Nathan Sobo: Exactly. Right. And that is a reality. You can’t just go off into some ethereal realm and like build software and divorce from the realities of scarcity in the world, right? The sign that we built something valuable is that people will be willing to pay for it, ultimately. But I’m very patient, though. I’ve been working on this for a long time… But yeah, all that said, I’m excited to take some real revenue… Even though the margins won’t be that great, because AI is expensive. It’s compute-intensive.

Austin Parker: Yeah, well… Yeah. We’ve got V6 now. We’re good. People trying to impose scarcity, I think, on the idea of space on the internet is just very – it doesn’t sit well with me, and it’s why I’m so excited about stuff like AT Protocol, and this idea of a web and an internet that you invest in. This is your space online. This is your part of this global network of human beings, and you aren’t just renting time from some large company, or a billionaire, right?

Jerod Santo: Yeah, but that ruins the scarcity problem.

Jerod Santo: Well, if I was the person issuing IPv4 addresses for a price, I’d kind of like this scarcity. It’s helping me out. I can just keep raising my prices and issuing them… It’s not too bad if you’re holding the keys, right?

Damien Riehl: Really, what we’re describing here is there’s a scarcity mindset, and there’s an abundance mindset. The scarcity mindset is that – around 1979 accountants were really worried with this artificial intelligence that’s called the spreadsheet. Because they said, “Wow, all we do all day is use ledgers, and we add and subtract numbers, and machines can do that in seconds. That’s gonna put us all out of work.” But what happened was that when the clients realized, “Oh, it’s not going to take me a week to get that ledger back, but it’s going to take seconds… Let’s do scenario two, and scenario three, and scenario four, and run more scenarios”, and now we have more accountants than ever, because the tools are actually a force multiplier, that now there’s more accounting work, rather than less. So that is an abundance mindset, that is not a scarcity mindset.

So the real question in my mind, and maybe it should be on all of our minds, is “Is the scarcity mindset that I described with worlds one, two, three and four - is that going to be our future? Or is there an abundance mindset where we just have 10x or 100x productivity and we keep growing and growing?”

Varun Mohan: [05:33] One of the really sort of unique things about GPUs is that, unlike CPUs, it’s kind of tricky to virtualize. One common thing that we have with CPUs is you can put a bunch of containers on a single VM, and then you can kind of make use of the CPU compute effectively. You can basically dump 10 applications onto a CPU and it’s perfectly fine. For GPU, it’s a little bit more messy, because the GPU doesn’t have a ton of memory. So you can’t just load up infinitely many models on there. Let’s imagine you have a GPU with 16 gigs of memory, and each of these models takes like 10 gigs. You can’t really even put two applications on there. So then that already becomes a big issue. And that’s sort of what a lot of these large deep learning workloads were struggling with.

When I was at Nuro, one big problem we had was we had around tens of models, but we had these workloads that needed hundreds of GPUs. Some of them even thousands of GPUs. And we struggled to basically make it so that we were even able to use the hardware properly. And then you can imagine the complexity then stacks with now we’re in a state where companies have trouble even getting access to 10 GPUs, because of NVIDIA sort of scarcity issues. And then also the cost of a GPU is not like a CPU; it’s significantly more expensive. The cost of a single H-100 chip is well over 30k. So these aren’t like very cheap chips. So there was a big need at the time to figure out how do we leverage the hardware properly? …and sort of that’s what we had to build software for.

Adam Jacob: Alright, so there’s three levers in sort of open source business models. There are no open source business models; open source isn’t a business model, blah, blah, blah, blah. Let us just admit that that’s true, and then we’re going to go on calling it open source business models, because that’s just what you have to do.

So open source business models - three levers: copyrights, trademarks, patents. All three of those things - you can think of them as just ways that you create scarcity. And then in order to get people to pay you for things, there needs to be scarcity. So you don’t tend to pay for things you can get for free, is the way to think about it. So the one people are most familiar with is copyright. So we say, “Hey, I made System Initiative, therefore you can’t have it unless you pay me money. And if you think it’s valuable, you pay me; if you don’t think it’s valuable, you don’t. No harm, no foul.” That’s the copyright lever.

