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Accelerating adoption
The discussions at the Innovation Realized in Focus city series highlighted the potential for AI and Web3 to symbiotically address each other’s shortcomings, leading to faster and more extensive adoption of both. In particular, Web3 could help AI address its trust deficit, while AI could help Web3 overcome its adoption challenge.
Trust had already become a challenge in the Web 2.0 era, which has seen an explosion in online misinformation. AI could supercharge the problem. Hallucinations — misinformation output by models that is often indistinguishable from accurate information — are a growing challenge and are finding their way into the larger internet, polluting the repository of information on which we collectively rely. Taking this further, GenAI could be weaponized by malicious actors to generate synthetic media — not just fake news articles, but also synthetic data injected into enterprise systems or videos and avatars spewing conspiracy theories — at lightning speed and tremendous scale.
Innovation Realized in Focus participants noted that Web3 could help with verification and confidence building. For instance, Web3 can help combat misinformation through blockchain notarization. Content developers can “hash” an article or video — essentially, creating a digital fingerprint that is unique to any piece of content — put the result on the blockchain, and sign it with a public key. Any reader or viewer can then use the public key to hash the content themselves and, if they get the same result as the one stored on the blockchain, be confident that the content has not been tampered with. Such techniques, in combination with methods such as digital watermarking, could go a long way to building trust in GenAI and its outputs.
A similar approach would be valuable for enabling the multi-organizational teaming that will be essential for extracting value from GenAI. GenAI’s ability to work with unstructured data — and ultimately, to combine structured and unstructured data — will open the floodgates to new opportunities for companies to extract value from pooled data, including knowledge about processes and best practices, also known as knowledge graphs.
But such information pooling will have to contend with regulations and company policies that constrain the ability of data to move across jurisdictions, or otherwise limit data sharing to protect consumer privacy. To extract value from shared data while working within these limitations, companies will increasingly turn to protocols such as multi-party computation or zero-knowledge proofs, which allow them to conduct analysis or computation on data from multiple parties without any entity revealing its data to others. Blockchain can then be used to verify the validity of outputs generated.
In ways such as these, Web3 can boost trust and confidence, thereby helping accelerate the adoption of GenAI. By the same token, GenAI could boost adoption of Web3 in several ways.
One factor standing in the way of mass adoption of Web3 is the lack of user-friendly interfaces and experiences. Using Web3 can be technologically daunting, often requiring new users to learn abstruse terminology while navigating confusing interfaces and complex workflows. AI could help overcome this hurdle. Much as GenAI will become a copilot in many jobs and roles across the workplace, it could become a copilot for Web3, helping users navigate the complexity of the Web3 ecosystem by providing user-friendly interfaces and personalizing experiences for individual preferences.
More fundamentally, GenAI could create the ideal setting for Web3 applications. The elements of Web3 are digital-first constructs; as such, they may be more suited for machines than humans. The average person may not see a compelling reason to pay for purchases using a cryptocurrency. But for GenAI, it may well be easier and more efficient to store and exchange value using cryptocurrencies rather than fiat money, or to work with smart contracts instead of paper contracts. As GenAI becomes more prevalent, this could catalyze widespread adoption of Web3.
We don’t want to overstate the case. Web3 and AI are not going to solve every challenge these emerging technologies face. Indeed, the participants across the six cities highlighted several challenges — from Web3’s scalability problem to the carbon footprint of both technologies. But in specific ways, the combination of GenAI and Web3 could help mitigate some key risks and challenges, setting the stage for increased adoption.