How to Survive in a World of Disruption – Innovation in Large Organizations

The team at Innovation Leader had me over to share some observations on how to survive in a world of disruption in large organizations.

It’s worth a listen – here.

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8:30: Not everyone is an innovator
15:15: How to find and foster innovation talent in your company

Driven to Distraction – the future of car safety

If you haven’t gotten a new car in a while you may not have noticed that the future of the dashboard looks like this:


That’s it. A single screen replacing all the dashboard gauges, knobs and switches. But behind that screen is an increasing level of automation that hides a ton of complexity.

At times everything you need is on the screen with a glance. At other times you have to page through menus and poke at the screen while driving. And while driving at 70mph, try to understand if you or your automated driving system is in control of your car. All while figuring out how to use any of the new features, menus or rearranged user interface that might have been updated overnight.

In the beginning of any technology revolution the technology gets ahead of the institutions designed to measure and regulate safety and standards. Both the vehicle’s designers and regulators will eventually catch up, but in the meantime we’re on the steep part of a learning curve – part of a million-person beta test – about what’s the right driver-to-vehicle interface.

We went through this with airplanes. And we’re reliving that transition in cars. Things will break, but in a few decades we’ll come out out the other side, look back and wonder how people ever drove any other way.

Here’s how we got here, what it’s going to cost us, and where we’ll end up.


Cars, Computers and Safety
Two massive changes are occurring in automobiles: 1) the transition from internal combustion engines to electric, and 2) the introduction of automated driving.

But a third equally important change that’s also underway is the (r)evolution of car dashboards from dials and buttons to computer screens. For the first 100 years cars were essentially a mechanical platform – an internal combustion engine and transmission with seats – controlled by mechanical steering, accelerator and brakes. Instrumentation to monitor the car was made up of dials and gauges; a speedometer, tachometer, and fuel, water and battery gauges.
By the 1970’s driving became easier as automatic transmissions replaced manual gear shifting and hydraulically assisted steering and brakes became standard. Comfort features evolved as well: climate control – first heat, later air-conditioning; and entertainment – AM radio, FM radio, 8-track tape, CD’s, and today streaming media. In the last decade GPS-driven navigation systems began to appear.

Safety
At the same time cars were improving, automobile companies fought safety improvements tooth and nail. By the 1970’s auto deaths in the U.S averaged 50,000 a year. Over 3.7 million people have died in cars in the U.S. since they appeared – more than all U.S. war deaths combined. (This puts auto companies in the rarified class of companies – along with tobacco companies – that have killed millions of their own customers.) Car companies argued that talking safety would scare off customers, or that the added cost of safety features would put them in a competitive price disadvantage. But in reality, style was valued over safety.

Safety systems in automobiles have gone through three generations – passive systems and two generations of active systems. Today we’re about to enter a fourth generation – autonomous systems.

Passive safety systems are features that protect the occupants after a crash has occurred. They started appearing in cars in the 1930’s. Safety glass in windshields appeared in the 1930’s in response to horrific disfiguring crashes. Padded dashboards were added in the 1950’s but it took Ralph Nader’s book, Unsafe at Any Speedto spur federally mandated passive safety features in the U.S. beginning in the 1960’s: seat belts, crumple zones, collapsible steering wheels, four-way flashers and even better windshields. The Department of Transportation was created in 1966 but it wasn’t until 1979 that the National Highway Traffic Safety Administration (NHTSA) started crash-testing cars (the Insurance Institute for Highway Safety started their testing in 1995). In 1984 New York State mandated seat belt use (now required in 49 of the 50 states.)

These passive safety features started to pay off in the mid-1970’s as overall auto deaths in the U.S. began to decline.

Active safety systems try to prevent crashes before they happen. These depended on the invention of low-cost, automotive-grade computers and sensors. For example, accelerometers-on-a-chip made airbags possible as they were able to detect a crash in progress. These began to appear in cars in the late 1980’s/1990’s and were required in 1998. In the 1990’s computers capable of real-time analysis of wheel sensors (position and slip) made ABS (anti-lock braking systems) possible. This feature was finally required in 2013.

Since 2005 a second generation of active safety features have appeared. They run in the background and constantly monitor the vehicle and space around it for potential hazards. They include: Electronic Stability Control, Blind Spot Detection, Forward Collision Warning, Lane Departure Warning, Rearview Video Systems, Automatic Emergency Braking, Pedestrian Automatic Emergency Braking, Rear Automatic Emergency Braking, Rear Cross Traffic Alert and Lane Centering Assist.

