Subscribing to Platformer and joining Sidechannel

I subscribed to Casey Newton’s Platformer last week. It was the first time I have paid for a so called “sovereign writer” since the trend took off last year. There were some fairly obvious motivations for this. First, the quality of Newton’s writing. He’s been covering the sector for quite some time now and that experience comes through. He’s probably a bit more tolerant of platform harms than I am, however, he’s not the acolyte that some would paint him as. He sees the problems they cause, but won’t be drawn into knee jerk reactions either. This later point also means that his access to the leaders in the industry is as good as it gets. They know he won’t fire “gotcha” style questions at them, and, whether it’s right or not, Zuckerberg et al. are under no obligation to talk to anyone. Just ask Parliament.

It was the launch of Sidechannel — a Discord community for paid subscribers — that pushed me into parting with my money, however.

For a long time now I’ve wanted an outlet for my interest in platforms and the impact digitisation has had on the wider economy. Reading about it myself could only take me so far. And while enrolling in a master’s at King’s has been an incredible experience, it will soon come to an end and I’ll lose the opportunity to share papers and debate with people who are interested in the same topic.

Sidechannel may not be quite as intense as the rhythm of research and seminar debates, but it’s in the same ball park. Everyone who is part of the Discord server is paying to be there, so the motivation to get value from it is real. Similarly, the total number of subscribers is in the thousands, not the tens or hundreds of thousands, so there is opportunity for meaningful discourse. What’s more the community an interesting mix of engineers, policy wonks, legal experts, designers and people who are just motivated to learn more about the platformification(?) of life. Conversation often centres around Newton’s writing, but it’s more than a comments thread as subscribers can start discussions too. And, of course, there are the live interviews.

I have been tempted to blog about the sovereign writers / indie journalists thing for a while. I think focussing the debate on Silicon Valley’s desire to undermine the institutions of journalism (however accurate that may be) fails to recognise some of the benefits the trend will bring to the wider journalism ecosystem.

I shall save that for another post, but in the mean time, I will leave you with the thought that the creation of niche audiences, anchored on the rigour of a lead journalist, adds something positive to the media landscape that didn’t exist before. If more journalists of note follow in Sidechannel’s footsteps I’m sure it will bring other readers to the table.


Generativity, uncertainty and digital entrepreneurship


This post has been submitted as part of an assessment for my master’s degree.


In 2006 The Harvard Law Review published Jonathan Zittrain’s seminal paper on The Generative Internet. In it Zittrain explains how technological generativity – put simply, technology’s capacity for unprompted change – has been a driving force behind many of the innovations we rely on today (Zittrain, 2006, p. 1980). Since its publication the concept of technological generativity has been discussed by a diverse field of scholars. However, it’s within the domain of digital entrepreneurship that it first caught my eye, no doubt a product of my own time as a digital entrepreneur.

One example comes from Nambisan’s 2017 paper paper that details how the generative nature of digital technology is one of the reasons digital entrepreneurship is uniquely affected by uncertainty. If networked technology is capable of unprompted change, it becomes harder for digital entrepreneur’s to control the trajectory of the products and services they create (Nambisan, 2017). This idea caught my attention as it’s widely known that tech startups are living through a moment of record investment (Tech Nation, 2021). And, if it’s common sense that greater uncertainty leads to a decline in the propensity for entrepreneurial action (McKelvie et al., 2011, p. 274), what then is enabling digital entrepreneurs to overcome apparent contradiction?

In this post I will argue that the generative forces contributing to this entrepreneurial uncertainty are also the forces that enable digital entrepreneurs to overcome it. In the first half I will briefly review the nature of uncertainty within entrepreneurship, before introducing the concept of generativity and explaining how it creates a uniquely uncertain environment for digital entrepreneurs. In the second half I will explain how generativity contributes to new affordances that motivates digital entrepreneurs and enables them to succeed.

