The easy part of tokenization is making the token. The hard part is telling the market what the asset is worth.

That question shows up immediately. If the price is weak or stale, the asset becomes hard to trade, hard to lend against, and hard to insure. Your valuation deck put that point plainly: without dependable valuation, you cannot define a fair buy or sell price, lenders will not accept the asset as collateral, and insurers cannot price coverage properly. That is why this part of the stack deserves more attention.

The numbers in the deck make the bottleneck easier to see. Traditional valuation for real estate can take 2-5 days and cost about $300-$2k per valuation. Art can take 1-4 weeks. Collectibles can run about $500-$5k or more depending on complexity. On top of that, the process can be subjective, different appraisers can give different answers, and the methods are often hard to verify. That is a bad setup for markets that want faster trading, updated collateral marks, and regular reporting.

The AI case is simple because the comparison is so wide. Your deck shows AI valuation cutting time from 2-5 days to under 1 minute and cost from about $300-$2k down to roughly $5-$50, while also making it possible to run thousands of valuations at once. If those numbers hold up in practice, valuation stops being a side process and starts looking like core infrastructure for tokenized assets.

The market opportunity is large enough that this matters. The deck uses a $10T-$16T 2030 range for tokenized real-world assets, while the broader DVLT market framing puts current tokenized RWAs excluding stablecoins around $26.7B today. That gap tells you the market is still early, and it also tells you growth depends on more than blockchains and custody. Somebody has to keep pricing the assets in a way buyers, lenders, and investors will trust.

This is why **DVLT** keeps standing out to me. The company talks about DataValue and DataScore, which puts valuation and scoring near the center of the story. But the extra piece is the data side. DVLT is also a data broker and data monetization company, so it is not walking into valuation with only a model and a pitch deck. It may have a growing base of information to lean on when pricing assets. That matters because the same deck makes a strong point about the data network effect: more assets lead to more data, more data leads to better models, and better models are hard for later competitors to catch.



There is also a reason this fits the bigger DVLT setup. On Mar 18, the SEC approved Nasdaq's proposal to allow certain securities to trade in tokenized form. On Mar 19, **DVLT** announced its NYIAX deal. The NYIAX side brings exchange-linked infrastructure and technology tied to AI valuation, smart contracts, and compliant liquidity mechanisms. So when valuation becomes the point of discussion, DVLT is already sitting near a lane where pricing, data, and market rails overlap.

What I like about this angle is that it gives the story a more concrete shape. Issuing a token is one event. Valuing the asset can happen again and again across issuance, collateral, trading, reporting, and liquidation. That means the pricing layer can touch the same asset multiple times and keep generating work long after the token is created. Your deck laid that out clearly, and it is one of the better monetization arguments in the whole tokenization stack.

So when people talk about tokenization, I think the better question is not only who can issue the asset. I think the better question is who can keep pricing it in a way the market will actually use. That is where trust gets built, and that is where a company like DVLT may have more edge than people assume.



My take only. Not financial advice

Everyone talks about tokenization. Almost nobody talks about the part that decides whether it works