles]
- ACAaron Cannon
So, so it was, like, a super, super bare bones, and so that's all it did, right? Is, like, you sent it to almost like a survey. You sent it to your users, and they would answer questions by AI.
- HTHarj Taggar
So it was like an interactive survey.
- ACAaron Cannon
It was basically a conversational survey, was the way to think about it.
- HTHarj Taggar
Why did these demos not convert? What was the resistance you met from buyers?
- ACAaron Cannon
So I think w- we found pretty early on that there's a lot of interest in from enterprises, which we love. Right? It's like, should you be so lucky from the beginning to actually work with enterprises. The problem was that, like, these, these teams and buyers were not-- they did not have budgets for this. They did not have any framework for fitting this into their, to their budgets. So it's, it's like a... When an enterprise buyer, like, you're talking to them, you, you... Like, they already have line items. They're already there. So either something is swapping something else out of a line item, or they need to make a case for an entirely new line item. And to make that case, they need enough external validation that their, their boss or their boss's boss believes it. So the problem is, when we're coming in with something novel, um, and it does not fit into an existing line item, then it's, like, on, it's incumbent on us to, to kind of will this new category into existence so that line item appears.
- HTHarj Taggar
What, what was sort of the altern- Like, what were they doing-
- ACAaron Cannon
Yeah
- HTHarj Taggar
... as the alternative, and maybe where was the budget for that coming from?
- ACAaron Cannon
So there's a bunch of incumbents in the research world, right? So we're talking about, like, uh, Qualtrics and Medallia and these, like, uh, user testing, these kind of older school companies, and those had line items, right? So, but those were, like, s- just static research. You know, the, the idea of hearing from lots of people at once. You just send a survey, and it asks very static questions. And this idea of a conversational one, it didn't fit into that bucket because it's not quantitative research in the way that people think about surveys. And then it wasn't qualitative research in the way you think about a focus group or a one-on-one interview. And so there just, like, wasn't a nice home for it when we started. And so what the, the kind of-- Yeah, the challenge was how do you convince them that this is the best of both, that we've, like, changed the economics of this thing and put that new line item there?
- HTHarj Taggar
And so was the challenge getting the, the teams to understand how to use the tool and adopt the tool because it wasn't clearly qualitative or quantitative, or was it, um-- Did you have a different pricing model and people didn't know how to think about that and compare the price to, like, Qualtrics?
- ACAaron Cannon
I think it was more of the former. So it was more about how people, uh... The hard part was to get them to understand where this fits into what they do, right? Um, do they replace a project where they otherwise would have done interviews? Do they replace a survey? And the hard part is, like, if it is something net new, which it definitely was, then, you know, sometimes it's not a exact rip and replace. It's actually behavior change.
- HTHarj Taggar
Mm.
- ACAaron Cannon
And so behavior change, like, especially when they're a prospect, is really, really hard. And so, uh, we had to ultimately do a lot of market education. So it's like our, our first kind of couple years of existence, it was all about kind of market education, not straight sales, not classic sales, right? It was like, we have to help- You know, bring you on the journey of this thing
- HTHarj Taggar
So you launch sort of as quickly as you can during YC, lots of demos. Demos don't convert. You realize you have to do more sort of educating the buyers 'cause the category's not... The feature isn't quite sort of where you want it to be. Um, what does that look like again, like tactically? Like how do you educate buyers?
- ACAaron Cannon
Yeah.
- HTHarj Taggar
I mean, it's so hard to get their attention j- just for a sale, so how do you, how do you kind of get them around
- 6:31 – 8:14
Landing the First Enterprise Customer
- HTHarj Taggar
with it?
- ACAaron Cannon
Our first customer ever was Weight Watchers, which was, was a amazing logo to get as your first one. I happened to have, at the time, somebody I knew there, which is my advice always for YC founders of like-
- HTHarj Taggar
Yeah
- ACAaron Cannon
... shamelessly use your own network. Um, but we had somebody we knew there, and so-
- HTHarj Taggar
Were they the buyer for the product or did they know someone who was the buyer?
