Elimination of Egospend – The Real Reason We Need Lean Enterprise

The project that has been 90% done for six months. The program that is 5 years late. The company division that continually loses money, but somehow, survives, getting more good money thrown after bad. The larger the company you work for, the more likely you have seen this phenomena in action.

My term for this is a concept called Egospend. Egospend is what happens when you combine:

  • a large sum of financial and/or political capital
  • little or no discipline around relevant business metrics, measured frequently
  • a culture with a strong sense of loss aversion / high penalties for perceived failure
  • decision making by feel and relationship versus results

The degree of Egospend in an organization tends to be the product of those four factors.

Why Traditional PMOs Are Ineffective Against Egospend

Most organizations recognize and want to combat Egospend. Combating Egospend is one of the reasons PMOs came to be, as well as various other forms of governance that occur at various levels of an enterprise. Intentions are always quite good, as nobody enjoys taking multi-million dollar writeoffs after failed projects.

What occurs, unfortunately, as it does in all political systems, is some form of regulatory capture. On the X axis, we have political support for an idea…. let’s call this CSAPS, “Combined Salaries of All Project Sponsors”. On the Y axis, we have HCI, “Historical Costs of Initiative”.

Failed Project Political Economics

What does “regulatory capture” mean in this context? It admits to the reality that political power can defeat governance functions. In nearly all companies, if the CEO decides to continue funding an initiative that should have died, it will continue to get funding, despite the opinions of the PMO. This is true for traditional PMOs, Lean PMOs, or any other kind that exists in any structure where political power is centralized, such as nearly all modern enterprises.

This effect, of course, is accelerated under corporate cultures that are prone to loss aversion. All it takes are a few executives who have had careers ended over high profile failures – something that isn’t atypical – in order to lock loss aversion into the corporate culture for a very long time.

Where Traditional PMOs Go Wrong

The issue with traditional PMO isn’t the intent. They are often very good at making sure projects are delivered (of course, this isn’t always true either). The issue is deeper – do the projects achieve the business results they were designed to achieve. A key tenent of the Lean Startup movement is that you don’t claim done upon product creation. Rather, you orient around building a product or service where you create a virtuous learning cycle. A learning cycle that works in a manner where you end up with a product that delivers continuous improvement of business results.

Where does this differ from traditional PMOs? Most PMOs try to control change (see, Change Control Board), where Lean PMOs expressly enable and expect change. Successful outcome for a traditional PMO is project delivery, successful outcome for a Lean PMO is results delivery. Traditional PMOs expect projects to finish and move into “maintenance”, Lean PMOs expect products to continually evolve and pivot in order to meet new and evolving customer needs.

By focusing on delivery of projects, with the key metric being project completeness and delivery status, i.e. “did we deliver software – regardless of whether it is the software that a customer actually wants”, the Traditional PMO is blind to actual business results.

Combating This with Lean Enterprise

The idea of Lean Enterprise is to take the lessons from Lean Startup and apply them to the enterprise. How do you take the discipline a startup must have to survive – laser focus on customers, early feedback, real, non-vanity metrics, obsessive focus on your product – and make that work in multi-billion dollar organizations? Surely, there are adaptations – there are regulatory constraints, audit committees, and sheer size that changes how you approach the problem. There are also technical constraints – protection of data is a much bigger deal for a financial services behemoth than it is, say, for a social media startup. You can’t just walk into a Fortune 50 company with Lean Startup as your playbook and be successful.

However, in order to avoid the projects that have both high CSAPS and HCI – the kinds of projects that kill companies, something has to be done. Since CEOs will continue to have ideas, the only real way to combat the perils of Egospend is to lower HCI for initiatives that are doomed to failure. Failure defined by poor business results, not just mere poor project delivery.

How do you do this? We start with Continuous Delivery – the idea that instead of delivering things in large chunks, you deliver incrementally, so you can measure what each change does. You deliver an MVP early, so you can start to get feedback as soon as possible and know whether you need to pivot. You change the way funding works, moving away from annual budgets with fixed scope, and towards contingent budgets based on meeting business objectives.

As you evolve, you start to build a culture that embraces early failure – and learning from same as part of the path towards success. At some point, even the CEO becomes someone who can admit, in a company-wide meeting, that his or her pet idea did not work. By setting such an example, the organization becomes one where hubris and bravado are seen as negative traits, not forms of faux leadership.