Autonomous Cars
Today, a fourth wave of safety features is appearing as Autonomous/Self-Driving features. These include Lane Centering/Auto Steer, Adaptive cruise control, Traffic jam assist, Self-parking, full self-driving. The National Highway Traffic Safety Administration (NHTSA) has adopted the six-level SAE standard to describe these vehicle automation features:

Getting above Level 2 is a really hard technical problem and has been discussed ad infinitum in other places. But what hasn’t got much attention is how drivers interact with these systems as the level of automation increases, and as the driving role shifts from the driver to the vehicle. Today, we don’t know whether there are times these features make cars less safe rather than more.

For example, Tesla and other cars have Level 2 and some Level 3 auto-driving features. Under Level 2 automation, drivers are supposed to monitor the automated driving because the system can hand back control of the car to you with little or no warning. In Level 3 automation drivers are not expected to monitor the environment, but again they are expected to be prepared to take control of the vehicle at all times, this time with notice.

Research suggests that drivers, when they aren’t actively controlling the vehicle, may be reading their phone, eating, looking at the scenery, etc. We really don’t know how drivers will perform in Level 2 and 3 automation. Drivers can lose situational awareness when they’re surprised by the behavior of the automation – asking: What is it doing now? Why did it do that? Or, what is it going to do next? There are open questions as to whether drivers can attain/sustain sufficient attention to take control before they hit something. (Trust me, at highway speeds having a “take over immediately” symbol pop up while you are gazing at the scenery raises your blood pressure, and hopefully your reaction time.)If these technical challenges weren’t enough for drivers to manage, these autonomous driving features are appearing at the same time that car dashboards are becoming computer displays.

We never had cars that worked like this. Not only will users have to get used to dashboards that are now computer displays, they are going to have understand the subtle differences between automated and semi-automated features and do so as auto makers are developing and constantly updating them. They may not have much help mastering the changes. Most users don’t read the manual, and, in some cars, the manuals aren’t even keeping up with the new features.

But while we never had cars that worked like this, we already have planes that do.
Let’s see what we’ve learned in 100 years of designing controls and automation for aircraft cockpits and pilots, and what it might mean for cars.

Aircraft Cockpits
Airplanes have gone through multiple generations of aircraft and cockpit automation. But unlike cars which are just first seeing automated systems, automation was first introduced in airplanes during the 1920s and 1930s.

For their first 35 years airplane cockpits, much like early car dashboards, were simple – a few mechanical instruments for speed, altitude, relative heading and fuel. By the late 1930’s the British Royal Air Force (RAF) standardized on a set of flight instruments. Over the next decade this evolved into the “Basic T” instrument layout – the de facto standard of how aircraft flight instruments were laid out.

Engine instruments were added to measure the health of the aircraft engines – fuel and oil quantity, pressure, and temperature and engine speed.

Next, as airplanes became bigger, and the aerodynamic forces increased, it became difficult to manually move the control surfaces so pneumatic or hydraulic motors were added to increase the pilots’ physical force. Mechanical devices like yaw dampers and Mach trim compensators corrected the behavior of the plane.

Over time, navigation instruments were added to cockpits. At first, they were simple autopilots to just keep the plane straight and level and on a compass course. The next addition was a radio receiver to pick up signals from navigation stations. This was so pilots could set the desired bearing to the ground station into a course deviation display, and the autopilot would fly the displayed course.

In the 1960s, electrical systems began to replace the mechanical systems:

  • electric gyroscopes (INS) and autopilots using VOR (Very High Frequency Omni-directional Range) radio beacons to follow a track
  • auto-throttle – to manage engine power in order to maintain a selected speed
  • flight director displays – to show pilots how to fly the aircraft to achieve a preselected speed and flight path
  • weather radars – to see and avoid storms
  • Instrument Landing Systems – to help automate landings by giving the aircraft horizontal and vertical guidance.

By 1960 a modern jet cockpit (the Boeing 707) looked like this:

While it might look complicated, each of the aircraft instruments displayed a single piece of data. Switches and knobs were all electromechanical.

