Dispatches from the Frontier

Failing Effectively in Order to Succeed

The odds against successfully commercializing a given invention are low, yet there are successful inventors.  Just because luck is an important element of success, it is not the only driver of success.  Successful inventors understand that taking good shots at a high rate can improve the overall odds of success.  However, taking a "shot" toward commercializing an invention can be expensive: it requires the expenditure of time, money, the activation of social networks, intellectual effort, and draws upon emotional reserves.  Therefore, given the likelihood of failure, it's incumbent upon the inventor to learn how to fail quickly and cheaply.

In his latest post, Ashton Udall at Product Global comments on advice given by Andrew Krauss of Inventors Alliance and Stephen Key of InventRight.  Both offer solid, practical insights on how to effectively poke holes in your own business plan in order to help ensure that you spend most of your time, money, and energy on inventions that have the greatest likelihood of commercial success.

Superstar CEOs as Free Riders

Superstar CEOs' celebrity typically doesn't reflect their performance:

We find the firms of CEOs who achieve "superstar" status via prestigious nationwide awards from the business press subsequently underperform beyond mere mean reversion...

It could be that kleptocrat CEOs - those who lay claim to a disproportionate amount of credit and compensation for their firms' past success - are a particularly visible and pernicious form of free rider: their narcissistic behavior drives out top performers at an accelerated rate, leaving slackers in their wake.

The Seeds of Corporate Failure

Over recent decades, there has been an explosion in business knowledge.  Furthermore, pervasive networks facilitate the dissemination of such knowledge.  Nevertheless, businesses - even very large, formerly very successful businesses - fail at a consistent rate.  Conventional thinking suggests that the causes of failure are largely extrinsic - unexpected shocks.  In such cases, one might expect that a company's size might insulate it from failure.  Contrary to such thinking is evidence that the primary cause of failure is intrinsic, and large companies may bear the seeds of their own destruction.

As Paul Ormerod, author of Why Most Things Fail, notes:

More than 10 per cent of all economically active firms in the US become extinct each year.  It is a distinctive feature of firms, and any economic theory of the firm should attempt to explain it.  Conventional economic theory can only do so by positing an endless supply of completely unexpected shocks, for otherwise the perfectly informed, rational decision-making firm should never die.

In other words, conventional thinking assumes that a key cause of failure is extrinsic.  If extrinsic shocks were the key driver of failure, one would expect that large firms would have a better chance of survival.  After all, large firms can benefit from economies of scale and scope.  Furthermore, their characteristics accumulations of resources can serve as buffers.  Nevertheless, size is no guarantee of longevity:

Very basic mistakes...weed out very quickly those least adapted to survive...surprisingly, there seems to be very little connection between the size of a firm, once the first few fraught years of existence have passed, and its probability of surviving any given period.

Philip Ball, author of Critical Mass, makes a similar observation:

Of the largest five thousand U.S. firms operating in 1982...only 35 percent still existed as independent entities in 1996.

Robert Axtell, a sociologist, found a typical pattern in the lifecyle of large firms: a period of exponential growth is followed by a sudden decline and gradual dwindling.  He was able to simulate the pattern using an agent-based computer model that demonstrates that the rise and rapid decline of firms can be explained intrinsically.  Mark Buchanan, author of The Social Atom, offers this explanation:

In a small firm, each person's effort has a large impact on the total output, so what a worker gets out depends on what he or she puts in.  In small firms, therefore, no one has the incentive to free ride; all have the incentive to work hard.  In a large firm, however, any one person's contribution to the overall effort becomes much smaller.  So if someone doesn't really put in much effort, but only pretends to work hard, he or she will still get just as much because the overall productivity of the company will barely suffer...

Ball elaborates:

...collapse is a consequence of the firm's own success.  Once it grows big enough, it comes a haven for free riders who capitalize on the efforts of others.  So the firm gradually becomes riddled with slackers, until suddenly the other workers decide they have had enough and jump ship...longevity in a company stems from being able to attract and retain productive workers.  A firm fails not when its profit margins are eroded but when it is infiltrated by slackers.

Buchanan makes a similar conclusion:

In short, firms grow out of cooperation and the benefits it brings, but their success sets the stage for later cheating, which undermines the cooperation on which the firm depends.