- ACAaron Cannon
They were, call it an influencer, but not the buyer.
- HTHarj Taggar
Yeah.
- ACAaron Cannon
So it was not... It was a door open, but it wasn't necessarily like a, you know, co- contract closed, right? So, so I, I think the best way with enterprises is validation, and so what that looked like for us is getting a case study. And I remember our first contract ever, uh, z- we, we, uh, we, we pretended we had some, you know, a, a perfectly, uh, rational pricing, you know, whatever, but we were still trying to figure it out, and we priced them at, uh, 10K for the year with a 75% discount-
- HTHarj Taggar
[chuckles]
- ACAaron Cannon
... just for that case study, which by the way is like a fraction of what we charge now, and now we have seven-figure contracts. But at the time, it was, uh, all about the case study, and that was the most valuable thing we could get out of it. And then we brought that case study and, and to the next customer and the next customer, and that was, I think, the best way to start educating the market, was like through examples.
- HTHarj Taggar
So getting Weight Watchers to understand the right way to think about the product and, and adopt it, how, how did you overcome that?
- ACAaron Cannon
I mean, [chuckles] I, I just... There's no replacement for just spending a lot of time with them, and so I think it was a very, uh... Felt pretty forward deployed, right? [chuckles] We were like spending like a kind of a irrational amount of time with that early customer, and, uh, we kind of convinced them to try it in a few instances, and once they actually tried it in kind of a particular instance where it was really successful, that became the blueprint for them, and it became much easier, so that was like boulder rolling downhill for them. Now we had to bring that knowledge to the next
- 8:14 – 12:57
How Better AI Models Changed the Product
- ACAaron Cannon
one and the next one.
- HTHarj Taggar
You guys seem to me like the classic case of a startup where it's like, as the models have got better, you've benefited from that massively. Um, maybe talk us through that. Like how exactly have the underlying models becoming more powerful and there being more intelligence to build on top of, how has that improved the product you're able to build, and how has that maybe changed-
- ACAaron Cannon
Yeah
- HTHarj Taggar
... the attitudes to the enterprise buyers you're talking to?
- ACAaron Cannon
So I think it's like actually worth stepping back and like think about the world of customer insights or customer research. Like y- y- out in the market, there's actually like a, uh, insatiable demand for it. It's like an uncapped thing. So we're not just automating an existing workflow, but rather we're saying, "Hey, you can actually get way more insight about your user, about your customer, which means you can build a much better product, which means you can do much better marketing, which means you can grow your business." And so the way we sell is actually a growth story, right? It's like a how, how, like you can, by accelerating your ability to do research, you can accelerate your own business. And so because of that, there is not like a single kind of beginning and end to a use case that needs to be automated, and so, hey, a, you know, second-tier model will do it. It's like the better the models get, the better we can do interviews, the better we can synthesize data and pull in other kinds of data, the better we can actually drive more insights. And so our first product was on DaVinci, right? Like forever ago. Um, and now we use all the frontier models in various ways that work together. So we, we positioned ourselves basically as like the more the models get better, the more insight you are going to get.
- HTHarj Taggar
Have there been particular types of insights, maybe even specific insights some of your customers have gotten where it's, it's very clear they would never have been able to get that from a traditional survey or like a focus group? They could only have got it from their customers talking to the Outset AI?
- ACAaron Cannon
Yeah. Okay, so I'll, I'll give you one example, uh, which I'll, I'll actually stick with the Weight Watchers example from early days. So I think what, you know, when we started this product, we, we, we could tell, hey, there's an op- opportunity to do something in a more scaled way than we've done before. And so we tested that, and Weight Watchers had the first pilot project. They did a study about weight loss and what motivates people, and you could imagine for a company like Weight Watchers, that's actually a really, uh, kind of strategic question. That matters a lot for how they speak to their market and how they advertise, how they do everything, right? And so we did a study, uh, where 100 people were interviewed, um, about what motivates them and about their journey, and what was so interesting is not only did they get 100 interviews done in a matter of a couple of hours, which the traditional methods would never have gotten anywhere close to that, 100 interviews takes months.
