
You have been worried about your business. 

You have been successful but there are some nagging issues.

You have a capable team but sometimes they keep fighting over and over about the same thing>.

You’ve invested time in <learning methodologies, applying techniques, and consulting experts>. This has helped alleviate some pains.

But the real nagging issues keep coming back—personnel problems, shipping products, or setting priorities.

We’re stuck in Groundhog Day, but no Hollywood ending in sight.

What to do when you don’t know what to do?

Luckily, you are already familiar with the solution. 
Science is what you do when you don’t know what to do. 
For centuries, Science has been our primary tool for conquering unexplained phenomena. Science is the invisible force that helps us reform the world around us, from micro to macro scales. We used Science to harness the power of atoms, and then use that power to help us travel to the stars.


This is all sounding abstract and academic. Let’s try an example. 
Say you have been stranded with no signal. <In a jungle, or a desert. Somewhere you don’t recognize.> You are trying to get back home, which at the moment seems quite improbable. You are stuck. Don’t know what to do. 

So you can’t get to your home in one step (flick your fingers and get home). There are a lot of unknowns…

but perhaps there is something you could do to get yourself closer to get home. Well, what could this be? Well, if I don’t survive tonight then I most probably won’t arrive home (alive anyway). 

You see what we just did? We took a global imperative that we had no levers to control and turned it into something which presents us with choices, and (perhaps a limited) set of trials.

<ton of unknowns> <doesn’t mean we are totally at a loss> 
 If we look at this problem at its total scale, we don’t have a solution.
But this does not mean we cannot make any progress. All we need is a foothold. 

If I can stay alive, I can leave open the possibility that I can find my way home or I would be rescued. 

If I can make it through today and tonight.  And repeat that! I am increasing my chance of returning home or being rescued.

But how do I stay alive? 

Turns out, there is a simple set of rules that govern successful survival <~>.
If we have had knowledge of these rules of survival we would increase our chances of survival or being rescued because it’s infinitely better if we survive than if we die. 

Shelter, Water, Fire, Food. 
 Or alternatively, 
 Avoid: hyperthermia or hypothermia. 
Avoid: dehydration 
Avoid: predators, macroscopic and microscopic.
Avoid: starvation

Where did these words come from? 
Science. 
 What do these words entail? 
Knowledge. 

… 

dx•MBA - Nagging Problem 1

KISS METHOD mantra is a popular dictum around product circles, and is hard to argue. However, it can also be an excuse for simplistic explanations of complexities.

What we are aiming for are good explanations.  Good explanations are simple. But the process of arriving at the explanation may be complex. 

Here we dig into the KISS method, the good, the bad, and the ugly, and argue for a better explanation of the relationship between process and the kiss method. 

Simple ≠ Simplistic
Complex ≠ Complicated 

Need a quadrant chart for this! 


Goodbye KISS

America is facing an existential problem. We need widescale testing (high demand) but we are unable to make tests (low supply.) What do we do? 

When we look at it that way, the answer is simple: a low-end attack on testing. 

The temperature tests outside places are good, but they are not scalable, and intrusive. 
They also are not knowledge-building. 
Finally they require coordination of two people, and agreed upon time & place. This limits scale. 

What we would like is to have 'standard tests' taken by millions of Americans. This can be a simple website that asks a series of questions. And then records the resutls. However, a website doesn't know where you are, a website doesn't have persistence. A website doesn't have notifications. A website isn't always with you. OTOH a smartphone has all these. This needs to be an app, with a network backend. 

So we look for asymmetries in supply. What is it that almsot everyone in the population posesses at all times? A mobile phone. 
Obviously a computational solution to corvid testing would be awesome. We can look at <????>Health to see how well they can tell. Unfortunately, iphone doesn't come with a thermometer. 

So their (integrative) solution is to bundle a Bluetooth thermometer. However, this breaks modularity priorities. It requires sync in time, space and network of two devices. If we could do this...

Simplest idea is to create a bluetooth temperature device. But still there is manufacturing and distrubution costs. And likely result in inaccurate. 

**Scalability Principles**
1. Use existing supply. 
2. Distribute rote work to the edges 
3. Be willing to compromise individual quality for whole-system outcome
4. Build systems to create structure

So we asked what is that everyone has in their pocket? A smartphone, which is totally capable to do compuation and capture and network. We only have a last mile problem. 

Applying Scalability: What's the one thing that everyone has in their medicine cabinet? A thermometer. 

We have a network computing device, we have a theromoeter, we need to connect it. What's the universal information adaptor on the smartphone? Not Bluetooth. It's the CAMERA. <this itself is worth an article...how camera is an information capture device, but later> Note that it's not just the Camera. It's the Camera & a Person. Which means we have a universal cosntructor as our adapter. All we need to do is to give it the right knowledge. 

>  ### The Universal Adapter
Using Universal Constructor as your adapter is not new. In the 1990s Apple tried to create the Newton, with a very high end handwriting recognizer that could read different types of handwriting. This was a huge disaster. PalmPilot instead came up with Graffiti, a script they invented for easier recognition. So they could spend more time on other required innovations (like building an AppStore) and making it cheaper. Results: millions of people loved using the PalmPilot and had no problem learning it.
Another Example: Apple created an aura of User Friendly software, from the Xerox PARC ethos. Macintosh went a long way to be user friendly, steve jobs removed the cursor keys so new software would be written. Boards were integrated, and everything was in one box. this was useful, but Apple failed to scale this. Windows instead said we are not going to get too far into the depth of user, we will let *them* be the adapter of our (half baked) software and their outcomes. So it's true that <link> typical Windows machine requires much more maintenance than Mac, even today. However, this works to Windows's advantage in scaling, because it allowed people at contexts to adapt it, and they adapted it to specific needs they had, as opposed to centralized method of Apple. 


So basically, part of the steps of the 'daily quiz' is the phone wakes up, notifies you at a certain time to answer some questions, and depending on some factor, asks you to take your temperature. and take a photo of it. The rest will be backend magic, turns out it's not that hard to recognize these specific LCD images and also even the mercury theromometers aren't too bad because they have predefined structure. Worse case an army of people who are bored at home can mechanical turk this and the system will learn along with them. 

What about the error? Well, we can probably tell what thermometer is being used, that is, if the same theormometer. And we are interested in the fluctuations in a person's temp, not that much about their absolute temp. 

The reason this is good is that it's statically more sound. It can be available to billions of people. And it requires no new purchases or equipment. It can be built in a matter of week, and can spit out results on day 1. It has no limits to scale, and it can create gloabl and local knowledge. 

<need reference to knowledge vs. information> 

A Viral Strategy for Virus Testing

Quibi is not doing as well as expected. They have fallen well short of their subscriber target. And the app peaked briefly in the top ten downloads, but has quickly fallen out of the top 100. 

Quibi’s woes are not due to a lack of funding or genius at the top—it raised ~$1.75B, was founded by Jeffrey Katzenberger and is led by CEO Meg Whitman. So what’s going on?

Let’s put on our lenses to understand.

Quibi didn’t position itself as competitor to successful streaming services like Netflix or Disney+. Katzenberger [has said](https://www.vanityfair.com/hollywood/2019/06/quibi-jeffrey-katzenberg-streaming-platform-interview) that Quibi isn’t about sitting down and watching TV in the evening, but about what people ages 25-35 do from “7am to 7pm” on their phones. ([Link](https://www.vanityfair.com/hollywood/2019/06/quibi-jeffrey-katzenberg-streaming-platform-interview)) Instead, Quibi pitched itself as content you would consume “while waiting in line for coffee.”

