Economic Laws of R&D | How Tesla and Germany Built Success in Cleantech
R&D. Innovation Labs. We all want and need tech and science to grow and improve. But how? To the outsider such as myself, it’s quite difficult to get a grasp on how products will change for the best — not only on how tech will get better, but also how it’ll get cheaper. With the influx of issues associated with climate change, we need more, and faster R&D to get what we need cheaper, quicker, and better.
Luckily, there are laws that not only govern but narrate the results of R&D; not only literal laws of the land, but also “laws” observed through data.
The most famous of R&D laws, Moores Law predicts that the number of transistors within a microchip will double around each year. Gordon Moore, the co-founder of Intel who pioneered this “law”, was quite accurate:
In 1965, Gordon Moore, who was working at Fairchild Semiconductor, made this prediction based on his observations he made on transistor density in microchips.
Moore’s law was never made on the basis of pure scientific explanations, or on an engineering principle placed on the pedestals of physics and chemistry. Simply, the law is just a prediction based off data points. Moore was smart enough to understand the process of increasing transistor density, in addition to understanding how long it would take for each root sector of microchip manufacturing to improve.
While Moore’s law is significant in that it gave a prediction for companies to follow in terms of transistor R&D, the law’s existence and proof gives evidence that certain processes in innovation can follow “rules” or dependable patterns. Moore’s law could have been wrong, and it may be false in the coming future. Just recently, Nvidia CEO Jensen Huang says that Moore’s law is dead. With transistor densities getting smaller and smaller, it’s getting much harder, expensive, and tiresome to follow with Moore’s law.
Although Moore’s law may not stand true for the coming decades, it was relatively true for the past 5 decades. Moore’s law demonstrates how it gives a prediction for companies to follow in terms of semiconductor R&D. It lets competitors and chip manufactures set benchmarks, plan product releases, and helps all other stakeholders plan accordingly. More importantly, it helps keep costs, for both manufacturers and consumers, reduced in relation to computing power and complexity.
Moore’s law did much more than provide a benchmark. It proved that R&D can and does follow patterns. Similar “laws” or predicted patterns of innovation are also just as important for other sectors, and more so with the challenge of cleantech.
Wright’s Law — Experience Curves
In 1936, Theodore Wright published a paper called the “Factors Affecting the Costs of Airplanes”. Wright, an experienced educator and aeronautical engineer in both the public and private sector, described how for a doubling in airplane production, the labor requirements reduce by 10–15%.
Moore’s law gave a pattern for production’s technological achievement, transistor density, plotted against time. Wright’s law plots the cumulative production against cost. Both Ark investment and MIT stated that Wright’s law, also known as the Experience or Learning Curve, proves more accurate that Moore’s Law.
Wright’s Law focused on how the cost to produce a unit decreased when the amount of said unit increased. Rather than using smart policies or intuitive techniques to explain the pattern, the obvious was stated: more experience with building a product helped whoever and whatever building said product to be more efficient, and therefore cheaper.
Furthermore, Wright’s law predicted an exponential decrease in cost in relation to cumulative production. In the scope of cleantech, Wright’s law is rather relevant to lithium-ion battery systems:
This graph shows how the cost of a Li-ion battery declines proportionally with the cumulative kWh produced. What’s important to notice, is how the axis are on a logarithmic scale. Thus, Wright’s law is demonstrated as the cost has exponentially decreased.
With current climate-friendly innovations going through R&D, it’s difficult to know when a certain product will reduce enough of its cost. The experience curve could be applied to predict when a certain units cost would dip into a reasonable rate, thus helping one either manufacture goods to the right audience under the right cost, or to allows consumers to know when a certain product can be purchased for an appropriate price.
Understanding the patterns of R&D helps nearly all stakeholders in the system. However, understanding “laws” such as the experience curve, Moore’s Law, or other laws unbeknownst to myself
Tesla’s use of “Laws”
It’s likely that Elon Musk used this idea of the Experience Curve to create “The Secret Tesla Motors Master Plan”. Written in August 2, 2006, Musk introduces the plan of Tesla Motors, starting with the purpose of the Tesla Roadster (the one we all forgot about) to the longer term models of Tesla: the actual cars that will contribute to decreasing carbon in the atmosphere.
“Almost any new technology initially has high unit cost before it can be optimized and this is no less true for electric cars. The strategy of Tesla is to enter at the high end of the market, where customers are prepared to pay a premium, and then drive down market as fast as possible to higher unit volume and lower prices with each successive model.”
Musk planned according to Wright’s Law and succeeded accordingly. With the success of the Tesla Roadster came the Model S, and shortly after came the Model 3. Even within the localized product of the Model 3 there’s strong proof of the experience curve.
The Public Sector’s Use of the “Laws
In 2000, Herman Scheer, a long-time member of the German Parliament, vouched for Germany to step up its usage of solar and wind power. Scheer successfully passed through the Erneuerbare-Energien-Gesetz, in English being the Renewable Energy Sources Act.
The EEG took form in a feed-in-tariff in order to economically encourage citizens and power providers to transition to renewable sources. The EEG allowed anyone to install renewable power sources (wind, solar) and to feed energy into the electrical grid. The utility provider would pay a preset rate back to the citizen-power-contributor. This preset rate was locked in by the government and allowed others to calculate costs. The EEG also provided other parts of aid to help the law succeed: agent-aggregators aided citizens in installation and maintenance of clean energy; the extra costs of the pay-rate would be added on to the homeowners bill, something that wasn’t quite high.
All in all, the EEG made sure that the cumulative volume of renewable’s increased. The state used its power to make sure that the cost to run each unit was subsided. The EEG was successful. It allowed for 100,00 solar panels, made Germany a leader in renewables, and cut down the green premium.
This law, Scheer’s Law, was largely successful due to Scheer’s clever understanding of economical pattern’s and the “laws” of innovation.
The Importance of R&D Patterns to Solving our Climate Crisis
Both Tesla and Germany, through Herman Scheer’s ingenuity, utilized the laws and patterns of R&D. One used the experience curve to decrease costs of batteries and to propel his business to the most valuable car company in the world. The other used Wright’s Law to push his country to the forefront of renewable energy adoption, staying far ahead of most countries in terms of adoption and market innovation in cleantech.
Understanding these sorts of patterns in R&D is pivotal when tackling climate-related issues. We’re all working against a clock counting down, while the speed of R&D and innovation seems like it’s a clock counting up. When we keep time as our variable, we need to make sure that we match supply, demand, and money in order to transition into a cleaner world, all while we use the existing techniques and systems of the current to build the next.