Steve Blank's Blog, page 17

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:

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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. [image error]

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.

[image error]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 Speed, to 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.

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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:

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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.)[image error]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.[image error]

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:[image error]

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:

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

So today a modern airplane cockpit (an Airbus 320) looks like this:[image error]

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.)[image error]

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.[image error]

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.

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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.”)[image error]

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

Why Founders Need a Moral Compass

I’ve been thinking why the ethical boundaries of todays founder/VC interactions feel so different then they did when I was an entrepreneur. I’ve written about the root causes in an HBR article here and an expanded version here. Worth a read.

Stanford eCorner captured a few minutes of what I’ve been thinking in the video below.

If you can’t see the video click here

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.[image error]

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.[image error]

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.

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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 rent. Examples 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 FCC, SEC, FTC, 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.

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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.

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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.

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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

The Apple Watch – Tipping Point Time for Healthcare

I don’t own an Apple Watch. I do have a Fitbit. But the Apple Watch 4 announcement intrigued me in a way no other product has since the original IPhone. This wasn’t just another product announcement from Apple. It heralded the U.S. Food and Drug Administration’s (FDA) entrance into the 21stcentury. It is a harbinger of the future of healthcare and how the FDA approaches innovation.

Sooner than people think, virtually all home and outpatient diagnostics will be performed by consumer devices such as the Apple Watch, mobile phones, fitness trackers, etc. that have either become FDA cleared as medical devices or have apps that have received FDA clearance. Consumer devices will morph into medical grade devices, with some painful and well publicized mistakes along the way.

Let’s see how it turns out for Apple.

Smartwatches are the apex of the most sophisticated electronics on the planet. And the Apple Watch is the most complex of them all. Packed inside a 40mm wide, 10 mm deep package is a 64-bit computer, 16gbytes of memory, Wi-Fi, NFC, cellular, Bluetooth, GPS, accelerometer, altimeter, gyroscope, heart rate sensor, and an ECG sensor – displaying it all on a 448 by 368 OLED display.

[image error]When I was a kid, this was science fiction.  Heck, up until its first shipment in 2015, it was science fiction.

But as impressive as its technology is, the Apple’s smartwatch has been a product looking for a solution. At first, positioned as a fashion statement, it seemed like the watch was actually an excuse to sell expensive wristbands. Subsequent versions focused on fitness and sports – the watch was like a Fitbit– plus the ability to be annoyed by interruptions from your work. But now the fourth version of the Watch might have just found the beginnings of “gotta have it” killer applications – healthcare – specifically medical diagnostics and screening.

Healthcare on Your Wrist

Large tech companies like Google, Amazon, Apple recognize that the  multi-trillion dollar health care market is ripe for disruption and have poured billions of dollars into the space. Google has been investing in a broad healthcare portfolio, Amazon has been investing in pharmacy distribution and Apple…? Apple has been focused on turning the Apple Watch into the future of health screening and diagnostics.

Apples latest Watch – with three new healthcare diagnostics and screening apps – gives us a glimpse into what the future of healthcare diagnostics and screening could look like.

The first new healthcare app on the Watch is Fall Detection. Perhaps you’ve seen the old commercials where someone falls and can’t get up, and has a device that calls for help. Well this is it – built into the watch. The watch’s built-in accelerometer and gyroscope analyze your wrist trajectory and impact acceleration to figure out if you’ve taken a hard fall. You can dismiss the alert, or have it call 911. Or, if you haven’t moved after a minute, it can call emergency services, and send a message along with your location.

If you’re in Apple’s current demographic you might think, “Who cares?” But if you have an aged parent, you might start thinking, “How can I get them to wear this watch?”

The second new healthcare app also uses the existing optical sensor in the watch and running in the background, gathers heart data and has an algorithm that can detect irregular heart rhythms. If it senses something is not right, up pops up an alert. A serious and common type of irregular heart rhythm is atrial fibrillation (AFib). AFib happens when the atria—the top two chambers of the heart get out of sync, and instead of beating at a normal 60 beats a minute it may quiver at 300 beats per minute.[image error][image error][image error]

This rapid heartbeat allows blood to pool in the heart, which can cause clots to form and travel to the brain, causing a stroke. Between 2.7 and 6.1 million people in the US have AFib (2% of people under 65 have it, while 9% of people over 65 years have it.) It puts ~750,000 people a year in the hospital and contributes to ~130,000 deaths each year. But if you catch atrial fibrillation early, there’s an effective treatment — blood thinners.

