Coming back to the internet analogy, how did Google, Amazon etc ended up so successful? Metcalf’s law explains this. It states that as more users join the network, the value of the network increases thereby attracting even more users. The most important thing here was to make people join your network. The end goal was to build the largest network possible. Google did this with search, Amazon did this with retail, Facebook did this with social.

Collecting as much data as possible is important. But you don’t want just any data. The real competitive advantage lies in having high-quality proprietary data. Think about it this way, what does it take to build an AI system? It takes 1) data, which is the input that goes into the 2) AI models which are analogous to machines and lastly it requires energy to run these models i.e. 3) compute. Today, most AI models have become standardized and are widely available. And on the other hand, the cost of compute is rapidly trending to zero. Hence AI models and compute have become a commodity. The only thing that remains is data. But even data is widely available on the internet. Thus, a company can only have a true competitive advantage when it has access to high-quality proprietary data.

Recently, Chamath Palihapitiya gave an interview where he had this interesting analogy. He compared these large language models like GPT to refrigeration. He said “People that invented refrigeration, made some money. But most of the money was made by Coca-Cola who used refrigeration to build an empire. And so similarly, companies building these large models will make some money, but the Coca-Cola is yet to be built.” What he meant by this is that right now there are lot of companies crawling the open web to scrap the data. Once that is widely available like refrigeration, we will see companies and startups coming up with proprietary data building on top of it

Social networking is much more than just a dominant discourse. We need to go beyond text and images and include its software, interfaces, and networks that depend on a technical infrastructure consisting of offices and their consultants and cleaners, cables and data centers, working in close concert with the movements and habits of the connected billions. Academic internet studies circles have shifted their attention from utopian promises, impulses, and critiques to “mapping” the network’s impact. From digital humanities to data science we see a shift in network-oriented inquiry from Whether and Why, What and Who, to (merely) How. From a sociality of causes to a sociality of net effects. A new generation of humanistic researchers is lured into the “big data” trap, and kept busy capturing user behavior whilst producing seductive eye candy for an image-hungry audience (and vice versa).

We need to politicize the New Electricity, the privately owned utilities of our century, before they disappear into the background.

What remains particularly unexplained is the apparent paradox between the hyper-individualized subject and the herd mentality of the social.

Before we enter the social media sphere, everyone first fills out a profile and choses a username and password in order to create an account. Minutes later, you’re part of the game and you start sharing, creating, playing, as if it has always been like that. The profile is the a priori part and the profiling and targeted advertising cannot operate without it. The platforms present themselves as self-evident. They just are—facilitating our feature-rich lives. Everyone that counts is there. It is through the gate of the profile that we become its subject.

We pull in updates, 24/7, in a real-time global economy of interdependencies, having been taught to read news feeds as interpersonal indicators of the planetary condition

Treating social media as ideology means observing how it binds together media, culture, and identity into an ever-growing cultural performance (and related “cultural studies”) of gender, lifestyle, fashion, brands, celebrity, and news from radio, television, magazines, and the web—all of this imbricated with the entrepreneurial values of venture capital and start-up culture, with their underside of declining livelihoods and growing inequality.

Software, or perhaps more precisely operating systems, offer us an imaginary relationship to our hardware: they do not represent transistors but rather desktops and recycling bins. Software produces users. Without operating system (OS) there would be no access to hardware; without OS no actions, no practices, and thus no user. Each OS, through its advertisements, interpellates a “user”: calls it and offers it a name or image with which to identify. We could say that social media performs the same function, and is even more powerful.

In the age of social media we seem to confess less what we think. It’s considered too risky, too private. We share what we do, and see, in a staged manner. Yes, we share judgments and opinions, but no thoughts. Our Self is too busy for that, always on the move, flexible, open, sporty, sexy, and always ready to connect and express.

Platforms are not stages; they bring together and synthesize (multimedia) data, yes, but what is lacking here is the (curatorial) element of human labor. That’s why there is no media in social media. The platforms operate because of their software, automated procedures, algorithms, and filters, not because of their large staff of editors and designers. Their lack of employees is what makes current debates in terms of racism, anti-Semitism, and jihadism so timely, as social media platforms are currently forced by politicians to employ editors who will have to do the all-too-human monitoring work (filtering out ancient ideologies that refuse to disappear).