Is Blockchain Stuck in the Valley of Death?

Updated: September 30, 2026

Key Points

• Blockchain development is ongoing, while investment has stalled. 

• The blockchain Valley of Death occurred due to unresolved uncertainty about its value-generating applications.

• The industry needs to keep innovating on the business model to shorten its distance to monetization and close the Valley of Death gap.

Introduction

Last week, I was listening to an Unchained podcast episode with Illia Polosukhin from NEAR and Charles Guillemet from Ledger discussing the post-quantum future of blockchain. The conversation was mostly technical: emerging approaches to post-quantum cryptography, how different chains may adopt them, and so on. But it made me think about a related question: why doesn't such an R&D-intensive industry as blockchain receive investor attention and capital similar to the AI industry today?

It is certainly not because blockchain is all "scam" and "fraud". If anything, AI enables a comparable (if not greater) number of fraudulent opportunities. The point in question is the disproportionate demand for investment relative to its supply. And this mismatch is drastically observed between the two industries. This is not to say the AI industry is less R&D-intensive, but the scope and diversity of the R&D required to make the technology vision work seem to be narrower in AI than in blockchain.

One possible reason is the distance to the monetization frontier. AI, as a technology, clearly has a recognizable value-generating product: a model that can code, write, search, analyze, create images, operate software, or perform some other economically valuable task. Blockchain, by contrast, is stuck in the valley of uncertainty - what its most valuable application is is not clear yet. This article explores how the disproportionate capital allocation between industries emerged and what may come next for blockchain.

Economics of R&D and Innovation Cycle

To get to the answer to the question of how the disproportionate capital allocation between industries emerged, it is worth looking back at the economic theory. There is an old economic problem here.

Nelson (1959), writing about the economics of basic scientific research, explained why private firms tend to underinvest in research whose eventual commercial value is difficult to appropriate. The benefits of basic knowledge spill over, the direction of discovery is uncertain, and the time between research and commercial payoff may be very long. Arrow (1962) similarly emphasized that the uncertainty and limited appropriability make knowledge production fundamentally difficult to finance. When it is not clear how the monetization occurs, investors are less likely to put their money into it. 

This problem becomes particularly visible in what is called the Valley of Death: the gap in the technology readiness level when it is still under development but fails to demonstrate a commercially viable use case. Beard et al. (2009) represent the investment process as a sequence of funding decisions where each next stage of financing occurs after the information revelation obtained from the previous stages. The Valley of Death occurs when early-stage funding is non-competitive (e.g., government grants) and later-stage investment decisions are not adequately considered. 

When the information uncertainty does not get resolved, the expected payoff remains unclear, and consequently private investment attractiveness goes down. Importantly, the core assessment is not about the quality of technology, but about the clarity of its economic value. 

The Abernathy–Utterback technological lifecycle offers a strategic implication for firms: investments must be matched to the phase of technology maturity, eventually transitioning to capital efficiency and scale optimization. Importantly, a dominant design should eventually emerge: customers' needs become clearer about the value of technology.

Figure 2 summarizes the mechanism that determines the investment attractiveness of technology. 

So, Why Not Invest in Blockchain R&D

Blockchain may still be looking for the end of its pipeline. In my interpretation, the core value proposition of blockchain is the combination of cryptography that creates trust and decentralized network organization that distributes control and value. The problem is that, almost two decades after Bitcoin, we are still not completely sure what the dominant monetizable application of this technological combination is.

Payments? Maybe.

Tokenized real-world assets? Possibly.

Stablecoins? Increasingly convincing.

Decentralized finance? In some contexts.

Digital ownership? Perhaps.

However, the clear answer to "How is blockchain useful?" is still not comparable in its obviousness to the “What are large language models useful for?”. The lack of an answer may explain why the vector of technological development of blockchain is still intense and not settled. The Unchained conversation about post-quantum cryptography is a revealing example. Different blockchain ecosystems are still making decisions about the cryptographic architecture, rather than are not adding features to a finished product. NEAR and Ledger are thinking about how assets across different chains could survive a transition toward different post-quantum standards. This is not just product development. It is development of the technological rail itself. And this leads me to the artifact that I think is useful for understanding the difference between blockchain and AI: distance to monetization.

The farther a firm is from monetization, the more technological uncertainty remains and the more foundational R&D is required. At the same time, future cash flows are harder to see. Closer to monetization, the product, customers, and revenues become more legible, and investors can start pricing a business rather than a technological possibility.

Then why did ICOs happen?

Far-from-market technologies can still attract capital because investors value the option on a potentially large future market. ICOs provided blockchain with an unusual form of early-stage speculative financing, even when future cash flows were unclear. Nevertheless, the option eventually needs to move toward realization. This is where the Valley of Death becomes relevant. Early technologies can attract speculative capital because the upside appears enormous; mature technologies attract conventional capital because cash flows are visible. The difficult stage is in between: the initial excitement has faded, while commercialization is still uncertain.

Blockchain got stuck in limbo: The technological problems remain, but R&D can not yet be connected to visible economic value. Capital therefore may not flow toward the industry with the most unresolved technological problems. Right now, blockchain may be somewhere in that Valley of Death.

Conclusion

Right now, blockchain seems to be somewhere in the Valley of Death. The capital that financed the first wave has declined, but the technological work has not stopped. This is where distance to monetization becomes useful as an idea. It is both an outcome of not yet finding a large, repeatable, and appropriable use case and a cause of weaker private investment, which makes closing that distance harder. AI appears to be further along this path, while blockchain may still be in the valley, with the option alive as long as the technological work continues.

References:

1. Arrow, K. J. (1962). Economic welfare and the allocation of resources for invention. In The rate and direction of inventive activity: Economic and social factors (pp. 609–626). Princeton University Press.

2. Beard, T. R., Ford, G. S., Koutsky, T. M., & Spiwak, L. J. (2009). A Valley of Death in the innovation sequence: An economic investigation. Research Evaluation, 18(5), 343–356.

3. Gbadegeshin, S. A., Al Natsheh, A., Ghafel, K., Mohammed, O., Koskela, A., Rimpiläinen, A., Tikkanen, J., & Kuoppala, A. (2022). Overcoming the Valley of Death: A new model for high technology startups. Sustainable Futures, 4, 100077.

4. Nelson, R. R. (1959). The simple economics of basic scientific research. Journal of Political Economy, 67(3), 297–306.

5. Shin, L. (Host). (2026, August 25). One type of post-quantum cryptography is most popular. Why is crypto trying out three? (No. 1212) [Audio podcast episode]. In Unchained. Unchained. Episode page 

Please cite this article as: 

Petryk, M. (2026, September 30). Is Blockchain Stuck in the Valley of Death?. MariiaPetryk.com. https://www.mariiapetryk.com/blog/post-35