[54:10] The trademark lever says “Only I am allowed to make System Initiative and call it System Initiative. And if you want to build something similar, and also call it System Initiative, you can’t, because that’s my brand. That’s the thing I do. That’s how I make money. That’s my brand promise.”

And then patents are the third, where you can be like “Hey, we do this in a novel way, and nobody but us are allowed to do it in this novel way, because if you try, we’ll find our little patent and we’ll waggle our finger at you and be like “You can’t do it, because my patented algorithm says you can’t, unless you pay me money.”
So most open source business models hinge on copyright. They say by keeping some amount of features proprietary, and so you can’t have them without paying the money, then I find someone who’s willing to pay for those features, and they do, and therefore we’re happy. Or you say, “Hey, here’s my SaaS, and I’ll run that here… But I’m the only one who’s allowed to, blah, blah, blah, blah.” Lots of shenanigans sort of around copyright.

So the Red Hat model says, “Okay, the actual value, the value people get is in products, not in software.” So when you buy a product – for example, when you record this podcast, y’all want us to record a separate audio track, just in case the one we’re doing right now doesn’t work. And I’m using Audacity to do it. And it’s not a very good product. And so if – I mean, it is; I’m so sorry, Audacity people…

Beth Rudden: Right. Yeah, ask it about a product that you are not selling, that you’re not marketing. Ask it about something you know, and you know to be true, and ask it to say, “I would like you to market a blue tomato. We have blue tomatoes. Blue tomatoes grow on trees. Could you market that for me?” And it will give you marketing for blue tomatoes that grow on trees.

And so I really want people to come from a space of abundance, not scarcity. I really want people to think about what do they have right now, and what every human being has is their own experience. And what this AI has been trained on is a very small number of people’s experience who have been on the internet and been writing on the internet. And so my opinion is that every single human being is already impacted by AI, they should be using it. And they should be using it in a way that I used it to help my daughter come up with an analogy on reciprocals. I asked it to come up with a good metaphor in order to explain what an ontology does. There’s so many different things that you can use it for, and I really think that the best way that people can do is go out and use it and ask it about things that you know. Many authors are like “Oh, so I wrote 10 books, not for 4. Hah!”

People are starting to see that it’s going to generate the next proper noun, the next predicate. [laughs] The next syntactically correct – and I’m doing it in a sentence, but it’s so smart, because it has been fed so much data… But here’s a myth. You know, I talked a lot technically about using ontologies, and knowledge graphs, and concept hierarchies, and all these things. But here you go - all of what I just talked about can run on something that was like the very first iPhone one. The myth - you do not need big data and big honking machines to create AI. And I would ask who does that myth serve? And if everyone could understand how to use the data that they have, that is special to them, that they understand, that they – like, if you take your grandfather’s journals and put them into an AI so that you can have a conversation with your grandfather, this is what the technology is enabling us to do, and we want it to be distributed to every human on Earth, because every human on Earth is being impacted by AI.

Adam Stacoviak: Scarcity and demand.

Leo Mutuku: [20:28] So when I think about open data, it is in a way democratizing access to data… And this means that if some investments are put in place to collect this data, it’s still beneficial to our wider group of people that just those who collected the data.

Now, specifically in AI and agriculture, as I’ve mentioned earlier, it’s quite difficult to get accurate groundtruth data to support interventions. So there are a few projects now that are supporting the collection of that data; this data has been collected from a small sample of the farms in the region, so the applicability of this data is only as useful as whether it can be reused on repurposed in other areas, or at least used as training datasets for algorithms that may not necessarily impact the places where the data has been collected, but are for similar contexts.

Traditionally, we have been saying that one of our biggest development challenges here in Africa is the scarcity of data. So open data I think creates mechanisms and communities around sort of meeting that data gap, and being able to support further application of datasets.

