Bitwise and SVRN Target NEAR’s AI Infrastructure Opportunity
Bitwise and SVRN are pursuing different investment strategies centered on NEAR Protocol and its potential role in an economy increasingly shaped by artificial intelligence. Bitwise offers exposure through its NEAR ETF, NRR, while SVRN combines a token accumulation strategy with infrastructure operations and commercial development.
The two approaches reflect a broader investment thesis: if AI agents begin carrying out more online economic activity, blockchain networks could provide infrastructure for transactions between software, applications and users. However, the long-term investment case depends on whether this activity creates sustainable demand for network services and economic value for NEAR token holders.
Bitwise Chief Investment Officer Matt Hougan and SVRN CEO Sal Ternullo have outlined different ways investors might participate in that opportunity. One approach packages exposure and staking within an exchange-traded fund, while the other combines a corporate treasury strategy with businesses operating within the ecosystem.
Neither approach guarantees returns. Both depend on NEAR’s ability to develop its technology, attract users and convert adoption into lasting economic activity.
Why Bitwise Selected NEAR for Its ETF
Hougan’s investment thesis is based on the idea that individual crypto projects could increasingly deliver different returns depending on their technology, execution and target markets.
“Increasingly in the future, I think we’re going to see idiosyncratic returns specific to individual projects, how they execute and what market they’re going after,” Hougan said.
For Bitwise, NEAR offers exposure to an established blockchain ecosystem alongside ambitions to support AI-driven commerce. The investment rationale includes the network’s transaction capabilities and its potential to support interactions involving AI agents.
Hougan also highlighted the importance of investor interest and long-term community support. “The thing that kills an ETF is if no one cares,” he said, pointing to NEAR’s existing capabilities and its broader AI-related ambitions as factors that could help attract attention.
The thesis remains dependent on execution. Interest in the intersection of AI and crypto does not by itself establish that NEAR will capture a meaningful share of future AI-related transactions or that those transactions will generate lasting token demand.
Financial Advisers Show Interest in the NEAR ETF
Hougan said early internal observations showed financial advisers allocating to NRR sooner than Bitwise had anticipated. These observations offer an initial indication of interest, but they do not establish the fund’s full investor composition or demonstrate long-term demand.
“I think the reason for that is the intersection of crypto and AI is just too attractive for anyone to ignore,” Hougan said. “They’ve been hearing about that for the last two years, and now they have a way to express it.”
The fund also incorporates staking rewards. Rather than distributing those rewards as cash payments, Hougan explained that “the staking yield gets added back into the net asset value. It’s not distributed.”
The fund’s official NRR website provides information about its structure, holdings, staking approach and investment risks. Because staked tokens can involve withdrawal delays, liquidity management is another consideration. Hougan pointed to NEAR’s relatively short unstaking period, settlement arrangements for market makers and additional liquidity capabilities for extreme conditions.
Staking rewards are not guaranteed, and fund investors do not hold the underlying tokens in the same way as someone who buys NEAR directly. The fund’s performance also depends on token prices, expenses and its operating structure.
SVRN Combines Token Accumulation With Network Businesses
SVRN has stated a long-term ambition to accumulate 10% of NEAR’s total token supply. That target represents a substantial treasury strategy, but the company’s approach also includes operating infrastructure and developing commercial services around the network.
CEO Sal Ternullo described the business as having started with infrastructure that supports network operations.
“What this meant in the first chapter was building out the infrastructure that powers the network,” he said. “And so on our side, we now run six white-labeled validators.”
Validators help maintain a proof-of-stake blockchain by participating in transaction validation and network consensus. SVRN’s stated operations also include infrastructure associated with NEAR Intents and confidential transactions, alongside its acquisition of Fastnear, which Ternullo described as a cash-generating infrastructure business.
The company’s strategy therefore extends beyond holding NEAR tokens. Its potential results also depend on the performance of its operating businesses, its capital allocation decisions and its ability to develop products that attract paying customers.
NEAR Intents and Private AI Inference
NEAR Intents is a key part of the ecosystem’s transaction infrastructure. The official NEAR Intents platform describes a system designed to connect assets, blockchain networks and applications through intent-based transactions. Such infrastructure could be relevant to AI agents that need to exchange assets or execute transactions across different networks.
Ternullo said NEAR Intents had processed more than $34 billion in volume, with approximately 98% of interactions still driven by humans. These figures were supplied in the interview material and should not be treated as independently verified here. They also indicate that AI-agent commerce remains an emerging use case rather than the dominant source of activity described.
SVRN is also targeting private AI inference. This refers to running AI models while protecting sensitive inputs, such as prompts, and outputs. Privacy may matter when AI agents handle business information or conduct transactions, although commercial adoption will depend on technical capabilities, user demand and the economics of providing the service.
Developing infrastructure for AI agents does not automatically establish demand for NEAR tokens. The investment case depends on how services are used, how revenue is generated and whether the network’s economic design connects that activity to token value.
Token Value Accrual Remains a Key Question
For investors, an important distinction is whether increased network activity translates into economic benefits for token holders. Transaction volume can demonstrate usage, but it does not by itself establish profitability, recurring revenue or sustained demand for a blockchain’s native asset.
Ternullo argued that NEAR is the asset through which ecosystem value accrues. “There is one asset; it is NEAR. All value that’s generated in this ecosystem accrues to it, and that will always be the case,” he said.
He also referred to the potential for a NEAR AI revenue model and token buyback system to increase value accrual. These are elements of the company’s investment thesis, not guaranteed outcomes. The actual effect would depend on the design and implementation of the model, the revenue generated and how any buybacks are funded and executed.
Investors can consult NEAR’s official developer documentation to understand the network’s technical infrastructure and capabilities. However, technical documentation alone cannot establish whether the token will capture sufficient economic value to support a particular investment valuation.
How Bitwise and SVRN Differ
Bitwise and SVRN offer distinct approaches to the same broad thesis. NRR provides exposure to NEAR through a fund structure that incorporates staking, while SVRN combines token accumulation with infrastructure operations and commercial development.
The ETF approach brings fund-level considerations, including expenses, liquidity, custody and staking arrangements. SVRN’s strategy adds corporate execution risks, including the cost of operating infrastructure, the success of its businesses and decisions about deploying capital beyond token holdings.
Both strategies ultimately depend on the evolution of the NEAR ecosystem. If AI agents and cross-chain applications generate sustained demand for network services, the infrastructure thesis could gain support. If activity fails to produce recurring economic value, adoption alone may not be enough to justify the investment case.
For now, the key question is whether NEAR can turn its AI ambitions, transaction infrastructure and growing ecosystem activity into durable revenue and meaningful value for token holders. Bitwise and SVRN are approaching that possibility through different structures, but neither removes the uncertainty surrounding adoption, execution or future returns.
