The Blockchain-Enabled Intelligent IoT Economy

The 21st century would be the “century of complexity” (Hawking, 2000)

I. Setting the stage

The IoT and consumer hardware industry have seen multiple failures and a few exits over the last 12–18 months (while the B2B side has been doing a bit better overall) and some criticism has been recently made to the industry to slow down.

In spite though of the current push back, the sector is still increasing and attracting capital and talents. Clearly, there are multiple reasons as to why this is the case, but I firmly believe that one of those reasons is the convergence of IoT and Artificial Intelligence with Blockchain as the infrastructural backbone, which is unlocking the next step not only on the tech side but also on the business side.

The industry has indeed evolved from merely creating products, to create networks of products (namely, Internet of Things), to eventually creating Intelligent networks of products (I-IoT). The transition between the first and the second class was straightforward: it was enough to create more and different products and link them together. This generated many new possibilities, but it was clear from day one that it came with a series of issues hard to tackle, such as security/privacy, validation/authentication, and connectivity bottlenecks.

This is where AI and Blockchain come in. The second transition indeed is made possible through a combination of improvements in computing powers, device miniaturization, ubiquitous wireless connectivity and efficient algorithms (Porter and Heppelmann, 2014). The new class of smart products will be (and already are, to some extent) able to monitor, control, optimize, and automatize processes and products with an accuracy previously not imaginable.

Of course, as often happens, the bonus of integrating those fundamental technologies is that they ended up modifying IoT as much as IoT was impacting them in turn.

This convergence is however not accidental, but rather an inevitable necessity almost designed by default: AI needs data, IoT needs intelligence and insights, and both need security and transparent marketplaces.

The magnitude of this convergence is so high that will affect several sectors swinging from energy and manufacturing to home environment, robotics and drones, supply chain, logistics, and healthcare. Every field which is historically data-rich but information-poor will be touched (or should I say brutally hit?) by those technologies.

I will explore how in the next few sections.

II. How Blockchain is changing IoT

Blockchain as a technology is basically providing the IoT stack with a secure data infrastructure to capture and validate data. As simple as that. At least it is a simple statement that contains three different nuances:

  • Securing data better: The first one is indeed the concept of storing data securely. We know that blockchain protocols are not designed to heavily store data (they are indeed ledgers, not databases), but they can provide “control points” to monitor data access (Outlier Ventures, 2018).

III. How Blockchain can change AI

As I have already previously mentioned, blockchain can affect AI in multiple ways:

  • Help AI explaining itself (and making us believe it): The AI black-box suffers from an explainability problem. Having a clear audit trail can improve the trustworthiness of the data as well as of the models and also provide a clear route to trace back the machine decision process, i.e., where data are coming from, who wrote the original algorithm, what data was used for training, etc. It can establish the foundations for “algorithms standards,” as for example which main algorithms, packages, and framework have been developed using a specific training set. This is also essential in machine-to-machine interactions and transactions (Outlier Ventures, 2017), and provides a secure way to share data and coordinate decisions, as well as a robust mechanism to reach a quorum. This is extremely relevant for swarm robotics and multiple agents scenarios, as mentioned by Rob May, who is a tech investor and Talla’s CEO.

IV. How AI can change IoT

AI is feeding itself with the new stream of data coming from the physical world and the billions (if not trillions) of sensors and “things” that are capturing and monitoring everything we do.

At the same time though, as soon as an AI starts making sense of IoT data flows, it will:

  • Increase data efficiency: An AI will inform those sensors on what data should be captured and stored, and above all where those sensors should be placed to be both more efficient and more effective.

V. How AI can change Blockchain

Although extremely powerful, a blockchain has its own limitations as well. Some of these are technology-related while others come from the old-minded culture inherited from the financial services sector, but all of these can be affected by AI in a way or another:

  • Consensus mechanisms: The proof of work or proof of stake are the first consensus mechanisms created but definitely neither the only ones nor the most efficient. AION has recently created a new consensus mechanism called “Proof of Intelligence” where validators are asked to train a neural network and using the parameters of that NN as proof of computation.

VI. How IoT is affecting AI

The generation and analysis of data that were not available earlier open a new spectrum of possibilities for an AI to:

  • Become more efficient: This is pretty straightforward, but new both structured and unstructured data can feed an AI and be used for new use cases or achieve a better performance on the existing ones.

VII. How IoT could change blockchain

If there is a clear trend emerging, it is that decentralized systems are hard to work with and expensive to maintain. Although the relationship is less intuitive than other more direct links, IoT can help blockchain in:

  • The nodes structure: IoT devices often act as lightweight nodesof the chain, which are those nodes that simply pass data to the full nodes that instead store the data, create new blocks, and ensure validity. Better and more powerful devices, possibly powered by AI, can turn every lightweight node into a full one.

VIII. Conclusions

As you might have noticed, the edges of the impact of one technology on the others and vice-versa often blur, and this is not by chance but an inevitable consequence of technologies that are born and developed to create an “intelligence flywheel.

In addition to unlocking a set of new technological scenarios, the integration of blockchain, IoT and AI has generated new powerful business models. The shift from product to service and ownership to access is the key to understand the magnitude of the changes in the tech ecosystem. Even more radically, product-as-a-service and product-sharing business models are emerging and winning in almost every markets, leaving the manufacturer in charge of the ownership as well as maintaining the full responsibility of the product and service operation.

It is counterintuitive and even a bit absurd, if you think about it, that the surge in the hardware industry is in fact shifting the attention toward a “servitization” model (Porter and Heppelmann, 2014), which clearly makes more sense where the cost of service is a significant part of the greater cost of ownership (that is the case in the current technology landscape).

This integration does not come without issues, as we have seen, both technical and commercial, as much as of design. Data democratization may also soon erode the data moat barrier AI companies are nowadays building their empires on. Software and algorithms are no longer private but rather open-source. Computational power is now affordable and will be processed directly on-device. What does it all mean for the evolution of the industry? Who knows. I have no idea of how these phenomena will shape our businesses and lives, but I am sure that the changes will happen at an exponential rate.


Skier, solution architect, investor, traveler, cryptocurrency enthusiast, blockchain expert