Steve is the co-inventor of the Lean Startup movement, a serial entrepreneur-turned-best selling author, and educator who has changed the way startups are built, how entrepreneurship is taught, and how big companies and the U.S. government innovate.
I’m a big fan of McKinsey’s Three Horizons Modelof innovation. (if you’re not familiar with it there’s a brief description a few paragraphs down.) It’s one of the quickest ways to describe and prioritize innovation ideas in a large company or government agency.
However, in the 21stcentury the Three Horizons model has a fatal flaw that could put companies out of business and government agencies behind their adversaries. While traditional analysis suggests that Horizon 3 disruptive innovations take years to develop, in today’s world this is no longer the case. The three horizons are not bound by time. Horizon 3 ideas – disruption – can be delivered as fast as ideas for Horizon 1 – existing products.
In order to not be left behind, companies / government agencies need to focus on speed of delivery and deployment across all three horizons.
When first articulated by Baghai, Coley and White in the 20th century, the Three Horizons model was a simple way to explain to senior management the need for an ambidextrous organization – the idea that companies and government agencies need to execute existing business / mission models while simultaneously creating new capabilities.
The Three Horizons provided an incredibly useful taxonomy. The model described innovation occurring in three time horizons:
Horizon 1 ideas provide continuous innovation to a company’s existing business model and core capabilities.
Horizon 2 ideas extend a company’s existing business/model and core capabilities to new customers, markets or targets.
Horizon 3 is the creation of new capabilities to take advantage of or respond to disruptive opportunities or to counter disruption.
Each horizon requires different focus, different management, different tools and different goals. McKinsey suggested that to remain competitive in the long run a company allocate its research and development dollars and resources across all three horizons.
And here’s the big idea. In the past we assigned relative delivery time to each of the Horizons. For example, some organizations defined Horizon 1 as new features that could be delivered in 3-12 months; Horizon 2 as business/mission model extensions 24-36 months out; and Horizon 3 as creating new disruptive products/business/mission models 36-72 months out. This time-based definition made sense in the 20th century when new disruptive ideas took years to research, engineer and deliver.
That’s no longer true in the 21st century.
Today, disruption – Horizon 3 ideas – can be delivered as fast as Horizon 1 ideas.
For example, Uber took existing technology (smartphone app, drivers) but built a unique business model (gig economy disrupting taxis) and the Russians used existing social media tools to wage political warfare. Fast disruption happens by building on existing technologies uniquely configured, packaged and/or delivered, and combining them with a “speed of good-enough deployment as a force multiplier” mindset.
What’s an Example of Rapid Horizon 3 Implementation? In the commercial space AirBnB, Uber, Craigslist, Tesla, and the explosion of machine learning solutions (built on hardware originally designed for computer graphics (Nvida)) are examples of radical disruption using existing technologies in extremely short periods of time.
What’s Different about Rapid Horizon 3 Disruption? These rapid Horizon 3 deliverables emphasize disruption, asymmetry and most importantly speed, over any other characteristic. Serviceability, maintainability, completeness, scale, etc. are all secondary to speed and asymmetry.
To existing competitors or to existing requirements and acquisition systems they look like minimum viable products – barely finished, iterative and incremental prototypes. But the new products get out of the building, disrupt incumbents and once established, they then refactor and scale. Incumbents now face a new competitor/threat that obsoletes their existing product line/infrastructure/business/mission model.
Why Do the Challengers/new Entrants Have the Edge? Ironically rapid Horizon 3 disruption is most often used not by the market leaders but by the challengers/new entrants (startups, ISIS, China, Russia, etc.). The new players have no legacy systems to maintain, no cumbersome requirements and acquisition processes, and are single-mindedly focused on disrupting the incumbents.
Four Strategies to Deal With Disruption For incumbents, there are four ways to counter rapid disruption:
Incentivize external resources to focus on your goal/mission. For example, NASA and Commercial Resupply Services with SpaceX and OrbitalATK, Apple and the App Store, DARPA Prize challenges. The large organizations used startups who could rapidly build and deliver products for them – by offering something the startups needed – contracts, a distribution platform, or prizes. This can be a contract with a single startup or a broader net to incentivize many.