Here is my interpretation of the dynamics described by Axtell, Ormerod, Ball, and Buchanan (please refer to the diagram at left):  A firm's capacity for success and growth is a function of its resources and capability (intrinsic) as well as good fortune (extrinsic).  Over time, capacity depends upon the interaction of two feedback loops.  The first, which I've labeled the "Rich Get Richer", is a reinforcing loop that describes how success and growth increases a firm's access to resources.  Capacity for future success, for a given level of capability, increases with an increase in resources.  The second feedback loop, which I've labeled the "Free Rider Problem", is a balancing loop.  It describes how accumulated success increases the chances of attracting free riders - slackers and kleptocrats - who drag down a firm's average capability over time.  For a constant level of resources, a decline in capability reduces the capacity for future success.

A thought experiment shows that even this simple model can simulate the patterns of growth and decline observed in the real world.  Consider the following narrative:

• A start-up typically has few resources.  Its workers tend to by highly motivated, but as researchers such as Amar Bhide have discussed, are not, on average, particularly skilled.  Consequently, a start-up's capability is usually modest.  Modest capability and low resources suggest a humble capacity to succeed.  Most don't, as reflected in the power law distribution of market outcomes and data that suggests that at least 75% of business initiatives fail to survive longer than two years.
• Sometimes, however, good fortune strikes, and humble capacity is sufficient.  Growth happens, and success accumulates.  During this phase, the Rich Get Richer feedback loop is virtuous: success improves access to resources and, thus, the capacity for future success.  In my experience, the relationship between accumulated success and capability may even be virtuous: capable people like to attach themselves to a rising star.  If so, the firm benefits from rising capability as well as increasing resources.  The rocket roars from the launching pad.
• Over time, though, the Free Rider Problem emerges.  In spite of careful (and rather expensive) efforts to mitigate agency risk through careful board supervision and measures to improve accountability, average capability diminishes over time (notwithstanding the possibility that a very large company may still have a large number of extraordinarily capable workers).
• At some critical point, success and growth stalls, and the benefit of incremental access to resources is offset by the Free Rider Problem.  At this point, the firm's best (and most mobile) workers start to hit the exit door.  At some threshold, the outflow becomes a stampede, and average capability starts to plummet.
• As capacity falls with the drop in capability, the reinforcing Rich Get Richer loop turns into a viscious cycle, in which a drop in success results in a withdrawal of resources and a further diminishment of the firm's capacity to succeed (absent miraculous good fortune).

Most new firms don't benefit from enough good fortune to ensure that their modest endowments of capability and resources translate into sufficient capacity for success.  So, small firms die.

Of the minority of firms that do benefit from a surge of success and exponential growth in capacity, few are able to recognize the true extent of their growing Free Rider Problem.  That may because we tend to internalize success, believe that we are better than average, and are convinced that we work hard.  Some (many?) large companies may even foster an environment that is corrosive to cooperation, which seems to accelerate the demise of firms.  So, big firms die.

Growing wisely would seem to be much more challenging than growing rapidly.

What Kobe Bryant Taught Me About Innovation

I'm not a big basketball fan.  Nevertheless, my digging around on the statistics pages at NBA.com has yielded some interesting data that I think have relevance for innovation.  Specifically: competition will push you to take more risk in the face of greater possible payoffs; take shots at a higher rate than your competition in order to score more than your competitors; and those who take reasonably good (but not necessarily spectacular) shots at a higher rate tend to get more shots.

Forgive me, I'm getting ahead of myself.  I should explain why I was looking at basketball statistics in the first place.  It started with my recollection of a quote from our friend Doug Hall's book, Jump Start Your Business Brain:

A 10 to 25% chance of success is terrible odds.  Most business owners would have a greater probability of success if they went to a Las Vegas casino and gambled their investments.

Doug was referring to the high rates of business initiative failure observed consistently over the last 25 years.  Per John Gourville:

In the U.S. packaged goods industry, for instance, companies introduce 30,000 products every year, but 70% to 90% of them don't stay on store shelves for more than 12 months.

Those odds do seem "terrible."  On the other hand, consider the following from Nassim Taleb, a student of uncertainty:

The frequency or probability of the loss, in and by itself, is totally irrelevant; it needs to be judged in connection with the magnitude of the outcome.

What Taleb is saying is that a 75% percent chance of failure doesn't mean anything unless you know the cost of trying and the potential rewards from winning.  If the prize for hitting the goal is big enough, it may make sense to throw up a lot of shots, knowing that most will miss, as long as the cost of shooting is relatively low.