The role of uncertainty within entrepreneurship

Uncertainty is the condition that arises as a result of the unknowable nature of the future (McMullen & Shepherd, 2006) and because action impacts the future, action is inherently uncertain (Mises, 1949 cited in McMullen & Shepherd, 2006, p. 133). The need for action and an uncertain future are at the heart of theories of entrepreneurship (Cunningham and Lischeron, 1991). Launching a new product requires action on the behalf of the entrepreneur (McKelvie et al., 2011). That same product will launch in an uncertain environment; and how it affects customers and how they respond to it is also uncertain (McMullen & Shepherd, 2006).

With all of this in mind it’s unsurprising that uncertainty is a regular feature in the discussions of entrepreneurship. Scholars have examined how uncertainty impacts the likelihood of entrepreneurial action (Herbert & Link, 1988); whether business planning can mitigate the inevitable uncertainty that exists when launching a venture (Brinckmann et al., 2010) and the extent to which uncertainty attracts certain people to the role (McKelvie et al., 2011). Zittrain himself alludes to this later point in an early article for the Harvard Business Review in which he claimed that:

The information technology explosion was set off by visionaries who thrive on uncertainty (Zittrain, 2005).

Uncertainty and digital entrepreneurship

This post is specifically concerned with the nature of uncertainty within digital entrepreneurship. With that in mind it’s worth briefly unpacking by what I mean by digital entrepreneurship before moving on.

In simple terms digital entrepreneurship is a sub-categorisation of entrepreneurship that differs from the traditional form in that a significant part of the business process has been digitised (Hull at al., 2007). However, a definition as wide as this would include companies who, for example, had digitised internal processes but not digitised any of their customer facing components. While there is no doubt that these companies should still be considered “digital” it’s important to clarify that for the purpose of this post I am concerned with ventures that produce digital products and services for use by their customers.

Digital entrepreneurship, in which digital ventures launch digital products and services, is a domain uniquely affected by uncertainty (Nambisan, 2017). At the root of this observation is the generative nature of the digital artefacts being created by digital entrepreneurs (Huang et al., 2017). Generativity means that they can evolve in an unpredictable fashion and outside of the control of their creator (Zittrain, 2006, p. 1980). Put another way, when a digital entrepreneur launches a new digital product, it’s underlying generativity will lead to change that the entrepreneur will not have been able to predict or control.

Nambisan (2017) synthesises these ideas and offers two observations about the nature of uncertainty within digital entrepreneurship. First, that the introduction of digital technology to the entrepreneurial process has made outcomes less bounded. This is driven by products that are “incomplete and in a state of flux”, themselves impacted by other digital artefacts and the capability of digital platform owners to infuse new generative potential into the products that make use of them (Nambisan, 2017, p. 1033). Second, that digital technology makes the role of the entrepreneur less predefined than traditional entrepreneurship, as it introduces a network of actors who have varying goals and motives and yet contribute to the same initiative (Nambisan, 2017).

It is evident that, when compared to the traditional image of an entrepreneur, the digital entrepreneur faces a significantly more uncertain future. And, if greater uncertainty leads to a decline in entrepreneurial action (McKelvie et al., 2011, p. 274), why is it that countries across the globe are seeing record levels of investment in tech startups (Tech Nation, 2021)?

Generative potential

For the remainder of this blog post I will argue that, as well as being a key contributor to entrepreneurial uncertainty, the generative nature of digital technology supports digital entrepreneurs in four important ways. However, before I introduce these observations, it’s worth returning to Zittrain’s paper briefly.

While Zittrain’s paper has informed to our understanding of uncertainty within digital entrepreneurship, the original intention of the paper was to point out the power of generative technology, a force that had contributed to the:

…rapid technological innovation to which we have become accustomed. (Zittrain, 2006, p. 1976).

And, how any attempt to centralise the Internet in the name of security and stability will dampen it’s generative potential (Zittrain, 2006, p. 2013).

This is significant for our discussion because, if we recognise generativity as an enabling force, we are closer to understanding how it can help digital entrepreneurs overcome the uncertainty it creates.