Enterprises Need Lean More Than Startups

The irony, of course, is it is the large enterprises of the world that need these ideas the most, since they tend to be where most of the large amounts of money are spent on technology. I have personally seen initiatives in single companies, doomed to failure, that ate up budgets equal to the entire GDP of a developing world country. Initiatives for whom admitting failure would mean the CEO would lose his job (and, thus, more continued investment). Imagine the capital that could be put to better use if we could put an end to this kind of wasteful Egospend.

If you are interested in reading more about this, you can get four chapters of the book, Lean Enterprise, for free, authored by my colleagues Barry O’Reilly and Joanne Molesky, as well as ThoughtWorks Alum Jez Humble.

Elimination of Egospend – The Real Reason We Need Lean Enterprise

Traits of Analytics Led Companies

Imagine you are the CEO of a retailer. The economy is roaring, people are starting to shop more at your higher end shops, increasing margin. Quarter over quarter top line revenue growth is coming in, albeit at a slow, measured growth rate. You are even getting some level of margin growth by implementing some measures to remove excess cost out of the business. Things are going great!

Then, some year, everything changes. A new competitor emerges. Similar brand strength, similar cost structure, even similar locations. However, they seem to get twice as much revenue per square foot, based on some new traffic pattern analysis that they are doing. And they seem to have very savvy staff who are empowered by their systems to recommend products for customers that they seem to actually want to buy. This, when combined with the investment they have made in omni-channel customer experience, is allowing this new upstart to drastically cut into your marketshare.

You are paralyzed by fear. You’ve read a few articles about big data, but given you are a brick and mortar retailer focusing on the high end, you did not think this was as much of a priority. You’ve been focused on cost control and squeezing an extra 10 basis points of margin out of your existing model, and your competitor has leapfrogged you by creating an entirely new model that increases margin 2%. In retail. Where such a margin increase can mean profits rise by 40%.

You used to be aware of analytics. Now you need to lead the charge to be analytics led, before your competitor ends up beating you so badly that you become an aquisition target – with your declining brand and your real estate becoming the only assets that remain. You are faced with a new imperative – how do I drive this organization to become analytics led, and in a way where I can “re-leapfrog” this new upstart competitor?

Becoming an analytics led company – a company that drives strategic advantage through analytics – is a journey that requires rethinking of how your entire business operates. It requires agility to be able to change tactics and strategies in response to data coming from how your customers interact with your products, retail locations, and your brand in general. You will not get there overnight. There are, however, traits that such companies share – traits that you can use as a marker to know whether you are at least on the way to becoming such a company.

Contextual Intellectual Capital is Valued

For purposes of this discussion, contextual intellectual capital is the sum of learning from analytics that has taken place and exists in the minds of people actively involved in shaping the business. For example, a tuned collaborative filtering model that forms the basis of a recommendation engine, paired with data scientists who know how to evolve the model, could be considered such capital. It is contextual, because it’s value is derived from properties unique to the company – the brand, the people, the culture, the customer base. Even if you “copied the code” to a different company, it’s value would deteriorate, because it is optimized for that particular brand.

Analytics led companies have a great deal of contextual intellectual capital. It is the models, the learning, and the people who know how to leverage and improve the model. It is the ability to create new models in response to changing business conditions – that build on what is learned from prior models whose value is derived not just from the math, but the context they come from.

Driven by Data, But Intuition Still Matters

One of the more surprising things you find in analytics led companies is that, while they are naturally driven by data, as you would expect them to be, they do not completely discount intuition. Intuition is a unique capability humans have for processing lots of complex information from diverse sources in parallel. This ability is something that humans excel at much more than computers do.

What does this mean? A human will have the context to intuitively know, based on data on whether a model has worked (or not) how a model may need to be tweaked. Intuition will give us a safe harbor to know when results from a model that is new should be called into question. For example, if a new model for managing up-sell recommendations is having great results well beyond what it should, intuition will tell us to look at the data closer so we can know whether some one time black swan style event is influencing the recommendation.

For example, during a cold snap, more people may buy face-masks that cover your entire face while they are buying new winter coats – but that does not imply that such face-masks should always be up-sold, as this condition only exists perhaps when the temperature goes below 10 degrees Celsius. Models running 100% unmanaged, if they are not including weather in the analysis, would not pick up this detail, where a model combined with intuition is much more powerful.