Enter the Glass Cockpit and Autonomous Flying
Fast forward to today and the third generation of aircraft automation. Today’s aircraft might look similar from the outside but on the inside four things are radically different:

  1. The clutter of instruments in the cockpit has been replaced with color displays creating a “glass cockpit”
  2. The airplanes engines got their own dedicated computer systems – FADEC (Full Authority Digital Engine Control) – to autonomously control the engines
  3. The engines themselves are an order of magnitude more reliable
  4. Navigation systems have turned into full-blown autonomous flight management systems

So today a modern airplane cockpit (an Airbus 320) looks like this:

Today, airplane navigation is a real-world example of autonomous driving – in the sky. Two additional systems, the Terrain Awareness and Warning Systems (TAWS) and Traffic Condition Avoidance System (TCAS) gave pilots a view of what’s underneath and around them dramatically increasing pilots’ situation awareness and flight safety. (Autonomy in the air is technically a much simpler problem because in the cruise portion of flight there are a lot less things to worry about in the air than in a car.)

Navigation in planes has turned into autonomous “flight management.” Instead of a course deviation dial, navigation information is now presented as a “moving map” on a display showing the position of navigation waypoints, by latitude and longitude. The position of the airplane no longer uses ground radio stations, but rather is determined by Global Positioning System (GPS) satellites or autonomous inertial reference units. The route of flight is pre-programmed by the pilot (or uploaded automatically) and the pilot can connect the autopilot to autonomously fly the displayed route. Pilots enter navigation data into the Flight Management System, with a keyboard. The flight management system also automates vertical and lateral navigation, fuel and balance optimization, throttle settings, critical speed calculation and execution of take-offs and landings.

Automating the airplane cockpit relieved pilots from repetitive tasks and allowed less skilled pilots to fly safely. Commercial airline safety dramatically increased as the commercial jet airline fleet quadrupled in size from ~5,000 in 1980 to over 20,000 today. (Most passengers today would be surprised to find out how much of their flight was flown by the autopilot versus the pilot.)

Why Cars Are Like Airplanes
And here lies the connection between what’s happened to airplanes with what is about to happen to cars.

The downside of glass cockpits and cockpit automation means that pilots no longer actively operating the aircraft but instead monitor it. And humans are particularly poor at monitoring for long periods. Pilots have lost basic manual and cognitive flying skills because of a lack of practice and feel for the aircraft. In addition, the need to “manage” the automation, particularly when involving data entry or retrieval through a key-pad, increased rather than decreased the pilot workload. And when systems fail, poorly designed user interfaces reduce a pilot’s situational awareness and can create cognitive overload.

Today, pilot errors — not mechanical failures– cause at least 70-80% of commercial airplane accidents. The FAA and NTSB have been analyzing crashes and have been writing extensively on how flight deck automation is affecting pilots. (Crashes like Asiana 214 happened when pilots selected the wrong mode on a computer screen.) The FAA has written the definitive document how people and automated systems ought to interact.

In the meantime, the National Highway Traffic Safety Administration (NHTSA) has found that 94% of car crashes are due to human error – bad choices drivers make such as inattention, distraction, driving too fast, poor judgment/performance, drunk driving, lack of sleep.

NHTSA has begun to investigate how people will interact with both displays and automation in cars. They’re beginning to figure out:

  • What’s the right way to design a driver-to-vehicle interface on a screen to show:
    • Vehicle status gauges and knobs (speedometer, fuel/range, time, climate control)
    • Navigation maps and controls
    • Media/entertainment systems
  • How do you design for situation awareness?
    • What’s the best driver-to-vehicle interface to display the state of vehicle automation and Autonomous/Self-Driving features?
    • How do you manage the information available to understand what’s currently happening and project what will happen next?
  • What’s the right level of cognitive load when designing interfaces for decisions that have to be made in milliseconds?
    • What’s the distraction level from mobile devices? For example, how does your car handle your phone? Is it integrated into the system or do you have to fumble to use it?
  • How do you design a user interface for millions of users whose age may span from 16-90; with different eyesight, reaction time, and ability to learn new screen layouts and features?

Some of their findings are in the document Human-centric design guidance for driver-vehicle interfaces. But what’s striking is that very little of the NHSTA documents reference the decades of expensive lessons that the aircraft industry has learned. Glass cockpits and aircraft autonomy have traveled this road before. Even though aviation safety lessons have to be tuned to the different reaction times needed in cars (airplanes fly 10 times faster, yet pilots often have seconds or minutes to respond to problems, while in a car the decisions often have to be made in milliseconds) there’s a lot they can learn together. Aviation has gone 9 years in the U.S. with just one fatality, yet in 2017 37,000 people died in car crashes in the U.S.

