Accelerating Adoption
Discussions in the “Innovations Realized in Focus City” series highlighted how AI and Web3 can symbiotically solve each other's shortcomings, leading to faster and more widespread adoption of both. In particular, Web3 can help solve the trust gap in AI, and AI can help overcome the challenges of Web3 adoption.
Trust was already a challenge in the Web 2.0 era, when online misinformation exploded. AI has the potential to exacerbate this problem. Hallucinations (misinformation output by models that is often indistinguishable from accurate information) are a growing challenge, permeating the internet and poisoning the repositories of information we collectively rely on. Taking this further, bad actors could weaponize GenAI to generate synthetic media (not just fake news articles, but synthetic data injected into corporate systems, videos and avatars spewing conspiracy theories, and more) at incredible speed and scale.
Attendees at Innovation Realized in Focus noted that Web3 could help with verification and building trust. For example, Web3 could help combat misinformation through blockchain notarization. Content developers could “hash” articles or videos, essentially creating a unique digital fingerprint for their content, and store the result on the blockchain and sign it with their public key. Any reader or viewer could then hash the content themselves using the public key, and if they get the same result as what's stored on the blockchain, they can be confident that the content hasn't been tampered with. Such technology, combined with methods like digital watermarking, could go a long way toward building trust in GenAI and its output.
A similar approach can be valuable in enabling the multi-organization teamwork that is essential to extract value from GenAI. GenAI's ability to work with unstructured data, and ultimately combine structured and unstructured data, opens up a host of new opportunities for companies to extract value from pooled data, including knowledge about processes and best practices (also known as knowledge graphs).
However, such information pools must contend with regulations and corporate policies that restrict moving data across jurisdictions or limit data sharing to protect consumer privacy. To derive value from shared data while working within these restrictions, companies will increasingly turn to protocols such as multi-party computing and zero-knowledge proofs, which allow any entity to perform analysis or calculations on data from multiple parties without disclosing the data to others. Blockchain can then be used to verify the validity of the generated outputs.
In this way, Web3 can increase trust and confidence, accelerating the adoption of GenAI. Similarly, GenAI may accelerate the adoption of Web3 in several ways.
One of the barriers to mass adoption of Web3 is the lack of a user-friendly interface and experience. Using Web3 can be technically challenging, and new users often have to learn arcane terminology while navigating confusing interfaces and complex workflows. AI can help overcome this hurdle. Just as GenAI acts as a co-pilot for many jobs and roles in the workplace, it can become the co-pilot for Web3, helping users navigate the complexity of the Web3 ecosystem by providing a user-friendly interface and personalizing the experience to their individual preferences.
More fundamentally, GenAI could create an ideal environment for Web3 applications. Because Web3 elements are digital-first structures, they may be better suited to machines than humans. The average person may not see a reason to pay for purchases using cryptocurrency. But for GenAI, it may be easier and more efficient to store and exchange value using cryptocurrency instead of fiat currency, or to use smart contracts instead of paper agreements. As GenAI becomes more widespread, it could help drive widespread adoption of Web3.
We don't want to exaggerate: Web3 and AI will not solve all the challenges these emerging technologies face. In fact, participants in six cities pointed out several challenges, ranging from Web3's scalability issues to the carbon footprint of both technologies. But in concrete ways, GenAI and Web3 combined can mitigate some of the key risks and challenges, laying the groundwork for greater adoption.