Optimized for Learning

Analytics led companies usually think quite a bit differently about what the source is for sustainable value. At first glance, a competitor may think it is the presence of a killer model that tells them what customers want to buy with incredible accuracy. However, as valuable as that is, in a competitive market, competitors will quickly figure out ways to reverse engineer and replicate the model. It is not a static model that really provides the value in an analytics led company, it is the organizational capability to learn from and quickly adjust the model to changing conditions.

Imagine you are a retailer who has been selling products in primarily western economies for the last ten years. As you move into emerging markets, how does the system that evaluates product mix change? Organizations that do not have agility to update and evolve models risk using inappropriate models for new conditions and situations. Companies that are optimized for learning can quickly adjust to new realities and change models to meet new business conditions.

Powered By Science

Is there any type of science that isn’t driven by data? Analytics led companies understand that data science is really *business science*. Such science has a process, and that process is the scientific method. You form hypotheses, you test them, and if they fail, you learn and move onto the next hypothesis. Data science as a term may be a fad, but the scientific method, and application thereof to business, is most certainly not.

Part of science, of course, is embracing failure of models, even if the ideas behind them come from a high ranking executive. Proving that models don’t work, in science, is just as important as proving that they do. In analytics led companies, while intuition matters (see above), when a hypothesis is falsified when under test, the outcome is accepted in favor of alternative hypotheses. The rank of the idea’s progenitor does not apply.

Practitioner Driven Tool Choice

In analytics led companies, the CIO does not buy analytics tools based on conversations that occur on a golf course. While tools are used, tool choice is vetted by the data science team.

Thankfully, most data scientists tend to be very pragmatic about tool choice. The tools of choice these days tend towards free or open source when possible – things like Hadoop, R, Python, and related libraries. Paid, proprietary tools have their place in certain situations, but the defaults tend to be tools that lower the cost of experimentation, so that too much capital does not get spent in yet to be unproven ideas. Nobody wants to invest seven figures in tooling for a model that may not work – for at some point, too much investment in an unproven model will create pressure to “make it work”, even if it turns out to be wildly wrong.

Analytics Driven Strategy

The most important trait of analytics led companies – above all – is that there is confidence that science properly applied to business has potential to deliver breakthrough value. The executives have not only seen it in competitors or upstarts encroaching on their turf, but they are prepared to compete by executing an analytics strategy that plays to their own strengths.

Does this mean that brick and mortar retailers all replicate the strategy of Amazon? Of course not. It means a contextual strategy that plays to the retailer’s strength. If it is a retailer that has strong location coverage in certain kinds of communities, the analytics strategy will consider that. If it is a retailer that has a brand that appeals to a different kind of consumer, it will consider that as well. They deeply understand that context matters – and that the unique combination of analytics model, people, corporate culture, and brand for the basis of a successful analytics strategy.

Traits of Analytics Led Companies

The Analytics Maturity Spectrum

There is no doubt “Big Data” has taken the tech world by storm. I have spent much of 2013 talking about analytics and data science with people all around the US, going to conferences like Strata, and immersing myself in this world for the last 12 months. Over the course of this journey, I have started to notice some patterns about how various people in various kinds of organizations understand and invest in analytics.

The analytics led company is a concept I will define here as a company that seeks to use analytics (predictive, prescriptive, or descriptive) as one of their chief competitive weapons. The canonical example is Amazon, whose use of analytics is part of the DNA of the company. However, there are other more traditional companies that are analytics led, such as Walmart, Proctor and Gamble, Kolhs, and dozens of others.

In companies that are analytics led, analytics capabilities are spread throughout the company. They are not siloed off to some group in IT that does “analytics stuff”. Such organizations, knowing that analytics has to be a core competency of the company, invest in people – data scientists, data engineers, data savvy analysts and developers, and free them to use whatever tools and techniques are required in order to generate business results.

The next category in the continuum are analytics aware companies. These organizations see the competitive threat. Many may be piloting technologies or starting to do some discovery work in small areas. They see the value, but have not yet integrated analytics into the DNA of the company and made analytics something that would be considered business as usual. These organizations often have a siloed group doing experimentation, and this siloed group often has ties, or is directly part of, the traditional IT department.