You’ve mentioned the issue of community, and I find the concept of open data serves communities in two ways. So there’s the community of the producers, that can pool together resources, they can pool together data, and reused them, reproduce whatever experiments or research that has been done, without having to expend resources. But at the same time, open data I think has a close relationship to what is usually called citizen science, that we can also encourage the communities themselves to contribute to the creation of this data, and then when it’s placed in an open format and in these open repositories, it’s also accessible back to them, either as outputs of technologies applied on this data, or they can use it directly.

So I think open data creates this community resource that, again, preserves some of that indigenous knowledge we alluded to earlier, but at the same time promotes the development of new technologies where there might be sparse data previously, because of this increased availability or relevant datasets.

Adam Stacoviak: Very trivial, because it’s like, that should be table stakes, right? That should just be how it works anyways. I don’t understand why–I mean, I guess I do understand why. Greed, probably. Greed and scarcity, right? If you make things scarce, you can drive the price up, you can lock certain people out, and you can essentially be very biased and you can showcase your bias. It can be class bias, essentially; like, if you have enough money, you can play in my park, or come to my house, or whatever it might be. But if you don’t, then you’re not welcome here, and I won’t let you access these markets and make the money that we make. I like the way you pair up this side of the business. It’s really, really interesting.

I want to mention something that was said by Anish, one of your newly founded board members partner at a16z, Andreessen Horowitz, who recently threw a bunch of money at you, $58 million in a series B early this year. Congratulations, by the way. But one thing that Anish talked about was just like this change of psychology from what might be the current day market, essentially, Gen Z, right? That they’re able to embrace more risk. And you’ve got things like you alluded to earlier, if a million people hearted something on Instagram - well, they kind of did that, with GameStop. Like, this whole big thing changed.

So we saw how everyday folk with hundreds or thousands, not tens of thousands, but hundreds and thousands of dollars can really change the way the markets drive. That’s an interesting place. And what you’re giving is this is where we can take more risk. Do you see people being more risky with their money because of the access to the market and the feedback loop for what you give? Because you said $20,000 as an opening account, or on average account balance, versus hundreds or thousands with a Robinhood or something like that? Do you see people taking more risk?

Daniel Whitenack: Yeah, so if I’m understanding right, Abba, you’re sort of using the GANs for data augmentation in the case of data scarcity… So do you take the actual observed imagery and use that with your discriminator in this framework to create these augmented images? And could you describe a little bit – was that the initial solution that made sense to you, or is that something you stumbled on later? Could you describe maybe a little bit more of that process and how you came to that solution?

Adam Stacoviak: It’s definitely the beginning, the early innings of what will become of this way to create and share the value of what is created out there… And I think what’s more – what’s happening now, like any gold rush, or any rush of sorts, there’s gonna be bad actors, and there’s gonna be people that are just in it to create scarcity, and get that value. I actually saw a TikTok recently where it was like “Do you wanna get rich? Go to Canva, and whatever-this. And then go to this marketplace and find somebody to create you 1,000 pieces of artwork, and then put those out there as NFTs”, and this and that. It’s like, okay, you’re not really bringing anything of value, really. You’re just selling – at that point it’s just JPEGs. But maybe that’s the code smell, so to speak, is if you’re just buying a JPEG, you’re doing it wrong. Because if we ever got into the NFT space, it wouldn’t be to sell a JPEG. That might be the thing you can look at, and say “I own this thing” and –

Nader Dabit: Yeah, we have a way to actually get around that by programming our own digital scarcity. All you have to have is an internet browser. You can now receive and send payments between parties.

The other thing is state. So to save information - how do we do that today? Well, we have a centralized database somewhere, like AWS, that if we don’t pay the bill, goes away forever and we lose all of our information. Maybe the data leaks, maybe it gets hacked, or maybe we just – I don’t know, there’s all types of stuff… Maybe it goes down because AWS has an outage in U.S. East 1. But there are a lot of things that are not ideal, and I think that with blockchain technology we’ve unlocked native payments and native state… And early on, with Bitcoin, it kind of jsut showed that these types of decentralized protocols were possible, but you couldn’t really write a program in Bitcoin. And I think the reason that you’re seeing it start to become a thing now is that the evolution of that technology and the improvement - you’ve had increased transactions per second, so higher throughput per second, reduced costs…