Combine the existing strengths of a company/agency and its business/mission model by acquiring external innovators who can operate at the speed of the disruptors. For example, Google buying Android. The risk here is that the mismatch of culture, process and incentives may strangle the newly acquired innovation culture.
Rapidly copy the new disruptive innovators and use the incumbent’s business/mission model to dominate. For example, Microsoft copying Netscape’s web browser and using its dominance of operating system distribution to win, or Google copying Overture’s pay per click model and using its existing dominance in search to sell ads. The risk here is that copying innovation without understanding the customer problem/mission can result in solutions that miss the target.
Innovate better than the disrupters. (Extremely difficult for large companies/government agencies as it is as much a culture/process problem as a technology problem. Startups are born betting it all. Large organizations are executing and protecting the legacy.) Successful examples, Apple and the iPhone, Amazon and Amazon Web Services (AWS). Gov’t agency and armed drones.
The Three Horizons model is still very useful as a shorthand for prioritizing innovation initiatives.
Some Horizon 3 disruptions do take long periods of development
However, today many Horizon 3 disruptions can be rapidly implemented by repurposing existing Horizon 1 technologies into new business/mission models
Speed of deployment of a disruptive/asymmetric product is a force multiplier
The attackers have the advantage, as the incumbents are burdened with legacy
Four ways for the incumbents to counter rapid disruption:
Five years ago we brought evidence-based entrepreneurship to Life Sciences – teaching the first Lean Lean Launchpad class at UCSF, then the NIH and Imperial College. But it’s been awhile since I was in a room made up entirely of Life Science entrepreneurs. So I was excited to visit IndieBio, a life science accelerator in San Francisco. Think of IndieBio as “Y-Combinator for Life Sciences with a wet lab” and you get what they are trying to do. It’s a 4-month program to help biotech startups build their company and it comes with $250k in seed funding.
I sat down with Arvind Gupta, Founder and Managing Director of IndieBio and talked about how Lean methods apply to Life Sciences.
Designing Science with Arvind Gupta & Steve Blank - YouTube
If you’re an early employee at a startup, one day you will wake up to find that what you worked on 24/7 for the last year is no longer the most important thing – you’re no longer the most important employee, and process, meetings, paperwork and managers and bosses have shown up. Most painfully, you’ll learn that your role in the company has to change.
I’ve seen these transitions as an investor, board member and CEO. At times they are painful to watch and difficult to manage. Early in my career I lived it as an employee, and I handled it in the worst possible way.
Here’s what I wish I had known.
I had joined MIPS Computers, my second semiconductor company, as the VP of marketing and also took on the role of the acting VP of Sales. During the first year of the company’s life, I was a fireball – relentless in creating and pursuing opportunities – getting on an airplane at the drop of a hat to fly anywhere, anytime, to get a design win. I worked with engineering to try to find product/market fit (big endian or little endian?) and get the chip designed into companies building engineering workstations – powerful personal computers, all while trying to refine how to find the right markets, customers, and sales process. I didn’t get much sleep, but I was having the time of my life.
And after a year there was good news. Our rent-a-CEO was being replaced by a permanent one. Our chip was nearing completion, and I had convinced early lighthouse customers to design it into their computers. I had done amazing things with almost no resources and got the company on the radar of every tech publication and into deals we had no right to be in. I was feeling 10 feet tall. Everything was great… until the new CEO called me in for a chat.
I don’t remember much about the details, but I do remember hearing him tell me how impressed he was with what I had accomplished so far, then immediately the visceral feeling of shock and surprise when his next words were that now the company needed to scale, and I wasn’t the right person to do that… Wait! What??
For a minute I couldn’t breathe. I felt like I had been punched in the gut. How could that be? What do you mean I’m not the right person??? Hadn’t he just listed all the great work I had done? He acknowledged it was a lot of progress but offered that it was a flurry of disconnected tactics without a coherent strategy. No one knew what I was doing, and I couldn’t explain why I was doing it when asked. “You’re just throwing stuff against the wall. That doesn’t scale.” I was speechless. Wasn’t that what the first year of a startup was supposed to be like?
