[User] Please transcribe this text

[Assistant] Yeah, so we didn't, uh, the question is like how do you go about pricing something, uh, like this where it's 500 lines of code, it seems like it solves a small problem for people.


[User] Please transcribe this text

[Assistant] Uh, we thought it was a small problem for people and we underpriced hugely, uh, for a long time. It turns out that that problem that we saw, that, you know, the 500 lines of code solves, uh, is actually really valuable. Like,


[User] Please transcribe this text

[Assistant] We have lots of customers now that pay over $100,000 a year to solve that problem.


[User] Please transcribe this text

[Assistant] And, you know, there's more to the product now than the 500 lines of code, uhm, a lot more, but it's nevertheless is, the size of the business problem has almost nothing to do with the amount of code written, right?


[User] Please transcribe this text

[Assistant] And I think it did take us a little while to like revalue things in that respect. Uhm, but to answer your question most directly, we spent the first year just accumulating customers. So we just had lots of people adopting it. Got to maybe like a thousand or two thousand companies sending data through Segment. Uhm, and then we tried experimenting with pricing.


[User] Please transcribe this text

[Assistant] And then I'd say the biggest kick in the butt we had to actually get to the right point here is we had a sales advisor who said,


[User] Please transcribe this text

[Assistant] We would go, you know, there were reasonably sized companies using us, like, uhm,


[User] Please transcribe this text

[Assistant] Go to sales meetings at these companies and the sales advisor would basically get me pumped up ahead of time. He'd be like, Peter, you have to ask for $120,000 a year. I'm like, that's crazy.


[User] Please transcribe this text

[Assistant] Like, what are you talking about? And he's like, Peter, if you don't ask for $120,000 a year, I'm quitting as your sales advisor. He's like, well, alright.


[User] Please transcribe this text

[Assistant] Here we go, right? Uhm, and so I would ask, I'd turn beat red, and then like, they would negotiate it down to like 18k a year. But if you keep doing it, eventually someone is like, well that seems reasonable. So.


[User] Please transcribe this text

[Assistant] Cause you don't know how much value you're delivering until you start asking for money.


[User] Please transcribe this text

[Assistant] I think if I had to do it over again, I'd start asking for money earlier and I'd be a lot more comfortable with it. Like, if you're solving a real business problem, people are gonna be happy to pay for it. I'm not sure I totally answered your question, but.


[User] Please transcribe this text

[Assistant] Yeah, this is actually why it's so hard to find product market fit the first time and why it's so much easier the second time once you have some product market fit.


[User] Please transcribe this text

[Assistant] Because once you do have a defined customer set that you sell to,


[User] Please transcribe this text

[Assistant] You can pretty easily figure out by going back to the same people whether or not you're solving an adjacent problem for them.


[User] Please transcribe this text

[Assistant] It's like you have two things and you can either slide the product or you can slide the market by talking to different people.


[User] Please transcribe this text

[Assistant] I'm not sure that there's a magic rule to know when to slide which, but I think, I think founders get slippery with themselves, like they aren't totally honest with themselves about which thing they're doing when.


[User] Please transcribe this text

[Assistant] So I think it's, I think it's most important just to recognize that like, okay, if we make this shift, we are like shifting the audience versus shifting the product.


[User] Please transcribe this text

[Assistant] Cause I think otherwise you can shift the product and the audience simultaneously and think you're doing well but then realize you're not.


[User] Please transcribe this text

[Assistant] I'm not sure that there's like a slam dunk solution to that, other than just being really honest with yourself about which thing you're moving when.


[User] Please transcribe this text

[Assistant] I think there's a pretty big difference between finding that first product market fit, where you can like kill a product, immediately move to a totally new audience, like Codecademy tried a whole bunch of different stuff.


[User] Please transcribe this text

[Assistant] Building a company around it and you have product managers and each product manager is sort of like searching for a big breakthrough.


[User] Please transcribe this text

[Assistant] It's a little bit easier in that you can't shift the audience anymore. The audience is fixed. And so now, uh,


[User] Please transcribe this text

[Assistant] It's a, it's a slightly more straightforward problem for, uh, sort of helping a product manager understand how to go talk to customers and sort of reveal what problems those customers have and then actually solve them.


[User] Please transcribe this text

[Assistant] Uhm, so it's mostly training in that loop and it comes from talking to either larger customers or your best customers or your worst customers and sort of like trying to push the boundaries around like the core of very happy people have to like what other problems you can solve for them. Yeah.


[User] Please transcribe this text

[Assistant] I think the biggest mistake is trying to pitch your existing ideas. Like there's a place for that. Uh, right when you have some product market fit and you want to test some of these things. Uhm, but I think most founders and certainly we made this mistake early on of like pitching rather than trying to sell rather than actually trying to understand what their problems were.


[User] Please transcribe this text

[Assistant] So now when I talk to a customer, I just start by asking what their business problems are, right?


[User] Please transcribe this text

[Assistant] Hey, uhm, like, person at retail company, like, uh, do you spend a lot on Facebook? They're like, oh yeah, yeah, yeah, we spend a lot on Facebook. I'm like, oh, like, how do you measure whether that spend is efficient or not? And they're like, well, we don't really know.


[User] Please transcribe this text

[Assistant] Tell me why you haven't solved it. And they're like, well, we haven't solved it cause like, blah, blah, blah. And you just start like digging in to like these problems.


[User] Please transcribe this text

[Assistant] And then, you're actually really set up to do a sale then, right? You're like, well I have just the thing, right? Uhm, and they're like, holy crap. Uhm, but, but,


[User] Please transcribe this text

[Assistant] If you don't have a product yet, now you know exactly what can, what problem you can fix, right? So, I think it's more about listening and digging, uh, than it is about, like, pitching an idea. Yeah.


[User] Please transcribe this text

[Assistant] E-mailing people cold, it's getting intros through everyone you know, it's like...


[User] Please transcribe this text

[Assistant] Scratching and clawing your way through your social network and through introductions.


[User] Please transcribe this text

[Assistant] After you have customers in an area, then it gets easier because you have people coming to you all the time, mostly, if you're inbound, and so then there's like a steady stream of people to talk to. Uhm, but the initial piece, like that's why people talk about hustle being such an important founder quality is because


[User] Please transcribe this text

[Assistant] Very low, like single digits percentages maybe. Uhm, you're gonna send a lot of emails that are unanswered. Uhm, that's, that's the nature of sales. Honestly, if you're selling in like a business to business kind of environment, you're gonna send a lot of emails that go unanswered. Like sales is basically the, the exercise of getting the door slammed in your face like nine times out of ten, right? Yep.


[User] Please transcribe this text

[Assistant] Stream processing, so we have both the ability to collect data across a broad section of stuff here, as well as the ability to fan it out to a whole bunch of different places.


[User] Please transcribe this text

[Assistant] And so what we wanna do is basically provide a platform that all of these tools can build on tele. Uh, that's a super short version. So you can imagine basically every team in a company needs access to customer data, and we wanna become the platform on which those tools are built. Whether that's email marketing, or push notifications, or analytics, or help desks, or CRMs, or payment systems, fraud detection, etc. All of those things operate on a core set of customer data.


[User] Please transcribe this text

[Assistant] And that's the customer data that already flows through Segment by nature of us being that integration connection layer. And so we just want to expose that data to partners to build on top of it. So when I talked about building a platform at the beginning, uhm, that's the next step for us.


[User] Please transcribe this text

[Assistant] Yeah, and actually that's a super clueful presentation I have to say, right? And you don't hear that from all people in the PR industry.


[User] Please transcribe this text

[Assistant] Yeah, yeah, sure, yeah, I'm dealing with this a lot, uhm, you know, for what I do, uhm, you know, I much prefer exclusives, uhm, you know,


[User] Please transcribe this text

[Assistant] There's a company that does X and, you know, there's four reporters sitting there with a story.


[User] Please transcribe this text

[Assistant] Well over half of the embargoes I've ever been involved with get broken.


[User] Please transcribe this text

[Assistant] Uhm, but I think I will argue for the ex- if you have an important story to tell, and particularly as of a complex story,


[User] Please transcribe this text

[Assistant] It can't be summed up neatly in a sentence, uh, but what you have is really cool and needs explanation, then you really want an exclusive story, a reporter who's going to


[User] Please transcribe this text

[Assistant] Which was, uh, people who used to do the Siri product there. Uhm, uh, I did it for Wired. It was actually my last story, uh, in the public, in the magazine until this week actually. It was a couple years ago.


[User] Please transcribe this text

[Assistant] Uhm, they actually use that on their website to explain themselves for a couple years.


[User] Please transcribe this text

[Assistant] Yeah, well sure. I think, well, I mean, ultimately, uh, you know, when you're going to do your pitch, you're going to do what, what, what Sharon says and really get yourself familiar with them. But, you know, the, the larger distinctions is that a publication like TechCrunch


[User] Please transcribe this text

[Assistant] Yeah, yeah, because they're going to do a lot of stuff there and they're interested in the business.


[User] Please transcribe this text

[Assistant] Traditional publications, again, they're not going to do as much. They're going to figure out is, is it significant in a business sense or a technology sense? In which case, actually, you might have to consider a different reporter for one of those publications there. You know, uh, if your company is an AI company, uhm, you're going to talk to the person who covers AI.


[User] Please transcribe this text

[Assistant] As opposed to the retail thing, if you're, you know, a commerce company.