There Are No Safety Ratings for Your Car As You Drive
In the U.S. aircraft safety has been proactive. Since 1927 new types aircraft (and each sub-assembly) are required to get a type approval from the FAA before it can be sold and be issued an Airworthiness Certificate.

Unlike aircraft, car safety in the U.S. has been reactive. New models don’t require a type approval, instead each car company self-certifies that their car meets federal safety standards. NHTSA waits until a defect has emerged and then can issue a recall.

If you want to know how safe your model of car will be during a crash, you can look at the National Highway Traffic Safety Administration (NHTSA) New Car Assessment Program (NCAP) crash-tests, or the Insurance Institute for Highway Safety (IIHS) safety ratings. Both summarize how well the active and passive safety systems will perform in frontal, side, and rollover crashes. But today, there are no equivalent ratings for how safe cars are while you’re driving them. What is considered a good vs. bad user interface and do they have different crash rates? Does the transition from Level 1, 2 and 3 autonomy confuse drivers to the point of causing crashes? How do you measure and test these systems? What’s the role of regulators in doing so?

Given the NHTSA and the FAA are both in the Department of Transportation (DoT), It makes you wonder whether these government agencies actively talk to and collaborate with each other and have integrated programs and common best practices. And whether they have extracted best practices from the NTSB. And from the early efforts of Tesla, Audi, Volvo, BMW, etc., it’s not clear they’ve looked at the airplane lessons either.

It seems like the logical thing for NHTSA to do during this autonomous transition is 1) start defining “best practices” in U/I and automation safety interfaces and 2) to test Level 2-4 cars for safety while you drive (like the crash tests but for situational awareness, cognitive load, etc. in a set of driving scenarios. (There are great university programs already doing that research.)

However, the DoT’s Automated Vehicles 3.0 plan moves the agency further from owning the role of “best practices” in U/I and automation safety interfaces. It assumes that car companies will do a good job self-certifying these new technologies. And has no plans for safety testing and rating these new Level 2-4 autonomous features.

(Keep in mind that publishing best practices and testing for autonomous safety features is not the same as imposing regulations to slow down innovation.)

It looks like it might take an independent agency like the SAE to propose some best practices and ratings. (Or the slim possibility that the auto industry comes together and set defacto standards.)

The Chaotic Transition
It took 30 years, from 1900 to 1930, to transition from horses and buggies in city streets to automobiles dominating traffic. During that time former buggy drivers had to learn a completely new set of rules to control their cars. And the roads in those 30 years were a mix of traffic – it was chaotic.
In New York City the tipping point was 1908 when the number of cars passed the number of horses. The last horse-drawn trolley left the streets of New York in 1917. (It took another decade or two to displace the horse from farms, public transport and wagon delivery systems.) Today, we’re about to undergo the same transition.

Cars are on the path for full autonomy, but we’re seeing two different approaches on how to achieve Level 4 and 5 “hands off” driverless cars. Existing car manufacturers, locked into the existing car designs, are approaching this step-wise – adding additional levels of autonomy over time – with new models or updates; while new car startups (Waymo, Zoox, Cruise, etc.) are attempting to go right to Level 4 and 5.

We’re going to have 20 or so years with the roads full of a mix of millions of cars – some being manually driven, some with Level 2 and 3 driver assistance features, and others autonomous vehicles with “hands-off” Level 4 and 5 autonomy. It may take at least 20 years before autonomous vehicles become the dominant platforms. In the meantime, this mix of traffic is going to be chaotic. (Some suggest that during this transition we require autonomous vehicles to have signs in their rear window, like student drivers, but this time saying, “Caution AI on board.”)

As there will be no government best practices for U/I or scores for autonomy safety, learning and discovery will be happening on the road. That makes the ability for car companies to have over-the-air updates for both the dashboard user interface and the automated driving features essential. Incremental and iterative updates will add new features, while fixing bad ones. Engaging customers to make them realize they’re part of the journey will ultimately make this a successful experiment.

My bet is much like when airplanes went to glass cockpits with increasingly automated systems, we’ll create new ways drivers crash their cars, while ultimately increasing overall vehicle safety.

But in the next decade or two, with the government telling car companies “roll your own”, it’s going to be one heck of a ride.

