Marshall McLuhan famously asserted that “the medium is the message”. Meaning, the communication structure enabled by technology (“the medium”) shapes society as much as the content of the communication (“the message”). For example, the rise of radio broadcasting in the 1920s/1930s and the rise of the Internet in the 1990s/2000s both have had large societal impacts.
AI is enabling a new communication structure, in which many senders communicate with one receiver. This happens in two ways: 1) asynchronously in AI training, where millions of human communications are absorbed by one AI system, and 2) in real-time chats with large language models, where thousands of people can simultaneously initiate a conversation with the same AI model hosted in a datacenter.
While it’s still a bit early to understand the societal effects of this, it establishes AI models as new public knowledge access institutions, it may allow AIs to become the institutional memory of companies, and digital replicas of famous people can now simultaneously respond to many people.
Three classic communication structures
A classic way to discuss communication structures is by referring to the number of senders and receivers of a communication.
One-to-one
In one-to-one communication, such as in a face-to-face discussion, email, or phone call, there is one sender and one receiver. One-to-one is the oldest communication structure and our ‘natural’ form of interaction.
Trust & privacy: Unlike group or public interactions, one-to-one exchanges offer a controlled environment where people feel secure to share more openly, which is why it’s often preferred for sensitive or confidential topics.
Empathy: Eye contact and physical presence can affect the emotional depth of conversations.
Digital one-to-one: Various technologies have enabled some form of one-to-one communication over large distances and asynchronously. These include letters and the post service, the telephone, email, text messaging and video calls.
One-to-many
One-to-many communication has one sender and many receivers. The archetypal one-to-many medium is the radio broadcast. This was later complemented by television.
Mass culture: Standardized cultural tastes and experiences, blending regional differences into a unified popular culture. For example, radio and TV have enabled cultural sensations like ‘Beatlemania’. When The Beatles performed on American television, they reached tens of millions of viewers in a single night. Something that was impossible before the rise of one-to-many media
Standardized language: For example, in the UK, BBC radio and television broadcasting adopted ‘Received Pronunciation’, as its broadcast standard, reducing regional accents in England and Wales, particularly among the educated and elite.
Mass propaganda: The low-cost Volksempfänger (‘people's receiver’) was mass produced in Nazi Germany in the 1930s at the request of Joseph Goebbels to disseminate propaganda directly to citizens. This has helped to reinforce state ideology and war support.
Many-to-many
In many-to-many communication there are many senders and many receivers. The Internet has enabled the rise of many-to-many platforms, such as Facebook, YouTube, TikTok or WhatsApp group chats, where users can form communities, engage in debates, and create collaborative knowledge.
Erosion of authority of traditional institutions: The Internet has dismantled the traditional gatekeeping role of mainstream institutions by giving the public unprecedented access to publish and share information. This shift has empowered previously marginalized groups and niche communities to gather, voice dissent, and critique established authorities.
Cultural specialisation: The ‘Gigacity Internet’ allows niche communities to flourish because people with unusual interests can find each other regardless of location.
Decentralized collaboration: Encompasses various forms, including citizen science, crowdfunding, Wikipedia, and open-source software development.
Many-to-one
There are two primary forms of many-to-one AI communication: 1) asynchronous training, where AI absorbs human content, and 2) real-time dialog, where many people interact with copies of the same AI model.
Training AI on human content as unidirectional many-to-one
The pre-training of AI models with vast amounts of human-created content can be framed as an asynchronous, unidirectional, many-to-one communication. The rise of this “many-to-one” communication is in part a response to the increasing abundance of content. “Words supplied” is growing exponentially, whereas “words consumed” is limited by the natural bandwidth and time constraints of humans. As a consequence economist Hal Varian has argued as early as 1998 that “the fraction of the information produced that is actually consumed is asymptoting towards zero”.
Take academia as an example. While the claim that “the average academic article is read by about 10 people, and half of articles are never read at all” is an urban legend, its memetic ancestor still showed that, in the 1980s, 55% of academic papers have never been cited within five years of publishing. A more recent study found that as the volume of publications within a scientific field grows, it can overwhelm scholars' ability to identify and integrate novel ideas, leading to a concentration of citations on already well-known work.
AI does not have the same bandwidth constraints as the human brain. AI models are giant data “sponges” that can absorb volumes of communication that an individual human could not read in a lifetime. So, Tyler Cowen’s notion of “writing for the AIs” does make sense.
Chatting with datacenters as bidirectional many-to-one
In many-to-one, you’re not talking to other humans via a datacenter, you’re really talking to the datacenter.
First, when you’re talking to a specific ChatGPT instance,1 you’re not talking to it alone. The GPU’s on which AI models run have a fixed memory to FLOPs capacity. The memory required for inference is fixed by the size of the AI model, whereas the amount of FLOPs used by a request depends on factors such as how long the user’s prompt is and how many tokens are generated in response. To ensure efficient resource use it’s common to serve multiple user requests together as a batch to one model instance. So, about 10 to 100 human users are simultaneously talking to same AI instance as you.
Second, one AI instance maybe hosted on one single GPU or sharded across multiple GPUs based on its memory requirements. A large model like GPT-4 may be split across 8 to 16 GPUs. A very large datacenter like xAI’s Colossus can host 100’000 GPUs. Doing the math (100’000:8) would indicate that a large datacenter could host on the order of 10’000 instances of a large AI model or on the order of 100’000 instances of small AI models. So, up to about a million human users could talk to exact copies of the same AI model in one big datacenter at the same time.