Below is a blank Job Definition template, something we use to assist our understanding of the crucial elements of a JTBD.

<lens-image:job-definition>

From a JTBD perspective, Quibi has only made it halfway. 

**When I am** *In line waiting for coffee* **I hire** *Quibi*...

What’s missing, though, is the "**so I can**.” A first pass at this would be something like

So I can get something interesting or meaningful out of that time, while maintaining some awareness of my surroundings. 

(Think: when my child exits the school looking for me; while waiting for my coffee order to come up; or when filling up my car, and so on.)

One of the great things about the high-quality content that Quibi (and others) make is that it’s *engrossing*. And, in general, the more engrossing, the better. But that’s not true if you’ve got anything more to do than just immerse yourself in content. In fact, the better the content is, the worse it’s suited to situations—as in my examples above—where you need partial attention on your surroundings. 

Moreover, by positioning the app on those small slivers of time in our lives, Quibi has unwittingly put themselves in a space more crowded than they think. Those small bits of semi-free time are not only crowded with video content, but other competitors outside of the video entertainment category, such as magazines (digital or analog), chatting with someone, Twitter, texting, etc. 

By framing its competition in a producer-focused way—as more engaging and high-quality produced video content than YouTube or TikTok—Quibi has missed important details of the JTBD that recurs during our day. With an incomplete understanding of the circumstance of struggle, it fails to fit the actual job, and puts itself in competition with other activities outside its product category. 


Quibi through the lens of JTBD


In a recent post (link), I pointed out that Quibi has gotten itself in trouble (link to selling off article) because it had only done half of the work of understanding Quibi’s Job to be Done (JTBD). As I noted in the previous post, the pitch that raised $1.75B—and the pitch to potential subscribers—was that Quibi is what you’d reach for when you’re “waiting in line for coffee.” 

There are two problems with use cases. First, use cases only go half the distance to a real job. The “waiting in line” use case is a great start. But it fails to express the underlying drivers that cause one to seek a solution for that—and parallel—circumstances. In fact, if we discover that there are different causal paths that lead people to seek out Quibi or YouTube or TikTok, we might have two different JTBD on our hands. Without some notion of the progress people seek, we cannot tune an offering to the deeper JTBD.

The second problem is that use cases typically address how an already created or specified product/service can be used. The Jobs to be Done methodology inverts the high-level steps of a product launch process.

[Pyramid here. Bottom: some idea of JTBD. Middle layer: specification and creation; Top: marketing, sales, promotion, positioning...

Then the JTBD pyramid w/ top and middle layers switched.]

In JTBD work, we narrow the JTBD and all of the dimensions of a product experience before making the product. It runs counter to conventional practice, but it makes life easier. Instead of working to try to figure out how to “position” a product, the JTBD makes it clear how to do that. [Need example.]


The Problem with Use Cases

Thanksgiving...<a href='https://www.youtube.com/embed/wkptQwR5sKU'><img width="50%" src='https://s3.amazonaws.com/strapi.dx.mba/5025e22f3ebb4acfa4110bcf34668c89.png' align='right'/></a>the time of year that brings us family joy...and the usual share of people problems and disagreements, especially during a pandemic! Not to worry...a healthy serving of <lens:tools-of-cooperation> can go a long way to help you avoid people hickups. 

[Watch](https://www.youtube.com/embed/wkptQwR5sKU) how to use this very helpful People lens lens to make progress toward resolving conflicts and a happy thanksgiving. 

<br/>

<iframe width="560" height="315" src="https://www.youtube.com/embed/wkptQwR5sKU" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>


Tools of Thanksgiving Cooperation

Apple's recent release of the M1 series Macs, based on the newly minted Apple Silicon technology, was a hot topic in November. This led us to take a new look at Apple's past, present, and future through the lens of Disruption, published here in three parts. 
<img src='https://s3.amazonaws.com/strapi.dx.mba/6fa1be626170409a80a33bda9f42c22d.png' width='25%' align='right'/>

## Part 1. Is Apple Disruptive? 

**Farshad**: Horace & Dave, thanks for joining this long lens look about Apple & <a href='/lenses/disruptive-market-entry'>Disruption</a>. I'd like to set the stage with an brief synopsis, and then ask three fundamental questions about Apple.

Apple has done an amazing job in advancing personal and mobile computing. They have brought some of the greatest products in the world to billions of happy customers. They have entered new markets methodically, taking on fierce, entrenched, and well-healed competitors. They’ve done so while growing market share, improving products and increasing margins. 

Most recently Apple has introduced their own M1 SoC, based on their foundational Apple Silicon technology, leaving behind Intel, the leading supplier of PC CPUs. 

Taking a long-lens look at Apple & Disruption, let us explore three questions: 

1. Is Apple Disruptive?
2. Is Apple Over-serving with  M1 Macs?
3. How does Apple defy Disruption?

Let's dive right in. ***Is Apple Disruptive?*** Horace, what do you think? 

**Horace:** Actually the reason I do Apple analysis is this. Apple is  the anomaly or the exception that makes the rule of disruption. The history of Apple has been both in confirmation of and in contradiction to the theory of disruption, thus observing Apple helps us better explain disruption. This is what Clay always had as a goal: Let’s find the anomaly, so that we can improve the theory.

<center><img src='https://s3.amazonaws.com/strapi.dx.mba/0f22291722994bc082e7346cdc54eeda.png' width='75%' style=' text-align:centert;'/></center>

In looking at the graph of moving to higher margin versus a lower margin and lower performance is also present for Apple, however, the twist to that story with Apple has been that when looking at the upper end of the trajectory, they are often creating a new market.

In his classic disruption diagram, [Clay] puts a new market as a separate parallel plane, the Z dimension, which suggests there's a parallel market that can be created. Often what Apple does is combines the two markets so that that, Apple’s move looks like an up-market move in the original market, but it's also a down-market move in another parallel market. The iPhone is a classic case in point: it's far too good as a phone, but it's far, far too low-end to be a computer.

![Disruptive Market Entry](https://s3.amazonaws.com/strapi.dx.mba/ffa3f437d9eb4b508b435d3da4af0281.png)

And so when do you understand that, you realize that the disruption that occurred, it’s a market creation of an alternate way of spending your time. It's an alternate way of creating applications, which came to be known as apps. It’s a new way of communication through social media. It's new things you can do that you wouldn't dream of doing either with a phone or with a computer.  Architecturally, it’s a computer. Computer engineers will look at it and say, “oh yeah, I recognize within it a computer running on an operating system,” which used to run on a desktop or a workstation, or even the mini computer called Unix. And it runs on the microprocessor that eventually gets to be good enough and fast enough to be a desktop as they just did, but it's been packaged in a container and the market that did not exist before. And as a result, it redefines the P, or the Measure of Performance.

It redefines *P*, which is what the goal of an entrepreneur should be. Redefining *P* or recalibrating *P* is what is really at the heart of disruption. To create products that are worse or not good enough in the original dimension of performance, but awesome on a new dimension of performance. This is what Apple does over and over again. 

**Dave:** Jumping on the iPhone, it's been my long-held view that the iPhone was not disruptive, but sustaining to the mobile phone market—most users look at an iPhone and say this is a better phone. Certainly iPhone helped catapult consumers’ use of mobile services. So we can safely assume that the iPhone definitely sustained mobile phones. 