[40:07] For instance, Solana is a good example of a blockchain that has fractions of a penny for a transaction… So you have all of these things that are kind of happening, that are now starting to become mature enough for people to build on, that people are taking advantage of them… And the things that they’re unleashing are also, I would say, really interesting to people that are developers already, but they also enable people to make money. In the United States, where we can make like a couple hundred grand a year for a senior developer, we might not be that interested in this stuff… But let’s say you’re making 10k as an intermediate or senior developer somewhere else in the world, and you can partake in a protocol and increase your wage by 10x because all these projects are international and they’re completely decentralized, and they’ll hire anyone, anywhere, it doesn’t matter where you live… It’s just very, very interesting to a lot of people.

Adam Jacob: It’s always easier to go from a business to something else than it is to go from something else to a business. The thing about starting something that’s open source is your optionality is only to figure out how to close something off. You have to create that scarcity somehow.

So if you have an open source project and you wanna create a business out of it, if you wanted to follow the free software product model, which I think you should do, then the number one thing you have to do is stop releasing builds with your branding on them, and you have to make it so that no one else could ever release a version of that software that is called - you know, I’m wearing a McLaren hat - McLaren. Like, you’re the only one who can do that. And then people who want it have to agree to your commercial terms, which means at some point they have to pay you. And doing that - you could do that, and somebody would pay you, if it was valuable; they’d do it.

Now, you still have to do all the work of designing the business itself, which is “Who’s my target customer? How do I reach those people? How do I convince them that it’s worth buying? How do I get them to try it the first time?” And all that stuff - that’s just stuff you have to do whether what you’re doing is open source or what you’re doing is closed source. That’s just business.

So I think the real question for people trying to go from zero to one is “What kind of business do you want?” and “How big can it be?” And you start by making spreadsheets, by just making it up… Because you can’t actually answer any of those questions. And all you’re really trying to do is get to a place where you can believe that success is possible. And if you can’t create a spreadsheet that tells you success is possible, it’s not possible. So that’s like step one. And I’ve thrown away so many, what I thought were really great business ideas, because I couldn’t build a spreadsheet, where when I looked at the spreadsheet, I was like “That’s gonna work.” Like, I looked at the spreadsheet and I was like, “Oh, I’m doomed. I’m done. There’s no way.” So you do that.

Adam Stacoviak: Yeah, in Second Life. No names dropped here, for scarcity and security and anonymity… But I knew somebody who literally made their income… It wasn’t a ton, but it was enough to live on, from just making in-game art things that you can use, wear, share, ride, horses… All the things, objects in the game that you can use in some way that was unique, because it was by them. Scarce, because they could only make so many, and I suppose artistic, because creativity required.

Kris Brandow: Part 2: Storytelling

When you step on stage you become a storyteller. Just because you’re not acting doesn’t mean you can’t employ the same techniques that have been used for centuries to captivate your audience and tell great stories. Before the stage though, you’re storytelling on the page, weaving the narrative of your talk through a proposal. But how do you achieve this?

First, note that your proposal’s audience differs from that of your talk. The former consists of a group of Go community veterans, who read hundreds of proposals to organize an exciting and intellectually stimulating program. The latter is a group of Gophers looking to learn and grow. The story you tell in your proposal to captivate your first audience differs from the story you’ll tell on stage for your second.

When I started crafting my first proposal I searched for articles and blog posts to aid in the process. I easily found resources that guided structure, but few on how to tell an engaging story. Through this guide I aim to fill that gap, helping you tell your stories in a captivating way for both the review committee and the audience at GopherCon.

Before I dive in, I want to emphasize that while this advice may help increase the odds of selection, it will not guarantee acceptance of your proposal.

How To Think About Your Proposal

When starting something new I use analogies to make connections between what I’m doing and what I’ve done. As an author, I connected that the storytelling techniques I use in my prose translate to the writing done for a proposal and a talk.