Scrambling to save my job, I regained the power of speech, and asked him if I could be the person to take the company to the next level. And to his credit (which I only appreciated years later) he agreed that while he was going to start a search, I could be a candidate for the job. And to top it off he got me a coach to help me understand what taking it to the next level meant. In preparation I remember buying all the management books I could find and reading what little literature there was at the time about how small company management transitioned into a larger one.
And herein lies the tale…. I vaguely remember going to lunch with my coach, a nice white-haired “old guy” who was trying to help me learn the skills to grow into the new job. The problem was I had shut down. Even as we were meeting, I was obsessively thinking about the change in my role, my title and my status. “I don’t get it, I did all this work, and everything was great. Why does anything have to change?” But I never shared any of how I felt with my coach. To do this day I am really embarrassed to admit that I have no idea what my coach tried to teach me over multiple lunches and weeks. As we went to lunch, all I could think about was me and how I was being screwed. I literally paid zero attention. In my righteous anger I was unreachable.
I shouldn’t have been surprised, but yet again I was, when a month later the CEO said, that the report from the coach said, “I had a long way to go”. The company was going to hire a VP of Marketing. I was devastated.
It’s Not About Change – It’s About Loss If you had asked me a decade later what had been going on in my head and why I handled this so badly, I would have simply said, that: 1) I was resistant to change, and that 2) I had made this all about me and never once considered that our new CEO was right. All true – to a point.
It took me another decade to realize if I had been really honest with myself it wasn’t about fighting change at all. Heck every day something new was happening at our startup. I was agile enough to keep up with innumerable changes and I was changing lots of things myself. It was actually about something much more personal I wouldn’t admit to myself – it was that these changes made me fear what I was losing;
I felt a loss of status and identity – I had been judged inadequate to continue in my role and my stature and the value of my skills and abilities had dropped.
I felt a loss of certainty – I was now competing to hold a job I thought was mine forever in the company. At least that’s what I thought my business card said. Now I was adrift and didn’t know what the future held.
I felt a loss of autonomy – Up until now I used my best judgment of what was needed and I was doing what I wanted, when I wanted it. I was fine making up a strategy on the fly from disconnected tactics. Now we were going to have plans and a strategy.
I felt a loss of community – we had been a small tight team who had bonded together under extreme pressure and accomplished amazing things. Now new people who knew none of that and appreciated little of it were coming in. They had little trust and empathy with us.
I felt the process lacked fairness – no one had warned/told me that the job I was doing needed to change over time, and no one told me what those new skills were.
Looking back over the decades it’s clear that the new CEO was right. Even though these losses triggered something primal, I did need to learn discipline, pattern recognition, time management, separating the trivial from the important and the difference between tactics and strategy. I needed to learn to grow from being a great individual contributor to being a manager and then a leader. Instead I walked away from learning any of it.
I probably added five unneeded years to my career.
What should I have done? Today it’s understood that all startups go through a metamorphosis as they become larger companies. They go from organizations struggling for survival as they search for product/market fit, to building a repeatable and scalable business model, and then growing to profitability. And we are all hard-wired for a set number of social relationships. This mental wiring defines boundaries in growing an organization – get bigger than a certain size, and you need a different management system. The skills needed from employees differ at each stage.
What I wish I knew was that if you’re an early company employee, it’s not likely that the skills you have on day one are the skills needed as the company scales to the next level. This sentence is worth reading multiple times as no one – not the person who hired you, the VC’s or your peers -is going to tell you when you’re hired that the company will likely outgrow you. Some (like your peers or even the founders) don’t understand it, and others (the VCs) realize it’s not in their interest to let you know. The painful reality is that products change, strategies change, people change…things have to change for your company to stay in business and grow.
