Welcome back to nerd corner. Today a note about Healthcare Capacity.

Yesterday a friend asked a really smart question. She’d seen the data on the 2016-7 flu season. That year there were 80,000 deaths and 900,000 hospitalizations. The question is, why are we in such a state now, but weren’t so during that flu season?

[For background, the seasonal flu vaccine is a very informed bet by WHO, CDC, and others that the strains used to make the vaccine will be similar enough to provide those of us who get the vaccine will have enough of the right antibodies to fight off the flu. (https://www.cdc.gov/flu/prevent/vaccine-selection.htm) Some years’ predictions about what will be circulating are better than others. And sometimes the viruses in circulation change (antigenic drift) during the same flu season. Both of these were at work in 2016-7.]

Most of us know that nCoV-19 is different from the flu.
-It spreads more easily
-Symptoms, even in those with good health and who will recover, can be severe
-It appears to have a higher case fatality ratio
-It lingers, even for people who are asymptomatic
-No one has immunity to it
-The flu has a long-established surveillance program that includes adequate testing &c.
-We know who is most at risk for complications of the flu; even though we know that older people and people w certain conditions have a higher mortality risk; we’re still learning who is most likely to be hospitalized w nCoV-19

One of the most important things we’re trying to do is manage the capacity of our hospitals. The US has 2.9 beds per thousand people—as do Lebanon and Albania, ahem. Because of a lapse in regulation (a story for another day), rural hospital closures have surged over the last few years. 

Our capacity can be overwhelmed because—even among those that will recover—coronavirus patients are more acutely sick and therefore need more ICU beds and a higher standard of care. This puts a strain on things. 

But there’s another complication. In order to make sense of this, we need to look at what hospitals generally refer to as “average length of stay” (LOS)—how long does a person admitted to the hospital stay? Standards for both admission and discharge vary across the globe and even across the US. I’ve been trudging through reports and it appears that LOS for nCoV-19 is somewhere between 2.5 and 3 times the seasonal flu from 2016-7 (LOS_flu = 5.3; LOS_nCov-19 = 12 - 16). 

This means that over the same period of time and with the same number of hospitalizations, coronavirus patients need between 2.5 and 3 times as many beds. It’d be like if the 2016-7 flu season had 2.25 to 2.7 million hospitalizations. New York is more or less a live look at the problem of peak capacity requirements. A few days ago they estimated they’d need 110,000 hospital beds. The estimate is now 140,000 beds—7.3 beds per thousand people, two and a half times the national average.

Dave Sundahl
*This is not *

Beds(Coronavirus) = Beds(Flu) x 3

I'd tweeted [this article from The Economist](http://www.economist.com/news/science-and-technology/21699098-makeshift-weapons-are-becoming-more-dangerous-highly-sophisticated) when it was first published. It shows disruption perfectly—both in the *upmarket* trajectory of weapons (see <lens:disruptive-market-entry>) and in the asymmetry of motivations—we’re trying to stop killing and protect civilians, they don’t distinguish between combatants and non-combatants, and they’re going to keep it going until they get their way.  

![home made smart munition](https://s3.amazonaws.com/strapi.dx.mba/8a539b794f824b8e993552ce0bd98205.png)

This is a photo of smart munitions built from what looks to be a two-wheeled trailer for agriculture, a propane tank, and some other spare parts. Early on, the rebels were delivering rudimentary and smaller yield indirect fire—basically molotov cocktails fired by slingshots. In fact, at that point direct fire from individually armed rebels were the most common and effective choice. But then they moved their artillery capabilities “upmarket” to improved yields, delivered from a greater with more precision. 

Finally, this very mobile unit uses Google Maps and GPS data to target not only a specific location, but the point at which—say dropping down through several floors in a building—to detonate for maximum damage from as far as a mile away. Finding these mobile units after they’ve struck, but before they’re good and hidden, just won’t work. And the future is even scarier—with the increasing simplicity of creating biological agents, metal-free mines, etc.  

This is classic disruption: off-the-shelf technologies configured in a way that gets a job done as well as (and sometimes better than) expensively developed systems that do everything and nothing (think: F-35 Joint Strike Fighter).  

[![](https://s3.amazonaws.com/strapi.dx.mba/afc306dc07f9433c995c2cebfce0f3b9.png)](/lenses/disruptive-market-entry)
<lens:disruptive-market-entry> as applied to a market of weapons </a>

To complicate matters, often our defense and intelligence agencies are “fighting the last war.” That is, we are learning the lessons that would have served us well in our last conflict. Remember, disruption is a theory of competitive response. Our tendency to look back is good, but real disruption happens only when a firm creates a new business model—yes, I’m using business model in the context of warfare, but as a technical term, with a rigorous definition—by changing its process, priorities, and value proposition.  

In my next entry on this issue, I will show how the US is attempting to address innovation by our enemies, as well as how the technique can be profitably used in a business context. 

*This article was originally posted in the Christensen Institute Blog*

*Lenses uses in this example: <lens:disruptive-market-entry>*



Disruption in a New Age of Warfare

When I do a search for the term “big data,” I get 8.47 *billion* results in just over a half second. On the first page of my search results there is a listing of the most recent articles on big data as well as big data services offerings. But followers of Disruptive Innovation and related theories know that we take a theory-first approach to understanding the world.

First, by “theory” we mean something devilishly simple: a statement of what causes what and why (see <lens:scientific-process>). But what we’ve come to understand is that rather than pursue causality, there are huge investments in artificial intelligence, machine learning and data science. 

Second, while most people know the dictum that *correlation is not causation*, they speak and behave in a way that’s inconsistent with this. In fact, the most ardent promoters of big data claim that as we master data, [we won’t need the scientific method or theory building](https://www.wired.com/2008/06/pb-theory/). 

This is nonsense. And I’d like to use an example from one of my former teachers, K. Codell Carter, a historian and philosopher of medicine. During the late 19th century research changed from being empirically driven to being causally driven—in our terms theory driven. One example from the book Carter cites about the different trajectories that causal and non-causal thinking in curing disease is a comparison of scurvy and beri beri—both diseases caused by a deficiency of a single nutrient.

Scurvy, a severe vitamin C deficiency, was described by the famed father of medicine Hippocrates (460 BC - 370 BC), but not understood causally until 1927. Throughout the two millennia that preceded a true understanding of the cause of scurvy many remedies were prescribed, almost all of them close to the true cause of scurvy. And by the 1830s something like clinical guidelines were developed for the prevention and treatment of the disorder. Fresh meat and citrus generally contain enough vitamin C to cure scurvy. However, lacking a deep understanding of the mechanism for scurvy led to confusion among physicians and researchers. For example, the storage and transport of citrus juices in copper or their exposure to air and sunlight degraded vitamin C content to sub-therapeutic levels, often meaning that their interventions failed. Researchers got what we might refer to as “noisy” data from their clinical trials with citrus juices and meat. Sometimes lime juice cured scurvy and sometimes not. To make matters worse, the advent of faster steam ships and a proliferation of ports got sailors from port to port faster, where they could consume good sources of vitamin C. So although the on ship sources of vitamin C had been attenuated to the point of being ineffective, sailors reached port sooner, where they got better nutrition and so felt few, if any effects, of scurvy. Without a drive to understand the causal mechanism for scurvy, these guidelines did not evolve much. Researchers were satisfied with knowing that (most of the time) lime juice cured scurvy. And so medical discoveries and powers stalled.

Beriberi, an insufficiency of vitamin B1, had a different trajectory. Although known in the 17th century, the serious study of beriberi did not arise until the 1880s. At this point, the causal approach to understanding and curing disease had risen to some prominence. Because of their causal mindset and theory-driven approach, in fact, by 1884, a Japanese researcher had determined that beriberi was only found among sailors whose diet consisted almost entirely of polished rice. One can imagine empirical researchers being satisfied with knowing this and working to ensure that sailors had a more diverse diet, as they had been satisfied knowing that usually meat or citrus cured scurvy. However, where scurvy researchers stopped due to pretty good empirical work, the drive toward causal mechanisms kept scientific progress on beriberi moving forward. In the 1880s researchers had done controlled animal studies, and shortly thereafter settled definitively that a dietary deficiency from some element of unpolished rice caused beriberi. And by 1913 researchers knew with certainty the very extract of rice bran whose lack was the cause beriberi. 

