How the combination of AI and Web3 could reinvent business

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. 

Reinventing business
 

The combined impact of AI and Web3 has the potential to reinvent business models and the shape of the enterprise in fundamental ways.

This will start with rethinking business functions. Already, companies are exploring AI to streamline business functions by boosting productivity and efficiency. Over time, the focus will shift from performing existing functions more efficiently to rethinking these functions from the ground up — and this rethinking will work best when it combines the power of AI and Web3.

A prime candidate for such business function reinvention, referenced repeatedly in the IR 2023 sessions, is supply chain. A reinvented supply chain of the future could be built on the blockchain for security, integrity and inventory control. It could use smart contracts to deal with suppliers and other third parties. It could deploy AI to make the supply chain more predictive and dynamic in anticipating and responding to supply chain disruptions.

Over time, disruption will likely spread from individual business functions to entire business models. This will happen in at least a couple of ways. First, as AI and Web3 automate key skills — such as supply chain management, research and development, or sales and marketing — these value drivers will become increasingly commoditized. Companies with value propositions built on such competencies will find their competitive advantage shrinking, and will need to find new ways of creating, delivering and capturing value — pushing them to develop new business models to remain relevant.

Second, Web3 has tremendous potential to disrupt business models because it embraces decentralized and open-source architectures. The evolving GenAI space is currently comprised of both open-source and proprietary models, and some IR 2023 participants noted that Web3 could tip the balance toward open-source. Even more disruptive for business models is the decentralization that Web3 is built on. Web 2.0, the era of social media and user-created content, enabled tremendous market concentration by large platform companies because of the potency of network effects. Web3 has the potential to disintermediate such platforms, by creating truly peer-to-peer models — with huge implications for companies with business models dependent on network effects.

Lastly, AI and Web3 could transform the organizational structure of the company. Web3 has already given us the construct of the DAO, a collectively-owned and/or run organization without the need for centralized leadership. The addition of GenAI could take the DAO model to the next level, by making DAOs more adaptive and intelligent, and bringing the organization ever closer to a concept we have been exploring for some time: the superfluid enterprise.