What should my CEO have done? When my CEO was explaining to me how the company needed to change to grow, he was explaining facts while I was processing deeply held feelings. The changes in the organization and my role represented what I was about to lose. And when people feel they’re going to lose something deeply important, it triggers an emotional response because change feels like a threat. It’s not an excuse for my counterproductive behavior, but explains why I acted out like I did.
Startup CEOs need to think about these transitions from day one and consider how to address the real sense of loss these transitions mean to early employees.
Loss of status? It’s almost impossible to take away a title from someone, give it to someone else and still retain that employee. Think hard about whether titles need to be formal (VP of Engineering, VP of Marketing, VP of Sales, etc.) before the company finds product/market fit and/or tens of people – as you can almost guarantee that these people won’t have those roles and titles when you scale.
Loss of Certainty? Startups and VC’s have historically operated on the “I’ll deal with this later” principle in letting early employees know what happens as the company scales. The common wisdom is that no one would want to work like crazy knowing that they might not be the ones to lead as the company grows. I call this the Moses-problem – you work for years to get the tribe to the promised land – but you’re not allowed to cross over. The company needs to give formal recognition for those individuals who brought the tribe to the promised land.
Loss of Autonomy? This is the time you and your employees get to have a discussion about the next steps in their career. Do they want to be an individual contributor? Manager of people and process? Special projects? These shouldn’t be random assignments but instead, offer a roadmap of possible choices and directions.
Loss of Community? Your original hires embody the company culture. Unless you have them capture the unique aspects of the culture, it will become diluted and disappear among the new hires. Declare them cultural co-founders. Help them understand the community is growing and they’re the ambassadors. Have them formalize it as part of a now needed on-boarding process as the company grows. And most importantly, make sure that they are celebrated as the team that got the company to where it is now.
Loss of Fairness? Just telling employees “a change is going to come” it is not sufficient. What are the new skills needed when you scale from Search to Build to Grow – from tens to hundreds and then thousands of people? How can your existing employees gain those new skills?
If you haven’t gotten a new car in a while you may not have noticed that the future of the dashboard looks like this:
That’s it. A single screen replacing all the dashboard gauges, knobs and switches. But behind that screen is an increasing level of automation that hides a ton of complexity.
At times everything you need is on the screen with a glance. At other times you have to page through menus and poke at the screen while driving. And while driving at 70mph, try to understand if you or your automated driving system is in control of your car. All while figuring out how to use any of the new features, menus or rearranged user interface that might have been updated overnight.
In the beginning of any technology revolution the technology gets ahead of the institutions designed to measure and regulate safety and standards. Both the vehicle’s designers and regulators will eventually catch up, but in the meantime we’re on the steep part of a learning curve – part of a million-person beta test – about what’s the right driver-to-vehicle interface.
We went through this with airplanes. And we’re reliving that transition in cars. Things will break, but in a few decades we’ll come out out the other side, look back and wonder how people ever drove any other way.
Here’s how we got here, what it’s going to cost us, and where we’ll end up.
Cars, Computers and Safety
Two massive changes are occurring in automobiles: 1) the transition from internal combustion engines to electric, and 2) the introduction of automated driving.
But a third equally important change that’s also underway is the (r)evolution of car dashboards from dials and buttons to computer screens. For the first 100 years cars were essentially a mechanical platform – an internal combustion engine and transmission with seats – controlled by mechanical steering, accelerator and brakes. Instrumentation to monitor the car was made up of dials and gauges; a speedometer, tachometer, and fuel, water and battery gauges. By the 1970’s driving became easier as automatic transmissions replaced manual gear shifting and hydraulically assisted steering and brakes became standard. Comfort features evolved as well: climate control – first heat, later air-conditioning; and entertainment – AM radio, FM radio, 8-track tape, CD’s, and today streaming media. In the last decade GPS-driven navigation systems began to appear.
At the same time cars were improving, automobile companies fought safety improvements tooth and nail. By the 1970’s auto deaths in the U.S averaged 50,000 a year. Over 3.7 million people have died in cars in the U.S. since they appeared – more than all U.S. war deaths combined. (This puts auto companies in the rarified class of companies – along with tobacco companies – that have killed millions of their own customers.) Car companies argued that talking safety would scare off customers, or that the added cost of safety features would put them in a competitive price disadvantage. But in reality, style was valued over safety.