I believe the real disruptive force of the iPhone was not what Apple produced directly, but in their enabling a new form of software development. The iPhone, by way of the AppStore, was disruptive to software development. Before the AppStore, software development required many more people and expertise—both deep and broad—to essentially wrap their work around an operating system or to create something that interfaced with “programs” as we used to call them. But the AppStore enabled many more people with fewer resources, skills, and experience to make money creating software.

<div style='border: 1px solid #DA3B26; min-height: 5em; margin: 50px;'>
<h4 class='title' style='text-align: center; padding: 30px;'>
<center><a href='https://twitter.com/intent/tweet?text=The+real+disruptive+force+of+the+iPhone+was+in+Apple+enabling+a+new+form+of+software+development.+via+@dxMBA&url=https://www.dx.mba/blog/:is-apple-disruptive-long-lens-apple-and-disruption'> ❝The real disruptive force of the iPhone was in Apple enabling a new form of software development.❞</a></center>
</h4>
</div>

With this, the iPhone passed one of our “disruptive innovation litmus tests”: enabling a larger population of people with less expertise and fewer resources to do something that in the past could only be done by experts or was available to people and orgs that had more resources.

**Horace:** And now apps are constantly being refreshed and updated without developers having to box them and put them on the shelf, and without the licensing terms which used to be onerous: end-user license agreements, corporate deals and so on.

They changed distribution. And as a result of that, they changed the economics and they changed the fabric of computing, from the software side. It's hard to get this across because software businesses are complex to explain. But the scope is vast—they enabled different layers of software to be created, for example, the whole ride-hailing industry wouldn't exist for, well, not for the smartphone because it required your location when you summon it outdoors.

We have as, a result, not only billions and billions of dollars of income that's being generated through the AppStores, but also we have tens of millions of new workers in that value chain whom we call developers. They used to be employed in large corporations, but now increasingly they're self-employed or working in small companies.

**Farshad:** This very much reflects my personal experience as an app developer around the birth of the iPhone. The iPhone was definitely a marvel of engineering but I just didn't see it as a business. When Apple opened the iPhone up to developers, I imagined it would be a fun hobby. I just didn't see it in the same class as our client work. But my partner Lauren, insisted that we figure out a way to release some apps on the AppStore. She thought this was a great opportunity. By piggybacking on Apple’s brand, customer relationships and distribution network, we would have a new class of users—the consumers. When I mentioned to an indy software developer friend, he asked bluntly: “how many $1 apps do you have to sell to pay your bills?”

This was too good an opportunity to pass up. So I created a simple app called Digital Clock signed it and uploaded it to Apple’s backend—it turned out to be the first of its kind on AppStore! Apple's developer documentation back then was even more scant than now, and Apple had put the developers under NDA so we couldn't even use resources like StackOverflow. We were flying blind the first couple of weeks. We didn’t even know how many downloads we had. Finally we found the right person to call and they turned on  app “sales” reports. By then we figured we may have a couple of hundred free downloads. When they finally sent us the report, it was not at all what we expected. We had 17,000 downloads! Not total over two weeks, 17,000 on a single day! In one day, we had served more users than all our previous work combined. This was until the next day, when our downloads more than doubled!

<iframe width='100%' height='480' src="https://www.youtube.com/embed/xo9cKe_Fch8" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

My point for telling this story is that, even in its early days, iPhone’s scale was staggering. Before we could blink, we had millions of downloads by customers we didn’t know in half of the countries around the world, by simply signing the App and uploading it to Apple’s servers. Apple took care of the rest of the business stuff, tally the downloads every day and deposit money into our account every month. At the end, the app sales helped us offset the loss of our corporate customers during the financial meltdown. And this was 2008. Today, Digital Clock is a genre that includes thousands of apps. There are hundreds of thousands if not millions of developers like us. The scale is unfathomable. 

**Horace:** Yes. Like you said, Farshad, the AppStore enabled innovation in packaging, pricing, and distribution. And as the market has grown, it’s now morphing into subscriptions where developers can go from $1 per download to $1 per month. When Apple touts that they have 600 million subscriptions, it’s not their own but the subscriptions sold by third-party apps enabled by Apple devices. Imagine that: as a software developer, you're being told that you can get customers to give you a monthly payment! Wow!

**Farshad:** That’s true. When I now replay my early AppStore experiences through the lens of disruption, I can see how the iPhone and AppStore met the *litmus tests* you guys mentioned: that iPhone was a worse computing device than computers, but it also was awesome in another dimension! It was characteristically different than working for corporations. 

**Horace:** And it created [a whole new universe around this] but it's so subtle and so difficult to convey. I use this notion that it's a computer only to simplify and make it more palatable.

The fact of the matter is that the iPhone completely reshaped computing, which means it created a new ecosystem, a value network. And by this, I mean tha software distribution, oftware creation, the business model in software has completely changed. 

And all of those things meant that you had to have a cellphone. And we don't think about it now, but the imagined that that pre-smartphone we could not have had ride-hailing. We could not have had micromobility. We could not have had social media as we know it today, which itself created a niche consumption of media. Where we used to think of media as either half an hour sitcom, or a one hour drama, or a two hour movie, and now we have a six-second TikTok.

So this gives the apps the ability to slice content down to such tiny, tiny increments of time that fill up spaces that would be otherwise unoccupied. This is classic non-consumption disruption.

So the iPhone enabled all these things: it enabled new communications, that enabled new consumption, that enabled new transportation that…  So do you give credit to the iPhone and let Apple just sitse there and collect rents on all this? Or do you give credit to the whole system that was created? 

Are the disruptors the engineers who wrote the apps? Or are the disruptors the Facebooks and Twitters? In their own right, they changed media, they changed the way we think about our approach to information.

The idea of the disruptive theory is that you want to use it to point to causality of the innovation—what and who caused the transformation? But what we find in reality, is that the world is multi-causal, it’s hard to pinpoint which is the real cause. And all it can do is to tell multiple causes, but it’s kind of hard to predict them. It’s fascinating.

<div style='border: 1px dashed #DA3B26; padding: 30px; margin: 50px;'>
<h4 class='title' style='text-align: center'>
<center>Should you apply a disruptive or sustaining strategy? </center>
<center>Meet up with the authors for a <a href='https://dxmba.squarespace.com/engage/unstuck-assist-trial'>free consult</a>.
</div>

**Dave:** Yes! 
Maybe I sound like a broken record, but the key thing Apple did that helped in the success of the iPhone was the AppStore. And to make AppStore successful, Apple had to use a different business model type. This topic probably requires its own discussion, but we have briefly introduced it here. 

We’ve so far talked about two kinds of disruptive innovation in this conversation: First, low-end disruption, when an entrant sells a cheaper solution to customers who are over-served by the incumbent players (ex: Excel → Google Sheets). The second is by creating a new market—finding non-consumption and exploiting it (ex: pocket transistor radio or the Walkman, used by teenagers).

But there is yet a [third type of disruptive innovation]( /blog/:three-kinds-of-disruption-the-3rd-kind), where a firm causes fundamental changes in its business model to enable the transformation of the market. With the AppStore, Apple changed how they and their developers did business—their relationship with each other and with their respective customers. To make this happen, Apple maintained what we call a “value-added process" business model,—really, just making stuff and selling the end product—while also creating a “facilitatednetwork" business model, where people pay to participate in the network.  