- HTHarj Taggar
Mm-hmm.
- ACAaron Cannon
But also, people shared more with AI than they would, than they would otherwise with humans, and that's the really like counterintuitive thing here. So like it's not just that this was a kind of better economic decision to run AI interviews, but it's actually that you are getting data that was impossible to, to unearth otherwise, and I think that's like a... It's the result of like, you know, how people like love those anonymous apps where they can say anything and they, they feel like they're able to express themselves, but there's no kind of personal judgment and you're not trying to come off a certain way? It's like that, that's just transferred to research now, right? Which is like the most kind of, uh, incredible thing you can get, is like people's ground truth, like what actually matters to them. What do they really think of your idea? All of those things really matter.
- HTHarj Taggar
It kind of reminds me when, um, when ChatGPT was first taking off. I remember the people who really were deep in, like early power users of it, um, could understand that this was clearly gonna replace Google Search, but I think the people sort of at Google working with search engines felt like, oh, like people will still want like, like the fastest way to find a result when it takes too long to like chat to a thing and wait for it to tell you like which things you should buy or go for. But the, like once OpenAI, once the, um, once the models have all this context on you, like because you share so much with them, they can actually get you to the thing that you want really, really quickly, um, and it just feels actually more efficient than trying to like browse through it or look through it yourself. And so I imagine you have like the similar thing. Like early on, I bet a lot of incumbents were, "No," like, "If we're gonna do surveys, and we need to like go through all of like the, um, the responses ourselves to understand what's going on, or focus groups need to be really- Um, high touch and done in person, but people will, like, bear their souls to these things.
- ACAaron Cannon
Yeah.
- HTHarj Taggar
That's just, like, really rich information for you.
- ACAaron Cannon
Well, I, I think about this, like, even on a more macro level, where you just... AI can make us more efficient at things we wanna do anyways. But then there's, like, AI-
- HTHarj Taggar
Mm
- ACAaron Cannon
... you know, the, the, the, the, uh, has changed actual human behavior. [laughs] And so I think what I didn't realize when we started, I thought about it being an efficient way to get a thing done, and the reality was is it was creating a new set of human behaviors-
- HTHarj Taggar
Mm
- ACAaron Cannon
... that we've really, really benefited from. It, it's funny because, like, e- understanding humans is, of course, what our product does, but it's also, like, we had to do that to take advantage of what's now possible
- 12:57 – 14:45
When the Market Finally Caught Up
- ACAaron Cannon
with AI.
- HTHarj Taggar
And you guys have had a real inflection point on growth over the last year, so tell us what's d- um, what's been driving that.
- ACAaron Cannon
Yeah. Well, I think it goes to the through line of, like, we create a new category. After YC, uh, we continued to push on the market, and I know my co-founder and I at the time, we were, like, wanting things to move faster, wanting to hit an inflection point, but we kept hitting conversations with people where they were really intrigued, but did they have budget yet, and were they overco- like, were they able to overcome their skepticisms, um, of the category? And so that's where we pushed on the education, we pushed on the awareness, we, we brought case studies to bear, and we kind of scraped and clawed our way through. And then really in 2025, I think there was a he- a, a big inflection point. And what happened is the market... Like, this became-- went from experimental thing that, "Are you willing to take a bet?" to consensus. And so this is like a, you know, if, if you're, you know... If you think about, like, hitting tailwinds of AI, right? Like, this is a you want to be there and already have mindshare when the inflection point hits.
- HTHarj Taggar
Yeah.