[User] Please transcribe this text

[Assistant] Lots of different kinds of stuff. Some of them are really focused on like,


[User] Please transcribe this text

[Assistant] What startup life is like? And then, in which case, you might have something unique to offer. Uhm, we do a thing, in my publication, cause a lot of people in this community read us, are called war stories.


[User] Please transcribe this text

[Assistant] Uhm, that if, you know, someone has a really interesting story to tell, uhm, you know, about what they do. Typically, actually, it's people who've already established themselves and talked about, you know, uh,


[User] Please transcribe this text

[Assistant] And a crucial part in the formation of their company, but you know, even so, you know, uhm, some of the younger companies I've, I've, I've seen there, uhm, you know, has stories interesting enough we did it. We did one with, uhm,


[User] Please transcribe this text

[Assistant] Uh, insta- what's it? Insta-art? Instacart? Insta-art. The art one. Oh, okay. It does Chris Chan, who, you know, I was somehow captivated with, you know, he was, you know, this, uh, young guy from Detroit, you know, who wound up, uh, you know,


[User] Please transcribe this text

[Assistant] Being a sole founder who did a few pivots and wound up to have these people in Hong Kong, you know, create


[User] Please transcribe this text

[Assistant] He was helpful to me, you know, talking about his life as a startup when I did a story about that batch and then, you know, later I was connected with him and wound up writing about his company.


[User] Please transcribe this text

[Assistant] It's a little, you know, less, you know, delicious for a reporter, uh, to do that. I did a story about a startup, uhm, called Bonsai, uh, recently and interestingly that came from, you know, uh,


[User] Please transcribe this text

[Assistant] A relationship I had with, uh, uh, a PR person who really knows my work and, you know, knows, okay, you know, this is a guy who likes to do things that really move the ball, you know, and this is something different there, uhm, you know, that I think is going to meet Stephen's needs. Uhm, and they had done some stuff on Medium.


[User] Please transcribe this text

[Assistant] And I looked hard at that to say, boy, did they give the story away, even though obviously if you have, you just put up stuff on Medium, you're not necessarily, it's not gonna be pretty rare to get a huge readership from that.


[User] Please transcribe this text

[Assistant] Uhm, you know, uh, so, I say ultimately then, it doesn't really matter, not that many people read it, but if, you know, uh, if you got what you wanted and


[User] Please transcribe this text

[Assistant] You know, and took off, it might make a reporter think twice to say, gee, that story's been told already.


[User] Please transcribe this text

[Assistant] Well, I mean, you're gonna need to, you know, focus in turn to explain what it is, but, you know, once the things get rolling, the reporter is gonna wanna take his or her, uh, take on the thing there, and, you know, uhm, people, reporters just don't like being


[User] Please transcribe this text

[Assistant] Directed or saying, oh, this is our story. Don't, don't pay attention to that.


[User] Please transcribe this text

[Assistant] You know, once you open the door, we're in. So, you know, uh,


[User] Please transcribe this text

[Assistant] I hope what you want to say is interesting enough to keep us from what we find interesting.


[User] Please transcribe this text

[Assistant] The price and cost estimation problem for the home services market


[User] Please transcribe this text

[Assistant] So this is much better than calling multiple moving companies, staying on the phone for about half an hour each to get arbitrary wrong codes or waiting at home for an onsite estimator to come visit.


[User] Please transcribe this text

[Assistant] So, uh, we are targeting the same accuracy. So, uh, uh, so the way we are thinking about it is today the estimation process takes about a week for you to get a quote or you get multiple quotes and you don't really understand why a quote is what it is.


[User] Please transcribe this text

[Assistant] Uh, so our goal is to give you a complete breakdown on what it is. And we still have, uh, some, uh, uh, human labor in the background cleaning up the, uh, mistakes the AI is making. So our goal is to reduce the human time to less than five minutes.


[User] Please transcribe this text

[Assistant] One main reason is if you're scheduling an on-site estimator visit, so, uh, uh, it costs the moving company a lot, more than $200 per visit, uh, and you have to schedule something, uh, schedule someone to come visit, and these guys are able to do two to three estimates a day, so, uh, uh, in the summer time when it's busy, it gets really backed up.


[User] Please transcribe this text

[Assistant] On the, uh, so most companies don't end up actually sending an onsite estimator, and they give price codes over the phone. Our research shows that 90% of this is wrong, so consumers shop around, and they don't really understand, uh, uh, why, uh, why


[User] Please transcribe this text

[Assistant] Uh, so our initial MVP that we deployed doesn't have any computer vision. People just take videos and someone watches it in the backend and to give a quote. That takes around 90 minutes per person. So, uh, uh, and also you need to, uh, hire and scale human, uh, workforce to do that. And there are a few other companies that do it, that's doing this.


[User] Please transcribe this text

[Assistant] And uh, keep the human workload to as, as uh, less as possible.


[User] Please transcribe this text

[Assistant] Uh, and from a product perspective, uh, what we want to be also, uh, able to deliver to consumers is an interactive way of them customizing their quotes. So let's say, uh, you have a $2,000 budget and, uh, and everything, moving everything is maybe going to cost $3,000. Then you can decide, pick and choose, uh, how you want to spend that money.


[User] Please transcribe this text

[Assistant] Yeah, so we, uh, we signed our first LOI, uh, for a paid, uh, uh, trial with a moving company in San Diego, uh, where we are doing this GPM solution where it's $10 with no AI and $50 with AI.


[User] Please transcribe this text

[Assistant] Uhm, and, uh, uh, we are talking to multiple large moving companies, uh, from 20 million dollars annual revenue to a few billion dollars, uh, in, uh, and we are in the final stages of negotiations with some of them on the timelines as well as the features of the, of a paid pilot. So it's like enterprise level contracts that we're going after.


[User] Please transcribe this text

[Assistant] That's something that we've considered, uh, but moving has a lot more, uh,


[User] Please transcribe this text

[Assistant] So for, for the $10 solution, the, uh, uh, the assumption is if you're the moving company, you watch the video and provide the code yourself. So, uh, uh, so there's some human, uh, if you're the moving company, you have to hire people to actually watch the video. Oh, so you're not hiring people to do it? On the $10, no.


[User] Please transcribe this text

[Assistant] Uhm, right now our AI is not completely ready yet, so that's the product that we are selling right now.


[User] Please transcribe this text

[Assistant] So, uh, uh, that's a good question. So two reasons. The first one is, uh, we want to train our AI on, uh, real world people's homes. The, like, you can imagine when you're moving, your house looks nothing like the beautiful catalog picture.


[User] Please transcribe this text

[Assistant] Uh, annotators, uh, that are outsourced where, uh, as our AI is improving, their workload is reducing. So that's something that we are doing.


[User] Please transcribe this text

[Assistant] Uhm, so we are actively also web scraping for data from Google Flickr, so we have a few millions of images already downloaded.


[User] Please transcribe this text

[Assistant] So, uh, the goal, the goal with, uh, again with AI is, uh, the accuracy keeps increasing with time and as more consumers use it and, uh, and we have a fail safe mechanism of, uh, humans filling in the gaps in the meantime.


[User] Please transcribe this text

[Assistant] That's a good question. So, and that's one thing I think startup school has actually helped us out with. Uh, so we are going after large enterprise contracts, uh, and, uh, it's, uh, it's taking some time because our AI is not ready yet. So, uh, we are looking at ways, uh, of how to deploy a solution while simultaneously being, uh, can monetize it as well.


[User] Please transcribe this text

[Assistant] So in the meantime, uh, because of, uh, uh, our, our mentor, uh, from startup school, we are doing this GPM solution so that we get our product out the door. We did a non paid, uh, non paid pilot. So this is a way to get, uh,


[User] Please transcribe this text

[Assistant] And, uh, because we are using video, we need to track, uh, these objects across frames. So we understand that this is one chair, even though I'm seeing it for, from the fifth second to the fifteenth second. So we need to identify the size of the object because that contributes to cost as well.


[User] Please transcribe this text

[Assistant] So there are multiple things that we are doing to, uh, uh, so we're working simultaneously and making progress in all of this.


[User] Please transcribe this text

[Assistant] So we have more than half a dozen of LOS already signed, so we're still working on the contract dollar amount with a few customers. We are in the final stages of figuring out what that amount actually is going to be.


[User] Please transcribe this text

[Assistant] Uh, for the first year, we are, uh, we are pitching a flat fee, uh, so a few hundred thousand dollars, uh, where, uh, they can use their, use our product as, as much as they want, where we actually work, uh, together with them in, uh, in refining the process of how consumers actually use, uh, use our technology to get a code.


[User] Please transcribe this text

[Assistant] So we are looking at signing a maximum of up to three in the first year for a few hundred thousand dollars.


[User] Please transcribe this text

[Assistant] That's what you're doing. Uh, I mean, uh, that's, that's our, that's our goal once we sign this. But, uh, they also want like more, uh, like a turnaround time of a few hours. So that's something that we're working with actually. So that's a very good point.


[User] Please transcribe this text

[Assistant] Yeah, so it depends. So we can have our turnaround time as less as one hour. It just means that we need to make sure that we have someone on call.


[User] Please transcribe this text

[Assistant] Uhm, because this is very new to them and, uh, we want to make sure that, uh, they are incentivized to use it as much as possible. And, uh, uh, and like pitching on a poor use fee, we feel like maybe...


[User] Please transcribe this text

[Assistant] So, uh, I don't think initially the target is, let's say you have like a five bedroom or six bedroom house, they may still want to send an estimate over.