Big data is just more sophisticated empiricism, and sometimes that’s just fine. If tests on our data show a tight fit to a regression line—if they have a high r-squared—we might be able to make future predictions that are sufficient to solve our problems. However, while literally billions of dollars are being poured into big data’s statistical (i.e., non-causal) methods, it’s worth remembering that sound application of the scientific method is both more powerful in its implications for all sorts of ills, and is also vastly cheaper and faster. At the Christensen Institute, we have found the power of theory for many issues in management research and application. And we want to invite skeptics and boosters alike to approach their work by developing sound causal theories.

Why bother with theory when you've got big data?

I’ve worked with Clayton Christensen, co-founder of the Christensen Institute and architect of Disruptive Innovation, for many years and discussed everything from business strategy to the elliptical orbits of planets around the sun. Over the last few years one idea has come up time and time again: data are not discovered but created. And I see this struggle in how some people feel about Microsoft Excel.

The computing program is an excellent tool and has undeniable utility, though a cursory Google search reveals hundreds of memes that express people’s frustrations with some of absurdities of the tool. More important than this, though, Excel embodies two serious problems about data. To Christensen’s point, the measurements and decisions we make determine what goes into our spreadsheets, before any calculations are even made (see <lens:knowledge-hierarchy>). The second problem is that the rows and columns of figures in an Excel spreadsheet sometimes obscure causality.

*We create data*

Every executive who has sat in a board room has witnessed how data can cause rather than solve problems. Typically, when a firm faces an important decision or a serious threat, different departments come armed with their data in support of their preferred course of action. They can do this because they’ve created data in support a of decision they’ve already made. They’re not simply acting cynically; each group truly believes that they know the right answer. They build their data to help them show others the light. But as the renowned economist Ronald Coase said in 1981, “If you torture the data enough, nature will always confess.”

In academic research, scholars typically try to bypass the problem of their own bias by using “sharper knives”—essentially more sophisticated tools than Excel. But a sharper knife doesn’t eliminate the fundamental problem faced by researchers. Day in and day out, researchers cannot avoid the struggle of turning complex real-world events into numbers that they hope will enable them to find the truth.

As big data has become more prevalent, decisions about how and what to measure often wreak havoc. As an example, data science struggles with what’s called “the data fusion problem.” Researchers would love to have at their disposal a big, neat and tidy data set formed from a group of related studies. Instead, they’re confronted with the often impossible task of creating coherence among heterogeneous sources of data, each of which has been measured, collected, or recorded differently. Try as they might, they can’t escape the reality that data doesn’t exist on its own, waiting to be discovered.

*Causality is hidden*

The essence of causation is regular change from one state to another. But as the spreadsheet from a recent blog of mine about baseball shows, causality isn’t always obvious. In Excel some data are entered manually (or imported/pasted in) and some are generated by formulas. The “hidden” formulae that turn one column into another are actually causal mechanisms—they predictably turn one thing into another.

The problem is not just that people unfamiliar with the software don’t know how to find the formulae, but that there are myriad events that underlie the data. In my baseball example, there is information about payroll and winning percentages in Major League Baseball. Yet outside of this data set, there are literally thousands of small things that influence both of those things: aggressiveness of an agent, injury, a player’s desire to stay with his team or go, how good a team’s pitching is, whether batters will swing at pitches they can’t hit, the league’s minimum salary, the peculiarities of the field each team plays on, regression to the mean, etc.

Put another way, while Excel and other tools may enable us to see the formulae—the human-made causal mechanisms in the data—it still cannot reveal to us all relevant causal factors like field layout and player motivation.

The world is not made of numbers, or as Christensen likes to say, “data don’t descend on us as a gift from heaven.” For this reason, we’ll never be free from the need to make consequential decisions about what to measure and what to record. What’s more, even if we have very good tools and training, causes are in most cases literally hidden. That’s why we need to be humble as we create our own or use others’ data. Our decisions about data can easily lead us to confusion rather than clarity about what we really seek: a model or explanation of the causal relationship that binds cause to effect.

Reference: (https://www.christenseninstitute.org/blog/dont-yankees-dominate-major-league-baseball-every-year/)

Our Love-Hate Relationship with Excel

Last year, more than one billion “energy shots” were sold in the US—almost all of them were part of the 5-hour Energy line. The closest competitor sold just over 17 million units, or a little less than 2% of the market share--the other 18 competitors fare much worse.

In many ways, 5-hour energy has no business being so successful. The packaging is gaudy, it claims no celebrity endorsements, and it tastes like the cough syrup I took as a kid. It clearly was not developed through needs analysis or other traditional market techniques. 

The genius of 5-hour energy is that it captured a "job to be done" According to “jobs” theory, people do not buy products or services—they hire them to do a job. The classic example is that no one ever really set out to buy a one-quarter inch drill bit, they set out to drill a quarter-inch hole.  (See <lens:jobs-to-be-done>.)

![forces of progresst](https://s3.amazonaws.com/strapi.dx.mba/6cd96ece78c34e34afa226281dcec649.png)

 At a deeper level, what this means for innovators and marketers is that they must replace a customer-need and product-benefit mindset with a “circumstance of struggle” mindset. What do I mean by the circumstance of struggle? I’ve hit the early afternoon slump, but I’ve still got lots of work to do. When I get home I need to get dinner ready for the kids or go to the gym or whatever. For years, there were many things we used to resolve this struggle: willpower, coffee, a candy bar, a nap, etc.  

But then in 2004, many of saw a low-budget commercial that promised, “If you want to get out of that afternoon slump and not hit the inevitable sugar-crash later, try 5-hour energy.” They placed the product where we experiencing the struggle (like at a convenience store buying an energy drink) and we tried it. Though actual sales figures are not publicly available, plant utilization and staffing models indicate that in that first year many millions of us tried 5-hour Energy and that demand was growing. 

Since then, the product has remained much the same. 5-hour Energy absolutely dominates a field of at least 20 competitors because it nailed the “job” perfectly and didn’t stray. It still fulfills the job of resolving a slump in energy without too much of a subsequent crash.  <lens:forces-of-progress>

Of course, customer needs and product features factor into the success of 5-hour energy. However, those needs and features are clearer and easier to specify when framed from a “jobs” perspective. In an interview with Forbes, Manoj Bhargava, the founder of 5-hour energy, remarked that as he sought to resolve his problem of the afternoon slump he asked himself, “If I am tired, am I also thirsty?” It turns out that by framing the problem from the circumstance of struggle, he realized that his product should only be a couple of ounces.  

By using jobs theory and identifying first the struggle (which includes many needs) and the circumstance under which the struggle arises, this gaudily-packaged, cough-syrupy stuff created and continues to dominate a multibillion dollar market.  

See also [The Job that 5-Hour Energy is Hired For](http://jobstobedone.org/radio/the-jobs-that-5-hour-energy-drink-is-hire-for/).

5-Hour Energy: Tastes Terrible. It looks Weird. And It Kills... its Category.

A few days ago I wrote a post on how easy it is to have “false negatives”—negative for virus, but the person is sick. I made a table to show how easy it is to overestimate the efficacy of a test. 

Paul Romer a co-winner of the Nobel Prize in Economics has a simple blog post where he shows that if we can increase testing, I don’t need to be so worried. He runs 50 simulations for the spread of the virus, based tests of different false-negative rates. 

“The simulated data here contrast policies that isolate people who test positive using four different assumptions about the quality of the test. Even a very bad test cuts the fraction of the population who are ultimately infected almost in half. And when I say bad, I mean bad – an 80% false negative rate, which means that 4 out of 5 of people who are truly infectious will get a negative test result – i.e. a result saying that they are not infectious.”

https://paulromer.net/covid-sim-part3/

***There are big, important limitations to this.*** These simulations assume that each day 7% of the population is randomly selected for testing and completes that testing. The first limitation is practicability. It’s not likely that we’ll achieve (1) agreement on and (2) implementation of such a policy of random testing. Also, (3) compliance with testing might be low. However the biggest challenge could be (4) compliance with self-isolation requirements. The (5) scale of testing is also an important limitation. Romer’s model relies on the US testing 23.1 million people EVERY DAY. Cumulatively, the US has conducted 432,655 tests. (104P, EDT, 3/25/2020)

Nevertheless, this is strong evidence that my worry about insufficient oversight can be overcome if enough firms can—basically without impediment—develop, produce and distribute lots of test. 