Listening to one person talk for 25 minutes can be difficult if the speaker is not telling an interesting story; this is even more true when it’s 45 minutes. Similarly, when reviewing hundreds of proposals on a tight deadline, it’s difficult to engage with each proposal individually, unless the material pulls the reader in and excites them. The key component in both situations is time: an abundance in your talk and a scarcity in your proposal. Techniques like suspense and foreshadowing are handy in your talk, but not so much in your proposal, where you need to get straight to the point. In the latter, opt for concise but exciting language, where each word can serve a purpose.

[36:01] Another element to consider is competition. At the maximum, there are four options for attendees at the conference: the three session tracks and a hallway track. That gives you an odds of 25%. Contrast this with the proposal review process, where your odds are closer to 10%, and your opportunity to hold the audience’s attention is far shorter. Holding that attention requires not just writing in a concise manner, but also adding elements to your proposal that set it apart from the rest, and this is where storytelling elements are helpful.

Combining these two elements, we arrive at an important piece of advice: don’t bury the lede. The structure of your proposal does not need to match that of your talk. Let’s use an analogy. Your talk is like a book; you want to pull the audience through all the pages, even those treacherous middle ones, through to the end. You make that task more difficult if you front load all of the interesting information. Your proposal though? That’s more like a newspaper article; you want to put all the interesting information upfront, and then extrapolate over the remainder of the space. In your proposal, that end space is where you discuss elements like foreshadowing and suspense, which can be conveyed either directly in prose, or indirectly through an outline. This is also where you can discuss elements like timing, context, and why you’re the best person to give this talk.

Finally, while there are a variety of formats that conference talks come in, the structure of your proposal is static. You want to address the same requirements and criteria I laid out in part one, while ensuring that you include as much context (including storytelling elements) as you can compactly fit.

At the end of the day, storytelling is about the way you use words and less about the structure used to contain them. Whenever you’re storytelling, think about your audience and cater your story to them. Craft it based on the time you have, the structure the audience is expecting, and the other things competing for your audience’s attention.

Elad Walach: But that doesn’t really work that way. If you wanna create an effective system, you have to steer the learning process in a way that it’s most efficient to learning that specific domain. So the question we asked ourselves is how to train AI most effectively to solve the specific problems of medical imaging. I’ll give a few examples, and then I can talk about what elements are we using.

[24:01] So the first issue that you get when you go to medical imaging is data scarcity. So you think about ImageNet, you think millions of images annotated, very easy, you do a Mechanical Turk or something, you can outsource people flagging… It’s not the same with radiology. We have what we believe is the biggest annotated database in the world right now, and we have hundreds of thousands of images. We’re talking about roughly an order of magnitude, hundreds of thousands, a million of studies. Each contains hundreds of images, so it’s tens of millions of slices of images, but a lot of the imaging is 3D stacks, so you have one patient with 500 images. So that’s one big challenge that we had to tackle.

The second is class imbalance. A small patient image is like a million pixels, something like that, and a disease can be on a 3 by 3 by 3. We’re talking eight pixels out of a million. And that’s in positive exams. If I’m talking Air Force lingo, I would say – so if I’m creating an algorithm to detect bunkers, it’s like it would give you a map of the world and tell you “Hey, there’s a bunker in here somewhere. Go and have fun.” So it really is trying to handle this class imbalance where you have all these normal areas. That’s a big [unintelligible 00:25:22.04] challenge.

And the third challenge for us was that humans have a lot more information oftentimes, if the system is accessible. I’m talking about the clinical context. So radiologists cheat, in the sense that they look at historical exams, they look at patient data, and it helps them focus on what they really wanna do, and that’s another thing that we really wanted to focus on. And those are just three examples of how we basically built the underlying architecture to support those pain points.

So basically what we’re using - it’s not totally different. It’s a convolutional neural network on the medical imaging, but what we really innovated is on the training process to handle the class imbalance on some of the specific layers in the network, to try and replicate as much as possible how a physician would look at the image… And we added a lot of clinical information from other imaging, from other clinical sources, for the neural network to have as much information as possible when doing it. Before I dive any deeper, I wanna pause here and see if you have any questions on that.