[User] Please transcribe this text

[Assistant] Uh, but there's a significant, maybe 75% of the cases where they don't really send an estimator. So our, so initially, uh, based on our conversations, they may start using us for that specific segment of the market, uh, before


[User] Please transcribe this text

[Assistant] That's one of the options that we're working with with one of these companies.


[User] Please transcribe this text

[Assistant] So again, we started with moving, but we've also found opportunities in other parallel verticals, like, you know, painting and home renovations and so on. So where the data that we are collecting and the AI that we are training will help us get to a solution faster. So our goal is solve


[User] Please transcribe this text

[Assistant] It's actually slightly more difficult because uhm, if an onsite estimator comes to estimate


[User] Please transcribe this text

[Assistant] Uh, so, so, we looked at it and then, uh, we found, we thought that, uh, uh, we think that moving is an easier problem to solve initially. Technically speaking. Technically speaking.


[User] Please transcribe this text

[Assistant] My name is Mitchell Lee. I'm one of the two co-founders of Penny, which is a personal finance app that you interact with by way of conversation. We are a five person team.


[User] Please transcribe this text

[Assistant] So I guess I should address the elephant in the room, which is I am a white male on a DNI panel.


[User] Please transcribe this text

[Assistant] We raised a round of funding for Penny, uhm, and started to look to expand our team. At the time, we were just two people. And what, a little bit of backstory here, Penny is a conversational, uhm,


[User] Please transcribe this text

[Assistant] Kind of tap me on the shoulder and she says, I think Penny's flirting with me.


[User] Please transcribe this text

[Assistant] And I was like, whoa, uh, no, I just use emoji. That's my style. But kind of experiences like that, and I heard from other friends as well.


[User] Please transcribe this text

[Assistant] Really hammered home the point that if we want to build a product that's accessible to everyone, we need to have a team that looks like the people that we're trying to help and is kind of representative of the group of people that we're trying to build for. So that was a


[User] Please transcribe this text

[Assistant] That was one of the turning moments for why we need to focus on this right now.


[User] Please transcribe this text

[Assistant] I will say that we're pretty early on and so uhm, for us I don't view it so much as mistakes, it's just kind of learning opportunities.


[User] Please transcribe this text

[Assistant] There was a lot of learning opportunities as we started this process. Uhm, how do you approach the subject with people that you're interested in recruiting? How do you position it within your own company and have an open dialogue there?


[User] Please transcribe this text

[Assistant] There were also just small missteps like uhm, we had one woman that actually ended up joining our team, uh, we had a dinner with the team and


[User] Please transcribe this text

[Assistant] There are a lot of things like that. But I do think that we're learning and we're getting better at this. And uhm, step one is just talk about it openly.


[User] Please transcribe this text

[Assistant] So I think the thing that we probably have done best is start early. We started thinking about this when we were two and three people.


[User] Please transcribe this text

[Assistant] That's important both because the people that you hire early on are eventually going to go on to be the leaders at your company and hire their own teams and so you want to.


[User] Please transcribe this text

[Assistant] Make sure that you're building the team now that you, uh, eventually wanna have.


[User] Please transcribe this text

[Assistant] It's also important because if you end up as a team of 15 white and Asian dudes sitting in a room and start trying to incorporate diversity into your team at that point, good luck. Because, I mean, people don't want to be a


[User] Please transcribe this text

[Assistant] Like, a token diversity, uh, candidate or, or feel alienated by a culture that's already in place.


[User] Please transcribe this text

[Assistant] So that's a big one. We've also been pretty proactive. So, uh, you mentioned kind of, uh,


[User] Please transcribe this text

[Assistant] Blinding the resume, uhm, or putting together good job descriptions.


[User] Please transcribe this text

[Assistant] That, uhm, at a very early stage is not quite enough, uhm, because you're working with a much smaller, uh, funnel. So we've been very proactive about saying.


[User] Please transcribe this text

[Assistant] No, we're actually seeking out people of different backgrounds, uhm, and different cultures to, to try to incorporate them into the team early on, uh, and, and so like, partnering with, uhm,


[User] Please transcribe this text

[Assistant] Different organizations, uh, that can kind of help broaden your, your recruiting funnel there was, um, something that we I think did well early on.


[User] Please transcribe this text

[Assistant] Written by Nigel Pepperhouse Focus Question


[User] Please transcribe this text

[Assistant] Where can you see the color white?


[User] Please transcribe this text

[Assistant] Connections. Writing and art.


[User] Please transcribe this text

[Assistant] Yeah, we're bootstrapped in France, and then it wasn't working, so we moved here, got into YC, and that really, really, really helped. Uhm, so anyway, nine years.


[User] Please transcribe this text

[Assistant] It depends who you ask, but yea, it counts the same company, same problem we're trying to solve. Uh, so 350 employees, lots of growth, lots of excitement. Uhm, the biggest challenge for us, the biggest challenge last year was proving that we could build a business, a viable high growth business on top of


[User] Please transcribe this text

[Assistant] This excitement for stuff that is in large part free and open source.


[User] Please transcribe this text

[Assistant] Uhm, and that was last year we, we proved that model and this year the challenge is to scale it.


[User] Please transcribe this text

[Assistant] And to scale it faster than our competitors who now figured out that the model is proven. So it was a big competitive battle and a really lucrative space. So that's the challenge this year. Good.


[User] Please transcribe this text

[Assistant] So, so the background here is, so nine years in we started out as a company called Dot Cloud.


[User] Please transcribe this text

[Assistant] And now we're a different company called Docker, but there is a common thread, right? It's the same company, it's the same team with the same mission, but we changed how we do it in the first phase as dot clouds. We, uh, well, let me start with what the common thread is. The common thread is we help developers be more productive. We give them tools to create applications.


[User] Please transcribe this text

[Assistant] And, uh, then we help the businesses behind those developers, uhm, solve their business problems with the creation of software. And we're surfing on this trend of more people, uhm, creating software to do more things.


[User] Please transcribe this text

[Assistant] So the first phase, we did that by, uh, hosting the applications. So we said, just write the code, upload it to us. We'll take care of everything. We'll run it for you. We'll manage it for you. All of it. And, um, and then now as Docker, we don't do the hosting. We provide the tools.


[User] Please transcribe this text

[Assistant] In the first phase, we're very small. We didn't have any sort of hype behind us. We were just trying to convince developers to use our stuff. And uhm,


[User] Please transcribe this text

[Assistant] And we used, honestly we used our own community.


[User] Please transcribe this text

[Assistant] Programmers, we write software and we know a lot of other programmers and we just annoyed the hell out of all of them to use our stuff. We gave it to them for free.


[User] Please transcribe this text

[Assistant] And then kind of through, through the individual programmers, eventually made our way to the business behind the programmer. So the difference I think is that, uhm, for us it's a two step


[User] Please transcribe this text

[Assistant] We joined, uh, White Combinator in 2010 and, uh, you know, raised our first small round of funding, uhm, at the end of 2010, so within six months. And we just got, went at it gradually.


[User] Please transcribe this text

[Assistant] Uhm, I mean it's nothing, nothing particularly unique there.


[User] Please transcribe this text

[Assistant] Yeaah, so the first part was really tough, it was bootstraps, we did consulting.


[User] Please transcribe this text

[Assistant] We did whatever we had to do to pay the bills. Uh, it was really tough. My advice would be


[User] Please transcribe this text

[Assistant] They're very useful, but uh, you gotta get out of it really quickly because it just, the consulting takes all of your time. You will never get off the ground as a product company.


[User] Please transcribe this text

[Assistant] There's too much distraction in consulting. So eventually we just took a leap of faith.


[User] Please transcribe this text

[Assistant] Uhm, and landed in our feet in Y Combinator, so.


[User] Please transcribe this text

[Assistant] Yeah, so honestly I don't know how useful my insights are here because really the trick is to get really lucky.


[User] Please transcribe this text

[Assistant] The open source technology that we develop, Docker, is extremely popular.


[User] Please transcribe this text

[Assistant] It, you know, we were in the right place at the right time. There's this whole trend around containers which we kind of.


[User] Please transcribe this text

[Assistant] You, you start having a completely different set of problems once you're in that situation. So honestly, the problems we had before that moment in 2013 when we launched the open source Docker and after.


[User] Please transcribe this text

[Assistant] Now honestly it's more, uh, how do we deliver on the incredible demand fast enough before competitors do it instead? So there is no outbound in that case. The question is too much inbound.


[User] Please transcribe this text

[Assistant] Which seems like a nice thing, but it's actually. It's noise. It's noise and it's also you drop a lot of it on the floor. Yeaah. And your competitors pick it up.


[User] Please transcribe this text

[Assistant] I don't know about the biggest, but we made a lot.


[User] Please transcribe this text

[Assistant] We made a lot. I think everyone, I don't know anyone who hasn't. Everyone, yes, but we can learn.


[User] Please transcribe this text

[Assistant] Yeah, I think, I mean, the biggest challenge is once you grow into full-fledged, uhm, company, then...


[User] Please transcribe this text

[Assistant] If you're on the technical and product side, then you have to hire not, you know, business people. If you're on the business side, you have to hire more technical and product leadership.


[User] Please transcribe this text

[Assistant] And, you know, it's things that at some point you don't know.


[User] Please transcribe this text

[Assistant] And you don't know what you don't know. And I remember as an engineer, hiring non-engineers, it was very difficult for me because I knew I needed help.


