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0:00:22 a16z.com/disclosures. Hi, welcome to the A16Z podcast. This is
0:00:27 Frank Chen. Today’s episode is called “The Future of Decision-Making, Three Startup Ideas.”
0:00:33 It’s a conversation I had with Chad Nouse, originally as a YouTube video. You can watch
0:00:39 all of our YouTube videos at youtube.com/a16zvideos. Now on to the episode.
0:00:45 Hi, welcome to the A16Z YouTube channel. I am Frank Chen, and today I am here with
0:00:51 Chad Nouse. Chad is part of the enterprise investing team, and he’s noticed something,
0:00:55 and so let’s just get right into it. So you’ve noticed something around the way that big
0:01:00 companies are trying to do digital transformation. So why don’t we start there. What are the
0:01:04 big companies doing? What is digital transformation?
0:01:10 Yeah, digital transformation is something that gets thrown around quite a bit. I think
0:01:16 there’s a big shift now. We’re starting to see a lot of industries actually starting
0:01:20 to go through digital transformation, and I would bucket the things that people do in
0:01:27 digital transformation into two areas. The first one is around moving from these manual
0:01:34 paper processes to more digital ones that are easy to change, faster to modify, more
0:01:38 agile. The second thing that people tend to do when they’re doing digital transformation
0:01:46 is move from these manual processes to more automated processes, and so automation. And
0:01:53 I think that this shift is now starting to happen in earnest, and we’re going to start
0:01:59 seeing three things pop out. The first one is people’s roles and functions to a certain
0:02:06 degree are going to start shifting around. The second one is we’re going to start seeing
0:02:12 new demand for new technology and new tools as these new functions and roles actually emerge
0:02:18 and start to change. And third, that’s going to also lead eventually to a change in market
0:02:24 dynamics and how companies run, who will become successful, who wins in certain spaces.
0:02:30 Interesting. So anywhere there is a fax machine or a clipboard or sort of a big bundle of
0:02:34 papers, there’s opportunity. We’re going to go from analog to digital, and then we’re
0:02:38 going to automate whatever business process was behind that piece of paper that you had
0:02:43 fill out in triplicate. So why don’t we talk about a couple of examples of these? What
0:02:45 are good examples?
0:02:52 So I’ll talk a little bit about product management. So earlier on, people, the way they used to
0:03:00 decide what products to build, how to prioritize features or bugs to fix, is they’d go and
0:03:06 they’d run these surveys that are manually, and they send them out to people, or the product
0:03:10 managers go and talk to people. They spend a ton of time doing these, collecting all
0:03:14 the data and figuring out, okay, these are, this is the segment of people I care about
0:03:17 the most. Here’s the issues that they care about. Let me figure out what the problem
0:03:19 is and so on.
0:03:22 As an old product manager, I went on those calls.
0:03:26 You flew to a customer and you dutifully listened to what they wanted, and you’d sort of come
0:03:28 back and try to sort them all.
0:03:33 And that, I mean, it’s a huge time sink. A lot of the product manager’s time used to
0:03:39 be that. What’s happening now is we have a new generation of tools that actually allow
0:03:44 the automation of data collection from the product. What’s actually happening? What
0:03:51 features are people using? Where are they getting stuck? And so where the product manager
0:03:57 now, instead of having to go and do all these surveys, would look at a dashboard that describes
0:04:02 what people are doing in their product. And then they would be able to analyze it and figure
0:04:08 out from that what features, what areas of the product are they getting stuck in, and
0:04:12 be able to communicate with engineers. Here’s the things that we need to do.
0:04:17 And then once they fix some of these, they can actually roll them out gradually and do
0:04:22 A/B tests to figure out did this actually fix the problem or did it not fix the problem.
0:04:25 And decide that if something actually did fix the problem, then continue to roll out
0:04:27 to the rest of the population.
0:04:30 So that’s on the product management side.
0:04:37 You see another example actually happening on the marketing side. I’m sure you’ve heard
0:04:45 of growth hacking. So for a long time, marketers used to be this madman kind of thing where
0:04:49 you spend a lot of time figuring out the creative aspect of what you do, you spend a lot of
0:04:54 time on a lot of money on advertising campaigns and you kind of spray and pray for the most
0:05:03 part. What has happened over the past few years is the rise of this marketing engineering
0:05:11 role to a certain degree. This is one where a marketer who understands numbers, who understands
0:05:18 engineering systems, who understands pipelines would work with these data systems and actually
0:05:25 try to figure out ways that are low cost that would actually increase growth in a certain
0:05:32 segment of the population. And that requires a lot of data instrumentation, a lot of understanding
0:05:38 people and a lot of creativity in figuring out how to spur growth or how to get traction
0:05:40 in a certain area.
0:05:47 So Don Draper’s tools were typewriters and stories, right? And so the tool set around
0:05:52 this is going to change dramatically if we make this transition from sort of the old
0:05:56 world analog un-automated to the new world. And by the way, I think you have a name for
0:05:57 the new world.
0:06:04 Yeah. So let me first say like what’s actually going to happen. So as people’s jobs become
0:06:10 more and more automated, a lot of the things that they get, a lot of the things that they
0:06:16 used to do that are work will go away. And what’s actually left in their jobs is mainly
0:06:22 decision making, figuring out like what am I going to do? What am I going to focus on?
0:06:27 How should I do it? And communication or other things that are actually related to their
0:06:35 job like creative work, human aspects that can be automated, buy in, alignment, et cetera.
0:06:40 But the road work goes away. And so that means that there’s a ton of decisions, a lot more
0:06:46 decisions that they’re doing more frequently on a daily basis that they have to go through.
0:06:51 So what that means is that to a certain extent, everybody is going to end up becoming more
0:06:56 of an analyst in that sense in the enterprise. When I say everybody, I kind of mean like
0:07:05 the middle of the enterprise. And what that really means is they’re going to have these
0:07:10 questions that they’re going to need to ask on a daily basis, but with no tools to actually
0:07:16 help them do these. So you might say, well, you know, people used to do this for a very
0:07:19 long time. They used to use BI tools to actually answer questions.
0:07:24 Yeah, business intelligence, right? So you build the data warehouse, you build the tables
0:07:27 on top of it, right? Then you build your reports. So.
0:07:33 Exactly. And so I think that BI tools are not going to be enough in this world. And I’ve
0:07:36 come up with a term for like the type of tools that we need that I’m calling operational
0:07:42 intelligence, because it’s actually targeting the operational people. It’s the type, it’s
0:07:47 questions that people need to answer on a daily basis and they have to answer them immediately.
0:07:54 Questions like, where is the bottleneck in my funnel right now? And how do I eliminate
0:08:03 it? Or I have my competitor is having a flash sale. How do I figure out how much of my revenue
0:08:09 is impacted? Which customer segment should I target? And what should I, what should I
0:08:13 put on sale? And those are things that you’re going to have to answer in the moment. You
0:08:19 can’t have, so for BI, you would need this army of analysts, where you would just ask
0:08:23 a question and then they would go off into your enterprise and rummage through all the
0:08:29 data sources, try to understand kind of like what the question that you’re asking is kind
0:08:33 of try to understand what the business context is and then show you, build you a dashboard
0:08:37 and hope that that’s the one that you want. Yeah. Well, there’s the old joke about BI,
0:08:42 right? Which is it’s $10 million to your first report. And then you realize, oh, I didn’t
0:08:47 want this question answered anyway. Wrong question. Exactly. And so the solution there
0:08:52 is kind of what I’m calling operational intelligence. And there’s three pieces to it. The first
0:09:01 one is that it has to be, it has to be immediate. It can’t be eventual like BI. You can’t just
0:09:06 say, oh, I need to answer this question and then get an answer like three months later.
0:09:10 It has to be answered in the moment. And that involves a few things. Like first, that you
0:09:15 have to actually be able to do it yourself. Like you have to actually get the data in
0:09:21 real time as opposed to it being late. The second piece is that it has to be kind of
0:09:28 continuous. It has to be real time. You can’t have your data being sent into these systems
0:09:35 on a batch basis every day or every week or whatever. The data that you actually see to
0:09:41 make your decisions has to be what’s happening at this point. Right now. So the classic example
0:09:44 of this would be sort of social listening on Twitter, right? Which is that’s got to
0:09:49 be an ongoing process because things can blow up with your brand either in a good way or
0:09:53 a bad way at any time. So you can’t say, hey, I’m done analyzing Twitter for the quarter.
0:09:57 I’m done. Exactly. Exactly. Another example, I mean, I said
0:10:03 this earlier about the A/B testing. I mean, if you’re looking at, if you’re trying to
0:10:09 do A/B tests, you can’t just let it go and come back next week and see whether the thing
0:10:14 worked or not. You actually have to be continuously monitoring what’s actually happening in the
0:10:21 A/B test space and figure out did the B test work or did the A test work better? And am
0:10:25 I going to flip the switch now? Because if, I mean, you’re doing an A/B test on a segment
0:10:29 of the population, you don’t want them to completely fail in the end.
0:10:33 In fact, we’re seeing with some of the more sophisticated machine learning systems that
0:10:38 you actually have multiple models, machine learning models that are live at any given
0:10:43 time. And you’re actually doing nightly bake-offs against these models, right? Which is model
0:10:47 A will get 40% of the traffic, and then model B will get 20% of the traffic. And then we’ll
0:10:52 just sort of let them run and the best models get promoted to receive more of the traffic
0:10:57 over time. So that’s an example of what you’re talking about is this sort of continuous process.
0:11:04 It’s really interesting that like what we’ve seen is this kind of monitoring, this kind
0:11:08 of continuous monitoring, like what I’m calling operational intelligence, has actually been
0:11:12 kind of standard on the engineering side for a very long time. People have been monitoring
0:11:21 systems and engineering for a very long time. And they would kind of run A/B tests continuously
0:11:25 to try to improve performance. And now we’re actually seeing these kinds of engineering
0:11:29 disciplines kind of migrate into other functions of the org, right? Like marketing seems to
0:11:33 have been the first one to go after that and then product management. And we’re actually
0:11:39 seeing now people trying to do this for salespeople, trying to like look, okay, here are the things
0:11:45 that salespeople have done. And in order to close a deal, let’s actually learn from that
0:11:48 as a pattern and like figure out how to get everybody, every salesperson on the team to
0:11:51 get to the level of the top performer.
0:11:55 Yeah. Cresta.ai is a great example of this, right? So you’re chatting and you’re getting
0:11:59 real time advice about, hey, maybe this is the time to mention we have a product in this
0:12:01 space. Yeah, that’s a real time recommendation.
0:12:02 Exactly.
0:12:08 Yeah. So in the old days, engineering typically was first because like websites were coming
0:12:12 online and you needed to watch those things, right? Because everybody knows the statistics
0:12:16 that if like, you know, your webpage loads this much slower, you’re going to lose that
0:12:20 much more people through the conversion funnel. And so like you had to watch all these things
0:12:25 in real time. And now that’s getting outside of IT, right?
0:12:31 Yeah. It’s interesting also that, so I used to work at AppDynamics. I was there for a
0:12:38 few years and AppDynamics sells APM tools, application performance monitoring tools.
0:12:43 It’s probably one of the easiest things to sell because you go up to your customer and
0:12:48 you’re like, well, how much does it cost for your engineering systems to be down for, you
0:12:58 know, five minutes, 10 minutes, an hour? And then you say, hey, we prevent that from happening.
0:13:02 That same kind of sale hasn’t yet happened in these other works. It’s a little harder
0:13:06 to prove the ROI. But I think it’ll get there.
0:13:11 Right. So now this is about sales performance, marketing performance of those people.
0:13:12 Exactly.
0:13:17 And we’re going to sort of treat them as if they were websites, right? What’s the downtime?
0:13:22 What’s the dollars lost if you have a salesperson being non-optimal at this point in time?
0:13:23 Exactly.
0:13:30 Yeah. And so to recap sort of the tool change from business intelligence to operational intelligence,
0:13:36 sort of, I need it now. I don’t need it in three months. Three months is too late. That’s
0:13:41 one. Two is I need it ongoing. I don’t need a one time, hey, I’m done.
0:13:42 Right.
0:13:46 I need to, and then I think there was another aspect of the tools that you expect to change
0:13:47 and what is that?
0:13:49 It has to be self-service, not full service.
0:13:50 Oh, I see.
0:13:56 You can’t have somebody else going and doing all the work for you. Those tools have to
0:14:01 actually give you insights that are catered to you and you have to actually be able to
0:14:03 ask the questions yourself out of these tools.
0:14:04 Right.
0:14:06 They have to enable you to do all these things by yourself.
0:14:12 Yeah. So basically the tools need to be easy enough to use such that the average business
0:14:17 analyst can basically just poke at the data and then any answer comes out as opposed to
0:14:22 you think of a question some team later, six weeks later, turns that into a very complicated
0:14:24 SQL query and then the report comes back.
0:14:30 Yeah. I wouldn’t even say it’s an analyst that actually is doing this. These are tools
0:14:36 for the actual operational people as opposed to the, as opposed to, I call them meta-operational
0:14:40 because they’re like analysts. They’re about the business. They’re not the business.
0:14:41 I see.
0:14:45 So what a good example of somebody who now needs to consume these tools directly, which
0:14:47 is different, Brent, a marketer.
0:14:54 The growth hacker, the product manager, the customer support manager, the sales person,
0:15:00 these are all the actual functional operational people that need to consume this data.
0:15:04 Got it. So that would be a big change, right? Because in the past it was sort of a very
0:15:09 sophisticated technical consumer, right, who would be the interface between the business
0:15:12 person and the system and now you’re saying the business person needs direct access to
0:15:13 the system.
0:15:14 Exactly.
0:15:20 So it can be easy, right? So if we think about the entire stack of how it came to be that
0:15:26 you’ve got a BI answer, right, there was ETL, there was storage, there were data cubes,
0:15:35 there were analytics, right? So do you think each layer of the stack is going to need to
0:15:40 change or do you think these are just features that the incumbents can add?
0:15:41 Yeah. Good question.
0:15:49 So I think that the breakdown of the stages of data pipeline is a functional breakdown,
0:15:54 not really so much legacy. Like you’ve got ETL at the top, you’ve got, well, maybe at
0:15:59 the bottom depending on how you like to draw your pancake from the left to the right.
0:16:05 You’ve got ETL at the, right after your data sources, you’ve got storage where all the
0:16:10 data that you’ve processed goes in, like these are your data warehouses, your databases,
0:16:15 data lakes, et cetera. You’ve got processing that happens to extract the data from the
0:16:21 storage layer and turn it into insights or whatever. You’ve got analytics that’s actually
0:16:32 used to turn a question into actual execution. You’ve got the access layer which controls
0:16:37 and governs who is allowed to access what. And then you’ve got processing at the end,
0:16:40 I’m sorry, a presentation at the end that actually.
0:16:41 That’s where your answer comes out.
0:16:43 This is the dashboard.
0:16:49 I think every layer, functionally each layer is going to remain the same, like at the core
0:16:54 it’s going to be doing the same things. But each layer is going to have new non-functional
0:17:00 requirements. Each layer is going to have to be usable by a non-technical person who
0:17:08 is trying to ask their own questions. And we see that happen in large companies. These
0:17:14 large companies have already built these stacks. So Airbnb, for example, built SuperSet and
0:17:22 they luckily open sourced it to the world. And now it’s used by hundreds of companies.
0:17:28 It’s a presentation layer product that’s focused toward more technical engineers or data scientists
0:17:34 to be able to get ad hoc access to their data and answer questions immediately.
0:17:42 One of our investments imply is doing this for the analytics and the processing layer.
0:17:49 So they’re able to store streaming data directly into their database and allow you to do OLAP
0:17:54 types of queries and analytics on top. And they provide a presentation layer that allows
0:18:00 you to slice some dice on problems. Databricks is another one. They’re focused on the processing
0:18:09 layer. So we’re seeing a bunch of things happening in each of these layers. And I think probably
0:18:15 the layer that hasn’t yet seen the most changes is the ETL layer.
0:18:20 And what do you think that is? Is that the hardest layer? Is it just, well, that’s going
0:18:24 to be the hardest to turn a business user into a direct customer of? Because traditionally
0:18:26 that’s been very wonky.
0:18:34 Yeah. I think two reasons why ETL has been so hard. The first one is it actually requires
0:18:43 domain specificity. ETL for healthcare is not going to look the same as ETL for financials.
0:18:44 Ridesharing.
0:18:49 For ridesharing, for whatever. The ontologies, the things that they care about are different.
0:18:56 And so any company that does these has to really get deep into that domain. The second
0:19:04 one is it’s a lot of integration and a lot of kind of heavy manual work. And engineers
0:19:08 don’t really like to build these kinds of things. So they’re going for the lower hanging
0:19:09 fruit at this point.
0:19:14 Got it. But it seems like overall you’re arguing there are a lot of startup opportunities
0:19:19 here that the incumbents are going to have a hard time retrofitting their products, right?
0:19:24 So it’s pretty hard to change a product that was designed originally for a technical user
0:19:29 to turn that into a non-technical. Is that sort of a fair summary of where you’re going?
0:19:34 Yeah. So if you think about the opportunities in operational intelligence, I’d probably
0:19:41 break them into maybe three categories. The first one, actually the first two are maybe
0:19:46 like related to each other. It’s basically you want to become an operational intelligence
0:19:55 vendor. So you sell software and tools that enable existing incumbents to become operationally
0:20:02 more capable. You enable them to do operational intelligence. And within that category there’s
0:20:07 a breakdown. So you can either target a specific role. So I’m going to enable the salesperson
0:20:11 to become successful or I’m going to enable the product manager or I’m going to enable
0:20:20 the customer success manager. And so we see products in each of these categories today.
0:20:26 There hasn’t yet been complete breakout success in any of these, but it’s super crowded and
0:20:35 I think it’s probably the hardest one to win in at this point. The second category is within
0:20:49 that vendor superset is segment focused vendors. So companies that sell operational intelligence
0:20:59 tools to existing incumbents, for example, companies that sell sensors and analytics
0:21:07 for oil and gas companies. So these are people who will collect data from your wells, optimize
0:21:13 it and then collect that data from your wells, put it into dashboards, tell you how your
0:21:18 wells are doing and tell you how to optimize it in order to improve efficiency. So like
0:21:23 a vertical solution for oil and gas. For oil and gas. So those are those are still vendors
0:21:28 selling software, maybe some hardware into an existing industry. And then finally you
0:21:38 have the vertically integrated, you know, operationally intelligent company that competes
0:21:43 against the existing incumbents. And so we’ve got plenty of examples of that at this point.
0:21:48 So we’ve got Airbnb that’s in the hospitality business. We’ve got some Sara in the logistics
0:21:59 industry. We have Lyft and Uber in transportation. And I think that’s where the biggest value
0:22:06 is, but also one of the hardest to go into. Yeah, the classic full stack startup, right?
0:22:10 Which is I’m going to build these operational intelligence tools, but nobody else gets to
0:22:14 use them. I’m using it to serve my own business. And I’m going to win the market by winning
0:22:20 the customers directly. Yeah. And I think that the industries that are going to win
0:22:25 the most out of operational intelligence are going to be these kind of like traditionally
0:22:35 non-IT buyers. So oil and gas, groceries, construction, these are businesses that are
0:22:43 really, you know, trillion dollar industries, or trillions. But they have very low margins.
0:22:47 Like they’ve existed for such a long time that they’ve they’ve operationally become
0:22:58 really efficient. And at the same time, commoditized. So I’ll give you an example. The largest
0:23:05 construction group in the world is called the ACS group. The revenues are about like
0:23:12 34 billion per year, but their margins are about six and a half percent. And so a small
0:23:18 change in the gross margins for these businesses, a small change in how operationally efficient
0:23:26 they are translates into huge increases in their profit margins. Another example is Costco.
0:23:32 So in 2017, their revenues were about 12 and a half billion. And they were operating on
0:23:40 about 11% gross margin. Again, another another place where a change in operational efficiency
0:23:46 can lead to huge changes in revenues. The final example is a little different. This
0:23:50 one is less about gross margins, but more about capital deployed. And so the example
0:23:58 here is ExxonMobil, the mobile. If you were to guess what their like the value of the
0:24:01 capital that they have deployed around the world, what would you what would you guess?
0:24:07 Oh, ExxonMobil. Yeah. Hundreds of billions. Is it the order magnitude?
0:24:16 So ExxonMobil is about 230 billion capital. And they’re the way they measure their performance
0:24:26 is on return on capital invested ROIC. It’s it’s it’s a it’s very different. It’s different
0:24:30 than how you know the grocery example I gave earlier, which was based mainly on the gross
0:24:37 margins. And their return is about nine and a half percent or so. So again, a small change
0:24:43 in the operational efficiency of the of the capital that they have deployed can translate
0:24:49 into huge additional gains. I mean, they’re deploying about like 23 billion dollars additional
0:24:55 capital this year. That’s a lot of spending. Yes. And that’s that’s that’s the I mean,
0:25:00 it’s really interesting, like helping these companies on the that that are capital heavy.
0:25:06 So it sounds like you’re excited about a whole sort of gamut of startups. One would be, hey,
0:25:12 look, I’m going to sell a particular technology to enable you to be more operationally intelligent.
0:25:17 Right. You’re also interested in the full stack startups, which is I can sell an entire
0:25:23 solution to a customer directly and nobody else gets my oh, I goodness, so to speak.