Daniel Whitenack: Yeah, definitely. It seems like there’s a lot of great potential there. And I know that, of course, one of the things, like you were talking about some of the data that’s available in the language you’re working with… And I know Josh, you’ve worked a little bit with bias in datasets, and it seems like, of course, as data scarcity might be a problem with some of these lower-resource languages, there’s probably a heavy bias in those speech datasets for the lower-resource languages towards whatever it is - maybe it’s only males in the corpus, or only a very small representation of the potential accents in the data… You’ve done some work in this area; I remember reading a recent blog post about a dataset that you were working with to help researchers diagnose bias in speech datasets… Could you give us a little bit of an introduction to that work and what you’re doing there?

Jeff Meyerson: Something special, and they got their answers promoted, and they got a nice fleece in the mail, they got invited to these amazing events, where all the top writers were… They created this form of scarcity to reward the people who were really putting in the work in developing their platform. And that level of honesty between the platform owner and the platform creator, basically the platform owner saying “Look, you guys are building this for us. We understand that, we’re gonna reward you”, that adds fuel to the fire, and I would like to do that at some point. We kind of did that in the early days with the FindCollabs hackathons, where basically I just like, you know, earned money by rewarding people for their projects on FindCollabs. It was a little too early to do that, but I would like to do other things like that. But yes, I mean… You get it.

Adam Jacob: There’s a couple. For me personally, I realized that the things I disliked – well, I’ll back up. Let’s tell the whole story of how I got started on that journey… And then the business side comes in a little later. So I had decided to leave Chef, but it’s on good terms; I’m on the board, and everything’s happy. I was there for more than a decade. But I was just done, because… I’ve gone to a customer meeting, it was a delightful meeting, and the person who was sitting to my right basically made all the technical decisions for this massive Fortune 100 company. And we described how he could solve his problem, and he high-fived me.

He was just like “What’s up!? I’m in!” And all I wanted to do was go home. I didn’t get a single squirt of dopamine in my brain that was like “Good job!” I was just like “Oh. Great.” And I was like “Oh, I have to stop. I have to quit now, because I don’t wanna be like a caricature of myself.” Do you know what I mean? I know I could keep going, I could show up tomorrow and do this again… And I wasn’t wrong, and it was gonna work, but it’s wasn’t healthy; it wasn’t where I wanted to be. I don’t want people to remember me as that guy; I want them to remember as that person who is stoked when that happens, and was a part of that journey for them… So I decided to leave.

When you make those sorts of monumental life decisions, you sort of talk to your own small council. You go to the people that know you well, and care about you, and know your context, and you talk about your decisions. So I was doing that with a previous investor of ours, Sunil Dhaliwal, who asked me “Hey, would you start another open source company?” And without hesitation, I just said no. And it wasn’t like a weak no. And as soon as it came out of my mouth, I was like “Is that true? Do I mean that? Because that’s intense.” A lot of my life has been dedicated to open source, and it’s done so much for me; I learned to program in the Perl community… So much of my identity and my life is tied up with open source… And it just came out, instantaneously and [unintelligible 00:23:03.08]

I was ramping down what I was doing at Chef, because – I don’t know if you’ve ever exited a company that you’ve built over a long period of time, but people just assume that you’re really busy all the time, doing very important things, and if you’re ramping your way out, you’re doing the opposite; you’re rapidly unloading things, to make sure that people don’t miss you when you’re gone. Because if it’s gonna go badly, you wanna still be there to help them out.

So I’ve found myself with time, in-between when I had to do things, where this existential dread just kept washing over me, of like “Ugh, is that really real? Is that really what I think?” So I started looking into myself and trying to decide why I said what I said… And it turned out that it was because being the sole arbiter of everyone’s hopes and dreams as they related to Chef - I was sort of the benevolent dictator, so from a technical point of view, whatever I said goes. And very rarely did I have to use that power, but sometimes I did.