[User] Please transcribe this text

[Assistant] But I didn't have the know-how to distinguish the different kinds of non-engineers that I needed. It seems really silly to say that, but to me, you know, marketing was just the, you know, marketing and sales, it was like the, the, the not coding thing. And uhm, also,


[User] Please transcribe this text

[Assistant] And so you're right here and you meet 10 people and you're like, wow, these are all rock stars. Plan grid and box and you just want to hire them right then and there. And you do. And then you realize, okay, there's more to this. And the biggest problem is when you hire the wrong person, you're, you're


[User] Please transcribe this text

[Assistant] The state of mind is, oh, it's, it's a bad person. You know, they're, they're bad and, and my company's good and now I hired the bad person. But really what's happening is.


[User] Please transcribe this text

[Assistant] It, everyone's wasting their time. It's the, it's, it's, it's not the right combination of people. And you're gonna find out six months later and it's gonna be.


[User] Please transcribe this text

[Assistant] I don't really know if I've learned anything how to avoid, but every time we've made a mistake.


[User] Please transcribe this text

[Assistant] We've tried to really invest the time to go back and find out why, what we did wrong, so that we don't make the same mistake twice, honestly. Yeaah. Uhm, and uhm, I guess it gets- It's a good insight, it's the higher, it's not the highest fault.


[User] Please transcribe this text

[Assistant] Yeah, exactly. That's exactly right. And my feeling is it's the hardest at the beginning. But if you find, once you have a core group of people, like five key people in areas that you don't really...


[User] Please transcribe this text

[Assistant] Understand and master, but you build a relationship of trust with those people, then it gets easier because you trust them to hire more people.


[User] Please transcribe this text

[Assistant] Uh, the really bad situation is if you don't sort out that stuff early on, like you make a hire, you feel like it might be a bad hire, but you don't deal with it right away. You allow it to, uhm,


[User] Please transcribe this text

[Assistant] Fester. And then before you know it, you're growing and that person has hired 20 more people and now you're in big trouble because now you gotta deal with the mismatch potentially with 20 people.


[User] Please transcribe this text

[Assistant] So I would, my, my only advice would be you're gonna make mistakes, just deal with them early and, and listen to your gut. You know, if your gut says, oh, maybe this is the wrong thing, you know, trust yourself, even if it's not your field of expertise. I mean, probably.


[User] Please transcribe this text

[Assistant] Repeating, uh, common sense here, but, uh. Uh, well, we all make the same mistake.


[User] Please transcribe this text

[Assistant] I think the same thing is happening in a lot of industries. There's a sort of a bottom up go to market, right? Where first you get the practitioners on boards and then you get their bosses on board, maybe later, maybe uhm,


[User] Please transcribe this text

[Assistant] You know, maybe they learn about your product only once


[User] Please transcribe this text

[Assistant] Dozens or hundreds of people in the organization are using it. Even today at Docker.


[User] Please transcribe this text

[Assistant] So, it's this, yeah, so we use that, we use that heavily, yes, yeah. And so today, as a business, we target enterprises, very large businesses, that is our focus. We sell also to small businesses, we're kind of in a similar situation, you got the tens of thousands, and then the majority of the revenue comes from a few hundred. Uhm, but all of them,


[User] Please transcribe this text

[Assistant] Are in this relationship with us as a vendor because someone on their team at some point said new app.


[User] Please transcribe this text

[Assistant] Awesome, Docker, I went to meet up, sounds great, or I saw a discussion online about it. So, we owe everything to that, to that community.


[User] Please transcribe this text

[Assistant] You know, it, it's honestly, we, we started something very small and the day 10 people, we showed it to a few people one by one and then, uhm,


[User] Please transcribe this text

[Assistant] Ten of them came back to our office saying, hey, we want to know more.


[User] Please transcribe this text

[Assistant] And it was just the most fun three hours of my life. Basically we're just.


[User] Please transcribe this text

[Assistant] You're, you're the janitor now, like make sure, you know, organize things for us because this is our thing now. Uh, and so we just took that job really seriously of.


[User] Please transcribe this text

[Assistant] Enabling that community to do whatever it decides to do. So we've been following that community ever since and just removing obstacles for that community to grow.


[User] Please transcribe this text

[Assistant] Yes, correct. Yeah, and so it's anyway that when you see a community, when you see anyone super excited about what you're doing, just drop everything you're doing and make sure you put themselves as your service at their service. In our case, it's developers using an open source tool.


[User] Please transcribe this text

[Assistant] Uh, you know, but it, it looks like there's something similar going on. I'm sure there's, um,


[User] Please transcribe this text

[Assistant] I'm going to say it wrong, but whatever the job is of someone actually working on a construction site, uh, some of them I'm sure are just huge fans of the tool and they go everywhere and they just, they tell all their buddies about it. And I think,


[User] Please transcribe this text

[Assistant] That's how consumer products used to grow. I think now it's increasingly seeing the best B2B products also grow that way.


[User] Please transcribe this text

[Assistant] I think there's probably, there's a generic answer that's true for every business and then there is for every startup and then there's maybe some, uh, domain specific answers. Uhm, I think


[User] Please transcribe this text

[Assistant] The, the, personally I think something that's true for all startups is that you have focus and you have speed. And I had never worked at a large company before, so I did not appreciate exactly the advantage, uh, we have as a smaller company. But now that we're growing a little bit, I can already tell what happens when you grow.


[User] Please transcribe this text

[Assistant] And so the goal really is to take advantage of our small size while we're small, faster than we accumulate problems as we grow. When you're big and you have a successful business, everyone working there, the majority of people working there,


[User] Please transcribe this text

[Assistant] First of all, we're not there at the time when the business became successful. So they all, it's long forgotten how they became successful in the first place. They're administrating an existing cash cow.


[User] Please transcribe this text

[Assistant] And so there's a completely different kind of people, generally speaking, right? Except maybe at the very top. You got a few VPs that are, you know, the original guy who did it and then he's running something. But usually these are people who,


[User] Please transcribe this text

[Assistant] Uhm, at a large, at your average large company, they're, they're not united as one group to make, uh, to make that, that company succeed against, uhm, outside threats like you.


[User] Please transcribe this text

[Assistant] Uhm, they are focused usually on their career.


[User] Please transcribe this text

[Assistant] Are they going to get promoted? Are they going to get more people under them? You know, the, it's, it's inside facing rather than outside facing.


[User] Please transcribe this text

[Assistant] The particular parts of that company that is actually competing with you for all you know might also be 10 people. And they might be underfunded, they might be on the wrong side of the big boss who just doesn't care about the product, and over time as you start competing you'll find out which ones are serious threats, uh, and which ones are basically to be ignored.


[User] Please transcribe this text

[Assistant] It's hard to tell from the outside. Hard to tell, but as, as you start getting, becoming an expert and you talk to customers, I mean it's pretty, it's pretty easy. Customers are going to say, well I'm thinking about you and these other two, three things and some names are going to come up a lot.


[User] Please transcribe this text

[Assistant] And, uh, some of them will be other startups. Some of them will be other big companies. And then you narrow down your focus and you figure out, okay, what makes that big company smarter than the others?


[User] Please transcribe this text

[Assistant] Witness Worldwide is part social experiment, part reality show, part talk show and part promotion for Katy Perry's new album.


[User] Please transcribe this text

[Assistant] Uhm, I struggle to sort of just- That's okay. Didn't know how to sit still. So when I was with the monk in his monastery and we were meditating, he kept on asking me every 15 minutes, what are your thoughts? They were naughty thoughts.


[User] Please transcribe this text

[Assistant] So I'm going to be with Katie, the most amazing cooking lesson. We're going to be cooking side by side. I hope to perfect Katie's meatballs today. I hope to perfect her tomato sauce today, so there's no excuse going forward. So I know these...


[User] Please transcribe this text

[Assistant] So just crack it in your hands, let the egg white seep through the fingers, and then go from there.


[User] Please transcribe this text

[Assistant] We are live, and then we'll move on to the sauce.


[User] Please transcribe this text

[Assistant] Bland, bland, bland, bland, bland. Unlike the music. Music's fantastic. Cooking, mediocre.


[User] Please transcribe this text

[Assistant] You know, I wouldn't even mind coming back here for a visit sometime. Yeah, I can do that.


[User] Please transcribe this text

[Assistant] 27, 28, 29, 30. Hmm. 30. 30 black and only 29 white. Looks like you're black with white stripes after all. Dilemma solved. Good night.


[User] Please transcribe this text

[Assistant] Oh nah, fella. That is the real deal right there.


[User] Please transcribe this text

[Assistant] I don't think that's your problem. First of all, that's not how you run in the wild.


[User] Please transcribe this text

[Assistant] I'll keep my mouth shut.


[User] Please transcribe this text

[Assistant] Yeah, here we are. Where exactly is here?


[User] Please transcribe this text

[Assistant] White sandy beaches. Cleverly simulated natural environment. Wide open enclosures. I'm telling you, this could be the San Diego Zoo. Complete with fake rocks. Wow! That looks real.


[User] Please transcribe this text

[Assistant] Is it really? So this must be one of George's lovely dresses.


[User] Please transcribe this text

[Assistant] Right, I'll help George tidy, and Mummy Pig can help Peppa.


[User] Please transcribe this text

[Assistant] What a tidy room. Hahaha, well done everyone.


[User] Please transcribe this text

[Assistant] Just a bunch of pansies. Oh no. There's still something about that one with the crazy hairdo that I find suspicious. Nonsense, Maurice!