**Horace:** Yes, it’s critical to be aware of the change in the customer definition, as in mobile computing we end up with a new value chain as did the developers. This may as well be the glaring omission in the PC and particularly on the Mac because this ecosystem didn’t emerge, certainly not thriving in the same way, partly because of legacy issues, partly because of corporate customers and perhaps because the lack of an appropriate network business model.  But we can see that something is fundamentally missing. 

**Farshad:**	This is  a good segue to our second question which is whether Apple is over-serving with the Mac. Thank you both for this very insightful conversation! Looking forward to taking on the second question! 

<hr/>
<em>
<strong>Do you have a <span class='highlight'>Disruptive Strategy</span> for your business? </strong>

Are you confident about it?
What about your competition?<br/>
<strong><em>
Sign up for a <a href='https://dxmba.squarespace.com/engage/unstuck-assist-trial'>free consultation</a>  to learn, <em>with aboslutely no strings attached.</em></strong>
</em>

A Long-Lens Look at Apple and Disruption


Apple’s recent release of the M1 series Macs based on Apple Silicon technology, was perhaps the biggest Apple technology advance in 2020. This led us to take a new look at Apple’s past, present, and future through the long lens of Disruption theory.
<img src='https://s3.amazonaws.com/strapi.dx.mba/6fa1be626170409a80a33bda9f42c22d.png' width='25%' align='right'/>

1. [Is Apple Disruptive?](../blog/is-apple-disruptive-long-lens-apple-and-disruption)
2. Is Apple Over-serving with M1 Macs?
3. How does Apple defy Disruption?

The first part of our discussion explored how Apple’s sustaining innovations (e.g., Smartphones) are typically disruptive to the markets where Apple itself operates (e.g. Computers). 
In this second part of the article, we discussed the Mac itself, and in particular, the introduction of M1.


# Part 2. Is Apple Over-serving with M1 Macs?

**Dave:**  After the M1 Macs launched last year, I told Farshad that I'm skeptical. That is, I see the M1 as over-shooting mainstream customers’ needs. Right now, most Macs provide performance along many dimensions that exceeds what users need or want. Having said that, I recognize there’s a good chance I’m wrong about this.

**Farshad**:		Since the M1 release, I’ve had a chance to talk with a few MacBook Air users, whom I would put in the median demand category. They seemed to be happy with their Intel MacBooks: the battery life, and general performance. I didn’t hear a lot of complaints about how long it took to zip files, or the number of tabs they can keep open. Sure the cooling fan kicks in sometime, but users didn’t seem very bothered with it. 

Anecdotal as it was, in my hallway testing, I couldn’t find counters to Dave’s hypothesis. At the same time, my feed is full of developers, video producers or other techies report  blazing speed in compression and compilation. 

**Horace:**  Let’s take the MacBook. It’s striking is that its form has not changed much in the past two decades. At least with the iPhone, we have a new input method with touch, whereas with the Mac we’ve had the exact same methods. 

Even the iPad has a different interface. It’s really a computer, as powerful as a Mac with a screen almost as big as a Mac, and now with a keyboard that gets bolted onto it as if it were a Mac, but it has a touch input and touch user experience. And so we can argue that iPad is in a different market because of the way it's used. But in the case of the Mac the question is: how much further up the sustaining trajectory can you go in terms of performance in the same dimension? 

Now, again, the devil's in the details. One thing I would add is that the Mac, under Apple’s stewardship, did move to a new dimension of performance by becoming thin and portable. Thus power consumption became a key. It’s been 25 years, since the modern laptop form factor was introduced in the 90s. Apple just pushed and pushed in that direction and arguably created the best examples of laptops in the world. So now, we can ask the the question: after more than two decades of that improvement, where are we with this? Is the M1 necessary? Or will the M1 going to cause an overshoot?

**Horace:**  And so the struggle for Mac, as a product, is going to be: will Macs become tools for content creators only? If so, it’ll be thought of as the creative tool, which it’s pretty good at. But then it's not going to scale to be the mainstream consumer tool because it’s already over-serving. 

**Dave:**  That’s my concern.

**Horace**:		With the M1 what we're seeing is more portability, certainly with better power management. But what about this performance?  What we need to ask is what could possibly absorb that extra performance? We see these amusing pundit reviews that say, “Oh, I can not get the system to slow down, or the fans to come up, even though I have a ridiculous number of tabs open in my browser”, or “I do some number of things at once, while on a zoom call plus this and that thing”.

**Dave:**		I think we can all agree that the M1 is overpowered for most users, at least at this point. It’s important to recognize that over-serving customer needs by a firm does not guarantee disruption by new entrants. Often firms overshoot because they’re setting the stage for some advance. Given your point about content creators, Horace, perhaps Apple is using this as an opportunity to discover what  new uses a much faster chip can be put to. In fact, it’s my belief that this is essentially how Apple used the iPhone and the App Store—to discover new Jobs to be Done.

**Horace:**  Exactly. This is where technology people sit around and ponder, well, what about virtual reality or augmented reality? Can we dream up some nice, cool applications? But the market will decide whether this will be a success. And so that's where we have yet to put the pieces together. And if there is a worry about the Mac, it is that it needs to find a job for itself beyond being a superlative creativity tool, which it already is.

**Farshad:**		Look guys, it’s hard to argue that what Apple has achieved with M1 is anything but a technological marvel as a result of a huge strategic bet. Like Horace said, the devil’s in the details. Let’s just look at M1 Mac's first release. There was speculation that Apple would start with just a single high-end M1 Mac first, or that Apple would charge a premium. Supply chain pundits thought there would be major delay. None of these guesses were correct. Apple release lower-end Macs, with performance guard rails, and the price in line with before, and twisted the supply chain to work during a global pandemic.

Apple is clearly committing to a whole new level of computing. Apple is no longer a niche company. They are world’s top technology company. In modern age of computing, making your own chips is as risky as it gets: the designs are getting [ever more complex](https://ourworldindata.org/technological-progress#the-exponential-increase-of-the-number-of-transistors-on-integrated-circuits), the [sizes in nanometers](https://www.hpcwire.com/2020/06/01/10nm-7nm-5nm-should-the-chip-nanometer-metric-be-replaced/), and volumes growing into billions. One bad chip bug can wreak havoc for an integrated brand like Apple, as the old computing stars learned the hard way.  I would compare the all-in, high-stakes wager that Cook has placed on Johny Srouji & company is as audacious bet as Jobs ever placed: on the Mac, the iPod and the iPhone teams.

*But why?* Why is Apple making such a big bet? Is it just that Apple can't stop itself from improving products? Is it further value chain evolution? Is it like the change to Solid State Disks (SSDs), which just moved consumer expectations to faster performance? Is it just an efficiency innovation, slashing Mac’s BOM by $150+ by removing Intel's expensive CPU and improve Apple's already-high margins? Or is there a higher level target beyond the horizon that we can't see?

**Horace:**  Playing devil's advocate, I think the argument that Apple would make is to say, look, we want to make a great experience, and we want to make the best product. And that’s always been a North star, a guiding light. And so here's how they see it. First of all, we're going to be able to deliver a product that doesn't look or have an outward appearance that's different, or represents easily recognizable leaps in functionality. Still, it's going to be just smoother and better in polished in every way. So this is classic Steve Jobs. We'll call it polishing rocks–you put things into a process of polishing, and what comes out is simply brilliant.

