What’s next for these five crypto and NFT trends in 2023?

1. Trust is the new killer app

Digital asset companies have indeed been working to build trust, but most have yet to make the organization-wide efforts that would most impress customers, investors, business partners and regulators. Many companies are acting to increase transparency in core products and related processes. Examples include proof of reserves reports and verifiable security related to the segregation of client assets.

But trust doesn’t come from a specific process or a single line of business. Instead, trust should come from an organization as a whole. We expect more digital asset companies to take this lesson to heart as the year proceeds. This will include dependable, organization-wide compliance and reporting — and evidence that they have an ethical culture built on transparency.

2. We’ll see more regulation, but not a complete framework (just yet)

As expected, we’re seeing more global regulatory activity. In the EU, the Markets in Crypto-Assets (MiCA) regulation has entered into force. Hong Kong SAR’s Securities and Futures Commission (SFC) has introduced various regulatory rules and guidelines for virtual asset services providers. In the United States, the SEC has taken regulatory enforcement actions and issued statements. But overall movement has been comparatively slow.

The slow pace of US regulators was expected — and it should persist through 2024. Congress is divided, an election is coming and policymaker priorities are mostly elsewhere (notably, on generative AI). But this lack of regulatory clarity could continue to encourage digital innovation to shift to jurisdictions with more clearly defined regulations.

3. Traditional finance will become more involved

A lack of regulatory clarity in the United States is a serious barrier for traditional financial institutions (TradFIs) interested in digital assets. But many are still advancing. Behind the scenes, they’re working to enhance skills, technology, risk management and compliance frameworks for a future digital asset business.

Until the regulatory framework advances further, we expect TradFi to continue to move cautiously. Given the tight scrutiny under which these institutions operate, that’s an understandable approach. But we also expect many TradFis to keep preparing. Eventually, they’re likely to apply their risk management skills, client relationships and powerful brands to a technology that needs them.

4. Digital assets will be increasingly common and invisible, driven by new advocates

These two predictions now belong together because these trends have both accelerated and converged. New companies are using NFTs and web3 as an invisible, back-end technology to help enhance operations and improve user experience. Some companies are using digital assets internally to track and verify data and assets. Some media firms are experimenting with smart contracts to share ownership rights and profits — and to make it easier for customers to pay for access or ownership.

We expect that digital asset technology will become increasingly common and more invisible to end users in a greater variety of companies. New innovation will help. Some interesting developments include protocol proposals for rentable NFTs and work on a non-tradable or non-transferable token (“soulbound”) for digital identity.

Digital assets have a new companion, generative AI

The biggest news in technology this year has been generative AI’s move from the lab to the top of the corporate agenda. That’s good news for digital assets, since generative AI can help accelerate many aspects of the digital asset ecosystem. For example, generative AI can help write or debug the code that underlies digital assets, memorize hashes for users, and help track, monitor and verify assets or identities — all through user-friendly chat-style interfaces.

This relationship is reciprocal. Digital assets can and likely will help generative AI to advance. As a case in point, digital assets can help verify and track the data that goes into generative AI models. That can improve model performance, reduce “hallucinations,” reduce bias in data sets and help avoid violations of privacy rights or intellectual property.