The Open, Decentralized AI Market: An Alternative to the Typical Tech Startup or Public Tech Company

For a typical tech startup, the internal structure is very simple: On paper, there is a Board of Directors and an Executive Suite composed of people like CEO, CFO, CTO, etc. In practice, in the early stages, there tends to be a small core team, each member of which carries out multiple functions. As the enterprise grows, there is a transition to a situation where practical leadership roles are better defined — for instance, in a startup it would be common for the same person to be the lead software architect and to handle interfacing with the firm’s lawyers and accountants; but in a mid-sized firm this would be very unusual.

Typically, in the beginning, the Board of a startup is run by the same few people who run the company; and then as the company grows, the Board comes to be dominated more by corporate investors and their representatives. This is often an important shift, because investors are often more interested in achieving a rapid and profitable exit, whereas company founders are often more interested in seeing the company realize its initial vision. Given the nature of a privately held company, big decisions such as whether to sell the company at a certain point tend to be “all or nothing,” which means that decision-making logjams between investors and founders on the Board are fairly common.

In the AI field as in some other areas of tech, this set of organizational and fundraising dynamics is a large part of the reason for the increasing concentration of AI software and developers within a small number of large corporations. A typical trajectory becomes: A group of young friends start a company, they develop some AI technology that is novel in its algorithmics or (more commonly) in the particulars of how it’s applied; they raise investment money; and their investors help them negotiate a profitable acquisition from a large tech company. Some of the staff stay with the acquirer, some move on. The founders hopefully got rich, the investors presumably got a decent return on investment, and the big tech company got some creative new software it wouldn’t have created on its own, and some smart new hires who wouldn’t ordinarily have felt like working for a big company.

SingularityNET provides an alternative: a more decentralized mode of organization, which still provides the creators of valuable AI tools a way to monetize their creations, but which leaves the control substantially in the hands of the creators even as radical growth occurs and increasing investment is injected. Via this decentralized mode, it will encourage a greater variety of AI developers and AI-using businesses to participate, more than would ever participate in any small company, and potentially more than participate even in modern tech giants.

Using SingularityNET mechanisms, anyone who creates AI code can put their code online and enter it into the SingularityNET — and then can thereby make money when others use their code. The amount of profit that can be made in this way is limited only by the number of customers available in the network, the quality of the AI processing provided, and the need for this particular kind of AI processing.

Of course providers of AI code within the SingularityNET are not islands; they are embedded in a complex social situation in which many parties have influence. But it is a quite different situation from a typical tech startup. Instead of being at the whim of venture investors with preferred shares of stock, it is the overall community of SingularityNET stakeholders that has the influence. This includes anyone who holds SingularityNET tokens: other AI service providers, customers, SingularityNET founders, and anyone who has bought the tokens for later use. This is fundamentally more democratic than the situation with typical tech startups, and it will lead to new dynamics that are not yet fully understood, but that appear very likely to put more power in the hands of developers and customers rather than investors.

In some ways the situation with SingularityNET is similar to that of a public company rather than a privately held tech startup. However, a significant difference is that the shareholders of a public company generally are not the same as the users of that company’s services, or the developers who have created the services the company offers. Rather, in a typical public company, the shareholders are generally a quite different group of people, and the developers of specific services offered by the company rarely interact directly with the customers using that service. (In a startup, developers and customers often directly interact, but public companies tend to be much larger.) So in a typical public company, one has three highly distinct though coupled dynamics, one among shareholders and other potential shareholders (investors), one between customers and the company, and one between developers and the company. In the SingularityNET model on the other hand, these various groups are all on par with each other — they are all just token holders — and they are encouraged by the structure to freely interact via their common participation in the network’s democratic governance mechanisms.

The business goal of this novel mode of organization is to provide higher-quality services, in a way that spreads around the wealth generated by the services more fairly and in a more broadly beneficial way. It happens that, via the potential for AI Agents in the network to interact with each other and outsource work to each other, this decentralized framework also provides an unparalleled platform for the emergence of AI with a greater degree of general intelligence — in the form of federations of Agents interacting with each other in manners found to provide optimally for (internal and external) customer needs. One thus has a powerful coupling of network effects: the same “AI cooperation” that leads to better, and more diverse practical services and lower prices, also militates toward greater general intelligence. But of course the more general intelligence is achieved within the network, the greater the quality and efficiency of the services provided. And the more revenue is generated from the network’s provision of services, the more funds will go into developing AIs to operate within the network, and the greater the general intelligence will grow.

We thus have an alternative to the traditional tech startup and public company structures, which is not only more likely to yield fair and sensible distributions of the wealth that AI generates, and to leave more freedom of choice in the hands of developers and customers, but also has a specific power to foster increasing intelligence as it grows. The decentralized, self-organizing structure adopted by SingularityNET can be valuable in a variety of different contexts, but is especially suited to AI and AGI.

In this context, democratic governance such as is provided in the SingularityNET charter serves two overlapping purposes. First, it ensures that the overall organization and maintenance of the network is carried out in a way that benefits the network’s overall growth. Token holders with reasonably high reputations are allowed to vote, and this means that anyone who has proved themselves a genuine and positive part of the network gets some say in how the network is operated. Customers, AI developers and anyone else who owns tokens (and thus has expressed their commitment to the network) gets to vote on any issues of importance to the networks’ operations.

Secondly, the democratic governance mechanisms of the network allow the network participants to guide the focus of the network, in terms of which applications and which AI algorithms and approaches it pays more attention to. New AGI tokens, progressively generated, are dedicated to multiple purposes: some to AI Agents that are democratically approved as carrying out “benefit tasks” that contribute toward the common good, and some to AI Agents that are chosen by a democratic voting mechanism among token holders. The reputation system is used as a filter here, both in terms of who gets to vote, and in terms of which Agents are qualified to receive newly created tokens as a result of voting.

In cognitive science terms, the voting mechanism may be considered as a kind of “deliberative attention allocation” — in which the players in the network are consciously choosing which sorts of Agents should get more of the attention of the overall evolving network. This deliberative attention allocation will work alongside, and coupled with, the “automatic attention allocation” that emerges from the ordinary economic activity of the network.

This may seem a bit complicated, but I would argue that the dynamics of the current tech ecosystem is also quite complicated, even though we generally don’t see this, because we take it for granted. There are large public companies like Cisco and Huawei that nobody fully understands, because they are involved in so many different sorts of businesses, which interact in complex ways — these are self-organizing systems or socio-cognitive organisms of a uniquely modern sort, though we don’t tend to think of them that way. The overall ecosystem of a place like Silicon Valley or Shenzhen is also extremely subtle and complex, with interplay between different actors such as companies of different size, universities, government agencies and so forth (and the subtlety is witnessed by the difficulty of replicating such tech wonderlands in other places, even when large sums of government money are devoted to such attempted replication). A decentralized organization like SingularityNET is not particularly more complicated than existing entities involved in the production and consumption of technology — it’s just complicated in different ways. The potential is tremendous, but we are still in the early stages of discovering how this kind of organization should best work, and for that reason a flexible and effective democratic governance mechanism is key to SingularityNET.