Dealing with climate change has always felt like a slog. Like we need to take our medicine in order to fight off calamity. In some respects this is correct, especially for countries without access to a lot of low-emissions power.
But reading Superpower by Ross Garnaut makes me realise that there is a huge opportunity. Especially for countries with access to large quantities of wind, solar, hydro and tidal power.
…Australia’s resource base placed it well for the energy transition: it had a wide range of high-quality renewable energy resources and economically favourable opportunities for geosequestration of emissions from traditional coal and gas generation… Australia’s hydro-electric resources and potential for pumped hydro-electric storage (PHS) in the Snowy Mountains and Tasmania, and perhaps its proximity to the immense hydro-electricity resources on the island of New Guinea, would play big roles in balancing solar and wind….
There’s already a company raising funds to supply a fifth of Singapore’s energy via an undersea cable from a solar farm in Australia.
But Garnaut goes further, pointing out that the more other countries put a price on carbon, the greater advantage there is for a country with Australia’s capacity to generate low-emissions electricity.
A price on embedded carbon would make imports from polluting industries and countries more expensive.
Australia is the largest exporter in the world of mineral ores requiring energy-intensive processing for conversion into metals. Australia in the post-carbon world could become the locus of energy-intensive processing of minerals for use in countries with inferior renewable energy resource endowments. Second, there are opportunities for export of hydrogen produced by electrolysis from renewable energy, through liquefaction or through ammonia as a hydrogen carrier. The natural markets are the renewable-energy-resource-poor countries of Asia, notably Japan and Korea.
Tackling climate change could move Australia up the industrial stack. It could rejuvenate manufacturing, countering some of the advantages of automation, geography and low cost labour. It should even benefit regional areas, as manufacturing is located near power generation.
But, most importantly, this isn’t just an argument for investment in renewable energy. Countries like Australia have a clear incentive to encourage everyone to cap, price and reduce emissions, to invest in moonshot technologies.
The more action on climate change the more competitive we become.
As I slowly wrap–up The creativity code by Marcus Du Sautoy, this paragraph makes me consider how our training and experience shape, and in some sense even limit, our world:
Various attempts at learning jazz have taught me that there is a puzzle element to a good improvisation. Generally a jazz standard has a set of chords that change over the course of a piece. The task of the trumpeter is to trace a line that fits the chords as they change. But your choice also has to make sense from note to note, so playing jazz is really like tracing a line through a two-dimensional maze. The chords determine the permissible moves vertically, and what you’ve just played determines the moves horizontally. As jazz gets freer, the actual chord progressions become more fluid and you have to be sensitive to your pianist’s possible next move, which will again be determined by the chords played to date. A good improviser listens and knows where the pianist is likely to head next.
Of course this is by a mathematician and mathematics is a subject of the book. And this grab comes amid an exploration of music and algorithms. But the explicit mathematical digression in the midst of this musical romp sticks out.
I think it’s because I do this all the time (I’m pretty sure we all do). Just like Du Sautoy pulls music through his mathematical lens (and vice versa), we are constantly filtering and analogising. It shapes our world.
As a journalist I have a hard time ignoring the decisions made in stories. Wondering at other angles or how the medium itself (text, audio, video etc.) inherently limits choices.
In that sense my experience has me constantly stuck in the role of participant. Viewing through a lens of construction rather than strict consumption. I consume stories as one of a number of options, as a version rather than a totality.
Perhaps we all do this when consuming news. But I’ve made these exact decisions thousands of times. It’s hard not to envision the dirty carpet of the newsroom, the white walls above my desk, the mild panic as deadline approaches. To wonder how the availability of talent, the domain knowledge of the reporter, any number of other factors; pushed and pulled on what’s before me.
Similarly with economics. After university I have comparative advantage and opportunity cost tattooed on my brain. I’m constantly searching for impacts at the margins. I reason under a cloud of ceteris paribus.
Your training, what you do every day, equips you with easy heuristics. But it can slowly carve grooves in your thinking. You mustn’t let it control where you end up.
The trick is to be aware of it, and, hopefully, leverage it as Du Sautoy has. For greater understanding. Not to be sucked into thinking this is all there is.
As always my emphasis
If you have thought seriously about inequality or capitalism then the thesis of The code of capital by Katharina Pistor is not going to be too shocking. Still, it’s one of the best articulated explanations for wealth and inequality I’ve seen.
Fundamentally, capital is made from two ingredients: an asset, and the legal code. I use the term “asset” broadly to denote any object, claim, skill, or idea, regardless of its form. In their unadulterated appearance, these simple assets are just that: a piece of dirt, a building, a promise to receive payment at a future date, an idea for a new drug, or a string of digital code. With the right legal coding, any of these assets can be turned into capital and thereby increase its propensity to create wealth for its holder(s)…
This gives you the crux of the argument. That how assets are turned into capital, and so generate wealth and confer certain rights, is contrived. This may seem like a simplistic and obvious point, but we often take for granted what protections are afforded to certain assets, why some stakeholders are held over others, or the myriad frameworks and legal fictions we use to interact with them.
