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.
A.I. refrained from overhyping itself prematurely, allowing ample time for development and refinement over the past decade, before it was ready for prime time. During that gestation period, developers dedicated themselves to fine-tuning the technology, tackling intricate challenges, and only now are we witnessing the true impact of A.I. on the average consumer.
In contrast, the blockchain industry continues to expose its tinkering to the public, resulting in a large gap between hype and reality. Several participants in that industry persist in promoting unproven products or exaggerated business models, exposing their experimental ventures to public scrutiny and inviting criticism or skepticism. Some examples include the heightened expectations around DAOs or GameFi that was supposed to revolutionize gaming.
With a combination of practical, realistic, and compelling use cases, A.I. has now entered various sectors and segments one by one. Artificial intelligence is already being verticalized and specialized, whereas blockchain’s ambitions to permeate industry remain a work in progress. Although blockchain’s impact has primarily been felt within finance, most other forays in different sectors have been feeble or, at best, hopeful.
There is much that the blockchain community can glean from the success of A.I. I hope the upcoming generation of blockchain entrepreneurs will not only draw inspiration from their predecessors but also look to their counterparts in the A.I. field for valuable insights and guidance. The lessons are there for the taking.
William Mougayar has four decades of tech industry experience and is the author of . The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
