Decoding Greatness: How the Best in the World Reverse Engineer Success

Why Should You Read This?

In today’s hypercompetitive world, it isn’t enough that you have the right skills and knowledge. You need to channel them into blockbuster products. But how do you know what will be a hit and avoid what’s going to bomb?

In Decoding Greatness, Ron Friedman, Ph.D., reveals that many top performers have figured out the answer to this question.

Friedman starts by describing 3 possible paths to greatness:

• Discover your inner talent and find a profession that allows it to shine
• Practice a prized skill deliberately and diligently so success will be the natural outcome
• Study great works and reverse engineer them to uncover the blueprints for success

The first approach argues you only need to find your “calling.” Find the career that leverages your talents. Do that and you’re on the way to success.

There are several challenges, however:

• What if you don’t know what your talent is?
• What if it’s good but not great?
• What if it’s great but in something that you don’t care for?

Path 1 requires that skill, interest, and the success you desire all align.  That’s asking a lot. And in practice doesn’t happen that often, so not very useful nor reliable.

The second approach offers more control. It emphasizes effort. If you work at a valued skill or subject and improve via deliberate practice, you achieve expertise that will be highly prized. This is the traditional approach most top performers follow.

The drawbacks are it’s hard, unpleasant, and takes time. The often quoted, “10,000 hours” to become an expert summarizes this thinking. (Professor K. Anders Ericsson, the man behind deliberate practice, states it doesn’t take 10,000 hours but varies considerably. But the effort has to be challenging).

Another issue with deliberate practice is that it works well in fields with a “ranking” definition of success. In other words, there is a ladder of progression. You start at 0 and slowly but surely work your way up. For example, chess has a ranking system that monitors wins and losses. To move up the ranking, you need to win X number of games at major tournaments. Thus, you have a clear sense of how to advance.

But what happens if you’re in a field where the success criteria are subjective.

That is the reality every book, film, song, or knowledge/creative product faces. When it’s released, it either climbs the chart or not. It doesn’t matter how much blood, sweat, and tears are invested.

That’s where reverse engineering comes in and is the subject of Decoding Greatness.

Friedman shows how you can increase your odds of success. By looking at what is great, figuring out the blueprint, and then duplicating that with some minor adjustments, you minimize the risk of failure.

In his book, he divides the process of reverse engineering into 2 parts. Part 1 is called the “The Art of Unlocking Hidden Patterns” and describes how to identify hidden insights, acquire new skills, and spark creativity via reverse engineering. He also explains why copying is not sufficient and ignoring proven formulas is a mistake.

In part 2, “The Vision-Ability Gap” deals with the challenges of transforming knowledge into mastery. Identifying the formula and the key ingredients are necessary but not sufficient for achievement. Execution presents a whole set of issues that Friedman refers to as the vision-ability gap. When you start to copy great works, you immediately realize the considerable gulf between where you are and where you want to be.

In this section, he describes various strategies to accelerate your expertise, such as improving via using scoreboards, using better practice techniques, and learning to get the proper feedback.

For this review, Friedman’s insights are captured in 7 takeaways organized into 2 sections. The first one describes why you need reverse engineering now, while the second goes into the challenges of doing it right.

Why You Need Reverse Engineering as a Skill

At its core, reverse engineering is about learning through copying. And while mimicry has been around since antiquity, in modern times, its practice is frowned upon.

In school, you learn that you should not plagiarize another’s work. This is true. You should not take credit for other’s efforts by passing them off as your own.

And in industry, copying a competitor’s work can be viewed as infringing on intellectual property. So again, this is a no-no and not to be done.

But that is not the goal of reverse engineering. Instead, the objectives are first to learn and then improve your expertise by dissecting a great product.

And when you do that, you uncover additional layers of richness.

For example, all blog posts should have a headline, intro, body, and call to action. But having these elements doesn’t tell you why one post gets crickets while the other goes viral.

This is the heart of reverse engineering. While it has elements of copying, at its core, it’s about finding the hidden formulas, patterns, and structures that differentiate the successful from the mundane.

By studying top performers in fields as diverse as high technology, cooking, screenplays, music, and book writing, Friedman demonstrates that reverse engineering was at the core of their success.

