The End of More – The Death of Moore’s Law

 A version of this article first appeared in IEEE Spectrum.

For most of our lives the idea that computers and technology would get, better, faster, cheaper every year was as assured as the sun rising every morning. The story “GlobalFoundries Stops All 7nm Development“ doesn’t sound like the end of that era, but for anyone who uses an electronic device, it most certainly is.

Technology innovation is going to take a different direction.


GlobalFoundries was one of the three companies that made the most advanced silicon chips for other companies (AMD, IBM, Broadcom, Qualcomm, STM and the Department of Defense.) The other foundries are Samsung in South Korea and TSMC in Taiwan. Now there are only two pursuing the leading edge.

This is a big deal.

Since the invention of the integrated circuit ~60 years ago, computer chip manufacturers have been able to pack more transistors onto a single piece of silicon every year. In 1965, Gordon Moore, one of the founders of Intel, observed that the number of transistors was doubling every 24 months and would continue to do so. For 40 years the chip industry managed to live up to that prediction. The first integrated circuits in 1960 had ~10 transistors. Today the most complex silicon chips have 10 billion. Think about it. Silicon chips can now hold a billion times more transistors.

But Moore’s Law ended a decade ago. Consumers just didn’t get the memo.

No More Moore – The End of Process Technology Innovation
Chips are actually “printed,” not with a printing press but with lithography, using exotic chemicals and materials in a “fab” (a chip fabrication plant – the factory where chips are produced). Packing more transistors in each generation of chips requires the fab to “shrink” the size of the transistors. The first transistors were printed with lines 80 microns wide. Today Samsung and TSMC are pushing to produce chips with features few dozen nanometers across.That’s about a 2,000-to-1 reduction.

Each new generation of chips that shrinks the line widths requires fabs to invest enormous amounts of money in new chip-making equipment.  While the first fabs cost a few million dollars, current fabs – the ones that push the bleeding edge – are over $10 billion.

And the exploding cost of the fab is not the only issue with packing more transistors on chips. Each shrink of chip line widths requires more complexity. Features have to be precisely placed on exact locations on each layer of a device. At 7 nanometers this requires up to 80 separate mask layers.

Moore’s Law was an observation about process technology and economics. For half a century it drove the aspirations of the semiconductor industry. But the other limitation to packing more transistors onto to a chip is a physical limitation called Dennard scaling– as transistors get smaller, their power density stays constant, so that the power use stays in proportion with area. This basic law of physics has created a “Power Wall” – a barrier to clock speed – that has limited microprocessor frequency to around 4 GHz since 2005. It’s why clock speeds on your microprocessor stopped increasing with leaps and bounds 13 years ago.  And why memory density is not going to increase at the rate we saw a decade ago.

This problem of continuing to shrink transistors is so hard that even Intel, the leader in microprocessors and for decades the gold standard in leading fab technology, has had problems. Industry observers have suggested that Intel has hit several speed bumps on the way to their next generation push to 10- and 7-nanometer designs and now is trailing TSMC and Samsung.

This combination of spiraling fab cost, technology barriers, power density limits and diminishing returns is the reason GlobalFoundries threw in the towel on further shrinking line widths . It also means the future direction of innovation on silicon is no longer predictable.

It’s the End of the Beginning
The end of putting more transistors on a single chip doesn’t mean the end of innovation in computers or mobile devices. (To be clear, 1) the bleeding edge will advance, but almost imperceptibly year-to-year and 2) GlobalFoundaries isn’t shutting down, they’re just no longer going to be the ones pushing the edge 3) existing fabs can make current generation 14nm chips and their expensive tools have been paid for. Even older fabs at 28-, 45-, and 65nm can make a ton of money).

But what it does mean is that we’re at the end of guaranteed year-to-year growth in computing power. The result is the end of the type of innovation we’ve been used to for the last 60 years. Instead of just faster versions of what we’ve been used to seeing, device designers now need to get more creative with the 10 billion transistors they have to work with.

It’s worth remembering that human brains have had 100 billion neurons for at least the last 35,000 years. Yet we’ve learned to do a lot more with the same compute power. The same will hold true with semiconductors – we’re going to figure out radically new ways to use those 10 billion transistors.

For example, there are new chip architectures coming (multi-core CPUs, massively parallel CPUs and special purpose silicon for AI/machine learning and GPU’s like Nvidia), new ways to package the chips and to interconnect memory, and even new types of memory. And other designs are pushing for extreme low power usage and others for very low cost.

It’s a Whole New Game
So, what does this mean for consumers? First, high performance applications that needed very fast computing locally on your device will continue their move to the cloud (where data centers are measured in football field sizes) further enabled by new 5G networks. Second, while computing devices we buy will not be much faster on today’s off-the-shelf software, new features– facial recognition, augmented reality, autonomous navigation, and apps we haven’t even thought about –are going to come from new software using new technology like new displays and sensors.

The world of computing is moving into new and uncharted territory. For desktop and mobile devices, the need for a “must have” upgrade won’t be for speed, but because there’s a new capability or app.

For chip manufacturers, for the first time in half a century, all rules are off. There will be a new set of winners and losers in this transition. It will be exciting to watch and see what emerges from the fog.

Lessons Learned

  • Moore’s Law – the doubling of every two years of how many transistors can fit on a chip – has ended
  • Innovation will continue in new computer architectures, chip packaging, interconnects, and memory
  • 5G networks will move more high-performance consumer computing needs seamlessly to the cloud
  • New applications and hardware other than CPU speed (5G networks, displays, sensors) will now drive sales of consumer devices
  • New winners and losers will emerge in consumer devices and chip suppliers

Is the Lean Startup Dead?

A version of this article first appeared in the Harvard Business Review

Reading the NY Times article “Jeffrey Katzenberg Raises $1 Billion for Short-Form Video Venture,” I realized it was time for a new startup heuristic: the amount of customer discovery and product-market fit you need to find is inversely proportional to the amount and availability of risk capital.

And while the “first mover advantage” was the rallying cry of the last bubble, today’s is: “Massive capital infusion can own the entire market.”


Fire, Ready, Aim
Jeff Katzenberg has a great track record – head of the studio at Paramount, chairman of Disney Studios, co-founder of DreamWorks and now chairman of NewTV. The billion dollars he just raised is on top of the $750 million NewTV’s parent company, WndrCo, has raised for the venture. He just hired Meg Whitman. the ex-CEO of HP and eBay, as CEO of NewTV. Their idea is that consumers will want a subscription service for short form entertainment (10-minute programs) for mobile rather than full length movies. (Think YouTube meets Netflix).