0:25:28 What are some examples of sort of startups that you are? What are some examples of things
0:25:36 that you’re personally excited about? I can give you some some examples on the on the
0:25:40 infrastructure side. So I’m excited about the SuperSep project. I’m excited about what
0:25:51 implies doing. I think I think there’s a lot of I think a lot of what’s actually happening
0:25:59 is people are now starting to see analytics and observability as as urgent, as necessary
0:26:05 to running their business. And so I think that there’s a really great opportunity in
0:26:15 that space. I’m also really interested in companies or vendors, software vendors, into
0:26:19 incumbents, into large existing industries, like into construction, companies that sell
0:26:26 into construction or companies that sell into groceries. We’ve seen a few startups in that
0:26:34 domain. The hardest, some of the hardest problems here is that these are startups that are going
0:26:41 to have very different economic profiles than the traditional, you know, Silicon Valley
0:26:43 startup that that we know.
0:26:54 So first off, these are you’re selling into markets that are stagnant, that are very low
0:26:58 margin. They don’t have a lot of margin to go around, right? They can’t afford to pay
0:27:04 a lot. Exactly. And they’re not used to buying new technology. They kind of understand one,
0:27:09 two, and three, and like they don’t really know about four, or they don’t know how to
0:27:15 digest it. And so a lot of the effort there is going to be around educating and the sales
0:27:22 cycles are going to be very long. The pie at the end of that, like the other, the flip
0:27:26 side of this is that these are huge businesses, right?
0:27:30 Yeah. Construction, oil gas, retail chains, right?
0:27:36 Once you’re in, you’re in. And so when you’re actually starting a company in this area,
0:27:42 there’s a few things that you want to keep in mind. One, you need to educate your investors.
0:27:45 Like these are usually investors are not going to understand these businesses really well.
0:27:51 And they might not know the difficulty of actually selling into them, like what it takes.
0:27:59 And so you need to prep your investors for this like long haul thing for the long term.
0:28:05 And they need to understand that this is at the end of this, there’s a really bright light.
0:28:11 The second piece is you need to get domain expertise. Like you need to become the expert
0:28:19 in that business. And you need to become a kind of trusted advisor to these companies.
0:28:24 And so when they say things like, oh, you know, we want to go through digital transformation,
0:28:27 you need to help them understand like, here’s what that means. We’re going to be here for
0:28:33 you. We’re going to guide you through it and actually help them with both a significant
0:28:37 amount of services as well as software on the back end.
0:28:42 So don’t shy away from the services. Don’t shy away from the services, especially in these industries.
0:28:49 Well, Jan, thank you so much for coming and sharing this idea. The good thing about this is that
0:28:55 the world really is changing fast. If you are a retailer, Amazon has scared the bejesus out of
0:29:02 you, right? And so what used to be a very long tedious sales cycle has gotten a little quicker
0:29:06 because Amazon’s in the rearview mirror. And so everybody sort of knows that they need to go
0:29:10 faster. They need to make decisions sort of lower in the organization. They need to make them in
0:29:16 real time. And so it’s exciting to see startups helping that transition to real time decision
0:29:22 making pushed lower in the organization. So thanks for joining the YouTube channel.
0:29:27 If you liked what you saw, go ahead and subscribe. Feel free to leave comments. Maybe the question
0:29:32 that I’ll use to prime the comments is, what are your favorite examples of decisions that now need
0:29:39 to be made in more real time? And look forward to joining the conversation there. See you next episode.

0:00:02 Hi, and welcome to the A16Z podcast.
0:00:06 I’m Hannah, and this episode is all about building a software company in healthcare.
0:00:11 In this conversation, Jorge Conde, A16Z general partner in bio and healthcare, previous founder
0:00:16 of the genomics company Nome, and Julie Yu, partner on the deal team for the Bio Fund,
0:00:21 and previous founder of the patient provider matching system Kyrus, explained what it is
0:00:25 that makes building a company in the healthcare space so fundamentally different from in other
0:00:26 sectors.
0:00:28 And why exactly it’s so damn hard.
0:00:32 So let’s start with basically just the very fundamental difference between building a
0:00:36 software company full stop and building a software company in the healthcare space.
0:00:39 What are the most foundational, crucial differences?
0:00:45 Well, historically at least, software had two very important sort of qualities in healthcare.
0:00:48 The first one, the actual quality of software deployed in healthcare system historically
0:00:49 has not been great.
0:00:51 User interface wise and experience wise.
0:00:52 Bad track record.
0:00:53 Bad track record there.
0:00:56 And the second one is that it was usually not highly valued.
0:01:01 So at least a lot of times it was considered either free or cheap.
0:01:02 And why was that?
0:01:05 That in it from the very beginning, there was not a lot of value attached to this.
0:01:09 On the healthcare system, a lot of things still have a very human component to them,
0:01:14 automating things and sort of creating frictionless experiences or delightful experiences.
0:01:17 The things that software is really good at doing, it’s just really hard to do in the
0:01:18 healthcare system.
0:01:21 The second one is, I’m going to generalize for a second, but I think a lot of times in
0:01:26 the healthcare system, software has sold us a component of a broader service or of a
0:01:27 broader offering.
0:01:31 And so therefore it’s the piece that tends to get sort of devalued first because it obviously
0:01:32 has the lowest marginal cost.
0:01:36 It’s going to create this weird dynamic for software companies that are trying to build
0:01:37 in healthcare.
0:01:41 There’s a higher degree of sensitivity in this particular market for things that get
0:01:44 in the way of the patient provider experience.
0:01:47 One of the challenges/opportunities within healthcare is that it tends to be much more
0:01:50 risk averse when it comes to adoption of new technologies.
0:01:55 One meaningful difference in introducing a software product to this market versus other
0:02:02 markets is the level of scrutiny and the bar that you need to hit from a not even usability
0:02:07 perspective but just utility and actually having validation of if you are going to introduce
0:02:13 something new into the care delivery flow, it better work because the stakes are so high.
0:02:16 If you get it wrong, you could either send a patient in the wrong direction or they might
0:02:20 not get the care that they need or it could actually harm the individuals involved.
0:02:23 So not just higher barrier to entry but higher stakes, correct?
0:02:24 Immediately.
0:02:26 They’re a reticent buyer, generally speaking.
0:02:30 They’re running on very thin margins if we’re selling into the healthcare system, into provider
0:02:37 space and it needs to work because if it doesn’t, obviously there can be patient harm so the
0:02:42 probability that a newcomer, an upstart can come in and make that case in a convincing
0:02:44 way is a very, very difficult challenge.
0:02:48 So does that mean you have to have certain prerequisites that you may not need to have
0:02:49 in other spaces?
0:02:52 If you know you have these challenges and you know that you’re entering this space with
0:02:55 a lot more barrier to entry and a lot higher stakes, are there certain things you need
0:03:01 in place, a certain kind of proof of concept that you might not have to have otherwise?
0:03:04 Well, first of all, I think you’re touching on a very important thing which is in the space
0:03:07 and I’m going to specifically focus on sort of the healthcare system.
0:03:11 So let’s call it provider systems, payers and the like.
0:03:17 You have to really understand what the workflows are, what the problem space is and how to
0:03:19 actually address any of those things.
0:03:24 And so one of the biggest challenges I think that companies have when they want to build
0:03:28 software products here is to really understand what problem they’re going to solve because
0:03:33 I think you have this weird sort of intersection between it’s very non-intuitive, it’s still
0:03:39 very human driven and centric, there are regulatory barriers, you don’t want to get in between
0:03:42 say a provider and a patient, you know, most people aren’t born with the ability to say
0:03:48 like I know I can insert a piece of software into this part of the workflow and I will
0:03:50 solve an acute pain point for the system.
0:03:51 That’s not obvious.
0:03:53 And some of that is actually lack of standardization.
0:03:57 You would think that medicine is an industry that has a tremendous amount of standardization
0:04:01 and protocols around how people make decisions and do things.
0:04:04 But it actually turns out that healthcare is an industry that actually is characterized
0:04:06 by a tremendous amount of variation.
0:04:07 And variation in what kinds of ways?
0:04:11 It could be variation in terms of actually literally the decision that if you have ten
0:04:16 doctors who are all presented with the same patient, you might see ten different decisions
0:04:18 about how to treat that patient.
0:04:22 Some physicians might be more aggressive about using invasive surgical techniques versus
0:04:27 others who are more holistic, even just how I was brought up religiously or culturally
0:04:29 might impact the way I think about that problem.
0:04:33 From a product perspective, you could have multiple drugs that all treat the same condition,
0:04:35 that all have different implications and whatnot.
0:04:39 So even there, even though you have a patient population that is characterized by the same
0:04:43 diagnosis, you could have dozens of different ways that those patients play out.
0:04:47 And so it makes it very hard for a technology company to come in and sort of generalize
0:04:53 and say, you know, there is one single method for, you know, manufacturing this thing or
0:04:57 for making this decision and managing this patient population, ultimately that reflects
0:05:01 as differences in the financial profile of different patients.
0:05:04 Healthcare, it’s like politics, it’s very local.
0:05:09 Thinking that you’re going to have an out-of-the-box, one and done solution, even in systems that
0:05:14 look similar from either a size standpoint or reach standpoint or even a geographic standpoint,
0:05:16 these are all kind of end-of-ones.
0:05:18 So what does that mean?
0:05:24 So we have kind of knowledge of workflow, the knowledge of variety and spectrum and that
0:05:27 you are ultimately working in weirdly an N=1 scenario.
0:05:31 I want to bring it back to like actual practicalities of this sort of company building.
0:05:38 In your experiences, you both founded companies, what do you wish you had known or done differently
0:05:42 from the very beginning, given the complexity of that space and the unique challenges that
0:05:44 building a company in healthcare presents?
0:05:48 With Kairis, one of the products that we had was a product that was used by call center
0:05:50 agents in hospitals.
0:05:53 And our thesis when we first launched the product was, oh, well, we’re just going to
0:05:58 go after every hospital that has a call center and they probably all operate similarly.
0:06:02 And what constitutes the job of a call center agent is probably relatively homogenous.
0:06:06 And so we can make all sorts of assumptions about how it’s built, how it’s deployed and
0:06:07 how it’s managed over time.
0:06:11 The thing that strikes me already is that feels like a reasonable assessment of the
0:06:12 lay of the land.
0:06:13 Yeah.
0:06:15 And especially, I think it’s very easy to get fooled in healthcare by looking at other
0:06:20 industries and seeing how it works in the rest of the world because certainly…
0:06:21 And then you pull up the…
0:06:22 Yeah.
0:06:24 And then you pull up the wool and it’s like, oh, it’s completely the opposite.
0:06:25 Call centers.
0:06:28 I mean, that’s definitely an industry that if you look at retail or even all the airline
0:06:33 companies and how they operate their customer service operations tend to be pretty standardized
0:06:36 and pretty sophisticated in a lot of cases.
0:06:39 When did you start to realize this wasn’t maybe your average call center?
0:06:45 Like on day one, first of all, there’s heterogeneity in the actual scope of services of pretty
0:06:47 much every call center that we encountered.
0:06:51 Some call centers might be fully centralized and they’re like a central 800 number that
0:06:56 receives every call that comes into the hospital versus others that are decentralized that
0:07:01 only serve the primary care line versus the cardiology line versus the dermatology line.
0:07:05 And because of that, they will have just fundamentally different starting points of where they have
0:07:07 to be in the workflow for the thing to work.
0:07:12 The other aspect is the scope of functions that the call center plays.
0:07:15 It could be everything from just a general marketing service where a customer might call
0:07:18 in and say, do you provide these kinds of services?
0:07:20 Can you give me directions to the clinic?
0:07:23 All the way to I need a prescription refill.
0:07:24 I’ve been diagnosed with this thing.
0:07:27 I need to figure out what kind of surgery I need.
0:07:29 So again, much bigger range of possibilities.
0:07:30 Correct.
0:07:31 Yeah.
0:07:36 Like I’m a call center agent and how do you define in my job so that when I give you another
0:07:41 piece of software to use to do that job, it’s going to be seamless.
0:07:44 And when you have that kind of heterogeneity around even the sheer definition of what the
0:07:48 job is, it makes it very hard to design a scalable solution that can kind of fit into
0:07:50 all those different environments.
0:07:55 So day one, we actually were fortunate to get a customer that did have a pretty robust
0:08:00 centralized call center group that was hundreds of people who literally were answering every
0:08:02 call that was coming into the health system.
0:08:06 And so the immediate sort of leap that we made was, oh, they must all look like this.
0:08:11 Even if 80% of it was the same and there was 20% sort of buffer that needed to be modified,
0:08:12 we can deal with that.
0:08:16 Yes, they all had central call centers, but the fundamental scope of jobs that they were
0:08:19 doing were completely different across the board.
0:08:22 And some were more clinical in nature, some were more marketing in nature, some were more
0:08:24 financial in nature, et cetera.
0:08:26 So what were the knock on effects of that?
0:08:27 Yeah.
0:08:32 The impact on like, go to market, product design and spend product strategy.
0:08:36 Most importantly, the service model of you could either say, we’re going to design our
0:08:40 software to be so flexible that it could work in any environment.
0:08:46 Or you could say, we’re going to provide services to come train your people to behave in a more
0:08:49 standardized way relative to the rest of our book of business.
0:08:52 And so we ultimately ended up taking a hybrid approach to both.
0:08:55 But the latter, you know, that services approach is something that we hadn’t thought about
0:09:00 that allowed us to sort of abstract out the variation to some degree, but also provide
0:09:04 value back to the customers in a pretty unique way because then we had the best practices
0:09:07 for, you know, how it should work.
0:09:10 So ultimately it was a good thing, but it was a major fork in the road.
0:09:11 Absolutely.
0:09:15 Because there is so much variability, because there’s so much localization, the notion of
0:09:20 the pure SaaS model where you’re just throwing technology over the fence and assuming that
0:09:25 it will fit into whatever environment you’re deploying it into, that is a moot point in
0:09:30 healthcare, you actually do need to think about the services component of things.
0:09:34 There was a whole generation of companies that got started like a decade ago that took
0:09:39 these sort of tech-only approaches and failed to get scale or had to fundamentally pivot
0:09:43 their models to actually take into account more of the human element of the service delivery
0:09:44 model.
0:09:45 I mean, even there’s a term for it now, right?
0:09:50 Tech-enabled services is a way of doing things now in digital health that I think is well
0:09:56 recognized that it’s necessary to wrap the technology with a human component to essentially
0:10:00 address and be able to accommodate all the variation that you see across different customer
0:10:01 bases.
0:10:04 And it changes your cost structure fundamentally, the nature of how we talked about the business
0:10:05 and how it scales.
0:10:08 And even our fundraising strategy fundamentally changed because of that.
0:10:13 And so we did have to, you know, raise more and give ourselves more runway and think about
0:10:15 different ways to manage our margin.
0:10:19 It sounds like everything that could have been changed by that.
0:10:23 Let’s go back to a specific example where you really put your foot in it.
0:10:26 Well, so in our experience at Noam, it was interesting because here, this is a company
0:10:32 with, the sole purpose of the company was to provide software capability to analyze
0:10:33 genomic information.
0:10:36 And so, you know, when you launch that, your assumption is, well, this could be used to
0:10:39 power all kinds of applications.
0:10:43 It could be used for research, either an academia and industry, it can be used for, you know,
0:10:44 clinical diagnostics.
0:10:45 Flexible.
0:10:46 We thought it was very flexible.
0:10:50 But challenge one is, you know, a solution looking for a problem is always a very, very
0:10:51 dangerous thing.
0:10:52 I think that’s universally true.
0:10:54 I think it’s especially true in the healthcare space.
0:11:00 And challenge two was understanding exactly where, in the case of the clinical setting,
0:11:03 where this technology would be used in the workflow.
0:11:07 So here we wanted to go after the clinical labs.
0:11:08 That was your initial hypothesis?
0:11:12 Our initial hypothesis for an application in a clinical setting.
0:11:17 You have technicians and docs that are inside of the laboratory setting, receiving samples,
0:11:23 running a test, analyzing the results of that test, generating a report that gets signed
0:11:25 off by a lab director that goes back to a physician.
0:11:27 Usually it’s in the form of a diagnosis, right?
0:11:29 And it gets signed off and it goes to the physician.
0:11:36 The physician now takes that report and basically decides what to do based on that information.
0:11:42 So our assumption was, well, if you have the ability to sequence DNA now in a way that
0:11:46 you couldn’t before, before you’d have to do all of these specific tests, you have to
0:11:49 know what to test and then you’d test it and then you’d get a report.
0:11:53 You had to know what street lamp the keys were under, right, like there in that case.
0:11:57 Whereas once you had the full genome, you could just sequence everything and just run
0:11:59 a bunch of software queries.
0:12:04 So our thought going into this was, well, that’s an incredibly powerful tool for clinical labs
0:12:08 because first of all, you can sequence just once and analyze over time.
0:12:09 Right.
0:12:12 It seems like a totally legitimate assumption to make.
0:12:15 And it turns out that there was a lot of challenges with that assumption.
0:12:17 The first one is every lab is different.
0:12:21 A lot of them didn’t have the budget or the willingness to basically pay the upfront
0:12:28 piece to buy the capability to use this technology or they didn’t have the ability to sequence
0:12:29 everything upfront.
0:12:33 Even if all of the subsequent queries would be technically free later.
0:12:34 Why not?
0:12:35 The way they’re reimbursed.
0:12:36 Oh, how fascinating.
0:12:37 Too expensive, basically.
0:12:38 It’s too expensive.
0:12:43 So even the theoretically there’s an ROI, a return on the investment of sequencing upfront,
0:12:48 just the way the industry structure, the way reimbursement flows, the way payments flow.
0:12:50 It just didn’t make sense for a lot of labs to do this.
0:12:53 So how was that not just a complete roadblock at that point?
0:12:54 It was a big roadblock.
0:12:58 So what that required us to do was to then focus on clinical labs that had the ability
0:13:00 to make certain investments in upfront costs.
0:13:04 And those tended to be very sophisticated labs that do a lot of research work in addition
0:13:08 to patient care and they tended to be on the sort of on the bleeding edge and they wanted
0:13:11 to incorporate new technology and they were great partners and all of that.
0:13:13 But then it goes back to your end of one problem.
0:13:18 So you sell something into that lab and you go next door and next door has a totally different
0:13:21 set of capabilities, a totally different set of constraints, a totally different set
0:13:22 of expectations.
0:13:27 And so therefore, all of a sudden the solution you created for lab A is not relevant or unattainable
0:13:29 for lab B.
0:13:33 Now, to just add to the stepping in it, you know, when you’re analyzing genomic data,
0:13:36 there’s a massive amount of computation required.
0:13:40 And so we went in there assuming, well, this is easy, we’re just going to shoot all of
0:13:44 this up to the cloud, we’ll run the analysis, we’ll send the data back to the lab, the lab
0:13:48 could verify it, generate a report and off we go.
0:13:51 It turns out labs weren’t comfortable sending data up into the cloud, full stop.
0:13:55 At that time, it was just completely– At that time, arguably even today, arguably
0:13:59 even today in 2019, but definitely at that time, we probably should have known that earlier
0:14:03 that would have changed how we thought about going into the clinical lab space.
0:14:04 How would you have done your homework?
0:14:06 I mean, what would that have actually looked like?
0:14:11 It was frankly, I think just defining the specs of what would be required to bring in
0:14:17 our technology, because I think people intuitively know that genomic data is massive, but I don’t
0:14:22 think they know sort of the level of computation required to run the interpretation.
0:14:23 Right.
0:14:24 So like really running the numbers.
0:14:26 Running the numbers for them and by the way, we tried everything.
0:14:30 I mean, we brought representatives from AWS that could show them that they had a HIPAA
0:14:35 compliant cloud that they had received all the certifications and it came back to risk
0:14:36 aversion.
0:14:39 So someone, the lab director, saying like, “Look, I’m sure all of that’s true, but I’m
0:14:41 not going to risk sending all of this data up into the cloud.”
0:14:46 So that was a big, big challenge for us and it ended up being a major limitation for our
0:14:50 ability to expand into the clinical setting because of all of those barriers.
0:14:51 So what did you do?
0:14:56 We had to do a plan A and a plan B. And so the plan A was we assumed that there would
0:15:01 be a couple of forward looking labs or forward thinking labs that would be willing to work
0:15:05 in the cloud environment, much easier to deploy there.
0:15:10 The plan B was we had to create a box and we had to create a box and the box had to have
0:15:11 essentially the competition.
0:15:12 A normal appliance.
0:15:13 Yeah.
0:15:14 We had a normal appliance.
0:15:15 Remember that.
0:15:16 Oh my gosh.
0:15:17 Because they didn’t want the data to go outside.
0:15:21 And it’s for the reasons that we’d expect, you know, there’s regulatory, there’s risk
0:15:24 associated with that today in 2019.
0:15:28 In fact, the companies that have managed to use this technology have taken the sort of
0:15:30 full stack service approach.
0:15:35 So that sort of high low strategy became the approach is get folks to deploy into the cloud
0:15:37 when they were willing to.
0:15:43 And in the case where folks needed an appliance, we basically had to go to labs that had enough
0:15:47 a sample volume that an appliance made sense for them and make basically the case there
0:15:48 from an investment standpoint.
0:15:53 So again, multiple choice, variety and like addressing in different ways.
0:15:58 A pure software company in healthcare is a really hard thing to do.
0:16:01 Because on the one side, you have this challenge that it’s hard to create a sort of a solution
0:16:03 that’s going to fit everyone.