1.     Conventional learning approaches aren’t sufficient

If you want to learn a subject or skill, what do you typically do? Three common approaches are you take classes, watch videos, or read books. (You can also hire a coach, but unless you have $$$’s that’s going to be expensive, so that won’t be covered here).

Friedman says these don’t work anymore because of 3 shortfalls:

• The modern landscape is more competitive and changes faster than ever before. If you wait for someone to teach you, then you incur 2 opportunity costs. The first is you are on their timetable. You can’t learn until they create the teaching material. With reverse engineering, there is no time lag. As soon as you get your hands on a product, you can take it apart and analyze what makes it tick. It is one of the fastest ways to innovate. The second is you are subject to the instructor’s agenda and bias—more on this in the next bullet.
• Experts are great at execution, but that doesn’t mean they can teach. Friedman cites scientific studies where it’s not unusual for top performers to miss up to 70% of the details explaining what they do. In other words, you are getting only 30% of what’s needed. The researchers believe that because professionals know the subject so well that it’s in their unconscious. They just know what to do and what to avoid. In other words, they forget that a newbie may see a multitude of options whereas the expert only sees one. And that one is, of course, “intuitively obvious.” Hence, books, workshops, and classes are limiting unless you take several since something is always missing. This is an important revelation for perfectionists and book bound learners who feel if they just read it, they will know it. You have to read a lot, and even then, you won’t learn what you missed until you apply it.
• Reverse engineering makes a trade-off by going for depth over breadth. With a finished product, you’re examining the choices a top performer has made and studying the details. In contrast, if you’re reading a book or taking a class, you cover the breadth of a topic, and not all of it is relevant. Reverse engineering involves less material since it is targeted.

Compared to conventional learning approaches, reverse engineering provides a fast, relevant, targeted way to learn a subject or a skill. However, it makes a trade-off by going deep into what is successful versus covering the breadth of a topic.

2.     True greatness does not have to be original or novel

When you think of something being awesome, you believe it must be original or novel, right? Decoding Greatness debunks this idea and shows that true originality may actually be a barrier to success.

Friedman says that Prof. Jennifer Mueller of the University of Southern California [Fact checked, she’s never been at USC but has been at the University of San Diego] found an alarming trend: the more novel an idea is, the more likely it is rejected. So, despite folks asking for bold, innovative concepts, her studies find that people reject them all the time in practice. On top of it, people see those proposing the ideas as weaker leaders.

Why is that? She explains that novel ideas present challenges because novelty makes us uncomfortable, and discomfort is unpleasant.

A study conducted by Harvard researchers in 2014 back Mueller’s findings on the “novelty paradox.” These scientists had 142 subject matter experts (SME’s) review medical research proposals submitted for grants. Winning these awards is a very competitive process. Each submission was rated across metrics such as quality, feasibility, novelty, and how strongly the SME’s felt if it should receive funding. What researchers observed is grant-winning submissions contained a small amount of novelty. In other words, what got awarded was something familiar with a slight twist.

So, while folks may ask for novelty, the reality is too much is not great either.

Friedman says this is encouraging because it lowers the barrier to greatness.  You don’t need to come up with something that is 100% original. Instead, look for great works, find their essential parts, and borrow successful elements to create your product.

As an example, in Decoding Greatness, Friedman describes a pivotal element to Marvel’s film success is they deliberately place “a leader with limited genre exposure at the helm for the purpose of introducing a fresh perspective.” In other words, while they make superhero movies, they go for a proven director with non-superhero experience, so their films seem fresh.

The takeaway is to leverage what’s proven and add a little bit of spice to it.

3.     Success leaves clues in the form of patterns

So, why does reverse engineering provide such a competitive advantage over other approaches to expertise?  These 3 quotes from Decoding Greatness explain:

History teaches us that a striking number of top performers appeared naturally drawn to collecting works they admired long before entering and later dominating their field.

Why is collecting outstanding examples so important? Because the first step to achieving mastery is recognizing mastery in others.

Studies indicate that simply consuming examples with an underlying structure leads you to detect their patterns, even when you’re not consciously trying to learn a thing.