Safety systems in automobiles have gone through three generations – passive systems and two generations of active systems. Today we’re about to enter a fourth generation – autonomous systems.
Passive safety systems are features that protect the occupants after a crash has occurred. They started appearing in cars in the 1930’s. Safety glass in windshields appeared in the 1930’s in response to horrific disfiguring crashes. Padded dashboards were added in the 1950’s but it took Ralph Nader’s book, Unsafe at Any 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.
Today, a fourth wave of safety features is appearing as Autonomous/Self-Driving features. These include Lane Centering/Auto Steer, Adaptive cruise control, Traffic jam assist, Self-parking, full self-driving. The National Highway Traffic Safety Administration (NHTSA) has adopted the six-level SAE standard to describe these vehicle automation features:
Getting above Level 2 is a really hard technical problem and has been discussed ad infinitum in other places. But what hasn’t got much attention is how drivers interact with these systems as the level of automation increases, and as the driving role shifts from the driver to the vehicle. Today, we don’t know whether there are times these features make cars less safe rather than more.
For example, Tesla and other cars have Level 2 and some Level 3 auto-driving features. Under Level 2 automation, drivers are supposed to monitor the automated driving because the system can hand back control of the car to you with little or no warning. In Level 3 automation drivers are not expected to monitor the environment, but again they are expected to be prepared to take control of the vehicle at all times, this time with notice.
Research suggests that drivers, when they aren’t actively controlling the vehicle, may be reading their phone, eating, looking at the scenery, etc. We really don’t know how drivers will perform in Level 2 and 3 automation. Drivers can lose situational awareness when they’re surprised by the behavior of the automation – asking: What is it doing now? Why did it do that? Or, what is it going to do next? There are open questions as to whether drivers can attain/sustain sufficient attention to take control before they hit something. (Trust me, at highway speeds having a “take over immediately” symbol pop up while you are gazing at the scenery raises your blood pressure, and hopefully your reaction time.)If these technical challenges weren’t enough for drivers to manage, these autonomous driving features are appearing at the same time that car dashboards are becoming computer displays.
We never had cars that worked like this. Not only will users have to get used to dashboards that are now computer displays, they are going to have understand the subtle differences between automated and semi-automated features and do so as auto makers are developing and constantly updating them. They may not have much help mastering the changes. Most users don’t read the manual, and, in some cars, the manuals aren’t even keeping up with the new features.
But while we never had cars that worked like this, we already have planes that do.
Let’s see what we’ve learned in 100 years of designing controls and automation for aircraft cockpits and pilots, and what it might mean for cars.
Airplanes have gone through multiple generations of aircraft and cockpit automation. But unlike cars which are just first seeing automated systems, automation was first introduced in airplanes during the 1920s and 1930s.
For their first 35 years airplane cockpits, much like early car dashboards, were simple – a few mechanical instruments for speed, altitude, relative heading and fuel. By the late 1930’s the British Royal Air Force (RAF) standardized on a set of flight instruments. Over the next decade this evolved into the “Basic T” instrument layout – the de facto standard of how aircraft flight instruments were laid out.
Engine instruments were added to measure the health of the aircraft engines – fuel and oil quantity, pressure, and temperature and engine speed.
Next, as airplanes became bigger, and the aerodynamic forces increased, it became difficult to manually move the control surfaces so pneumatic or hydraulic motors were added to increase the pilots’ physical force. Mechanical devices like yaw dampers and Mach trim compensators corrected the behavior of the plane.
Over time, navigation instruments were added to cockpits. At first, they were simple autopilots to just keep the plane straight and level and on a compass course. The next addition was a radio receiver to pick up signals from navigation stations. This was so pilots could set the desired bearing to the ground station into a course deviation display, and the autopilot would fly the displayed course.