To expand on this point further I will use the rest of this post to argue that the generative nature of digital technology supports digital entrepreneurs in four ways:

  1. That digital products and infrastructure enable digital entrepreneurs to test and scale their concepts.

  2. That generative network effects help protect a digital entrepreneurs from competition.

  3. That digital networks create new sources of financing to support digital ventures.

  4. That generativity creates the outsized financial returns that motivates entrepreneurs to push ahead into the unknown.

Digital products and infrastructure enable rapid testing and scaling

Digital products are generative by virtue of their reprogrammable, recombinable and open properties (Nambisan, 2017, p. 1033). These features enable digital entrepreneurs to react quickly to the effects their products produce and the responses from customers (Nambisan, 2017). Huang et al. (2017 p. 302) highlight how the separation of function and form contribute to this process. With a digital product’s instructions (function) being independent from how the customer interacts with it (form), a digital entrepreneur is able to introduce new functionality without the need for a complete redesign of the product (Huang et al., 2017). Moreover, the nature of web-based applications in particular means that the digital entrepreneur can continuously release these new features with minimal effort required on the part of the customer (O’Reilly, 2005).

Digital infrastructure also plays a role in overcoming the uncertainty a generative digital ecosystem creates. Makerspaces and data analytics platforms, for example, enable digital entrepreneurs to find small audiences in order to test their products (Hatch, 2013, cited in Nambisan, 2017). By conducting a series of tests (as opposed to a traditional launch) they can search out product market fit before making use of those same platforms to scale rapidly (Ries, 2011). This same process is also supported by cloud computing, which allows for the horizontal scaling of the computing power necessary to support larger audiences (Nambisan, 2017, p. 1032).

Zittrain argues that generative technology has four features: capacity for leverage; adaptability; ease of mastery and accessibility (Zittrain, 2006, p. 1981). From my brief observations above we can see how digital products, platforms and infrastructure align with each of these features. They provide digital entrepreneurs with set of new set of tools that can be repurposed in a variety of ways; they are unique to the digital ecosystem; and they democratise the entrepreneurial process through their ease of use and widespread adoption (Aldrich, 2014). In short, the generative capacity of digital technology has created the uncertain environment digital entrepreneurs face, but also provided them with the tools to overcome it.

Network effects, disruption and moats

Many digital products benefit from the increasing returns to scale that are driven by network effects (Eisenmann et al., 2006). What’s more, these same network effects can build a defensive moat around the new business, one that can near on insurmountable for competitors (Schilling, 2002). As Mark Zuckerberg, founder and CEO of Facebook, explained in an email about mobile social networks to his Chief Financial Officer in 2012:

These businesses are nascent but the networks established, the brands are already meaningful, and if they grow to a large scale the [sic] could be very disruptive to us (Zuckerberg, M., 2012 [email] quoted in Newton, 2020).

This kind of network growth is a generative process – as users join a service their presence increases the likelihood that other users will join (Huang et al., 2017, p. 302). And while this is a process that digital entrepreneurs are not entirely in control of, it’s also one that they look to manage and influence through the strategies employed by their team (Huang et al., 2017).

Zuckerberg’s admission about network effects exemplifies the dual nature of generativity within the entrepreneurial process. It creates an uncertain, often disruptive, environmental force, while also acting as an enabler of rapid growth and competitive dominance.

Funding from the crowd

Despite the risk created by the generative power of digital technology there is no shortage of investment into digital ventures. Here in the UK, in the six years since 2015 there has been a 300% increase in investment into UK tech companies (Tech Nation, 2021).

As well as being able to access traditional forms of venture capital and angel investing to finance their startups, digital entrepreneurs are able to access crowdfunding, a new investment vehicle that is itself a product of the generative power of digital platforms (Nambisan, 2017 p. 1033).

Reward based crowdfunding platforms like Kickstarter and Indiegogo allow consumers to support nascent ventures with seed capital through the advanced purchasing of products (Mollick, 2014). Much like the digital infrastructure discussed in the earlier section, crowdfunding platforms enable digital entrepreneurs to test a market for their product (Mollick, 2014, p. 3) while also making their first customers part of a more collective entrepreneurial process (Nambisan, 2017, p. 1030). Similarly, equity based crowdfunding platforms like Crowdcube and Seedrs enable entrepreneurs to gain access to a new type of investor, one previously unable to invest in companies at such an early stage due to regulatory limits (Mollick, 2014, p. 3). Like reward based platforms, this process contributes capital to finance the future development of the venture, and presents a new route for entrepreneurs to secure funding. Investors in equity crowdfunding platforms have been shown to be motivated by the potential for financial returns, but also by some consumption utility that arises from the support of a given project (Agrawal et al., 2011 p 19).

