Blockchain should benefit from GenAI’s ‘disillusionment’ moment

After just over a year or two under the thumb of venture capitalists and global executives, generative AI already appears to have fallen into what’s been called the “trough of disillusionment.” Can long-running blockchain AI projects turn this situation into a David vs. Goliath moment and seize the opportunity to shine?

GenAI’s rapid fall can be explained by several factors. A failure to deliver promised productivity gains is one reason it’s falling out of favor with executives, while regulators, activists, and creators have raised a host of concerns about the underlying data. These include antitrust lawsuits over abuse of power by big tech companies, lawsuits from copyright owners like The New York Times, and accusations of algorithmic bias along political or racial lines.

Of course, all technologies follow a similar trajectory, otherwise Gartner would not have been able to create such an accurate model to describe the phenomenon. However, the fact that Sam Altman has publicly stated that ChatGPT requires copyrighted material to exist indicates a certain lack of willingness to attempt to address the many problems that exist with the underlying data.

However, in a competitive market, refusing to tackle the problem only creates a void in the market for those willing to go the extra mile. While none of the big tech companies have yet managed to come up with solutions that are robust enough to satisfy their various stakeholders, some intriguing projects at the convergence of blockchain and AI show promise in solving some of the problems faced by AI companies.

Moreover, some of these initiatives have demonstrated remarkable prescience, having been around much longer than ChatGPT and most current AI platforms. AI-meets-blockchain projects such as SingularityNET
SingularityNET
and DeepBrainChain were designed nearly a decade ago to address challenges that seem much more recent to most of us. SingularityNET is a decentralized marketplace for sharing and collaborating on AI algorithms, while DeepBrainChain aggregates GPUs for AI computing, dramatically reducing costs and, therefore, making AI more accessible. Another example is Ocean Protocol, created in 2017, which effectively turns data into an asset, allowing it to be monetized and its owners fairly compensated by AI algorithm developers who need training data.

Upsetting the status quoWhile blockchain is not a panacea, it offers a very simple and elegant answer to many of the underlying challenges of the established generative AI model. Integrating transactions and algorithms into the chain will improve transparency and provide the means to provide fair compensation to copyright holders. Making data and algorithms more freely available will also help avoid scrutiny from antitrust regulators. Developments such as zero-knowledge technology can promote user privacy.

Yet blockchain-based models disrupt the centralized economic model, which focuses on generating profits for shareholders. As a result, blockchain projects struggle to compete with the scale of OpenAI or Anthropic, with their large technology backers,

However, in a David and Goliath scenario, it’s about maximizing your own strengths while exploiting your enemy’s weaknesses to the fullest. Of course, the biggest challenge for any startup is gaining traction, and this challenge is even more acute in a blockchain scenario, where network effects are the very heart of success. In this regard, going up against the tech behemoths is a daunting prospect, because they have the odds stacked against them: cash-rich companies with established user and investor bases and access to all the AI ​​training data they need.

For this reason, entering the “trough of disillusionment” represents a significant opportunity for AI blockchain projects to demonstrate how they can offer something different and seek to create a stark contrast to the big tech-funded AI companies that seem to simply shrug their shoulders.