Lessons Learned

  • There’s a (r)evolution as car dashboards move from dials and buttons to computer screens and the introduction of automated driving
    • Computer screens and autonomy will both create new problems for drivers
    • There are no standards to measure the safety of these systems
    • There are no standards for how information is presented
  • Aircraft cockpits are 10 to 20 years ahead of car companies in studying and solving this problem
    • Car and aircraft regulators need to share their learnings
    • Car companies can reduce crashes and deaths if they look to aircraft cockpit design for car user interface lessons
  • The Department of Transportation has removed barriers to the rapid adoption of autonomous vehicles
    • Car companies “self-certify” whether their U/I and autonomy are safe
    • There are no equivalents of crash safety scores for driving safety with autonomous features
  • Over-the-air updates for car software will become essential
    • But the downside is they could dramatically change the U/I without warning
  • On the path for full autonomy we’ll have three generations of cars on the road
    • The transition will be chaotic, so hang on it’s going to a bumpy ride, but the destination – safety for everyone on the road – will be worth it

What Your Startup Needs to Know About Regulated Markets

Often the opposite of disruption is the status quo.

If  you’re a startup trying to disrupt an existing business you need to read The Fixer by Bradley Tusk and Regulatory Hacking by Evan Burfield. These two books, one by a practitioner, the other by an investor, are must-reads.

The Fixer is 1/3rd autobiography, 1/3rd case studies, and 1/3rd a “how-to” manual. Regulatory Hacking is closer to a “step-by-step” textbook with case studies.

Here’s why you need to read them.


One of the great things about teaching has been seeing the innovative, unique, groundbreaking and sometimes simply crazy ideas of my students. They use the Business Model (or Mission Model) Canvas to keep track of their key hypotheses and then rapidly test them by talking to customers and iterating their Minimal Viable Products. This allows them to quickly find product/market fit.

Except when they’re in a regulated market.

Regulation
All businesses have regulations to follow –  paying taxes, incorporating the company, complying with financial reporting. And some have to ensure that there are no patents or blocking patents.  But regulated markets are different. Regulated marketplaces are ones that have significant government regulation to promote (ostensibly) the public interest. In theory regulations exist to protect the public interest for the benefit of all citizens. A good example is the regulations the FDA (Food and Drug Administration) have in place for approving new drugs and medical devices.

In a regulated market, the government controls how products and services are allowed to enter the market, what prices may be charged, what features the product/service must have, safety of the product, environmental regulations, labor laws, domestic/foreign content, etc.

In the U.S. regulation happens on three levels:

  • federal laws that are applicable across the country are developed by Federal government in Washington
  • state laws that are applicable in one state are imposed by state government
  • local city and county laws come from local government.

Federal Government
In the U.S. the national government has regulatory authority over inter-state commerce, foreign trade and other business activities of national scope and interest. Congress decides what things needs to be regulated and passes laws that determine those regulations. Congress often does not include all the details needed to explain how an individual, business, state or local government, or others might follow the law. In order to make the laws work on a day-to-day level, Congress authorizes certain government agencies to write the regulations which set the specific requirements about what is legal and what isn’t.  The regulatory agencies then oversee these requirements.

In the U.S. startups might run into an alphabet soup of federal regulatory agencies, for example; ATF, CFPB, DEA, EPA, FAA, FCC, FDA, FDIC, FERC, FTC, OCC, OSHA, SEC. These agencies exist because Congress passed laws.

States
In addition to federal laws, each State has its own regulatory environment that applies to businesses operating within the state in areas such as land-use, zoning, motor vehicles, state banking, building codes, public utilities, drug laws, etc.

Cities/Counties
Finally, local municipalities (cities, counties) may have local laws and regulatory agencies or departments like taxi commissions, zoning laws, public safety, permitting, building codes, sanitation, drug laws, etc.

A Playbook for Entering a Regulated Market
Startup battles with regulatory agencies – like Uber with local taxi licensing laws, AirBnB with local zoning laws, and Tesla with state dealership licensing – are legendary. Each of these is an example of a startup disrupting regulated markets.

There’s nothing magical about dealing with regulated markets. However, every regulated market has its own rules, dynamics, language, players, politics, etc. And they are all very different from the business-to-consumer or business-to-business markets most founders and their investors are familiar with.