- ACAaron Cannon
Right? And, like, we had already been there at beating the drum, and so the moment, you know, the conversation shifted from, "Should we explore this?" to, "We should explore this," we were already synonymous with the category, which was, like, I think in category creation, like, we just wanted to be associated with that net, net new category, and that's the position we got. And so the last 18 months have, you know, been, been completely, uh, have been massive.
- HTHarj Taggar
I feel like, Ian, I feel like going back to the fact that it was such a non-consensus, non-obvious idea in 2023, going back in time and explaining to people then that you'd be able to, like, talk to your agents, 'cause voice would have really taken off, would've just seemed like crazy talk. Um, if you project out forward sort of what's coming next for you guys, like, what are some of, like, the, the crazy capabilities that you're excited for that will let AI go even deeper into sort of figuring out what people want and, and going and probing
- 14:45 – 19:40
From AI Interviews to Customer Simulations
- HTHarj Taggar
much further?
- ACAaron Cannon
Well, it, it, it's funny actually. Uh, I remember in 2023 during the batch having calls with people where I literally had to educate them on what were LLMs.
- HTHarj Taggar
Yeah.
- ACAaron Cannon
Like, I, I actually had calls with people who were, uh, were like, "What? What, what do you mean LLM?" And I had to g- So it's, like, such a different world. In the last three years, now we, like... AI can do so much. It's talking to your user's voice, and we've actually included a ton of visual intelligence too. So now we're using computer vision models to say, let's just say, you know, AI is running an interview, and it, it can watch your screen as you're interacting, it can watch your face, it can watch your environment, probe on those, dig deeper. We even have a way where AI will get feedback from people on a design, and then literally change the design in real time and get the person's further feedback. We call that co-design. So there's, like, so many ways that AI is now, like, kind of, uh, uh, uh, become this superpower for research. But, um, I'm really excited to announce and to launch that we're, uh, we're shipping Outset Digital Twins this month.
- HTHarj Taggar
Oh, cool.
- ACAaron Cannon
And so what that is is we're, we're able to start synthesizing actual customers, actual users, into what we call digital twins. So the idea here is that when you're doing research, sometimes you're able to and have the time and money and investment to go out and learn from people. But as you do, you're able to put together actual twins that you can interact with as well, which are representations of those people. And so, um, this is part of our new Simulations Lab that we've stood up at Outset, and we've developed a methodology where you're actually able to very accurately, uh, simulate what a person would say. If you think forward, right, businesses, um, ultimately just want to predict human behavior, right? That is the thing that matters. That's why research exists. Predict will somebody buy a thing? Will somebody use a thing, right? You're all, you're... It's always, you know, you're always trying to figure out the future. And so now we have the ability to both gather the data to inform the future and also actually simulate it with real, uh, digital twins.
- HTHarj Taggar
So that sounds really cool. Could you tell us maybe a little bit about how exactly do you generate these digital twins? Like, what, what inputs are going into creating them? And then what's the value to your customers of using the digital twin for feedback versus just going to, like, the actual human customers and asking them?
- ACAaron Cannon
Yeah. Yeah, good question. So when we work with a customer, we identify what are the audiences they care most about, right? Maybe it's their own customer base, maybe it's net new prospects they wanna get, you know, bring into their, to their business. And so we generate the set of twins based on them. We reach out to real people, and we actually do hour-long grounding interviews. These are extensive AI-run interviews that are digging into all sorts of dimensions about their personality, about what they value, about what they care about, and through that, we're able to develop this persona core that can best represent that person. So we've now created this digital twin that represents them, and what's cool is now anybody in your business can interact with that digital twin at any time. Traditional research is great, and you, you know, you wanna keep doing it in all sorts of, uh, uh, use cases, but it costs money. It takes time. Not everybody can go do this research. But, um, with a digital twin, anybody across the business can be tapping into that knowledge, can be asking... For example, maybe your marketer wants to test a new messaging with that kind of digital twin, or your finance person is working on new pricing models and wants to simulate what would happen if I changed that price. This is this infrastructure that should exist for companies to simulate the future.