If your watch gives you an irregular heart rhythm alert you can run the third new healthcare app – the Electrocardiogram.

The Electrocardiogram (ECG or EKG) is a visual presentation of whether your heart is working correctly. It records the electrical activity of the heart and shows doctors the rhythm of heartbeats, the size and position of the chambers of the heart, and any damage to the heart’s muscle. Today, ECGs are done in a doctor’s office by having you lie down, and sticking 10 electrodes to your arms, legs and chest. The heart’s electrical signals are then measured from twelve angles (called “leads”).[image error]

With the Apple Watch, you can take an ECG by just putting your finger on the crown for 30 seconds. To make this work Apple has added two electrodes (the equivalent of a single lead), one on the back of the watch and another on the crown. The ECG can tell you that you may have atrial fibrillation (AFib) and suggest you see a doctor. As the ECG is saved in a PDF file (surprisingly it’s not also in the HL7’s FHIR Format), you can send it to your doctor, who may decide no visit is necessary.

These two apps, the Electrocardiogram and the irregular heart rhythms, are serious health screening tools. They are supposed to ship in the U.S. by the end of 2018. By the end of next year, they can be on the wrists of tens of millions of people.

The question is are they are going to create millions of unnecessary doctors’ visits from unnecessarily concerned users or are they going to save thousands of lives?  My bet is both – until traditional healthcare catches up with the fact that in the next decade screening devices will be in everyone’s hands (or wrists.)

Apple and The FDA – Clinical Trials

In the U.S. medical devices, drugs and diagnostics are regulated by the Food and Drug Administration – the FDA. What’s unique about the Apple Watch is that both the Electrocardiogram and the irregular heart rhythms apps required Apple to get clearance from the FDA. This is a very big deal.

The FDA requires evidence that medical devices do what they claim. To gather that evidence companies enroll volunteers in a study – called a clinical trial – to see if the device does what the company thinks it will.

Stanford University has been running a clinical trial on irregular heart rhythms for Apple since 2017 with a completion date in 2019. The goal is to see if an irregular pulse notification is really atrial fibrillation, and how many of those notified contacted a doctor within 90 days. (The Stanford study appears to be using previous versions of the Apple Watch with just the optical sensor and not the new ECG sensors. They used someone else’s wearable heart monitor to detect the Afib.)

To get FDA clearance, Apple reportedly submitted two studies to the FDA (so far none of the data has been published or peer reviewed). In one trial with 588 people, half of whom were known to have AFib and the other half of whom were healthy, the app couldn’t read 10% of the recordings. But for the other 90%, it was able to identify over 98% of the patients who had AFib, and over 99% of patients that had healthy heart rates.

The second data set Apple sent the FDA was part of Stanford’s Apple Heart Study. The app first identified 226 people with an irregular heart rhythm. The goal was to see how well the Apple Watch could pick up an event that looked like atrial fibrillation compared to a wearable heart monitor. The traditional monitors identified that 41 percent of people had an atrial fibrillation event. In 79 percent of those cases, the Apple app also picked something up.

This was good enough for the FDA.

The FDA – Running Hard to Keep Up With Disruption

And “good enough” is a big idea for the FDA. In the past the FDA was viewed as inflexible and dogmatic by new companies while viewed as insufficiently protective by watchdog organizations.

For the FDA this announcement was as important for them as it was for Apple.

The FDA has to adjudicate between a whole host of conflicting constituents and priorities. Its purpose is to make sure that drugs, devices, diagnostics, and software products don’t harm thousands or even millions of people so the FDA wants a process to make sure they get it right. This is a continual trade-off between patient safety, good enough data and decision making, and complete clinical proof. On the other hand, for a company, a FDA clearance can be worth hundreds of millions or even billions of dollars. And a disapproval or delayed clearance can put a startup out of business. Finally, the rate of change of innovation for medical devices, diagnostics and digital health has moved faster than the FDA’s ability to adapt its regulatory processes. Frustrated by the FDA’s 20th century processes for 21st century technology, companies hired lobbyists to force a change in the laws that guide the FDA regulations.