It also means that we might be able to rely on thermometers. Even if 40% of infected people are asymptomatic—see my post on Iceland’s testing—that’s still enough to reduce overall infections so we can manage the crisis. 

Here we run up against what I think is the biggest obstacle of all: if people aren’t sure the test is reliable, they’re not likely to be willing to immediately accept the draconian measure of self-isolation. 

No less an expert than Dr. Deborah Birx illustrates this. At a briefing Birx mentioned that she had a fever over the weekend and was careful and got a test. But when her fever receded, Birx figured she was okay to go back to work—before she got the results of her test. Coronavirus causes an intermittent fever (https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients.html). And I can’t imagine anyone is more conscious of this than Birx. Of course, I’m not saying she was irresponsible—she might’ve taken extra precautions and she might not have let her guard down until she’d reached a sufficient time to be sure her fever wasn’t returning. But if Birx can skirt the requirements a bit, unreliable tests, unfortunately, may not do the trick.

False Negatives

(This is a follow-up “<a href=http://disruptiveinnovation.org/discuss/disruption-in-warfare'><b>Disruption in a New Age of Warfare</b></a>.”)
In the past, US—and other countries—defense and intelligence agencies would set up small working groups tasked in designing countermeasures against innovations in our enemies’ ability to wage war. It was not particularly successful. However, there is hope. And it could provide a lesson for businesses. DARPA has created a section called “Improv,” responsible for figuring out how to stay ahead of—or at least up to date on—the capabilities of our enemies. The leader of Improv, John Main, could follow past practice and gather a handful smart engineers and tacticians to attempt to figure out how to overcome advances in enemy tech and war fighting. But Mr. Main is looking to change this.

The future of warfare is unlikely to be state-based—that is, we’ll be engaged less in conventional war against nation-states. Rather, we will be fighting revolutionaries and radicals, who are not in concentrated locations, but in loosely syndicated networks. We won’t be able to “soften” infrastructure targets with air strikes, followed by Marine Expeditionary Units and US Army Armor and Mechanized infantry, who take the area and drive enemy forces out, leading to a definitive surrender. In fact, this isn’t really a future problem. Last year, General Joseph L. Votel—then Commander of all US Special Operations Forces (SOF)—attempted to address how the military can cope with the breakneck OPTEMPO of SOF.[1] The reduced use of conventional forces with a radical increase in demand for and use of SOF, is an indication that the future is now.
To make matters worse, a radicalized enemy can take losses and more. For one thing, we can’t hit all the bad guys all at once, because they’re spread nearly all around the world. For another, groups of radicals dispersed around the world will be “moving up-market” in their tools and techniques of warfare.
As a result of the change in how war is waged and how this will spur innovation, Main is planning to create as he says, “very, very large ‘Red Team.’”[2] Red teaming is often standard practice among modern armies. When done right, a group of people—troops, intelligence personnel, etc.—learn everything about how and why the enemy fights, including the resource constraints and motivations for fighting both on an individual and aggregate level. A good Red-teaming exercise can lead to war gaming, where what was a thought experiment can be put into practice to increase learning.
In my view, Main’s approach is the only way to succeed in this fast, non-state state of affairs--using hundreds, maybe even thousands, of engineers, warfighters, biologists, and many others constituting our Red Team--thinking both about countermeasures, but also getting ahead of our enemies, by innovating at the low end.
The strategic thinking that arises from good Red-teaming, can be useful to businesses, as well. Most firms spend a great deal of time, effort, and money devising winning strategies for their products and services. Some companies even think about their competition. But when faced with the potential of disruption, I think Mr. Main at DARPA is onto something. I suggest that established firms engage in rigorous Red-teaming. In my experience, the more realistic and intensive the process is, the more likely a an organization is to find their way out of a bind and onto a new, winning trajectory. The leadership of an established firm can, when facing competition, create a temporary Red Team, composed of a vertical and horizontal cross-section of the firm that can spend a couple of weeks getting into the mindset of the competition, and then hatch a strategy designed specifically to disrupt their own organization. Of course, this means a substantial commitment from leadership to buffer the Red Team from the grind of daily meetings and tasks for that short period. But doing so should be worth it. Remember, disruptive innovation is principally a theory of competitive response. Consequently, an incumbent firm under threat that can gain a more complete picture of their situation, can mount a superior strategic response—and possibly innovate in ways that they would otherwise never have imagined.<br><br>

<b>Notes</b>
[1]        http://www.military.com/daily-news/2015/05/19/socom-commander-says-op-tempo-stressing-special-operations.html
[2]        http://www.economist.com/news/science-and-technology/21699098-makeshift-weapons-are-becoming-more-dangerous-highly-sophisticated?fsrc=scn/tw_ec/hell_s_kitchens"

Red-Teaming: A strategy against constantly evolving competition


Chances are you’ve never heard of Simpson’s paradox, but it matters. 

In sum, Simpson’s paradox—sometimes referred to as the “reversal paradox”—says that how we choose to analyze a data set may cause us to draw opposite conclusions. For instance, in the 1980s, there was a study about the most effective way to treat kidney stones, using existing outcome data. When looking at the outcomes for all patients, procedure A showed better results. However, when scientists cut the data by stone size, they found that for all stone sizes, procedure B was better. To make matters worse, there is no boundary to Simpson’s paradox. If researchers had cut the data by gender or height or BMI or whatever, they could have seen reversal after reversal after reversal.

Fortunately, if you can design an experiment, you don’t run into this problem. But there are many studies that would be unethical. We often hear about drugs, for instance, that have not passed all of the FDA’s gates for safety and efficacy but still are administered to patients. Unfortunately, these are all instances where the patient’s alternative is death. But imagine a condition like depression, where there are lots of alternatives. And imagine that we wanted to study the effectiveness of a treatment on mild to moderate depression—so that the alternative to failure of the intervention is not catastrophic. The new pill, however, has a 1 in 1000 chance of causing slow, painful, but certain death. It would be unethical to study this drug in a randomized controlled trial (RCT), which is considered to be the gold standard of research. Because we have alternatives, including doing nothing, that are better than dying a slow painful death, we are in a situation where we have to use existing data to figure out how to treat mild to moderate depression.</p>

So, in the real world, where we have strong ethical controls on experiments, we will <em>always</em> be confronted with Simpson’s Paradox and no way for our data to tell us how to slice it up so that we’re assured that procedure A truly is better than procedure B.</p>

In business, we’re even in a worse situation. We really have only data about the past. Other than what’s called A/B testing—where a company sends out two mailers with different fonts or text to entice potential customers to sign up for their credit card, or a website feeds people many different looks/feels to see which keep people on the site longest—we are stuck with using existing data to try to draw conclusions about the right way to proceed in the future. But Simpson’s Paradox tells us that quite literally, we can never be sure in advance whether we have analyzed our marketing, financial, and other data in the right way or not.</p>

But there is good news. First, Judea Pearl of UCLA has worked out a calculus of causality, with tools to model it. That is, there is a way to use information from the past to tell us what causes what. We don’t have to sweat Simpson’s Paradox, we can know from past data whether procedure A or procedure B will (or is more likely) to produce our desired outcome. Second, if we do not need to make something happen in the real world, then data analysis—which will give us nice correlations—is good enough. Put another way, if we don’t need to cause people to buy a product, no problem. However, if we want to figure out how to get people to buy our product or use our service, we either need to have special intuition or we need to understand what <em>causes</em> someone to buy our product vs. all other options.



Why should business care about Simpson’s Paradox?

"A teacher of mine, K. Codell Carter, a philosopher and historian of medicine has captured the importance of what he calls the ""aetiological stance""--the orientation of medicine toward understanding the causes of disease. 

In his book The Rise of Causal Concepts of Disease: Case Histories he points out that medical science up to the 19th century made relatively few advances and little progress. However, once physicians and researches focused on the mechanisms that caused disease, medical science’s “discoveries and powers”--to steal a phrase from Francis Bacon about the proper object of science--skyrocketed. Everything from the virtual end of leechcraft to the effective treatment of many other ailments occurred within the brief period of the late 19th and early 20th centuries and has continued at breakneck speed since. Carter’s careful examination of the progress of medical science shows repeatedly that the attempt by researchers to isolate causes of illness was the key to moving beyond much of what we now think of as superstition.