Again we see how the generative features of leverage, accessibility and the ease of use of crowdfunding platforms is directly contributing to a venture’s chances of success. Critically, this provides entrepreneurs with another source of financing for their projects, but also the opportunity to test their ideas and observe market effects and responses, strategies which are vital to overcoming the otherwise uncertain nature of digital ecosystems.

A billion dollars

In The Social Network, the 2010 movie dramatisation of the creation Facebook, Sean Parker, Facebook’s first president, meets the two founders of the nascent social platform for lunch. The scene is best remembered for the quote about Facebook’s future revenue:

SEAN PARKER: A million dollars isn’t cool, you know what’s cool?

However, it’s the line before this that I find more interesting:

SEAN PARKER: You don't even know what the thing is yet. How big it can get, how far it can go. This is no time to take your chips down.

NB: You can watch the entire scene on YouTube.

It’s hard to imagine a scenario in which an entrepreneur would happily admit to not knowing what their company was with a would be investor/advisor. In the traditional view of entrepreneurship business plans and strategy are considered critical to the likely success of a venture and that means having a clear understanding of your product, market and the size of the opportunity (Brinckmann et al., 2010). And yet in this scene it is Parker, the more experienced digital entrepreneur of the group, who is most comfortable with this state of uncertainty.

This is unsurprising.

As the founder of Napster, a music sharing website that gained tens of millions of users within 12 months of launching, Parker had seen first hand the generative power of digital technology and the potential for it to yield significant financial returns (Kirkpatrick, 2010). Viewed in this light, “you don’t even know what this thing is yet” is a positive, not something to be ashamed of. In the words of Yoo (2013, p. 230) it has been, “designed without fully knowing the ‘whole’ design”.

Fast forward to 2021 and this idea is supported by the fact that, at the time of writing, Facebook is the sixth largest company by in the world by market capitalisation, and in total eight, of the ten largest companies, benefit from the same underlying generative forces (TradingView, 2021). Generativity may make the world of digital entrepreneurship uniquely uncertain, but it also contributes to the creation of huge financial returns.

Summing up

My aim for this post was to provide an explanation for a counter intuitive observation: that digital entrepreneurs are undeterred by the increased levels of uncertainty they face when starting a new venture. I’ve argued that there is a duality to Jonathan Zittrain’s theory of technological generativity that explains both where this uncertainty comes from, and how digital entrepreneurs are empowered to overcome it.

I should note that observing generativity’s duality is not a new idea. My argument was informed by the work Choi (2013) who when writing about anonymity noted that online abuse is both enabled and remedied by generative forces. Or, in more general terms, generativity’s “versatility is a double edged sword” (Choi, 2013, p. 505).

One of the challenges I have faced while researching for this post is finding many notable criticisms of Zittrain’s work. On one level this in itself is a criticism of his ideas, a good theory should hold against critique after all. But this too may be a byproduct of the theory’s simple elegance, as David Post notes in his review Zittrain’s 2008 book:

“Stating the obvious”—articulating a proposition in such a way that (pretty much) everyone involved in the discussion agrees that obvious—moves discussion and debate forward in constructive ways (Post, 2010, p. 104).

I wouldn’t expect this blog post to be met with such high praise, however, I too hope that the strength of my argument lies in the relative simplicity of what I have observed.


References

Agrawal, A. K. et al. (2011) The Geography of Crowdfunding. National Bureau of Economic Research. [Online]. Available from: https://www.nber.org/system/files/working_papers/w16820/w16820.pdf (Accessed 7 April 2021).

Aldrich, H. E. (2014) The Democratization of Entrepreneurship? Hackers, Makerspaces, and Crowdfunding. Academy of Management Proceedings. 2014 (1). [Online]. Available from: http://journals.aom.org/doi/10.5465/ambpp.2014.10622symposium (Accessed 8 April 2021).

