In my previous blog post, I outlined how the explosion of high-resolution, low-cost image sensors was transforming the nature of vision, as we rapidly evolve to a world where most pixels are never seen by humans, but captured, analyzed and used by embedded computing systems. This discontinuity is creating ample opportunities for new technologies, new business models and new companies. In this second part, we look at the basics ingredients of a startup, and two rival models of how to approach building a successful enterprise.
Let’s look at the basic ingredients of starting a technology business – not just a vision venture. We might call this “Startup 101”. The first ingredient is the team.
- You will need depth of skills. It is impossible to be outstanding in absolutely everything, but success does depend on having world-class capability in one or two disciplines, usually including at least one technology dimension. Without leadership somewhere, it’s hard to differentiate from others, and to see farther down the road on emerging applications.
- You don’t need to be world-class in everything, but having a breadth of skills across the basics – hardware, software, marketing, sales, fund-raising, strategy, infrastructure – will help enormously in moving forward as a business. The hardware/software need is obvious and usually first priority. You have to be able to develop and deliver something useful, unique and functional. But sooner or later you’ll also need to figure out how to describe it to customers, make the product and company visible, and complete business transactions. You’ll also need to put together some infrastructure, so that you can hire and pay people, get reasonably secure network access and supply toilet paper in the bathrooms.
- Some level of experience on the team is important. You don’t need to be graybeards with rich startup and big company track records, but some level of real world history is enormously valuable. You need enough to avoid the rookie mistakes and to recognize the difference between a normal potholes and an existential crevasse. Potholes you do your best to avoid, but it you have to survive a bump, you can. A bit of experience can alert you when you’re approaching the abyss, so you can do everything possible to get around it. Is there a simple formula for recognizing those crevasses? Unfortunately, no (but they often involve core team conflict, legal issues, or cash flow). Startups through a lot of issues, big and small, at the leadership team, so there will be plenty of opportunity to gain experience along the way.
- The last key dimension of team is perhaps the most important, but also the most nebulous – character. Starting a company is hard work, with plenty of stress and emotion, because of the stakes. A team, capable and committed to openness, patience and honesty, will perform better, last longer, and have more fun than other teams. It does NOT mean that the team should agree all the time – part of the point of constructing a team with diverse skills is to get good “parallax perspective” on the thorniest problems. It DOES mean trusting one another to do their jobs, being willing to ask tough questions about assumptions and methods, and working hard for common effort. More than anything, it means putting ego and individual glory aside.
The second key ingredient for a startup is the product. Every startup’s product is different (or it had better be!), but here are four criteria to apply to the product concept:
- The product should be unique in at least one major dimension.
- The uniqueness could be functionality – product does something that wasn’t possible before, or it does a set of functions together that were weren’t possible before.
- The uniqueness could be performance – it does a known job faster, at lower power, cheaper or in a smaller form-factor than anyone else.
- The uniqueness could be the business or usage model – it allows a task to be done by a new – usually less sophisticated – customer, or let’s the customer pay for it in a different way
- Building the product must be feasible. It isn’t enough just to have a Mat Lab model of a great new vision algorithm – you need to make it work at the necessary speed, and fit in the memory of the target embedded platform, with all the interfaces to cameras, networks, storage and other software layers.
- The product should be defensible. Once others learn about the product, can they easily copy it? When you work with customers about real needs, will you be able to incorporate improvements more rapidly and more completely than others? Can you gather training data and interesting usage cases more quickly? Can you protect your code, your algorithms, and your hardware design from overt cloners?
- You should be able to explain the product relative to the competition? In some ideal world, customers would be able to appreciate the magnificence of your invention without any point of comparison – they would instantly understand how to improve their lives by buying your product. In that ideal world you would have no competition. In the long run, you ideally want to so dominate your segment that no one else comes close. However, if you have no initial reference point – no competition – you may struggle to discover and explain the product’s virtues. Having some competition is not a bad thing –it gives a preexisting reference point by which the performance, functionality and usage model breakthrough can be made vivid to potential customers. In fact, if you think you have no competition, you should probably go find some, at least for purpose of showing the superiority of your solution.
The third key ingredient for a startup is the target market: the group of users plus the context for use. Think “teenage girls” + “shopping for clothes” or “PCB layout designers” + “high frequency multi-layer board timing closure”.
Finding the right target market for a new technology product faces an inherent dilemma. In the early going, it is not hard to find a group of technology enthusiasts who will adopt the product because it is new, cool and powerful. They have an easy time picture how it may serve their uses and are comfortable with the hard work to adapt the inherent power of your technology to their needs. Company progress often stalls, however, once this small population of early adopters has embraced the product. The great majority of users are not looking for an application or integration challenge – they just want to get some job done. They may tolerate use of new technology from an untried startup, but only if it clearly addresses their use case. This transition to mainstream users has been characterized by author Geoffrey Moore as “crossing the chasm”. The recognized strategy for getting into wider use is to narrow the focus to more completely solve the problems of a smaller group of mainstream customers, often by solving the problem more completely for one vertical application or for one specific usage scenario. So “going vertical” puts the fear (and hypothetical potential) of the technology into the background and emphasizes the practical and accessible benefits of the superior solution.