Carter briefly notes the difference in the scientific progress of curing scurvy vs. beriberi as an example of the power of causality. Scurvy, caused by a deficiency in vitamin C, was described by the famed father of medicine Hippocrates, but not understood causally until 1927. Throughout the two millennia that preceded a true understanding of the cause of scurvy many remedies were prescribed, almost all of them close to the true cause of scurvy. Even as early as the 1830s something like clinical guidelines were developed for the prevention and treatment of the disorder--fresh meat and citrus generally contain enough vitamin C to cure scurvy.

However, lacking a deep understanding of the mechanism for scurvy led to confusion among physicians and researchers. For example, the storage and transport of citrus juices in copper or their exposure to air and sunlight degrade vitamin C content to sub-therapeutic levels. Researchers and practitioners got what we might refer to as “noisy” data. Sometimes lime juice cured scurvy and sometimes not. To make matters worse, the advent of steam ships got sailors from port to port faster, where they could consume good sources of vitamin C. So sailors who might have suffered scurvy decades earlier appeared to be treated successfully with inadequate sources of vitamin C. Without a drive to understand the causal mechanism for scurvy, these guidelines did not evolve much. Researchers were satisfied with knowing that (most of the time) lime juice cured scurvy. 

Beriberi, an insufficiency of vitamin B1, had a different trajectory. Although known in the 17th century, the serious study of beriberi did not arise until the 1880s, when researchers were looking not just for guidelines to treat the disorder, but its underlying cause. Immediately, diet was identified as a causal factor for beriberi. In fact, by 1884, a Japanese researcher had determined that beriberi was only found among sailors whose diet consisted almost entirely of rice. One can imagine researchers being satisfied with knowing this and ensuring that sailors had a more diverse diet, as their counterparts had been satisfied knowing that usually meat or citrus cured scurvy. However, where scurvy researchers stopped due to pretty good empirical work, the ""aetiological stance""'--the drive to understand causation--kept scientific progress on beriberi moving forward. By the 1890s researchers had settled definitively that a dietary deficiency connected to unpolished rice caused beriberi. And by 1913 researchers knew that a particular extract of rice bran was the deficiency that caused beriberi. 

It's not just that causality enables 100% effective treatment, but the drive to understand causes--leads to faster progress. In a world, where big data and its analysis are gaining influence, we cannot forget that there is no substitute for understanding the causation at work in the world around us.
"

Why Causality Matters, or the Difference between Scurvy and Beriberi

The current widespread focus on the problems of replication in scientific studies—<a href='http://www.vox.com/2016/7/14/12016710/science-challeges-research-funding-peer-review-process'>especially those of the social and biomedical sciences</a>—has been to invest in ever more sophisticated analytical tools and techniques: more data, more sophisticated tools of analysis, faster computing, etc. All of these efforts have missed an alternative solution: causality. Most research in biomedical and social science begins with data analysis. But data analysis alone is not capable of yielding a genuine causal model. Essentially, all analysis is subject to the problem of induction—roughly, correlation is not causation. 

Our theory-building model is founded on building a causal model and testing that model in a variety of situations. The most obvious risk for such a process is that having built a model, people tend to unwittingly find corroborating evidence as they apply their model—confirmation bias. However, in our model, a theory is not tested in other environments to confirm it, but to find anomalies for the theory. Anomalies help to refine the causal model both by bounding the model and by working out the conditions of its application. Gravity looks different under different conditions—on the moon vs. the earth vs. open space—but is still the same mechanism. Sometimes we can explain phenomena without needing to refer to gravity—such as explaining superconduction. It’s not that the law of graviton is suspended; it’s just that the law has nothing to add to our understanding of lots and lots of phenomena.  

While an anomaly focus is crucial for improving a causal theory, correlational modes have trouble with “outliers.” Most often outliers are dropped from consideration. But whether to do so relies on extra-statistical thinking. A causal theory must account for any apparent anomaly—resulting in clarification, rejection or bounding of the original theory. Confirmation bias simply cannot enter into such reasoning, since corroborating evidence is interesting, but does not lead to better theory.

Put simply, an anomalies-first focus in theory building either forces biases out or prevents biases from entering into a researcher’s reasoning.


How an Anomaly Focus Eliminates Bias

There is another important consequence of the power of anomaly: all good theories must be susceptible to anomalies, otherwise there is no progress. Correlation is like a sliding scale of confidence--it’s akin to an analog signal that we receive with varying degrees of fidelity. Consequently, we can seldom state definitively if some event is an anomaly or merely “noise.” On the other hand, a causal model is more like a digital signal—it gives us discrete “yes” or “no” indicators for anomalies. As we mention in the first chapter, the causal approach to manufacturing created clear go/no-go signals that either kept work moving or initiated an investigation into a defect. Without the clear binary signals, sub-par work would inevitably sneak through.  

In building theories in management—and other social science disciplines—the causal model enables a clear yes/no indication for anomalies. Anomalies, as we discovered, are the mechanism that drives improvement in theories. Disruption has benefited greatly from anomalies. But what happens when good work stops at the level of correlation?
There are few more influential gurus than Jim Collins whose books <i>Built to Last</i> and <i>Good to Great</i> have dominated best-seller lists for years. Yet his research is a lightning rod for criticism – from some very smart people. This is our sense of why.

In Good to Great, Collins outlines seven characteristics that he and his team found common to all firms that had a particularly successful and durable performance. One of the characteristics of the CEOs in each of the good-to-great companies is that they were home-grown, humble and self-deprecating. He asserts that if such men and women were positioned atop other companies, they too would go from good to great. But how about Steve Jobs? He was not humble or self-deprecating. Yet Apple prospered under his leadership and has proven to be quite resilient without him at the helm. 

Can the theory account for this? If we accept that Apple at least appears to be an anomaly, what can be done? 

Collins’ theory is at risk of being skewered on the horns of a dilemma. On the one hand, he can ensure that the criteria for selecting good-to-great firms excludes Apple. But excluding a firm that everyone would agree has not only done well, but has come out of a decade-long slump to have the largest market cap in history, seems like a failure of the theory. On the other hand, if Collins admits that Apple is an anomaly, he’s got a serious problem. Are there exceptions to the theory? Or are some elements more important than other? Collins seems to think not:
 
No single finding by itself makes a great organization; you need to have them all working together as an integrated set. Then work sequentially through the framework, starting with “first who” and moving through all the major components. Meanwhile, work continuously on your own development toward Level 5 leadership. I have laid out this book in a sequence consistent with what we observed in the companies; the very structure of the book is a road map.

Collins’ theory is induced from all of the firms that were good to great at the time his study was conducted. His study concluded that all and only the firms that went from good to great had all of the components together. It has not fared well either in predicting the future of these firms or in predicting which firms will go from good to great. 

Our purpose is not to take pot shots at an incredibly smart guy like Jim Collins. What we wish to show is that research projects that stop at correlation cannot turn into longer programs of research that explore different areas and applications. When we stop short of the hard work of understanding the underlying causality of the phenomena we study, we put ourselves into a one-and-done situation. There is no clear way for research that stops short of causality to advance beyond “the literature.”
  
The hard work of theory-building does not stop with the articulation of a model. A causal model, however, makes it possible to accommodate anomalies that clarify the boundaries and conditional application of a theory—whether to apply the “invisible hand” or the “visible hand,” for instance. The improvability of processes in the quality revolution in manufacturing was essential. Likewise, if a revolution in the marketplace of ideas is to happen, ideas must be amenable to improvement through anomalies.

Anomalies: Improving Good Theory

I hadn't seen this before, but I coudn't stop watching once I started. In fact, I had to stop myself watching all the way through again so I could post <u><b><a href='https://youtu.be/rHdS_4GsKmg'>the link</a></b></u>.

Clay on where Growth Comes From

"Yesterday, <a href=http://www.businessinsider.com/tim-cook-apple-strategy-clayton-christensen-jobs-theory-2016-10’><I>Business Insider</i></a> reported that UBS’s Steven Milunovich grilled Tim Cook on whether Apple sees a next “job to be done”? The article was critical of Cook’s answer. I’m not here to make a judgement about either Cook’s answer or Apple’s strategy. I want to clarify what a job to be done is, and a bit about why it’s hard to talk about them. 