In short, with reverse engineering, you’re searching for patterns of success. Specifically, Friedman outlines 4 steps to do the pattern recognition:

• Collect positive and negative examples
• Look for essential variations, such as what features or their values, are common or different between the 2 classes
• Detect similarities; while Friedman says similarities, he actually talks about differences as well; it’s not clear to me how this is different from step 2, but I list it here for consistency with the text.
• Generate predictions from your feature set and see if they are right

By following these steps, you search for patterns hidden in the works you admire. Having some background in the field helps since you can use this knowledge to figure out what data to collect.

If you’re a newbie and have no idea, it’s trial and error. Measure what you think might be critical elements, and then be ready to iterate.

The key is step 4. With the data, can you make predictions about what makes something great vs. average? If you can’t, consider alternative measurements or the following 2 analysis techniques to help generate more pattern ideas.

Friedman describes 2 popular ways to dissect a work:

• Literally, just try to copy the work you admire as closely as possible. For writing, reproduce the prose word for word. For art, copy the painting or the sculpture as best you can. The theory is you learn by diffusion. By duplicating the work, you focus on details that may be lost through reading or taking a class. This gives you a sense of their style as you pick up clues as to what’s distinct.
• Reverse outline. Here you deconstruct the product into an outline. This applies more to literary work but operates by breaking it into components. For example, for writing, Friedman says to take each paragraph and summarize it in a single sentence. As this would be a lot for a book, you can do something similar but with chapter sections instead. Again, the goal is to detect writing structures. For instance, do the chapters start with a story, a problem, a quote, or with theory and principles? Is there a recurring pattern?

When you do these analyses, Friedman advocates measure everything you can. While that’s a bit overboard, you get the gist that you don’t know where/what the pattern is unless your measurements capture it.

So, by collecting samples, then dissecting and comparing them, you uncover the success patterns.

Challenges When Doing Reverse Engineering

Ok, now that you know hidden formulas are out there. It’s just a matter of identifying and following them, and then success is guaranteed, right?

Friedman says not so fast.  A significant drawback when you do reverse engineering is the Vision-Ability Gap. And that is, as you study great works, you find an enormous disparity between where you are and the top performers.

As Ira Glass says in the book:

What nobody tells people who are beginners – and I really wish someone had told this to me – is that all of us who do creative work, we get into it because we have good taste. But there is this gap. For the first couple of years you make stuff, and it’s just not that good. It’s trying to be good, it has potential, but it’s not. But your taste, the thing that got you into the game, is still killer. And your taste is why your work disappoints you. A lot of people never get past this phase. They quit.

Friedman adds:

… the price of having a clear vision is not simply disappointment with your own work. It’s also the risk factor for quitting. The stronger your radar for excellence, the harder it becomes to stomach mediocrity. And that’s a problem, especially when deconstructing the work of masters will invariably raise your standards.

This gap can seem impossible, especially for perfectionists or anyone with high standards. You expect a lot, and when your efforts fall short, as they inevitably will in the beginning, it can be frustrating.

So, in Part 2 of his book, Friedman shows how to close this vision-ability gap by leveling up your skills the right way.

1.     De-risk so you do not feel incompetent

The bad news at the beginning is you suck. The good news is research by UCLA Prof. Robert Bjork suggests you learn best when you’re in over your head. That’s the theory.

To do so in reality, means you need to push yourself, take some risks, and that means inviting failure. This is hard to do in practice. You might be encouraged to be creative, bold, and cutting edge in both school and work, but let’s be honest, you don’t get rewarded for failure.

So, what’s the answer?

By analyzing authors and businesses, Friedman found there are 4 ways to minimize (not eliminate) risk while getting the real-world feedback you need to get better. By doing this, you take on more challenges, conquer desirable difficulties, and shrink that vision-ability-gap.