So, the Apple announcement is a visible signal in Washington that the FDA is encouraging innovation. In the last two years the FDA has been trying to prove it could keep up with the rapid advancements in digital health, devices and diagnostics- while trying to prevent another Theranos.

Since the appointment of the new head of the FDA, there has been very substantial progress in speeding up mobile and digital device clearances with new guidelines and policies. For example, in the last year the FDA announced its Pre-Cert pilot program which allows companies making software as a medical device to build products without each new device undergoing the FDA clearance process. The pilot program allowed nine companies, including Apple, to begin developing products (like the Watch) using this regulatory shortcut. (The FDA has also proposed new rules for clinical support software that say if doctors can review and understand the basis of the software’s decision, the tool does not have to be regulated by the FDA.)

This rapid clearance process as the standard – rather than the exception – is a sea-change for the FDA. It’s close to de-facto adopting a Lean decision-making process and rapid clearances for things that minimally affect health. It’s how China approaches approvals and will allow U.S. companies to remain competitive in an area (medical devices) where China has declared the intent to dominate.

Did Apple Cut in Front of the Line?

Some have complained that the FDA has been too cozy with Apple over this announcement.

Apple got its two FDA Class II clearances through what’s called a “de novo” pathway, meaning Apple claimed these features were the first of its kind. (It may be the first one built into the watch, but it’s not the first Apple Watch ECG app cleared by the FDA – AliveCor, got over-the-counter-clearance in 2014 and Cardiac Designs in 2013.) Critics said that the De Novo process should only be used where there is no predicate (substantial equivalence to an already cleared device.) But Apple cited at least one predicate, so if they followed the conventional 510k approval process, that should have taken at least 100 days. Yet Apple got two software clearances in under 30 days, which uncannily appeared the day before their product announcement.

To be fair to Apple, they were likely holding pre-submission meetings with the FDA for quite some time, perhaps years. One could speculate that using the FDA Pre-Cert pilot program they consulted on the design of the clinical trial, trial endpoints, conduct, inclusion and exclusion criteria, etc. This is all proper medical device company thinking and exactly how consumer device companies need to approach and work with the FDA to get devices or software cleared. And it’s exactly how the FDA should be envisioning its future.

Given Apple sells ~15 million Apple Watches a year, the company is about to embark on a public trial at massive scale of these features – with its initial patient population at the least risk for these conditions. It will be interesting to see what happens. Will overly concerned 20- and 30-year-olds flood doctors with false positives? Or will we be reading about lives saved?

Why most consumer hardware companies aren’t medical device and diagnostic companies

Historically consumer electronics companies and medical device and diagnostic companies were very different companies. In the U.S. medical device and diagnostic products require both regulatory clearance from the FDA and reimbursement approval by different private and public insurers to get paid for the products.

These regulatory and reimbursement agencies have very different timelines and priorities than for-profit companies. Therefore, to get FDA clearance a critical part of a medical device company is spent building a staff and hiring consultants such as clinical research organizations who can master and navigate FDA regulations and clinical trials.

And just because a company gets the FDA to clear their device/diagnostic/software doesn’t mean they’ll get paid for it. In the U.S. medical devices are reimbursed by private insurance companies (Blue Cross/Blue Shield, etc.) and/or the U.S. government via Centers for Medicare & Medicaid Services (CMS). Getting these clearances to get the product covered, coded and paid is as hard as getting the FDA clearance, often taking another 2-3 years. Mastering the reimbursement path requires a company to have yet another group of specialists conduct expensive clinical cost outcomes studies.

The Watch announcement telegraphed something interesting about Apple – they’re one of the few consumer products company to crack the FDA clearance process (Philips being the other). And going forward, unless these new apps are a disaster, it opens the door for them to add additional FDA-cleared screening and diagnostic tools to the watch (and by extension a host of AI-driven imaging diagnostics (melanoma detection, etc.) to the iPhone.) This by itself is a key differentiator for the Watch as a healthcare device.