0:16:08 And therefore you need to have some level of services around that software.
0:16:09 That’s on one extreme.
0:16:12 So when you need to have humans in the process or in the loop.
0:16:16 And then the other extreme, if it is pure software, then it’s considered that it should
0:16:17 be free.
0:16:18 So it’s very hard to abstract value.
0:16:19 That’s so interesting.
0:16:23 Do you think that’s shifting at all with the kind of understanding of the importance of
0:16:24 data and some other things?
0:16:25 Yeah.
0:16:27 Look, I would argue it’s shifting on a couple of axes.
0:16:30 The first one is data is becoming more and more valuable.
0:16:36 Historically data was viewed as being either too small in terms of its impact, too narrow,
0:16:38 too dirty, et cetera, et cetera.
0:16:39 Too difficult.
0:16:40 Yeah.
0:16:41 Too unstructured.
0:16:42 So that historically has been the case.
0:16:47 So if you have ways to ingest data and clean it and make it meaningful, then I think that
0:16:48 is valued.
0:16:52 Probably the most public one is what Flatiron was able to do and ultimately getting acquired
0:16:55 by Roche for $2 million.
0:17:00 That’s viewed as using an electronic medical record to capture patient experiences, take
0:17:05 that information, and give researchers the ability to drive valuable insights from that.
0:17:06 That’s a relatively new thing.
0:17:08 So I think there is the ability to create value there.
0:17:10 So I think that’s one axis.
0:17:14 I think there’s a general shift in the model that having a tech-enabled service can be
0:17:18 a valuable thing and if done well can be a scalable business.
0:17:23 In other words, if you know what you’re trying to build and if the software layer reduces
0:17:29 sufficient friction in the system and allows you to add people, not linearly as you scale,
0:17:33 but in a leverageable way, then all of a sudden you could have tech-enabled services that
0:17:35 can grow and become large businesses.
0:17:40 So leaning into what it is that makes it difficult almost and then scaling that, leveraging that.
0:17:41 Exactly.
0:17:42 Finding ways to make that scalable.
0:17:43 Yeah.
0:17:45 That’s not easy to do, but I think it is now doable in a way that probably it wasn’t.
0:17:46 Yeah.
0:17:50 So we see that same trend actually happening in the consumer world where you used to have
0:17:55 a bunch of services like the marketplaces that were purely tech and were just matching
0:17:59 supply and demand and then getting out of the way, whereas now you see a lot more services
0:18:03 like in the real estate market where they’re actually managing properties.
0:18:06 We’re actually going to clean the place and make sure it has good furniture and all that
0:18:07 kind of stuff.
0:18:11 I think the same premise holds true in healthcare where you realize that in order to truly make
0:18:15 an impact, you kind of have to own certain parts of the full stack and that’s what you
0:18:17 see playing out in the rest of the world as well.
0:18:18 Okay.
0:18:22 So we’ve talked about kind of knowing the workflow and the complexity of the system,
0:18:26 running the numbers and specking it out as concretely as possible.
0:18:28 How about in terms of team building?
0:18:33 Are there ways that you, knowing what you knew down the road that you would have changed
0:18:36 how you thought about building the team from the very beginning?
0:18:38 My prior experience was not in healthcare.
0:18:42 And so a lot of my views on how to do these kinds of things were informed by a company
0:18:45 that was just a pure enterprise software company.
0:18:48 And one of the mantras was you want to, in the early stages of a company, hire for all
0:18:52 around athletes and just people who are utility players who can like roll with the punches
0:18:53 and figure it out.
0:18:57 It doesn’t matter what kind of experience they had as long as they’re scrappy, intellectually
0:19:00 motivated people, they’re going to figure it out.
0:19:04 So it certainly took that approach when we started Kyrus and hired folks not necessarily
0:19:09 from healthcare who maybe had some engineering experience or sales experience from elsewhere
0:19:12 in the world and said, “We’re just going to go in there and figure it out.”
0:19:15 But you surely had some deep experts in the space as well.
0:19:17 So my co-founder is a physician by training.
0:19:23 So we had sort of the deep clinical knowledge, but I would say actually we didn’t have that
0:19:28 many people who knew the specific market that we were going after.
0:19:32 And that’s another characteristic of healthcare startups is healthcare is so massive that
0:19:36 when you talk about market segment, you have to be very specific about what you’re talking
0:19:37 about.
0:19:41 So when people come and say, “Oh, I have a company that sells to providers,” I’m like,
0:19:42 that’s great.
0:19:43 That’s like, you know…
0:19:44 What does that actually mean?
0:19:45 Yeah.
0:19:47 Like there’s 20 billion ways that you could describe providers like, “Are you selling
0:19:48 to hospitals?
0:19:49 Are you selling to health systems?
0:19:50 Are you selling to individual practices?”
0:19:53 And each of those can be multi-billion dollar markets in and of themselves.
0:19:56 I used to work in publishing and it reminds me of people who would pitch their books to
0:19:58 us and be like, “It’s for the general reader.”
0:19:59 And you’re like, “Who?
0:20:00 Who?
0:20:01 There is no general reader.”
0:20:02 Exactly.
0:20:03 There’s like somebody who likes to read Amy Tan.
0:20:07 There’s somebody who likes to read like, you know, Dan Brown or whatever.
0:20:08 These are different people.
0:20:09 Yeah.
0:20:10 There you go.
0:20:11 So yeah.
0:20:14 So basically we had folks in our company who had “health care experience,” but maybe
0:20:18 it was from the pharma industry or from payer or even like a different segment of the health
0:20:22 of the provider market, but not the specific market that we were going after, which was
0:20:24 like a very esoteric…
0:20:28 We were going after the biggest health systems, like the top down approach in the enterprise
0:20:29 space.
0:20:32 And there’s very specific characteristics to those organizations that are very different
0:20:34 than even smaller hospital networks.
0:20:38 The areas of the team building exercise that I wish we had been more thoughtful about were,
0:20:43 you know, in terms of customer facing roles, where it was a team responsible for managing
0:20:47 the customer relationship longer term, you know, just how important it is for those people
0:20:54 to have some kind of understanding and empathy and ideally experience with the kind of people
0:20:55 that we were servicing.
0:20:59 There is total merit to saying, “Actually, we need some insiders who might not have any
0:21:05 technical skills whatsoever, but can help us understand the culture and the politics
0:21:08 and what it means to even like talk to a physician.”
0:21:11 You know, we had a bunch of folks who had never been in health care who walked into meetings
0:21:15 and called doctors by their first names, and that was a complete taboo in certain cultures
0:21:17 where you have to call them Dr. Jones or Dr. Smith.
0:21:21 Like “Stranger in a strange land” kind of like, “Here’s the language here.”
0:21:22 Yeah.
0:21:24 So I think from a team building experience, one of the biggest lessons that we certainly
0:21:31 learned was a valuing health care domain expertise earlier in the evolution of a company relative
0:21:36 to other sectors, and then also thinking about where that makes sense, like what functions
0:21:39 that makes sense, because it’s not a 100% universal statement across the board.
0:21:43 I would say our engineering team, it was actually better that they came from outside
0:21:44 of health care.
0:21:48 Oh, so in specific areas of where you need the knowledge and where you don’t.
0:21:51 Why was it a bad thing for engineers to have that?
0:21:53 Not a bad thing per se, but you wanted people who could like really think out of the box
0:21:58 and not be sort of married to the way it’s done today, because actually that’s exactly
0:22:02 the point of building companies in this space is to not do it the way it’s been done.
0:22:07 And so most of the technology systems that are in place are written on super legacy technologies
0:22:10 and don’t have things like APIs and whatnot.
0:22:13 You need to be like super creative about like how to get into these systems and get data
0:22:18 out because they were like fundamentally not designed to have liquidity around the data
0:22:19 that’s stored in them.
0:22:23 And so it was helpful to have people from the financial services industry, for instance,
0:22:27 who had figured those things out with similar banking systems and whatnot and could kind
0:22:30 of bring some of that creativity to the health care space.
0:22:33 So engineering is definitely a space where I felt there was a positive to not having
0:22:37 that health care domain knowledge, but certainly on the commercial side of the business.
0:22:39 I think it’s critically important.
0:22:42 Making sure that the engineering team is as modern as possible is the most valuable thing
0:22:43 you can do for your company.
0:22:48 Because I think what’s generally true and probably definitely true across the board
0:22:52 is that health care, the data sets are so complex, right?
0:22:56 They’re complex in terms of their variety, they’re complex in terms of their volume,
0:22:57 they’re unstructured.
0:22:58 There’s regulatory requirements.
0:23:02 There’s so many things that are challenging from a data handling standpoint.
0:23:06 So building the pipes in the most modern way possible, absolutely critical.
0:23:11 Whoever’s customer facing, I think has to be from that game, has to understand the space,
0:23:15 has to understand who the customer is, has to understand the cultural norms and all of
0:23:16 those things.
0:23:17 Those things are both true.
0:23:22 You need both in the get-go, industry specific on the customer spacing side and domain expert
0:23:24 from the engineering side, right?
0:23:27 And then let’s talk a little bit about the middle, the product, right?
0:23:28 That’s where the sausage gets made.
0:23:29 Totally.
0:23:32 I’m going to be biased because I was the chief product officer of my company.
0:23:35 And that’s where I would say it was split, where I do think it’s important for the leader
0:23:40 of that organization to have a pretty deep understanding of the market.
0:23:44 And so I happen to have had health care experience, not specifically in this particular segment
0:23:48 per se, but I understood some of those cultural nuances and just dynamics of how the market
0:23:51 worked to be able to set strategy.
0:23:55 Below me, however, some of my best product managers were not health care people at all.
0:23:59 And in fact, we had three products, one that was the call center product that I mentioned
0:24:04 earlier, where the end users themselves were not health care people, right?
0:24:09 And so some of them were like high school graduates who go home and they use their iPhone and
0:24:11 they’re used to all these modern technologies and the rest of their lives.
0:24:15 And then they come to work and they’re faced with these totally esoteric, crappy, hard
0:24:17 to use systems.
0:24:20 And so I wanted someone who actually had kind of a consumer mindset.
0:24:24 Did you find yourself doing a lot of sort of explaining and educating though to bridge
0:24:25 that gap?
0:24:26 Yeah.
0:24:28 My philosophy was just throw them in the deep end.
0:24:32 As part of the onboarding experience at Kyrus, you had to visit a hospital call center and
0:24:34 they actually let you listen in on calls.
0:24:37 It was like a religious transformation for these team members who went.
0:24:42 Some came back and said, I cannot believe that this is how these organizations operate,
0:24:43 right?
0:24:45 Cause like everyone thinks of health care as this very pristine, like I’m going to trust
0:24:49 you with my life and they’ll come back and be horrified because, you know, they see that
0:24:54 things are being run on paper and just how much burden they put on the customer, right?
0:24:57 Because part of what you hear when you’re listening in on these calls is like asking
0:24:59 the patient, what do you want to do?
0:25:01 And the patient’s like, well, why would I have understood?
0:25:04 I’m calling you guys a hospital and you’re supposed to tell me what to do.
0:25:05 So that was one reaction.
0:25:07 The other reaction was completely emotional, right?
0:25:11 Because a lot of these patients who were calling in had just been diagnosed with cancer and
0:25:16 they have no idea what they’re doing and they’re calling because they need help.
0:25:20 And then the call center agent sometimes felt helpless because they didn’t have the tools
0:25:21 or the workflows or the information.
0:25:25 Oh, it reminds me of like a 911 operator with like no training, somebody thrown into
0:25:28 the middle of like, I’m having a massive life crisis.
0:25:29 Yeah.
0:25:30 It was inspiring and motivational.
0:25:33 And so that became part of like our training process was to just go out there and see it
0:25:35 versus me explaining it.
0:25:36 That’s really interesting.
0:25:37 Okay.
0:25:38 So what about timing?
0:25:42 Do you think it’s different in the healthcare space, how you think about what’s the right
0:25:43 moment for your product?
0:25:48 One of the big challenges in healthcare is this idea that you can be too early.
0:25:50 You can be too early for a couple of reasons.
0:25:59 One is you need a lot of changes to workflows for the entire system to become much more modern.
0:26:04 But you think this is different from being too early with like pets.com.
0:26:05 That’s a good question.
0:26:09 So the way I would think about it, I described what was for us at the company, a very obvious
0:26:11 evolution of where genetic testing would go.
0:26:15 You would sequence everything first and you would test multiple times in silicone.
0:26:16 You could see the light at the end of the tunnel.
0:26:17 I mean, that’s a clear future.
0:26:22 And so the question is when is the system ready for your particular solution to a problem
0:26:24 that everyone agrees exists, right?
0:26:28 Everyone agrees that we have to do a better job at being able to diagnose folks with genetic
0:26:29 disease.
0:26:33 And I think everyone would agree that using genomics, the ability to do this at large
0:26:38 scale to query multiple times, to use software, to make intelligent queries would be a very
0:26:41 powerful tool, a very powerful solution for that.
0:26:47 But the reality was, continues to be, that just the structures of the industry are such,
0:26:50 even though that’s where I think we will end up, it’s just not ready for it now.
0:26:54 And I think this is true for any entrepreneur, timing is a big part of anything you do.
0:26:58 I think timelines are especially warped in healthcare because it just takes a long time
0:27:00 to adopt new technologies.
0:27:04 There actually is a peer-reviewed study of the average number of years it takes for
0:27:08 new technologies that are introduced into the medical setting to become mass-market
0:27:09 adopted.
0:27:10 Oh, how fascinating.
0:27:11 Wait, wait, let’s guess.
0:27:12 Two years.
0:27:13 17 years.
0:27:14 No!
0:27:15 I mean, we still have fax machines.
0:27:16 That’s true.
0:27:17 We still have fax machines.
0:27:18 We still use the same…
0:27:21 But we’re not talking about when technology leaves, but you’re right.
0:27:22 It’s the same thing, really.
0:27:23 Yeah, so it gets replaced.
0:27:26 Yeah, you can think about it as all the things that have tried to replace the fax machine
0:27:28 or not yet mass-market adopted.
0:27:29 And it’s the same.
0:27:30 You could see it in…
0:27:35 I think the study actually focused primarily on stethoscopes and thermometers and things
0:27:38 that literally have not been redesigned for hundreds of years because it’s been so hard
0:27:39 to disrupt them.
0:27:42 Over the last 17 years, there’s been a bajillion better versions of the stethoscope that we
0:27:43 are just not seeing.
0:27:45 The wheel could have been reinvented, but better.
0:27:46 Absolutely.
0:27:50 Those are the tangible examples, but the same applies to software and technology.
0:27:54 And that’s a lot of the reason why you see the market-leading companies that own the EHR
0:27:57 space today are literally 45 years old.
0:28:01 And by the way, those companies also didn’t hit their stride until like 20 years into
0:28:02 their journeys.
0:28:06 So time functions completely differently, basically, in this system.
0:28:07 It’s almost like…
0:28:08 It’s like a wormhole.
0:28:12 And second of all, it’s an incredible testament to the strength of these systems that…
0:28:13 Totally.
0:28:14 Yeah.
0:28:16 It’s like, once you do make it, it’s totally sticky.
0:28:21 The LTV, essentially, of tech companies that actually make it and get to a certain level
0:28:23 of scale is through the roof.
0:28:25 There’s no incentive to rip them out because if they work, they work.
0:28:29 The switching costs because of all the human and cultural elements that we described is
0:28:30 huge.
0:28:31 Yeah.
0:28:34 So the longevity of your company, if you’re looking at success, is also incredibly promising.
0:28:35 Yeah.
0:28:38 I mean, certainly at Kairis, the way we mitigated it was we thought about what our fundraising
0:28:42 strategy would be to give ourselves enough runway to have that model play out.
0:28:47 We needed to fund the sales cycles and the adoption cycles to create a new category of
0:28:48 solution that didn’t exist.
0:28:51 Did it hang out in the wormhole for a while?
0:28:52 It’s a big oxygen tank.
0:28:53 Yes.
0:28:57 Global happens in healthcare in under three years, and so you kind of have to give it
0:28:58 some runways.
0:29:01 This is one of the things that we’ve spent time talking about is what does a minimal viable
0:29:03 product in healthcare look like?
0:29:04 Doesn’t exist.
0:29:05 Big bang.
0:29:09 You’ve got to go in and you’ve got to create a category and you’ve got to get that adopted.
0:29:14 I think in other industries, you can sort of quote-unquote get away with having a product
0:29:18 that does one thing really, really well and then start there and yes, expand over time,
0:29:23 but at least you can get by and to prove your value with that initial use case.
0:29:26 I think going back to a lot of the points you made earlier in healthcare, when you’re
0:29:32 in the flow of impacting a patient encounter and saying you’re going to rip something out
0:29:36 or change the way that you’re doing something or what have you, you have to make sure that
0:29:39 it’s going to give you the right answer, so to speak.
0:29:43 Even if it’s just one feature, it might mean, okay, yes, it could be one feature, but you
0:29:46 have to be integrated into seven different systems to make sure that the data flowing
0:29:50 into that one feature is enough to inform the right outcome or decision.
0:29:52 So really fully baked.
0:29:55 If a transaction falls through the cracks, while you’re doing some kind of revenue cycle
0:29:59 type encounter, you might not get paid for a procedure that could have a severe impact
0:30:01 on your bottom line.
0:30:02 You need more funding.
0:30:04 You need to think differently about your strategy for product and what that footprint
0:30:05 looks like.
0:30:08 You have to have the full solution.
0:30:12 And the related point I would make to that is it’s really hard to have a point solution.
0:30:14 Even if that point solution is very, very good.
0:30:18 I think people in general in the healthcare system are looking to buy a complete solution.
0:30:23 So if you take the problem from A to B to C to D, that’s great, but they need A to Z.
0:30:25 They can’t get A to Z from you.
0:30:27 It’s very hard to get them to buy A to C from you.
0:30:29 I’ll go even further than Julie.
0:30:32 I will say, not only does MVP not exist in healthcare, I would argue that product-market
0:30:34 fit doesn’t exist in healthcare.
0:30:35 What do you mean by that?
0:30:40 The definition of product-market fit is when the right product meets a good market.
0:30:44 All of the things we talked about creates such distortions in the marketplace that by the
0:30:49 time you actually get through all the hoops, you have such a skewed product.
0:30:51 It’s not really product-market fit.
0:30:53 It’s almost like accepted product capture.
0:30:56 Here you have regulatory issues.
0:30:57 You have pricing concerns.
0:30:58 You have incumbents.
0:31:03 You have so many aspects that distort the market that I would argue that you don’t have a normally
0:31:06 functioning market for software in healthcare.
0:31:12 How would you both embrace that distortion early on and not get completely knocked off
0:31:14 your path by it?
0:31:19 It strikes me that a lot of what you’re describing is know-thyself, know-yourself very deeply.
0:31:22 That was the tagline I know, by the way.
0:31:25 Oh, was it really?
0:31:26 That’s really funny.
0:31:29 I did not work that in for you.
0:31:34 But also know where you’re going and do that deep, I want to say, soul-searching on a company
0:31:37 level and build out accordingly.
0:31:40 How do you get that big center of gravity of really knowing yourself, knowing where
0:31:44 you’re going, but be able to be flexible with that distortion along the way?
0:31:48 The only North Star you can have, and this is going to sound cliche, but really understanding
0:31:54 your value proposition truly from the customer standpoint, it becomes a critical guide for
0:31:55 what you do.
0:31:58 This is a debate that healthcare companies have all the time, which is should your value
0:32:02 proposition be I’m going to save the system money because the healthcare system is very
0:32:05 inefficient and it runs on very low margins generally.
0:32:08 Should it be that I am going to result in better outcomes for patients?
0:32:13 Is it going to be I’m going to create some sort of a lift in terms of return on investment?
0:32:16 There’s a bunch of different ways you can think about value proposition.
0:32:20 If you don’t have that crystal clear from the outset, the amount of obstacles that you
0:32:24 are going to hit along the way are going to make it such that it’s going to be very difficult
0:32:25 to get to the other side.
0:32:29 If you don’t really understand the workflow and the culture and the regulation and the
0:32:33 governance and the politics and all of the other things, you can have a theory on what
0:32:37 the value proposition is, but you need your customer to confirm that early on and sadly
0:32:40 the best way to confirm that is to have them buy something, obviously.
0:32:44 Julie and I have had this debate before, which is a lot of the software platforms that go
0:32:49 into healthcare have been sort of predicated on we’re going to cut costs.
0:32:54 I don’t know of any sort of solution out there that has meaningfully been able to make a
0:32:56 very, very strong case that they can cut costs.
0:32:59 And by the way, part of it is, I think, is because it’s really hard to measure costs.
0:33:03 It’s almost like a necessary evil where you have to say in some way, shape, or form you
0:33:06 are going to reduce costs, but that can’t be your primary value proposition.
0:33:09 Because at the end of the day, it’s aligned in the cost structure that can get wiped out
0:33:13 over time and potentially get commoditized.
0:33:17 So is the takeaway, know your value proposition as early as possible and test it?
0:33:20 That and then have the conversation of like, okay, if we are able to accomplish what we
0:33:23 just described, is it worth it?
0:33:24 Is the juice worth the squeeze?
0:33:29 Because it’s so expensive to distribute product in this market because of the sales cycles
0:33:34 and the nature of the enterprise sales motion and whatnot, that if you’re not able to envision
0:33:39 a path towards being like at least a half a million dollar kind of a year type solution
0:33:42 in this space, it’s actually not financially worth it to build a business in that area.