[User] Please transcribe this text

[Assistant] Only dead ones. I mean, if we had a lot of live people here, it wouldn't be called The Wild.


[User] Please transcribe this text

[Assistant] I heard Tom Wolfe was speaking at Lincoln Center.


[User] Please transcribe this text

[Assistant] Uh, hi everybody, my name is Chris Beltran. I'm the CEO and founder of Tummel. What we do is we connect, uhm, homeowners, private property owners, and all sorts of parking lot owners, uhm, to our app. And what our app does is it aggregates, uh, essentially their parking spaces lets them lease it out. The owners are able to make some passive income.


[User] Please transcribe this text

[Assistant] So this tool was meant for specifically San Francisco.


[User] Please transcribe this text

[Assistant] Uh, highly industrialized cities in which uhm, people need to park easier and faster rather than circling the block.


[User] Please transcribe this text

[Assistant] That is uhm, but right now it is more of the issue, is this a perceptive problem, or is this an actual problem?


[User] Please transcribe this text

[Assistant] More often than not, they say, this is awesome, why haven't anybody else created this?


[User] Please transcribe this text

[Assistant] Correct. So essentially, uhm, the percentage of sales that occur when the, uhm,


[User] Please transcribe this text

[Assistant] Commuter parks in their parking space will collect 15%, they collect 85%.


[User] Please transcribe this text

[Assistant] Currently, uh, what we're doing is we're creating lists and we're pitching the idea to them. So essentially what we're trying to do is we're trying to curate all these, uh, homeowners and private property owners throughout San Francisco that apply to, uh, oh, pretty much that play with San Francisco standards because you cannot park, um, or impede over onto the sidewalk. So obviously there's some,


[User] Please transcribe this text

[Assistant] There's some things that we have to do to double check to make sure that they are completely legal. Sure. Uhm, but otherwise...


[User] Please transcribe this text

[Assistant] Why so long? I would love to pitch it in, I would love to build this out in six days, but currently I'm not a technical founder, so that was, uh, or a technical person. Is your co-founder? My co-founder is technical. So she, uhm,


[User] Please transcribe this text

[Assistant] Well, I've been there physically and had a check board out and checking the times and stand there for eight hours a day. Uhm, but there are times where it does free up a little bit, but for the most part.


[User] Please transcribe this text

[Assistant] So what we're going to do is we're going to reach out to people during festivals.


[User] Please transcribe this text

[Assistant] You know, there's tons of festivals, there's outside lands, there's, uh, some that go on at Treasure Island, even though they know that's out of our reach.


[User] Please transcribe this text

[Assistant] Uhm, but there's many that happen around Golden Gate Park, there's uhm, you know.


[User] Please transcribe this text

[Assistant] Beta breakers, there's tons of festivals that we're gonna be pitching this around, or some type of theme, or some type of festival, some type of ballgame.


[User] Please transcribe this text

[Assistant] So 9 times out of 10, uh, people are either commuting into the city, so there are people that are not living in the city, obviously because a lot of people who live in the city don't necessarily own a car.


[User] Please transcribe this text

[Assistant] But they're commuting for what? Are they commuting for work? They're commuting for work and sometimes they're just commuting to do some contract where they gotta come into the city. Every so often there's people that come in for meetings, uhm, whole host of reasons that they come in.


[User] Please transcribe this text

[Assistant] Yeah, essentially just, so when that, when that time comes in which we do have a product launch, I'm going to create, you know, like maybe little signs at first, something cheap just to get started, uhm, you know, maybe laminated or whatnot to put it on those places where it needs to be. Right, you need something, in a lot of cases.


[User] Please transcribe this text

[Assistant] So right now, as far as the 24 hour booking and whatnot, I want to give the...


[User] Please transcribe this text

[Assistant] Sometimes maybe some owners or property owners say, let's just say like.


[User] Please transcribe this text

[Assistant] So I essentially wanted to focus on possibly like residential and private lots first and then start slowly building out. Do you have any sense yet for pricing?


[User] Please transcribe this text

[Assistant] Uhm, for private lots, obviously we're gonna be competing with private lot prices. So, we're just gonna base our model off of the model that San Francisco has already done.


[User] Please transcribe this text

[Assistant] Uh, an SF law attorney, uh, specifically for San Francisco themselves, and I've asked them.


[User] Please transcribe this text

[Assistant] Uhm, can I, it's kind of a funny question. The story goes is, uh, I called him up, I said, hey my, uh,


[User] Please transcribe this text

[Assistant] My dad is, uh, bedridden, but he's not able to work. He doesn't get enough from social security. And uhm,


[User] Please transcribe this text

[Assistant] Essentially he needs, uh, to make some passive income. Is it okay if we could rent this space out to our neighbor?


[User] Please transcribe this text

[Assistant] And they said, uh, as long as it's his house, we don't see any issue with it. Great. That's great.


[User] Please transcribe this text

[Assistant] Uhm, I think that's it. I'm pretty good. I'm excited to try it out.


[User] Please transcribe this text

[Assistant] Progress report. It's an older code, Skipper. I can't make it out. You, hire Mammal. Hmm?


[User] Please transcribe this text

[Assistant] Can you read? No, Phil can read though. Phil?


[User] Please transcribe this text

[Assistant] Most of what we do these days, our methods, our theories, our ideas, are shaped to some extent by Piaget's influence. And so what I want to do is begin this class that's going to talk about cognitive development by talking about his ideas.


[User] Please transcribe this text

[Assistant] His idea was that children are active thinkers. They're trying to figure out the world. He often described them as little scientists. Uhm,


[User] Please transcribe this text

[Assistant] An ambitious goal. He didn't start off because he was interested in children. He started off because he's interested in the emergence of knowledge in general.


[User] Please transcribe this text

[Assistant] Uhm, it was a discipline he described as genetic epistemology. The origins of knowledge.


[User] Please transcribe this text

[Assistant] But he studied development of the individual child because he was convinced, uhm, that this development would tell him about the development of knowledge more generally. There's a very snooty phrase that, I don't know if you've ever heard it before, it's a great phrase. It's, ontogeny recapitulates phylogeny.


[User] Please transcribe this text

[Assistant] Now, it's entirely not true, but it's a beautiful phrase, and Piaget was committed to this. He was very interested in saying, look, we'll figure out how the kid develops, and that will tell us about the development of knowledge more generally.


[User] Please transcribe this text

[Assistant] So assimilation would be the act of expanding the range of things that you respond to. Uhm, Piaget's example would be a baby who's used to sucking on a breast might come to suck on, on a bottle or on a rattle.


[User] Please transcribe this text

[Assistant] Is changing how you do it. A baby will form his mouth differently depending on what he's sucking on.


[User] Please transcribe this text

[Assistant] And so, these processes where you take in, I've given this in a very physical way, but in a more psychological sense, you have a way of looking at the world, you could expand it to encompass new things. Assimilation.


[User] Please transcribe this text

[Assistant] But you could also change your system of knowledge itself, accommodation.


[User] Please transcribe this text

[Assistant] And Piaget argued that these two mechanisms of learning drove the child through different stages. Uhm, and he had a stage theory which was quite different from the Freudian stage theory that we've been introduced to.


[User] Please transcribe this text

[Assistant] So his methods were to ask children to solve problems and ask them questions and his discoveries that he did them in different ways at different ages led to the emergence of the stage theory. So for Piaget, the first stage


[User] Please transcribe this text

[Assistant] The child just touches and sees, but doesn't yet reason.


[User] Please transcribe this text

[Assistant] Object permanence is the understanding that things exist when you no longer see them. So, those of you in front, you're looking at me, and I go...


[User] Please transcribe this text

[Assistant] So, adults have object permanence. Piaget's very interesting claim is that kids don't. Uhm, before six-month-olds,


[User] Please transcribe this text

[Assistant] Piaget observed, you take an object the kid likes, like a rattle.


[User] Please transcribe this text

[Assistant] You hide it, you put it behind something, it's like it's gone. And he claimed a child where he thinks it's, it's gone.


[User] Please transcribe this text

[Assistant] Things don't continue to exist when I'm not looking at them anymore.


[User] Please transcribe this text

[Assistant] And here's the idea. You take a nine-month-old, and for Piaget, a nine-month-old is just starting to make sense of objects and their permanence. You take an object and you put it here, in a cup, where the kid can't see if it's in the cup. So the kid, if you're the kid, will reach for it. You do it again. Reach for it.


[User] Please transcribe this text

[Assistant] It's like, they're not smart enough to figure out it's not there anymore, even if they see it move.


[User] Please transcribe this text

[Assistant] And this was more evidence that they just don't understand objects. And this thing takes a lot of time and learning to develop. The next stage is the pre-operational stage.


[User] Please transcribe this text

[Assistant] Uhm, the pre-operational stage, a child starts off grasping the world only in a physical way, in a sensory motor way. But when he gets to the pre-operational period, the capacity to represent the world, to have the world inside your head, comes into being. But, it's limited, and it's limited in a couple of striking ways.


[User] Please transcribe this text

[Assistant] One way in which it's limited is that children are egocentric.


[User] Please transcribe this text

[Assistant] But then you ask him to draw it as it would appear from the other side. And children find this extraordinarily difficult.


[User] Please transcribe this text

[Assistant] They find it very difficult to grasp the world as another person might see it.


[User] Please transcribe this text

[Assistant] Uhm, another significant finding Piaget had about this phase of development concerns what's called conservation.