It’s an almost $2-billion-dollar bet based on a set of hypotheses. Will consumers want to watch short-form mobile entertainment? Since NewTV won’t be making the content, they will be licensing from and partnering with traditional entertainment producers. Will these third parties produce something people will watch? NewTV will depend on partners like telcos to distribute the content. (Given Verizon just shut down Go90, its short form content video service, it will be interesting to see if Verizon distributes Katzenberg’s offerings.)

But NewTV doesn’t plan on testing these hypotheses. With fewer than 10 employees but almost $2-billion dollars in the bank, they plan on jumping right in.

It’s the antithesis of the Lean Startup.  And it may work. Why?

Dot Com Boom to Bust
Most entrepreneurs today don’t remember the Dot-Com bubble of 1995 or the Dot-Com crash that followed in 2000. As a reminder, the Dot Com bubble was a five-year period from August 1995 (the Netscape IPO) when there was a massive wave of experiments on the then-new internet, in commerce, entertainment, nascent social media, and search. When Netscape went public, it unleashed a frenzy from the public markets for anything related to the internet and signaled to venture investors that there were massive returns to be made investing in anything internet related. Almost overnight the floodgates opened, and risk capital was available at scale from venture capital investors who rushed their startups toward public offerings. Tech IPO prices exploded and subsequent trading prices rose to dizzying heights as the stock prices became disconnected from the traditional metrics of revenue and profits. Some have labeled this period as irrational exuberance. But as Carlota Perez has so aptly described, all new technology industries go through an eruption and frenzy phase, followed by a crash, then a golden age and maturity. Then the cycle repeats with a new set of technologies.

Given the stock market was buying “the story and vision” of anything internet, inflated expectations were more important than traditional metrics like customers, growth, revenue, or heaven forbid, profits. Startups wrote business plans, generated expansive 5-year forecasts and executed (hired, spent and built) to the plan. The mantra of “first mover advantage,” the idea that winners are the ones who are the first entrants in their market, became the conventional wisdom of investors in Silicon Valley.“ First Movers” didn’t understand customer problems or the product features that solved those problems (what we now call product-market fit). These bubble startups were actually guessing at their business model and did premature and aggressive hype and early company launches and had extremely high burn rates – all predicated on an IPO to raise more cash. To be fair, in the 20th century, there really wasn’t a model for how to build startups other than write plan, raise money, and execute – the bubble was this method, on steroids. And to be honest, VC’s in this bubble really didn’t care. Massive liquidity awaited the first movers to the IPO’s, and that’s how they managed their portfolios.

When VC’s realized how eager the public markets were for anything related to the internet, they pushed startups with little revenue and no profits into IPOs as fast as they could. The unprecedented size and scale of VC returns transformed venture capital from a financial asset backwater into full-fledged player in the financial markets.

Then one day it was over. IPOs dried up. Startups with huge burn rates – building leases, staff, PR and advertising – ran out of money. Most startups born in the bubble died in the bubble.

The Rise of the Lean Startup
After the crash, venture capital was scarce to non-existent. (Most of the funds that started in the late part of the boom would be underwater). Angel investment, which was small to start with, disappeared, and most corporate VCs shut down. VC’s were no longer insisting that startups spend faster, and “swing for the fences”. In fact, they were screaming at them to dramatically reduce their burn rates. It was a nuclear winter for startup capital.

The idea of the Lean Startup was built on top of the rubble of the 2000 Dot-Com crash.

With risk capital at a premium and the public markets closed, startups and their investors now needed a methodology to preserve capital and survive long enough to generate revenue and profits. And to do that they needed a different method than just “build it and they will come.” They needed to be sure that what they were building was what customers wanted and needed. And if their initial guesses were wrong, they needed a process that would permit them to change early on in the product development process when the cost of changes was small – the famed “pivot”.

Lean started from the observation that you cannot ask a question that you have no words for. At the time we had no language to describe that startups were not smaller versions of large companies; the first insight was that large companies executed known business models, while startups searched for them. Yet while we had plenty of language and tools for execution, we had none for search.  So we (Blank, Ries, Osterwalder) built the tools and created a new language for innovation and modern entrepreneurship. It helped that in the nuclear winter that followed the crash, 2001 – 2004, startups and VCs were extremely risk averse and amenable to new ideas that reduced risk. (This same risk averse, conserve the cash, VC mindset would return after the 2008 meltdown of the housing market.)

As described in the HBR article “Why the Lean Startup Changes Everything,” we developed Lean as the business model / customer development / agile development solution stack where entrepreneurs first map their hypotheses about their business model and then test these hypotheses with customers in the field (customer development) and use an iterative and incremental development methodology (agile development) to build the product. This allowed startups to build Minimal Viable Products (MVPs) – incremental and iterative prototypes – and put them in front of a large number of customers to get immediate feedback. When founders discovered their assumptions were wrong, as they inevitably did, the result wasn’t a crisis; it was a learning event called a pivot— and an opportunity to change the business model.

Every startup is in a race against time. It has to find product-market fit before running out of cash. Lean makes sense when capital is scarce and when you need to keep burn rates low. Lean was designed to inform the founders’ vision while they operated frugally at speed. It was not built as a focus group for consensus for those without deep convictions.

The result? Startups now had tools that sped up the search for customers, ensured that what was being built met customer needs, reduced time to market and slashed the cost of development.

Carpe Diem – Seize the Cash
Today, memories of frugal VC’s and tight capital markets have faded, and the structure of risk capital is radically different. The explosion of seed funding means tens of thousands of companies that previously languished in their basement are getting funding, likely two orders of magnitude more than received Series A funding during the Dot-Com bubble. As mobile devices offer a platform of several billion eyeballs, potential customers which were previously small niche markets now include everyone on the planet. And enterprise customers in a race to reconfigure strategies, channels, and offerings to deal with disruption provide a willing market for startup tools and services.

All this is driven by corporate funds, sovereign funds and even VC funds with capital pools of tens of billions of dollars dwarfing any of the dollars in the first Dot Com bubble – and all looking for the next Tesla, Uber, Airbnb, or Alibaba. What matters to investors now is to drive startup valuations into unicorn territory (valued at $1 billion or more) via rapid growth – usually users, revenue, engagements but almost never profits. As valuations have long passed the peak of the 2000 Internet bubble, VC’s and founders who previously had to wait until they sold their company or took it public to make money no longer have to wait. They can now sell part of their investment when they raise the next round. And if the company does go public, the valuations are at least 10x of the last bubble.