Further down the spectrum are analytics ignorant companies. When a company simply does not see the value of predictive analytics, and rather seeks to gain competitive advantage through other means. Finally, at the other end of the spectrum, are analytics hostile companies. They may have sought to use analytics and failed – and then soured on the idea. They may have a technology hostile culture in general. Regardless of reason, they make very good targets for analytics led companies that seek to steal marketshare.

From Analytics Ignorant to Analytics Aware

Most industries, though not all, have had the emergence of at least one new competitor who has used analytics to achieve some sort of competitive advantage. Whether it is organizations like Progressive Insurance who use analytics of how you drive via it’s Snapshot tool to allow for better underwriting, or its one of the many online and offline retailers who are using analytics to understand or predict customer behavior, if you are the CEO of any industry where one of these upstarts have emerged, you have likely at least made your executive team aware of the threat.

That said, in industries that tend to be less competitive, due to either higher barriers for entry or presence of a monopoly – the urgency for analytics is much less. These types of companies, utilities, some telecoms, and a few others, means that even if the potential for additional profit is there, the lack of urgent need tends to move analytics to the back burner. It is only when a competitive threat from a related industry emerges (i.e. Google cutting into the Yellow Pages revenue) that such organizations move from Ignorant to Aware.

Moving From Analytics Aware to Analytics Led

In analytics led companies, the approach towards data science will generally be to build the capability in house. Leaders of such companies understand implicitly that analytics is deeply business relevant. They know that predicting customer behavior and anticipating customer needs – and most importantly – connecting those insights to the rest of the business – drives profit margin, customer loyalty, and numerous other outcomes that are core to mission.

Analytics aware companies, on the other hand, will tend to know they need those outcomes, but do not have the capability to achieve them. They often try to achieve analytics by purchasing technology – usually applications that have some analytics capability. While this approach can help the company at least get to level compared to their peers, they do not allow a company to exceed very far beyond their peers, as if one company can purchase a product that does analytics, so can competitors. There may be a short term advantage, but it isn’t sustainable.

Some analytics aware companies may seek to purchase the capability either through acquisition – buying a company that is analytics led and hoping that the new company unit will enable the entire organization to also be analytics led. While such moves have a better chance of providing competitive advantage than buying a product, it is risky, as this pattern tends to lead to siloed analytics capabilities within a business unit that used to be the old acquired company, unless the acquisition is properly integrated (which seldom happens).

The Opportunity at “Analytics Aware”

Data science will obviously be more valued in organizations that are analytics led. However, the most interesting opportunities for change tend to be in the organizations that are analytics aware. The analytics aware organization is the class where the value is understood, but a brand new culture about how to leverage data in new and interesting ways can be fostered. In analytics led organizations, especially ones that have been analytics led for quite some time, certain conventional wisdom may already be in place about what is possible and what isn’t. Often, such “wisdom” constrains the idea-space, causing the most ambitious ideas to sound too big, audacious, or disruptive to be viable investments.

On the other hand, analytics aware companies have experience spending large amounts of money on product and acquisitions. Such costs tend to dwarf what the cost of a competent data science team would be. One can take the budget that is spent on tools and acquisitions, redirect it towards an innovation lab that serves business line leaders, and get a far superior return on the investment.

What is the takeaway of all this? Do not despair if you are not analytics led…. yet. Use it as an opportunity to redefine the kind of analytics that your organization will use, an opportunity to chase more audacious ideas than than people with an abundance of conventional wisdom would ever consider.

The Analytics Maturity Spectrum

On (not) Being Post-Technical

In ThoughtWorks, one of the most poignant insults one can throw at you is “so-and-so has gone post-technical”. This usually means one has entered the land of management, that place where you give back your brain in exchange for money (or prestige.. or nothing, as it turns out).

Lately, as I have taken on additional roles at ThoughtWorks, the temptation to go “post technical” has put itself forward. Imagine not having to think anymore. Imagine being able to just focus on “strategy” and “people issues”, without all that hard technology stuff.

I could take that path, but I’d rather not…

I remain technical. My day-to-day job may involve things like Statements of Work and such, and I do not write code 100% of the time, but I am making a decision to at least remain somewhat involved in the technical communities in which I have interest.

