Recent analyses have envisaged an impending scarcity of openly accessible, pertinent data indispensable for training AI models, prognosticating this deficit by 2026. Projections indicate that the worldwide AI market will burgeon to a staggering $1.8 trillion by 2030. This engenders an exigent demand for novel avenues to discover and harness alternative datasets. Furthermore, unearthing means to commercialize these AI models and datasets ushers in an entirely novel creator-centric economy, encompassing both corporate giants and individual innovators.
Consider ChatGPT, the beloved large language model (LLM) that graces us. Although it has recently acquired access to contemporary web data, its training regimen still hinges on the same publicly available, publicly harvested data utilized by other LLMs, including Google’s Bard. In order to differentiate and sustain the expansion of their extensive knowledge repositories, each LLM must undergo training utilizing proprietary data inaccessible to other AI models in the domain.
Why is this requisition imperative? To optimally harness these models, they necessitate pertinent, context-specific datasets. Moreover, these models and their creators necessitate the requisite framework to address prevailing quandaries such as copyright violations, data privacy, and source referencing. This culminates in the necessity to construct a foundation rooted in web3 technology. Here’s why AI is entwined with web3:
Generation of Revenue
Rendering data and AI models profitable bequeaths novel revenue streams to enterprises, developers, and publishers alike. As developers engender AI models, they can vend these on marketplaces as recompense for their laborious endeavors, which often encompass research, human resources, and model development. These models subsequently become tools for other innovators’ pioneering technologies. Furthermore, businesses can forge supplementary revenue streams by commercializing their APIs, enabling these AI models to be trained on non-public datasets. For instance, Reddit recently terminated its API, curtailing the capacity of AIs like ChatGPT to learn from information exchanged on subreddits. Yet, theoretically, Reddit could opt to vend subreddit APIs on a marketplace, benefiting their enterprise as well as the models yearning to train on that reservoir of information.
Consent as a Motivating Force
While monetizing AI and data models presents an appealing novel revenue model for developers and businesses, it is paramount to obtain informed consent from these developers and data publishers to grant access for additional model training and utilization of their data and models. Constructing AI technologies on the blockchain guarantees that this consent is meticulously recorded and traced publicly, alongside bestowing appropriate attribution for any utilized resources.
Innovation through Collaborative Data Sharing
The potential for collaboration in the realm of data and AI appears boundless, fostering ingenious pathways for collective pursuit of shared objectives via AI innovation. Inevitably, this will catalyze the formulation of more resilient, comprehensive, and precise AI models.
Enhanced Accessibility and Scalability of Efforts
To democratize access to AI models and data, this collaborative value-acquisition structure amplifies the scope of undertaken endeavors. As a consequence, AI models can be deployed on a more expansive scale, reaching a broader audience or diverse industry verticals.
With the manifest exigency for AI and data monetization to invigorate growth in this sphere, the query arises: how do we achieve it?
Blockchain technology emerges as pivotal for the efficacy of these assets. Smart Subscriptions, which manifest as tokenized renditions of outdated subscription models, amalgamate multiple data and AI model assets, empowering the asset owner to stipulate access parameters such as price and duration. This mode of progression is most apt for confronting the aforementioned current quandaries.
Through this genre of monetization model, enterprises and publishers can harness the transparent nature of blockchain technology, establishing the bedrock for a trust-based AI asset registry.
To encapsulate, harnessing web3 technology to construct AI forges a novel avenue of growth, enabling projects to germinate in a lucid and scalable manner. This strategy mitigates concerns held by consumers and creators alike. Web3 and AI stand as the archetypal technologies of the impending era. The amalgamation of these two realms is a natural evolution, bestowing dependable capabilities that lay the groundwork for the ensuing wave of innovation.