Brinckmann, J. et al. (2010) Should entrepreneurs plan or just storm the castle? A meta-analysis on contextual factors impacting the business planning–performance relationship in small firms. Journal of Business Venturing. 25 (1), 24–40. [Online]. Available from: https://www.sciencedirect.com/science/article/pii/S0883902608001109 (Accessed 1 April 2021).

Choi, B. H. (2013) The Anonymous Internet. Maryland Law Review. 72 (2). [Online]. Available from: http://digitalcommons.law.umaryland.edu/mlr/vol72/iss2/4 (Accessed 8 April 2021).

Cunningham, J. B. & Lischeron, J. (1991) Defining Entrepreneurship. Journal of Small Business Management. 19.

Eisenmann, T. et al. (2006) Strategies for Two- Sided Markets. Harvard Business Review.

Huang, J. et al. (2017) Growing on Steroids: Rapidly Scaling the User Base of Digital Ventures Through Digital Innovaton. MIS Quarterly. 41 (1).

Hull, C. E. et al. (2007) Taking advantage of digital opportunities: a typology of digital entrepreneurship. International Journal of Networking and Virtual Organisations. 4 (3). [Online]. Available from: http://www.inderscience.com/link.php?id=15166(Accessed 30 March 2021).

Kirkpatrick, D. (2010) With a Little Help From His Friends. Vanity Fair. [Online]. Available from: https://www.vanityfair.com/culture/2010/10/sean-parker-201010 (Accessed 30 March 2021).

McKelvie, A. et al. (2011) Unpacking the uncertainty construct: Implications for entrepreneurial action. Journal of Business Venturing. 26 (3), 273–292. [Online]. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0883902609001062 (Accessed 25 March 2021).

McMullen, J. S. & Shepherd, D. A. (2006) Entrepreneurial Action And The Role Of Uncertainty In The Theory Of The Entrepreneur. Academy of Management Review. 31 (1), 132–152. [Online]. Available from: http://journals.aom.org/doi/10.5465/amr.2006.19379628 (Accessed 22 March 2021).

Milliken, F. J. (1987) Three Types of Perceived Uncertainty about the Environment: State, Effect, and Response Uncertainty. 12 (1), .

Mollick, E. (2014) The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing. 29 (1), 1–16. [Online]. Available from: https://linkinghub.elsevier.com/retrieve/pii/S088390261300058X (Accessed 7 April 2021).

Nambisan, S. (2017) Digital Entrepreneurship: Toward a Digital Technology Perspective of Entrepreneurship. Entrepreneurship Theory and Practice. 41 (6), 1029–1055. [Online]. Available from: http://journals.sagepub.com/doi/10.1111/etap.12254 (Accessed 22 March 2021).

Newton, C. (2020) ‘Instagram can hurt us’: Mark Zuckerberg emails outline plan to neutralize competitors. The Verge. [Online]. Available from: https://www.theverge.com/2020/7/29/21345723/facebook-instagram-documents-emails-mark-zuckerberg-kevin-systrom-hearing (Accessed 1 April 2021).

O’Reilly, T. (2005) What Is Web 2.0? [Online]. Available from:https://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html (Accessed 8 April 2021).

Post, D. G. (2010) The Theory of Generativity. Fordham Law Review. 78 (6). [Online]. Available from: http://ssrn.com/abstract=165076 (Accessed 8 April 2021).

Ries, E. (2011) The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. New York: Crown Business.

Schilling, M. A. (2002) Technology Success and Failure in Winner-Take-All Markets: The Impact of Learning Orientation, Timing, and Network Externalities. Academy of Management Journal. 45 (2).

Tech Nation (2021) Tech Nation Report 2021. [Online]. Available from: https://technation.io/report2021/ (Accessed 6 April 2021).

TradingView (2021) Large Cap Stocks. [Online]. Available from: https://uk.tradingview.com/markets/stocks-usa/market-movers-large-cap/ (Accessed 6 April 2021).