The 4 approaches are:

• Use test audiences to get early feedback. In software, this is often referred to as A/B testing. The idea is to present users with variants of your product (version A or B) and ask them to evaluate. One top reason entrepreneurs fail is spending a lot of time creating their product only to discover there is either no market need or what consumers want is only a part of what they are building. This approach says to vet your ideas, as imperfect or incomplete as they may be, just to test the waters. The key is to determine if there is sufficient interest to merit further investment of time and energy. While everyone agrees it’s important not to flop, it’s worse to sink a lot of effort into something that bombs. It’s also important to note what aspects of each product are popular and not. Finally, use this feedback to iterate on the product. For example, B can be popular overall, but some of A’s elements might be worth capturing for the next iteration.
• Use pseudonyms or brand names to test out new approaches, products, styles, etc. This is an old practice. Some writers such as JK Rowlings and Stephen King have used pseudonyms to see if their writing chops were good enough in other writing genres. They didn’t want readers biased by their name recognition, so they used pseudonyms. Likewise, companies like The Gap have created brand names like Old Navy, Banana Republic, and Athleta to explore other market areas without risking The Gap’s brand recognition. For example, the previous names were successful market hits. Other lines The Gap tried include Forth & Towne and Piperlime, which all failed. The point is most of us don’t associate either successful or failed brands with The Gap. So, a company can test the waters without risking the brand recognition they spent years building.
• With the rise of the Internet, preselling has become easier to do. At its core, presell is about selling prospective customers the idea of your product before you build it. This was how online shoe retailer Zappos started. The founder, Nick Swinmurn, sold shoes to customers despite not having a store or any product. He simply went to existing shoe stores, took pictures of their inventory, posted them on his website, and then grabbed orders from customers. To fulfill orders, he went back to the stores, bought the shoes, and then sent them to the customers. Through this experience, he found strong support for online shoe purchases. The power of preselling is confirming demand before investing significant effort- this again reduces risk significantly.
• Diversify your investments. There are 2 schools of thought on how you spend your effort. One view is to go all-in. To maximize gain, you need to put all your eggs in one basket. The belief is unless you’re 100% invested, you will fall short as the lack of focus will distract you from your goal. This is true. If you pursue too many targets, you dilute your energy, and progress will be tiny compared to the all-in. But the danger is what if your investment fails. The reality is when dealing with risk, science favors diversification. Friedman cites research that says entrepreneurs who started their new business while keeping their day job were 33% less likely to fail than those who committed 100%.  As you don’t know which paths lead to a win, you need to keep some (not many) options open and let the data tell you where success lies.

By following any or a combination of the 4 above approaches, you will have the tools to de-risk and build the confidence to pursue more challenging tasks.

2.     Improve by measuring what’s relevant

To become proficient, you need to know what needs to be improved. It would be great if it was only one thing, but often several factors are involved. Thus, Friedman suggests a scorecard of metrics- a set of quantitative measurements on performance. These metrics provide you with clarity on what is pushing the needle of progress the most.

So, what should you measure? This depends on the task nature, your skill level, and ultimate goal, but 3 areas worth considering:

• Measure subskills: Take a critical activity and then break it down into multiple subskills. Try to quantify how to measure improvement in each. This allows you to assess how each component is doing and its impact. Does improving them make a difference or not? This will enable you to figure out where to invest your time and effort.

• Measure features: When you break down a product into elements, try to quantify them. You want to figure out if great works have something that mundanes do not, or do they have the same ingredients, but their values are different. Again, you’re looking for differentiating characteristics.

• Quantify daily habits: Look beyond your tasks and evaluate your performance over a specific time frame. At the end of the day, while tasks are essential, your behavior determines whether they get done or not. Friedman stresses that quantifying productive habits is an excellent way to determine how you spend your time.

In brief, you want to have a combination of behavior and outcome metrics. The reason is behaviors are controllable, whereas outcomes are not. But you need both to see if your actions lead to the desired results.

Another point is you need short- and long-term metrics. The former keeps your motivation up since progress is easier to appreciate, whereas the latter helps you stay on target.

Finally, Friedman adds that over time don’t be afraid to change your metrics. In fact, you should review them periodically to see if they are relevant. You don’t want to collect measurements for legacy reasons if they aren’t helping you improve. You have better uses of your time.

The takeaway is it’s hard to level up your skill if you don’t measure the specific areas to improve upon.

3.     Accelerate your skill level in 3 ways

Sooner or later, you need to practice if you want to level up. Friedman shows 3 ways to do this.