Of course, my time is more limited, given I have taken on some management responsibility lately. So I can’t pursue everything. And I do have to admit that with my reduced time, I am likely never to be the most “technical person” in any given group of TWers. In fact, given that TWers are all generally really good at their jobs, it would be utter arrogance for me to assume I could keep up with them when I only act in a technical role for 40% of my time. So my bar isn’t TWers – they will most likely all be more technical than I am. But it is remaining competent enough to understand at least 40% of what they talk about, and more importantly, remain excited about technology.

So what am I excited about these days, here is my own, personal, “tech radar” (no fancy graphics required):

  • Functional Languages and Big Data

Someday, the world will catch up to where the F#/Clojure people are now. I think the tie between FP and Big Data is strong, and given that is where much of the value will be created in the next 5 years with corporate IT spend, the use of FP will only increase.

  • IaaS (note, I don’t say cloud… too overloaded)

I got the cloud bug this year, and I will admit, I am a terminal case. I saw an app where someone deployed a load balancer with an http request. And not an http request that converts into an email that goes to a tech that puts a physical one on a rack. Infrastructure as code is changing this business. Imagine the ability to specify, in human readable code, an organization’s entire server configuration. And realize that configuration by executing the DSL it is written in. That day is approaching, fast. Imagine the possibilities – from everything to devops to disaster recovery.

Why don’t I say cloud? The term is so overloaded that it is meaningless. Anything that runs on the internet is putting “cloud” in front of it’s name, which is why I have stopped using the term and do everything I can to use more descriptive terms.

  • Lean Startup

Though not a technology, it is the first thing from a project management standpoint I have been truly excited about in years. At least since Agile, maybe more so. Why? It finally closes the loop, bringing in the entire scientific method into the business of software development. It seems so obvious now, it never ceases to surprise me that it took our industry 50 years to adopt it. Lean Startup, to me, is the application of the scientific method to how you run business. I recently did a webinar that nicely encapsulates how I feel about it in much more detail, but my general sense is that this builds on CD and gives organizations that embrace it a much more repeatable, sustainable path to successfully delivering business results with software.

The three things above are the tools I believe will create most of the value in corporate software over the next 5 years. And I fully intend to remain competent enough to discuss these topics with both technical and business audiences, even if I don’t write code every single day anymore. The trick, not being a full time software developer anymore, will be to:

  • Make sure I don’t stop doing some technical work – I still intend to pair program with the team when I can, as well as keep up with a selection of OSS projects I am involved with.
  • Continue to read new technical material – I am on planes a lot, should not be too hard. For example, I am learning Python now, despite that I don’t have a good reason to personally code in python.
  • Be aware – ultra-aware – that I am not the most technical person in the room. My team-mates will likely know more than me, and I will defer to them on technical arguments more often than not, especially if they have decent data on why a given technical decision needs to be one way or another.

Will this work? We will see. I am only in the starting stages of this journey – will be interesting to see the degree to which my technical skills atrophy from not being a full time developer anymore!

On (not) Being Post-Technical

The Link Between Continuous Delivery and Agile

Now that Agile has passed the 10-year mark, many people are starting to wonder what the next step should be in the evolution of Agile. As we start to think about what’s next, it doesn’t hurt to think for a moment about how we got here in the first place. As the saying goes, it’s hard to know where you’re going if you don’t know where you’ve been!

So let’s turn back the clock through the mists of time in the years leading to the Agile Manifesto in 2001. Back when this movement started, many of the “reasons for Agile” just seemed very intuitive. People over process? Sure. Responding to change versus following a predefined plan? Common sense. On the surface, few people would disagree with the assertions made in the Agile Manifesto. But is that enough? Can you following the Manifesto—do it all by the book—and be guaranteed to create software that delivers economic benefit? No. By itself, Agile doesn’t lead to business value!

How Can By-the-Book-Agile Fail?

Let’s be clear. With its increased collaboration with the customer, more frequent releases, and increased engineering and testing discipline, Agile makes delivering value more likely. It’s certainly a vast improvement over multi-year “too big to fail” Waterfall software projects. But even if you do everything right, even if you have the best practitioners in the world building your product, you can still create a product that fails to make money.

Agile—and here we mean the kind that includes the right engineering practices, such as test-driven development (TDD), pairing, SOLID principles, automated acceptance testing, and continuous integration—will ensure that you create a product that works…in the lab, at least. However, despite your best efforts, you might build a very functional eCommerce site that tries to sell something that nobody really wants. Or you might build an internal application that can’t go beyond the lab because the operations people can’t support it in production. These issues—and many other things outside direct control of the team practicing Agile—can thwart even your best efforts.