- HTHarj Taggar
Yeah. It seems like it should unblock, like, a huge amount of creativity, 'cause there, there have to be lots of good ideas that are generated by people in these companies that they talk themselves out of, and then it's too expensive to go out and get budget or whatever to go and run them, like, do, like, a focus group. But now, yeah, if you just have access to a digital twin, you can, like, test it out and build a case for it, um, without talking to anyone, and that, that should be pretty exciting.
- ACAaron Cannon
That's right. It, it's, like, amazing how many decisions are made at big companies based on someone's gut or instinct. [laughs] And, like, yes, they pull in their research team for the big projects, but there's so many decisions day to day that don't have that. You can't do that. You don't have the budget or time or, or capital to do that, and so- Being able to just talk to a twin of an actual customer or talk to 100 twins of an actual customer, like that is a really powerful mechanism.
- HTHarj Taggar
Yeah. That's definitely one of the things I feel founders talk about as the companies grow in scale is they sort of, in the sort of YC stage it's almost so, it's so easy in certain ways because you just, you have an idea and you go out and talk to users and like you just do it yourself and then the company gets bigger and there's all this other like stuff going on. But maybe with Outset you actually like help bigger companies be able to come up with new innovative ideas.
- ACAaron Cannon
That's right. And, and I think it like comes down to two things. It's like one, accelerating the ability to get this input, get the insight, right? It's like we're sh- if we're shipping code at the ridiculous rate that we are now, we're actually blocked on understanding what people actually want, right? That becomes more of a blocker, so we need to accelerate that too. But the other is like going and understanding humans in a way that we haven't before, right? And whether that's a direct Outset interview with a person or a digital twin that's representing the most, you know, impactful audience that you have, both are ways to just capture more insight more quickly.
- HTHarj Taggar
Yep.
- 19:40 – 21:18
Aaron’s Path to Becoming a Founder
- HTHarj Taggar
Okay, well, maybe take, changing gears here a little bit. A lot of people watching this are hoping to start a company themselves one day. Uh, maybe tell us a little bit about, um, your journey to becoming a founder. Um, so I think you've worked at some places before, so [laughs] maybe tell, tell us about that story.
- ACAaron Cannon
It all started, I walked [laughs] I walked into Harj's office.
- HTHarj Taggar
[laughs]
- ACAaron Cannon
And he ... No, but, but, but, uh, but my actual, my, my background was working, uh, doing product at a number of startups. So, so I worked, uh, uh-
- HTHarj Taggar
Yeah, we worked together at Triplebyte.
- ACAaron Cannon
We worked together.
- HTHarj Taggar
You were our first product hire.
- ACAaron Cannon
That's right. That's right. Harj, Harj took a bet on me.
- HTHarj Taggar
[laughs]
- ACAaron Cannon
Um, and I was the, the head of product under, under Harj and team at Triplebyte. And, and I think like for me, working very closely with, uh, at existing startups, so if I ... You know, at the time I wasn't ready to go start my own thing, and so like being able to work really closely with founders who are in the trenches and understand that experience and start, you know, pattern matching like all the different phases was a really big way that helped me. The other, the other, [laughs] the other advice I always give of like if, if folks are, you know, thinking about it one day is basically, uh, life is short and do it now. [laughs] And, uh, you know, I, I, uh, decided to, uh, leave my job and start a company, and I hadn't yet figured out exactly how that was gonna go or what that was gonna be, and I was still kind of exploring, you know, kind of, uh, I just disarted, decided to start working with Michael, my co-founder. Just deciding to do it and not sitting around waiting for the right idea to strike me, right, like walk, walking down the street, was the most powerful thing I think that, that happened.
- HTHarj Taggar
Cool. And I think that's all we have time for today. Thanks so much for being here, Aaron, and, uh, can't wait to see the Digital Twins launch from Outset.
- ACAaron Cannon
Thank you for having me. [upbeat music]
Episode duration: 21:18
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