0:33:43 Right.
0:33:45 Which goes back to your point of like run the numbers, basically.
0:33:48 At least like back at the envelope, like, you know, whiteboard kind of thing.
0:33:52 I mean, is there anything that you can figure out as you go?
0:33:57 It sounds like you need to know so much before you begin and be so self aware and so kind
0:33:59 of like have the end game in sight.
0:34:03 Are there things that you can leave sort of more organic and like feel out as you go?
0:34:04 Yeah.
0:34:05 No, I mean, absolutely.
0:34:08 There’s tons of things you can be doing on a daily basis with end users and just like
0:34:12 feedback mechanisms on like how people are, are they actually able to do their jobs, for
0:34:15 instance, and making minor tweaks to the workflows and whatnot.
0:34:19 So that was always, you know, a component of a more organic and dynamic aspect of how
0:34:20 we did things.
0:34:24 The other thing that you need to kind of think about doing in parallel is, you know, so much
0:34:29 of success of technology and healthcare is predicated on integrating into other ecosystem
0:34:30 players.
0:34:33 And so this is actually probably one industry where you definitely can’t like just build
0:34:34 in a vacuum.
0:34:38 You actually should understand, even if it’s not, you know, for another few years that you’re
0:34:42 really going to have to do this, like who are the players, we just need to get to know
0:34:46 so that we’re on their radar when time comes for us to take the hammer and like try to break
0:34:50 down the wall of integration with that vendor that we are on their good side and that they
0:34:53 know who we are so we can kind of make that happen faster.
0:34:58 So things like that, I think you can be doing in parallel to, you know, the kind of formulation
0:35:00 of what the footprint of the product is.
0:35:04 If you’ve got the right solution, you can get very creative in how you get paid.
0:35:09 So figuring out different pricing structures or value capture mechanisms, I think is something
0:35:13 that you can do pretty organically because if you are making a difference in the system,
0:35:17 the system has so much cost built into it and so much revenue flowing through it that
0:35:19 there are ways to be very imaginative there.
0:35:21 So that’s the first thing I would say.
0:35:26 The second thing I would say is thinking about adjacencies, you know, going from one, you
0:35:32 know, your core function to the next adjacent use case, not all adjacencies are created
0:35:33 equal.
0:35:34 One might be easier than the other.
0:35:38 It’s almost like, you know, jumping on stones across a pond or something, right?
0:35:41 What’s the next stone I can jump on that’s least likely to make me fall into the water,
0:35:42 right?
0:35:43 Yeah.
0:35:44 Even if it doesn’t get me as far as another one.
0:35:45 Right.
0:35:46 Always have that closer spot insight.
0:35:49 You’re almost, you’re creating the next thing and the next thing and the next thing and
0:35:50 you build out from there.
0:35:54 And eventually you cover so much surface area that, you know, you become a very sticky solution
0:35:58 and you hopefully become a complete solution sort of closer to the A to Z type vision.
0:35:59 Okay.
0:36:00 Last question.
0:36:01 Biggest takeaways.
0:36:04 Quick lightning round for your founder struggling right now.
0:36:05 What would you say?
0:36:06 Bullet points.
0:36:07 Know your market segment.
0:36:12 Be very specific about what segment you’re going after because that has major implications
0:36:14 for your go to market and your product.
0:36:15 Good one.
0:36:16 All right.
0:36:17 Let me get that to you.
0:36:20 One is build the multidisciplinary team early.
0:36:24 Two is understanding and if the person that suffers from the pain point can actually pay
0:36:29 for your solution because there’s a lot of misincentives in the healthcare system.
0:36:34 And three, with the right technology, you can have massive impact on patient lives and
0:36:38 the experience that we have with the healthcare system, which we will all touch in our lifetime.
0:36:42 And if there’s anything you can do to make it better as an entrepreneur, I would say
0:36:44 that is extraordinarily satisfying.
0:36:45 That’s fantastic.
0:36:46 And good bullets.
0:36:49 Thank you both so much for joining us on the A16Z podcast.
0:36:50 Thank you.

0:00:03 – Hi, welcome to the A16Z podcast.
0:00:05 This is Frank Chen.
0:00:06 This episode, which is called
0:00:09 “Inside the Apple Software Factory,”
0:00:11 originally aired as a YouTube video.
0:00:13 You can watch all of our YouTube videos
0:00:17 at youtube.com/A16ZVideos.
0:00:18 Hope you enjoy.
0:00:21 – Well, welcome to the A16Z YouTube channel.
0:00:24 I’m Frank Chen, and today I am so excited.
0:00:27 I feel like I have won the golden ticket
0:00:29 to Willy Wonka’s Chocolate Factory,
0:00:32 because look, if you’re in Silicon Valley,
0:00:34 the one chocolate factory you want
0:00:37 your desperate to go visit is Apple.
0:00:40 And the reason for that is Apple has consistently
0:00:44 over its history turned out some of the most intuitive
0:00:47 and delightful and just plain awesome products
0:00:51 that people use, and people are dying to find out
0:00:56 how is it that Apple makes such delightful products?
0:00:59 And so today, I’m here with Ken Cacienda,
0:01:01 and I’m so excited for him to tell us
0:01:06 all about the creative process that he used,
0:01:07 and his team used to create these products.
0:01:09 So Ken, thank you so much for coming.
0:01:10 – Well, thank you so much.
0:01:11 It’s great to be here with you.
0:01:12 – Well, let’s get right into it.
0:01:16 So maybe talk a little bit about how you ended up at Apple,
0:01:19 because like on paper, you don’t look like
0:01:20 the typical software engineer.
0:01:22 So go back and do the laundry.
0:01:24 Like, where were you born in?
0:01:27 – Oh, well, I was born in New York,
0:01:30 stayed there on Long Island, downstate,
0:01:33 grew up close to Beaches, lived there until
0:01:35 I went away to college, I went to Yale,
0:01:37 and got a degree in history.
0:01:40 And then after I graduated from Yale,
0:01:42 I didn’t do the typical thing.
0:01:44 I went to Motorcycle Mechanics School.
0:01:45 – Really?
0:01:47 All right, Ivy League, and what motivated that?
0:01:49 Like, you just learned motorcycles?
0:01:50 – I wanted to learn how to fix motorcycles.
0:01:53 – Well, when I graduated from college,
0:01:56 I wanted to do something that was as different
0:02:00 from Ivy League College as possible.
0:02:02 – I think that qualifies.
0:02:06 – Right, right, this was dismaying to my parents,
0:02:07 my father in particular, I can tell you.
0:02:08 – I’m sure.
0:02:10 – But, yeah, so I–
0:02:11 – At least you didn’t have an Asian parent.
0:02:16 – Well, I think my dad was pretty confused about the choice.
0:02:27 Anyway, but eventually, they got behind and supported that,
0:02:31 and so I fixed motorcycles, and then I wasn’t really
0:02:33 quite sure what I wanted to do.
0:02:38 I had this degree in history, but wanted to keep following
0:02:42 my nose, find new and interesting things to do.
0:02:47 I also did a lot of work in photography when I was at Yale.
0:02:50 I spent a lot of time in the Art and Architecture Library
0:02:52 on the Yale campus, just reading books,
0:02:53 and learning about art.
0:02:55 – Beautiful buildings on campus.
0:02:58 – Oh, for sure, yeah, very interesting architecture,
0:03:01 the Art and Architecture building in particular.
0:03:04 Well, anyway, so I became more interested in photography.
0:03:06 I wound up getting a job at a newspaper
0:03:08 in the New York area, Newsday.
0:03:11 Did two years there, working in their editorial library
0:03:16 with their photo archive, but then I kind of decided
0:03:19 that wasn’t really going anywhere fast enough,
0:03:21 so I moved to Japan.
0:03:21 – Wow.
0:03:24 – I had a three-part plan for going to Japan.
0:03:27 I was gonna photograph myself, make a portfolio
0:03:31 of my own work, and I thought that it might be interesting
0:03:35 to get some teaching experience, so I taught English,
0:03:37 and I was chasing a girl.
0:03:39 (laughing)
0:03:40 – And not an actor, right?
0:03:43 – That was the three-part plan, right?
0:03:45 Photograph, teach, chase a girl.
0:03:48 I wound up catching the girl, and so we’ve been married
0:03:51 for, it’s gonna be 25 years, and congratulations.
0:03:52 – Oh, congratulations.
0:03:53 – A couple months here.
0:03:54 – That’s so awesome.
0:03:59 – And so after that, I took that of the portfolio of work
0:04:01 that I put together two years in Japan
0:04:04 and applied to a fine arts program,
0:04:07 the Rochester Institute of Technology,
0:04:09 for a master of fine arts degree program.
0:04:14 But it was there that I discovered the World Wide Web.
0:04:18 And so I put my plans to be a fine art photographer
0:04:22 or maybe a professor of photography,
0:04:24 or putting together the teaching experience
0:04:25 with photography.
0:04:28 I just set that aside, because I saw the web
0:04:31 for the first time, it was probably 1994,
0:04:34 and I thought it was the most amazing thing.
0:04:39 Somozek, and the professor, oddly enough,
0:04:42 loaded up one of the few websites comparatively
0:04:45 that was available, Yahoo, when it was text only.
0:04:46 – Right, right.
0:04:49 – And so to me, the interest was,
0:04:51 I’m gonna make photos show up on this thing.
0:04:52 I’m gonna take my experience,
0:04:57 my love of fine art and the liberal arts,
0:05:00 and figure out how to make that come alive on the web.
0:05:02 And then just wound up getting more and more
0:05:03 into programming.
0:05:07 I graduated, or I left RIT without graduating
0:05:10 with any degree, but by that time,
0:05:11 I learned enough to go get a job
0:05:12 at a web development company,
0:05:14 and wound up making websites,
0:05:18 and this start-up, that start-up, the next start-up,
0:05:20 I wound up at a company called EZL.
0:05:21 – Oh, right, of course.
0:05:26 – I did Linux software development making,
0:05:27 desktop Linux.
0:05:29 – Right, every year is the year of desktop Linux.
0:05:33 – The desktop Linux, we thought that 1999 or 2000
0:05:35 was gonna be the year of desktop Linux,
0:05:37 it turned out, not to be, but–
0:05:38 – Not to be.
0:05:41 But you worked on the Nautilus file browser?
0:05:43 – I actually worked on the portion of Nautilus
0:05:47 that connected to these sort of proto-cloud services.
0:05:48 – Right.
0:05:49 – And interesting, our cloud–
0:05:50 – Dropbox before it’s time, right?
0:05:52 – Interestingly, for where I am here,
0:05:54 Andres and Harwitz, we hosted our cloud services
0:05:55 at LoudCloud.
0:05:56 – Oh, thank you very much.
0:05:57 – Yes.
0:05:58 – Yes.
0:05:59 – Very good customer.
0:06:04 – So, we went ahead with that project,
0:06:06 but of course that company didn’t succeed.
0:06:09 But of course, EZL had this long-standing connection
0:06:11 through some of its principles,
0:06:13 Andy Hertzfeld, Mike Boydch, Bud Tribble.
0:06:15 – Yeah, the legends, right?
0:06:16 Macromedia–
0:06:19 – And that got me an introduction to Apple.
0:06:19 – Yeah.
0:06:21 – And started Apple in 2001,
0:06:23 and started getting into making,
0:06:26 the web browser for Apple was my first project.
0:06:28 – That’s fantastic.
0:06:30 And why don’t we get into that story,
0:06:31 because as you tell in the book,
0:06:34 you sort of started experimenting
0:06:36 with the old Netscape code base, right?
0:06:36 – Right.
0:06:40 – Called Mozilla, I guess, by then.
0:06:42 But you ultimately didn’t go that way.
0:06:45 – Right, well you see, it’s sort of interesting,
0:06:48 and maybe we’ll get into this more as we talk.
0:06:51 The way that Apple worked in this period,
0:06:52 during the Steep Jobs era,
0:06:55 is that he would set this vision.
0:06:59 And so his vision was, Apple needs its own web browser.
0:07:02 So at the time, when I joined in 2001,
0:07:07 Mac OS X, the new version of the desktop operating system,
0:07:11 replacing the old classic version of Mac OS
0:07:15 that had been chipping on the computer since the 80s.
0:07:16 – Right.
0:07:18 – Right, so it came along with this Unix-based replacement.
0:07:23 But that system didn’t have its own web browser.
0:07:25 It was still part of the agreement
0:07:26 that had been made a couple of years earlier
0:07:30 with Microsoft to provide Apple with web browser.
0:07:32 So, Internet Explorer.
0:07:33 – Right, when Bill invested–
0:07:34 – That’s right.
0:07:35 – Right, he brought Office to the Mac,
0:07:37 and then IE became the default browser.
0:07:38 – Correct.
0:07:39 – People don’t remember this anymore.
0:07:43 – Correct, but that was the situation that Apple was in,
0:07:47 is that this exciting new technology, the web,
0:07:50 was something that wasn’t under its own control.
0:07:53 And so the vision for Apple, back then,
0:07:55 and even still today,
0:07:57 is that Apple wants to be in control
0:08:01 of what it considers to be critical technology
0:08:05 that gets critical to its user experience.
0:08:08 – Yep, and as all the operating system companies decided,
0:08:10 the web browser was critical,
0:08:12 it wasn’t an optional add-on component.
0:08:16 Netscape and Microsoft famously got into a legal battle
0:08:19 over this, so Apple arrived at the same insight.
0:08:21 And then interestingly, the two code bases
0:08:24 that you consider to get Safari off the ground
0:08:28 were Mozilla, the Netscape code base,
0:08:32 and then Conquer, which was a Linux web browser,
0:08:33 and they were both open source.
0:08:36 And so talk to me about what it felt like at the time
0:08:37 to be looking at open source inside Apple,
0:08:39 which is a famous sort of like,
0:08:41 we’ll build it all ourselves.
0:08:45 – It was interesting that the executives,
0:08:47 people like Avi Tavanian,
0:08:51 who was the chief software VP at that time,
0:08:55 and Steve, were willing to consider open source.
0:09:00 But just to give a brief summary of our full investigation,
0:09:05 we considered writing a browser from scratch,
0:09:06 we also considered going out and licensing
0:09:08 from a company like Opera.
0:09:09 That was the company that–
0:09:10 – There were many, yeah.
0:09:11 – Licensure browsers back then.
0:09:12 – Right, right.
0:09:14 And so, but we, Don Melton and I,
0:09:18 which was the two people we joined on the same day in 2001
0:09:22 to begin this browser investigation,
0:09:25 and we looked at open source because it was,
0:09:27 we were a team of two people.
0:09:29 And a web browser is a pretty complicated thing,
0:09:30 if you– – It’s pretty complicated.
0:09:30 – It’s harder than it looks.
0:09:31 – It’s harder than it looks.
0:09:36 So we thought that if we could make a compelling case
0:09:40 to use open source as a way to jump ahead in the effort,
0:09:44 stand on the shoulders of giants, right?
0:09:47 You know, it would get us to a point
0:09:50 where we would have something sooner.
0:09:51 And that was really the goal.
0:09:53 And being open source,
0:09:57 if we took this software from, say, another platform
0:10:01 that neither Mozilla nor Conqueror worked on the Mac.
0:10:03 So we were gonna have this opportunity
0:10:04 to bring this code from elsewhere
0:10:09 and make it Apple zone and really make it look and feel
0:10:12 like it was a native program to the Mac.
0:10:14 So that was, and looking at that,
0:10:16 it really just came down to Conqueror
0:10:18 was one-tenth the size of Mozilla.
0:10:20 And so as a two-person team,
0:10:23 soon thereafter, a three-person team,
0:10:26 this just was the easiest way
0:10:28 to get from where we were to where we wanted to be.
0:10:29 – Yeah, it makes sense.
0:10:31 I mean, people don’t remember this
0:10:32 about the early days of the browser,
0:10:34 but when we shipped Netscape,
0:10:36 we had to do it on 20 platforms.
0:10:38 So every build was a, all right,
0:10:39 here’s the one for ARIX,
0:10:40 here’s the one for Digital Unix,
0:10:41 here’s the one for AIX,
0:10:42 here’s the one for HPUX.
0:10:44 And here’s, by the way, is Windows 95,
0:10:47 Windows 98, Windows NT, right?
0:10:52 Like it was such a cross-platform exercise
0:10:54 that the code base sort of grew and grew.
0:10:56 – Sure, and so we only had to do that once
0:10:59 in that we took this Linux code
0:11:01 and brought it over to the Mac.
0:11:03 And of course, it was a challenge for us,
0:11:04 so I can only imagine what it would be
0:11:07 to kind of keep all of these platforms going.
0:11:09 Concurrently, as you’re trying to make improvements
0:11:11 and add features and make things better.
0:11:15 – Yeah, and so you ultimately decided
0:11:16 on the Conqueror code base,
0:11:17 the sort of your starting point.
0:11:19 And then pretty early in the development process,
0:11:23 you ended up building a stopwatch, the PLT.
0:11:24 – Right.
0:11:26 – And so maybe talk a little bit about
0:11:28 that why did you decide to do that?
0:11:29 And then ultimately flash forward,
0:11:32 like when Steve announced the browser,
0:11:34 he would say this is the fastest,
0:11:36 like it was one of the key features.
0:11:37 And did you know at the time
0:11:39 that you built the stopwatch that he was gonna do that,
0:11:41 or like would you get lucky?
0:11:45 – So no, no, we didn’t, it was not luck at all.
0:11:48 Steve was very, very clear to us
0:11:53 from at a very early stage in our browser development process
0:11:56 was that, well, of course,
0:11:59 he wanted to deliver the best experience out to customers.
0:12:00 That was it.
0:12:03 He wanted to put a smile on the user’s face, right?
0:12:07 And so if you think about the challenge that we had,
0:12:10 there was this existing browser on the platform.
0:12:11 – Right, Microsoft.
0:12:14 – That people were familiar with, right?
0:12:16 And so now we’re gonna come along
0:12:18 and we’ll say, well, no, well, you had that other thing.
0:12:22 Here is this new browser that we want you to use.
0:12:24 It’s Apple’s own browser.
0:12:28 And well, what is gonna convince people
0:12:29 to make the change?
0:12:30 And so Steve thought, well,
0:12:32 we’re gonna need a compelling argument.
0:12:36 And to be compelling, it needs to be simple.
0:12:40 And so his idea, his vision was,
0:12:43 look, we need to make this thing perform fast.
0:12:45 Again, thinking back to the time that,
0:12:47 oh, the network wasn’t so fast.
0:12:49 I mean, some people were getting,
0:12:51 maybe broadband at the office,
0:12:55 but certainly at home, you’re still doing dial-up, right?
0:12:59 And so anything that you could do to sort of speed up
0:13:04 the browsing experience was something
0:13:06 that would be attractive to people.
0:13:07 People would notice.
0:13:10 And so he said, browser team,
0:13:12 you need to figure out how to make this browser fast.
0:13:14 And he told us this.
0:13:18 A year plus ahead of time.
0:13:22 So this PLT, the page load test as a PLT stands for,
0:13:25 was this performance tool that we used
0:13:27 during our daily development.
0:13:29 So that every code check-in that we had,
0:13:33 we would run our page load test to see
0:13:35 that there were no speed regressions.
0:13:40 We had this idea, that was really Don Melton’s idea,
0:13:41 who was the manager of the team.
0:13:44 He had this little bit of sneaky logic
0:13:49 where he said, okay, team, if we check-in code
0:13:55 and it doesn’t make any speed regression,
0:13:57 only two things can happen.
0:14:00 Either the code will remain the same speed
0:14:02 or it’ll get faster, right?
0:14:05 And again, it’s just one of these simple things
0:14:08 that just turns out to be this profound truth.
0:14:13 Because as we would go over the weeks, the months,
0:14:17 hundreds and hundreds and hundreds of check-ins,
0:14:18 that’s what happened.
0:14:21 Either the code either stayed the same or it got faster.
0:14:24 And over time, because there was this speed priority
0:14:25 coming straight from Steve,
0:14:27 we would look for ways to make it faster.
0:14:32 And eventually, the Safari, when it was released,
0:14:37 it was three times faster than MSIE at loading web pages.
0:14:38 – Yeah, that’s fantastic.
0:14:43 – And the point is, again, Steve Jobs going out on stage,
0:14:48 he has this reputation of being this great marketer,
0:14:49 the reality distortion field,
0:14:52 anything that Steve says you’ll believe
0:14:54 just because he has this through the sheer force
0:14:55 of his personality.
0:15:00 But this was more of a matter of him just saying,
0:15:02 well, we executed on this plan,
0:15:04 we got a great result and here it is.
0:15:07 – So I love this idea that Steve set this goal early on,
0:15:09 ship the fastest browser that you can ship
0:15:10 ’cause when I launch it,
0:15:12 that’s what I’m gonna talk about.
0:15:14 And as I was thinking about
0:15:17 basically the software development process,
0:15:21 it’s rare for a CEO, a big company,
0:15:22 and Apple was a big company back then,
0:15:25 to be so intimately involved in the planning process
0:15:27 and how important do you think that was
0:15:30 to your age of design?
0:15:35 – Yeah, I think the way that Steve organized the company
0:15:40 and built the teams, built the culture,
0:15:44 was an essential part of how we did our work.
0:15:46 And the way I like to describe it
0:15:50 is that Apple was this wonderful combination
0:15:55 of top-down leadership and bottom-up contributions.
0:16:00 So Steve, the top-down part,
0:16:01 I think is almost well-known.