In the 1960s, electrical systems began to replace the mechanical systems:
electric gyroscopes (INS) and autopilots using VOR (Very High Frequency Omni-directional Range) radio beacons to follow a track
auto-throttle – to manage engine power in order to maintain a selected speed
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.
The Fixer is 1/3rd autobiography, 1/3rd case studies, and 1/3rd a “how-to” manual. Regulatory Hacking is closer to a “step-by-step” textbook with case studies.
Here’s why you need to read them.
One of the great things about teaching has been seeing the innovative, unique, groundbreaking and sometimes simply crazy ideas of my students. They use the Business Model (or Mission Model) Canvas to keep track of their key hypotheses and then rapidly test them by talking to customers and iterating their Minimal Viable Products. This allows them to quickly find product/market fit.
Except when they’re in a regulated market.
Regulation All businesses have regulations to follow – paying taxes, incorporating the company, complying with financial reporting. And some have to ensure that there are no patents or blocking patents. But regulated markets are different. Regulated marketplaces are ones that have significant government regulation to promote (ostensibly) the public interest. In theory regulations exist to protect the public interest for the benefit of all citizens. A good example is the regulations the FDA (Food and Drug Administration) have in place for approving new drugs and medical devices.
In a regulated market, the government controls how products and services are allowed to enter the market, what prices may be charged, what features the product/service must have, safety of the product, environmental regulations, labor laws, domestic/foreign content, etc.
In the U.S. regulation happens on three levels:
federal laws that are applicable across the country are developed by Federal government in Washington
state laws that are applicable in one state are imposed by state government
local city and county laws come from local government.
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.
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.
Finally, local municipalities (cities, counties) may have local laws and regulatory agencies or departments like taxi commissions, zoning laws, public safety, permitting, building codes, sanitation, drug laws, etc.
A Playbook for Entering a Regulated Market Startup battles with regulatory agencies – like Uber with local taxi licensing laws, AirBnB with local zoning laws, and Tesla with state dealership licensing – are legendary. Each of these is an example of a startup disrupting regulated markets.
There’s nothing magical about dealing with regulated markets. However, every regulated market has its own rules, dynamics, language, players, politics, etc. And they are all very different from the business-to-consumer or business-to-business markets most founders and their investors are familiar with.
How do you know you’re in a regulated market? It’s simple– ask yourself two questions:
Can I do anything I want or are there laws and regulations that might stop me or slow me down?
Are there incumbents who will view us as a threat to the status quo? Can they use laws and regulations to impede our growth?
Diagram Your Business Model
The best way to start is by drawing a business model canvas. In the customer segments box, you’re going to discover that there may be 5, 10 or more different players: users, beneficiaries, stakeholders, payers, saboteur, rent seeker, influencers, bureaucrats, politician, regulators. As you get out of the building and start talking to people you’ll discover more and more players.
Instead of lumping them together, each of these users, beneficiaries, stakeholders, payers, saboteur, rent seekers, etc. require a separate Value Proposition Canvas. This is where you start figuring out not only their pains, gains and jobs to be done, but what products/services solve those pains and gains. When you do that, you’ll discover that the interests of your product’s end user versus a regulator versus an advocacy group, key opinion leaders or a politician, are radically different. For you to succeed you need to understand all of them.
One of the critical things to understand is how the regulatory process works. For example, do you just fill out an online form and pay a $50 fee with your credit card and get a permit? Or do you need to spend millions of dollars and years running clinical trials to get FDA clearance and approval? And are these approvals good in every state? In every country? What do you need to do to sell worldwide?
Find the Saboteurs and Rent Seekers
One of the unique things about entering a regulated market is that the incumbents have gotten there first and have “gamed the system” in their favor. Rent seekers are individuals or organizations with successful existing business models who look to the government and regulators as their first line of defense against innovative competition. They use government regulation and lawsuits to keep out new entrants that might threaten their business models. They use every argument from public safety to lack of quality or loss of jobs to lobby against the new entrants. Rent seekers spend money to increase their share of an existing market instead of creating new products or markets but create nothing of value.
These barriers to new innovative startups are called economic 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.