Who decides this, and why, helps explain persistent and widening inequality in many societies.
The idea that people aren’t all equal before the law isn’t a novel concept. Nor is the notion that the already wealthy and powerful have greater ability to influence this. But Pistor makes a more subtle point.
She focuses on how private agents, through private law, have taken over this process. Lawyers, not legislators, conform assets to the law, and select and fashion the law to suit.
Law is the cloth from which capital is cut; it gives holders of capital assets the right to exclusive use and to the future returns on their assets; it allows capital to rule not by force, but by law. The cloth is woven of private law, of contracts, property rights, trust, corporate, and bankruptcy law, the modules of the code of capital. Capital owes its vibrancy and frequent transmutations (from land, to firms, to debt, to ideas, etc.) to the fact that private and not state actors code capital in law.
In that sense the issue is less about access to legislators and more about access to smart and creative lawyers. Especially interesting is how Pistor frames this as an implicit subsidy to those able to play the game.
Subsidies and other “entitlements” are typically viewed with great suspicion, because they are regarded as distortive of markets and lead to inefficiencies, even corruption. Yet, the legal protections capital enjoys are arguably the mother of all subsidies. Without the code’s modules and the possibility to fashion them to one’s liking, neither capital nor capitalism would exist.
What Pistor reveals is a deeper problem than is acknowledged by calls for higher taxes, different tax treatment, greater transfers, or pretty much anything else intended to decrease inequality. This is a structural issue, an invisible force behind much of our lives. It’s about who gets to set the rules behind closed doors.
Realizing the centrality and power of law for coding capital has important implications for understanding the political economy of capitalism. It shifts attention from class identity and class struggle to the question of who has access to and control over the legal code and its masters: the landed elites; the long-distance traders and merchant banks; the shareholders of corporations that own production facilities or simply hold assets behind a corporate veil; the banks who grant loans, issue credit cards, and student loans; and the non-bank financial intermediaries that issue complex financial assets, including asset-backed securities and derivatives…
…The law is a powerful tool for social ordering and, if used wisely, has the potential to serve a broad range of social objectives; yet… the law has been placed firmly in the service of capital.
As always my emphasis
One of the most interesting observations in Radical Markets is that artificial intelligence is (at least currently) beholden to the labour of humans. AI requires us to produce, and even process and mark up, the data used to train them:
…AIs are not actually the free-standing replacement for human labor they appear to be. They are trained with and learn from human data. Thus AI, just as much as fields or factories, offers a critical role for ordinary human labor—as suppliers of data, or what we will call data as labor. Failing to recognize data as labor could thus create what Lanier calls “fake unemployment,” where jobs dry up not because humans are not useful but because the valuable inputs they supply are treated as by-products of entertainment rather than as socially valued work.
This questions the fear that humans are bound to be obsoleted, or even completely displaced, by computers. But a couple of chapters in The creativity code by Marcus Du Sautoy makes me wonder how else humans and computers will work together.
Chess players have been teaming up with computers for a while. But the stories Du Sautoy tells of AlphaGo are something else. AlphaGo was famously the first computer program to beat a professional at the board game Go.
Go is thousands of years old and was thought too complex for computers to master. But not only did AlphaGo win, many of its moves shocked its opponent, commentators and spectators. Some were downright bizarre, mocked, and even described as “alien“.
AlphaGo had taught itself to play Go. And, as its moves were analysed, it has also taught the rest of us:
AlphaGo had taught the world a new way to play an ancient game. Analysis since the match has resulted in new tactics. The fifth line is now played early on, as we have come to understand that it can have big implications for the endgame. AlphaGo has gone on to discover still more innovative strategies. DeepMind revealed at the beginning of 2017 that its latest iteration had played online anonymously against a range of top-ranking professionals…Those games are now regarded as a treasure trove of new ideas.
I really like the “alien” description. Because while AlphaGo learned from databases of tens of thousands of Go games. It also played millions of games against itself. Where, through trial and error, it came up with iterations we hadn’t seen before.
The trouble with modern Go is that conventions had built up about ways to play… But by breaking those conventions AlphaGo had cleared the fog and revealed an even higher peak…
Because the humans had also been trained on a store of games past. By playing against itself, AlphaGo broke out of those ruts. And a version of the algorithm that wasn’t trained on past games at all became even more bizarre and unbeatable.
Just think of the possibilities when taken out of the narrow problem of games.
DeepMind now has an even better algorithm that can thrash the original version of AlphaGo. This algorithm circumvented the need to be shown how humans play the game… It was no longer constrained by the way humans think and play. Within three days of training, in which time it played 4.9 million games against itself, it was able to beat by 100 games to nil the version of AlphaGo that had defeated Lee Sedol. What took humans 3000 years to achieve, it did in three days. By day forty it was unbeatable.
As always my emphasis