0:16:03 Steve was very, very clear.
0:16:06 He could be almost, you know, domineering, right,
0:16:11 in pushing his vision forward, right?
0:16:15 So when you worked at Apple in software development,
0:16:18 you knew what the vision was.
0:16:21 That was always very, very clearly communicated.
0:16:22 But it still was just a vision.
0:16:24 Now, sometimes he would get specific,
0:16:27 but most of the time he just would tell us,
0:16:30 “I want a great browser and it’s gotta be fast.”
0:16:34 And so with that as a brief handed over
0:16:38 to the engineering team,
0:16:40 it was our job to figure out how to do it.
0:16:43 And so then that’s where the bottom-up contribution
0:16:44 comes from.
0:16:46 He didn’t say, “I want you to make a performance test
0:16:48 “and I want you to institute this policy
0:16:52 “where every check-in doesn’t allow any speed regressions.”
0:16:54 I don’t know, we came up with that.
0:16:57 Providing that bottom-up contribution
0:17:00 that helped to realize the vision.
0:17:02 And then one of these other things,
0:17:05 and perhaps we’ll get into it a little more as we go,
0:17:07 because it is such an important part of Apple’s culture,
0:17:09 is that there would be demos.
0:17:14 So we would periodically, I remember quite clearly,
0:17:18 there was a 0.1, there was a 0.2 demo
0:17:23 where we needed to demonstrate the strength
0:17:26 and the potential of this open source idea
0:17:29 of the conqueror source code that we had chosen
0:17:32 and of our porting plan and efforts
0:17:35 before they would commit to going through
0:17:40 to the project, to go from 0.2 to 1.0.
0:17:44 – And with Steve at the demo at that point?
0:17:48 – He would see the code very, very often.
0:17:49 So that’s a little unusual.
0:17:52 I compare that to sort of a typical Silicon Valley company
0:17:55 where you’re doing these demos frequently.
0:17:59 And so in general, you sort of think of the CEO of a company.
0:18:00 This side is not being involved
0:18:02 in every single milestone, right?
0:18:04 ‘Cause you’re Safari on Mac OS.
0:18:06 Mac OS is one of the many products
0:18:09 that Apple was shipping at the time.
0:18:12 And so it seems unusual that the CEO
0:18:14 would be involved in this many demo points.
0:18:16 And how important do you think that is to sort of–
0:18:17 – Well Steve, I’m actually gonna dispute
0:18:19 one of the things that you said, if I may,
0:18:22 is that certainly during the Steve Jobs era,
0:18:27 and I still think to today here in 2019,
0:18:30 Apple didn’t ship a whole lot of products.
0:18:32 Back then, Steve, quite famously,
0:18:36 when he reestablished control over the company,
0:18:41 he came up with that product matrix, right?
0:18:45 Where we’re gonna have consumer product, a pro product,
0:18:47 a desktop product, and a portable product, right?
0:18:49 And so we’ve got four products.
0:18:51 And it’s the same operating system, right?
0:18:55 Mac OS, and so there’s actually very, very few products.
0:19:00 Now interestingly, when I joined Apple in June of 2001,
0:19:02 Mac OS X had come out,
0:19:04 and so we had that two-part product matrix
0:19:05 that we were still working in.
0:19:06 And that was still four months
0:19:09 before the announcement of the iPod,
0:19:14 which was just that beginning of Apple expanding out
0:19:17 from being, well, Apple computer to being Apple Inc, right?
0:19:20 You get into more consumer-focused products
0:19:24 that weren’t really thought of as being computers.
0:19:27 But because, I mean, the point of going through all that
0:19:30 is that since there were so few products,
0:19:35 Steve could keep tabs on what the software teams were doing,
0:19:42 that there was this big initiative to make a web browser
0:19:47 so he could keep tabs on it.
0:19:50 He could find the time on his schedule
0:19:53 to get updates on how the software was doing, and he did.
0:19:55 – Yeah, so it was sort of a focused thing, right?
0:19:57 But Steve’s saying, look, we’re not gonna have
0:19:59 that many SKUs, we’re not gonna have that many products.
0:20:01 Like, then I can put all my eggs in one basket,
0:20:03 get in and watch the basket very carefully.
0:20:07 – You say the word, and it is one of the best words,
0:20:10 perhaps the best word to describe Steve’s approach,
0:20:11 which is focus.
0:20:12 Focus on what?
0:20:14 Great products.
0:20:17 I mean, in those three words, focus, great products.
0:20:22 You get, you can distill down Steve’s approach,
0:20:25 his formula to just a couple concepts.
0:20:30 – Yeah, so you ship Safari, awesome browser, fast native.
0:20:34 You get a lot of people to switch over.
0:20:36 And then, at that point in your career,
0:20:38 after having been this individual contributor
0:20:40 that shipped this awesome product,
0:20:42 you thought, like many people in your shoes,
0:20:45 time to be an engineering manager.
0:20:46 So maybe talk a little bit about that story
0:20:50 of sort of how you thought about it,
0:20:51 and then how you got the job,
0:20:52 and then what the job was like when you got it
0:20:54 as your first engineering manager job.
0:20:59 – Right, well, I always try to think about,
0:21:01 well, what’s next?
0:21:06 And I don’t really have a big career vision.
0:21:10 It’s because, especially the tech world,
0:21:12 it changes so fast, right?
0:21:15 And so it always seems like you come to the end of one thing
0:21:17 and then that’s the moment to really decide
0:21:19 what the next thing should be.
0:21:21 And as you say, I mean, engineering management seemed
0:21:25 to be like this new domain that I didn’t have
0:21:28 a lot of experience in.
0:21:31 So I thought that this would be an interesting opportunity.
0:21:34 And so I pushed for it, I asked for it,
0:21:38 and it was actually Scott Forstahl, the software executive,
0:21:43 really instrumental in coming up with a lot of the
0:21:47 interesting user interface work in the iPhone software,
0:21:49 a project later, which I’m sure we’ll get to.
0:21:52 But he was the one who was in my management chain
0:21:53 who gave me this opportunity.
0:21:58 And so I started working on the Sync Services software
0:22:03 for the Mac, which at that time was really still the software
0:22:08 that would be up in the cloud and would help
0:22:11 two Macs sync with each other.
0:22:13 I mean, we didn’t really have–
0:22:15 – There were no phones, no iPods.
0:22:18 – Right, okay, so it’s like you have a computer,
0:22:19 desktop computer in the office,
0:22:21 you have a desktop computer at home,
0:22:23 or maybe you have a portable and a desktop,
0:22:27 and it was to get those systems exchanging some data,
0:22:30 your contacts, your address book, things like that.
0:22:35 And so I thought this was an interesting challenge,
0:22:37 and people were gonna be getting more devices
0:22:40 and things like that, but I found that very soon
0:22:42 after I got into the job that I was miserable,
0:22:47 that I hadn’t really reckoned, at that point in my career,
0:22:53 with what management really is, it’s about people.
0:22:58 I was still, certainly at that point in my career,
0:23:02 I was still fascinated by the software itself.
0:23:03 That’s what was attracted to me about sync.
0:23:07 It seemed like this distributed computing problem,
0:23:10 and I was enamored of the technology,
0:23:14 and you had client server, and all of this,
0:23:18 and not really, again, thinking about how the right focus
0:23:21 was to build a team, build a team culture,
0:23:24 support the people so that they could do the technology.
0:23:27 And again, at that point in my career,
0:23:29 I wasn’t really ready for that,
0:23:31 and I found myself within just a couple of months
0:23:33 that I was miserable.
0:23:37 – Yep, it’s the lament of a lot of first-time managers,
0:23:39 which is you think, on the other side of it,
0:23:41 of course I want a manager job, it’s the way up,
0:23:43 it’s the natural hierarchy, and then you get there,
0:23:47 and your job is about shipping a team and not a product.
0:23:48 And a lot of people go through that,
0:23:51 oh, I don’t wanna ship a team, I wanna ship a product.
0:23:52 – Right. – All right.
0:23:53 So it sounds like that’s what you did,
0:23:55 you sort of went back to being–
0:24:00 – Yeah, well, I had almost the shape to say,
0:24:02 it was like a mini meltdown, I went to Scott Forstall,
0:24:05 and I said, hey, look, Scott, I don’t wanna do this,
0:24:08 I led you astray, led myself astray, I quit,
0:24:10 I offered a resign, ’cause I know,
0:24:13 and part of the thing was that it was a feeling
0:24:16 of responsibility that I had taken on a responsibility
0:24:18 that now I did not want to fulfill,
0:24:21 and I felt like, well, the only thing for me,
0:24:22 there’s really just two choices,
0:24:25 I could continue on being miserable about it,
0:24:28 or I could just go and say, look, I’m done with this,
0:24:31 I submit my resignation.
0:24:34 So Scott was like, whoa, whoa, whoa.
0:24:36 Just a second, stop right there,
0:24:38 I wanna understand what’s going on there.
0:24:40 So I explained to him what I just explained to you
0:24:43 about really wanting to still be in closer touch
0:24:44 with the technology, and so he said,
0:24:49 okay, well, just go away, he was not pleased with me.
0:24:52 – Yeah, yeah, we got to the management job you asked for.
0:24:53 – You said that you wanted it.
0:24:55 – Right in now, you’re coming back in now,
0:24:57 a couple months later saying that you want something else,
0:24:59 what’s going on.
0:25:02 So yeah, he wasn’t that happy, but he had–
0:25:05 – And at that time, you had sort of started taking calls
0:25:07 from Google recruiters, right?
0:25:09 – Yeah, I mean, because I thought that I was resigning,
0:25:11 so I just need to go get another job.
0:25:14 So I actually did, and I went to–
0:25:16 – Full interview cycle, right?
0:25:18 – I went and did the interview process at Google,
0:25:20 and they offered me a job.
0:25:22 – So you were serious, you were ready to go?
0:25:25 – Well, I was serious, I was serious.
0:25:29 But I turned it down, I turned down that job
0:25:32 because Scott continued to engage with me,
0:25:36 and he said, just kind of sit tight,
0:25:39 maybe we’ve got something for you,
0:25:42 and a couple of days later,
0:25:47 it was actually my direct manager at the time said,
0:25:52 come here, and he took me into his office,
0:25:55 and he said, we want you to work on this new project,
0:26:00 sign this paper, and I kind of thought
0:26:04 there was just the barest little hint on the grapevine,
0:26:07 so I just like, reach out, I signed the paper,
0:26:09 and he said, yeah, we’re making a cell phone.
0:26:10 – Yeah.
0:26:11 – And you’re now on the team.
0:26:12 – So that’s fascinating, right?
0:26:15 So this is a great part of Apple
0:26:16 that’s sort of very different than most Silicon Valley
0:26:18 companies, which is in most Silicon Valley companies,
0:26:20 if you get assigned to another project,
0:26:22 there’s not this level of secrecy,
0:26:24 you’re not signing papers saying,
0:26:25 so tell me a little bit about that,
0:26:28 like what did they read you into at the time?
0:26:30 It was purple at the time, right, what’s the code name?
0:26:34 – You know, the funny thing is that at Apple,
0:26:37 I was already under this blanket non-disclosure.
0:26:38 – You couldn’t say anything about it.
0:26:41 – I mean, for the whole time that I worked there,
0:26:44 I was under these document retention orders,
0:26:47 I would get these periodic emails from the lawyers saying,
0:26:52 do not destroy anything because of the work that I had done
0:26:55 was then submitted in patents and, you know,
0:26:57 perhaps there was gonna be patent litigation.
0:27:01 So this is just the whole mindset,
0:27:03 the whole culture of what Apple was.
0:27:06 There was secret, we were doing patentable
0:27:08 where we were trying to innovate,
0:27:12 and we were interested in treating that work
0:27:15 as a trade secret, something that was valuable
0:27:17 to the company.
0:27:19 – So already super secret culture.
0:27:21 And then you have to sign something,
0:27:22 which is I’m gonna introduce you
0:27:25 to an even more secret culture inside Apple.
0:27:27 It’s kinda like the, you know, when you do the logic classes,
0:27:30 like infinite sets can be larger than other infinite sets.
0:27:31 – That’s right.
0:27:31 – Like now you’re into the larger–
0:27:33 – That’s right, now you’re into a bigger,
0:27:37 bigger, deeper, darker infinity, that’s right.
0:27:40 It is a bottomless well, truly.
0:27:45 And so, yeah, so I had to sign this additional NDA
0:27:47 and yeah, I got introduced to this project,
0:27:51 it was called Purple, the code name for iPhone
0:27:52 and it was in development.
0:27:57 And my job was to join the software effort,
0:28:00 which at that point was maybe six or eight people.
0:28:01 – That’s a tiny team.
0:28:02 – It’s a tiny little team.
0:28:06 To do what I like to term the high level software.
0:28:10 The plan was that we were gonna take
0:28:13 as much of the Mac as possible
0:28:17 and bring it over and squeeze it into one of these,
0:28:20 you know, a tiny little, you know, smartphone form factor.
0:28:24 And so we were gonna take the operating system kernel
0:28:26 and some of the low level libraries,
0:28:29 you know, the networking stack, things like this,
0:28:33 the graphics stack, but above the level of core graphics,
0:28:37 which was the, you know, the low level graphics library.
0:28:40 Above that, it was then, I was invited onto the team
0:28:43 that was gonna invent the touchscreen OS.
0:28:44 So we weren’t gonna take any of the,
0:28:47 naturally the mouse tracking or handling
0:28:50 or anything of app kit, which was the, you know,
0:28:52 the user interface level software for the Mac,
0:28:55 we were gonna make that from scratch for the phone.
0:28:58 So what became UI kit for people who know
0:29:01 about the, you know, the technology for what became,
0:29:04 you know, the iPhone software, iOS, that was our job.
0:29:07 And so we started with it with a clean slate.
0:29:11 And that slate was pretty well clean when I joined again,
0:29:14 just about six or eight people on that effort at the time.
0:29:16 – Yeah, so they tap you on the shoulder,
0:29:18 you’re on the purple team, it’s like six to eight people.
0:29:20 So tell me about the people on the team.
0:29:21 Like what are the roles?
0:29:22 Are there product managers?
0:29:23 Are there UX designers?
0:29:24 – Right, right.
0:29:26 So when I say six or eight people,
0:29:27 that was software engineers.
0:29:28 – Yeah.
0:29:30 – There was also this other team of designers,
0:29:33 which in Apple, we called the human interface team,
0:29:35 the HI team, right, human interface.
0:29:39 And though that was the team of designers,
0:29:42 they would do graphic design, animation design,
0:29:44 but they would also do concepts.
0:29:48 They would provide the thinking behind what is going
0:29:50 to be the experience of the person
0:29:54 that is gonna be using this product that we make.
0:29:56 And so there was this small team,
0:30:01 half dozen software engineers and HI designers,
0:30:04 and then executives, managers.
0:30:06 So there was a fellow named Henri
0:30:09 who was leading the software engineering team.
0:30:10 There was a fellow named Greg Christie,
0:30:13 who was the day-to-day manager of the HI team.
0:30:15 They both reported to Scott Forstahl,
0:30:18 who was the executive, who reported to Steve.
0:30:19 And that was it.
0:30:21 That was the team.
0:30:23 Now, eventually we wound up adding,
0:30:26 over time, the more people,
0:30:29 we probably never had more than 20 software engineers
0:30:32 and maybe 10 designers.
0:30:36 Those two managers and the executive and Steve,
0:30:37 and that was it.
0:30:41 And so there were no product managers.
0:30:43 No product managers, no QA engineers.
0:30:44 No, like until later.
0:30:45 Until later.
0:30:49 So the core of it that got the whole product going
0:30:52 is software engineers, human interface designers,
0:30:52 and executives.
0:30:57 Yeah, we added then a program manager.
0:30:59 So there were maybe like two people
0:31:02 in just managing the schedule, tracking risk,
0:31:05 looking at the bugs.
0:31:07 A couple of QA people joined.
0:31:10 But at Apple, certainly from my standpoint,
0:31:12 I can consider them engineers.
0:31:15 Yeah, they’re the QA engineers.
0:31:18 And so, but still, that still is all encompassed
0:31:21 in the numbers that I gave you.
0:31:25 And in a way, I say there were no product managers,
0:31:30 but I would say that we had one product manager.
0:31:31 There’s two ways that I could say it.
0:31:34 We either had one product manager, Steve.
0:31:34 Right.
0:31:36 Yes, the ultimate decider.
0:31:37 Right?
0:31:39 Or that we all were.
0:31:40 We all were.
0:31:44 It was all our responsibility to make sure
0:31:47 that the product was going to be great for people.
0:31:50 We all shared commonly in that responsibility.
0:31:52 So that’s really interesting.
0:31:54 ‘Cause you sort of distribute the responsibility.
0:31:55 Now it’s everybody’s responsibility,
0:31:58 but a lot of companies would think,
0:32:00 ooh, I’ve got to have a throat to choke.
0:32:02 I’ve got to have like the one person.
0:32:03 But of course, at Apple, that’s one.
0:32:04 So we did, right?
0:32:05 One person with Steve.
0:32:07 Okay, well it’s, but then another way.
0:32:10 When you get down to the level of features,
0:32:11 we had this notion at Apple
0:32:14 of directly responsible individuals.
0:32:15 Oh yeah.
0:32:16 Let’s talk about this.
0:32:18 So we had DRIs, right?
0:32:22 And so when I started working,
0:32:25 when I was invited to join the Purple Effort
0:32:28 because of my experience on the web browser,
0:32:31 I started working on making, crunching down Safari,
0:32:33 optimizing Safari so that it could fit
0:32:37 on a smartphone operating system and form factor.
0:32:41 And, but then after a couple of months,
0:32:46 we had a bit of an impasse with the software keyboard.
0:32:50 And we had what was really quite unusual,
0:32:52 really unique in my experience at Apple,
0:32:56 is that this was judged to be
0:32:58 that the development of the software keyboard
0:33:01 was judged to be a sufficiently high risk.
0:33:04 And that the risk was not being matched
0:33:08 by a commensurate progress, right?
0:33:10 I mean, the whole thing was high risk, right?
0:33:13 We’re gonna make a whole new touch screen operating system,
0:33:13 right?
0:33:15 So the whole thing was high risk.
0:33:17 But the thing is, is that we were making
0:33:20 good incremental progress on most of those areas.
0:33:24 Touch screen and the UI kit and Safari
0:33:26 and messages and calendar and you know,
0:33:28 all of these, you know, the phone app and,
0:33:32 but the touch screen keyboard was lagging behind
0:33:34 all of these other projects.
0:33:37 And so one day, it really, really again,
0:33:39 a unique in my experience,
0:33:42 Henri, who was the software engineering manager,
0:33:46 called all of the engineers out of our offices
0:33:48 into the hallway, we had a group meeting,
0:33:49 again, about two dozen people,
0:33:51 probably even less than that.
0:33:53 And said, okay, you all stop.
0:33:54 Stop what you’re doing.
0:33:57 Stop working on the calendar, the phone app,
0:33:59 you know, the user interface, a level software,
0:34:01 everything, stop.
0:34:04 Starting from now, you’re all keyboard engineers.
0:34:06 – Wow, that is crazy.
0:34:07 Like the entire team.
0:34:09 – Tire team, stop. – Everybody’s a keyboard engineer.
0:34:12 – Because the idea was that if we don’t crack
0:34:16 this problem, we might not have a product.
0:34:18 – Yeah, so I think we need to take people back
0:34:19 to that era, right?
0:34:21 Because this seems super counterintuitive
0:34:24 that you’d put all 20 people on one project.
0:34:27 And so, take us back in time.
0:34:30 So the most popular phone at the time was the CrackBerry,
0:34:31 right? – Yeah.
0:34:33 – The RIM BlackBerry, and it has a physical keyboard.
0:34:34 – Has a physical keyboard.
0:34:39 And so, this was in the fall of 2005.
0:34:42 And again, to just give the time perspective,
0:34:44 Steve stood up on stage and announced the iPhone
0:34:47 in January of 2007.
0:34:50 So again, this is a really, really compressed time scale.
0:34:54 So where, just a little bit more than,
0:34:58 you know, less than a year and a half out from the day
0:35:01 where we were trying to hit that target.
0:35:02 – Yeah, 18 minutes, not a lot of time.
0:35:07 – And we still had really nothing to show
0:35:11 for this effort to give a solution for our phone,
0:35:13 which would compete with the BlackBerry, right?
0:35:16 Of course, the BlackBerry had this wonderful keyboard,
0:35:19 the hardware keyboard, the little plastic keys,
0:35:21 click, click, click, click, the little chick-lip keys.
0:35:23 And again, you said the word CrackBerry.
0:35:28 People loved the products, a great product, right?
0:35:32 But we were gonna provide this different vision
0:35:34 for what a smartphone would be,
0:35:35 is that it was gonna be this,
0:35:38 that there wasn’t going to be enough room
0:35:43 for a plastic keyboard with the keys fixed.
0:35:46 We were gonna give more of the front of the display
0:35:49 over to a screen, to software.
0:35:53 And so the keyboard had to be in software.
0:35:56 – And the idea of all the sort of software-based keyboard
0:35:59 was one of the design things that came from Steve early.
0:36:00 – Yes.
0:36:01 – Like it was just like, look, this is non-negotiable.
0:36:02 I’m not shipping a physical keyboard.
0:36:03 – That’s right.
0:36:08 No, his idea was that we need a keyboard some of the time,
0:36:13 but we certainly don’t need it all of the time.