For example, if the chance of a winning shot is 25%, then you can be 90% certain that you'll have at least one winner if you take eight shots.  In fact, it's conceivable that in a competitive environment, you may be compelled to expose yourself to a 75% chance of failure per shot in order to win the game.

Well, that sounds good in theory, but is that really how players in competitive games behave?  That's what lead me, uncharacteristically, to basketball.  Here's why:

• Basketball has both 2-point and 3-point field goals.
• The risk of missing a 3-point shot, given the greater distance from the goal, is higher than the risk of missing a 2-point shot.
• The cost (in time and energy) of taking a 3-point shot doesn't appear to be materially different than the cost of taking a 2-point shot.

From the preceding, I hypothesized that given the higher payoff from the 3-point field goal, players would be compelled to take 3-point shots as long as their 3-point conversion ratio was at least 2/3 that of their 2-point conversation ratios.  In other words, if their 2-point field goal percentage was .500, they will take 3-point shots as long as their 3-point conversion ratio was at least .500 x 2/3 = .333.  After all, if you make 50 out of 100 2-point shots, you'll score 100 points.  Likewise, if you make 33 out of 100 3-point shots, you'll score 100 points.  And, it's scoring that counts.

Actually, I would expect that players would be pushed to accept a success rate on 3-pointers of a bit over 2/3 of their 2-point field goal percentage.  Per Wikipedia,

Many coaches are known for disliking, even shirking, the three-pointer...Their reasoning in avoiding the three-pointer usually stems from its typically lower shooting percentage, the likelihood of a quick defensive rebound and fast break, and the missed opportunity of drawing shooting fouls...

So, with hypothesis in hand, I took a look at the numbers.  More specifically, I looked at the NBA's scoring leaders, ranked by points scored per 48 minutes played, for the 2006-2007 season.  At the top of the list is Kobe Bryant who scored 37.2 points per 48 minutes of playing time.  The average of the top 20 players was 32.2 points.

Kobe's career 2-point field goal percentage is .479.  So, per my hypothesis, competition should push him to accept a 3-point field goal percentage of a bit more than .319 (i.e., a failure rate of a bit under 68%).  In fact, his actual career 3-point conversion percentage is .337.  Similarly, the average career field goal percentages for the top 20 scoring players last year (excluding three centers, who apparently aren't allowed to attempt 3-pointers) are an almost identical .476 for 2-pointers and .338 for 3-pointers.

Presumably, basketball players who stand to lose multi-million dollar contracts if they perform poorly relative to their competitors are as risk averse as the rest of us.  Consequently, they might not be expected to expose themselves to the higher rates of failure associated with 3-point attempts, unless competition pushed them to do so (particularly if their coaches frown on 3-pointers).  Nevertheless, the data suggests that in the face of competition, players are pushed to accept a "natural" failure rate of about 68%.

I wonder if something similar holds true for business innovation.  That is, is there something about competition and the payoff distribution function that causes players to accept a failure rate of 75%?  (I'll take a stab at answering this in a subsequent post.)

While crunching the numbers on 3-pointers, I noticed something kind of funny about Kobe: he's the highest producing scorer in the NBA, but his shooting accuracy doesn't stand out - he's pretty average, in fact.  (Granted, the average NBA player can shoot the ball very well, but in a competitive world, it's relative, not absolute, performance that matters.)  So, how can an average shooter be the league's leading scorer?  Simple: he takes more shots per minute than his peers.  Kobe scores a lot of points because he's willing to miss shots at a higher rate than his competitors.

People in business don't like to miss - particularly us MBA-types.  We work incredibly hard to protect our fragile egos from any evidence of incompetence.  We aim...aim...aim...aim...

Shoot the damned ball!  Analyze what you can; develop what you can, but do it fast and then get the product to market as quickly and inexpensively as you can.  Unpredictable consumer behavior may account for 50% of your product's success, so there is probably a limit to how much you can improve your "field goal percentage" in any event.  Make a good shot as quickly as you can, and then forget about it.  Move onto the next play.

Players in big organizations really have it tough in this regard.  Often, they get the best angle on the goal, but they a) either fear the humiliation of tossing up an air-ball or b) think that they can squeeze out more risk than is really possible in a complex, dynamic world.  I suspect that to the extent the player is insulated from on-the-floor competition, the more risk averse they may be.  (That sounds like the differences between basketball coaches and basketball players.)