[User] Please transcribe this text

[Assistant] And the notion of conservation is that there's ways to transform things such that


[User] Please transcribe this text

[Assistant] If you take a glass of water and you pour it into another glass that's shallow or tall, it won't change the amount of water you have.


[User] Please transcribe this text

[Assistant] If you take a bunch of pennies and you spread them out, you don't get more pennies. But kids, according to Piaget, don't know that.


[User] Please transcribe this text

[Assistant] Kid says they're the same. Then she said, now she has to, which one has more?


[User] Please transcribe this text

[Assistant] And it's an amazing finding kids will do this, and it's a robust finding. Here's another example. So, they're the same.


[User] Please transcribe this text

[Assistant] So, the mathematical notions of infinity or logical notions like logical entailment are beyond a child of this age. The child is able to do a lot, but still it's to some extent stuck in the concrete world. And then finally at around age 12, uhm, you could get abstract and scientific reasoning.


[User] Please transcribe this text

[Assistant] Feared a lot better than did Freud or Skinner.


[User] Please transcribe this text

[Assistant] Uhm, for several reasons. One reason is these are interesting and falsifiable claims about child development.


[User] Please transcribe this text

[Assistant] Pulling together all sorts of observations in different ways, wrote many, many books and articles, and articulated his theory very richly. And most of all, I think, he has some really striking findings. Before Piaget, nobody noticed these conservation findings. Before Piaget, nobody noticed that babies had this problem tracking and understanding objects. At the same time, however,


[User] Please transcribe this text

[Assistant] Uhm, there are limitations in Piaget's theory. Some of these limitations are theoretical.


[User] Please transcribe this text

[Assistant] Uhm, it's an interesting question as to whether he really explains how a child goes from a concrete thinker to an abstract thinker. Or how he goes from not having object permanence to understanding object permanence.


[User] Please transcribe this text

[Assistant] There's methodological limitations. Piaget was really big into question and answer.


[User] Please transcribe this text

[Assistant] But one problem with this is that children aren't very good with language. And this might lead you to underestimate how much they know.


[User] Please transcribe this text

[Assistant] And this is particularly a problem the younger you get.


[User] Please transcribe this text

[Assistant] So, when you tickle somebody, under what circumstances will they laugh? Uhm, where do you have to tickle them? Can you tickle yourself? Does it have to be a surprise? And so on. It turns out very difficult to study this in a lab. You're not going to have your experimental credit. You come into the lab and say, okay, I'm the graduate student, uh, uh, uh, and he...


[User] Please transcribe this text

[Assistant] And finally there's factual. What do infants and children really know?


[User] Please transcribe this text

[Assistant] It's possible that due to the methodological limitations of Piaget, he systematically underestimated what children and babies know. And in fact, I'll present some evidence suggesting that this is in fact, that this is the case.


[User] Please transcribe this text

[Assistant] So, I want to introduce you to the modern science of infant cognition.


[User] Please transcribe this text

[Assistant] And to discover the intelligence of babies, we have to ourselves be pretty smart in developing different techniques. To study what a baby knows, you can't ask your questions. Babies can't talk.


[User] Please transcribe this text

[Assistant] One of the few things babies can do is they can suck on a pacifier.


[User] Please transcribe this text

[Assistant] And you might think, well, you know, how can you learn anything from that? Well, for instance, you could build machines that when babies suck on a pacifier, they hear music. Or they hear language. And then you could look at how much they suck on a pacifier to determine what they like.


[User] Please transcribe this text

[Assistant] And uhm, I have up here, this is a picture of, of Elizabeth Spelke, who's a developmental psychologist who's developed the most research on looking at babies' looking times and what you could learn from them. And I have here two ways you could learn from looking. One is preference.


[User] Please transcribe this text

[Assistant] So for instance, suppose you want to know, for whatever reason, do babies like the looks of dogs or cats? Well, you could put a baby down,


[User] Please transcribe this text

[Assistant] Have a picture of a dog here, a picture of a cat here, and see which one the baby looks at.


[User] Please transcribe this text

[Assistant] You could also do habituation and surprise. And much of the studies I'm going to talk about here involve habituation and surprise. Habituation is a fancy word for boredom.


[User] Please transcribe this text

[Assistant] What you do is, you show a baby something over and over again.


[User] Please transcribe this text

[Assistant] Now remember, from behaviorism, the baby will learn, this isn't very interesting. Then you show the baby something different.


[User] Please transcribe this text

[Assistant] If the baby really sees it as different, the baby will look longer.


[User] Please transcribe this text

[Assistant] And you could use that as a measure of what babies find different. For instance, suppose you want to know if a baby can tell green from red.


[User] Please transcribe this text

[Assistant] Well, you could show the baby a green patch, a green patch, a green patch, a green patch, the baby will get bored. Then a red patch. If they all look the same to the baby,


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[Assistant] Uhm, the baby will just continue to tune out. But if the red looks different, the baby will perk up. And this is in fact one way they study color vision in babies. Surprise is related to this.


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[Assistant] If babies are like, if babies also think it shouldn't happen, they might look longer. And essentially what happens is scientists do magic tricks.


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[Assistant] To start with some real examples, a lot of this infant research has gone back to the Piagetian question of object permanence, asking is it really true babies don't know


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[Assistant] That objects remain, that objects, uh, remain even when they're out of sight. So one very simple study by, uh, by Spelke and by Arjom.


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[Assistant] I'd maybe shown a block with a bar going back and forth. Like that. So the bar just goes back and forth. Now,


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[Assistant] There's something you do that's so obvious you probably don't even know you're doing it.


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[Assistant] And what that means is there's something in the middle that you've never seen before.


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[Assistant] But of course, if you were a simple perceptual creature, you would just see that there'd be a bar on top and a bar on the bottom. You wouldn't expect anything in the middle because you never saw anything in the middle. So what you do then, is you show babies this, and then you show them either B or C.


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[Assistant] And if we do this with adults, you expect B. C is almost a joke.


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[Assistant] And in fact, babies respond the same way. Babies expect there to be an entire, complete bar and are surprised and look longer at the broken bar.


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[Assistant] Well, here's another study by René Bayardjon looking at the same thing in a different way. You show the baby, say a six month old, uh, uh, a stage with a block on it. Then a screen rises and obscures the block. Now, if the babies expect


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[Assistant] The block to still be there, they should think the block should, should stop the screen.


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[Assistant] On the other hand, if out of sight, out of mind, they should expect the screen to keep going. So what you do is, you set up a couple of displays, one where the block keeps, is stopped, the other one where you take, you take this away if a trap door, and it keeps going.


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[Assistant] This research suggests that infants understanding of the physical world is there from the very start, but at the same time, not entirely. We know there are certain things babies don't know. Here's an example.


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[Assistant] Suppose you show babies this. You have a block here, and they have something above there, floating in mid-air.


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[Assistant] Babies find this surprising. Even six-month-olds find this surprising. It violates gravity.


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[Assistant] But six month olds aren't smart enough to know that a block just stuck over here is also surprising.


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[Assistant] 12-month-olds will think that it should fall, 6-month-olds don't. And even 12-month-olds don't find anything weird about this, while adults are sophisticated enough to understand that that should, that that's an unstable configuration and should fall over. So, although some things are built in, some things develop.


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[Assistant] And this raises the question of how do we explain development? How do we explain when babies come to know things that they didn't originally know?


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[Assistant] Well, one answer is, uh, neural maturation. Growth of the growth of the brain. Uhm, most adult, most of the neurons you've had, you have now in your head, right now, you had when you were in your mother's uterus.


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[Assistant] What happens in development isn't for the most part the growth of neurons, it's for the most part pruning, getting rid of neurons. So the neural structures change radically as babies kind of get rid of excess neurons through development. At the same time though, uhm, connections between neurons grow like crazy.


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[Assistant] And this process of synaptic growth where there's the connections across different synapses peaks at about two years.


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[Assistant] Finally, remember myelination, where you sort of get this fatty sheath over your neuron to make it more effective? That also happens through development.


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[Assistant] And so it could be the problem is the baby's brain doesn't develop yet. Another possibility is there's problems with inhibition.


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[Assistant] And there's all sorts of independent evidence that babies lack this control. A part of their brain that could control certain behaviors, uhm, is just not active yet.


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[Assistant] Uhm, there's a very nice illustration of inhibitory problems from a Simpsons episode that actually sort of covers anything you might want to know about developmental differences.


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[Assistant] As if they, they know it's there but they can't stop themselves from reaching. And we'll continue this theme a little bit later. Finally,


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[Assistant] So uhm, so here's a study by Michael McCloskey with college students. Here's the idea.


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[Assistant] You throw a ball through, so whips through the tube and comes out. Question is, what happens to it? Does it go in the path of A, or does it go in the path of B? Without looking around, who votes for A? Who votes for B?


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[Assistant] But here's the twist. Ask somebody, what if you took a tube and you squirted water through it? Where would the water go?


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[Assistant] Nobody chooses B. Everybody knows the water would continue in a straight line.


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[Assistant] Suggesting that when you have experience, that helps you out. But in the absence of experience, you're kind of lost.


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[Assistant] Babies start off with some social preferences. If you take newborn babies.


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[Assistant] It's very hard to do research with newborn babies, actually, because of the


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[Assistant] Uhm, there's some preferences with humans and with other primates to favor faces. Babies are also social animals, too. So they're natural mimics. Uhm, Andrew Meltzoff, for instance, has found that if you go to a newborn baby, and if you get it, if you find a newborn baby, this is the first thing, first thing you should do. Stick your face right up the newborn baby and go like this. And stick your tongue out.