With capital chasing the best deals, and hundreds of millions of dollars pouring into some startups, most funds now scoff at the idea of Lean. Rather than the “first mover advantage” of the last bubble, today’s theory is that “massive capital infusion owns the entire market.” And Lean for startups seems like some quaint notion of a bygone era.

And that explains why investors are willing to bet on someone with a successful track record like Katzenberg who has a vision of disrupting an entire industry.

In short, Lean was an answer to a specific startup problem at a specific time, one that most entrepreneurs still face and which ebbs and flows depending on capital markets. It’s a response to scarce capital, and when that constraint is loosened, it’s worth considering whether other approaches are superior. With enough cash in the bank, Katzenberg can afford to create content, sign distribution deals, and see if consumers watch. If not, he still has the option to pivot. And if he’s right, the payoff will be huge.

One More Thing…
Well-funded startups often have more capital for R&D than the incumbent companies they’re disrupting. Companies struggle to compete while reconfiguring legacy distribution channels, pricing models and supply chains. And government agencies find themselves being disrupted by adversaries unencumbered by legacy systems, policies and history.  Both companies and government agencies struggle with how to deliver innovation at speed. Ironically, for this new audience that makes the next generation of Lean – the Innovation Pipeline – more relevant than ever.

Lessons Learned:

  • When capital for startups is readily available at scale, it makes more sense to go big, fast and make mistakes than it does to search for product/market fit.
  • The amount of customer discovery and product-market fit you need to do is inversely proportional to the amount and availability of risk capital.
  • Still, unless your startup has access to large pools of capital or have a brand name like Katzenberg, Lean still makes sense.
  • Lean is now essential for companies and government agencies to deliver innovation at speed
  • The Lean Startup isn’t dead. For companies and government the next generation of Lean – the Innovation Pipeline – is more relevant than ever.

Brown University Talk

Every year I head to the East coast for vacation. We live in a semi-rural area, just ~10,000 people in town, with a potato farm across the street and an arm of the ocean in the backyard. While they own tech, smartphones and computers, most of my neighbors can’t tell you about the latest trends in AI, Bitcoin or Facebook. In contrast, Silicon Valley is an innovation cluster, a monoculture of sorts, with a churning sea of new tech ideas, sailed by entrepreneurs who each passionately believe they’re the next Facebook or Google, with their sails driven by the hurricane winds of investor capital.

The seas are calm here. Most years out here I spend my time reading. This year has been a bit more interesting. One of the things I did was to speak to the startup community in Providence Rhode Island at Brown University.

The talk is here

It’s worth a listen.

7:54: How we used to build startups

11:40: How the Lean Startup began

13:34: Why startups are not smaller versions of large companies

14:06: The three pillars of Lean

20:10: Customer Development is an art

26:42: How we changed the way science is commercialized in the U.S.

29:14: What’s a pivot?

37:34: Customer Discovery isn’t just a bunch of random conversations

39:03: Mistakes that blow a customer meeting

42:45: How you know you’ve talked to enough customers

48:51: Why corporations are mostly doing innovation theater

54:59: Tesla started in my living room

57:28: It takes two: Why world-class startups have both a great innovator and a great entrepreneur

1:04:05: Failure sucks

1:08:43: Avoiding the startup deathtrap

1:13:22: Talk to the crazy people

1:16:05: How you know when you stop being a startup

This 1 Piece of Advice Could Make Or Break Your Career

There’s no handbook on how to evaluate and process “suggestions” and “advice” from a boss or a mentor. But how you choose to act on these recommendations can speed up your learning and make or break your career. Here’s what to keep in mind:


I had a team of students working on an arcane customer problem. While they were quickly coming up to speed, I suggested that they talk to someone who I knew was an expert in the area and could help them learn much faster. In fact, starting in the second week of the class, I suggested the same person several times – one-on-one, in class and in writing. Each time the various team members smiled, nodded and said, “Yes, we’ll get right on it.”  Finally, eight weeks later when they were about to fly across the country to meet the customer, I reminded them again.

When they returned from the trip, I asked if the advisor I suggested was helpful.

I was a bit surprised when they replied, “Oh, we’ve been trying to connect with him for a while and he never responded.”  So, I asked:


Team,
As per our conversation about the lack of response from your advisor John Doe -please forward me copies of the emails you have sent to him.

Thanks

Steve


The reply I received was disappointing — but not totally surprising.


Dear Steve, 

Unfortunately, I believe our team has painted the wrong picture due to miscommunication on our part. It was our responsibility to reach out to John Doe, but we failed to do so.

We did not attempt to reach out to him up until Week 8 before our flight, but the email bounced. We got caught up in work on the trip and did not follow-up. What we should have done was to clarify the email address with our Teaching Assistant and attempt to contact him again.

Best regards,

Taylor


Extra credit for finally owning that they screwed up – but there was more to it.

Combine Outside Advice with Your Own Insights
Upon reflection I realized that this student team was missing a learning opportunity. They were soon heading for the real world, and they had no idea how to evaluate and process “suggestions” and “advice.”  Ironically, given they were really smart and in a world-class university, they were confusing “smart” with “I can figure it all out by myself.”

Throughout my entrepreneurial career I was constantly bombarded by advice – from bosses, mentors, friends, investors, et al. I was lucky enough to have mentors who took an interest in my career, and as a young entrepreneur, I tried to pay attention to what they were trying to tell me. (Coming into my first startup from four years in the military I didn’t have the advantage of thinking I knew it all.) It made me better – I learned faster than having to acquire every bit of knowledge from scratch and I could combine the data coming from others with the insights I had.

Have a Process to Evaluate Suggestions and Advice
Here was my response to my student team:

Dear Team:

Throughout your work career you’ll be getting tons of suggestions and advice; from mentors – people you don’t work for but who care about your career and from your direct boss and others up your reporting chain.