First, jobs to be done are neither customer nor technology focused. Typically, products and services grow around a sense of a customer need or around the potential of a technology--think: AI and Blockchain. In a very simple sense, though, a job to be done is defined by time and space. It’s a circumstance in which people are trying to make progress in their lives. I have many needs in my life and there are lots of technologies, but sometimes a need just has no relevance in the circumstance I find myself. For instance, I feel the need to be healthy, but, say, when I’m cleaning up the kitchen after dinner, that need has very little influence on how I do the dishes--maybe it should, I could be the next fitness guru: “Dishwashing your way to dishwasher abs!” Nevertheless, the fact is that the relevance of a need varies over circumstances, and jobs are defined by circumstances.


Second, all jobs have three components: practical, social and emotional. Finding a need or problem and matching it to a technology at best solves only for the practical aspect of a job to be done. And, frankly, the easiest one. For many people an iPhone hits all three marks; for others it doesn’t. But the magic of the iPhone was not that it disrupted smartphones or computers, it’s that it disrupted software development. The entire value network, of which the iPhone was an element, enabled many more people to create (affordable) software. And the sheer volume of new software opened up a vast discovery process for jobs to be done. Who knew that circumstances in their lives would arise that could be addressed by Flappy Bird or Snapchat or Slack or even Facebook? Consider the apps you are most attached to and ask yourself: do they satisfy a need or do they fit into your life/work in a very specific way that is more than just putting a hammer to a nail? 


For me, Apple may never explicitly consider jobs to be done. Certainly, it’s worth asking them if they see a job on the horizon. And nearly as certainly they’re not going to tell us what they see. On the other hand, Apple’s success may never have been nor never will be in matching jobs to technology in a direct way. Nevertheless, Tim Cook’s relentless advocacy for an app store created a whole network of things and protocols and business models that hits all three elements of a true solution for a job to be done for nearly a billion people in a way that we now take for granted. "

Evasive Answers about Jobs to be Done

"One of the things that hamstrings the social sciences is that they are stuck on the problem of induction--first and best characterized by David Hume. Hume points out that just because we have all our lives seen sequences of events of the form A then B--the sun rises in the east and travels westward and sets--doesn't show us causality. We do not observe, we cannot observe causality. We see regularity but causation,  ug it's merely a heuristic we employ or concept we impose on the observed regularities. The problem is that the work of smart statistitcians, philosophers and mathematicians was constantly working on cleverer and cleverer ways to analyze data (AKA ""observations""). In the absence of high-quality supplementary experimental work, correlation could never lead to causation. (In a future entry, I'll explain why Simpson's Paradox demonstrates this to be so.) But for now what matters is that the social sciences tend to start with analysis. 

My treat for you today is a link to Richard Fyenman on the scientific method. NB: Scientific education--no matter what discipline should be about having high-quality ignorance, asking high-quality questions.

<a href='https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwjWkc-du8bPAhWHvZAKHXK9DKQQtwIIHDAA&url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DEYPapE-3FRw&usg=AFQjCNGD0MW_440XSW4cooUuPI_o1-ijrw&bvm=bv.134495766,d.dmo' target='_blank'><b><u>Feynman on the Scientific Method</b></u></a>"

Feynman on the Scientific Method

Typically, disruption raids a mainstream market by causing customers who are “overserved” by existing solutions to buy a “worse” solution. We buy budget laptops and fly cheaper airlines. But in an industry such as the taxi industry, where prices are fixed by the state, firms have little reason to move up-market—to pursue better service or additional services. To make matters worse, the competition over medallions—which grant the right to operate a taxi—squeezes margins further.   Gaining dominant marketshare is doubly unattractive to a taxi company. First, if the company must pursue better service, they gain nothing in terms of the price they can charge. Second, capturing greater marketshare is either prohibitively expensive—according to my own informal research, medallions in Boston have traded as high as $1 million—or impossible—due to regulations that limit the total percentage of a city's medallions that one firm can control.   So here is the rub: Uber doesn’t look disruptive because it didn’t take root in the low end of the market. And many smart people have tried hard to make the case that it created a totally different job to be done. But I take a slightly different view. Because taxis could not move upmarket, Uber entered by changing the basis of competition. Nobody wants a worse ride than a taxi ride, but we’re willing to switch because Uber is (just a little) cheaper and (much, much) more convenient. Uber has changed the basis of competition by moving us from thinking about the solution provided to thinking about how Uber eliminates almost all of the hassles we associate with taxis.   The regulation of pricing and the artificial scarcity of medallions combined with open markets for medallions created a situation where disruption happened by changing the basis of competition. Regulation prevented upmarket movement or market domination, but could not do anything about cost, convenience and access to point-to-point transportation. 

Uber: Nobody Wants a Worse Ride

Today, I’m posting something I wrote earlier this week. This is an adaptation of work that my long-time collaborator and friend, Clayton Christensen, and I did together. (It was his birthday Monday--4/6--so this topic drifted to the top of my mind.) To be clear, this is not the original, but an adaptation of our joint work. 

**Anomalies Wanted: Models must be accountable to reality**

In 1994, the US passed the Violent Crime Act. At the time, there was a belief, based on observational study, that longer penalties would deter violent crime. Since that time, the US has become the leader in incarceration rate in the world (655/ 100,000); the collective cost of incarceration has quadrupled (~$16B - ~$70B—direct expenditures for the states, does not include federal expenditures or costs of healthcare, courts/parole systems and increased policing.); and the payoff is modest. (See graph below. Source data are here: http://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm)

![text](https://s3.amazonaws.com/strapi.dx.mba/89dacf1918e74d5980f98e859f004ec4.jpeg)

As you can see from the graph, there are two immediate problems with the 1994 Violent Crime Act. First, the downward trend in violent crime *predates* the Act. Second, this was an incredibly expensive intervention--in both financial and human terms--given its modest outcomes.

The biggest problem, though, isn’t just that the study was wrong, but that it wasn’t accountable to updated information. 

Models can end up in what I refer to as a modeling “cul de sac.” Teacher evaluations done by a number of companies, for example, are often “black boxes” that attempt to estimate the quality of teachers mostly by evaluating the before and after pictures of students in their classes, using “objective” measures like standardized tests.

Among the many problems with most of these evaluations is that it’s not possible to see if they are wrong. Many teachers receive wildly different scores from year to year. Is it really possible that a teacher’s performance is that variable? Of course not. But this is ignored because the models are developed and updated without an external check on their soundness. 

The reason you and I—though we may complain periodically—rely on weather forecasts—often in 15-minute increments—is because weather models have continued to improve, due to their unavoidable clash with reality. If today’s model predicted 57 degrees at 1000A, but it was 58 degrees, the model can be improved.

With credit to Karl Popper, this is the central insight of Clay’s and my work on this issue: anomalies for our models or theories are the most powerful engine for progress. What distinguishes science from superstition is that scientific claims can, in principle, be falsified. 

Finding anomalies (and taking them seriously—that is, not casting them aside as “outliers”) for our statistical, intuitive or causal models also helps us to understand the conditions and boundaries of their application. Without the “failure” of the Michelson-Morley experiments, Einstein wouldn’t have worked on the “puzzles” about the speed of light. Without Brahe’s careful examination of the movement of planets, Kepler would never have scrapped “epicycles” and advanced the theory of elliptical orbits. 

The lesson for all of us right now, thinking about models of the spread of nCoV-19, is that our mental models and the statistical models of what’s happening need to be susceptible to outcomes contrary to prediction. And we should treat those anomalies not as unwelcome intruders, but as treasures that can drive better predictions and deeper insights.

Anomalies Wanted: Models must be accountable to reality

Public officials have had varied responses to the emergence of 19nCoV. <p/>In this post, I’ll be looking at the governor of New York, Andrew Cuomo, since he has done nearly daily public briefings. This is not an evaluation of whether he’s done a good job. Rather, this entry addresses the Tools of Cooperation he’s employed to get people to comply with the order for strict mitigation measures.

If you recall, the <lens:tools-of-cooperation> lens is based on two dimensions. The x-dimension represents the pre-existing degree of agreement on how the world works (*belief*); the y-dimension is the degree of pre-existing agreement on what we want (*intent*). 

