After only a little more than a year or two spent at the thrall of VCs and executives everywhere, generative AI already appears to have plummeted into the so-called “trough of disillusionment.” Can long-standing blockchain AI projects turn this into a David and Goliath moment and seize the chance to shine?
The rapid fall from grace on the part of GenAI can be explained by several factors. A failure to deliver the promised productivity gains is one reason it’s falling out of favor with executives, while regulators, campaigners, and creators have raised a bevy of concerns about the underlying data. These include antitrust suits regarding an overreach of big tech firms, lawsuits brought by copyright owners such as The New York Times, and accusations of algorithmic bias along the lines of politics or race.
Of course, all tech undergoes a similar trajectory, or Gartner wouldn’t have been able to create such an accurate model to describe the phenomenon. However, the fact that Sam Altman has gone on record to state that ChatGPT needs copyrighted material to exist indicates a certain lack of willingness to attempt to tackle the many issues that exist with the underlying data.
However, in a competitive marketplace, refusing to engage with the problem simply creates a gap in the market for those who are willing to go the extra mile. While none of the big tech firms have yet managed to come up with solutions robust enough to satisfy their various stakeholders, there are some intriguing projects at the convergence of blockchain and AI that show promise for tackling some of the issues faced by AI firms.
What’s more, some of these initiatives have demonstrated remarkable prescience, since they’ve been around far longer than ChatGPT and most of the current AI platforms. AI-meets-blockchain projects such as SingularityNET SingularityNET and DeepBrainChain were conceived the better part of a decade ago with a view to addressing challenges that seem far newer to most of us. SingularityNET is a decentralized marketplace for exchanging and collaborating on AI algorithms, while DeepBrainChain aggregates GPU for AI compute, substantially reducing costs and, therefore, making AI more accessible. Another example is Ocean Protocol, established in 2017, which effectively turns data into assets, allowing them to be monetized and owners fairly compensated by developers of AI algorithms in need of training data.
Upsetting the status quoWhile blockchain isn’t a panacea, it does present a very simple and elegant answer to many of the underlying challenges of the established Generative AI model. Putting transactions and algorithms on-chain will improve transparency and provide the means of delivering fair compensation to copyright owners. Making data and algorithms more freely available will also avoid scrutiny from antitrust regulators. Developments such as zero-knowledge technology can support user privacy.
Even so, blockchain-based models upend the centralized business model, which is focused on generating profit for shareholders. As such, blockchain projects struggle to compete with the scale of OpenAI or Anthropic, with their big tech backers,
However, in a David and Goliath scenario, it’s all about maximizing your own strengths while exploiting maximum advantage from your enemy’s weaknesses. Of course, the biggest challenge for any startup is gaining traction, and this challenge is even more pointed in a blockchain scenario, where network effects are the very crux of success. In this regard, going up against tech behemoths is a daunting prospect indeed when they have all the odds in their favor – cash-rich companies with established bases of users and investors and access to all the AI training data they need.
For this reason, the entry into the “trough of disillusionment” represents an important opportunity for AI blockchain projects to demonstrate how they can offer something different and seek to draw a stark contrast with big-tech-funded AI firms that are apparently simply shrugging their shoulders.