  1. Treat advice and suggestions as a gift, not a distraction
    • Assume someone has just given you a package wrapped in a bow with your name on it.
    • Then think of how they’ll feel when you ignore it and toss it aside.
  2. When you’re working at full speed just trying to get your job done, it’s pretty easy to assume that advice/suggestions from others are just diversions. That’s a mistake. At times following up on them may make or break a career and/or a relationship.
    • The first time your boss or mentor will assume you were too busy to follow up.
    • The second time your boss will begin to question your judgment. Your mentor is going to question your willingness to be coached.
    • The third time you ignore suggestions/advice from your boss is a career-limiting move. And if from a mentor, you’ve likely damaged or ended the relationship.
  3. Everyone likes to offer “suggestions” and “advice.” Think of these as falling into four categories:
    • Some bosses/mentors offer “suggestions” and “advice” because it makes them feel important.
    • Others have a set of contacts or insights they are willing to share with you because they believe these might be useful to you.
    • A few bosses/mentors have pattern-recognition skills. They’ve recognized the project you’re working on or problem you’re trying to solve could be helped by connecting with a specific person/group or by listening to how it was solved previously.
    • A very small subset of bosses/mentors has extracted some best practices and/or wisdom from those patterns. These can give you shortcuts to the insights they’ve taken years to learn.
  4. Early in your career it’s hard to know whether a suggestion/advice is valuable enough to spend time following up. Here’s what I suggest:
    • Start with “Thanks for the suggestion.”
    • Next, it’s OK to ask, “Help me understand why is this important? Why should I talk to them? What should I learn?” This will help you figure out which category of advice you’re getting.If it’s a direct boss and others up your reporting chain, ask, “How should I prioritize this? Does it require immediate action?” (And it most cases it doesn’t matter what category it’s in, just do it.)
    • Always report back to whoever offered you the advice/suggestion to share what you learned. Thank them.

If you open yourself to outside advice, you’ll find people interested in the long-term development of your career – these are your career mentors. Unlike coaching, there’s no specific agenda or goal but mentor relationships can result in a decades-long dialog of continual learning. What makes these relationships a mentorship is this: you have to give as good as you are getting. While you’ll be learning from them – and their years of experience and expertise – what you need to give back is equally important – offering fresh insights to their data.

If your goal is to be a founder, having a network of mentors/advisors means that not only will you be up to date on current technology, markets or trends, you’ll be able to recognize patterns and bring new perspectives that might be basis for your next startup.

Lessons Learned

  • Suggestions/advice at work are not distractions that can be ignored
    • Understand the type of suggestions/advice you’re getting (noise, contacts, patterns, insights)
    • Understand why the advice is being given
    • Agree on the priority in following it up
  • Not understanding how to respond to advice/suggestions can limit your career
  • Advice is a kickstarter for your own insights and a gateway for mentorship
  • Treat advice and suggestions as a gift, not a distraction

Hacking for Defense @ Stanford 2018 – wonder and awe

We just finished our 3rd annual Hacking for Defense class at Stanford. Six teams presented their Lessons Learned presentations.

Watching them I was left with wonder and awe about what they accomplished in 10 weeks.

  • Six teams spoke to over 600 beneficiaries, stakeholders, requirements writers, program managers, warfighters, legal, security, customers, etc.
  • By the end the class all of the teams realized that the problem as given by the sponsor had morphed into something bigger, deeper and much more interesting.

Each of the six teams presented a 2-minute video to provide context about their problem and then gave an 8-minute presentation of their Lessons Learned over the 10-weeks. Each of their slide presentations follow their customer discovery journey. All the teams used the Mission Model Canvas, Customer Development and Agile Engineering to build Minimal Viable Products, but all of their journeys were unique.

The teams presented in front of several hundred people in person and online.

Team: TrackID

If you can’t see the TrackID video click here

If you can’t see the TrackID slides click here

Team: Polaris

If you can’t see the Polaris video click here
If you can’t see the Polaris slides click here

Team: Acquiforce

If you can’t see the Acquiforce video click here

If you can’t see the Acquiforce slides click here

Team: Intelgrids

If you can’t see the Intelgrids video click here
If you can’t see the Intelgrids slides click here

Team: See++

If you can’t see the See++ video click here
If you can’t see the See++ slides click here

Team: Theia

If you can’t see the Theia video click here
If you can’t see the Theia slides click here
Video of the teams live presentation are here.  Worth your time to watch.

The Class
Our mantra to the students was that we wanted them to learn about “Deployment not Demos.” Our observation is that the DOD has more technology demos than they need, but often lack deep problem understanding.  Our goal was to have the students first deeply understand their sponsors problem – before they started building solutions. As you can imagine with a roomful of technologists this was tough. Further we wanted the students to understand all parts of the mission model canvas, not just the beneficiaries and the value proposition. We wanted them to learn what it takes to get their product/service deployed to the field, not give yet another demo to a general. This meant that the minimal viable products the students built were focused on maximizing their learning of what to build, not just building prototypes.

(Our sponsors did remind us, that at times getting a solution deployed meant that someone did have to see a demo!)

Note: The Hacking for Defense class was designed as “fundamental research” to be shared broadly and the results are not subject to restriction for proprietary or national security reasons. In the 10 weeks the students have, Hacking for Defense hardware and software prototypes don’t advance beyond a Technology Readiness Level 4 and remain outside the scope of US export control regulations and restrictions on foreign national participation.

Goals for the Hacking for Defense Class
Our primary goal was to teach students Lean Innovation while they engaged in a national public service. Today if college students want to give back to their country they think of Teach for America, the Peace Corps, or Americorps or perhaps the US Digital Service or the GSA’s 18F. Few consider opportunities to make the world safer with the Department of Defense, Intelligence Community or other government agencies.

Next, we wanted the students to learn about the nation’s threats and security challenges while working with innovators inside the DoD and Intelligence Community. While doing so, also teach our sponsors (the innovators inside the Department of Defense (DOD) and Intelligence Community (IC)) that there is a methodology that can help them understand and better respond to rapidly evolving asymmetric threats. That if we could get teams to rapidly discover the real problems in the field using Lean methods, and only then articulate the requirements to solve them, could defense acquisition programs operate at speed and urgency and deliver timely and needed solutions.

Finally, we wanted to familiarize students about the military as a profession, its expertise, and its proper role in society. And conversely show our sponsors in the Department of Defense and Intelligence community that civilian students can make a meaningful contribution to problem understanding and rapid prototyping of solutions to real-world problems.

Origins of the class
Hacking for Defense has its origins in the Lean LaunchPad class I first taught at Stanford in 2011. It was adopted by the National Science Foundation in 2012 to train Principal Investigators who wanted to get a federal grant for commercializing their science (an SBIR grant.) The NSF observed, “The class is the scientific method for entrepreneurship. Scientists understand hypothesis testing” and relabeled the class as the NSF I-Corps (Innovation Corps). The class is now taught in 81 universities and has trained over 1500 science teams. It was adopted by the National Institutes of Health as I-Corps at NIH in 2014 and at the National Security Agency in 2015.