The other interesting observation: Unlike other medical device companies, Apple’s current Watch business model is not dependent on getting insurers to pay for the watch. Today consumers pay directly for the Watch. However, if the Apple Watch becomes a device eligible for reimbursement, there’s a huge revenue upside for Apple. When and if that happens, your insurance would pay for all or part of an Apple Watch as a diagnostic tool.

(After running cost outcome studies, insurers believe that preventative measures like staying fit brings down their overall expense for a variety of conditions. So today some life insurance companies are mandating the use of an activity tracker like Apple Watch.)

The Future of SmartWatches in Healthcare

Very few companies (probably less than five) have the prowess to integrate sensors, silicon and software with FDA regulatory clearance into a small package like the Apple Watch.

So what else can/will Apple offer on the next versions of the Watch? After looking through Apple’s patents, here’s my take on the list of medical diagnostics and screening apps Apple may add.

Sleep Tracking and Sleep Apnea Detection

Compared to the Fitbit, the lack of a sleep tracking app on the Apple Watch is a mystery (though third-party sleep apps are available.) Its absence is surprising as the Watch can theoretically do much more than just sleep tracking – it can potentially detect Sleep Apnea. Sleep apnea happens when you’re sleeping, and your upper airway becomes blocked, reducing or completely stopping air to your lungs. This can cause a host of complications including Type 2 diabetes, high blood pressure, liver problems, snoring, daytime fatigue. Today diagnosing sleep apnea often requires an overnight stay in a sleep study clinic. Sleep apnea screening doesn’t appear to require any new sensors and would be a great app for the Watch. Perhaps the app is missing because you have to take the watch off and recharge it every night?

Pulse oximetry

Pulse oximetry is a test used to measure the oxygen level (oxygen saturation) of the blood. The current Apple Watch can already determine how much oxygen is contained in your blood based on the amount of infrared light it absorbs. But for some reason Apple hasn’t released this feature – FDA regulations? Inconsistent readings?  Another essential Watch health app that may or may not require any new sensors.

Respiration rate

Respiration rate (the number of breaths a person takes per minute) along with blood pressure, heart rate and temperature make up a person’s vital signs. Apple has a patent for this watch feature but for some reason hasn’t released it – FDA regulations?  Inconsistent readings?  Another essential Watch health app that doesn’t appear to require any new sensors.

Blood Pressure

About 1/3rd of Americans have high blood pressure. High blood pressure increases the risk of heart disease and stroke. It often has no warning signs or symptoms. Many people do not know they have it and only half of those have it under control. Traditionally measuring blood pressure requires a cuff on the arm and produces a single measurement at a single point in time. We’ve never had the ability to continually monitor a person’s blood pressure under stress or sleep. Apple filed two patents in 2017 to measure blood pressure by holding the watch against your chest. This is tough to do, but it would be another great health app for the Watch that may or may not require any new sensors.

Sunburn/UV Detector

Apple has patented a new type of sensor – a sunscreen detector to let you know what exposed areas of the skin of may be at elevated UV exposure risk. I’m not big on this, but the use of ever more powerful sunscreens has quadrupled, while at the same time, the incidence of skin cancers has also quadrupled, so there may be a market here.

Parkinson’s Disease Diagnosis and Monitoring

Parkinson’s Disease is a brain disorder that leads to shaking, stiffness, and difficulty with walking, balance, and coordination. It affects 1/% of people over 60. Today, there is no diagnostic test for the disease (i.e. blood test, brain scan or EEG). Instead, doctors look for four signs: tremor, rigidity, Bradykinesia/akinesia and Postural instability. Today patients have to go to a doctor for tests to rate the severity of their symptoms and keep a diary of their symptoms.

Apple added a new “Movement Disorder API” to its ResearchKit framework that supports movement and tremor detection. It allows an Apple Watch to continuously monitor for Parkinson’s disease symptoms; tremors and Dyskinesia, a side-effect of treatments for Parkinson’s that causes fidgeting and swaying motions in patients. Researchers have built a prototype Parkinson’s detection app on top of it. It appears that screening for Parkinson’s would not require any new sensors – but likely clinical trials and FDA clearance – and would be a great app for the Watch.

