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The Lean Startup - Eric Ries [82]

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should be engineered to design and run this experiment as quickly as possible, using the smallest batch size that will get the job done. Remember that although we write the feedback loop as Build-Measure-Learn because the activities happen in that order, our planning really works in the reverse order: we figure out what we need to learn and then work backwards to see what product will work as an experiment to get that learning. Thus, it is not the customer, but rather our hypothesis about the customer, that pulls work from product development and other functions. Any other work is waste.


Hypothesis Pull in Clean Tech

To see this in action, let’s take a look at Berkeley-based startup Alphabet Energy. Any machine or process that generates power, whether it is a motor in a factory or a coal-burning power plant, generates heat as a by-product. Alphabet Energy has developed a product that can generate electricity from this waste heat, using a new kind of material called a thermoelectric. Alphabet Energy’s thermoelectric material was developed over ten years by scientists at the Lawrence Berkeley National Laboratories.

As with many clean technology products, there are huge challenges in bringing a product like this to market. While working through its leap-of-faith assumptions, Alphabet figured out early that developing a solution for waste thermoelectricity required building a heat exchanger and a generic device to transfer heat from one medium to another as well as doing project-specific engineering. For instance, if Alphabet wanted to build a solution for a utility such as Pacific Gas and Electric, the heat exchanger would have to be configured, shaped, and installed to capture the heat from a power plant’s exhaust system.

What makes Alphabet Energy unique is that the company made a savvy decision early on in the research process. Instead of using relatively rare elements as materials, they decided to base their research on silicon wafers, the same physical substance that computer central processing units (CPUs) are made from. As CEO Matthew Scullin explains, “Our thermoelectric is the only one that can use low-cost semiconductor infrastructure for manufacturing.” This has enabled Alphabet Energy to design and build its products in small batches.

By contrast, most successful clean technology startups have had to make substantial early investments. The solar panel provider SunPower had to build in factories to manufacture its panels and partner with installers before becoming fully operational. Similarly, BrightSource raised $291 million to build and operate large-scale solar plants without delivering a watt to a single customer.

Instead of having to invest time and money in expensive fabrication facilities, Alphabet is able to take advantage of the massive existing infrastructure that produces silicon wafers for computer electronics. As a result, Alphabet can go from a product concept to holding a physical version in its hand in just six weeks from end to end. Alphabet’s challenge has been to find the combination of performance, price, and physical shape that is a match for early customers. Although its technology has revolutionary potential, early adopters will deploy it only if they can see a clear return on investment.

It might seem that the most obvious market for Alphabet’s technology would be power plants, and indeed, that was the team’s initial hypothesis. Alphabet hypothesized that simple cycle gas turbines would be an ideal application; these turbines, which are similar to jet engines strapped to the ground, are used by power generators to provide energy for peak demand. Alphabet believed that attaching its semiconductors to those turbines would be simple and cheap.

The company went about testing this hypothesis in small batches by building small-scale solutions for its customers as a way of learning. As with many initial ideas, their hypothesis was disproved quickly. Power companies have a low tolerance for risk, making them unlikely to become early adopters. Because it wasn’t weighed down by a large-batch

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