But what we are asking ourselves is: can there be a quantum leap, not an incremental one. And the leap would be towards something, and we just don't know what jobs the Mac could be hired for beyond what it is today. And I think Apple's answer, if I were to put it to them would be well, yes. 

They would look at the creation of content that is now democratized—for example, video editing. So YouTube videos by the billion are enabled by the software that runs on a Mac, now with 4K and frame rates that are just incredible. Where does that lead? You can sort of see two, three steps ahead, but you don't know the full journey. Look at the neural engine in the chip. Whether it ends up being some kind of artificial intelligence, part of high-end decision systems, or entertainment systems that are mind-boggling and unforeseeable right now, we just don't know. But where is it leading?I don't know. I struggle with this all the time. I just don't know. I just wish I could see that future.

**Farshad:**		Yes! I have been pondering that too.  What is a job that’ll require more computing than a Desktop PC? One lesson I remember from graduate school that Thinking Machines’ early architectures had these super fast processors but compute-time tended to be bogged down by I/O time. That is, big computations usually require big data to be streamed in, and a PC is simply limited because it’s gated by the network. If I were to have a big computer that needed to have a lot of data from the internet or whatnot, it's much better to place it on the backbone, on Google or AWS cloud, and not in my office. And I doubt if Apple is getting into cloud server business.

I could think of *one* other way to get a lot of data to fill the pipeline of an M*n* processor. And that’s with lots of sensory data. But if this were a handheld product, it would use the A-series chips. So what I ended up with is a *large* mobile device, which needs access to lots of power that helps it drive lots of processing, perhaps AI &amp; ML.

What I ended up with is the autonomous car.

So now, with the M-series, is hitting the high end for one market (computers) that is too low end for another (autonomous drive).  As speculative as this sounds, it does fit the criteria.

**Dave:**	The car totally makes sense to me—in more than one way. First of all, Apple can learn while underwriting the development of a better chip or chipset by putting these M1 chips in computers. Second, and possibly more important, is an issue that comes down to one of the fundamental principles of disruption: the theory of integration and modularity—which Horace has helped to advance. The theory says that because in an early market, firms typically need to improve, their products need an integrated architecture. You can only maximize overall performance by having end-to-end control of a product or service. 

This is similar to what you’re saying Farshad. You’re saying they need more control. But there’s another aspect to this: modular architectures just can’t be “tuned” to maximal performance in the same way an integrated architecture can.

**Farshad:** 		Yes. Also, Horace, when you were talking about the interface changes from Mac to iOS, it struck me that the desktop&#8594;laptop transition required much less adjustment. However, for Apple, designing a car is an opportunity to completely redefine an old interface, as they did with the watch. They may not be able to cut the steering wheel right way, like the Home button, streeing wheel will be a vestiage of the physical world they'll remove later. But it's hard to imagine  Apple designing a car that doesn't have a completely new user experience. I bet they would like some to think of Apple Car to Tesla as early iPhones were to Blackberries.

**Horace:** 	There’s another thing. Remember we talked last time about photography? Apple’s progress on this was not principally driven just by lenses or sensors, but through the application of *computational power*. And that’s certainly going to be necessary for a car.

**Dave:** 		This can explain why the iPad has LIDAR!

**Horace:**		Getting a little speculate here…

**Dave:** 		Fair enough. One last thing from me is that Apple’s corporate structure enables their progress and innovation. There’s a recent Harvard Business Review article on this. Apple is not organized like most other large-scale enterprises. Most product companies  are organized by divisions—usually around a product or group of them—each with its own P&L pushed as far down into the organization as possible. And an article of faith in the modern firm is that accountability and decision rights should be matched. Apple, on the other hand is organized functionally with the principal driving aim of which is matching decision rights with domain expertise. This enables more technical advances, as well as productive exchanges between functional units—since those interactions take place among Apple’s top experts in each function.  

**Horace:** 		Yes. Now we’re wandering toward another important area where Apple is unique or unusual. Why don’t we punt this to a future discussion?

**Farshad:** 		Let's do that. Thank you, Horace &amp; Dave, for a great discussion! Now, I am <em>really</em> looking forward to the next part. We've saved the best for the last: a study of how Apple continues to defy the forces of disruption year after year, and product after product.

--- 
END
--- 

<div style='border: 1px solid #DA3B26; min-height: 5em; margin: 50px;'>
<h4 class='title' style='text-align: center; padding: 30px;'>
<center><a href='https://twitter.com/intent/tweet?text=The+real+disruptive+force+of+the+iPhone+was+in+Apple+enabling+a+new+form+of+software+development.+via+@dxMBA&url=https://www.dx.mba/blog/:is-apple-disruptive-long-lens-apple-and-disruption'> ❝The real disruptive force of the iPhone was in Apple enabling a new form of software development.❞</a></center>
</h4>
</div>


<div style='border: 1px dashed #DA3B26; padding: 30px; margin: 50px;'>
<h4 class='title' style='text-align: center'>
<center>Should you apply a disruptive or sustaining strategy? </center>
<center>Meet up with the authors for a <a href='https://dxmba.squarespace.com/engage/unstuck-assist-trial'>free consult</a>.
</div>

<hr/>
<em>
<strong>Do you have a <span class='highlight'>Disruptive Strategy</span> for your business? </strong>

Are you confident about it?
What about your competition?<br/>
<strong><em>
Sign up for a <a href='https://dxmba.squarespace.com/engage/unstuck-assist-trial'>free consultation</a>  to learn, <em>with aboslutely no strings attached.</em></strong>
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A Long-Lens Look at Apple and Disruption. Part 2: Is Apple Overserving with M1 Macs?

What does the *Science* in Product Science signify? It means we will apply <lens:scientific-process> to products. But what does it really mean? 

<div style="position: relative; padding-bottom: 75%; height: 0;"><iframe src="https://www.loom.com/embed/4c3c869b3bee4e59b60db0734335be8a" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="position: absolute; top: 0; left: 0; width: 90%; height: 90%;"></iframe></div>

What are the workings of what you could also call the scientific method? And I want to be clear here. I'm sure you've heard some stuff about this in school.

You might think of some examples. Typically when we ask people, we don't get real clarity. So a long time ago, more than 20 years ago, clay Christianson, I click, let me see more than 20 years ago, clay Christianson.

And I realized this problem. And so we use some other people's research, our own observations to develop this model of the scientific method.

And it starts with this pyramid that you see here. And it also starts with the foundation, which is the phenomena.

What are the phenomenon? They're just the things that you see, hear, taste, taste, touch, smell, that kind of thing. And imagine now that you've been dropped into a completely new environment, everything is unfamiliar to you.

At first, all you can do is just start to catalog what you see. You see different kinds of organisms. This thing's green, that thing runs whatever.

There are a whole bunch of things you can do. Eventually you notice a pattern of similarities that you see a lot of things that have feathers and two legs, and turns out those things with feathers and two legs also always have a beak.

So now you have a category based on the similarities of certain kinds of organisms. We call them birds, right? That's a categorization.

Now you have a category. Birds will put it in square scare quotes, even though you can't see me. Now, one of the things that happens is you observe that of all these bird type things, all these things that fall into this category, they all are capable of flight.

So now I have a theory. Now you're going to say theories tend to be either super scary, like they're big and hairy and messy, or they're scare scary in the sense of scare quotes, right?

It's theoretical, right? It means non-practical not useful. Well, in our case, we mean something very simple and very practical. A theory is just a statement about what causes, what hopefully, why?