0:36:16 And so the idea of the keyboard being in software
0:36:18 is that it could get out of the way,
0:36:20 it could go off the screen,
0:36:24 which would then make the rest of that screen real estate
0:36:27 available for a customized user interface that was great,
0:36:30 that was optimized for either the phone app,
0:36:35 or if it’s the calendar, you can see more of your appointments
0:36:37 or see more of a month view for the calendar.
0:36:40 So it was absolutely essential that the keyboard could get
0:36:42 out of the way when you weren’t using it,
0:36:44 so that the device could be opened up
0:36:48 for these other better, richer experiences in the apps
0:36:49 that we were gonna be shipping.
0:36:52 – And what problems were you running into at the time?
0:36:54 Like were people missing keys,
0:36:55 were the keys not big enough?
0:36:56 Like what caused the–
0:36:59 – Yeah, okay, again, I mean, it’s in some ways,
0:37:04 it’s hard to think back given how history has played out,
0:37:07 right, that we have our phones now,
0:37:11 and maybe you’ve got, I’ve got my phone here today,
0:37:14 and I’m two thumb typing, and I’m hardly even looking
0:37:18 at whatever, back when we were working at this early stage,
0:37:23 and we were all new to interacting with touch screens,
0:37:27 we found that we had this real sense of apprehension,
0:37:30 apprehension whenever we were gonna touch a target
0:37:35 on the screen that was smaller than our fingertip, right?
0:37:39 That was actually a really interesting threshold
0:37:42 that, a constraint that we were dealing with
0:37:44 when we were designing the user interface,
0:37:46 is that if the target that you were going for
0:37:49 was larger than your finger, you could target,
0:37:51 because you could maybe move your head
0:37:52 a little bit out of the way,
0:37:55 and you could see what you were going for.
0:37:59 If the target was smaller than your fingertip,
0:38:00 it’s like, did I get it?
0:38:01 I don’t know, right?
0:38:05 And so we started, we didn’t have the tactile feedback
0:38:07 of that blackberry, right?
0:38:10 You could feel the edges of the keys with your fingers,
0:38:11 and of course with the touch screen,
0:38:14 it was just this sheet of glass.
0:38:16 And so that’s the challenge with the keyboard,
0:38:18 is that you needed enough keys
0:38:21 to have a typing experience, right?
0:38:24 But in order to give the number of keys necessary,
0:38:26 the keys needed to be smaller than your fingertip.
0:38:27 So what do you do?
0:38:32 And so it turns out that through investigation,
0:38:36 and lots of demos, and lots of sleeveless nights,
0:38:39 that the way to close that gap
0:38:42 was to give software assistance, yeah.
0:38:44 And so on Rewave the Magic Wand,
0:38:46 everybody now is a keyboard engineer,
0:38:48 everybody needs to figure out
0:38:50 how we’re going to make a reliable keyboard
0:38:51 that’s delightful.
0:38:53 And so what happened from that point?
0:38:56 Was it like a series of demos,
0:38:57 where people just demo, yeah.
0:38:59 – Yeah, we did this series of demos.
0:39:03 We see, again, going back to the way
0:39:05 that it was on that hallway,
0:39:07 and it was just one hallway,
0:39:09 since it was so few people.
0:39:11 It was sort of 20-ish people.
0:39:13 And we all had our individual offices at the time.
0:39:16 This was not open plan office, right?
0:39:17 Everybody had their office.
0:39:19 Mine, when I was working and thinking,
0:39:21 I had my door closed, right?
0:39:23 But then, okay, so I would be in my office
0:39:24 with my door closed,
0:39:27 and I would come up with a demo, an idea, right?
0:39:29 That could be represented in a demo.
0:39:30 Then I opened the door, and I go and see
0:39:34 who else’s door is open, and say, here, try this, right?
0:39:36 And so we would have this culture.
0:39:38 We were all demoing to ourselves,
0:39:40 all the time, and when we were set off
0:39:42 on this thing, you’re all keyboard engineers now,
0:39:44 well, we all just went in our own directions.
0:39:48 Some of us had already well-established,
0:39:51 collegial relationships,
0:39:52 where I would collaborate a lot with you,
0:39:53 and some other people,
0:39:56 they had maybe, they worked by themselves.
0:39:57 Some people had a good relationship
0:39:59 with one of the H.I. designers, or whatever.
0:40:01 So we just cobbled together our own little teams,
0:40:06 our own little efforts, and started making demos.
0:40:08 And again, trying to combat this problem
0:40:10 of the keys being too small.
0:40:12 So one idea that we experimented with
0:40:16 was making larger keys with multiple letters on the keys.
0:40:20 I started experimenting with software assistance.
0:40:22 Maybe there could be a dictionary on the phone
0:40:25 that the software could consult
0:40:31 to provide suggestions that may be much like we have today,
0:40:34 that there’s this bar on top of the keyboard
0:40:36 that is updating as you’re typing keys,
0:40:39 giving you some notion of what the software thinks
0:40:40 you’re trying to do.
0:40:42 – AutoCorrect, the author of AutoCorrect,
0:40:45 which is now not only super useful on the phone,
0:40:47 but probably my favorite comedy genre.
0:40:51 So go watch the Facebook videos
0:40:53 on AutoCorrect comedies, they’re fantastic.
0:40:55 – Yeah, well, sorry about that.
0:41:00 So eventually, the breakthrough, if you will,
0:41:06 that made it possible for software keyboards
0:41:10 to really work in a shipable product
0:41:13 was a software assistance to the extent
0:41:18 that the software may change the letters that you type.
0:41:19 – Right, right.
0:41:21 – That it’ll change it to what it thinks
0:41:22 rather than what you did.
0:41:26 And it’s actually, this phrase is really, really important.
0:41:28 I think really, really, one of the important
0:41:31 organizing concepts for so much that we did
0:41:34 to make the touchscreen operating system work
0:41:37 is because you didn’t get this tactile feedback
0:41:41 because you couldn’t feel the edges of either keyboard keys
0:41:44 or any button or anything in the user interface
0:41:46 is that the software had to be there
0:41:50 working behind the scenes to give you what you meant
0:41:52 maybe differently than what you did.
0:41:53 – Yeah.
0:41:55 And how did you come up with this idea?
0:41:57 ‘Cause this is a classic thinking outside of the box idea,
0:41:59 right, like if you were gonna try to solve this problem,
0:42:03 I bet you saw a lot of variations of sort of key sizes
0:42:06 and that type of thing, but like consulting a dictionary,
0:42:09 putting up suggested words, like where did the idea come from?
0:42:11 – It’s just this iterative process.
0:42:13 It just takes a long, long time.
0:42:16 You start with ideas, maybe somebody else.
0:42:20 It does a demo that does an idea and you had your idea
0:42:23 and you think, oh, maybe if I can combine those two ideas
0:42:28 and make a demo that does the best of everything that I see.
0:42:34 And it was just this collaborative soup
0:42:37 of ideas all swirling around and you just take the,
0:42:42 all of us were, there was a sense of friendly competition.
0:42:45 And it was both of those.
0:42:47 We all wanted to do the best.
0:42:48 We all wanted to be the one.
0:42:53 I mean, I think we all had a sense of maybe a sense of ego
0:42:56 that we wanted to be the one to crack this hard problem
0:43:01 that we were given, but it was all very friendly
0:43:06 in the end that if your idea wound up winning,
0:43:12 proving useful, yeah, you got a little bit of sort of geek,
0:43:15 you know, cred for that on the hallway.
0:43:19 Everybody knew who it was that came up with the idea.
0:43:20 – I wanna talk to you a little bit
0:43:22 about this sort of secrecy, right?
0:43:23 You got read into the Holy of Holies.
0:43:27 It’s more secret than sort of other parts of Apple.
0:43:29 And at one point you decided
0:43:32 as you were refining the auto-correct algorithm
0:43:35 that there were actually experts outside of the purple team
0:43:36 that might be able to help.
0:43:39 But of course they hadn’t been disclosed.
0:43:43 And so like, what was that like to try to go get their help?
0:43:47 – It was tough, it required getting approval.
0:43:50 It’s like, well, I’m gonna go and talk to these people.
0:43:55 But there was no process really at that point
0:43:56 to get them disclosed.
0:44:00 I mean, really, at a certain point,
0:44:04 Steve was still personally approving every person
0:44:07 that was submitted to get disclosed on the project.
0:44:08 But I did get permission to talk to them.
0:44:10 So as long as I told them,
0:44:13 I can’t tell you why I want to know
0:44:18 how, say, the Japanese input method works.
0:44:21 You know, the way the Japanese works
0:44:23 is that there is this input method
0:44:26 that there is a sophisticated way
0:44:29 to take the keys that a user types
0:44:32 and turn it into the Japanese language,
0:44:36 a text that actually reads as Japanese.
0:44:40 And so that just won’t get into the details of that,
0:44:43 but it seemed like it was similar in a way,
0:44:45 I mean, at least in the thought processes,
0:44:49 is that we have this real software
0:44:50 wearing away in the background,
0:44:52 other than, you know, different than, say,
0:44:54 just like a desktop keyboard,
0:44:57 where if you type the A, you get an A, right?
0:45:00 And so I went and talked to them.
0:45:02 But, you know, in the end,
0:45:07 it was just more of a conceptual help
0:45:11 than really anything concrete
0:45:13 that I could put into the software.
0:45:16 It just turns out really that the problem
0:45:17 that I was trying to solve,
0:45:19 which is really input correction,
0:45:22 that you weren’t sure what key you hit,
0:45:26 was a class of problem that was different enough
0:45:28 that it really required different solutions.
0:45:30 – Yeah, looking back at it now,
0:45:31 which is sort of the extreme secrecy,
0:45:34 you couldn’t really describe the problem, right?
0:45:36 And so as a result, you got some conceptual help,
0:45:37 but not sort of concrete design help.
0:45:40 Would you think of this sort of tiers of secrecy
0:45:42 inside Apple as a feature or a bug,
0:45:43 or somewhere in between?
0:45:44 – Yes.
0:45:45 (laughing)
0:45:46 Yes.
0:45:52 You know, the thing is, I think there is a really
0:45:57 underestimated power in keeping your team small.
0:46:03 The cohesion, the small unit cohesion that you have,
0:46:09 where simple things like we’re gonna have a meeting,
0:46:11 who do we invite?
0:46:13 Well, everybody, right?
0:46:16 We’re gonna have a team meeting, right?
0:46:18 Where we’re gonna talk about important milestones,
0:46:20 where we’re gonna call everybody out of their office.
0:46:22 Henry could say, “Hey, everybody,
0:46:25 “come out of your offices, please.”
0:46:30 And within 30 seconds, everybody was standing there, right?
0:46:34 So you get these, there are advantages
0:46:36 to keeping things really, really small.
0:46:40 And of course, then there is the disadvantage
0:46:45 that when you are trying to tackle difficult problems,
0:46:49 you may not have all of the talent that you need.
0:46:54 And you may not have a sufficient amount of diversity.
0:46:55 Right?
0:46:56 Right?
0:46:59 That all the, you know, especially a company like Apple
0:47:01 is trying to make products for everybody.
0:47:04 Well, how do you design for everybody, right?
0:47:09 If the design team is in a microcosm of everybody.
0:47:15 And so there are these really profound challenges, right?
0:47:18 Back in these times, we did the best that we could
0:47:21 within the constraints.
0:47:24 And we tried to then really tap into the benefits
0:47:28 that the smallness and the secrecy gave us as well.
0:47:29 Yeah.
0:47:31 Another funny thing that I learned reading your book
0:47:33 is the secrecy was so extreme
0:47:35 that like you didn’t even know what the product
0:47:36 was gonna be named.
0:47:38 And so like the word iPhone wasn’t even in the dictionary.
0:47:39 That’s right.
0:47:40 It’s like after Steve launched.
0:47:41 That’s absolutely true.
0:47:46 So there was, we were all heading toward this announcement
0:47:51 for the iPhone in January of 2007.
0:47:56 And so if you remember how Steve introduced the product,
0:48:02 he said, give his very dramatic introduction.
0:48:07 As we said, that something to the effect of,
0:48:10 well, we’ve got a groundbreaking product
0:48:14 and you privilege to be involved in a product like this,
0:48:16 maybe once in your career,
0:48:19 but Steve, he had been involved with the Mac
0:48:20 and then the iPod.
0:48:25 And he said, we’re gonna have three new products
0:48:26 of this class today.
0:48:27 And I’m saying like, wait,
0:48:29 there were two other secret projects
0:48:30 that I didn’t know about.
0:48:34 I mean, truly for a moment, I didn’t get.
0:48:35 And it’s like, oh, no, no, no.
0:48:37 It’s just how he’s gonna tell the story.
0:48:38 My product he’s talking about.
0:48:39 That’s right.
0:48:41 That’s gonna be the phone
0:48:44 and it’s gonna be the touch screen music player
0:48:46 and then the internet communicator
0:48:48 and that how, no, this is actually all just one product.
0:48:50 Then we call it iPhone.
0:48:53 And when he said that, that’s when I knew
0:48:56 that I was gonna have to go back the next day
0:48:59 and add iPhone to the auto correction dictionary.
0:49:00 – That’s awesome that he fooled you too.
0:49:02 ‘Cause he fooled me, like, click line.
0:49:04 Now I got, like, you were working on it
0:49:05 so I don’t feel quite as bad.
0:49:06 – Well, you just, I mean, again.
0:49:08 – I fell for it. – The secret is that,
0:49:10 oh, you know, I have to admit that it was just a moment
0:49:12 where it’s just like, wait, wait a second.
0:49:13 Is there something that I don’t know?
0:49:15 I was like, no, it can’t be.
0:49:20 But, yeah, it was, that was just the culture
0:49:23 and the times and the way Steve liked to run things.
0:49:25 – Yeah.
0:49:27 – Now a feature we all take for granted now
0:49:30 actually didn’t appear in iOS until several releases later
0:49:32 and that’s copy and paste.
0:49:34 So I wonder, at the time, did you guys talk about that
0:49:36 and did you make an explicit decision to sort of like,
0:49:38 yep, let’s ship without copy and paste
0:49:39 and was that contentious?
0:49:40 ‘Cause on the surface of the scene,
0:49:42 like, that’s contentious?
0:49:43 – Yes, yes, it was.
0:49:48 But one of the other things that we were really expert at,
0:49:52 to bring back the word that we talked about earlier was focus.
0:49:58 In that we were very, very good,
0:50:03 really very, very early in the development process
0:50:05 to say what was in and what was out.
0:50:06 – Right, physical keyboard.
0:50:08 – Out, that was super early.
0:50:10 – That’s right, very, very early.
0:50:14 And that it was clear that this was,
0:50:19 that getting the text entry system working at all
0:50:21 was going to be one of the real challenges.
0:50:26 I mean, I got used to being in the team meetings
0:50:29 where Anri, team engineering meetings,
0:50:30 again, everybody’s in the room,
0:50:31 so we’ve got 20 people in the room
0:50:35 and Anri is up at the front of the room
0:50:39 and he’s got a keynote slide deck
0:50:42 and he’s saying, okay, big challenges,
0:50:44 well, keyboard, of course,
0:50:46 and then whatever other challenge they may have been
0:50:48 and those challenges came and went,
0:50:49 but keyboard was just a constant
0:50:53 throughout the whole 18 month development cycle.
0:50:57 And so we knew that we wanted cut copy paste,
0:51:00 but we knew that there was simply not gonna be time for it.
0:51:04 So we didn’t spend any real development effort on it.
0:51:07 The one thing that I did implement
0:51:11 for the first iPhone was the loop.
0:51:12 So you press and hold
0:51:14 and it would give that little magnifying glass
0:51:17 above your finger that would show.
0:51:20 And the whole idea of that is that we wanted your finger
0:51:23 to be right where the insertion point,
0:51:25 the little cursor would move.
0:51:28 And so then we needed to show you what,
0:51:31 and so this was an idea that I came up with,
0:51:34 but then there was no time to capitalize that
0:51:36 and expand on that to do cut copy paste.
0:51:41 And it even got delayed an extra year
0:51:43 because in the second year,
0:51:46 after we did the initial release of the iPhone
0:51:47 and then we had that six month delay
0:51:50 before we did the first customer shipments.
0:51:54 And then that whole next year was taken up
0:51:58 by making third party APIs.
0:52:01 – Yep, so two releases before you had copy and paste.
0:52:02 – That’s right, that’s right.
0:52:04 – And so I wanna get right into this
0:52:08 ’cause look, Apple was famous for having exquisite taste
0:52:11 around these design trade-offs.
0:52:13 And a feature like copy and paste kind of feels like,
0:52:15 wait, you’re arguing against copy and paste?
0:52:18 Like, that’s not a great user experience.
0:52:22 And so like, how did the argument evolve?
0:52:25 And sort of the big setup is, look, there’s taste,
0:52:27 taste making, making hard decisions like this.
0:52:29 And then there’s sort of another style of decision making,
0:52:31 which sort of Google made super popular,
0:52:34 which is just relentlessly A/B testing everything, right?
0:52:35 – Right.
0:52:37 – And so like maybe the way Google would have come
0:52:40 at this challenge is, all right, let’s give people tasks.
0:52:41 This one has copy and paste,
0:52:43 this one doesn’t have copy and paste, let’s A/B test it.
0:52:46 – But Apple made sort of like what I would argue
0:52:49 is a pretty courageous call, right,
0:52:54 that seems to fly against the user intuition to exclude it.
0:52:56 – Yeah, well, it was simply a matter
0:52:59 of setting the constraints and keeping them.
0:53:03 And again, maybe if we had doubled the size of the team,
0:53:05 we could have gotten some other things done,
0:53:06 but maybe not to the same level of quality.
0:53:09 And again, once you start adding people,
0:53:11 other things begin to break down, right?
0:53:13 You can’t invite everybody to the team meetings,
0:53:16 so you can’t find a conference room big enough, right?
0:53:18 – And now there’s 40 people who can break the build.
0:53:19 – That’s right, that’s right.
0:53:21 I mean, how you start to have problems like this.
0:53:25 And so we just decided that, well,
0:53:29 it’s like a Steve way of maybe communicating this was,
0:53:32 look, this is the greatest product ever, right?
0:53:35 The touchscreen iPod,
0:53:37 it’s the greatest iPod that we’ve ever shipped.
0:53:39 It’s got all these great features.
0:53:41 It’s a phone, it’s got web browsing
0:53:43 that you can take anywhere with you now.
0:53:45 And there’s no copy-paste, well, who cares?
0:53:46 Well, we’ll get to it, right?
0:53:48 I mean, in the meantime, you’ve got this,
0:53:51 the most amazing product that we’ve ever made.
0:53:56 And so that was, and Steve just was,
0:53:57 in his mind,
0:54:03 he believed that the things that we did do
0:54:05 were good enough to counterbalance
0:54:06 for the things that we couldn’t do.
0:54:09 – So that’s great.
0:54:10 Great segue to sort of the next segment.
0:54:12 I’d love to sort of take us into
0:54:14 what it was like to demo for Steve.
0:54:17 Like, what was the room like, who’s in there?
0:54:19 Like, what’s the emotion of it?
0:54:20 (laughing)
0:54:22 – Everybody wants to know this, right?
0:54:22 – It’s pretty–
0:54:24 – It’s probably the scariest room in Silicon Valley.
0:54:27 – It was pretty, it was pretty scary.
0:54:30 Steve could be intimidating though,
0:54:33 is there is absolutely no doubt about it.
0:54:37 But to get back to this point I mentioned before
0:54:39 of the top down and the bottom up,
0:54:42 as I mentioned, except for this very brief
0:54:44 interlude where I was a manager,
0:54:46 throughout my whole Apple career,
0:54:48 over 15 years, almost 16 years,
0:54:50 I was an individual contributor.
0:54:53 And yet I got the opportunity to demo to Steve
0:54:56 some of the latest work that I did
0:54:59 at various points in my career.
0:55:04 Because he wanted to see from the person who did the work.
0:55:08 And because when he would ask questions,
0:55:11 well, go and ask the expert, right?
0:55:15 And go ask the person who is the DRI, right?
0:55:16 The directly responsible individual,
0:55:19 the person who is, at least according to plan,
0:55:21 the person who when they lose sleep,
0:55:24 they are losing sleep over that thing
0:55:26 that they’re gonna be demoing to me.
0:55:28 So that’s what he wanted to do.
0:55:31 And these demos were very, very small affairs.
0:55:36 Now, interestingly, the demo room for Steve,
0:55:39 the software demo room was this really
0:55:41 just shabby little room.
0:55:42 – That’s not what you would expect
0:55:44 for Steve Jobs’ command performance, right?
0:55:47 – This pristine room that it’s–
0:55:48 – Beautiful lawn mood.
0:55:52 – It’s not like an air filter, the air is clean,
0:55:55 or like the scent of redwoods
0:55:57 or something like that, piped in and no.
0:55:59 No, it was this shabby little room
0:56:01 with this mangy old couch
0:56:04 and just standard issue office furniture.
0:56:07 And that’s what there was.
0:56:10 I don’t know why he didn’t want better,
0:56:14 but the only reason that I can say
0:56:16 is that again, it was a matter of focus.
0:56:18 He was focused on looking at the software
0:56:19 and not worried about the decor.
0:56:21 – Yeah, all right, so take us in the room.
0:56:23 It’s a mangy couch who’s in the room.
0:56:26 Let’s do the version where you’re trading off
0:56:28 sort of the keyboard with the big keys
0:56:28 or the keyboard with the little keys.