The first is what he calls imagery, but a better description is mental rehearsal. Popular among athletes, they use this technique to prep before game time. They mentally replay what to do and what to expect.  This method works because it provides you one more practice round. You refresh your strategy and tactics one more time. You also review how to overcome obstacles before they happen, so you don’t panic. The benefits are less stress and fewer mistakes.

Note, another popular form of imagery is where you imagine yourself in the winner’s circle. Unfortunately, Friedman says this type of visualization backfires. Research finds that folks who focused on the outcome had the worst performance than the control group who did no visualization or the top-performing group that did the mental rehearsal.  The reasoning is when you focus on the outcome, you get satiated and have less desire to do the work needed for success.

While you should still do actual practice, another benefit of mental rehearsal is when you’re short on time. According to the research, the optimal length for this kind of practice is no more than 20 minutes, with some seeing benefits in as little as 3 minutes. In other words, the brain and body don’t have a strong distinction between physical and mental practice. So, if you don’t have time, mental rehearsal is the next best thing you can do.

The second approach to maximize your learning progress is deliberate practice.  Here top performers achieve mastery not by mere repetition but by targeting weaknesses, pursuing stretch goals, and pushing the bounds of their abilities. Prof. K. Anders Ericsson advocated this approach after studying how top performers prepped in different fields. (You could say that he did reverse engineering on the idea of practice!)

The critical steps of deliberate practice are breaking complex tasks down into subtasks, focusing on them one at a time, getting immediate feedback, making incremental adjustments, and repeating to see if the changes improved performance. Unfortunately, this makes the practice hard and unpleasant, since it forces you to address your shortcomings directly. But this is a common approach among top performers in many fields who strive to be world-class. (While not covered in Decoding Greatness, Ericsson points out that most people can only do deliberate practice for 4-5 hours a day due to its intensity. Any more, and you see a drop off in benefits for the effort invested. So, it follows the “Less but intense” model)

Finally, the third method of practice involves reflection. That is periodically review how you have performed and see if you can do better.

To illustrate, Friedman cites research where 2 groups trained to draw for 3 days. One group practiced sketching object after object for 3 days straight. The other group also sketched for 3 days but was asked to copy a professional drawing on the second day and then went back to drawing objects on day 3.  After the 3 days, both groups were evaluated on the creativity of their work. The study found that the second group, who had interspersed their practice by copying an expert’s work, displayed far more creativity than the first group, who had practiced consistently.  Friedman writes:

“Not only did copying an artist’s drawing inspire far more creative illustrations later on, it did so by stimulating ideas that had nothing to do with the copied artist’s work. In other words, copying didn’t simply lead people to mimic an established approach. It unlocked a mindset of curiosity and openness that motivated them to take their work in fresh, unanticipated directions.”

In brief, when you are forced to copy someone’s work, you become more reflective. Since you have a reference you’re measured against, you pay more attention to your actions. You ask if what you’re doing is hitting the target (quality) versus copying object after object (quantity).

If you find reflection hard to trigger, Friedman offers 2 approaches. One is to video record your performance and critique it to see if there are any noticeable shortcomings.  This is popular among film directors, athletes, and coaches. Directors often view various cuts to see which ones work better, while athletes and coaches review past plays to target areas of emphasis for their practice.

If recording is not feasible, try pausing and reflecting when performing. A Harvard study found that when you introduce this reflection period, performance can go up by as much as 20% versus individuals who simply took a break. Reflection provides benefits because it allows you an opportunity for feedback to readjust your efforts and make you think if there are better ways of doing things, which can prompt insight.  In other words, rather than blindly doing the task based on current assumptions, you ask yourself if you can do better by looking for areas of improvement.

Friedman says that capturing your thoughts in a journal is an excellent way to aid reflection.

Whichever practice method you follow, the takeaway is to focus not on simple repetition but on ways to improve if you want to level up your skill ASAP.

4.     Ask for feedback the right way

As hinted above, critical to improving your skills during practice is getting the proper feedback.