So now that I've observed these things with beaks feathers in two legs, I predict that I will see flight among them.

Every time I'm observing a new group of them or one that I haven't seen before. And if I just keep reinforcing my theory and my predictions come out to be true, I corroborate with more and more, um, force my hypothesis, my theory, my predict test mode, hang on.

I'm gonna go back. So as I do more and more predicting and testing, I shore up that theory, right? But that's not progress necessarily.

What really makes progress for us is if we go to the south pole, what are we encountered there? We encounter these things that have beaks feathers and two legs, two feet, and there's no way they could fly on their own power.

We have an anomaly. What this means then is we have to go back to our categories and figure out, should this count into our category, our prodo bird category, or should it fit somewhere else?

That's where progress in science happens. I'm going to give you an example of a case like this, which changed the course of scientific history.

So I'm going to pause there so we can, The way that people in medieval and early modern times saw the movement of heavenly bodies.

And we're just going to stick to Mars here, just to simplify. Everything was the way Aristotle said they should be, which is all heavenly bodies should move in circular orbits.

Now it turns out that does not fit with the observations. It's not on track. It's off that perfectly circular orbit path.

So what we did, what our predecessors did was actually create what are known as epicycles they're perfect circles on perfect circles.

And it kind of went deep and it helped to explain the phenomena. The problem was, it actually became incredibly complicated now to make matters worse while this is super complicated along comes Tyco, brah, Hey, who takes over at a very remote observatory?

And he's a stickler I put here. He's the kind of person that was very happy to retreat from public life, not be around other people.

And they were also glad not to have brah around them. But the thing about it was with new lens technology in telescopes, he was able to make much more precise observations than had ever been made.

So being a stickler meant that he was incredibly meticulous. His successor was a guy named Johannes Kepler, and he saw that there was this gap between what was predicted and what was observed and what could be accounted for in that error.

Some of it might be of course, the instrumentation, the, the, the defects in the S in the, uh, let me just say that again, some of this could be down to the instruments, right?

It's not like these new lenses were perfect. So there was some margin of error. It turns out that margin of error was just too big to be accounted for by that.

And so the problem there is now we have the epicycle thing, but we have an anomaly. Now it might seem like a bad thing, but remember, this is how progress happens.

And it turns out Kepler really spent his life trying to persuade people that we could not. We could no longer based on gathered evidence and the model of epicycles latch on, or hold on to this view of the moving of Mars and other heavenly bodies, as a consequence, he promoted the new theory of elliptical orbits, which was correct, uh, but required new math, like, uh, calculus, all, there was a whole bunch of new stuff required to do this, but without that gap, right, you can sort of see in the background that little circle without that perfect circle, there was a new, I don't like this very much so let's and add, it was very complicated and I'm sure.


Intro to Scientific Process

<img src='https://s3.amazonaws.com/strapi.dx.mba/7d10e104a301469bb63b485013084981.png' align='right' width='25%'/>A chance multitasking snafu reveals a new job-to-be-done for AI with broad impact in our everyday experience. 

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On a recent trip to NYC, I encountered a frustrating yet enlightening multitasking incident. I was using the Maps app to get directions to my next appointment—a straight, leiserly 30-minute walk to midtown—when I received a call from my mother. Twenty minutes into my call, I glanced at my phone to notice I was running late! It turned out that unbeknownst to me, the Maps app had defaulted to 'driving' mode, leading to an unnecessary detour around a long block.

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<p class='inset'> Driving vs. Walking: the usually hard distinctions between different modes of transportation in Maps app can get blured in a city.</p>

The technology world today is full of grand visions of AI as an all-encompassing mastermind, capable of making complex, high-level decisions that can potentially supplant humanity in the future. But what can AI do for the regular humans today? This incident, although trivial, does bring to surface a decent job for AI, with potential to impact everyday user experiences. My Maps mishap hints at where AI could begin: as a humble, silent assistant who handles simple, thankless tasks that can easily be undone, such as picking the default mode of transportation based on user's specific context, location, and geography.

The true promise of AI today is not in constructing fantastic, all-encompassing, ideal visions of the future but in ever-so-slightly augmenting our current, mundane, real lives. This approach positions AI as a supportive tool, an assistant that does not replace but enhances our experiences by freeing us from the trivial and tiresome. What we need today is an AI that is eager to start at the entry level of human cognition, not one carrying out a hostile takeover at the executive level of humanity.

An AI-integrated smartphone is in a unique position to serve billions of users in trillions of mundane situations. In this, we can find a great application for AI in the background, utilizing those idling supercomputers we carry in our pockets, and, on the occasions when AI makes the wrong initial choice, great learning opportunities.


A First Job to be Done for AI

[Charles Duhigg](https://www.charlesduhigg.com/) faced a dilemma. <img src='https://s3.amazonaws.com/strapi.dx.mba/cf168e981316404f9de4215ef46824e8.png' width='15%' align='right'/> By all public accounts, he was considered a great communicator: a New York Times columnist, a bestselling book author, and a Harvard MBA. Yet, he found his daily interactions with his family and at work to be trying and at times unsuccessful. Confounded, he surveyed the field of research for the best communication practices today. His latest book, [**Supercommunicators**](https://www.goodreads.com/book/show/157981748-supercommunicators) catalogs his findings. 

### Multiple Conversations at Once
Central to the book is what Duhigg calls the *Matching Principle*: <img src='https://s3.amazonaws.com/strapi.dx.mba/7471ac1b073843c380077883ae4816f8.png' align='right' width='33%'/>

> *Whenever we speak, we're actually participating in one of three conversations: practical (What's this really about?), emotional (How do we feel?), and social (Who are we?). If you don't know what kind of conversation you're having, you're unlikely to connect.*

Stated more formally: a participant in communication may be using one of three **mindsets** at any time: *practical*, *emotional*, or *social*. For optimal communication, it's best for all participants to be using the same mindset. Two participants are *matching* when they are using the same mindset.

Take for example, and exchange in a stereotypical scenario: a couple’s driving spat.
```
Passenger: 
Slow down! You’re giving me whiplash!

Driver:
Yeah, I know, the seatblet sticks. 
If you tug on it, it'll tighten and hold you closer to the seat.
```

Here we can infer the *Passenger* is in an *emotional* mindset: expressing anxiety about the way the *Driver* is driving, likely expecting empathy from the *Driver*. Instead of addressing the emotional mindset of the *Passenger*, the *Driver* offers a solution—in a *practical* mindset. We can guess how the rest of this story may go…

### The [Hotspot](/lenses/hotspots) Lens
<img src='https://s3.amazonaws.com/strapi.dx.mba/0545f8c25b034cb3a3a4b3cb1e5cb75b.png' width='33%' align='right'/>

Duhigg's categorization of conversations is almost identical to that of the [**Hotspots Lens**](/lenses/hotspots): *functional* (also referred to as *practical*), *emotional*, and *social* are the three types of hotspots we look for in [Jobs to be Done](/lenses/jobs-to-be-done) analysis. The Hotspots lens is not just a subsidiary lens; it is the go-to lens for starting a Jobs to Be Done analysis. 

Beyond similarities in categorizations, we can see how the prescribed matching technique is used by [skilled interviewers](/contributors/bob-moesta) in real-time during an interview. Hotspots turn out to be productive anchors because they are usually easy to detect even when the interviewer doesn't grasp the domain or the context. All it takes is to sense the energy in the subject’s voice. Just listen for exclamation points! However, even when a hotspot is captured, the interviewer may not be able to guess the type of hotspot. A qualifying question helps the interviewer correctly identify the type of hotspot.

