Disrupting Tech Monopolies & AI Tycoons — Part 2

A second, a longer-term implication of a global shared data layer is blockchain-enabled AI. Agents (not even particularly intelligent agents) can use blockchains as a ‘substrate’ as Fred Ehrsam has put it in the past.Deploying and using agents on blockchains rather than using proprietary tools and platforms like Facebook Messenger will be more attractive to developers, users and regulators.

For developers, first, they have access to a vast amount of free (on public chains anyway) and structured data, data that they would never be able to buy or generate themselves at first. Second, they have structured and high-quality data (right now, just transaction data, but increasingly all sorts of value store and exchange). Third, native automation tools in smart contracts and hopefully very soon production sidechains make it easier to build reasonable agents that can perform reasonably complex actions. Finally, developers and companies that deploy agents have a native payment channel to be paid almost immediately based on all sorts of conditions like usage or user utility. The business models with tokens and smart contracts are not limited to up-front payment or a paywall. All sorts of new business models will be available for experimental developers.

Users benefit because unlike any other environments, they will have direct access to token capital, investment and real interest in the system. When users use a Facebook messenger bot, they get some utility. When they use an agent on a blockchain they can be rewarded or paid with tokens. Depending on the token economics a user can ‘own’ a stake in the agent or company behind the agent. The more the user uses or evangelises the product, the stronger the product and underlying blockchain become. Network effects with a direct monetary reward thrown in. In a sense, a user is no longer a passive consumer of a service; they are a stakeholder. This model begins to look more and more like a digital cooperative. (Something we are actively exploring at Outlier Ventures with the tokenization of Botanic Technologies’ bot platform, a project named SEED that allows the fair exchange of information between AI and people)

The last stakeholder, and potentially the deciding factor will be regulators and Governments that demand some element of control or access to AI algorithms. The public and political tide are turning against technology companies. Certainly many Governments around the World are waking up to the power amassed by large US-based tech firms through their exploitation of data. Without overselling it, it seems to me that an open-source, auditable data structure would be an ideal technical solution for regulators that want a window into AI decision making and data used to train models. This would at the very least allow scrutiny of training data to check for bias as well as potentially providing an audit trail for exploration if an agent makes a bad decision. It’s not a leap to imagine regulators actually mandating the use of either a public blockchain or demanding a node in private networks for audibility of AI.

If this scenario plays out you have more developers, more users and happy regulators. There are many different descriptions, I like Autonomous Economic Agents (AEAs), these new types of decentralised AI are the logical next step when autonomous agents start using blockchains (something FETCH.ai, an Outlier portfolio company are working on). The level of human involvement with the agents will vary; some AIs can be managed by a traditional organisation, others will be managed by decentralised autonomous organisations (DAOs). Regardless of the human involvement, the fact is AIs will be accumulating tokens (seen another way, wealth). For example, an autonomous vehicle can be paid in tokens for rides and can pay for re-charging and servicing with tokens. Or an AI DAO could manage a neighbourhood distributed energy grid in which energy is exchanged using smart contracts based on real-time supply and demand.

I don’t think many people have truly thought through the implications of this. A non-human and non-human controlled entity will have the ability to acquire resources and wealth. When people talk about exponential growth, this is exactly what they are talking about. Society and politics are simply not ready to even begin a discussion about these sorts of issues. Can an autonomous agent generate wealth? What is the optimal level of taxation that doesn’t act as a disincentive to activity? We already have enough trouble collecting taxes as it is, how and who will collect taxes from an AI DAO?Blockchain-enabled AI might seem pie in the sky. But unlike say artificial general intelligence (AGI) we know exactly the problems that need to be solved to bring this vision to reality. There are already rudimentary versions of these agents available today. For more on AI DAOs you must read Trent McConaghy’s AI DAOs, and Three Paths to Get There.

This is a working thesis and a high-level description of the work we are doing at Outlier Ventures. I am looking for feedback so please tweet me, Lawrence Lundy. The thesis can certainly be improved upon. I am particularly keen to explore the potential impact of improved unsupervised learning algorithms and reinforcement learning on the need for large datasets. If large datasets are no longer required the outcome would also be that data becomes less valuable which serves to reduce the value of data but wouldn’t enable blockchain-enabled AI.

Thanks to Aron Van Ammers, Joel John & Jason Manolopoulos of Outlier Ventures; Jeremy Barnett a Barrister at St Paul’s Chambers; Omar Rahim of Energi Mine; Toby Simpson of FETCH.ai (an Outlier portfolio company); Mark Stephen Meadows of Botanic Technologies (an Outlier portfolio company) for reviewing and providing valuable feedback. Also to Trent McConaghy for driving and pushing forward the thinking on AI DAOs.