0:56:31 – Okay, so now, so skipping ahead a couple of years
0:56:35 after the original iPhone
0:56:37 when we were then doing the original iPad.
0:56:41 So this is now 2009 as I recall.
0:56:43 So a couple of years later.
0:56:48 And so this is actually an original iPad right here.
0:56:50 And it’s actually a really good one,
0:56:53 which is actually autographed by Steve Jobs.
0:56:55 So this was the iPad that I got
0:56:59 at the end of the iPad development process.
0:57:02 But back at the beginning of the iPad process,
0:57:05 I would have a prototype that looked pretty much like this.
0:57:08 And so we were thinking of, well,
0:57:11 what’s the typing experience gonna be like?
0:57:13 And so here’s an original iPhone and original iPad.
0:57:14 Well, we’ve obviously got a bigger screen.
0:57:15 – A lot of pixels now.
0:57:17 – Right, so now what are we gonna do
0:57:20 to make great use of these additional pixels that we have?
0:57:23 And one thing that I also noticed was
0:57:25 if you turn the iPad to landscape,
0:57:30 that screen distance is actually just about the same
0:57:32 as the distance between the Q key
0:57:35 and the P key on a laptop keyboard.
0:57:38 So I was thinking, hey, like, wait a minute,
0:57:41 we could maybe fit a full-size,
0:57:46 something that is a full-size keyboard on a landscape iPad.
0:57:51 Now it turns out that right around at the same time,
0:57:53 one of the H.I. designers,
0:57:55 one of my favorite H.I. designers
0:57:56 that I really loved working with
0:57:58 and who I had also collaborated with
0:58:01 on the iPhone keyboard, Basa Orting,
0:58:04 he was starting to think about iPad keyboards as well.
0:58:06 And so he had come up with this demo
0:58:10 where he had all of these variations,
0:58:11 all of these ideas.
0:58:15 And so he gave me a demo where he went through,
0:58:17 he showed me 10, 20 different ideas,
0:58:21 but one of them really made, really struck me,
0:58:25 which was he had a design that showed pretty much
0:58:28 just a shrunk down laptop keyboard to fit in this space.
0:58:32 And so what that meant is that I had two ideas,
0:58:36 is that maybe I could use this larger screen real estate
0:58:40 to make a version of the keyboard that had big keys
0:58:43 that was almost the same size as a laptop keyboard,
0:58:46 but then one that also gave you like the number row
0:58:48 and all of the punctuation keys,
0:58:50 exactly where you would expect to find them
0:58:52 on a laptop keyboard.
0:58:56 And so I figured, well, and I started talking with Basa
0:58:59 and we came up with this demo
0:59:04 where we would have a special key,
0:59:08 we called the zoom key that would take you
0:59:11 from this keyboard that had the small keys
0:59:12 that would zoom up to the larger keys
0:59:15 and then back down to the smaller keys
0:59:19 as a kind of a complement to the globe key
0:59:21 that changes the keyboard language.
0:59:23 So we would have this other key,
0:59:25 this kind of complimentary key
0:59:28 that would change the keyboard layout.
0:59:29 We thought this was a great idea.
0:59:34 And again, the idea of what are we gonna do
0:59:38 with this larger screen real estate for the iPad, right?
0:59:40 – So the idea was give the user choice.
0:59:41 – Give the user choice.
0:59:44 – Give the user choice, use these new pixels
0:59:46 that are available on this new platform,
0:59:51 this new form factor, and have that be the pitch
0:59:53 that we make to people.
0:59:55 And so before, of course, you can make the pitch to people.
0:59:56 You need to make the pitch to Steve.
0:59:57 – To the man.
0:59:58 – That’s right.
1:00:01 And so I got to demo this for Steve.
1:00:04 And so the way that this worked is that
1:00:09 there was a very small team that was like
1:00:14 the chief demo review team,
1:00:17 the small group of people that Steve wanted around him
1:00:19 as he was reviewing demos.
1:00:23 And this was Scott Forstahl, Greg Christie, Henri,
1:00:23 people that I’ve mentioned.
1:00:26 So the chief managers for iOS.
1:00:28 And then a couple of H.I. designers.
1:00:30 It’s like Boss Orting, the fellow that I collaborated with
1:00:34 on this keyboard, was almost always in this meeting.
1:00:37 Another fellow, Steve LeMay, was another H.I. designer,
1:00:39 was often in the meetings.
1:00:42 But as I recall, he wasn’t in this particular one
1:00:44 where I was demoing the keyboard.
1:00:46 – So half a dozen people.
1:00:48 – Half a dozen people in the room.
1:00:51 And so then what would happen is that people like me
1:00:53 who had individual demos,
1:00:55 and so it’s like there were circles inside of circles.
1:00:58 So I was in the circle of people who could demo to Steve.
1:01:01 But then there was this circle inside of that
1:01:03 who would stay for all the demos.
1:01:07 And so my role would be that, or how I would figure
1:01:11 is that I would go in, give my demo, and then leave.
1:01:13 And so, think of that beforehand,
1:01:15 is that I’m sitting there with my iPhone
1:01:19 out down the hallway, waiting for Henri to text me.
1:01:20 – Waiting for my turn.
1:01:21 – That’s right.
1:01:25 And so he sends me a text, go stand outside the door,
1:01:27 and then the door is gonna open.
1:01:29 I’m gonna get invited in.
1:01:31 So I get the text, I go stand outside the door,
1:01:34 and now I’m waiting, and I’m waiting, and I’m waiting,
1:01:35 and it just seemed like, well, he just texted me.
1:01:37 Why did he text me?
1:01:39 And so then the door opens, I get invited in,
1:01:41 and I figure I’m on.
1:01:43 Gonna go do this iPad keyboard demo,
1:01:46 and I come around the corner and turn into the room,
1:01:50 and Steve is over there, and he’s like this.
1:01:52 He’s like, he’s on the phone.
1:01:54 He’s on the phone, staring at the ceiling,
1:01:57 like, you know, going back and forth in his office chair.
1:02:01 And I’m like, gulp, I was like, what do I do?
1:02:05 Like, now I’m eavesdropping on Steve on his phone call.
1:02:06 – Yeah.
1:02:08 – Right, and so, you know, it’s pretty uncomfortable.
1:02:09 – Yeah.
1:02:12 – And I think, I actually do think
1:02:17 that he was talking to Bob Iger, the head of Disney, right?
1:02:20 And so he’s like, yeah, Bob, yeah, yeah, that sounds great.
1:02:21 Yeah, yeah, I’ll call you next week.
1:02:22 Yeah, great talking to you.
1:02:27 Right, so then he hangs up, and so then he does this thing.
1:02:30 He takes his iPhone, he puts his phone back to his pocket,
1:02:34 and then he does this, right?
1:02:36 It’s like, you know, I mean, out of you know,
1:02:39 like the eye of Saran, right, the Lord of the Rings, right?
1:02:42 You know, the great eye turns to focus on you,
1:02:44 and that’s what it feels like.
1:02:48 And so it’s very, very interesting
1:02:52 then how the demos go from that point
1:02:54 in that he didn’t want a lot of words.
1:02:57 He didn’t want a lot of, you know,
1:03:01 used car salesman pitches, right?
1:03:03 All he really wanted to know was what was next.
1:03:06 And so what happened is he hung up the phone,
1:03:09 he turns towards me, and then Scott Forstall
1:03:10 was the one who then stepped up.
1:03:14 He goes, and the iPad was already in the room,
1:03:17 and so he goes and wakes it up and brings my demo up,
1:03:19 and says, Steve, we’re gonna be looking
1:03:20 at iPad keyboard options.
1:03:23 Now Ken, he did work on the iPhone keyboard,
1:03:25 and now he’s got ideas for the iPad keyboard.
1:03:28 So Ken, and so I said, yes, Steve,
1:03:31 go and look at the demo, it’s on the screen now,
1:03:33 try the zoom button.
1:03:36 And that’s it?
1:03:38 That’s it, that was the intro.
1:03:43 And so then Steve goes, he slides his office chair over,
1:03:48 and he starts looking at the iPad screen.
1:03:51 And what was up was one of the two keyboards,
1:03:53 let’s say it was the big key keyboard,
1:03:56 the one that was more suitable for touch typing.
1:04:00 And he’s looking at it, he took a long time to look at it.
1:04:03 It’s like, he even did this little thing
1:04:07 where he was turning his head to see what it looked like,
1:04:09 like in his peripheral vision.
1:04:14 It’s like, he’s just incredible to see what does Steve do
1:04:16 when he evaluates a product.
1:04:18 Okay, so this is what Dan, that’s what he did.
1:04:20 And so he hadn’t even touched it yet,
1:04:21 he’s just looking at it.
1:04:24 – Yeah, and this is going on for a long time.
1:04:26 – It seems, it’s like one of those things
1:04:28 where it was probably maybe 20 or 30 seconds
1:04:31 that’s felt like 20 minutes, right?
1:04:33 But he took a long time to study,
1:04:37 and then eventually he goes out and touches the zoom button,
1:04:40 and this zoom button to change between the two keyboards,
1:04:42 in this case shrinking the keys down
1:04:46 to be the more laptop-like keyboard layout.
1:04:49 The animation that Boss Orting had designed
1:04:51 was one of the most beautiful things I’d ever seen.
1:04:53 I mean, it really looked like they were,
1:04:56 like the keys were just like morphing.
1:04:57 It was absolutely beautiful.
1:05:01 But Steve just was like, no reaction.
1:05:03 He does the zoom and then he does this study again.
1:05:05 He’s like, look at all the,
1:05:08 look at all the keys, looking at how the screen changed.
1:05:09 Then he does the zoom again,
1:05:12 and it goes back to the state that it was in the beginning.
1:05:15 And then he studied a little bit more
1:05:20 and tapped the zoom button again to see that it’s like,
1:05:22 okay, there are just two states that we’re going here
1:05:24 between, right?
1:05:26 We’ve got two keyboards.
1:05:29 I see the animation, go between one, then the other,
1:05:31 back to the first one.
1:05:35 He satisfies himself that he’s seen what there is to see.
1:05:38 And so then he turns to me and he says,
1:05:40 we only need one of these things, right?
1:05:44 And you’re like, I’m on the hot seat.
1:05:47 – Yeah, I guess so.
1:05:49 And then he says, I mean, this is again,
1:05:51 the interesting part.
1:05:54 He asks me, which one do you think we should use?
1:05:56 He asks me.
1:05:58 He doesn’t ask, you know, Scott Forstall,
1:06:00 who was, you know, he knows much better.
1:06:03 He doesn’t ask, you know, any of the other people in there.
1:06:05 He asks me, the individual contributor,
1:06:07 you know, just coming in.
1:06:09 But I’m the DRI, you see, that’s the thing.
1:06:11 He wanted the answer from me.
1:06:15 Now the thing was, I had to give an answer.
1:06:16 – Yeah.
1:06:17 – You know, if I didn’t give a good answer,
1:06:19 maybe I would never be invited back again.
1:06:21 – Not the DRI anymore, without answering.
1:06:24 – See, but you know, and I had no idea
1:06:26 that this is what he was going to ask in.
1:06:28 But in that moment, I came up with an answer.
1:06:30 Because I thought about my experience
1:06:32 with these two keyboards, and I thought that, you know,
1:06:35 the one with the bigger key is I found more comfortable.
1:06:38 I was getting to be, you know, that maybe with, you know,
1:06:41 like four or five fingers that I could touch type.
1:06:42 And auto correction was helping.
1:06:44 So that’s why I said to Steve, I said,
1:06:45 well, I like the bigger one.
1:06:46 You know, the auto correction is kind of helping.
1:06:49 And I’m starting to get a feel for touch typing.
1:06:53 And he says, okay, we’ll go with that one.
1:06:56 – Wow.
1:06:57 – Demo over.
1:07:00 And you know, the interesting thing is that then
1:07:02 that’s the keyboard that chipped on the product
1:07:06 with the slight modification of taking away the zoom button,
1:07:09 which was now no longer needed, right?
1:07:14 And so Steve had this amazing ability to simplify
1:07:19 and to rely on his people to have a good enough idea
1:07:26 about what they were doing and to be involved enough
1:07:31 in the work that even when you get asked difficult questions,
1:07:33 you know, about it, that you’ve been thinking about it.
1:07:38 You have this background of just context
1:07:40 of having been thinking about the problem
1:07:45 for weeks and weeks that that experience was then
1:07:47 something he was interested in topping into
1:07:50 to provide a way forward for the product.
1:07:51 – What was going through your head
1:07:54 when you were just watching him sort of head tilt
1:07:57 in silence, were you like tempted to like explain things?
1:07:58 Were you?
1:07:59 – Yeah, well, you just know that you,
1:08:00 that’s not what you’re.
1:08:01 – That you’re not supposed to do that.
1:08:02 – That you’re not supposed to do that.
1:08:03 – Yeah.
1:08:04 – Yeah.
1:08:06 I mean, I would imagine that if he had done so,
1:08:08 he would have been in no uncertain terms.
1:08:09 He’s like, let me look at the thing.
1:08:10 – Yeah.
1:08:14 – Because now he’s like, what was he doing?
1:08:17 He was in my view, in my view,
1:08:19 I don’t know what’s going on inside his head,
1:08:22 but just having seen him do that,
1:08:25 having at least enough experience with him
1:08:28 and his approach to evaluating work
1:08:33 is that he was putting himself in the position of a customer.
1:08:38 He was envisioning himself that being in an Apple store,
1:08:40 as a customer walking up to a table,
1:08:43 seeing this new iPad thing for the first time,
1:08:44 what’s gonna be my impression of it?
1:08:48 So he pictured himself as customer number one.
1:08:52 And so, you know, I don’t want anybody,
1:08:53 I don’t want the engineers,
1:08:55 the engineers aren’t gonna be there
1:08:58 to be whispering in the ear of the person in the Apple store.
1:09:00 Sure, they can maybe get the help of one
1:09:04 of the nice people working in the Apple store,
1:09:07 but gosh, wouldn’t it be better
1:09:09 if I can figure this thing out for myself
1:09:14 and decide for myself and see the evidence of the care
1:09:18 that the engineers and designers had put into the work,
1:09:19 I can decide for myself.
1:09:22 Yeah, this is the thing I want to take home with me, right?
1:09:26 Yeah, so obviously if you have a leader like Steve
1:09:30 that’s that into being able to emulate the user
1:09:33 who has great taste, like you want to make this person
1:09:35 benevolent design dictator for life, right?
1:09:37 Now the downside of that, you know,
1:09:39 Silicon Valley is getting a lot of criticism
1:09:42 for these sort of super charismatic reality distortion
1:09:46 field generating CEOs where like,
1:09:47 you might not agree with them, right?
1:09:50 And, you know, in the sort of ultimate downside case,
1:09:53 there’s sort of just too much hero worship of CEOs.
1:09:55 Like, do you think that ever became part
1:09:58 of the Apple culture, right?
1:10:01 Sort of the blind obedience to the fearless leader.
1:10:06 Yeah, I think the Steve’s reputation
1:10:11 and his success causes people to draw the wrong conclusions,
1:10:14 to take away the wrong lessons.
1:10:20 I think that if you go back and look on YouTube
1:10:22 of old videos with Steve, maybe, you know,
1:10:25 on stage with Walt Mossberg and Kara Swisher
1:10:28 at their, you know, All Things D conference,
1:10:33 or I just had a reason to go back
1:10:36 and look at the antenna gate.
1:10:37 Oh, right?
1:10:38 I forgot about that.
1:10:40 Because I, and the reason that I did this
1:10:43 is because this, you know, it’s current now
1:10:46 that there was a bug in group FaceTime
1:10:48 and Apple issued an apology.
1:10:50 They were sorry that we had this problem
1:10:52 and that we’re gonna be fixing it, whatever.
1:10:53 And so I wanted to go back and say,
1:10:56 well, what did Steve say about antenna gate?
1:10:59 You know, which was the issue with the iPhone 4
1:11:01 where you’re holding it wrong
1:11:03 and the signal strength would go down.
1:11:04 And I wanted to see what he said.
1:11:06 And it was, it’s really interesting.
1:11:06 This is on YouTube.
1:11:08 You can go and look at it.
1:11:10 And Steve held a little press event.
1:11:14 And, you know, he was just very, very clear,
1:11:16 very, very upfront saying,
1:11:18 our goal is to make our customers happy.
1:11:23 And so that’s the kind of lesson
1:11:24 that people should be taking away.
1:11:26 It’s not that he was domineering.
1:11:30 Not that he was this, you know, absolute monarch,
1:11:33 you know, 21st century absolute monarch now
1:11:35 in a company rather than a government.
1:11:39 All of that, you know, that he had this, yeah,
1:11:41 reality distortion field personality
1:11:44 is that he had this focus on doing great work
1:11:46 and making customers happy.
1:11:48 That’s really what he cared about.
1:11:49 – Yeah.
1:11:52 And then sort of how did the organization morph itself
1:11:54 to sort of reflect that you had this, you know,
1:11:57 great taste maker who wanted to make these decisions
1:12:00 at a sort of very granular level in the design.
1:12:03 So there was an example where you were designing
1:12:06 an animation, I think it’s sort of the scrunch zooming demo
1:12:08 and you got to the point where like Steve
1:12:11 and Scott Forstall actually disagreed.
1:12:12 – Right.
1:12:13 – So maybe tell us a little bit about that.
1:12:17 – Yeah, and so this was for iOS 5.
1:12:20 So this was, you know, maybe the second version,
1:12:22 second or third version of iPad software.
1:12:25 And we wanted to come up with multitasking gestures
1:12:26 is what we called them.
1:12:28 So that you would have some way of interacting
1:12:30 with your whole hand on the screen.
1:12:32 Well, obviously from the beginning,
1:12:34 even though multi-touch was something
1:12:36 that shipped even in the first Apple product,
1:12:38 there was no way that you could have
1:12:41 sophisticated gestures, multi-finger gestures
1:12:43 on a screen that size, but with the iPad,
1:12:44 we thought that you could.
1:12:47 And so you have this idea of, well,
1:12:49 what if you’ve got the home button that way,
1:12:51 you still maybe want some gestures to interact
1:12:54 with the device to control going between app to app.
1:12:59 So I came up with this idea of using this five finger gesture
1:13:01 like you take a sheet of paper and crumple it up
1:13:06 and throw it away to go from an app back to the home screen.
1:13:09 There was then this other interaction
1:13:11 where you would swipe side to side
1:13:15 to just go between one app directly to some other app.
1:13:20 So you launch mail and then you launch Safari.
1:13:23 Well, then I can just swipe to go from Safari back to mail.
1:13:24 So that the system would keep track
1:13:28 of the history of apps that you launched.
1:13:31 So now here’s the part that Scott didn’t like.
1:13:36 So let’s say you start up your iPad from nothing, right?
1:13:38 Yeah, you know, you take it out of the box
1:13:39 and you bring it home.
1:13:42 And yeah, you launch mail and you launch Safari.
1:13:43 You’ve only ever launched two apps.
1:13:46 So you swipe to go from Safari back to mail.
1:13:48 Well, what happens if you continue swiping
1:13:50 in that direction, right?
1:13:51 There’s no other apps.
1:13:52 – End of list.
1:13:53 – End of list.
1:13:56 And so what I came up with was this sort of
1:14:00 morphing, stretching, rubbery distortion of the app
1:14:03 to show you that you were at the end of the list.
1:14:05 And it would kind of do this bloop, bloop, bloop,
1:14:08 sort of animation when you let your fingers up
1:14:09 off the screen.
1:14:12 And Scott Forstall hated it.
1:14:14 He hated it.
1:14:16 And his argument went like this.
1:14:18 He said, you know, that’s not fair
1:14:20 to the designers of the apps
1:14:24 because they really didn’t design for
1:14:28 what their apps would look like when you stretched them.
1:14:29 – That’s super interesting.
1:14:31 They didn’t have a say in what it’s gonna look like.
1:14:31 – That’s right.
1:14:32 – So you’ve taken away their taste.
1:14:35 – And it’s an interesting aspect to what happens
1:14:37 as you evolve a product.
1:14:40 They would then, for the subsequent version,
1:14:41 but we would be shipping a version
1:14:44 that added a new feature, multitasking gestures.
1:14:45 And it would have to work with all the apps
1:14:47 that were already in the world.
1:14:49 Of course, there was a huge ecosystem by that point.
1:14:52 So this was Scott’s argument is that the designers,
1:14:54 you’ve done something to the designers
1:14:56 that they couldn’t really have accounted for
1:14:58 in the design of their apps.
1:15:01 Okay, so I got the chance to demo this to Steve too.
1:15:03 And I remember that Steve, what he did was
1:15:06 he had the iPad in his lap.
1:15:08 So he was sitting like this
1:15:12 and doing the gestures, trying them side to side
1:15:13 and whatever.
1:15:16 And when he just discovered by himself
1:15:21 this rubbery animation, end of list animation,
1:15:25 he did it, he did it again, and he didn’t look up.
1:15:29 He said, “This is Apple.”
1:15:31 – Oh, awesome.
1:15:33 – Yeah, so it was a pretty good moment for me.
1:15:35 – And you just stopped yourself doing the victory lap.
1:15:37 (laughing)
1:15:39 – He thought that it was,
1:15:44 you know, sort of tapping into the, excuse me,
1:15:47 the little sort of whimsical aspect
1:15:49 that went all the way back to sort of like
1:15:52 the happy Mac on the original Macintosh.