So, how is it that Kobe can get more shots off per minute than his peers?  I suspect that he benefits from a virtuous cycle:

• Kobe is a good, but not great shot.  However, he puts up a lot of shots - including an appropriate number of riskier 3-point attempts.  As a result, he scores a lot of points per minute played.
• Kobe's teammates like to win.  To win, they must score.  Kobe scores.  Feed the ball to Kobe.

One sees this kind of behavior in, for example, venture capital circles.  A VC firm will underwrite the launch and growth of 10 promising companies.  A couple are hits.  The payoff from the two hits are sufficient to cover the capital costs of the entire portfolio.  Limited partners win.  LPs like to win.  They feed more money back to the VC.  Entrepreneurs like to win.  They need capital.  They feed opportunities to the VC.  As long as the VC continues to take shots - and an appropriate number of risky shots - at a relatively fast rate, the team has a pretty good chance of continued success.

Of course, in the game of business, the Red Queen is the referree, and the time on the shot clock keeps fallling.

New Opportunity in the Long Tail of Specialty Retail

Chris Anderson has written extensively on how supply side changes are revealing the extent of the long tail of consumer demand.  As a consequence, the opportunity for niche retailing is expanding, and the private equity world is responding.

Decreases in the cost of inventory and distribution make it affordable to make an increasing number of unique offerings available to consumers.  Improvements in search and marketing techniques facilitate the matching of supply with demand over a broadening range of niches.  Although socially contingent decision-making on the part of consumers ensures that market leaders dominate market share, the introduction of new, unique offerings means that, in aggregate, the share represented by the long tail of demand is of increasing consequence.  In other words, those who can bring economies of distribution and effective search to the long tail can profit.

Anderson's analysis has focused on entertainment product retailers such as iTunes and Rhapsody (music), NetFlix (movies), and Amazon.com (books).  Nevertheless, he foresees an increase in the number of niche products in the world of tangible products and, presumably, new business models, including retail concepts, that profit from the extension of the tail and the relative flattening of the market share curve.

Of course, the trend in retail is not new, though it appears to be quickening.  An innovative Sears Roebuck & Co. combined economies of scope and scale with innovative direct marketing to offer greater variety and availability to new markets.  Wal-Mart helped the industry learn to use information technologies to reduce the cost of inventory and distribution.  Category killers such as Staples and PETsMART likewise have offered greater selection and lower prices to more and more people.

The extent of the co-evolution of distribution economies and every increasing variety can be seen in a retail concept called Oil & Vinegar.  It's a European retail concept that, yes, focuses on all things related to oil and vinegar.  In addition to the handful of franchises operating in Houston, Chicago, Charlotte, Seattle, and Milwaukee are two, apparently thriving, franchises in the micropolitan areas of Missoula and Bozeman, Montana.  An international company that offers a range of relatively affordable, micro-niche products to markets as small as Bozeman: that's remarkable.

It would seem that any would-be micro-niche retailer would have to be able to address a number of questions:

• How to take advantage of economies of scale when offering a broad range of products, any one of which represents a rather small market share?
• How to connect with the segment of the population that represents your core customers?
• How to gain preferred access to specialized knowledge and differentiated products in order to maintain a distinct identify with consumers?

Although challenges abound, the opportunity in specialty retail is compelling to some.  In yesterday's Wall Street Journal, Thomas Stemberg, the founder of Staples and a director of PETsMART, asserted:

...most categories have been killed...The predominance of our opportunity will come from America becoming more and more segmented in its demographics...Many needs aren't being met.

We discover new needs because more choice is possible.  Yesterday, Stemberg and his partners at Highland Capital announced a new, $300 million private equity fund aimed at making investments in retail chains like those in which Highland has already invested, such as lululemon athletica (yoga-oriented sportswear).

Of course, we at EIP will be busily performing our roles as innovation capitalist and, increasingly, new product incubator in order to help the specialty retailers backed by Mr. Stemberg and others access differentiated products faster and less expensively.  Given Staples' experience with InventionQuest, the embrace of open innovation by specialty retailers is, presumably, not foreign to Mr. Stemberg.

The Underappreciated Role of the Consumer in Innovation

After my previous post, I was prepared to like The Myths of Innovation by Scott Berkun.  I did like it, but my enthusiasm for the book was tempered by Berkun's treatment of "the best ideas win" myth.  I believe his observation is correct: the relationship between the subjective quality of a prospective innovation and the rate and extent of its adoption is ambiguous.  However, I suspect that Berkun may underestimate the crucial role played by interdependent decision-making among consumers.