Friedman says this is easier said than done. The majority of the feedback you get is not helpful. The reasons are two-fold. When you ask experts, he finds that knowing a skill and coaching it require different proficiencies.  As said earlier, experts have a difficult time relating to a newbie, so they often fail to point out key details (recall as much as 70% can be missed in an expert’s recollection). At the other end of the spectrum, if you ask peers or friends for feedback, you can get 2 extreme responses. Some might hesitate to give you bad news, while the more vocal may focus on criticism that is more towards making them look competent than helping you get better. In fact, a study in Psychological Bulletin found that over 1/3 of feedback actively damaged performance.

So, what do you do?

The key is you need to get specific and helpful feedback. So, for example, if you’re receiving positive feedback, you want to know what was good and why. Similarly, if it’s negative feedback, you need to know how it was bad and where to improve.

Friedman provides 2 strategies to achieve the above. One is for interacting with experts, and the other is for peers or customers.

For experts, you need to ask them 3 kinds of questions to steer them in the right direction:

• Journey questions ask the expert to reflect on how they got to where they are. The goals are two-fold: to unearth the expert’s roadmap for success and then to remind them of their experience when they started as a newbie. In other words, ask them to view things from the beginning when they started vs. where they are now.
• Process questions focus on the nitty gritty of execution. You want to interrogate experts on the details by drilling on specific steps they apply to bring their work to life. Typical questions may target specific tools they use or the exact sequence of steps they follow and why. For example, for some tasks, the step order may not seem important to the newbie, but the expert follows it religiously. For instance, an experienced chemist knows that there is a difference between adding acid to water versus water to acid (The latter risks major splash effects). It isn’t until you ask them why does the reasoning come out.
• Discovery questions are about initial assumptions or expectations that might seem reasonable initially but wrong based on experience. For example, what are things experts now know that they didn’t realize when they first started. The goal is to gain insights.

For peers or customers, you need a different strategy. The focus is not on recall but constructively getting their honest critique. You want to get a sense of how to improve versus just a straight-out assessment.

Friedman says the easiest way to do this is to reframe feedback as a request for advice instead.  By this mental shift, you acknowledge that you have a weakness and ask people for their recommendations. To do this, you need to guide them to where and how you want the feedback.

A 2019 Harvard study found that this approach was better in figuring out what worked and what didn’t. Researchers found when you ask for advice, you allow reviewers to consider opportunities for improvement. In contrast, traditional feedback tends to have people compare your current performance to either your past or someone else’s. In other words, it is common to ask, “Did I get better?” The answer, “Yes,” is not very informative.  But if you ask, “Are there ways to improve my delivery?” that’s asking for corrective action on a target area.

Another element about feedback is asking the right people, not those who are conveniently available but those whose opinions matter. Again, it’s quality over quantity.

The next point is feedback needs to be at the right frequency. For some tasks, like writing, getting critiqued at every sentence or paragraph is disruptive. But then waiting 3 years to finish a product to find there is no market might be too late.

In short, there is an art to getting the right frequency and type of feedback to accelerate your proficiency. But if done correctly, this can be invaluable.

Cons: Gaps and Issues

First off, Decoding Greatness is a fantastic book on why you should do reverse engineering to identify the blueprint for success. However, there are some minor shortcomings, and they fall into 2 buckets. The first is writing style, with the second on technical implementation.

Friedman writes in a narrative format with great stories showing how and why reverse engineering should be done. And his chapters end with a transition preview of what to expect in the next chapter. Normally, I enjoy this style of writing, however, in this case, I found it hard to navigate.

The reason is there is a lot of information. So, organization and structure are critical to helping you keep track of what’s going on. Having a brief overview at the beginning of each chapter would have helped. There Friedman can do a quick “These are the concepts that are going to be covered” or even, at the end, summarizing “These are the key takeaways you should get” would have been a significant improvement. 

In Decoding Greatness, you find nuggets of insight buried in the text, often devoid of tables or bulleted lists. In short, the gold is there, but it’s not screaming out at you. Instead, you have to sift through the narrative to extract the core principles.

For example, in one of his chapters, Friedman writes a section on the 3 secrets to functional personal scoreboards. The first secret is to have multiple metrics. Only one short paragraph is devoted to this, and it comes off more as an introduction to the next secret than anything stand-alone. For example, you don’t know what kinds of metrics to collect, only that you should have many. Hard to be actionable if you stop there.