How do you know you’re in a regulated market? It’s simple– ask yourself two questions:

  • Can I do anything I want or are there laws and regulations that might stop me or slow me down?
  • Are there incumbents who will view us as a threat to the status quo? Can they use laws and regulations to impede our growth?

Diagram Your Business Model
The best way to start is by drawing a business model canvas. In the customer segments box, you’re going to discover that there may be 5, 10 or more different players: users, beneficiaries, stakeholders, payers, saboteur, rent seeker, influencers, bureaucrats, politician, regulators. As you get out of the building and start talking to people you’ll discover more and more players.

Instead of lumping them together, each of these users, beneficiaries, stakeholders, payers, saboteur, rent seekers, etc. require a separate Value Proposition Canvas. This is where you start figuring out not only their pains, gains and jobs to be done, but what products/services solve those pains and gains. When you do that, you’ll discover that the interests of your product’s end user versus a regulator versus an advocacy group, key opinion leaders or a politician, are radically different. For you to succeed you need to understand all of them.

One of the critical things to understand is how the regulatory process works. For example, do you just fill out an online form and pay a $50 fee with your credit card and get a permit? Or do you need to spend millions of dollars and years running clinical trials to get FDA clearance and approval? And are these approvals good in every state? In every country? What do you need to do to sell worldwide?

Find the Saboteurs and Rent Seekers
One of the unique things about entering a regulated market is that the incumbents have gotten there first and have “gamed the system” in their favor. Rent seekers are individuals or organizations with successful existing business models who look to the government and regulators as their first line of defense against innovative competition. They use government regulation and lawsuits to keep out new entrants that might threaten their business models. They use every argument from public safety to lack of quality or loss of jobs to lobby against the new entrants. Rent seekers spend money to increase their share of an existing market instead of creating new products or markets but create nothing of value.

These barriers to new innovative startups are called economic rentExamples of economic rent include state automobile franchise laws, taxi medallion laws, limits on charter schools, cable company monopolies, patent trolls, bribery of government officials, corruption and regulatory capture.

Rent seeking lobbyists go directly to legislative bodies (Congress, State Legislatures, City Councils) to persuade government officials to enact laws and regulations in exchange for campaign contributions, appeasing influential voting blocks or future jobs in the regulated industry. They also use the courts to tie up and exhaust a startup’s limited financial resources. Lobbyists also work through regulatory bodies like the FCCSECFTC, Public Utility, Taxi, or Insurance Commissions, School Boards, etc.

Although most regulatory bodies are initially created to protect the public’s health and safety, or to provide an equal playing field, over time the very people they’re supposed to regulate capture the regulatory agencies. Rent Seekers take advantage of regulatory capture to protect their interests against the new innovators.

Understand Who Pays
For revenue streams figure out who’s going to pay. Is it the end user? An insurer? Some other third party?  If it’s the government, hang on to your seat because you now have to deal with government procurement and/or reimbursement. These payers need a Value Proposition Canvas as well.

Customer Relationships
For Customer Relationships, figuring out how to “Get, Keep and Grow” customers in a regulated market is a lot more complex than simply “Let’s buy some Google Adwords”. Market entry in a regulated market often has many more moving parts and is much costlier than a traditional market, requiring lobbyists, key opinion leaders, political donations, advocacy groups, and grassroots and grasstops campaigns, etc.

Diagram the Customer Segment Relationships
Start diagraming out the relationships of all the customer segments. Who influences who? How do they interconnect? What laws and regulations are in your way for deployment and scale? How powerful are each of the players? For the politicians, what are their public positions versus actual votes and performance. Follow the money. If an elected official’s major donor is organization x, you’re not going to be able to convince them with a cogent argument.

The book Regulatory Hacking calls this diagram the Power Map. As an example, this is a diagram of the multiple beneficiaries and stakeholders that a software company developing math software for middle school students has to navigate. Your diagram may be more complex.  There is no possible way you can draw this on day one of your startup. You’ll discover these players as you get out of the building and start filling out your value proposition canvases.

Diagram the Competition
Next, draw a competitive Petal diagram of competitors and adjacent market players.  Who’s already serving the users you’re targeting? Who are the companies you’re disrupting?

I’ve always thought of my startup as the center of the universe. So, put your company in the center of the slide like this.