To use the tool, we take a specific issue and two parties—two individuals, two groups, or a group and an individual—and plot the extent to which they agree. So, for social distancing and other measures, we can plot the degree to which Governor Cuomo and his constituents agree about
1. the efficacy and necessity of strong mitigation and
2. how much they agree on the desired outcome of these policies. 

For ease of use, we break the diagram into 4 quadrants, each quadrant comprises a group of tools that are similar in their requirements and characteristics. 

<img src='https://s3.amazonaws.com/strapi.dx.mba/544e8e3253494ddfb330408b61a7e8ce.png' align='right' width='60%'/>

Once we know the issue and understand the parties’ pre-existing degree of agreement, we can plot it in the diagram to see which tool(s) will be effective in getting people to cooperate—to coordinate their actions to achieve an outcome. 

In preparation for this example, I watched a few of Gov. Cuomo’s news conferences. Interestingly, he employs more than one tool as he tries to get people to comply with the mitigation measures. 

I have seen him use tools in the “**socialize**” quadrant. He appeals to what one might call civic religion: we are a community that looks out for one another, so stick to the guidelines for the sake of others.

I have also seen him use role modeling from the “**lead**” quadrant. He and the other briefers sit at least 6 feet apart from each other. 

His daily run through the numbers is a tool from the “**manage**” quadrant. This works like an informal measurement system intended to teach people the consequences of their actions—for good and ill.

Finally, I witnessed a briefing after a weekend where spring weather had brought people out in large numbers, many of whom ignored social distancing guidelines. Cuomo resorted to the “**dictate**” quadrant through a coercive threat of tripled fines for anyone violating the rules. 

But if the issue is between Cuomo and his constituents, why the scattershot approach? Simply put, the pre-existing agreement between him and his constituents varied on both dimensions. So, instinctively, Cuomo and his advisers made use of tools in all four quadrants to achieve cooperation and to cover that variability in agreement. 

Cuomo's Tools of Cooperation

Jobs to be Done (JTBD) comprise three types of *hotspots*.

1. **Practical**. This is the best understood element and often the only aspect used to persuade people to buy a product/service.
2. **Emotional**. This is how the experience of using a product/service makes one feel. 
3. **Social**. This is both connection to others and the sense one has about how others view them.

Peloton sells stationary bikes and treadmills. The practical aspect of the stationary bike (I’ll focus just on the bike) is fairly straightforward: get in shape, have more energy, etc.

But this is not enough. In a very unscientific poll among friends, I have found that their stationary bikes and treadmills are more likely to be used as a holding place for clothes or some such, 6than to be used for regular workouts. 

Peloton has evolved from being just a high-end training aid to a place to find community and for individuals to feel engaged when they use the equipment and app. 

Peloton has dozens of live and on-demand classes that range from challenging to what they call “feel-good” rides. Peloton promises that their trainers will keep you feeling motivated.They have also created structured workouts that appeal to the differing emotional needs of their riders—feeling good, feeling like you gave it your all, feeling like you’re improving, etc.—to keep users engaged. These are all aspects of the emotional hotspot: how people feel about themselves when they use Peloton.

Finally, Peloton has worked to plug people into a social system. For instance, if you and your friends are on at the same time, you can work out together or challenge each other to a race. There’s also a whole “Community” section of Peloton’s website, devoted to stories about how the growing Peloton community has been invaluable in user’s progress, and how to find the right social group for your goals.






JTBD Hotspots: Peloton Fitness Equipment

Since solutions for JTBD don’t fit into a product category, the structure of a job definition helps to see why people bring solutions into their lives. One example is that social media competes with—really, has basically replaced—“the mall” as a way for youth to gather and connect. 

Roll back the clock a few decades and you’d see lots of parents dropping their kids at the mall, where the kids would meet up with their friends. But they weren’t at the shopping center to shop. Using the job definition, we’d come up with roughly:

When I am bored at home, 
I hire the mall,
So I can eliminate boredom and connect with my friends.

Currently, social media and various other technological solutions fit in the “I hire” spot. Instead of hiring the mall to eliminate boredom and connect with friends, teenagers tend use Snapchat, Instagram and Tik Tok. 

When I am bored at home,
I hire social media,
So I can eliminate boredom and connect with my friends.

Of course, there are other things that teenagers hire(d) for this JTBD, but for purposes of this example, what’s important to note is (1) that the structure of a job definition can help to frame a job in a way that’s not product-dependent and (2) that the structure can help to structure our thinking in a way that helps us to identify true Jobs to be Done.

Job Definition: Mall vs. Social Media

This is placeholder <lens-card:basis-of-competition>

How to do market segmentations?

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.

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<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/>
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A Long-Lens Look at Apple and Disruption

In our discussion of  [Apple and Disruption](/blog/:is-apple-disruptive-long-lens-apple-and-disruption), I mention that beyond low-end and new-market Disruptions, there is a third way that firms can disrupt: by means of a different business model type. 

There are three general business-model types, first described in [Configuring Value for Competitive Advantage: On Chains, Shops and Networks](https://www.jstor.org/stable/3094221).

**Solution Shops**: A solution shop, or a business with such a business model deals with problems and solutions that are unknown, unstructured or unpredictable. Examples of solution shops—or businesses that use this business model type—are general hospitals, many consulting firms and R&D groups. Because the problems these firms address are not (yet) defined and their connections to outcomes of interest are also not (yet) known, Solution Shops must be paid for inputs, not outcomes. 

**Value-added Process Businesses**: Value-added Processes Businesses (VAPs) transform inputs into predictable outputs. Manufacturing—indeed all product companies—are VAPs. Education often operates as a VAP, taking in the raw material of students and, say, in two years mints them as new Harvard MBAs. VAPs are paid for outcomes, not inputs.

**Facilitated Network Businesses**: These businesses facilitate interactions between parties. eBay is a Network business—it facilitates buyers and sellers. In the discussion of Apple and disruption, I note that Apple disrupted software development by creating a part of the firm that operated on this different business-model type. Sometimes, such business are referred to as having a “two-sided” market. Other examples are things we would call “platform” businesses. 

This is why, in my experience, most “platform plays” fail—as they assume the “Field of Dreams” rule: if you build it, they will come.” This is false. In order to succeed, a platform has to provide a way for people outside the firm to make money by using their platform. (Think: Uber.) 

This is precisely what Apple did with the AppStore. They enabled first musicians and music companies a new way to distribute their content and be paid for it. (Compare with Napster.) The iPhone’s success is due principally to this business model shift. Remarkably, Apple has maintained both business-model types under one roof—an exceedingly rare achievement that wasn’t without controversy internally. 

The success of the iPhone has facilitated the development of other networks, built so that non-Apple developers can create software for Macs, iPads and now the Apple Watch.

This third type of Disruption is less common and, as with other types of disruption, rarely happens inside the same firm. Nevertheless, as with all “flavors” of Disruptive Innovation, it has repeatedly transformed industries.

*Note: I use terminology slightly different from the original, but it has become standard among those of us who do research on the theories related to <lens:disruptive-market-entry>.

Disruptive Encounters of the Third Kind

A friend of mine who is very bright makes his living as a data scientist that specializes in fantasy sports. Recently, he said, “I’m steeped in data and modeling. And I looked at the studies and some of the models on SARS-CoV-2 and hydroxychloroquine [HCQ] and found some of them persuasive,” to the point that he wanted to acquire HCQ and ignore advice to wear a mask.

I was surprised that, lacking any domain expertise,* he was confident in his capabilities. But it reminded me of the puzzle of Socrates’ wisdom—or something I’m calling the “Pauling Effect.”

In Plato’s “Apology,” Socrates recounts the story of the Oracle at Delphi saying that none was wiser than him. This pronouncement set Socrates on a collision course with the powerful men of Athens—ultimately leading to his being put to death. Why? Because Socrates set out to interrogate the matter of his supposed wisdom by interrogating men considered wise—politicians, poets, physicians, artisans, etc.—in hopes of proving the Oracle wrong. His discomfiting cross-examinations of prominent Athenians led him to a realization: Socrates was wise only in the sense that he understood he wasn’t wise. Put another way, he understood the limits of his wisdom.

What Socrates discovers is something I’ve named “The Pauling Effect.” Linus Pauling is the only person to receive two individual Nobels—Chemistry, 1954; Peace, 1962. Pauling’s most influential work, however, was his misguided advocacy for Vitamin C and other supplements, claiming that they could treat things ranging from cancer to the common cold to brain damage in children. Although  many well designed studies demonstrated that Vitamin C—and most other supplements—provide no health benefit and can be toxic, Pauling championed Vitamin C as a cure-all until his death in 1994. 