The second secret is to aim for balance in your metrics. This is a powerful message, but the guiding principle is buried. Friedman cites example after example of the diverse kinds of metrics available and writes about them for 2 pages. But what do you use as a guide to assess what metrics you need?

From the reading, the short answer is to collect a lot of different kinds of metrics. Valid in an abstract sense but hard to execute since a lot can be a lot.

A good guiding principle is to collect metrics on a time scale that helps you course correct and on aspects of tasks and outcomes Friedman has found are essential to measuring progress or quality. Of course, that means you may collect more than you need only to find some are useless. That’s ok. It’s trial-and-error.

The final secret is to evolve your metrics from time to time instead of mindlessly following them. Again, minor style complaint, but this can be viewed as it’s essential to periodically review your metrics for relevancy. Again, great idea but lacks concrete actionables. For example, no mention on how often the top performers he studied review their metrics?

These are minor stylistic issues but might be a challenge for readers short on time or wanting a crisp, actionable recommendation.

The second bucket of issues is technical implementation.  Here, there are 2 shortcomings.

One interesting pattern in Decoding Greatness is the lack of negative examples. Friedman hints that comparing and contrasting is essential but doesn’t cite the value of looking at bad examples. Instead, he merely states in one use case that it’s important to look at things you don’t like, but doesn’t emphasize it as a core principle.

This is interesting because you can assume that great works have something that mundanes do not. But that’s rare. Often, both the bad and the great have the same elements. For example, most blogs have a headline, intro, body, and call to action, yet some get crickets while others go viral.

The difference lies in the details. So, unless you look at poor examples, you may not catch what makes the great stuff stand out.

The next shortcoming is the lack of detailed how-tos in the book. Full disclosure: I took Friedman’s Masterclass on Blueprints for Greatness, a workshop for the book. In this class, Friedman provided detailed worksheets on how to dissect Ted talks, viral posts, and popular books. He offered specific questions and angles to guide how you should reverse engineer.

This class was immensely valuable in helping to apply reverse engineering in practice. It would have been great if this information was directly in the book, but it isn’t. Having said that, as of Sept 5, 2021, you can access the Masterclass I took for free and get other bonuses if you submit a receipt of purchase for Decoding Greatness at the book website https://decodinggreatnessbook.com.

If you get the book, I highly recommend taking the Masterclass and looking at the bonuses. This will make applying reverse engineering more practical. The class doesn’t cover everything in Decoding Greatness but does go into the reverse outlining section in detail.

Overall, these gaps and issues are minor relative to the value you get out of the book.

Summary and Recommendation

In Decoding Greatness, Dr. Ron Friedman illustrates the key to success for many top performers was reverse engineering great works.  By first collecting and then analyzing what makes great great, you learn the underlying patterns and structures.  But he also cautions that in doing so, you may stumble into the Vision-Ability Gap where you question your abilities which are pale in comparison to the experts.  In his book, Friedman highlights how you can bridge this gap with minimal risk.

In this review, 7 takeaways from the book are listed:

• Conventional learning approaches aren’t sufficient
• True greatness does not have to be original or novel
• Identification of hidden patterns is the key to achieving success
• De-risk so you do not feel incompetent
• Improve by measuring what’s relevant
• Accelerate your skill level in 3 ways
• Ask for feedback the right way

In terms of recommendation, I rate Decoding Greatness as a Must Have purchase, especially if you take the Masterclass. The main reason is there aren’t many books on the topic. Most of the current literature focuses on how to take apart a physical product or dissecting code.  They aren’t about reverse engineering as a general skill to learn what makes something successful.

Friedman’s contribution to the field of achieving success is that he captures and highlights content often found only in the biographies of top performers. And that is, reverse engineering along with deliberate practice have played critical roles in differentiating great work from average. This is especially essential in fields where reproducing success is challenging.

Fundamentally, there are many ways to leverage new skills and subjects. Some will lead to success, while others will not.  Top performers have already figured out which paths are the most promising.  Use Decoding Greatness to help you rediscover those hidden routes to accelerate your timeline to achievement.

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