Third, in reality, AI companies host copies of AI models in multiple datacenters closer to the users. This network collectively scales to support millions of parallel conversations.
These human-AI conversations are bidirectional. However, since it’s the many, decentralised human users that initiate and steer the communication with the datacenters as initial receivers, this fits the many-to-one label, much better than a one-to-many label. For the human user, chatting with an LLM like ChatGPT or Claude feels personal and tailored, a one-to-one exchange where the LLM focuses on the user’s questions and interests. However, the data flows in a many-to-one pattern in which many endpoints of a network communicate with a central hub rather than with each other. Furthermore, most of these conversations later on become training data helping to refine, adapt, and guide the way ChatGPT responds.
Impacts of many-to-one communication
Many of the potential societal impacts of many-to-one, from love, to democracy, to religion will still take years, if not decades, to unfold at scale. Still, the following are some societal patterns of the new communication paradigm.
a) LLMs as new knowledge access institutions
In the pre-Internet world knowledge could be accessed through books, libraries, and Universities. The rise of the Internet has created a whole toolkit of new knowledge access institutions, with many-to-many collaborative knowledge platforms, such as Wikipedia. Now, we are in the early stages of many-to-one knowledge access through LLMs like ChatGPT which have, amongst other things, read all of Wikipedia and are essentially “talking Internet libraries”.
The optimist in me believes that this could supercharge education with everyone having a worldclass personal tutor on tap. Historically, better knowledge access institutions have coincided with the Industrial Revolution and the widespread access to LLMs means this could be positive for global equality of opportunity for learning.2 In the past such hopes have been a recurring pattern for new information and communication technologies but they have rarely been as transformational to education as hoped (“schools of the air”, “instructional television”, “MOOCs”).
b) The institutionalization of institutional knowledge
There are still many corporate datasets that cannot simply be scraped from the public Internet due to privacy concerns and intellectual property rights. However, giving AI models sufficient context is crucial to their usefulness. Hence, we can expect many foundation models to be fine-tuned on specific institutional environments and given extended context on institutional policies and documents.
Corporate AI already exists today (e.g. ChatPWC, Sinequa, Guru, Coral, Starmind). Some of these providers identify human expertise within a company and route questions to the right place. Others let AI provide answers to employees directly. Both models still have limitations today.
Still, by ingesting a lot more data from internal communication channels (e-mail, Slack etc.), history, policies, and strategies, as well as chatting with a high number of employees, a future “company AGI” may become the dominant form of internal institutional knowledge. This could improve access to institutional knowledge within companies. It could also provide employers with a potential incentive to surveil their employees to gain more corporate AI training data. Lastly, it could reduce the dependence of companies on long-term employees, which have often been the implicit institutional memory so far, thereby lowering their bargaining power.
c) Many fans can simultaneously chat with one digital replica
Combining personal data with an AI agent, gives you an agent that understands you well and can be an effective life coach, but that can also represent your interests and preferences. With the U.S. No Fakes Act individuals have a property right on their image, voice, and likeness when used in a “digital replica” and you can make a digital representation of yourself publicly available.
One key advantage of the digital representation of you is that it has (nearly) unlimited communication bandwidth. So, your digital representation cannot just have one-to-one, one-to-many, or many-to-many communication, it can also do many-to-one, listening to and responding to hundreds or thousands of individual requests simultaneously. In other words, individuals can now be “scaled”. Anyone that wants can talk to your digital model over the Internet.
This concept has existed under a variety of names from “digital ghosts”, “digital models”, to “Universal You”, to “mirrors”, to “digital selves”. Some possibilities that this creates:
“Date” the digital replica of a celebrity crush: How we manage this is part of the “From AGI with Love” mini-series.
“Talk” to a digital replica of a political leader: In the future many could have the opportunity (or duty!) to talk to a digital replica of their country’s leader. We’re not quite there yet, but the New York Mayor now makes robocalls in Yiddish, Mandarin & Haitian Creole, and the 2024 Democratic candidate Dean Philipps attempted to create a chatbot of himself.
“Talk” to a digital replica of a deceased person: The visions of William Gibson and Eric Steinhart are slowly becoming reality.
“Talk” to a digital replica of a religious leader: Praying is kind of a many-to-one communication already…
Facebook’s 2023 attempt to let AI replicas of celebrities become the new digital friends of teenagers has largely failed after one year. Still, a signal on why it might still be worth it to think through societal implications of many-to-one is CharacterAI. The AI persona company has 20 million+ active users, most of whom are young and do not have fully developed brains yet. Based on self-reporting by the company itself and users on Reddit many chat with AI personas multiple hours every day.
If we would want to extremize this, we could imagine people staring at screens six hours per day, but instead of these screens being portals to the rest of humanity, its users are entirely absorbed into fake worlds dreamed up by the nearest datacenter.
It’s still early days
As of today, we’re barely 2.5 years into the many-to-one era, and this change in communication structure is only one aspect of a broader AI transformation. Still, many-to-one looks poised to be a significant new communication pattern, so at a minimum it seems worth spending a few brain cycles to explore potential implications of this.
You are not consistently routed to the same AI instance across turns in a conversation. The chat history is re-sent as input, and can be handled by any AI instance (these are perfect clones of each other).
Chess is a good example for this. Historically, elite chess talent was concentrated in a few strongholds, such as the Soviet chess schools. The rise of the Internet and chess engines as training partners has somewhat decreased the geographical concentration of chess skills.