1:15:54 Right, that it was this whimsical little animation
1:15:58 that showed that the system has this playful character to it.
1:16:00 And that was an aspect that he really loved.
1:16:05 And so, and it also just goes to show
1:16:09 that there could be disputes even up at the highest level.
1:16:11 Scott knew that I was very excited about this feature
1:16:14 and wanted to show Steve, so he let me.
1:16:17 And Steve was the one who had the final vote.
1:16:21 And he sided with me, and then that instance.
1:16:24 – And do you feel like that slowed decision-making down
1:16:26 at all in the org, where basically,
1:16:27 we’re just gonna wait for Steve to decide
1:16:30 so like why bother making a decision?
1:16:33 – See, but again, the DRIs were responsible.
1:16:38 You needed to bring him proposals, right?
1:16:46 You might think of that keyboard demo example was,
1:16:47 well, we were bringing him two keyboards
1:16:49 and we wanted him to pick which one.
1:16:51 No, that wasn’t it.
1:16:55 We were presenting him with a design
1:16:57 we wanted to ship in the product.
1:17:01 The design was going to have these two keyboards.
1:17:04 He was the one who unpacked it.
1:17:06 And to say we only wanted one of these.
1:17:11 So no, and the point is that if you brought him shoddy work,
1:17:15 that was like the equivalent of a shoulder shrug.
1:17:16 Yeah, Steve, we’ve got five things
1:17:19 we don’t really know which one we think we like.
1:17:23 That was a way to–
1:17:24 – Never get invited back to a demo, right?
1:17:26 – It’s a way to get invited, not invited back to the demo.
1:17:28 And that was the way that Scott Forstall then
1:17:32 would have gotten blowback from Steve Offline
1:17:35 to say, Scott, why aren’t you presenting me
1:17:37 with solid designs?
1:17:39 I’m not here wasting my time.
1:17:44 I wanna see the full result of that bottom-up process
1:17:48 so that he could then give his top-down approval,
1:17:51 disapproval, no, send this back for more work
1:17:54 with specific feedback on what to change.
1:17:56 That was the outcome of every demo with Steve.
1:18:01 Approved, not approved, bring me something different
1:18:06 next time, or not approved, give me these specific changes.
1:18:08 It was one of those three things.
1:18:11 – So Steve himself is sort of legendary
1:18:14 for sort of fusing liberal arts and engineering thinking.
1:18:17 And if you think about the classic Silicon Valley stereotype,
1:18:19 companies are a lot more about
1:18:22 the pedigreed computer science engineer, right?
1:18:23 Like, that’s the stereotype of like,
1:18:25 that’s what we’re looking for now.
1:18:28 But your own background and other people at Apple
1:18:31 who’ve sort of had the valued liberal arts
1:18:33 and engineering degree talk about like,
1:18:36 what are the advantages of sort of melding the traditions?
1:18:38 What’s an example of a decision they got made
1:18:39 that was a better decision?
1:18:40 Because you’re sort of–
1:18:45 – Well, I mean, it’s all the process
1:18:49 of designing experiences for people
1:18:52 that are useful and meaningful, right?
1:18:54 And I think that how do we define
1:18:56 what’s useful and meaningful?
1:18:59 Well, we look to literature, right?
1:19:01 We look to philosophy, right?
1:19:06 We look to art, we look to the creative media, right?
1:19:08 To decide what’s useful and meaningful.
1:19:11 And so, you know, I think, and you know,
1:19:14 I don’t know, I didn’t know Steve well enough
1:19:15 to know what he thought.
1:19:20 But the culture that he helped to create
1:19:24 and that I found my place in that culture
1:19:25 was, you know, the part of the approach
1:19:30 was that these devices are part of people’s lives, right?
1:19:34 More and more now, to the extent that now, right,
1:19:36 we think that there’s a problem
1:19:37 with the number of amount of time
1:19:40 that we’re spending looking at these screens, right?
1:19:43 That we need apps and features on the phone
1:19:45 to help us track, right?
1:19:47 Too much screen time, right?
1:19:51 And so, if we’re going to have this object,
1:19:54 this device, these experiences that are so important to us,
1:19:59 so deeply ingrained, well, then it requires,
1:20:01 I think, the care and attention
1:20:05 and the thought about it’s not just a technology artifact,
1:20:08 it’s a social artifact, right?
1:20:12 It’s a human artifact, right?
1:20:15 And so, that’s where liberal arts comes in.
1:20:18 Yes, you do need to have the technological background
1:20:22 to come up with the hardware and the software
1:20:24 and the networking and the services
1:20:26 to get everything packed together
1:20:28 so that a product like this is possible.
1:20:32 But if you’re gonna ask, well, what is it good for?
1:20:34 You know, why do we do this feature
1:20:35 rather than that feature?
1:20:39 I think that, yeah, that’s a liberal arts process.
1:20:40 – Tell the story, if you would,
1:20:45 of how you guys arrived at the homescreen app icon size.
1:20:49 Right, there’s a fun liberal arts twist to this.
1:20:51 – Yeah, so, okay, so now, you know,
1:20:55 going back to a phone that looks more like this
1:20:58 is my original iPhone that I still have.
1:21:00 So, you know, this is the screen size
1:21:02 that we were dealing with.
1:21:05 Now, one of the, you know, again,
1:21:08 now jumping back all the way to 2005,
1:21:12 18 months out from the, you know, the product announcement,
1:21:14 we were still in the early stages
1:21:16 of trying to figure out, well,
1:21:20 what is the homescreen of apps gonna look like
1:21:21 and how is it going to work?
1:21:24 And one of the fundamental questions that we had was,
1:21:26 well, how big should the icons be?
1:21:28 And again, I mentioned before this apprehension
1:21:31 of touching targets that were smaller than your finger.
1:21:32 And we were still in the phase
1:21:37 where we didn’t know how big on-screen objects should be.
1:21:39 And so we had some experiments,
1:21:42 but this was still, we didn’t have a good handle on it.
1:21:46 And so one of the engineers on the hallway had an idea.
1:21:48 And his name was Scott Herz.
1:21:50 He was doing work on Springboard,
1:21:53 the icon launching program himself.
1:21:56 And so he had this idea is I’m gonna make a game.
1:22:00 It’s the first ever iPhone game, right?
1:22:04 Truly, because this is a point where we didn’t even have
1:22:07 all of our units still needed to be tethered to a Mac.
1:22:11 We didn’t have standalone enclosures yet.
1:22:14 So we were still at this phase where we had touch screens
1:22:17 that still needed to have a wire tether to it.
1:22:19 But still we were trying to figure out,
1:22:21 well, what the ideal size is.
1:22:23 And the game was the solution.
1:22:24 And the game went like this.
1:22:26 You would launch the game
1:22:28 and there was a minimal user interface.
1:22:31 All it was was a rectangle on the screen
1:22:34 that was a random size and a random position.
1:22:37 And the game was tap the rectangle.
1:22:38 And as soon as you did,
1:22:41 it didn’t tell you if you succeeded or failed
1:22:44 because the idea was just go tap the rectangle
1:22:45 as quickly as possible.
1:22:48 You tap the rectangle, the next one would show up
1:22:49 at some other random size
1:22:51 and some other random position on the screen.
1:22:54 And the idea was to just go as quickly as possible
1:22:56 without, again, being sort of weighed down
1:22:59 by the feedback of whether you were succeeding or failing.
1:23:01 And you would get then 20 of them
1:23:04 and then it would give you your score, right?
1:23:06 And so it was fun, right?
1:23:08 – Before Angry Birds.
1:23:10 – Before Angry Birds, we had the little–
1:23:11 – Random rectangles.
1:23:13 – Angry rectangles as you’re going around.
1:23:15 Now, naturally what he was doing,
1:23:17 he also wrote the software so that he was tracking
1:23:22 rectangle by rectangle,
1:23:24 whether people were succeeding or failing
1:23:27 and also based on where the rectangles showed up
1:23:29 on the screen.
1:23:30 And within a couple of days,
1:23:32 of course, the game was actually fun, right?
1:23:35 I finally got 20 out of 20, right?
1:23:42 We determined that if you made a rectangle
1:23:45 that was 57 pixels square,
1:23:50 that pretty much everybody could tap it 100% of the time.
1:23:51 No matter where it was, again,
1:23:53 since you were going quickly,
1:23:55 you could tap it comfortably.
1:23:58 And that number, he just then,
1:24:01 since he was working on Springboard and it was his game,
1:24:04 it was his app, he put that number into the app,
1:24:07 he made the pixels 57 pixels square.
1:24:09 And since that was a good number, we never changed it.
1:24:12 And so that’s what wound up shipping on the iPhone.
1:24:14 – Yeah, I love that story,
1:24:16 that it was sort of a game that led to it,
1:24:17 as opposed to, all right,
1:24:20 we’re just gonna do every possible pixel variation,
1:24:22 we’re gonna bring people in to test it
1:24:23 and we’ll see what works.
1:24:26 – Yeah, no, it was, again, he was the DRI for Springboard.
1:24:29 It was his job to figure out how big the pixels should be.
1:24:32 And he came up with a good solution so we didn’t change it.
1:24:34 – Yeah, yeah.
1:24:37 So let’s switch gears a little bit
1:24:39 and talk about sort of your advice for young people
1:24:42 who are thinking about getting into the computer industry,
1:24:45 sort of, you know, a broad degree,
1:24:47 computer science degree, what set of life experiences,
1:24:49 like what’s your general advice
1:24:52 for people who want to join a tech company?
1:24:55 – Yeah, I think it needs to be a mix.
1:24:59 I think if you’re going to be a programmer,
1:25:00 yeah, go write programs.
1:25:02 I mean, the only way to get better
1:25:04 at things is to do them.
1:25:05 You know, and one of the wonderful things
1:25:09 we mentioned, open source, a bit earlier,
1:25:14 the barriers now have never been lower to get involved.
1:25:19 I knew that when I was a young person in college,
1:25:22 I actually started in college in 1984,
1:25:24 I couldn’t afford a Mac, right?
1:25:26 I wanted one.
1:25:27 – Yeah, they were thousands of dollars.
1:25:28 – Thousands of dollars.
1:25:32 There was no way that I could afford one.
1:25:36 And so now the barrier to entry is much lower.
1:25:41 So if you’re interested in making projects,
1:25:44 well, just go out and join a community
1:25:45 and start making them.
1:25:47 Or maybe you don’t even, you can even lurk in the community.
1:25:49 You can download the software
1:25:51 and try to make something of it yourself.
1:25:57 So I think that, again, if you want to do something,
1:25:59 just start doing it.
1:26:00 So that’s one piece of advice.
1:26:02 And then the other piece of advice is,
1:26:07 yeah, you do need to look at more than technology.
1:26:10 Again, for the reason that I said a few minutes ago,
1:26:13 which is these technological artifacts
1:26:17 that we’re making now have become so important to people
1:26:19 that if you don’t know anything about people, right?
1:26:24 I don’t think that you’re going to be successful
1:26:25 in the long term.
1:26:30 And so, yeah, read books, read books,
1:26:36 study, philosophy, go to art museums,
1:26:42 learn about what’s beautiful and meaningful to you.
1:26:46 Answer those questions for yourself.
1:26:47 I don’t think, you know,
1:26:49 if you can’t answer those questions for yourself,
1:26:53 it would be then hard as, say, a product designer
1:26:54 to then take on the responsibility
1:26:57 of answering those questions for other people.
1:27:00 Because that’s what you do when you’re a technologist
1:27:02 and say a product company like Apple,
1:27:06 you’re going to be making decisions on products
1:27:07 that are then going to go out in the world
1:27:08 and it’d be affecting other people.
1:27:10 Other people are going to be putting those things
1:27:12 and bringing them into their lives.
1:27:14 And so, how do you know what’s good?
1:27:18 And so that’s a question that you should be prepared
1:27:19 to answer for yourself.
1:27:21 What do you like?
1:27:22 And why?
1:27:23 What are your goals?
1:27:26 Why do you make a choice to make the product turn like this
1:27:27 rather than that?
1:27:31 And so, it’s this combination of learning about the technology
1:27:34 so that you can actually implement your ideas.
1:27:38 But then you’ve got to actually have good ideas.
1:27:40 And again, it’s the liberal arts
1:27:42 that provides the grounding for that.
1:27:43 – Super, and that’s counterintuitive
1:27:44 in Silicon Valley, right?
1:27:47 The suite of interview questions you typically encounter
1:27:50 when you’re interviewing for jobs are about linked lists.
1:27:51 And do you know TensorFlow?
1:27:54 And can you program in Python or whatever
1:27:55 as opposed to what’s good?
1:27:58 – Okay, and really, it’s unfortunate
1:28:00 that there are so many questions like that.
1:28:03 Well, obviously linked lists we’re still going to have
1:28:05 need for those as we go into the future.
1:28:10 But the work that, much of the work that I did in my life,
1:28:14 there was no way that I could have predicted, right?
1:28:18 When I was handed a piece of hardware like this
1:28:20 and it’s like making a touchscreen operating system
1:28:25 for a smartphone, well, there were precious few examples
1:28:27 that we could have looked at.
1:28:30 And so, how do you have experience in that thing?
1:28:33 So again, I think getting a flexibility
1:28:36 and being able to answer the sort of more general questions
1:28:37 about what you like and what’s good
1:28:41 and what your higher level goals are,
1:28:43 ’cause technology is gonna change.
1:28:48 – Yeah, and then sort of thinking about a company,
1:28:51 like how important do you think it is
1:28:52 if you’re thinking about joining a company
1:28:55 that there be a figure like a Steve Jobs
1:28:58 who has a trusted new tenant like a Scott Forstall?
1:29:00 Like, is the absence of those ingredients
1:29:02 like I’m not gonna join that company?
1:29:06 Or, right, how universal is the Apple experience
1:29:08 is another way of asking this question
1:29:11 versus how sort of specific to a set of characters
1:29:13 and a time in history?
1:29:16 – Yeah, it’s a hard question, right?
1:29:18 I mean, Steve was unique, right?
1:29:21 And unfortunately, he’s not around anymore.
1:29:26 And so, I think it’s kind of a fool’s errand
1:29:31 to go out and find who is the direct successor
1:29:32 to Steve Jobs.
1:29:35 It’s just like the questions are always changing.
1:29:41 And so, I think it’s a matter of finding a place
1:29:42 where you feel comfortable,
1:29:45 where you feel some sort of connection
1:29:48 to what the organization is trying to accomplish
1:29:51 and that you like the people
1:29:54 and that you feel that you’re bringing something,
1:29:55 you know, it’s, again,
1:29:58 this kind of this interesting contrast of both fitting in,
1:30:02 but then also, I think, providing more diversity.
1:30:04 I mean, that’s an ongoing challenge
1:30:07 for our high-tech companies is that, again,
1:30:09 as the products become more and more important
1:30:13 for our culture, the people who are making the products
1:30:18 need to be a better reflection of the world as it is, right?
1:30:23 That it’s not just a bunch of computer geeks
1:30:28 who went to maybe just a few high-powered schools
1:30:30 that have good computer science departments.
1:30:33 – In your book, there’s sort of a couple key ingredients
1:30:35 that you would sort of distilled
1:30:37 the Apple experience down to.
1:30:40 Like this is basically, in reflection,
1:30:43 this is what made the iPhone team so productive
1:30:45 and you talk about things like collaboration
1:30:47 and taste and decisiveness.
1:30:50 So we’ll pick up sort of a few of these things
1:30:53 as we sort of finish up the segment.
1:30:55 So collaboration, right?
1:30:58 Every company says we have a collaborative culture.
1:31:00 What do you think made Apple’s unique?
1:31:05 – Yeah, well, it’s interesting that we were very, very good
1:31:09 at combining complimentary strengths, right?
1:31:13 So we had this human interface design team
1:31:16 and I worked very, very closely over time
1:31:19 with a couple of the folks in there.
1:31:22 Of course, there were only a few folks in there in total.
1:31:26 And what we would do is, let’s say,
1:31:28 the example of me working with boss ordering
1:31:30 on the iPhone keyboard.
1:31:33 And so I was coming from the project
1:31:35 primarily from an engineering direction.
1:31:36 He was coming from the project
1:31:38 primarily from a design direction,
1:31:41 but boss was pretty good at writing code.
1:31:43 And I would fire up Photoshop and Illustrator.
1:31:46 And so we would come up with these ideas
1:31:48 and we would compliment each other.
1:31:51 And to the extent, and again,
1:31:53 whatever you think of software patents,
1:31:55 we got them for the work that we did in Apple.
1:31:57 And one of the constraints that you have
1:31:58 when you apply for patents
1:31:59 is that you need to list the inventors.
1:32:01 You actually need to be honest
1:32:04 about who contributed to the specific invention.
1:32:05 And so they would ask us,
1:32:09 well, which one of you two came up with this specific idea
1:32:11 so that we could write it into the claim language?
1:32:13 And maybe if we’re gonna take that claim
1:32:14 and move it to a separate patent,
1:32:17 we know we have to know who to put as the inventor.
1:32:19 And we would, boss and I would look at each other
1:32:20 and we would go, I don’t know,
1:32:21 we both came up with it.
1:32:24 And so that’s the sign of collaboration,
1:32:27 is that where the collaboration is so good
1:32:30 that you don’t know where it begins and where it ends.
1:32:33 You’re complimenting each other so well
1:32:38 that we did it and there is no other way to describe it.
1:32:44 And part of, as a sort of concrete piece of advice
1:32:51 or maybe a way of describing that more at Apple
1:32:54 is that we didn’t have a lot of politics.
1:32:58 When boss came up with the idea, I came up with an idea,
1:33:00 it just didn’t matter.
1:33:02 – Was it a strong attribution culture?
1:33:05 – Oh, that’s his idea and like, how dare you claim it.
1:33:06 – And I can’t work on that.
1:33:08 And now my manager is gonna get involved
1:33:12 because now I’m not gonna get the credit for it
1:33:15 and whatever, it just wasn’t like that.
1:33:17 – But you still had to have strong DRIs, right?
1:33:19 – Yeah, right.
1:33:21 – But that is also one of the ways
1:33:26 that just made it clear about if I was collaborating
1:33:30 with someone like Boss or just some other engineer
1:33:35 on the iOS engineering hallway,
1:33:37 if I was the DRI for the keyboard,
1:33:39 well, I was the one making the calls.
1:33:43 And as long as I kept making good calls, right?
1:33:45 I mean, if somebody else had an idea
1:33:47 that they really, really thought
1:33:48 they were gonna go to the mat and they’re gonna say,
1:33:52 “No, I think Ken made the wrong call on this.”
1:33:56 Yeah, they could buck that up, the management hierarchy,
1:33:58 but that was relatively unusual.
1:34:01 Because again, part of being a DRI
1:34:03 is recognizing strong ideas that are coming
1:34:05 from other people and including them in the work.
1:34:09 And so that helps to describe some of the character
1:34:11 of the collaboration that we had.
1:34:14 – Well, Ken, it’s been a fascinating conversation.
1:34:17 Thanks so much for taking us inside the chocolate factory.
1:34:19 Look, the chocolate factory did not have very many people.
1:34:23 So I feel really blessed that one of those people made it out
1:34:26 and is willing to lead the tour and talk to us.
1:34:29 And maybe that’ll be the last question I asked you,
1:34:33 which is famously secretive Apple corporation, right?
1:34:35 Did you have to get their approval
1:34:37 to actually write the book and tell the stories?
1:34:40 – Well, no, I didn’t.
1:34:43 I don’t know if I was supposed to, but I didn’t.
1:34:46 And I took a certain approach to it,
1:34:51 which is that it’s a positive take on Apple.
1:34:53 I loved my career at Apple.
1:34:55 So I didn’t throw anybody under the bus
1:35:00 because there was nobody that I thought deserved it.
1:35:06 And I limited myself to the Steve Jobs era,
1:35:09 which is now sadly, or for good or for bad,
1:35:11 passing into history.
1:35:12 And again, I was one of the few people
1:35:17 who had this perspective, this opportunity to be there
1:35:21 during the time that some of these products
1:35:22 were getting made.
1:35:26 And so again, with my background being in history
1:35:27 and being in the liberal arts,
1:35:29 I thought that it would be good
1:35:33 if I collected these recollections
1:35:38 while I still do remember them well and tell the story.
1:35:43 And so I thought that it was really more of a personal story.
1:35:50 And so no, I didn’t, I was imagining that maybe
1:35:53 I would ask forgiveness if somehow
1:35:55 they didn’t really approve,
1:35:59 but I thought that I wouldn’t really run into trouble.
1:35:59 – Well, that’s great.
1:36:01 Thank you for taking the time here
1:36:03 and for putting the stories down
1:36:06 so they don’t fade into the mists of history.
1:36:07 It’s been great having you.
1:36:09 – Well, I’ve had a great time, thank you.
1:36:10 – Great.
1:36:13 So for those in the YouTube audience,
1:36:16 if you liked what you saw, go ahead and subscribe.
1:36:18 And then in the comments thread on this video,
1:36:21 let’s talk about things that you might wanna try
1:36:22 in your own culture.
1:36:25 And now having listened to sort of Ken describe
1:36:27 what it was Apple, what Apple did,
1:36:29 sort of what would work in your environment
1:36:31 and what wouldn’t work in your environment.
1:36:32 We’d love to have a conversation
1:36:35 about how would you implement some of the ideas
1:36:36 that we talked about
1:36:38 in your own software development life cycle.
1:36:40 So see you next episode.