In 2016, brainstorming with Pete Newell of BMNT and Joe Felter at Stanford we observed that students in our research universities had little connection to the problems their government as well as the larger issues civil society was grappling with. Wondering how we could get students engaged, we realized the same Lean LaunchPad/I-Corps class would provide a framework to do so. Both Hacking for Defense and Hacking for Diplomacy with the State Department were born. Hacking for Energy at Columbia, Hacking for Impact (Non-Profits) at Berkeley and Hacking for Conservation and Development at Duke quickly followed.

 

The Innovation Insurgency Spreads
Hacking for Defense is now offered at eleven universities in addition to Stanford – Georgetown, University of Pittsburgh, Boise State, UC San Diego, James Madison University, University of Southern Mississippi, University of Southern California and Columbia University. Over the next year it will expand to 22 universities. Hacking for Defense.org a non-profit, was established to train educators and to provide a single point of contact for connecting the DOD/IC sponsor problems to these universities.

We’ve been surprised was how applicable the “Hacking for X…” methodology is for other problems. It’s equally applicable to solving public safety, energy, policy, community and social issues internationally and within our own communities. In the next year we’ll see three new variants of the class:

  • Hacking for the Environment
  • Hacking for Oceans
  • Hacking for Cities

It Takes a Village
While I authored this blog post, this classes is a team project. The teaching team consisted of:

  • Pete Newell is a retired Army Colonel currently a Senior Visiting Research Fellow at the National Defense University’s Center for Technology and National Security Policy and CEO of BMNT.
  • Steve Weinstein a 30-year veteran of Silicon Valley technology companies and Hollywood media companies.  Steve is CEO of MovieLabs the joint R&D lab of all the major motion picture studios.
  • Jeff Decker is a social science researcher at Stanford. Jeff served in the U.S. Army as a special operations light infantry squad leader in Iraq and Afghanistan.

Two of our teaching assistants were prior students: Samuel Jackson our lead TA, and Will Papper and Annie Shiel and Paricha Duangtaweesub also assisted.

Special thanks to our course advisors – Tom Byers, Professor of Engineering and Faculty Director, STVP, Arun Majumdar and Sally Benson Co-directors of the Stanford Precourt Energy Institute, and John Mitchell, Stanford Provost of Teaching and Learning.

A special thanks to Rich Carlin and the Office of Naval Research for supporting the program at Stanford and across the country.

We were lucky to get a team of mentors (VC’s and entrepreneurs) who selflessly volunteered their time to help coach the teams. Thanks to Tom Bedecarre, Kevin Ray, Daniel Bardenstein, Rafi Holtzman, Craig Seidel, Michael Chai, Lisa Wallace and Dave Gabler.

We were privileged to have the support of an extraordinary all volunteer team of professional senior military officers representing all branches of service attending fellowship programs at Stanford’s Hoover Institution, and Center for International Security and Cooperation (CISAC) and Asia Pacific Research Center (APARC) at the Freeman Spogli Institute (FSI). These included: Colonel Bradley Boyd, Lieutenant Colonel James “Gumbo” Coughlin, Lieutenant Colonel Marcus Ferrara, Lieutenant Colonel Jer “Jay” Garcia, Lieutenant Commander Nick Hill, Commander Michael Nordeen, Commander Rebecca Ore, Commander Michael Schoonover, Colonel Jason “Shrek” Terry and Todd Forsman.

And of course a big shout-out to our sponsors. At SOCOM, Matt Leland and Angel Zajkowski, at MITRE, Suresh Damodaran, at NAVFAC, Ben Wilcox, at the 9th ISR, Ian Eishen, at AFRL, Jeff Palumbo and Mike Rottmayer at the Defense Acquistion University, Shirley Franko and at ERDC Thomas Bozada.

Thanks!

The Innovation Stack: How to make innovation programs deliver more than coffee cups

Is your organization full of Hackathons, Shark Tanks, Incubators and other innovation programs, but none have changed the trajectory of your company/agency?

Over the last few years Pete Newell and I have helped build innovation programs inside large companies, across the U.S. federal science agencies and in the Department of Defense and Intelligence Community. But it is only recently that we realized why some programs succeed and others are failing.

After doing deep dives in multiple organizations we now understand why individual innovators are frustrated, and why entrepreneurial success requires heroics. We also can explain why innovation activities have generated innovation theater, but few deliverables. And we can explain why innovation in large organizations looks nothing like startups. Most importantly we now have a better idea of how to build innovation programs that will deliver products and services, not just demos.

It starts by understanding the “Innovation Stack” – the hierarchy of innovation efforts that have emerged in large organizations. The stack consists of: Individual Innovation, Innovation Tools and Activities, Team-based Innovation and Operational Innovation.

Individual Innovation
The pursuit of innovation inside large companies/agencies is not a 21st-century invention. Ever since companies existed, there have been passionate individuals who saw that something new, unplanned and unscheduled was possible. And pushing against the status quo of existing process, procedure and plan, they went about building a demo/prototype, and through heroic efforts succeeded in getting a new innovation over the goal line – by shipping/deploying a new innovation.

We describe their efforts as “heroic” because all the established procedures and processes in a large company are primarily designed to execute and support the current business model. From the point of view of someone managing an engineering, manufacturing or operations organization, new, unplanned and unscheduled innovations are a distraction and a drag on existing resources. (The best description I’ve heard is that, “Unfettered innovation is a denial of service attack on core capabilities.”) That’s because until now, we hadn’t levied any requirements, rigor or evidence on the innovator to understand what it would take to integrate, scale and deploy products/services.

Finally, most corporate/agency innovation processes funnel “innovations” into “demo days” or “shark tanks” where they face an approval/funding committee that decides which innovation ideas are worth pursuing. However, without any measurable milestones to show evidence of the evolution of what the team has learned about validity of the problem, customer needs, pivots, etc., the best presenter and flashiest demo usually win.

In some companies and government agencies, innovators even have informal groups, i.e. an Innovators Alliance, where they can exchange best practices and workarounds to the system. (Think of this as the innovator’s support group.) But these innovation activities are ad hoc, and the innovators lack authority, resources and formal process to make innovation programs an integral part of their departments or agencies.

