As soon as the word "blockchain" is uttered, speculation about cryptocurrency prices springs to mind. In contrast, initial thoughts about artificial intelligence revolve around what users are already doing with it.
That stark disparity alone is enough to make many blockchain enthusiasts, such as myself, incredibly jealous.
What can the blockchain learn from A.I. given that adoption of A.I. is off to such an impressive start?
To answer that question, let’s review the similarities and differences between the two sectors.
First, a quick summary of the parallels between their evolution.
Both A.I. and the blockchain have brought transformative advancements in technology and garnered significant market attention.
With the blockchain, the fundamental innovation was the seamless peer-to-peer transfer of digital assets, independent of intermediaries. With A.I., machine learning and its languages are pivotal elements revolutionizing the application of knowledge everywhere.
With so many imaginable use cases, these two technologies have sparked the interest of millions. Both industries are fast-growing, with the potential to create millions of jobs as thousands of new businesses have already attracted billions of dollars in investments.
In the past decade, both technologies have significantly matured and evolved, offering equal potential to reach billions of people. But the similarities end there.
A.I. has done much better pertaining to market introduction and user adoption.
A.I.’s consumer experience is more elegant but also simpler. For example, OpenAI’s ChatGPT has spawned the creation of dozens of applications for every possible sector. A user doesn’t need to download anything or have specialized knowledge about machine learning or natural language processing. Often, users can try a new product without even formally signing up.
A.I. technology has done an excellent job of appealing to developers who want to inject their applications with its power. The APIs offered are generally simple to understand and functional, from start to finish. Notable ones from OpenAI, Google, Microsoft’s Azure Cognitive Services, and AWS are just a few examples. In contrast, blockchain developers are confronted by a patchwork of technical resources.
Under the hood, A.I. is fairly complex, but while its developers were sifting through technical jargon—Deep Learning (DL), Large Language Models (LLM), Natural Language Processing (NLP), Machine Learning Languages (MLL)—end users weren't asked to do so. The blockchain sector, by contrast, remains dominated by technical discussions that leave many potential participants grasping for clarification instead of just experiencing the tech.