In this example the startup is creating a new category – a lifelong learning network for entrepreneurs. To indicate where their customers for this new market would come from they drew the 5 adjacent market segments they believed their future customers were in today: corporate, higher education, startup ecosystem, institutions, and adult learning. To illustrate this they drew these adjacent markets as a cloud surrounding their company. (Unlike the traditional X/Y graph you can draw as many adjacent market segments as you’d like.)

Fill in the market spaces with the names of the companies that are representative players in each of the adjacent markets.

Strategy diagram
Finally, draw your strategy diagram – how will you build a repeatable and scalable sales process? What regulatory issues need to be solved? In what order?  What is step 1? Then step 2? For example, beg for forgiveness or ask for permission? How do you get regulators who don’t see a need to change to move? And do so in your lifetime? How do you get your early customers to advocate on your behalf?

I sketched out a sample diagram of some of things to think about in the figure below. Both The Fixer and Regulatory Hacking give great examples of regulatory pitfalls, problems and suggested solutions.

Politicians
If you read Tusk’s book The Fixer you come away with the view that the political process in the U.S. follows the golden rule – he who has the gold makes the rules. It is a personal tale of someone who was deep inside politics – Tusk was deputy governor of Illinois, Mike Bloomberg’s campaign manager, Senator Charles Schumer’s communication director, and ran Uber’s first successful campaign to get regulatory approval in New York. And he is as cynical about politicians as one can get. On the other hand, Regulatory Hacking by is written by someone who understands Washington—but still needs to work there.

Read both books.

Lessons Learned

  • Regulated markets have different rules and players than traditional Business-to-Business or Business-to-Consumer markets
  • Entering a regulated market should be a strategy not a disconnected set of tactics
    • You need to understand the Laws and Regulations on the federal, state and local levels
    • You and your board need to be in sync about the costs and risks of entering these markets
    • Strategic choices include: asking for permission versus forgiveness, public versus private battles
  • Most early stage startups don’t have the regulatory domain expertise in-house. Go get outside advice at each step

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.

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

Why Entrepreneurs Start Companies Rather Than Join Them

If you asked me why I gravitated to startups rather than work in a large company I would have answered at various times: “I want to be my own boss.” “I love risk.” “I want flexible work hours.” “I want to work on tough problems that matter.” “I have a vision and want to see it through.” “I saw a better opportunity and grabbed it. …”

It never crossed my mind that I gravitated to startups because I thought more of my abilities than the value a large company would put on them. At least not consciously. But that’s the conclusion of a provocative research paper, Asymmetric Information and Entrepreneurship, that explains a new theory of why some people choose to be entrepreneurs. The authors’ conclusion — Entrepreneurs think they are better than their resumes show and realize they can make more money by going it alone.  And in most cases, they are right.

I’ll summarize the paper’s conclusions, then share a few thoughts about what they might mean – for companies, entrepreneurs and entrepreneurial education. (By the way, as you read the conclusions keep in mind the authors are not talking just about high-tech entrepreneurs. They are talking about everyone who chooses to be self-employed – from a corner food vendor without a high school diploma to a high-tech founder with a PhD in Computer Science from Stanford.)

The authors’ research came from following 12,686 people over 30+ years. They found:

  1. Signaling. When you look for a job you “signal” your ability to employers via a resume with a list of your educational qualifications and work history. Signaling is a fancy academic term to describe how one party (in this case someone who wants a job) credibly conveys information to another party (a potential employer).
  2. Capable. People choose to be entrepreneurs when they feel that they are more capable than what employers can tell from their resume or an interview. So, entrepreneurs start ventures because they can’t signal their worth to potential employers.
  3. Better Pay. Overall, when people choose entrepreneurship they earn 7% more than they would have in a corporate job. That’s because in companies pay is usually set by observable signals (your education and experience/work history).
  4. Less Predictable Pay. But the downside of being an entrepreneur is that as a group their pay is more variable – some make less than if they worked at a company, some much more.
  5. Smarter. Entrepreneurs score higher on cognitive ability tests than their educational credentials would predict. And their cognitive ability is higher than those with the same educational and work credentials who choose to work in a company.
  6. Immigrants and Funding. Signaling (or the lack of it) may explain why some groups such as immigrants, with less credible signals to existing companies (unknown schools, no license to practice, unverifiable job history, etc.) tend to gravitate toward entrepreneurship. And why funding from families and friends is a dominant source of financing for early-stage ventures (because friends and family know an entrepreneur’s ability better than any resume can convey).
  7. Entrepreneurs defer getting more formal education because they correctly expect their productivity will be higher than the market can infer from just their educational qualifications. (There are no signals for entrepreneurial skills.)