Early in his book, Berkun acknowledges how adoption by consumers is a socially contingent process:

The love of new ideas is a myth: we prefer ideas only after others have tested them.  We confuse truly new ideas with good ideas that have already been proven, which just happen to be new to us.

But, even as he acknowledges the "evolutionary advantage in this fear of new things," Berkun seems to consign consumers to a secondary role in the process of innovation:

Even early adopters, people who thrive on using the latest things, are at best adventurous consumers, not creators.  They rarely take the same risks on unproven ideas as the innovators themselves.

In fact, he seems to suggest that consumers timidity and inertia slows the rate of adoption of the best ideas.  That is, Berkun posits that there is an inverse relationship between the rate of adoption and the barriers to adoption:

The reason why Internet and cell phone usage climbed faster than previous technologies isn't because things happen faster today...It's simply because the barriers of entry were low.

Furthermore, he suggests that the "goodness" of an idea is inversely related to the ease of adoption (see Figure 8-2):  "This suggests that the most successful innovations are not the most valuable or the best ideas, but the ones that appear on the sweet spot between what's good from the expert's perspective, and what can be easily adopted, given the uncertainties of all the secondary factors combined.  The idealism of goodness and the notion that goodness wins is tempered by the limits and irrationalities of people's willingness to try new things, the culture of the era, and the events of the time."  So, as I understand him, Berkun argues that there is a causal relationship between the net benefit of an invention and the speed and extent of adoption.

It's a plausible and intuitive argument.  Doug Hall, in his marketing physics framework, makes a similar case.  Hall concludes that a clear articulation of overt benefit ("What's in it for the customer?"), real reason to believe ("Why should the customer believe you will deliver on the promise?"), and dramatic difference ("How revolutionary and new-to-the-world is your benefit/reason to believe pair?) drives adoption.  Importantly though, Hall acknowledges that there is not a linear cause-and-effect relationship: having a great concept means that the chances for widespread and sustained adoption can be increased to over 50%.

So, if the quality of an invention might explain 50% of success, what explains the other 50%?  Recent research suggests that the dynamics of the inter-relationships among consumers is a very likely candidate.  Consider the following, from a recent New York Times article by sociologist Duncan Watts:

Conventional marketing wisdom holds that predicting success in cultural markets is mostly a matter of anticipating the preferences of the millions of individual people who participate in them...The common-sense view, however, makes a big assumption: that when people make decisions about what they like, they do so independently of one another.  But people almost never make decisions independently--in part because the world abounds with so many choices that we have little hope of ever finding what we want on our own; in part because we are never really sure what we want anyway; and in part because what we often want is not so much to experience the "best" of everything as it is to experience the same things as other people and thereby also experience the benefits of sharing...

Yet our mutual dependence has unexpected consequences, one of which is that if people do not make decisions independently--if even in part they like things because other people like them--then predicting hits is not only difficult but actually impossible, no matter how much you know about individual tastes...The reason is that when people tend to like what other people like, differences in popularity are subject to what is called "cumulative advantage," or the "rich get richer" effect.  This means that if one object happens to be slightly more popular than another at just the right point, it will tend to become more popular still.  As a result, even tiny, random fluctuations can blow up, generating potentially enormous long-run differences among even indistinguishable competitors...

Watts goes on to describe a really interesting experiment that measured the popularity of products (songs) in independent and socially influenced conditions:

This setup let us test the possibility of prediction in two very direct ways.  First, if people know what they like regardless of what they think other people like, the most successful songs should draw about the same amount of the total market share in both the independent and social-influence conditions--that is, hits shouldn't be any bigger just because the people downloading them know what other people downloaded.  And second, the very same songs--the "best" ones--should become hits in all social-influence worlds.

What we found, however, was exactly the opposite.  In all the social-influence worlds, the most popular songs were much more popular (and the least popular songs were less popular) than in the independent condition...Introducing social influence into human decision-making, in other words, didn't just make the hits bigger; it also made them more unpredictable.