[24:10] And then from a corporate point of view I was the CTO, so my job was to definitely balance the business’ needs and the software’s needs… And then there’s this community of people who love Chef, and use Chef, and contribute to Chef, and I had to balance all of their hopes and dreams… And it felt a little like I was surrounded by the Cookie Monsters that I talked about earlier. I would pay attention to one, and then they’d be like “Oh, you’re giving too many cookies to that person. I want more cookies.” It was just this constant circle. And doing that in business is hard enough. Just the normal stakeholders and the day-to-day business is hard. Doing it with this massively broad community of people who have this huge number of stakes is even harder… And what I realized was that I don’t want to be a part of a community where my job is to manage all of their hopes and dreams, and to be the person who decides whose needs are met. Do you know what I mean? I don’t wanna be the person who decides if you can have what you want from this thing that you care about.

And when I reflected on the communities that I had been a part of in open source that I loved the best, it was because there was no arbiter for that. When I loved the Perl community, it was because Perl enabled me to do the things that I needed to do. It made my life; my entire life is built on the fact that I was a part of the Perl community, in a very direct way… And if I was gonna be a part of an open source community or build one myself, that I was never gonna set it up with those incentives ever again. And the only way that I could find to get out of that trap was to say that the project itself needs to be sustainable by the people who love it. If it has a single overlord, if it has Chef at the head of it, well that means that it’s my job to sustain it, and I’m giving you a gift.

I’m giving you the gift of my money, of my time, of my sustaining. But if you challenge my point of view… If you, Jerod, want to make a business on top of Chef and it conflicts with my business - well, now you’re not in my community anymore. We’re at odds, and I have to try to manage that by deciding “Do I let you exist? Do I fight you? What do I do?” And my answer to that was “I wanna let you do your thing. I wanna welcome you into this community, and I want you to do what you want to do with it, and we have to find a way to do that together as people. Because otherwise I, Adam Jacob - I’m just not interested in doing it, because it sucks; it’s a terrible job, and it’s not rewarding for the people who are part of it, because at any moment you know that someone could just take from you the thing that you value and love… And I just didn’t like that setup. I don’t like it now, I still don’t.

At the same time, Chef was going through its own business hurdles. Docker has happened… All kinds of things. We built a new product called Habitat - it’s incredibly dope - and we’d done a bunch of really good work, but we were at a moment where we could make those decisions, where we could decide to change our model. And the hardest thing at Chef had always been if you’re a sales rep at Chef, if the feature that a particular customer wants is proprietary, then you’re stoked, because you can use that as leverage. If they don’t, then you spend a lot of time trying to convince them that the features that they do want aren’t the most important features. [laughs]

So you have this constant debate, like “What’s in the bag? What’s not in the bag?” And the truth is, all of it is in the bag; it’s the same piece of software. That’s the value - the software itself, but also our ability to produce it is the value. You trust me that I can build this thing for you, that you can use it, that I can support you in the thing… That’s the value of the product.

[27:55] And in business, what you need is a way to create scarcity. I need a way to say “You can’t have what you want unless you give me money.” That’s how I take money, it’s not rocket science. So we started looking for a way to resolve that fundamental friction… Which is also a community friction, because if I’m a community member who wants that feature and it’s only in the proprietary part, can I send a pull request that adds that feature to the open source part? Would you accept it? Should you accept it? And to me, the answer has to be yes. Of course it should be yes. But man, that’s a tough sell if you’re a sales rep who is feeding your kids on that feature.

So it was just this perfect storm of my own existential dread… Barry Crist, CEO of Chef, who is an incredibly smart, and one of the most trustworthy and honest people I’ve ever known… He’s so honest that people tend to read into his behavior, because they can’t imagine that he’s just telling you the truth all the time… You know what I mean? I don’t know if you’ve ever met anybody like that, but he’s like that; he’ll tell you exactly what he’s thinking, and you’ll be like “Oh man, was that calculated?” And it’s not calculated; it’s just exactly what he was thinking in that minute.

And you put all of that together – Corey Scobie, who’s the SVP of Product and Engineering at Chef, and took over for me, who also had started seeing these dynamics… It was just sort of this perfect storm of all of that coming together all at once to make that choice… And I’m proud of that decision.