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.
For Customer Relationships, figuring out how to “Get, Keep and Grow” customers in a regulated market is a lot more complex than simply “Let’s buy some Google Adwords”. Market entry in a regulated market often has many more moving parts and is much costlier than a traditional market, requiring lobbyists, key opinion leaders, political donations, advocacy groups, and grassroots and grasstops campaigns, etc.
Diagram the Customer Segment Relationships
Start diagraming out the relationships of all the customer segments. Who influences who? How do they interconnect? What laws and regulations are in your way for deployment and scale? How powerful are each of the players? For the politicians, what are their public positions versus actual votes and performance. Follow the money. If an elected official’s major donor is organization x, you’re not going to be able to convince them with a cogent argument.
The book Regulatory Hacking calls this diagram the Power Map. As an example, this is a diagram of the multiple beneficiaries and stakeholders that a software company developing math software for middle school students has to navigate. Your diagram may be more complex. There is no possible way you can draw this on day one of your startup. You’ll discover these players as you get out of the building and start filling out your value proposition canvases.
Diagram the Competition
Next, draw a competitive Petal diagram of competitors and adjacent market players. Who’s already serving the users you’re targeting? Who are the companies you’re disrupting?
I’ve always thought of my startup as the center of the universe. So, put your company in the center of the slide like this.
In this example the startup is creating a new category – a lifelong learning network for entrepreneurs. To indicate where their customers for this new market would come from they drew the 5 adjacent market segments they believed their future customers were in today: corporate, higher education, startup ecosystem, institutions, and adult learning. To illustrate this they drew these adjacent markets as a cloud surrounding their company. (Unlike the traditional X/Y graph you can draw as many adjacent market segments as you’d like.)
Fill in the market spaces with the names of the companies that are representative players in each of the adjacent markets.
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.
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.
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..
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. 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 dollarhealth 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.
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”).
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-approved 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..
For most of our lives the idea that computers and technology would get, better, faster, cheaper every year was as assured as the sun rising every morning. The story “GlobalFoundries Stops All 7nm Development“ doesn’t sound like the end of that era, but for anyone who uses an electronic device, it most certainly is.
Technology innovation is going to take a different direction.
GlobalFoundries was one of the three companies that made the most advanced silicon chips for other companies (AMD, IBM, Broadcom, Qualcomm, STM and the Department of Defense.) The other foundries are Samsung in South Korea and TSMC in Taiwan. Now there are only two pursuing the leading edge.
This is a big deal.
Since the invention of the integrated circuit ~60 years ago, computer chip manufacturers have been able to pack more transistors onto a single piece of silicon every year. In 1965, Gordon Moore, one of the founders of Intel, observed that the number of transistors was doubling every 24 months and would continue to do so. For 40 years the chip industry managed to live up to that prediction. The first integrated circuits in 1960 had ~10 transistors. Today the most complex silicon chips have 10 billion. Think about it. Silicon chips can now hold a billion times more transistors.
But Moore’s Law ended a decade ago. Consumers just didn’t get the memo.
No More Moore – The End of Process Technology Innovation Chips are actually “printed,” not with a printing press but with lithography, using exotic chemicals and materials in a “fab” (a chip fabrication plant – the factory where chips are produced). Packing more transistors in each generation of chips requires the fab to “shrink” the size of the transistors. The first transistors were printed with lines 80 microns wide. Today Samsung and TSMC are pushing to produce chips with features few dozen nanometers across.That’s about a 2,000-to-1 reduction.
Each new generation of chips that shrinks the line widths requires fabs to invest enormous amounts of money in new chip-making equipment. While the first fabs cost a few million dollars, current fabs – the ones that push the bleeding edge – are over $10 billion.
And the exploding cost of the fab is not the only issue with packing more transistors on chips. Each shrink of chip line widths requires more complexity. Features have to be precisely placed on exact locations on each layer of a device. At 7 nanometers this requires up to 80 separate mask layers.
How a CPU is Made / Microchips (Animation) - YouTube
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.
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.
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
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.
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.