Blockchain AI Lags as Firms Prioritize Core Infrastructure

On-chain data marketplaces such as Ocean Protocol and Grass try to enable direct transactions between data providers and model developers. The report argued that data distribution is dominated by convenience and scale. Transparency alone does not trigger demand shifts when incumbents offer speed and proven execution.

Zero-knowledge machine learning can theoretically prove that an AI model followed specified rules without exposing sensitive information. Tiger Research said the market signal remains weak. Most companies are not yet compelled to pay the costs of adopting these tools voluntarily. The report described adoption in this lane as likely regulation-led, driven by frameworks like the European Union's AI Act that could create explicit requirements around data provenance and verifiability.

AI agent frameworks with their own wallets and identities, capable of transacting via stablecoins and settling autonomously, imply a machine-to-machine economy rather than incremental enterprise automation. The obstacle is market maturity. Most firms are still working to prove ROI for basic AI deployments and ensure safety. Multi-agent systems operating in open environments are not yet a top budget priority.

Tiger Research characterized the current lull as a business time lag – a period often seen before next-generation infrastructure meets a clear market need. The sector has not yet produced a killer use case that changes behavior for consumers or enterprises. Without that proof point, blockchain AI projects struggle to attract conservative mainstream capital. Whether the segment converges on today's performance benchmarks or builds toward a future paradigm will depend on how individual projects position themselves, the report said.