Himani Agrawal: I definitely felt that way. I realized that during my Ph.D. I was working on solving optimization problems, which are very similar to the problems in the real data science industry, but I was not using the same jargons that are being used in the tech industry… So by being part of the Galvanize program I got to learn data science in the tech way. That really helped me a lot, and it helps me even today in my job at AT&T.

Coming to your second question, how can people from different backgrounds enter into data science - I believe that machine learning and data science is very ubiquitous right now. There is a huge scarcity of machine learning and data science expertise, so it’s great if people from different backgrounds can enter into that field, because that would really spark creativity.

Ron Evans: Oh, absolutely. And just to riff on the same idea, the idea that the difficulty of a thing is equal to the goodness of a thing, to the benefit, to the value of the thing is only because of scarcity. Like, if relatively few people know how to do a thing, then it’s more valuable. But we live in the open source era. With information, we can have our cake, eat it, and give some to all our friends. It’s not a zero-sum game anymore. So there’s no upside ultimately to restricting this information. When it was a professional programming priesthood, as we used to call it back at Apple, the people we were against. The people who would wear white coats and would be typically hairy-armed engineers, as a female colleague of mine called them… You know, the people who were going to restrict your access to this knowledge and information so that they could use it and be the ones to be the keepers of the knowledge. But now we live in a whole other era. It’s more about the democratization of knowledge, it’s about the accessibility of knowledge, and it’s about unlocking the things we can do with this knowledge; it’s not just having it for its own sake.

It’s like having a book about something that you haven’t read, but you keep on the shelf. It’s not the same thing as actually possessing and assimilating that knowledge and doing something with it. It’s a poor shadow.

Anna Derbakova: [23:52] Yeah, I wrote it, I can point it to you. It’s bitly.com/hyperledger-basics. It is in my personal repo, because I published it, but there’s links from my GitHub page to the Docker.io official page and other official documentation for Hyperledger, and people can start there. Or they can just certainly go to the docs for Hyperledger and get started there, but there’s a lot of docs, get ready. It might be a tiny bit overwhelming, but people just need to have little – if they have some background in blockchain, great. But if they don’t, they just need to be a little patient; there’s a lot of information, it’s a very complex subject, and we’re trying to make the docs better and slimmer and more applicable, so just bear with us. We don’t want to scare people away by the overwhelming amount of docs, but we also don’t wanna turn them away by the scarcity of the docs. We’re still kind of walking that fine line. So yeah, certainly check it out.

Adam Stacoviak: But they said that. He’s like “When life gives you lemons, make lemonade.” He’s like “No. When life gives you lemons…” And then he goes on.

[17:53]

“First you roll out a multimedia campaign to convince people lemons are incredibly scarce… Which only works if you stockpile lemons, control the supply. Then a media blitz. Lemons are the only way to say I love you. The must have accessory for engagements or anniversaries. Roses are out. Lemons are in. Billboards that say “She won’t have sex with you unless you’ve got lemons. You cut the beers in on it, limited edition lemon bracelets, yellow diamonds called lemon drops… You get Apple to call their new operating system OS Lemon. Little accent over the O. You charge 40% more for organic lemons, 50% more for conflict-free lemons. You pack the Capitol with lemon lobbyists. You get a Kardashian to suck a lemon wedge in a leaked sex tape… Timothee Chalamet wears lemon shoes at Cannes. You get a hashtag campaign. Something that isn’t cool, or tight, or awesome… No, it’s lemon. Did you see that movie? Did you go to that concert? It was effin’ lemon. Billie Eilish. OMG. Hashtag. Lemon. You get Dr. Oz to recommend four lemons a day and a lemon suppository supplement to get rid of toxins, because there is nothing scarier than toxins. Then you patent the seeds. You write a line of genetic code that makes lemons look just a little more like tits. And you get a gene patent for the tit-lemon DNA sequence. You cross-pollinate… You get those seeds circulating in the wild… And then you sue the farmers for copyright infringement when that genetic code shows up on their land. Sit back, rake in the millions, and then, when you’re done, and you’ve sold your lempire for a few billion dollars, then and only then you make some f***ing lemonade.”