Lemons Versus Cherries. The most provocative conclusion in the paper is that asymmetric information about ability leads existing companies to employ only “lemons,” relatively unproductive workers. The talented and more productive choose entrepreneurship. (Asymmetric Information is when one party has more or better information than the other.) In this case the entrepreneurs know something potential employers don’t – that nowhere on their resume does it show resiliency, curiosity, agility, resourcefulness, pattern recognition, tenacity and having a passion for products.
This implication, that entrepreneurs are, in fact, “cherries” contrasts with a large body of literature in social science, which says that the entrepreneurs are the “lemons”— those who cannot find, cannot hold, or cannot stand “real jobs.”

So, what to make of all this?
If the authors are right, the way we signal ability (resumes listing education and work history) is not only a poor predictor of success, but has implications for existing companies, startups, education, and public policy that require further thought and research.

Companies: In the 20thcentury when companies competed with peers with the same business model, they wanted employees to help them execute current business models (whether it was working on an assembly line or writing code supporting or extending current products). There was little loss when they missed hiring employees who had entrepreneurial skills. However, in the 21stcentury companies face continuous disruption; now they’re looking for employees to help them act entrepreneurial.  Yet their recruiting and interviewing processes – which define signals they look for – are still focused on execution not entrepreneurial skills.

Surprisingly, the company that best epitomized this was not some old-line manufacturing company but Google. When Marissa Mayer ran products at Google the New York Times  described her hiring process, “More often than not, she relies on charts, graphs and quantitative analysis as a foundation for a decision, particularly when it comes to evaluating people…At a recent personnel meeting, she homes in on grade-point averages and SAT scores to narrow a list of candidates, many having graduated from Ivy League schools, …One candidate got a C in macroeconomics. “That’s troubling to me,” Ms. Mayer says. “Good students are good at all things.”

Really.  What a perfect example of adverse signaling. No wonder the most successful Google products, other than search, have been acquisitions of startups not internal products: YouTube, Android, DoubleClick, Keyhole (Google Maps), Waze were started and run by entrepreneurs. The type of people Google and Marissa Mayer wouldn’t and didn’t hire started the companies they bought.

Entrepreneurship. When I shared the paper withTina Seelig at Stanford she asked, “If schools provided better ways to signal someone’s potential to employers, will this lead to less entrepreneurship?”  Interesting question.

Imagine if in a perfect world corporate recruiters found a way to identify the next Steve Jobs, Elon Musks, or Larry Ellisons. Would the existing corporate processes, procedures and business models crush their innovative talents, or would they steer the large companies into a new renaissance?

The Economic Environment. So, how much of signaling (hiring only by resume qualifications) is influenced by the economic environment? One could assume that in a period of low unemployment, it will be easier to get a traditional job, which would lead to fewer startups and explain why great companies are often founded during a downturn. Those who can’t get a traditional job start their own venture. Yet other public policies come into play. Between the late 1930s and the 1970s the U.S. tax rate for individuals making over $100,000 was 70% and 90% (taxes on capital gains fluctuated between 20% and 25%.) Venture capital flourished when the tax rates plummeted in the late 1970s. Was entrepreneurship stifled by high personal income taxes? And did it flourish only when entrepreneurs saw the opportunity to make a lot more money on their own?

Leaving a Company. Some new ventures are started by people who leave big companies to strike out on their own – meaning they weren’t trying to find employment in a corporation, they were trying to get away from it.  While starting your own company may look attractive from inside a company, the stark reality of risking one’s livelihood, financial stability, family, etc., is a tough bar to cross.  What motivates these people to leave the relative comfort of a steady corporate income and strike out on their own?  Is it the same reason – their company doesn’t value their skills for innovation and is just measuring them on execution? Or something else?

Entrepreneurial Education. Is entrepreneurship for everyone? Should we expect that we can teach entrepreneurship as a mandatory class? Or is it calling? Increasing the number of new ventures will only generate aggregate wealth if those who start firms are truly more productive as entrepreneurs.

Lessons Learned

  • Entrepreneurs start their own companies because existing companies don’t value the skills that don’t fit on a resume
  • The most talented people choose entrepreneurship (Lemons versus Cherries)
  • Read the paper and let me know what you think

 

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