🎓 intangible assets + the new economy, podcasts and a new social network
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The rise of intangible assets
Capitalism without capital by Jonathan Haskel and Stian Westlake explores the growing role of intangible assets within the global economy. Intangibles are non-physical assets which derive value, these can be software, IP, brand, know-how, training etc. The authors shine a light, whilst building a cohesive framework on this under analysed and increasingly important part of our modern economy. This is an important work as we continually transition from the industrial revolution which was predominantly centered around the mass coordination and centralisation of individuals, firms and physical goods, to the information revolution where intangibles like software have near infinite replicability at nearly zero marginal costs and firms can grow exponentially. This is not a book review (the book is f*cking great - ok that’s the book review done) but merely an exploration of the most captivating ideas outlined by the authors.
The book starts by exploring the four Ss which are inherent to intangibles;
scalability: intangible assets can, unlike physical ones, be used over and over - arguably intangible dominant firms may in part create ‘winner take most’ markets (Google, Facebook etc)
sunk costs: intangibles as a whole have higher sunkenness as failed products arguably have much lower recoverability (ie you’re unlikely to be able to sell ineffective software)
spillovers: many forms of intangible such as design and R&D have high replicability, for example the iPhone spurred copycat design from its rivals, arguably its defensibility was in Apple’s hardware, supply chain and vertical integration
synergies: ideas, R&D and incremental improvements are worth more when you combine them
The authors then touch upon a few macro economic concepts which they believe to be partly explained by the rise of intangibles
Secular stagnation: is multivariate though can be characterised long-term low unemployment with high growth yet a lack of inflation. The authors believe that mismeasurement of intangibles to be a primary problem, for which we cannot value, we cannot account for. They also believe there is a widening gulf between leading and laggard firms, which discourages investment in the latter. This concentration of investment in the leaders allows for less absorption of spillovers from laggards and lower overall investment from investors/speculators (in part inequality, more on this below)
Inequality: of wealth is being exacerbated by the continuing growth of dense cities, where synergies and spillovers amongst firms and workers are plentiful - this outsized demand increasingly pushes up housing prices which are largely owned and afforded by those working at intangible-dominant firms (ie San Francisco or Tel-Aviv)
Future financing: historically lending against tangible assets such as vehicles, real estate and equipment has carried lower risk due to the assets’ partial recoverability. What happens when financing assets with negligible recoverability? In part, this might explain the rise of venture capital as a financial instrument which has historically funded intangible firms, high in R&D. If we believe in a continuing rise in intangibles, we might assume that we will see a shift from bank lending to securitisation of speculative IP and R&D. If this were to take place, we would need an improved regulatory environment, one which encourages institutions to better understand intangible value, invest across ecosystems to capitalise on spillover effects and rethink the short-termism inherent in public markets (why invest in long term R&D, if you’re playing for the quarter?). Beyond this, a dramatic rethink of current accounting practices - more specifically how intangibles are accounted for on balance sheets (WSJ: The end of accounting) would be required
Whilst we’ve all witnessed a digital revolution which has redefined the way in which we do business, many of our frameworks are more relevant to the old economic paradigm.
Many of the largest companies on the planet specialise in bits not atoms - they’re products are largely not physical. Unfortunately this new market paradigm is forcing us into difficult unintended consequences, exacerbating certain market dynamics, inequality and a lack of understanding around accountability. The rules which currently govern finance and economics do not fit our future economic model and it will be up to policymakers to make the necessary changes.
News + Articles
In Pod we Trust - Apple is making another push into original content - this time it’s commissioning its own podcasts only available within the Apple ecosystem - we’re seeing increasing efforts to offer paywalled exclusive content from Luminary, Spotify and now Apple. For the latter two this is particularly important as a shift of listening hours away from music allows them realise some margin expansion on their fixed cost base of royalties
The social network hiding in plain sight: How Google Photos got to over 1bn users, incredibly this is the 8th Google product to hit that milestone. Google Photos ships with most Android units - this is another case study on the power of pre-installs
AV winter: Big data or big model in autonomous driving? After a number of years of racing to gather enough training data for autonomous vehicles, it seems that many of companies are turning their attention back to novel AI techiques in order to solve the final x percentile to allow AVs to move beyond level 3 autonomy. Google subsidiary, and Waymo sister company DeepMind outlines its method of reinforcement learning has been applied to Waymo’s neural nets. These NNs control many of the fucntions within the perceptive and routefinding stack of their vehicles. Deepmind used a survivalist method which sets parameters, trains and replaces new neural nets a quantum faster than Waymo’s existing models // Lyft are working on this problem too, they recently open sourced a large, labelled dataset in conjunction with a research competition encouraging researchers to solve a number of problems such as agent prediction, scene segmentation and object detection. Whilst the focus on data gathering and simulation is by no means over, it seems AV companies are taking // Our friends at FirstMile / Atlantic Labs produced an awesome, detailed break-down of the current AV landscape
Thanks for reading!
Sam