Imagine, for example, if the *Passenger* was the subject in an interview: 

```
Subject:  (Passenger in the original example)
I said: "Slow down! You’re giving me whiplash!"

Interviewer: 
Wow. Were you really worried you'd get a whiplash? Or…[pause]

Subject: 
Gosh no! I was just scared of how fast we were going.
```

Of course, this makes sense because the Jobs to be Done Interview *itself* is a form of interactive conversation, and as such, subject to the same Matching Principle. By matching, the interviewer helps the subject open up more and be more honest, thus improving the capture of their story. Skilled interviewers learn to match in the same frame as the subject used in describing their hotspot, for example:

**Functional** → *It’s amazing how seatbelt technology hasn’t improved in years.*

**Emotional** → *I feel you—I am always a bit anxious when in the passenger seat.*

**Social** → *I might've expected a kid to whir their wheels, but a grown adult?*

### Mindsets ≡ Hotspots?

The fact that two distinct, independently researched domains of study have resulted in identical categorizations is significant and worthy of further study as we develop [lenses](/lenses) further. For one, it would be nice to understand if there is an underlying causality for this correlation. Another: if these three frames are so central, should we integrate frames more closely to [Job Definitions](/lenses/job-definition)?


#### Related

→ [**Hotspots** lens](/lenses/hotspots)<br/>
→ [**Job Definition** lens](/lenses/job-definition)<br/>
→ [Charles Duhigg](https://www.charlesduhigg.com/), [**Supercommunicators**](https://www.goodreads.com/book/show/157981748-supercommunicators)<br/>


Supercommunicators and Hotspots

Knowledge is information with causal power. Below is a diagram of how knowledge emerges—you’ll likely recognize the pattern (depends on diagram). Simply put data—both qualitative and quantitative—are unstructured. But when we take a perspective, a point of view, we turn data into information. When that information enables us to cause things to happen, it is knowledge. (Do I need a brief example/elaboration?)

The knowledge hill is useful to understand whether we’ve gained the minimum effective knowledge to bring about a desired outcome. For example, if someone asks if we’ve shipped a product, and we respond with “no, but we learned a lot,” we have not reached the critical point in our knowledge. The real world consistently requires us to reach these minima.

The knowledge hill characterizes the process of first learning and then doing. On the left side, we ascend the learning curve, on the right we execute. Our uphill/downhill image is intentional. In general, learning takes more time and effort than executing on what we know. 

Knowledge is information with causal power. Below is a diagram of how knowledge emerges—you’ll likely recognize the pattern (depends on diagram). Simply put data—both qualitative and quantitative—are unstructured. But when we take a perspective, a point of view, we turn data into information. When that information enables us to cause things to happen, it is knowledge. (Do I need a brief example/elaboration?)

The knowledge hill is useful to understand whether we’ve gained the minimum effective knowledge to bring about a desired outcome. For example, if someone asks if we’ve shipped a product, and we respond with “no, but we learned a lot,” we have not reached the critical point in our knowledge. The real world consistently requires us to reach these minima. 

Knowledge Hill.pdf

Knowledge Hill - thumb.jpeg

The knowledge hill characterizes the process of first learning and then doing. On the left side, we ascend the learning curve, on the right we execute. Our uphill/downhill image is intentional. In general, learning takes more time and effort than executing on what we know. 

Knowledge Hill

Most often we have learned about some of the components of the scientific method—hypotheses, testing, etc. Developed through thorough study of the history of science and decades of active application in research, we here present an operating model of the scientific method. 

Put another way, the scientific method is a process for developing and improving our understanding of our world. Harnessed to the overarching goals of product science, it tell us what to do when you don’t know what to do.

The scientific method is the foundation for product science, just as it is for any scientific discipline. It is the method that has led to the progress of the modern world. Understanding what it is and how it works enables one to create knowledge from observations, while keeping a rigorous skepticism about what is observed.

Most often we have learned about some of the components of the scientific method—hypotheses, testing, etc. Developed through thorough study of the history of science and decades of active application in research, we here present an operating model of the scientific method. 

Put another way, the scientific method is a process for developing and improving our understanding of our world. Harnessed to the overarching goals of product science, it tell us what to do when you don’t know what to do.

Scientific Process.pdf

Scientific Process - thumb.jpeg

The scientific method is the foundation for product science, just as it is for any scientific discipline. It is the method that has led to the progress of the modern world. Understanding what it is and how it works enables one to create knowledge from observations, while keeping a rigorous skepticism about what is observed.

9aa01c1408fe4a2497bbab5f63ce5c0c.jpeg

Scientific Process

At the base we have  ***data***. This corresponds to the "phenomena" portion of the model of the Scientific Method. It is raw and unstructured. 

The next level of the hierarchy is ***information***. Information is data that is structured—data that represent some point of view. Formally we call this *categorized*. (See <lens:categorization>.)

The top of the hierarchy is ***knowledge***. Knowledge, as <contributor:david-deutsch> defines it, is *information with causal power*. Knowledge includes explanations about how the world works—a special kind of information that can be used to explain what causes things to happen. 

This hierarchy provides a way to understand where we are in our understanding of the world around us. It makes valid the distinction between data, information, and knowledge expressed in quips such as "that is definitely good *information*, but is it *knowledege*?" 

In our quest to understand the world around us, the knowledge hierarchy is key because it helps us characterize our own progress in understanding the world. In every context, in our quest to cause progress around us, we must find out means to elevate from raw data to categorized information and to causal knowledge.

The *Knowledge Hierarchy* describes the underlying structure of the Scientific Method as means of creating knolwedge. This lens helps us distinguish three terms that are used interchangably in common language: *data*, *information* and *knowledge*. We treat these terms as distinct. Here we provide their relationship.

At the base we have  ***data***. This corresponds to the "phenomena" portion of the model of the Scientific Method. It is raw and unstructured. 

The next level of the hierarchy is ***information***. Information is data that is structured—data that represent some point of view. Formally we call this *categorized*. (See <lens:categorization>.)

The top of the hierarchy is ***knowledge***. Knowledge, as <contributor:david-deutsch> defines it, is *information with causal power*. Knowledge includes explanations about how the world works—a special kind of information that can be used to explain what causes things to happen. 

This hierarchy provides a way to understand where we are in our understanding of the world around us. It makes valid the distinction between data, information, and knowledge expressed in quips such as "that is definitely good *information*, but is it *knowledege*?" 

In our quest to understand the world around us, the knowledge hierarchy is key because it helps us characterize our own progress in understanding the world. In every context, in our quest to cause progress around us, we must find out means to elevate from raw data to categorized information and to causal knowledge.

Knowledge Hierarchy.pdf

Knowledge Hierarchy - thumb.jpeg

The *Knowledge Hierarchy* describes the underlying structure of the Scientific Method as means of creating knolwedge. This lens helps us distinguish three terms that are used interchangably in common language: *data*, *information* and *knowledge*. We treat these terms as distinct. Here we provide their relationship.

Knowledge Hierarchy

Of course, that’s not all that customers will invest. But experience across many product and service categories shows that if customers can make some progress in that first six seconds, they will allocate more time and energy to making use of the product or feature. 

This is a corollary of the maxim: no one ever got rich by “educating” their customers. If our customers need much education to use our products/services, that’s a problem for us, not them.