Watts also found that, while the quality of the product mattered, quality did not cause popularity in a straightforward manner:

So does a listener's own independent reaction to a song count for anything?  In fact, intrinsic "quality," which we measured in terms of a song's popularity in the independent condition, did help to explain success in the social-influence condition..."good" songs had higher market shares, on average, than "bad" ones...Overall, a song in the Top 5 in terms of quality had only a 50 percent chance of finishing in the Top 5 of success...social influence played as large a role in determining the market share of successful songs as differences in quality.

Here is what Watts concluded:

Our desire to believe in an orderly universe leads us to interpret the uncertainty we feel about the future as nothing but a consequence of our current state of ignorance, to be dispelled by greater knowledge or better analysis.  But even a modest amount of randomness can play havoc with our intuitions...That doesn't mean we should stop trying to anticipate the future, any more than we should stop trying to make sense of the past.  But it does mean that we should treat both the predictions and explanations we are served...with the skepticism they deserve.

So, read The Myths of Innovation.  Berkun's chapters on the myths of epiphany and the lone inventor are worth the price.  But be wary of the author's possible diminishment of the role of the consumer in the process of innovation, and be skeptical about his post hoc narratives regarding the success or failure of inventions.  Berkun's rationalizations are plausible and understandable, but they distract from the distinct possibility that the process of innovation is profoundly uncertain and luck plays a bigger role than we might wish.  Curiously enough, he seemed to acknowledge as much in the interview, about which I blogged previously:

Many stupid ideas have been successful and many great ideas have died on the vine and that's because success hinges on factors outside of our control.

On balance, I'd prefer to put my time and money behind a great idea.  In fact, I'd rather put my money behind three or four great ideas.  That way, I'll have a 90% chance of at least one success, if Watts and Hall's research is correct.  As long as I can keep the cost of trying down, I'll have a decent chance of surviving long enough in the marketplace to keep playing the game.

For more about cumulative advantage, check out The Social Atom, Critical Mass, and Six Degrees.  Also, check out the paper on venturesome consumption by Amar Bhide.

The Myths of Innovation

Ashton Udall over at the Product Global blog has relayed an interview of Scott Berkun, author of The Myths of Innovation, by Guy Kawasaki.  I like the following exchange, in particular:

How do you know if you have a seemingly stupid idea according to the "experts" that will succeed or a stupid idea that is truly stupid?

Don't shoot me, but the answer is we can't know.  Not for certain.  That's where all the fun and misery comes in.  Many stupid ideas have been successful and many great ideas have died on the vine and that's because success hinges on factors outside of our control.

The best bet is to be an experimenter, a tinkerer - to learn to try out ideas cheaply and quickly and to get out there with people instead of fantasizing in ivory towers.  Experience with real people trumps expert analysis much of the time.  Innovation is a practice - a set of habits - and it involves making lots of mistakes and being willing to learn from them.

Put a little differently, the practice of innovation benefits greatly from the application of the scientific method: the formulation of a reasoned hypothesis, the execution of an experiment designed to disprove the hypothesis, painstaking observation, and a willingness to modify the hypothesis in order to fit valid evidence.  Unfortunately, as Cordelia Fine points out in her book, A Mind of Its Own, most of us are not very good scientists:

Our first problem is that we are, at root, very poor scientists.  All sorts of biases slip in unnoticed as we form and test our beliefs, and these tendencies lead us astray to a surprising degree...Evidence that fits with our beliefs is quickly waved through the mental border control.  Counterevidence, on the other hand, must submit to close interrogation and even then will probably not be allowed in.  As a result, people can wind up holding their beliefs even more strongly after seeing counterevidence.  It's as if we think, "Well, if that's the best that the other side can come up with then I really must be right.  This phenomenon, called belief polarization, may help to explain why attempting to disillusion people of their perverse misconceptions is so often futile.

Our instincts, emotions, and subconscious minds evolved to help our hunter-gatherer ancestors survive.  Maybe it should not come as a big surprise or disappointment to learn that being an effective scientist-innovator takes a lot of work.  Nassim Nicholas Taleb offers, what I think is, a suitable starting point:

My lesson...is to start every meeting at my boutique by convincing everyone that we are a bunch of idiots who know nothing and are mistake prone, but happen to be endowed with the rare privilege of knowing it.

I'm fortunate to work with people who are smarter, more experienced, and more creative than me.  Consequently, when I slip into the mode of thinking I'm a friggin' super-genius that just knows the answer to the question at hand, I am usually brought quickly down to earth before I cause too much damage.  I'd like to think that there is hope for me, yet.