Talking to lots of people has made me realize that it is easy to be overwhelmed by the quantity of information out there about renewable energy.   Energy production and consumption is a complex topic and it is made more complex by those who have the most financial interest in the field tossing out truths and truthiness, often out of context, in order to solidify their position. And without some kind of base level of knowledge, its impossible to think critically about the news and propaganda that’s flying around in the media.

What I want to do is to give a quick overview of the state of the art in renewable energy – pros, cons, myths, and challenges. In addition, I’m going to talk about the size and scope of the “energy problem” that the world is facing and why its of utmost importance that we solve it, rather than deferring it or succumbing to it. I’m going to talk about why energy is the only true measure of wealth and how access to energy is a human rights issue. And, I’m going to end up by giving my perspective on what the ultimate solution will look like.

Its shaping up to be an exciting presentation.

The Deloitte Center for the Edge has published a report on the decline in the return on assets of American businesses over the past 40 years. Jon Taplin, a professor at USC, posted a very insightful summary of the report, likening this decline and how it was hidden to a shell game. What was interesting to me in his post was how the blind obedience to a particular metric has been in large part to blame for our current financial insanity.

What the Deloitte report points out is that companies have been able to “juice” their return-on-equity (ROE) numbers by consistently taking on more and more debt. Meanwhile, their return-on-assets (ROA) have fallen steadily. If you are even a casual investor or small businessperson, you’ve probably heard of ROE and why it is important. You may not have heard of ROA. Let me briefly explain the difference

Return on equity is a company’s annual net income divided by total shareholder equity. Shareholder equity is essentially how much money investors have put into your company, so ROE measures how effective you are at generating a return on invested funds.  Return on assets, on the other hand, is your annual net income divided by total assets. ROA, therefore, measures your effectiveness at generating a return on everything the company owns and is in the bank.

You may already be seeing the disconnect, just based on my choice of words when I defined ROE and ROA above. Let me give you an example: Let’s say you have two companies, A & B. Each of these companies generates 1 M$ per year in net income. Each of these companies has 5 M$ in equity on the books, meaning that the investors have 5 M$ in them. In each case, the ROE of the company is 20%. Not too shabby. But there is an important difference between them. Company B also has 5 M$ of debt outstanding. Company B will thus have 5 M$ more assets on the books than Company A, and thus their ROA will be lower.

If you’re a CEO and you’re managed by your board on the basis of your ROE, you thus have a substantial incentive to leverage your company with loads of debt in order to have more resources with which to expand your business, since that debt doesn’t show up directly on your measurements. People will still invest in your company on the basis of your keen ROE (so long as they don’t look at your debt-to-equity ratio, or your actual return on assets.)

The bottom line here is that a lot of people had a warning right in front of them about what was happening with GM and other companies, but couldn’t see it because one of their chief metrics hid it from them. As with so many other things, relying on a few simple metrics is dangerous and sloppy. Simple metrics are useful, but they must be cross-checked and reviewed with a constant eye on exactly what they tell you and what they do not.

A recent article in The Atlantic served as an excellent illustration of this point. In the article, Megan McArdle points out that the most selfish (i.e. most libertarian) bankruptcy laws serve to make everyone worse off and that having society defray the cost of failed entrepreneurship serves as an incentive to more entrepreneurialism, which makes everyone better off -  including non-entrepreneurs. Further, she lays out simply and elegantly why the current regime of tightening bankruptcy laws to “stick it to irresponsible” do little to prevent people from running up too much debt and a lot to disincentivize people from taking rational risks.

I think this is particularly timely as we see a rise in more socially networked entrepreneurs – a sign that we are growing to understand that collaboration is as much a part of capitalism as competition. As this transition occurs, I suspect that a distinction will grow between folks who run up tens of kilodollars in consumer debt and folks who run up tens of kilodollars in debt trying to start a business.

In my mind, this is a clear argument for open access scientific journals. While it would be possible to corrupt the process of peer review in an open access journal, it would be pretty difficult to hide the fact for very long.

I’m going to talk about how that love affair manifests, how you manage it, and how you move past it into a wonderful relationship that will hopefully last many years, bring in revenue, and perhaps even change the world.

