Some random thoughts about the Inflation Reduction Act and what a techie and tech might have to offer. I’ll be updating this post as I think about more things. This is not a refined and incisive essay, this is just me thinking out loud.

9-19-2022

First set of thoughts.

Overcoming a bias to avoid atoms in favor of bits

That tech valuations are unlocked by prefering bits (software) to atoms (physical things) and the subsequent near zero marginal scaling costs is a truism deeply engrained in many of us in tech. Obviously some people and organizations have unlearned this lesson: Amazon notably, but a number of the companies building health offices, or scooter rentals. Still many companies that seem like they would be atoms companies, e.g. Uber or some of those scooter companies, are actually outsourcing the ownership of the atoms in favor of sticking with the bits. The work of decarbonizing our economy (and the work funded by IRA, and CHIPS) are profoundly physical undertakings. I think that’s going to be hard for many of us coming from tech.

AI/ML/Robots

Obviously. Work out in the physical world is going to benefit from the really ridiculous rate of improvement around things like AI/computer vision with robot drones doing things like monitoring the state of the transmission system and deploying repair before things become catastrophic, or ditto pipelines and methane leaks to implement the methane charges from the IRA.

Learning curves

The standard theory about how you drive down costs, especially for atoms, is economies of scale. That remains true, but the work to decarbonize is often going to be more like the R&D we do in tech than the streamlined manufacturing of established industrial practices. Here we should be thinking about how learning curves drive down cost. The climate industry knows this, as no one can possibly have missed the precipitious decline in the cost of photovolatics and a number of other green energy technologies. That said tech also has a high concentration of people who think about socio-technical systems for innovation and have spent considerable hours contemplating things like how to apply the Toyota Production System to our domain. Feels like we may have something to offer as a source of talent and writing on the topics of continuous learning and improvement, the role human factors play, etc.

The data from industry suggests there are technologies that benefit from learning curves, and technologies that don’t. Technologies that benefit tend to be those being built in a controlled environment, like a factory. Technologies that don’t tend to be those built in the field (will AI/ML/robots change this?). Given that decarbonization is going to need to happen both in the factory and in the field, I wonder what insights we can share about modular design. (including what to do with the insight that almost all software teams that try to do modular design usually fail for a handful of predictable reasons, mostly around complexity)

Topics on which we may have something to teach if we are called upon

A lot of the conversation around knowledge work happens in and around tech for a few different reasons. The system we use to fund technology startups favors self aggrandizement, but also our roots in tech have always included a high degree of collaboration and information sharing. Either way we talk in public about a lot of things that other industries keep behind closed doors. Some of those topics:

  • Dealing with complexity (most of our best material comes via the DoD, but still)
  • The failures of Taylorism and how to actually enable people to do good work using frameworks like mastery-autonomy-purpose.
  • Great employee experiences and hiring.
  • How to deploy lots of money quickly.
  • Burnout.
  • Failure, innovation and moving fast.

Failure, innovation and moving fast

Much like dealing with complexity (which is an overlapping topic) tech’s foundational understanding of being okay with failure as the way to unlock innovation is learned from our Defense industry origins. That said we’ve added a significant body of practice and tooling around hypothesis, experimentation, risk, data collection and iteration in information rich environments.

More than anything what I worry about most with the climate change legislation is that the $400b dollars all goes to large, slow moving organizations that specialize in slow moving CYA style planning that attempt to boil the ocean with their big centralized plans, but whose actual function is to make sure that no one is accountable for the inevitable failures. If tech has anything to offer it will be how to get comfortable getting started without knowing all the answers, and how small wins create momentum on the way to understanding that our the foundation to big wins.

How to deploy these insights into a world of physical infrastructure and government funding is an interesting and open question, though organizations like Code for America and e.g. their work on CalFresh are interesting data points.