Glucose Monitoring

More than 100 million U.S. adults live with diabetes or prediabetes. If you’re a diabetic, monitoring your blood glucose level is essential to controlling the disease. However, it requires sticking your finger to draw blood multiple times a day. The holy grail of glucose monitoring has been a sensor that can detect glucose levels through the skin. This sensor has been the graveyard of tons of startups that have crashed and burned pursuing this. Apple has a patent application that looks suspiciously like a non-invasive glucose monitoring sensor for the Apple Watch. This is a really tough technical problem to solve, and even if the sensor works, there would be a long period of clinical trials for FDA clearance, but this app would be a game changer for diabetic patients – and Apple – if they can make it happen.

Sensor and Data Challenges

With many of these sensors just getting a signal is easy. Correlating that particular signal to an underlying condition and avoiding being confounded by other factors is what makes achieving medical device claims so hard.

As medical grade data acquisition becomes possible, continuous or real time transmission will store and report baseline data on tens of millions of “healthies” that will be vital in training the algorithms and eventually predicting disease earlier. This will eventually enable more accurate diagnostics on less data, and make the data itself – especially the transition from healthy to diseased – incredibly valuable.

However, this sucks electrons out of batteries and plays on the edge electrical design and the laws of physics, but Apple’s prowess in this area is close to making this possible.

What’s Not Working?

Apple has attempted to get medical researchers to create new health apps by developing ResearchKit, an open source framework for researchers. Great idea. However, given the huge potential for the Watch in diagnostics, ResearchKit and the recruitment of Principal Investigators feels dramatically under resourced. (It took three years to go from ResearchKit 1.0 to 2.0).  Currently, there are just 11 ResearchKit apps on the ITunes store. This effort – Apple software development and third-party app development – feels understaffed and underfunded. Given the potential size of the opportunity, the rhetoric doesn’t match the results and the results to date feel off by at least 10x.

Apple needs to act more proactively and directly fund some of these projects with grants to specific principal investigators and build a program of scale. (Much like the NIH SBIR program.) There should be as sustained commitment to at least several new FDA cleared screening/diagnostic apps every year for Watch and iPhone from Apple.

The Future

Although the current demographics of the Apple Watch skews young, the populations of the U.S., China, Europe and Japan continue to age, which in turn threatens to overwhelm healthcare systems. Having an always on, real-time streaming of medical data to clinicians, will change the current “diagnosis on a single data point and by appointment” paradigm. Wearable healthcare diagnostics and screening apps open an entirely new segment for Apple and will change the shape of healthcare forever.

Imagine a future when you get an Apple Watch (or equivalent) through your insurer to monitor your health for early warning signs of heart attack, stroke, Parkinson’s disease and to help you monitor and manage diabetes, as well as reminding you about medications and tracking your exercise. And when combined with an advanced iPhone with additional FDA cleared screening apps for early detection of skin cancer, glaucoma, cataracts, and other diseases, the future of your health will truly be in your own hands.

Outside the U.S., China is plowing into this with government support, private and public funding, and a China FDA (CFDA) approval process that favors local Chinese solutions. There are well over 100 companies in China alone focusing in this area, many with substantial financial and technical support.

Let’s hope Apple piles on the missing resources for diagnostics and screening apps and grabs the opportunity.

Lessons Learned

Apple’s new Watch has two heart diagnostic apps cleared by the FDA

This is a big deal


In a few years, home and outpatient diagnostics will be performed by wearable consumer devices – Apple Watch, mobile phones or fitness trackers

Collecting and sending health data to doctors as needed
Collecting baseline data on tens of millions of healthy people to train disease prediction algorithms


In the U.S. the FDA has changed their mobile and digital device guidelines and policies to make this happen
Insurers will ultimately will be paying for diagnostic wearables
Apple has a series of patents for additional Apple Watch sensors – glucose monitoring, blood pressure, UV detection, respiration

The watch is already capable of detecting blood oxygen level, sleep apnea, Parkinson’s disease
Getting a signal from a sensor is the easy part. Correlating that signal to an underlying condition is hard
They need to step up their game – money, software, people – with the medical research community


China has made building a local device and diagnostic industry one of their critical national initiatives

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

 A version of this article first appeared in IEEE Spectrum[image error].

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. [image error]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?

[image error]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.

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.


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.


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.


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.

[image error]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.

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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.

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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.)

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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.

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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.

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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 an sourcing funnel, 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.

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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.

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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