Innovators vs. Entrepreneurs
There are two types of people who engage in large company/agency innovation: Innovators – those who invent new technology, product, service or processes; and Entrepreneurs – those who’ve figured out how to get innovation adopted and delivered through the existing company/agency procedures and processes. Although some individuals operate as both innovator and entrepreneur, any successful innovation program requires an individual or a team with at least these two skill sets. (More detail can be found here.)

Innovation Tools and Activities
Over the last decade, innovators have realized that they needed tools and activities different from traditional project management tools used for new versions of existing products/customers.They have passionately embraced innovation tools and activities that for the first time help individual innovators figure out what to build, who to build it for and how to create effective prototypes and demos.

Some examples of innovation tools are Customer Development, Design Thinking, User-Centric Design, Business Model Canvas, Storytelling, etc. Companies/agencies have also co-opted innovation activities developed for startups such as Hackathons, Incubators, internal Kickstarters, as well as Open Innovation programs and Maker Spaces that give individual innovators a physical space and dedicated time to build prototypes and demos. In addition, companies and agencies have set up Innovation Outposts (most often located in Silicon Valley) to be closer to relevant technology and then to invest, partner or buy.

These activities make sense in a startup ecosystem (where 100% of the company is focused on innovation,) however they generate disappointing results inside companies/agencies (when 98% of the organization is focused on executing the existing business/mission model.) While these tools and activities educated innovators and generated demos and prototypes, they lacked an end-to-end process that focused on delivery/deployment. So it should be no surprise that very few contributed to the company’s top or bottom line (or an agency’s mission).

One of the ironies of the tools/activities groups is rather than talking about the results of using the tools – i.e. the ability to rapidly deliver new products/services that are wanted and needed – their passion has them evangelizing the features of the tools and activities. This means that senior leadership has pigeonholed most of these groups as extensions of corporate training departments and skeptics view this as the “latest fad.”

Team-based Innovation
Rather than just teaching innovators how to use new tools or having them build demos, we recognized that there was a need for a process that taught all the components of a business/mission model (who are the customers, what product/service solves their problem, how do we get it to them, support it, etc.) The next step in entrepreneurial education was to teach teams a formal innovation process for how to gather evidence that lets them test if their idea is feasible, desirable and viable. Examples of team-based innovation programs are the National Science Foundation Innovation Corps (I-Corps @ NSF), for the Intelligence Community I‑Corps@ NSA, and for the Department of Defense, Hacking for Defense (H4D).

In contrast to single-purpose activities like Incubators, Hackathons, Kickstarters, etc., these curricula teach what it takes to turn an idea into a deliverable product/service by using the scientific method of hypothesis testing and experimentation outside the building. This process emphasizes rapid learning cycles with speed, urgency, accepting failure as learning, and innovation metrics.

Teams talk to 100+ beneficiaries and stakeholders while building minimal viable products to maximize learning and discovery. They leave the program with a deep understanding of all the obstacles and resources needed to deliver/deploy a product.

The good news – I-Corps, Hacking for Defense and other innovation programs that focus on training single teams have raised the innovation bar. These programs have taught thousands of teams of federally funded scientists as well as innovators in corporations, the Department of Defense and intelligence community. However, over time we’ve seen teams that completed these programs run into scaling challenges. Even with great evidence-based minimal viable products (prototypes), teams struggled to get these innovations deployed at scale and in the field. Or a team that achieved product-market fit building a non-standard architecture could find no way to maintain it at scale within the parent organization.

Upon reflection we identified two root causes. The first is a lack of connection between innovation teams and their parent organization. Teams form/and are taught outside of their parent organization because innovation is disconnected from other activities. This meant that when teams went back to their home organization, they found that execution of existing priorities took precedence. They returned speaking a foreign language (What’s a pivot? Minimum viable what?) to their colleagues and bosses who are rewarded on execution-based metrics. Further, as budgets are planned out years in advance, their organization had no slack for “good ideas.” As a result, there was no way to finish and deploy whatever innovative prototypes the innovators had developed – even ones that have been validated.

The second root cause emerged because neither the innovator’s teams nor their organizations had the mandate, budget or people to build an end-to-end innovation pipeline process, one that started with innovation sourcing funnel (both internal and external sources) all the way to integrating their prototypes into mainstream engineering production. (see below and this HBR article on the innovation pipeline.)

Operational Innovation
As organizations have moved from – individual innovators working alone, to adopting innovation tools and activities, to teaching teams about evidence-based innovation – our most important realization has been this: Having skills/tools and activities are critical building blocks but by themselves are insufficient to build a program that delivers results that matter to leadership.  It’s only when senior leaders see how an innovation process can deliver stuff that matters – at speed—that they take action to change the processes and procedures that get in the way.

We believe that the next big step is to get teams and leaders to think about the innovation process from end-to-end – that is to visualize the entire flow of how and from where an idea is generated (the source) all the way to deployment (how it gets into users’ hands). So, we’ve drawn a canonical innovation pipeline. (The HBR article here describes it in detail.) For context, in the figure below, the I-Corps program described earlier is the box labeled “Solution Exploration/Hypotheses Testing.” We’ve surrounded that process with all the parts necessary to build and deliver products and services at speed and at scale.

Second, we’ve realized that while individual initiatives won “awards,” and Incubators and Hackathons got coffee cups and posters, senior leadership sat up and took notice when operating groups transformed how they work in the service of a critical product or mission. When teams in operating groups adopted the innovation pipeline, it made an immediate impact on delivering products/services at speed.

An operating group can be a corporate profit and loss center or anything that affects revenue, profit, users, market share, etc. In a government agency it can be something that allows a group to execute mission more effectively or in a new disruptive way. Operating groups have visibility, credibility and most importantly direct relevance to mission.

Where are these groups? In every large company or agency there are groups solving operational problems that realize “they can’t go on like this” and/or “we need to do a lot more stuff” and/or “something changed, and we rapidly need to find new ways to do business.” These groups are ready to try something new. Most importantly we learned that “the something new” is emphatically not more tools or activities (design thinking, user-centric design, storytelling, hackathons, incubators, etc.) Because these groups want an end-to-end solution, the innovation pipeline resonates with the “do’ers” who lead these groups.

(One example of moving up the Innovation Stack is that the NSA I-Corps team has recently shifted their focus from working with individual teams to helping organizations deploy the methodology at scale.  In true lean startup fashion, they are actively testing a number of approaches with a variety of internal organizations ranging in size from 40 to 1000+ people.)