Article continues here

The Link Between Continuous Delivery and Agile

Everybody’s Doing Agile–Why Can’t We?

Have you gone Agile? What are you doing this year to become Agile? We must become Agile in the next three months! These days, it is not unusual to hear about executives wanting to do an “Agile Transformation” on the entire company. Who knew that a bunch of relatively obscure techies would create a movement that is now the lingua franca of executives who are attempting to turn around companies! Agile really has “come a long way, baby.”

It’s amazing how things change in ten years. Once considered a methodology preferred by software developers because it “helped them avoid having to do status reports,” Agile has now gone beyond its original remit as a software development method. The word Agile, in many places, has become nearly synonymous with the word Good. As flattering this must be to the original founders, it probably means it would be wise to think, very candidly, whether Agile—as in Agile Software Development, or a broader Agile Enterprise—is really something that your company can achieve.

Read the rest of this article at InformIT here.

Everybody’s Doing Agile–Why Can’t We?

Welcome to the Revenge of The Nerds Economy

Many of you will remember Revenge of The Nerds, that fine classic movie where a bunch of, well, nerds take over the campus of Adams college by outsmarting and outwitting the jocks. For people who work in computers of a certain age and disposition – say, a late 30s geek from a western culture like myself who might have fit the nerd stereotype at various points in his early upbringing – the movie was somewhat influential. It told a story that, translated into economist-speak, equates to “intellectual capital may someday trump other kinds in terms of economic value creation.”

As it seems to have turned out, we are now in the Revenge of The Nerds economy. Despite 9%+ unemployment in the general economy, overall unemployment for software engineers is a tad below 5%. But this data, which is compelling enough, does not tell the entire story.

The bigger story is around what kinds of things are seeing investment. There is talk of another bubble right now in technical startups. If you are a technology based startup that has reached “Ramen Profitability”, you will almost certainly attract capital. Things are now even getting to the point where we are seeing companies that lack profits going to the IPO market (think Pandora and Groupon) – something that has been out of vogue since at least 2001. One could make the case that we will soon cross the line where it is easier to get a startup funded than it is to get a jumbo mortgage.

If you work in tech, there is a good chance you are feeling this, at least if you are currently employed and working for an employer that has some level of visibility. If you work for Google, Facebook, or some other high tech company, you likely do not have to work that hard to get a new job offer. Notwithstanding the cruel irony that people who are unemployed are often being systemically discriminated against, the job market for really good, currently employed software developers is as robust has it has been in 10 years.

So if the nerds are all right, mostly, what about the jocks? What occupations did they end up in? While some of them may have made it to the NFL as professional football players, most others end up in spilling into the general job market. While the stereotype is that jocks end up in menial jobs, construction, or manufacturing; research being hard to find, my own experience of knowing several such folk points to careers tending to be sales, low-end finance (think mortgages), real estate, and personal training. Given, a sample size of a couple dozen doesn’t really prove anything. However, if one did extrapolate, one could come to a conjecture that while “jocks” for lack of a better word, do not do worse then average now, the nerd/jock investment and employment dynamic has changed.

Why the change? Now that simply taking big leveraged risks with a big pile of money isn’t in fashion (i.e., as it was before the global financial crisis), you need advantages from superior intellectual capital in order to sustain a higher than mean return on investment. Why not in fashion? From 2003-2008, it was accepted that financial engineering was the way to riches. If you only structured your collateralized debt obligation in the correct way, you could invest at 40x leverage and still retain a AAA rating on your debt. Financial engineering gave you higher profits under such a regime than traditional “engineering engineering” could provide. So money flowed into CDS structures and away from the nerdy parts of the economy that invents things.

So now we find ourselves in an economy where capital flocks to things like Pandora, Groupon, Facebook, Linkedin, and other things that, at their core, have interesting algorithms inside them. And we have a situation where if you are a developer capable of writing an interesting and valuable algorithm – or helping a company scale out a system that leverages one of these inventions, you are in high demand.

Good thing for the nerds. What this means for everyone else remains to be seen.

Welcome to the Revenge of The Nerds Economy