Ready for the Machine Economy era?

In a convergence between IoT, Artificial Intelligence and Blockchain, the Machine Economy is expected to contribute up to $15 trillion to the global economy by 2030, say researchers from IoTeX and Siemens in a paper published this month by the Industrial IoT Consortium (IIC) journal.

The text also describes some of the disruptive business models the industry is witnessing and highlights examples of implementation. But before going into them, it is worth understanding the definition of Machine Economy.

The authors of the article define Machine Economy as one in which a network of economically independent devices and machines act as autonomous market participants, execute economic transactions and other activities with little or no human intervention. Therefore, characterized by the use of technologies that enable autonomous transactions between devices or machines. Which ones? IoT, DLTs and tokens, basically.

IoT devices and the data they generate are key assets in the machine economy. As a result, industry best practices must be applied to ensure end-to-end reliability of the IoT stack, from data collection, processing and transmission, to the lifecycle management level with device integration, upgrades and decommissioning. DLTs, in turn, provide infrastructure for decentralised financial markets and services.

Artificial Intelligence is also an important component of this ecosystem. Combined with IoT and Blockchain it helps overcome individual weaknesses of these technologies and create entirely new use cases that the individual technologies cannot grasp. For example, IoT provides sensor data that builds the basis for training AI models. On the other hand, to fully exploit the potential of new networks of IoT-enabled products and services, intelligence aggregation, in the form of informed decisions within these networks, is required. The central component in linking IoT and AI is data as the currency of the digital economy. The exponential growth in the volume of data is triggered and sustained by IoT. However, this flood of data only offers added value if information can be generated by adding context and meaning.

In the Machine Economy data will continue to increase in both volume and value. There is market consensus that the Machine Economy will usher in a radical new era of automation with billions of connected smart machines interacting with humans and other machines. The business landscape is heading towards a tectonic shift in the way companies are managed, where machines are considered as a whole new asset class.

After all, as machines become “smarter” (through the use of IoT, Machine Learning and Blockchain), they gain pre-programmed capabilities to make decisions, conduct transactions and consume goods and services with other devices. The combination of Web2 companies with significant presence in IoT markets and the vibrant, ultra-dynamic Web3 environment creates the perfect terrain for significant disruption.

Therefore, by bringing the three technologies together, the Machine Economy represents a paradigm shift in IoT-related business. And to successfully embrace it – and, in turn, build a reliable business and a sustainable position in the future – entrepreneurs should consider factors such as:

• Fairness and transparency of business models, to align stakeholder benefits and encourage participation in the ecosystem.

• Effectiveness of customer engagement strategies, important for increasing buy-in and loyalty. DAOs, for example, can offer customers the opportunity to participate in the brand-building process.

• Robustness of tokenisation models, to incentivise ecosystem stakeholders to continue the business, thus ensuring long-term sustainability and growth of IoT businesses.

Industrial Metaverse

Among the four key Machine Economy use cases the authors mention in the IIC article is the Industrial Metaverse, an important topic that participants at the World Economic Forum meeting in Davos also discussed this year. ABI Research estimates the Industrial Metaverse, heavily focused also on digital twin technology, to be a $100 billion market by 2030.

One of the most inspiring parts of the Industrial Metaverse lies in the potential of restructuring the industrial chain and adding value to all staakeholders. Its construction requires interdisciplinary efforts and knowledge of information science, computer science, data science, as well as specific applications from several professional domains.

One of the pieces of the Industrial Metaverse puzzle is to connect IoT devices and their trusted data streams with digital twins, to create near real-time simulation and prediction for real-world situations. Blockchain and other Web3 technologies are the perfect solution for this. They offer the Machine Economy a neutral digital infrastructure, alleviating a wide range of Web2 problems, from data privacy issues to interoperability. 

While there are challenges to be faced, we are undoubtedly entering a new era where AIoT and Machine Economy will shake up the status quo for IoT manufacturers and users.