The First Six Seconds is a lens that helps us to structure our thinking about an essential part of user experience (UX). Quite simply, this rule of thumb is that for every six months of our work on a feature of a product/service, we can expect new users to give it six seconds of effort. Naturally, this leads to parsimony in feature rollouts and lower friction between user and product.

Of course, that’s not all that customers will invest. But experience across many product and service categories shows that if customers can make some progress in that first six seconds, they will allocate more time and energy to making use of the product or feature. 

This is a corollary of the maxim: no one ever got rich by “educating” their customers. If our customers need much education to use our products/services, that’s a problem for us, not them. 

First Six Seconds.pdf

First Six Seconds - thumb.jpeg

The First Six Seconds is a lens that helps us to structure our thinking about an essential part of user experience (UX). Quite simply, this rule of thumb is that for every six months of our work on a feature of a product/service, we can expect new users to give it six seconds of effort. Naturally, this leads to parsimony in feature rollouts and lower friction between user and product.

First Six Seconds

Deliberate strategies are those we’re most familiar with and are aimed at executing what we understand. 

Emergent strategies, on the other hand, are planned, coordinated actions aimed at learning. For example, if we want to create a new product/service or expand the reach of one already in a market, we can design “experiments” to learn what we don’t yet know. 

Sustainable growth requires that we carefully distinguish deliberate and emergent strategies. We have observed the tendency to assimilate all planning to deliberate mode. For instance, I recently interviewed someone working in a long-established firm. They had decided that they needed “an incubator for new ideas.” But I discovered that even those in their “learning group” were designing all of their work as if they already understood the interplay of market, product and people. 

Both kinds of strategy must exist in various places and stages in a long-term successful firm. By distinguishing whether we are planning to execute or planning to learn—and feeding what we learn into a deliberate process—firms can more easily capture growth opportunities, while maintaining health in the core.

Traditional product strategies tend to drive toward deliberate execution. We present another essential kind of strategy: Emergent.

Deliberate strategies are those we’re most familiar with and are aimed at executing what we understand. 

Emergent strategies, on the other hand, are planned, coordinated actions aimed at learning. For example, if we want to create a new product/service or expand the reach of one already in a market, we can design “experiments” to learn what we don’t yet know. 

Sustainable growth requires that we carefully distinguish deliberate and emergent strategies. We have observed the tendency to assimilate all planning to deliberate mode. For instance, I recently interviewed someone working in a long-established firm. They had decided that they needed “an incubator for new ideas.” But I discovered that even those in their “learning group” were designing all of their work as if they already understood the interplay of market, product and people. 

Both kinds of strategy must exist in various places and stages in a long-term successful firm. By distinguishing whether we are planning to execute or planning to learn—and feeding what we learn into a deliberate process—firms can more easily capture growth opportunities, while maintaining health in the core.

Emergent vs. Deliberate Strategy.pdf

Emergent vs. Deliberate Strategy - thumb.jpeg

Traditional product strategies tend to drive toward deliberate execution. We present another essential kind of strategy: Emergent.

Emergent vs. Deliberate Strategy

To know which tool to use—incentives? standard operating procedures? consensus? negotiation?—we begin by framing what’s at issue and assessing the pre-existing degree of agreement on that specific matter. 

For instance, imagine that a software product manager wants to meet a rapidly approaching deadline to ship a product, but the developers see a privacy issue and are pushing back. We frame the issue: the product should be shipped on time. We assess, using a couple of dimensions of pre-existing agreement, to what extent developers and management agree on shipping on time in this particular case. Based on that assessment—and some other simple tips and tricks—the manager can choose the right tool to create a cooperative effort to resolve the issue. 

The Tools of Cooperation have many applications, including how to resolve what seem like intractable disagreements or getting customers to “pull the trigger” and purchase. Crucially, by understanding this framework you can select the right tool for the right job. 

**Example:**
[Tools of Thanksgiving Cooperation](https://www.dx.mba/examples/:thanksgiving-tools-of-cooperation-lens-example)

The Tools of Cooperation provide a way to understand how to get people to voluntarily coordinate their actions in accomplishing desired outcomes. We have seen that some leaders have an intuitive grasp of this, but recognize that choosing the right tool for the right situation can be learned quite easily. 

To know which tool to use—incentives? standard operating procedures? consensus? negotiation?—we begin by framing what’s at issue and assessing the pre-existing degree of agreement on that specific matter. 

For instance, imagine that a software product manager wants to meet a rapidly approaching deadline to ship a product, but the developers see a privacy issue and are pushing back. We frame the issue: the product should be shipped on time. We assess, using a couple of dimensions of pre-existing agreement, to what extent developers and management agree on shipping on time in this particular case. Based on that assessment—and some other simple tips and tricks—the manager can choose the right tool to create a cooperative effort to resolve the issue. 

The Tools of Cooperation have many applications, including how to resolve what seem like intractable disagreements or getting customers to “pull the trigger” and purchase. Crucially, by understanding this framework you can select the right tool for the right job. 

**Example:**
[Tools of Thanksgiving Cooperation](https://www.dx.mba/examples/:thanksgiving-tools-of-cooperation-lens-example)

Tools of Cooperation.pdf

Tools of Cooperation - thumb.jpeg

The Tools of Cooperation provide a way to understand how to get people to voluntarily coordinate their actions in accomplishing desired outcomes. We have seen that some leaders have an intuitive grasp of this, but recognize that choosing the right tool for the right situation can be learned quite easily. 

Tools of Cooperation

Ethos is character or, in a sense, credibility. This is the answer to, “why should I listen to this person or this message?” Sometimes we have credibility immediately—based on our connection to others our our reputation—but we can also build credibility in various ways. In the data-heavy world we live in, for instance, a grasp of data and its import generally builds ethos.

Logos is the logic or narrative that makes sense of facts and the world around us. This is the answer to, “what’s the message?” To engage people, listeners must be able to trace the reasoning, follow the story that the speaker tells. 

Pathos is the emotional resonance for the audience. This is the answer to, “how does it connect to me?” It’s not enough to have dry facts or even facts that have clear logic. To engage people we must connect (modestly) emotionally—listeners should be able to see themselves in the message. 

These interlocking principles have stood the test of time. To engage, to persuade we must balance these three elements.

The principles of engagement are as old as Western civilization. Aristotle broke persuasion into three elements: ethos, logos, pathos. As with hotspots, all three work together to engage people and inspire them to follow.

Ethos is character or, in a sense, credibility. This is the answer to, “why should I listen to this person or this message?” Sometimes we have credibility immediately—based on our connection to others our our reputation—but we can also build credibility in various ways. In the data-heavy world we live in, for instance, a grasp of data and its import generally builds ethos.

Logos is the logic or narrative that makes sense of facts and the world around us. This is the answer to, “what’s the message?” To engage people, listeners must be able to trace the reasoning, follow the story that the speaker tells. 

Pathos is the emotional resonance for the audience. This is the answer to, “how does it connect to me?” It’s not enough to have dry facts or even facts that have clear logic. To engage people we must connect (modestly) emotionally—listeners should be able to see themselves in the message. 

These interlocking principles have stood the test of time. To engage, to persuade we must balance these three elements.


Engagement.pdf

Engagement - thumb.jpeg

The principles of engagement are as old as Western civilization. Aristotle broke persuasion into three elements: ethos, logos, pathos. As with hotspots, all three work together to engage people and inspire them to follow.

Farshad Nayeri