Yoo, Y. (2012) The Tables Have Turned: How Can the Information Systems Field Contribute to Technology and Innovation Management Research? Journal of the Association for Information Systems. 14 (5). [Online]. Available from: https://aisel.aisnet.org/jais/vol14/iss5/4 (Accessed 15 April 2021).

Zittrain, J. L. (2005) In Praise of Uncertainty. Harvard Business Review. [Online]. Available from: https://hbr.org/2005/05/in-praise-of-uncertainty (Accessed 9 April 2021).

Zittrain, J. L. (2006) The Generative Internet. Harvard Law Review. 119. [Online]. Available from: https://dash.harvard.edu/bitstream/handle/1/9385626/zittrain_generativeinternet.pdf?sequence=1 (Accessed 4 April 2021).


AlgoTransparency, Cookies and Middleware

Earlier this week I came across AlgoTransparency, an interest group founded by Guillaume Chaslot who many people will recognise from The Social Dilemma. AlgoTransparency has commendable aims, they wish to:

  • Raise public awareness about the lack of transparency provided by the world’s most significant algorithms.
  • Influence international regulators with regards to policy approaches.
  • Pressurise the owners of significant algorithms to make changes to the way they operate.

This is an agenda I can get behind. End users should be provided with simple explanations about how the algorithms that power any given app or website work. And these explanations should satisfy both the need to understand the goal the algorithm has, for instance “time on site”, and (to the extent that it’s possible) the process that led to its final decision.1

There is a comparison to be made here with the EU’s fated “cookie policy”. While many people (myself included) lament the annoyance of consent pop-ups, it’s hard to deny that their presence has had a significant impact on the public’s understanding of how they are being tracked as they surf the web. And while it’s unlikely that many people spend time reading the descriptions of the different cookies a website uses, the mandated openness has created a new dataset for examination by journalists and privacy researchers. An effect I would expect to be replicated with greater algorithm transparency.

Importantly, the cookie policy enables users to opt-out of a website’s “non-essential” cookies. In the process of trying to do this you’re likely to come across all sorts of dark patterns that attempt to trick you into submission, but it should still be possible, if not, the website’s owner is breaking the law. There is no such option with algorithms. Partly because many of the algorithms we come into contact with provide essential functionality, but also because the website/app has a perfect monopoly over which algorithms you use.

I raise these points not because I think AlgoTransparency has picked the wrong fight – transparency will lead to a greater public understanding about algorithms and this is a worthy cause – but because transparency can only ever take us so far.

As I find myself regularly writing at the moment, algorithm choice, or more broadly, a marketplace for middleware, should be the direction we are headed. By creating choice for algorithms we diffuse the power they hold to multiple actors while retaining much of the value that comes with large platforms. I would also argue that a marketplace for middleware furthers the transparency cause because it makes transparency a vector by which consumers can vet their choice of algorithm. Developers who are open about the nature of their algorithms and the goals that its success is measured against, will surely be more appealing than those who choose to hide this information.

Another idea that I continually return to is that the successful regulation of tech/platforms/algorithms will be made up of many overlapping changes rather a single headline one. Break ups diffuse power, but aren’t going to make it easier to compete against network effects that protect YouTube. Full protocol interoperability will support new market entrants but would likely come with new kinds of risk to personal data. Algorithm transparency does little to alter the distribution of power, but will make developers more accountable.

I wonder if taking a more “agile” approach to regulation is what is needed, smaller changes, call them tests, that try to target specific problems rather than change everything in one go. I’m no expert on the history of regulatory approaches but my gut tells me that this isn’t a strategy that many policy makers would consider.


1 I add this caveat as it's important to remember that full algorithm transparency may not be possible. As Jenna Burrell points out in her excellent paper on opacity within machine learning.


Social Media and Middleware

I had a long conversation last week with some friends who were less than convinced by the idea of algorithm middleware. Their argument was that creating choice for algorithms within social media platforms would increase the likelihood of people seeing harmful content: hate speech, disinformation, lies, bullying, phishing attempts — that kind of thing.