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[Assistant] And Meltzoff finds that babies more often than not.


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[Assistant] To, that we've looked at for the physical world, look at the social world. And to illustrate one of the studies, I'll tell you about a study that I did, uh, with Valerie Kuhlmeier and Karen Nguyen. And so what we tested was 9-month-olds and 12-month-olds, and we showed them movies. Sort of sitting down or seeing a movie.


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[Assistant] Where one character is going to help a ball achieve a goal, and another character is going to hinder the ball.


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[Assistant] And then we're going to see whether they expect the ball to approach uhm, the one that helped it versus the one that hindered it.


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[Assistant] So, this is what a baby would see. This is literally the same movie a baby would see in the experiment. The thing is,


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[Assistant] For these sorts of experiments, there's a lot of control. So something that's a square in one movie will be a triangle in another movie. Something that's on the top in one movie will be, uh, on the bottom in another movie. So this is an example movie. But this is what babies would see.


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[Assistant] And what we find is, statistically, babies look longer when shown a movie where it approaches the one that hindered it versus helped it.


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[Assistant] That babies should themselves prefer the creature who's the helper versus the hinder. And to explore this, a graduate student in his department, Kylie Hamlin,


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[Assistant] Has started a series of studies where they show babies three-dimensional scenes and then give them the characters and see which one they reach for. So here's video so you can see how this experiment is done.


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[Assistant] Now the next trial is from a different study, different things were used, and the baby is given a choice. And uhm, one thing to know methodologically is the person giving a choice is blind to the study. And blind here is a technical term meaning she had no idea what the baby saw.


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[Assistant] And the point about this is to avoid either intentional or unintentional sort of trying to get the answer you want. She couldn't do that because she didn't know what the right answer is. So here's what the baby would see.


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[Assistant] Now I want to raise a huge developmental puzzle. And the puzzle is...


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[Assistant] That example, a little bit more detail. Any questions? What are your questions? Yes, I'm back.


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[Assistant] Typically, uhm, I don't know for those particular children, but typically, on those tasks, three-year-olds and young four-year-olds tend to fail. And around the age of four or five, kids tend to succeed. There's sort of a period around the age of four, four and a half, where kids make the transition from a failure to success.


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[Assistant] The question, by the way, was when do children, in that video, when were the, what were the ages of children, uh, who fail and who pass?


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[Assistant] Uhm, the question of whether discriminant conditioning has been used with babies to explore what sort of concepts they have. I, I don't know. Does anybody? It has been.


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[Assistant] It wasn't what they were interested in in the experiment. My bet is, when they chose the stickers, they had a pretty good sense of why, of which ones the boys would prefer in those studies. Uhm, the question of why a boy might prefer one sort of sticker,


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[Assistant] And you might get a different response if a girl is gonna come up later when we discuss different theories of sex differences. But that was something I think they were just assuming in the study to get it off the ground.


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[Assistant] About that you can do something with the intent to deceive. They don't understand.


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[Assistant] That somebody could choose what you chose in a malicious way.


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[Assistant] This is possible. This is one respectable theory. But the alternative is, they have the right knowledge, but they suffer from problems with inhibition.


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[Assistant] So consider both studies. The first study, the one of the deceptive, uh, dolls with the big shoes and little shoes, is actually fairly difficult. And it's possible that children kind of got overwhelmed with it. And when asked, uhm, what would the mother think, who the mother would think stole the food, responded with who really stole the food.


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[Assistant] And that there's some pull towards the right answer that makes this task difficult.


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[Assistant] Where, when asked to point to what he wants, he just couldn't help but point to the one he wanted. And that the extra work it takes to lie, uhm, was beyond him.


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[Assistant] And in support of the second alternative, uhm, even adults find these tasks involving lying and deception more difficult. We're slower at them, we make more mistakes than, than uhm, than tasks that don't involve lying and deception.


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[Assistant] So I'm raising this not to solve the problem. You'll read more about it in the Peter Gray textbook and more about it in the Norton Readings on Development.


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[Assistant] But just to raise this as an interesting area of debate Another interesting area of debate


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[Assistant] What's the relationship between different sorts of development? So I started off with Piaget. And Piaget, like Freud,


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[Assistant] Believed in general, across the board changes in how children think.


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[Assistant] An alternative though, is that they're separate modules.


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[Assistant] Who claim that the whole idea of a child developing as a single story is mistaken. What you get instead is their separate pre-wired systems for reasoning about the world.


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[Assistant] Uhm, these systems have some built-in knowledge and they have to do some learning. But the learning pattern varies from system to system. And there's a separateness to them. Why should we take this view seriously?


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[Assistant] Well one reason is that there are developmental disorders that seem to involve damage to one system, but not to another. And the classic case of this is a disorder known as autism. And autism is something


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[Assistant] I've always found a fascinating disorder for many reasons. It's actually why I entered psychology. I started off working with children, uh, with autism. And


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[Assistant] It could be taken as a striking illustration of how the social part of your brain is distinct from other parts of your brain. So what autism is is a disorder that strikes about one in a thousand people, mostly boys.


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[Assistant] And the dominant problems, concern, consist of a lack of social connectedness, problems of language, problems dealing with people, and more generally, a problem of what the psychologist Simon Baron Cohen has described as mind blindness. In that autistic people show no impairments dealing with the physical world.


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[Assistant] They show no impairments on, they, they don't necessarily show any impairments on mathematical skills or spatial skills. But they have a lot of problems with people.


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[Assistant] Now many autistic children, uhm, have no language, they're totally shut off from society, but even some of them who learn language and who manage to get some sort of independent life, nevertheless, will suffer from a severe social impairment.


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[Assistant] And this could be shown in all sorts of ways. A simple experiment developed by Simon Baron Cohen goes like this. You show this to three and four year olds. There's four candies there. And you say, this is Charlie in the middle. Which chocolate will Charlie take?


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[Assistant] For most children, and most of you, I hope, the answer is pretty clear. This one.


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[Assistant] Because they don't instinctively appreciate that people's interests and desires tend to be attuned to where they're looking.


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[Assistant] Another sort of task, which is a task that's been done hundreds, perhaps thousands of times, is known as the false belief task. And here's the idea.


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[Assistant] Maxie leaves and a second doll enters. The second doll takes the ball out of the cover and puts it under the bed.


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[Assistant] Maxie comes back, and the question is, where will Maxie look for the ball?


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[Assistant] Now this is a question about your understanding about minds.


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[Assistant] The question of where is the ball really is a question about the physical world. Everyone can solve it.


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[Assistant] But this question is hard. The right answer is Max will, Maxie will look in the cupboard. Even though it's not really there. Because Max, Maxie has a false belief about the world.


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[Assistant] Three year olds find this difficult. Two year olds find this difficult.


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[Assistant] Four year olds and five year olds are able to pass this task. Normal adults are able to pass this task. Children with autism have serious problems. And often people with autism who are otherwise very high functioning will fail this task. They'll say, oh he must think it's not, he's going to check under the bed.


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[Assistant] Any questions about, uhm, about autism?


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[Assistant] This is, they're both, they're both experiments designed to tap an appreciation of false belief. The deception one, with the shoes and everything,


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[Assistant] Looked at it in the course of deception. Can you understand that a mother might think it's that person even though it's really that person? And our kid failed. This is a sort of stripped down version without all the fanciness. But it tests exactly the same thing.


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[Assistant] Which won't answer your question, but we'll put it in a broader context. Simon Baron Cohen argues that there's certain abilities that tend to be more sequestered for males and other abilities that are more sequestered, more focused on females. Uhm, social abilities, he argues, tend to be more female than male.


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[Assistant] So the way Barron puts it provocatively is to be a man is to suffer from a very mild form of autism.


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[Assistant] And as such, it stands to reason that they'd be more sample from the male population than the female population. That's such an interesting issue that, again, when we return to talk about sex differences, we'll look at that in a little bit more detail to see if it's supported by the evidence. Yes?


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[Assistant] The question is, how do you think about the severity of autism with regard to developmental stages? And sort of surprisingly, autism can't really be thought of.


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[Assistant] So it's not like an adult with autism is like a three-year-old or a two-year-old. In some ways, somebody with autism isn't like any child at all. Uhm, any normally developing child at all. So it's not really a developmental


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[Assistant] Uh, it's not really a developmental delay in the way that it might make sense to think about certain forms of retardation.


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[Assistant] On the other hand, when we think about how severe autism is, we do look at things like how much language does a person have. And in that sense, it is related to development.


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[Assistant] Uhm, the majority of people with autism. It's a good question. The question is, uhm,


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[Assistant] What are the chances somebody with autism will be able to overcome their deficiencies? Uhm, autism is a funny disorder in that there's a lot of media publication and media presentation. Often the people who are showcased in the media tend to be very exceptional. So there's a woman, Temple Grandin.


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[Assistant] Who's autistic and has anybody here heard of Temple Grandin?


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[Assistant] Uhm, she wrote some wonderful books about her experience as an autistic person, but she's very unusual.


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[Assistant] So, a lot depends, to answer your question, how one defines autism. And whether one includes Asperger's Syndrome, which is a limited, a more mild syndrome as a form of autism.


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[Assistant] The answer is that the majority of people with autism have severe problems and will not, and at this stage, with this level of therapy, will not lead a normal life.