This narrowing of focus, however, can sometimes create a dilemma in working with potential investors. Investors, especially big VCs want to hit homeruns by winning huge markets. They don’t want narrow niche plays. The highly successful investor, Peter Thiel, dramatizes this point of view by saying “competition is for losers”, meaning that growing and dominating a new market can be much more profitable than participating in an existing commodity market.
The answer is to think about, and where appropriate, talk about the market strategy in two levels. First identify huge markets that are still under-served or latent. Then focus on an early niche within that emerging market which can be dominated by a concentrated effort, where the insights and technologies needed to master the niche are excellent preparation for larger and larger surrounding use-cases with the likely huge market. Talking about both the laser focus on the “beachhead” initial market AND the setup for leadership in the eventual market can often resolve the apparent paradox.
The accumulated wisdom of startup methods is evolving continuously, both as teams refine what works, and as the technology and applications create new business models [think advertising], new platforms [think applications as a cloud service], new investment methods [think crowd funding] and new team structures [think gig economy]. The software startup world, in particular, has been dramatically influenced by the “Lean Startup” principle. This idea has evolved over the past fifteen year, spawned by the writing of Steve Blank, more than any one individual. It contrasts in key ways to the long-standing model, which we can call “Old School”.
|Old School||Lean Startup|
|Funding||Seed Round based on team and idea, A Round to develop product, B Round after revenue||Develop prototype to get Seed Round, A Round after revenue, B Round, if any, for global expansion|
|Product Types||Hardware/software systems and silicon||Easiest with software|
|Customer Acquisition||Develop sales and marketing organization, to sell direct or build channel||CEO and CTO are chief sales people until product and revenue potential proven in the market|
|Business models||Mostly B2B with large transactions||Web–centric B2B and B2C with subscriptions and micro-transactions|
In vastly simplified form, the Lean Startup model is built on five elements of guidance:
- Rapidly develop a Minimum Viable Product (MVP) – the simplest product-like deliverable that customers will actually use in some form. Getting engaged with customers as early as possible gives you the kind of feedback on real problems that you cannot get from theoretical discussions. It gives you a chance to concentrate on the most customer-relevant features and skip the effort on features that customers are less likely to care about.
- Test prototypes on target users early and often – Once you have an MVP, you have a great platform to evolve incrementally. If you can figure out how to float new features into the product without jeopardizing basic functionality, then you can do rapid experimentation on customer usage. This allows the product to evolve more quickly.
- Measure market and technical progress dispassionately and scientifically – New markets and technologies often don’t follow old rules-of-thumb, so you may need to develop new more appropriate metrics of progress for both. Methods like A-B testing of alternative mechanisms can give quick feedback on real customer usage, and enhances a sense of honesty and realism in the effort.
- Don’t take too much money too soon – Taking money from investors is an implied commitment to deliver predictable returns in a predicable fashion. If you try to make that promise too early, people won’t believe you, so you won’t get the funding. Even if you can convince investors to fund you, taking money too early may make you commit to a product before you really know what works best. In some areas, like cloud software, powerful ideas can sometimes be developed and launched by small teams, so that little funding is absolutely necessary in the early days. Startup and funding culture have evolved together so that teams often don’t expect to get outside funding until they have their MVP. Some teams expect to be eating ramen noodles for many months.
- Leverage open source and crowd source thinking – It is hard to overstate the impact of open source on many areas of software. The combination of compelling economics, rapid evolution and vetting by a wide base of users makes open source important in two ways – as a building block within your own technical development strategy, and as part of a proliferation strategy that creates a wider market for your product. Crowd sourcing represents an analogous method to harness wide enthusiasm for your mission or product to gather and refine content, generate data and get startup funds.
As these methods have grown up in the cloud software world, they do not all automatically apply to embedded vision startups. Some technologies, like new silicon platforms, require such high upfront investments and expensive iterations that deferring funding or iterating customer prototypes may not be viable. In addition, powerful ideas like live (and unannounced) A-B testing on customers will not be acceptable for some embedded products, especially in safety-critical applications. The lean methods here work most obviously in on-line environments, with large numbers of customers and small transactions. A design win for an embedded system may have much greater transaction value than any single order in on-line software, so the sales process may be quite different, with a significant role for well-orchestrated sales and marketing efforts with key customers. Nevertheless, we can compare typical “Old School” and “Lean Startup” methods across key areas like funding, product types, methods for getting customers and core business models.