However, without a mandate for actually delivering innovation from senior leadership, scaling innovation across the company/agency means finding one group at a time – until you reach a tipping point of recognition. That’s when leadership starts to pay attention. Our experience to date is that 25- to 150-person groups run by internal entrepreneurs with budget and authority to solve critical problems are the right place to start to implement this. Finding these people in large companies/agencies is a repeatable process. It requires patient and persistent customer discovery inside your company/agency to find these groups and deeply understand their pains/gains and jobs to be done.

Lessons Learned

  • Companies/agencies have adapted and adopted startup innovation tools
    • Lean, Design Thinking, User-centric Design, Business Model Canvas, etc.
  • As well as startup activities and team-based innovation 
    • Hackathons, Incubators, Kickstarters, I-Corps, FastWorks, etc.
  • Because they are disconnected from the mainstream business/mission model very few have been able to scale past a demo/prototype
  • Use the Innovation Stack and start working directly with operating groups
    • Find those who realize “they can’t go on like this” and/or “we need to do a lot more stuff” and/or “something changed, and we rapidly need to find new ways to do business”
  • You’ll deliver stuff that matters instead of coffee cups

Why the Future of Tesla May Depend on Knowing What Happened to Billy Durant

A version of this article appeared in the Harvard Business Review

Elon Musk, Alfred Sloan, and entrepreneurship in the automobile industry.

The entrepreneur who founded and grew the largest startup in the world to $10 billion in revenue and got fired is someone you have probably never heard of. The guy who replaced him invented the idea of the modern corporation. If you want to understand the future of Tesla and Elon Musk’s role – something many want to do, given the constant stream of headlines about the company — you should start with a bit of automotive history from the 20th Century.

Alfred P. Sloan and the Modern Corporation
By the middle of the 20th century, Alfred P. Sloan had become the most famous businessman in the world. Known as the “Inventor of the Modern Corporation,” Sloan was president of General Motors from 1923 to 1956 when the U.S. automotive industry grew to become one of the drivers of the U.S. economy.

Today, if you look around the United States it’s hard to avoid Sloan. There’s the Alfred P. Sloan Foundation, the Sloan School of Management at MIT, the Sloan program at Stanford, and the Sloan/Kettering Memorial Cancer Center in New York. Sloan’s book My Years with General Motors, written half a century ago, is still a readable business classic.

Peter Drucker wrote that Sloan was “the first to work out how to systematically organize a big company. When Sloan became president of GM in 1923 he put in place planning and strategy, measurements, and most importantly, the principles of decentralization.”

When Sloan arrived at GM in 1920 he realized that the traditional centralized management structures organized by function (sales, manufacturing, distribution, and marketing) were a poor fit for managing GM’s diverse product lines.  That year, as management tried to coordinate all the operating details across all the divisions, the company almost went bankrupt when poor planning led to excess inventory, with unsold cars piling up at dealers and the company running out of cash.

Borrowing from organizational experiments pioneered at DuPont (run by his board chair), Sloan organized the company by division rather than function and transferred responsibility down from corporate into each of the operating divisions (Chevrolet, Pontiac, Oldsmobile, Buick and Cadillac). Each of these GM divisions focused on its own day-to-day operations with each division general manager responsible for the division’s profit and loss. Sloan kept the corporate staff small and focused on policymaking, corporate finance, and planning. Sloan had each of the divisions start systematic strategic planning.  Today, we take for granted divisionalization as a form of corporate organization, but in 1920, other than DuPont, almost every large corporation was organized by function.

Sloan put in place GM’s management accounting system (also borrowed from DuPont) that for the first time allowed the company to: 1) produce an annual operating forecast that compared each division’s forecast (revenue, costs, capital requirements and return on investment) with the company’s financial goals. 2) Provide corporate management with near real-time divisional sales reports and budgets that indicated when they deviated from plan. 3) Allowed management to allocate resources and compensation among divisions based on a standard set of corporate-wide performance criteria.

Modern Corporation Marketing
When Sloan took over as president of GM in 1923, Ford was the dominant player in the U.S. auto market. Ford’s Model T cost just $260 ($3,700 in today’s dollars) and Ford held 60% of the U.S. car market. General Motors had 20%. Sloan realized that GM couldn’t compete on price, so GM created multiple brands of cars, each with its own identity targeted at a specific economic bracket of American customers. The company set the prices for each of these brands from lowest to highest (Chevrolet, Pontiac, Oldsmobile, Buick, and Cadillac). Within each brand there were several models at different price points.

The idea was to keep customers coming back to General Motors over time to upgrade to a better brand as they became wealthier. Finally, GM created the notion of perpetual demand within brands by continually obsoleting their own products with new models rolled out every year. (Think of the iPhone and its yearly new models.)

By 1931, with the combination of superior financial management and an astute brand and product line strategy, GM had 43% market share to Ford’s 20% – a lead it never relinquished.

Sloan transformed corporate management into a real profession, and its stellar example was the continuous and relentless execution of the GM business model (until its collapse 50 years later).

What Does GM Have to Do with Tesla And Elon Musk?
Well, thanks for the history lesson but why should I care?

If you’re following Tesla, you might be interested to know that Sloan wasn’t the founder of GM. Sloan was president of a small company that made ball bearings that GM acquired in 1918. When Sloan became President of General Motors in 1923, it was already a $700 million company (about $10.2 billion in sales in today’s dollars).

Yet, you never hear who built GM to that size. Who was the entrepreneur who founded what would become General Motors 16 years earlier, in 1904? Where are the charitable foundations, business schools, and hospitals named after the founder of GM? What happened to him?

The founder of what became General Motors was William (Billy) Durant. At the turn of the 20th century, Durant was one of the largest makers of horse-drawn carriages, building 150,000 a year. But in 1904, after his first time seeing a car in Flint, Michigan, he was one of the first to see that the future was going to be in a radically new form of transportation powered by internal combustion engines.

Durant took his money from his carriage company and bought a struggling automobile startup called Buick. Durant was a great promoter and visionary, and by 1909 he had turned Buick into the best-selling car in the U.S. Searching for a business model in a new industry, and with the prescient vision that a car company should offer multiple brands, that year he bought three other small car companies — Cadillac, Oldsmobile, and Pontiac — and merged them with Buick, renaming the combined company General Motors. He also believed that to succeed the company needed to be vertically integrated and bought up 29 parts manufacturers and suppliers.