Pauling’s genius and unparalleled achievements gave him immense credibility, which still to this day influences millions of smart people. But Pauling suffered the same defect as the wise men of Athens: being wise in some areas, he thought he was wise across the board. 

<img src='https://s3.amazonaws.com/strapi.dx.mba/cb51d2d259bb40c0850d8201cf3538b8.png' width='40%' align='right'/>

Most of us are susceptible to the Pauling Effect, like my friend who has deep domain knowledge in sports and thus feels confident with any domain that uses models and numbers. For sure, he’s in a better position to understand these than some people. But without understanding epidemiology, virology, public health, etc., he’s on a course to make bad decisions about medicine and masks.

And this is the point of this post. This observed problem is not limited to my friend or Linus Pauling. In fact, we can all be wiser than Pauling (and lots of other folks) by knowing and admitting the boundaries of our knowledge and experience. 

*Full disclosure: I’m a member of my state’s Coronavirus Task Force.

The Pauling Effect

It seems obvious that there are advantages to standing out from the crowd—to differentiate your product or service from the others in your space. But successful differentiation doesn’t mean that people will choose your product. In fact, asking and acting on the issue of differentiation has at least two important problems when seen through the lens of Jobs to be Done (JTBD).

The first problem is that “differentiation” pushes focus onto the product, not the customer. Of course, discussions of differentiation are not completely untethered from customers. Still, differentiation tends to constrain considerations to what we can do with *our* solutions—added features, new promotions, etc.

But JTBD theory tells us that successful and repeatable sales emerge from understanding the demand side of the equation. Differentiation, unfortunately, directs attention to the supply side of things.

The second problem is that differentiation tends to confine considerations of a product that stands out in intra-category comparisons. The most common advice about differentiation is for a firm to understand what their competitor firms are offering and find opportunities to “differentiate,” to attract more attention in a way that delivers more customers.

But JTBD theory reveals something we all know intuitively: when we look for a solution, we do not confine our search to products in the same category or firms with the same SIC code. 

In fact, one of the best strategies for uncovering an unmet JTBD is to find what I call a “strange competitive set.” Years ago, when Clay Christensen and I were drafting early versions of a working paper, I used the example of the mid-afternoon slump. We all experience a nadir in our attentiveness around 3pm each afternoon. To fulfill the “afternoon slump” JTBD we seek out coffee, exercise, napping, will power, Snickers bar, and, my favorite, [5-hour energy](./five-hour-energy-tastes-bad-is-weird-kills-category). (Not my favorite solution, but a favorite example of mine.) 

None of the things commonly used for this universal daily human experience are in the same product category, and the firms that offer them are not in the same SIC grouping. 

Differentiation for its own sake doesn’t lead to more and better customers. The focus on what a firm supplies and the straightjacket of product categories cause firms to miss potentially very big opportunities that can only be understood from a demand-side JTBD approach.

The Problems of Product “Differentiation”


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
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<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

One of my favorite—and incredibly goofy—examples of how we instinctively "model" our environment comes from Amazon. 

When you search for an item, Amazon makes suggestions of other things, such as in their “Frequently bought together” links. Here, Amazon in the UK has served up items frequently sold with that particular baseball bat.       

![text](https://s3.amazonaws.com/strapi.dx.mba/44db2eb981224063b1943e6799ee579d.jpeg)

We humans immediately get the (unintentional) joke. This Amazon page is also a great example of how a theory can explain what correlation alone cannot. People had bought these items together frequently enough—"frequently enough" is undefined—that the algorithm served up more things a mugger might want to obtain. The search algorithm uses correlation to make suggestions; humans instinctively and immediately understand the larger context, based on our own "model" or "theory" of the world around us. 

The strength of Amazon’s algorithms is in linking things that seem unrelated, because other people have bought them at the same time in the past. On the other hand, the strength of humans is our ability to create a model of the world around us, test that model, and make corrections.

Recommended: Mugging

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

Jobs to be Done are not based on customer characteristics and product features. Jobs are discovered by looking at “struggling moments,” times when we’re searching to resolve a problem in our lives, but there isn’t a clear or great solution. 

I often use the example of the energy shot. &nbsp;<a href='/five-hour-energy'>5-hour energy</a> solves a job many of us have. In the mid-afternoon, our circadian rhythm causes a drop in attentiveness. But we’ve still got work to do now—and later when we get home from work. Options range from workout equipment to coffee to Snickers to pillows for a nap. 5-hour energy addresses the job well: keep you productive through the afternoon and early evening, without a sugar or caffeine crash. 

By understanding a place where people struggle, we can better design products and services, so that they fit perfectly in that struggling moment. If we match the solution to the job manifested in the struggling moment, customers will flock to the solution.

The Jobs to be Done lens helps us to see what causes people to bring products/services into their lives. We say that people don’t buy products, they hire them to do jobs for them. The classic way to understand this contrast is that no one goes to the hardware store seeking a 1/4-inch drill bit, they go to the store because they need a ¼"-hole. 

Jobs to be Done are not based on customer characteristics and product features. Jobs are discovered by looking at “struggling moments,” times when we’re searching to resolve a problem in our lives, but there isn’t a clear or great solution. 

I often use the example of the energy shot. &nbsp;<a href='/five-hour-energy'>5-hour energy</a> solves a job many of us have. In the mid-afternoon, our circadian rhythm causes a drop in attentiveness. But we’ve still got work to do now—and later when we get home from work. Options range from workout equipment to coffee to Snickers to pillows for a nap. 5-hour energy addresses the job well: keep you productive through the afternoon and early evening, without a sugar or caffeine crash. 

By understanding a place where people struggle, we can better design products and services, so that they fit perfectly in that struggling moment. If we match the solution to the job manifested in the struggling moment, customers will flock to the solution. 

Jobs to be Done.pdf

Jobs to be Done - thumb.jpeg

The Jobs to be Done lens helps us to see what causes people to bring products/services into their lives. We say that people don’t buy products, they hire them to do jobs for them. The classic way to understand this contrast is that no one goes to the hardware store seeking a 1/4-inch drill bit, they go to the store because they need a ¼"-hole. 

Jobs to be Done

To borrow an example from Malcom Gladwell, consider David and Goliath. Modern readers of the story see it as the victory of the little guy over the big guy. But little guy vs. big guy is the wrong way to understand the story. Goliath was built for conflict at close quarters—his size and strength were advantages, his slowness and poor eyesight were not hindrances. For our purposes, think of him as a grappler with a knife. David was small and there’s no indication he was particularly strong. But he was quick and he was skilled at defending his sheep from predators using a sling. Think of him as a rifleman. It’s pretty clear that a competent shooter will defeat a grappler, so long as the grappler can’t get near. The little guy/big guy categories obscure what David (and Saul) knew—David could reach out and touch the big man. 

Naturally, good categorization is context-sensitive. At close quarters the story of David and Goliath would have been entirely different—big vs. little would’ve been the right categorization. 

Categorization is necessary to connect the messiness of the real world to testable hypotheses. 

*Editor's note: <lens:categorization> does not meet the definition of lens (as of yet) it's foundational so it is included in the lense catalog.*

Categorization is a fundamental component of the scientific method. A key step in building knowledge about the world, is to categorize our observations. As such categorization is essential to understand the phenomena we observe.

To borrow an example from Malcom Gladwell, consider David and Goliath. Modern readers of the story see it as the victory of the little guy over the big guy. But little guy vs. big guy is the wrong way to understand the story. Goliath was built for conflict at close quarters—his size and strength were advantages, his slowness and poor eyesight were not hindrances. For our purposes, think of him as a grappler with a knife. David was small and there’s no indication he was particularly strong. But he was quick and he was skilled at defending his sheep from predators using a sling. Think of him as a rifleman. It’s pretty clear that a competent shooter will defeat a grappler, so long as the grappler can’t get near. The little guy/big guy categories obscure what David (and Saul) knew—David could reach out and touch the big man. 

Naturally, good categorization is context-sensitive. At close quarters the story of David and Goliath would have been entirely different—big vs. little would’ve been the right categorization. 