What do I mean by “technology” here? It might mean the latest programming language hotness or it might mean the latest thing in biofuels or in carbon sequestration. It could mean the latest gadget for helping you build a Twitter-enabled flowerpot. Technology, as a functional definition, can be said to be the use of knowledge to interact with our environment – both our physical environment and our social environment. How can you not fall in love with something that is so fundamental?

The problem comes when we expect everything from some bit of technology. You find it and it looks cool. Novel. You can do things with it that you’ve never done before. So you start using it and soon you want to use it to do some other things too. Things that are important, but maybe things that the technology really wasn’t supposed to do. Like using the butt-end of a screwdriver to pound in a nail. Or trying to write a fast Fourier transform in pure Java. You can probably think of your own examples.

The main thing that I do at my job is technology development. We see the seeds of a new technology – sometimes they’re at the “little black speck in a paper envelope” stage. Sometimes they’re at the “tiny sprout” stage or even at the “seedling ready to transplant” stage. We either develop the technology ourselves or partner with the people who are currently developing it, when we think there is a good fit. We take it, grow it until its ready to bear fruit and then commercialize it. And sometimes, we see a technology that sets our hearts pounding. Our pupils dilate, our palms get sweaty – we fall in love. And that love is a beautiful thing, because it means that the technology is likely to be useful and that there is going to be an internal champion for it.

The problem comes a little ways into the relationship. Sure, your new love is a fun to take into the lab and gives you that sizzle when you’re down and dirty with it. You want to solve every problem with it. It’s a sexy beast! But, after a few months with it, your new love’s moved into your apartment and you’d really like for him to – ya know, pay some rent? Plus, he drips on the toilet seat occasionally and leaves his dirty dishes all over the place. But still, you can’t let him go because, well…. he’s just so good!

At some point, you have to make a decision, just like you do with every relationship. Maybe it was just a fling – you learned something from it and had a good time doing it, but it’s just not for you. Or maybe this one is worth keeping. Sure, he’s rough around the edges, but a little time and effort and he’ll be something to take home to Mama. Or at least to your C-office.

What makes the difference between the one you kick out of bed and the one you go steady with is how honest you are with yourself when you’re learning about the technology. If you don’t quickly learn what the limitations of the new technology are, your new love is bound to disappoint. And as thorough as you might be up front about the technical limitations, you’re going to have a second round of limitations that come up each time you try to take that technology to a new market. It’s just like taking that new girl you hooked up with a few months ago to an office party and watching her get hammered and puke into the ficus. Quickly, you learn that while she’s an excellent choice for the “wild night on the town” market, she’s perhaps not quite the one for the “impress your clients” market.

One way we keep track of our current thinking about a technology is with a chart like this:

This sort of bubble chart keeps track of where the technology falls in your portfolio in terms of strategic fit, development stage, and expected financial return. The size of the bubble indicates the financial return, typically either by “best N years” performance or “N years post launch” performance. The chart above shows some typical positions in a portfolio. You tend to want to have a decent scatter over the chart, but ultimately, if you’re a technology-driven company, you don’t want to have a lot of things sitting in the lower left hand corner. That means your putting a lot of resources into things that are farther out and will ultimately bring you no new revenue.

During the course of a love affair with a particular technology, you will sometimes find that its trajectory across the chart will look something like this:

This happens a lot more frequently than you might suspect. By the time people take a long hard look at what they’ve invited into their bed … lab, that is, they’ve realized that the technology just isn’t as shiny as it used to be. Back when things were good, you dumped a lot of resources into it. Lots of fancy dinners, flowers, jewelry. You put time and effort, maybe even a sizable fraction of your available development staff, into making this technology go. And for what?

Some of this is inevitable. You’re never going to know a priori what a technology’s domain of applicability is. And if you spend too much time trying to map that out oh-so-carefully before you start, you’ll never bring anything to market. Further, your initial estimates are just that. You’re going to overestimate its financial impact – except in those rare cases where you’ve underestimated it. You’re going to completely misjudge how close to commercialization the technology is. But ultimately, the goal is to move past the love affair into a stable relationship, where there is a clear path from technology to product.