To sufficiently address this point I think it’s necessary to distinguish between the different editorial processes that are involved in publishing content on a social media platform. I see three broad areas:

  • Censorship, meaning the proactive and reactive removal of content from the platform.
  • Labelling, meaning the placement of notices next to content, ostensibly “fact checking”.
  • Ranking, meaning the ordering of content in a user’s feed.

If you assume that the developer of a middleware algorithm becomes responsible for each of these processes, then yes, creating a middleware market would increase the chances of harmful content being seen. In fact, it is inevitable that someone would create a system with minimal, if not zero, editorial oversight, something that would unleash a pretty nightmarish world on its users.

This consideration presents a more challenging flaw in the middleware model in my opinion. Could a middleware developer realistically handle the volume of censorship (moderation) currently carried out by social platforms today? It’s unlikely. Facebook’s Safety and Security budget for 2019 was reported to be nearing $4BN, a cost that would critically reduce the potential entrants to any market.

So what to do? I can see a solution coming from a few sources.

We could mandate that moderation remains the responsibility of the platform owner. If they are benefitting from the advertising revenue, it could be argued that they should foot the bill for moderation. This does nothing for our concern about centralised power, however.

An alternative would be to make the state responsible for moderation, probably through a statutory corporate body or QUANGO like Ofcom (the UK’s telecoms regulator) or the British Board of Film Classification (BBFC). A hybrid of the two could also work. For example, a British Board of Social Media Moderation (BBSMM) could create a framework that the platform was required to adhere to, enforceable by spot checks and fines. This body could also oversee the welfare of content moderators (assuming some were employed locally) as the challenging working conditions they face is well documented.

Whether these rules apply on a user level, meaning the moderation applies wherever the user happens to be located. Or they apply on an IP level, wherever the user accesses the platform is an interesting question, but not an insurmountable one.

With censorship taken care of two editorial processes remain, labelling and ranking, both of which are less labour intensive and therefore more likely to result in a dynamic middleware market.

Some labelling could be handled by algorithms written by a middleware developer, for example, labelling any content that includes references to Covid vaccines. However, some labelling may require human oversight, for example fact checking organisations like FullFact rely on a team of human checkers.

Fact checking services that rely on human input will not keep up with the velocity of social content. However, real-time fact checking need not be the goal. A user could choose to opt-out of content that has been flagged for fact checking, it could be labelled as requiring fact checking, or they could be shown the unchecked content but notified if it is labelled retrospectively. Once again, this is why choice is critical. Decisions as impactful as the few described above should not be left solely in the hands of the platform owner.

Ranking is the process most suited to a middleware marketplace. Sequentially it follows censorship and labelling meaning all the content available to rank has been cleared for publication. It’s then down to the user’s chosen algorithm to decide how the content available should be prioritised based on the preference data it has. This is not to say that this is an easy task, but that it is one more suited to a software only solution, one that could written by a single developer, in an ideal world, the end user themselves.

Returning to the opening question: how does a middleware marketplace reduce the impact of harmful content on social media? The short answer is that it doesn’t. But this misses the key point of the idea. The goal is to reduce the political power of social platforms, not clean up your timeline.

I think these changes can be part of a broader conversation about we regulate content on social platforms, but that conversation must start with the assumption that allowing social platforms to do it on our behalf is unacceptable. Users, in concert with the state, should have the power to decide what version the world they want to be exposed to.


Roon, Algorithmic Choice and Platform Middleware

My search for a better way to stream music led me to a service called Roon last week. Roon is great, but rather than write about that, I want to write about what Roon can tell us about algorithmic choice and the notion of platform middleware.1

Let’s start with the idea of abundance. As Albert Wegner puts in The World After Capital:

Digital information is already on a clear path to abundance: we can make copies of it and distribute them at zero marginal cost, thus meeting the information needs of everyone connected to the Internet.

This abundance has created a new economic challenge. Rather than the challenge of how we distribute scarce resources to a large group of people, we now have to decide which resources people should consume given a (near) infinite supply, and, in some cases a relatively limited amount of time to consume it. Some of these decisions are somewhat banal, for example, which movie or TV show I should watch next? But not all are like this. Which hairdryer should I buy might seem trivial, but when an algorithm decides which product is ranked first (and thus more likely to be bought) we are giving it an extraordinary amount of power. Similarly, when we allow an algorithm to decide which news article comes first, or which person’s voice is heard the loudest.