Categorization is necessary to connect the messiness of the real world to testable hypotheses. 

*Editor's note: <lens:categorization> does not meet the definition of lens (as of yet) it's foundational so it is included in the lense catalog.*

Categorization.pdf

Categorization - thumb.jpeg

Categorization is a fundamental component of the scientific method. A key step in building knowledge about the world, is to categorize our observations. As such categorization is essential to understand the phenomena we observe.

Categorization

Consider the archetypical scientist, Albert Einstein. Without the failure of the Michelson-Morley experiments to measure “ether drift,” Einstein would never have begun to consider the puzzle of why light always moves at a constant speed. 

In my work with Clayton Christensen, we came to recognize that the best path for continued research on Disruptive Innovation was to focus on anomalies. This led to a better understanding of how different circumstances affect the manifestation of disruption. And, more importantly, it helped us to understand where Disruptive Innovation should not be applied. 

When seeking progress in product science, we emphasize not the expected, but the unexpected—anomalies—as the engine for progress.

When testing hypotheses, sometimes we encounter an unpredicted result. Based on years of application and the history of science in the West, we see clearly that science progresses not by way of corroboration—strictly speaking, no hypothesis is confirmed by “successful” experiments—but by way of taking anomalies seriously.

Consider the archetypical scientist, Albert Einstein. Without the failure of the Michelson-Morley experiments to measure “ether drift,” Einstein would never have begun to consider the puzzle of why light always moves at a constant speed. 

In my work with Clayton Christensen, we came to recognize that the best path for continued research on Disruptive Innovation was to focus on anomalies. This led to a better understanding of how different circumstances affect the manifestation of disruption. And, more importantly, it helped us to understand where Disruptive Innovation should not be applied. 

When seeking progress in product science, we emphasize not the expected, but the unexpected—anomalies—as the engine for progress. 


Anomaly.pdf

Anomaly - thumb.jpeg

When testing hypotheses, sometimes we encounter an unpredicted result. Based on years of application and the history of science in the West, we see clearly that science progresses not by way of corroboration—strictly speaking, no hypothesis is confirmed by “successful” experiments—but by way of taking anomalies seriously.

Anomaly

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

The Forces of Progress cause customers to ***switch*** to or away from a ***current*** product/service to an ***alternative*** solution.

The primary forces are: *Push* and *Pull*. A ***Push*** is something that compels us to part ways from our current solution. A ***Pull*** is something we fancy in the alternative we are considering. A primary axiom of  <lens:jobs-to-be-done> is that a switch is unlikely if Push & Pull forces don't align in time and place. 

Once the Push & Pull forces are aligned, a switch becomes possible, but, secondary forces may *prevent* a switch: the ***Anxiety*** we may have of the alternative solution, and our ***Habit***s formed around the current solution from which we need to break away. 

**Example**:  [*mid-afternoon slump*](https://www.dx.mba/examples/:five-hour-energy-tastes-bad-is-weird-kills-category) —this is the point in our circadian rhythm that we are least alert. The *Push* of the situation *\[important meeting this afternoon with my boss and her boss\]* links up with the *Pull* of solutions that helps us make progress *\[alert and cogent so I look good to my superiors\]*. There are many products that we can ***hire*** to solve this slump: coffee, running shoes/kit, naps, energy drinks, etc. The forces must align in order for us to switch from our current solution *\[pick up fresh cup of coffee\]* to an alternative *\[gulp down an energy drink\]*. Usually, forces surrounding the current situation are somewhat balanced. <em>Push</em>es that may help consumers switch away from *current* solution are neutralized by the <em>Habit</em>s' inertia. This helps consumers create a somewhat stable system around their *current* use *\[looking forward to that fresh cup of cofee\]*. Only when this system is perturbed *\[unusually long line for coffee—I am going to be late!\]* when consumers are forced to make a choice to switch to an *alternative* solution available to them  that they consider switching. This forms an entry point to try an *alternative* solution: *\[a [5-Hour Energy](https://www.dx.mba/examples/:five-hour-energy-tastes-bad-is-weird-kills-category) shot\]*. But only if it clears our in-context *Anxiety* in that time and space: *\[right at the counter, and no line\]*. 

Proper attention to the Forces of Progress is a key to a successful product definition.

Forces of Progress are a representation of how customers hire and fire solutions in situations.

The Forces of Progress cause customers to ***switch*** to or away from a ***current*** product/service to an ***alternative*** solution.

The primary forces are: *Push* and *Pull*. A ***Push*** is something that compels us to part ways from our current solution. A ***Pull*** is something we fancy in the alternative we are considering. A primary axiom of  <lens:jobs-to-be-done> is that a switch is unlikely if Push & Pull forces don't align in time and place. 

Once the Push & Pull forces are aligned, a switch becomes possible, but, secondary forces may *prevent* a switch: the ***Anxiety*** we may have of the alternative solution, and our ***Habit***s formed around the current solution from which we need to break away. 

**Example**:  [*mid-afternoon slump*](https://www.dx.mba/examples/:five-hour-energy-tastes-bad-is-weird-kills-category) —this is the point in our circadian rhythm that we are least alert. The *Push* of the situation *\[important meeting this afternoon with my boss and her boss\]* links up with the *Pull* of solutions that helps us make progress *\[alert and cogent so I look good to my superiors\]*. There are many products that we can ***hire*** to solve this slump: coffee, running shoes/kit, naps, energy drinks, etc. The forces must align in order for us to switch from our current solution *\[pick up fresh cup of coffee\]* to an alternative *\[gulp down an energy drink\]*. Usually, forces surrounding the current situation are somewhat balanced. <em>Push</em>es that may help consumers switch away from *current* solution are neutralized by the <em>Habit</em>s' inertia. This helps consumers create a somewhat stable system around their *current* use *\[looking forward to that fresh cup of cofee\]*. Only when this system is perturbed *\[unusually long line for coffee—I am going to be late!\]* when consumers are forced to make a choice to switch to an *alternative* solution available to them  that they consider switching. This forms an entry point to try an *alternative* solution: *\[a [5-Hour Energy](https://www.dx.mba/examples/:five-hour-energy-tastes-bad-is-weird-kills-category) shot\]*. But only if it clears our in-context *Anxiety* in that time and space: *\[right at the counter, and no line\]*. 

Proper attention to the Forces of Progress is a key to a successful product definition. 

Forces of Progress.pdf

Forces of Progress - thumb.jpeg

Forces of Progress are a representation of how customers hire and fire solutions in situations.

Forces of Progress

Firms often do market research as a way to explore the possibilities for new products and services. But we have found that new-growth opportunities avoid already crowded markets by looking for where people are left out of a market. Successful FinTech firms, for example, have found their entry into the market among the unbanked. In India, Godrej Appliances recognized that only 18% of Indian households had refrigerators. Rather than competing in that small space, they set out to created ChotuKool to reach households that couldn’t afford a traditional refrigerator. 

The core idea of non-consumption is that you can avoid competition—and thus grow your business—by finding opportunities among people or organizations that don’t have access to a solution, or to find a new-growth opportunity in a Job to be Done that lacks a solution altogether.

In looking for disruptive opportunities, it is best to look for what we call “non-consumption.” Non-consumption is manifest by the inability of firms or individuals to obtain a solution. This can be due to the absence of a good solution or to an existing solution’s being inconvenient to acquire and use—including being too expensive. 

Firms often do market research as a way to explore the possibilities for new products and services. But we have found that new-growth opportunities avoid already crowded markets by looking for where people are left out of a market. Successful FinTech firms, for example, have found their entry into the market among the unbanked. In India, Godrej Appliances recognized that only 18% of Indian households had refrigerators. Rather than competing in that small space, they set out to created ChotuKool to reach households that couldn’t afford a traditional refrigerator. 

The core idea of non-consumption is that you can avoid competition—and thus grow your business—by finding opportunities among people or organizations that don’t have access to a solution, or to find a new-growth opportunity in a Job to be Done that lacks a solution altogether. 

Non-Consumption.pdf

Non-Consumption - thumb.jpeg

In looking for disruptive opportunities, it is best to look for what we call “non-consumption.” Non-consumption is manifest by the inability of firms or individuals to obtain a solution. This can be due to the absence of a good solution or to an existing solution’s being inconvenient to acquire and use—including being too expensive. 

David Sundahl