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[Assistant] The question is about so-called autistic savants. So Rain Man, the character played by Dustin Hoffman, had extraordinary mathematical abilities. And some people with autism have extraordinary


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[Assistant] But, this is a very small minority. This is a very, it's fascinating that happens at all, that you have severe damage, but compensated with some powerful skill. Now I know I'm answering a question I think in a better way. But, it's actually very rare.


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[Assistant] Most people with autism do not have any exceptional abilities that go along with it.


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[Assistant] And so far when reviewing the developmental data, we've talked about two of them, physics and people. An object module and a social module.


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[Assistant] But other people have argued that there's a special module in your brain for dealing with artifacts. That is, things like tables and chairs and cars and forks.


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[Assistant] Some people have argued there's a module for sociology, for dealing with human groups, races and classes and so on. Some have even argued that there's an intuitive biology, a common sense biological understanding of the world that's separate from, uhm,


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[Assistant] From your understanding of people and physics. And in fact, the most dominant proponent of the view is our very own Frank Kyle.


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[Assistant] Master of Morse College at Yale, who has strongly defended the notion of an intuitive biological module.


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[Assistant] There might also be profound general differences between children and adults. Not just specific to how you think about objects, or how you think about people, or how you think about this or how you think about that, but rather more general. And one claim, which we're going to return to briefly,


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[Assistant] Next class when we talk about language is that there's a very, very big difference between a creature that doesn't have language and a creature that does.


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[Assistant] And part of the claim is that learning a language, learning to speak, reconfigures the human brain in such a way that is really exceptional, and that has no parallel in any other species. And this is an interesting claim, and uhm, one we'll talk about.


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[Assistant] There are reasons to study development even if you are not interested in children.


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[Assistant] Questions about adults. And Stephen Jay Gould has a very nice example of this. He asked a question. Is a zebra


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[Assistant] A black animal with white stripes or a white animal with black stripes.


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[Assistant] Now, you could look at adult zebras all day long and you're never going to figure this out.


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[Assistant] But if you want to know the answer, I knew it, but I forget what it is. It doesn't matter. But if you wanted to know it,


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[Assistant] Mom and I are elephants.


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[Assistant] At the time, I didn't know it was illegal to sex up a cow on the Capitol steps.


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[Assistant] Damn it! There's some things they just don't teach in the police academy. Movies.


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[Assistant] When Captain Grandpa tells you all to do something, you must do it.


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[Assistant] Uhm, I don't know how to tell you this, but your baby doesn't have any legs!


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[Assistant] I am in the market for a backup mistress, possibly two.


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[Assistant] All the children have parts in the play.


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[Assistant] Peppa is going to be Little Red Riding Hood. Mummy Pig has made her costume.


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[Assistant] Danny Dog is going to play the Big Bad Wolf. Grandad Dog and Mummy Dog are helping him practice.


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[Assistant] Pedro Pony is going to play the Hunter. He will rescue Pepper from the Big Bad Wolf.


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[Assistant] Uhm, with a different sort of demonstration, I want to just show you one of the change blindness studies that has been done in the real world.


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[Assistant] And these videotapes are not available publicly, but you get them from the web and see them as little javascripts. So this is one of the first studies done by Dan Simons when he was at Cornell. And his advisor at the time was our Frank Kyle, who's now in our department. So here's the study.


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[Assistant] Surprise of the gorilla and the fact that it's hard to see the flickering, the object that's flickering, and appreciate the big moral of this, because the big moral of this is actually, I think, striking and quite important.


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[Assistant] You think, right now, that you're perceiving the world.


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[Assistant] I look down on you and I think I have a whole sense of where everybody is. I can't see everybody perfectly in back, you're kind of far away and blurry, but there's a sense in which I have a world around me.


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[Assistant] So you're looking up and, and you think you have a sense of the world both in perception and memory. The change blindness experiment suggests that this isn't true. The change blindness experiment suggests that if you look at me for a second, and during that second,


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[Assistant] Uhm, all of your classmates change positions, including those next to you. You are extremely unlikely to notice. The change blindness experiment suggests that if you turn your eyes away from me towards there for a second and turn back, and I'm dressed entirely differently,


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[Assistant] You wouldn't notice. The exceptions would be if you told yourself consciously, remember what this guy's wearing.


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[Assistant] Usually it's okay because your memory and your visual system exploits a basic fact about the universe, which is that most things stay the same most of the time.


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[Assistant] So one of the big surprises when people started making movies involving cuts was it is extremely difficult to get everything continuously right.


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[Assistant] And you need to work very hard to notice. So there's all these continuity errors that creep up into movies. And you have to be a film buff or writing it down to even notice this.


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[Assistant] And the overall moral here then is that your perception of reality is a lot


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[Assistant] Which is a sort of fraction of a second of sensory residue of what you're hearing and what you're seeing. Working memory, short term memory, and then long term memory.


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[Assistant] Is actually just, in fact, uhm, is storage differences. So, long-term memory.


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[Assistant] This is your memory like the hard drive of your computer.


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[Assistant] This is the memory you walk around with. Uhm, it includes all the words in English. Just for example. Sixty, eighty thousand words. It includes everybody you've ever met. Languages, faces, uhm, stories, locations, nursery rhymes, songs, TV programs. Nobody knows the storage.


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[Assistant] It is not true that you remember everything that has ever happened to you. That, there's no reason to believe that this is true.


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[Assistant] At the same time though, you have a huge amount stored in your brain, in long term storage. And nobody actually, it has to be limited because it's a finite limited brain.


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[Assistant] But nobody knows how big it is. Nobody knows how many terabytes you carry around in your brain. And uhm, but it's a lot. Compare this to working memory.


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[Assistant] The short-term memory, which is actually very limited.


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[Assistant] Your memory of what you could store on and what you could hold in consciousness right now is quite limited. Uhm, here is an exercise. Do not write these things down. I want you to remember them. I'm just going to give you a few numbers.


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[Assistant] Under normal circumstances, the cognitive psychologist George Miller said that this sort of suggested that the standard


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[Assistant] Memory storage of short-term memory is 7 plus or minus 2.


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[Assistant] And what that means is, uhm, anywhere from five to nine, roughly. Some of you, I bet, can beat that.


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[Assistant] Some of you, on a not so good day, maybe won't make it that much. Uhm.


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[Assistant] Now, 7 plus or minus 2 is what you're, so that's what you hold in consciousness. You know, I tell you, 1421.


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[Assistant] Walk around. Okay, fourteen, twenty-one. You hold that in conscious, no problem. But I throw eleven numbers at you, you can't, some dribble out, you can't hold that in your conscious window, in your short-term memory. Now this raises the question, seven plus or minus two, what? And the answer


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[Assistant] Seems to be what George Miller calls chunks. And a chunk is a basic memory unit. Something you think of as a single, individual entity. Uhm.


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[Assistant] So, suppose you see the string of letters. L-A-M-A-I-S-O-N.


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[Assistant] Uhm, if you don't know, if you can form these into words, and you have to remember them, these are eight chunks. You have to just pick them up separately. On the other hand, if you break them up into four words, you could just remember that's four chunks.


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[Assistant] Those of you who are computer scientists or mathematicians or for whatever reason know binary numbers could convert into a single binary number. Anybody know what a number is?


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[Assistant] It is murderously hard to remember that. They've done experiments. They've taken people in the lab who don't know how to play chess. They set up a chessboard. And then they say, okay, look at this for five minutes. Then they take it away, set it up again.


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[Assistant] And it's, it's murderously hard. You know, there's a horsey thing on the side there and everything. But!


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[Assistant] If these chess pieces are set up in some way that's logical for a chess player,


[User] Please transcribe this text

[Assistant] Similarly, uhm, football coaches have been tested on their memories of football diagrams. And they have like a photographic memory for football diagrams, because it corresponds to things that make sense. Architects, architects could have a photographic memory, a perfect memory for floor plans.


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[Assistant] And this is a theme we're going to return to when we get to long term memory. Depends on a large extent on how much you understand it.


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[Assistant] And this shows up in expertise effects. Now,


[User] Please transcribe this text

[Assistant] This is what's happening so far in short-term memory. How much you hold in there. The question is how do you get it into long-term memory? So you have long-term memory, your major storage system.


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[Assistant] To long-term storage. Well, there's one thing


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[Assistant] But this will not typically get things into long term memory. To get things into long term memory, rehearsal is usually not enough. You need to do other things.


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[Assistant] Typically what you need is structure and organization.


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[Assistant] Which nicely illustrates the fact that the more you structure something, the deeper you think about it.


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[Assistant] And all of the subjects saw the same strings of words. There were 48 words. They were not told to memorize the words.


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[Assistant] One third of the subjects was told, look, some of these words are going to come out in capital letters, some of them not capital letters. Press a button for capitals, non-capitals. Sure.


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[Assistant] The other group was told some of these words will rhyme with train. Others won't. Press a button if it rhymes in train.


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[Assistant] Then they were asked as a surprise, what words did you see? And the findings looked like this. When they were asked to focus on just what the word looked like, memory was very poor.


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[Assistant] The sound better, the meaning better. If you want to remember something, the best way to remember it is to give it meaning, to give it sense. Uhm, this is illustrated


[User] Please transcribe this text

[Assistant] Through a very ancient technique, which is that the way to remember things that are otherwise arbitrary, is to give them some organization through memory tricks. Through vivid imagery, or, or, or, uhm, songs, or poetry. Uhm, and there's a lot of examples of this.