It’s tempting to view algorithms as an exogenous force. Something independent of human biases and thus well placed to make important decisions such as the ones described. However, this couldn’t be further from the truth. Algorithms are socio-technical systems that, even with the best intentions, are imbued with all the biases inherent to humankind. They can be presented as independent while in reality promoting the agenda of their creator.

Centralised control over algorithms with power of this kind is something that society must reject. But that still leaves us with a new challenge of the second industrial revolution: in a world of abundance, how do we decide what people consume?

The state, peer recommendation, organisations, human editors and many other modes of discovery will play their role (as they always have), but algorithms powered by data are part of our future too. If we agree that algorithms represent new forms of power, and aren’t comfortable with the centralisation of that power, something needs to change.

Algorithmic choice is one idea that requires closer examination. If we are able to choose the algorithm that sorts, filters and flags content on our behalf the inherent power of algorithms becomes more diffuse. Algorithmic choice within e-commerce may allow someone to preference specific brands, products that were made locally or ones that meet certain environmental criteria. Similarly, within social media it could allow for a plurality of moderation services or attempt to make ones feed more representative of public opinion and less of a filter bubble.

Fortunately, these ideas are beginning to gain traction. Jack Dorsey, the CEO of Twitter, has voiced support for a “marketplace” of algorithms, and at the end of last year Francis Fukuyama released a report that called for the creation and enforcement of algorithmic middleware providers.

This brings me to Roon. First, consider how music streaming platforms like Spotify and Tidal choose to tackle the problem of abundance that we have just highlighted. From their growing libraries of 70M+ songs they present us with ideas of what to listen to next. If you’re like me, you find this grating, as you want to browse the music yourself, but if you like the recommendations you’re limited to the platform’s technology. And, as we already know, those recommendations come with the biases / economic goals of their creators. Now consider how Roon operates. After creating my account, Roon asks me to connect it to my streaming provider (Tidal or Quboz only I’m afraid). Roon then imports my entire collection and begins to re-organise the music based on their discovery interface. Similarly, it introduces a whole new set of algorithms to surface music based on my existing collection and based on things it thinks I’ll like.

The critical difference however is their economic incentive. Roon’s model isn’t based on micropayments dished out to the holders of music copyright. You can rent it off them for £12.99 a month, or your can purchase a lifetime license for £600.

As an aside, I really like the trend towards offering lifetime access for a single price, but I’ll save that for another post.

Roon is beholden to me, their only customer. Their goal should be to make my listening experience as enjoyable as possible. Tidal, Spotify et al. might argue that this is their goal too, but the truth is that as a multi-sided platform (or aggregator as Ben Thompson likes to call them) their motivation is not entirely aligned with mine.

It’s worth noting that the Room model creates a significant degree of disintermediation, or, in non-technical terms, I no longer use the Tidal apps to listen to music. Tidal may not be best pleased about this, but at the same time they are still receiving the £29.99 a month I pay them for access to the music library, so the impact on their business is not universally bad. Social media companies may be less comfortable with this outcome however as they are “free” to consume but monetised via ads. If I no longer access their front end, their revenues quickly drop to zero. This isn’t a blocker for the middleware model however, it just requires the platform to integrate a choice of algorithmic middleware themselves.

A basic middleware flow diagram

For those interested in algorithmic choice and the middleware model, Roon, and its relationship with Tidal / Quboz, is worthy of closer examination than a Friday morning blog post can afford. For my money, Roon is one of the best examples of commercially successful platform middleware available today. And only exists due to a quirk of history that has created multiple layers of rights over how digital music is consumed.

It’s probably obvious from my tone that I think algorithmic choice has the potential to untangle many of the messy issues the last 20 years of platform development has left us with. It is not a panacea however, we’ll need many new tools to fix the world we have today and however good I think this one is, it remains just one.


1 If you're interest to learn more about Roon I'd recommend reading this overview from What HiFi as it's not a service that can be explained that easily.