The key to managing this transition is flexibility. Once you’ve made a decision to try the thing out, you must constantly be looking for the mismatches between your technology and the problems you’re looking to solve with it. At the same time, you need to be flexible enough to realize that while the new technology may not solve the particular problem you’d set in front of it, there may be a completely different problem that it is particularly adept at solving.

There’s a lot left unsaid here about technology development: commercialization strategies, product migration maps, technology adjacencies, market adjacencies. I’m not going to talk about those here. They’re important and you should be thinking about them, but they don’t help you deal with the emotions behind finding a new technology, rubbing the shiny off of it, and dealing with the commercialization endgame.

Do you have to be in a research and development group to follow this advice? Not at all. The advice holds for anyone who is prone to love affairs with technology. The specifics of methods and desired outcomes will change. Some people will try Ruby on Rails and ultimately find that sleek hottie too much for them, returning to the stable, quiet hippie in Django. Some folks will be looking for a greener energy source to put their activism into, take that one look at Clean Coal, realize that she’s just a two-bit whore and pass on to something that they know they can support while keeping their self-respect. At the end of the day, everyone will fall in love with a technology at least once. The trick is to be honest enough to know when its not the technology you’re seeking and flexible enough to let it solve the problems at which it is best.

Recently, I’ve taken on the role of intellectual property champion for the central research division. This means that I’ll be keeping track of trends in what areas we’re inventing, what we’re filing on, and how well we’re doing in generating useful IP for the company. What’s most exciting is that I will be working to devise new, more useful metrics and measurements for the company’s IP performance. This has proved to be an interesting exercise thus far.

What strikes me most is how very little anyone understands about best practices in this area, despite the fact that IP has become central to most of the American economy. I’ve been perusing a report from the General Counsel Roundtable of the Corporate Executive Board that outlines what that group considers to be best practices and they are certainly good, but the implementation is very vague. I’m left with an outstanding question of whether the vagueness is due to the differences in best implementations that arise from legitimate differences between companies or due mainly to the inertia of corporate culture. I expect that I’m going to learn a lot from this.

[I]f we’re going to avert ecological destruction, we need to to not only do things differently, we need to do different things.

What he’s saying here is something that I’ve pointed out to my colleagues in the innovation community: sustainability is not about making things with less stuff, or that last longer, or that aren’t toxic, or even that can be infinitely cradle-to-cradle recycled. Sustainability requires us to invent things that make it possible to live more sustainably. If the things, the stuff, that we have and use make it easier to live sustainable lives than to not do so, then we will live sustainably.

Its not an easy problem to solve, for the same reason that truly groundbreaking innovation is not easy. It is pretty straightforward to imagine a novel solution for a market that already exists. It is much harder to invent a new market. I think that the kinds of products that will help people live sustainably are products for a market that doesn’t exist yet. Our business strategists don’t know how to value them, so our market analysts can’t compute a return on investment, so no investment is made. And truthfully, our scientists and engineers don’t always have the global perspective necessary to understand what types of solutions are necessary.

The point of Steffen’s article was to underline the importance of community in making these changes in our systems. I think that it is also important to understand the systems themselves. As we grow in our understanding the network of interactions and dependencies in our economy and our society, this understanding will allow us to break out of unsustainable patterns and replace them with ones that are equally understood, but are sustainable to the best of our knowledge. And because we’ll be building from a base of understanding, we’ll be able to look at them in a rational fashion 40 years from now when we understand the ways in which the new patterns are not sustainable.

It may be that at first, these more-sustainable patterns will be obvious. Things that folks like Steffen have been telling us for years, like community gardening, reducing sprawl, and increasing bike transport. But as with everything else, the low-hanging fruits will be quickly exhausted. At that point, progress will only be made by deeper understanding. It will be interesting to see how the tools for gaining that understanding develop.

This could be integrated with AdWords or Google Analytics, to return data from there that could feed custom analysis spreadsheets or could point to an internal database to use proprietary business data. I could also see data providers, e.g. the National Weather Service or Digg, providing statistics feeds that folks could slice and dice for their own purposes.

In a sense, this could be part of the long tail of the “super-cruncher”” phenomenon. Its hard to get good data to play with right now. A standard platform, or at least a standard access protocol, for raw numbers would open up opportunities to crowdsource data analysis.