The next year, 1910, trouble hit. While Durant was a great entrepreneur, the integration of the companies and suppliers was difficult, a recession had just hit, and GM was overextended with $20 million in debt ($250 million in 2018 dollars) from all the acquisitions and was about to run out of cash. Durant’s bankers and board fired him from the company he had founded.

For most people the story might have ended there. But not for Durant. The next year Durant co-founded another automobile startup, this one started with Louis Chevrolet. Over the next five years Durant built Chevrolet into a competitor to GM. And in one of the greatest corporate comeback stories, in 1916 Durant used Chevrolet to buy back control of GM with the backing of Pierre duPont. He once again took over General Motors, merged Chevrolet into GM, bought Fisher Body and Frigidaire, created GMAC GM’s financing arm and threw out the bankers who six years earlier had fired him.

Durant had another great four years at the helm of GM. At the time he was not only running GM but was a major Wall Street speculator (even on GM stock) and was big in the New York social scene. But trouble was on the horizon. Durant was at his best when there was money to indulge his indiscriminate expansion. (He bought two car companies – Sheridan and the Scripps-Booth – that competed with his existing products.) But by 1920, a post-World War I recession had hit, and car sales has slowed. Durant kept building for a future assuming the flow of cash and customers would continue.

Meanwhile, inventory was piling up, the stock was cratering, and the company was running out of cash. In the spring of 1920 with company had to go to the banks and he got an $80 million loan (about a billion dollars in 2018) to finance operations. While everyone around him acknowledged he was a visionary and a world-class fund raiser, Durant’s one-man show was damaging the company. He couldn’t prioritize, couldn’t find time to meet with his direct reports, fired them when they complained about the chaos, and the company had no financial controls other than Durant’s ability to manage to raise more money. When the stock collapsed Durant’s personal shares were underwater and were exposed to being called by bankers who would then own a good part of GM. The board decided that the company had enough vision — they bought out Durant’s shares and realized it was now time for someone who could execute at scale.

Once again, his board (this time led by the DuPont family) tossed him out of General Motors (when GM sales were $10 billion in today’s dollars.)

Alfred Sloan became the President of GM and ran it for the next three decades.

William Durant tried to build his third car company, Durant Motors, but he was still speculating on stocks, and got wiped out in the Depression in 1929. The company closed in 1931. Durant died managing a bowling alley in Flint, Michigan, in 1947.

From the day Durant was fired in 1920, and for the next half a century, American commerce would be led by an army of “Sloan-style managers” who managed and executed existing business models.

But the spirit of Billy Durant would rise again in what would become Silicon Valley. And 100 years later Elon Musk would see that the future of transportation was no longer in internal combustion engines and build the next great automobile company.

Days of Futures Past for Tesla
In all of his companies, Elon Musk has used his compelling vision of a future transformed to capture the imagination of customers and, equally important, of Wall Street, raising the billions of dollars to make his vision a reality.

Yet, as Durant’s story typifies, one of the challenges for visionary founders is that they often have a hard time staying focused on the present when the company needs to transition into relentless execution and scale. Just as Durant had multiple interests, Musk is not only Tesla’s CEO and Product Architect, overseeing all product development, engineering, and design. At SpaceX (his rocket company) he’s CEO and lead designer overseeing the development and manufacturing of advanced rockets and spacecraft. He’s also the founder at The Boring Company (the tunneling company) and co-founder and chairman of OpenAI. And a founder of Neuralink a brain-computer interface startup.

All of these companies are doing groundbreaking innovations but even Musk only has 24 hours in a day and 7 days in a week. Others have noted that diving in and out of your current passion makes you a dilettante, not a CEO.

One of the common traits of a visionary founder is that once you have proven the naysayers wrong, you convince yourself that all your pronouncements have the same prescience.

For example, after the success of the Model S sedan, Tesla’s next car was an SUV, the Model X. By most accounts, Musk’s insistence on adding bells and whistles (like the Falcon Wing doors and other accoutrements) to what should have been simple execution of the next product made manufacturing the car in volume a nightmare. Executives who disagreed (and had a hand in making the Model S a success) ended up leaving the company. The company later admitted that the lesson learned was hubris.

The Tesla Model 3 was designed to be simple to manufacture, but instead of using the existing assembly line Musk said, “the true problem, the true difficulty, and where the greatest potential is – is building the machine that makes the machine. In other words, it’s building the factory. I’m really thinking of the factory like a product.” Fast forward two years and it turns out that the Model 3 assembly line was a great example of over-automation. “Excessive automation at Tesla was a mistake. To be precise, my mistake” Musk recently tweeted,

Sleeping on the factory floor to solve self-inflicted problems is not a formula for success at scale, and while it’s great PR, it’s not management. It is in fact a symptom of a visionary founder imposing chaos just at the time where execution is required. Tesla now has a pipeline of newly announced products, a new Roadster (a sports car), a Semi Truck, and a hinted crossover called the Model Y. All of them will require massive execution at scale, not just vision.

Unlike Durant, Musk has engineered his extended tenure and this year got his shareholders to give him a new $2.6 billion compensation plan (and it could potentially be worth as much as $55 billion) if he can grow the company’s market cap in $50 billion increments to $650 billion. The board said that it “believes that the Award will continue to incentivize and motivate Elon to lead Tesla over the long-term, particularly in light of his other business interests.”

Elon Musk has done what Steve Jobs and Jeff Bezos did – disrupt a series of stagnant businesses controlled by rent seekers, permanently changing the trajectory of multiple industries – while capturing the imagination of consumers and the financial community. Just a handful of people with these skills emerge every century. However, fewer combine the talent for creating an industry with the very different skills needed for scale. Each of Tesla’s stumbles has begun to squander the very advantage that Musks vision gave the company. And what was once an insurmountable lead by having an economic castle surrounded by a defensible moat (battery technology, superchargers, autonomous driving, over the air updates, etc.) is closing rapidly.

One wonders if $2.6 billion in executive compensation would be better spent finding someone to lead Tesla to becoming a reliable producer of cars in high volume – without the drama in each new model.

Perhaps Tesla now needs its Alfred P. Sloan.

Lesson Learned

  • Founders/visionaries see things other don’t and the extraordinary ones create new industries
  • When technology changes are rapid you want the founder to continue to run the company
  • However, when success depends on exploitation and execution at scale their impatience for continuous innovation and invention often gets in the way of day-to-day execution
  • The best ones know when it’s time to let go
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