The report, titled Fundamental Value Analysis for Bittensor Subnet Tokens, was published by Yuma (a subsidiary of Digital Currency Group, DCG) in January 2026. It is authored by Greg Schvey, Jeff Schvey, Karl Osis, Kalen Boyles, Alex Bernbach, Youssef El Moujahid, and Lindsay Stone.
The stated purpose is to define Bittensor subnet tokens within the context of financial assets and to establish a framework that enhances their use in productive capital deployment. The report positions subnet tokens as a novel asset class, potentially as transformative as Bitcoin, shifting the focus from speculative hype to measurable operational utility and fundamental valuation.
The analysis draws on principles from corporate finance, adapted to the unique economics of the Bittensor network, including its token issuance mechanics and halving events (similar to Bitcoin’s halvings every ~4 years). It emphasizes that subnet tokens are not mere speculative instruments, but serve a practical role in decentralizing AI infrastructure.
Key Sections and Main Points
The report focuses on three core ideas:
- What subnet tokens are, and why they should be treated as financial assets
- What fundamentally drives their value
- How to value them using a structured, repeatable framework
Subnet Tokens as Financial Assets
Bittensor subnets are decentralized AI services (e.g., image recognition, fraud detection, deepfake detection) powered by miners and validators on the network.
Subnet tokens (also called alpha tokens or dTAO tokens) are described as “OpEx replacements.” In traditional companies, operational expenses (OpEx) like servers, data centers, and compute resources are funded through revenue or capital raises. In Bittensor, subnet tokens act as a decentralized equivalent: they are emitted to miners as payment for providing real-world infrastructure and computational resources.
Important constraints are also acknowledged:
- Subnet tokens are not legal tender
- They are not strictly required to access a subnet’s AI intelligence
- Miners therefore receive “digital cash” that may not directly offset fiat-denominated costs
This creates valuation challenges, frequently raised in community critiques.
Despite this, tokens strongly incentivize participation through Yuma Consensus (Bittensor’s mechanism for ranking and rewarding contributions) and are aligned with the network’s overall $TAO emissions (3,600 TAO daily post-2025 halving).
Fundamental Value Drivers
Subnet token value is driven by a combination of utility, market behavior, and network mechanics:
- Operational utility: Tokens derive value from a subnet’s ability to deliver tangible AI services. Subnets with real adoption and revenue potential attract more staking, increasing token value and TAO emissions.
- Market dynamics: Through staking via AMM exchanges, participants effectively “vote” on a subnet’s worth by locking TAO. Popular subnets experience rising token prices, reinforcing higher rewards.
- Issuance curves and halvings: Daily token emissions and periodic halvings reduce supply over time, introducing Bitcoin-like scarcity into long-term valuation.
- Risks: Adoption velocity, sales execution, competition from centralized AI providers (e.g., OpenAI), and network-specific risks such as validator consensus deviations.
Valuation Framework
The report introduces a Discounted Cash Flow (DCF) model adapted for crypto assets, forecasting subnet token value based on projected “cash flows” (OpEx equivalents) discounted to present value.
Key inputs include:
- Initial annual OpEx estimate: Derived from market comparables (e.g., deepfake detection subnets benchmarked against companies like Reality Defender or Hive, suggesting $10M starting OpEx)
- Growth rate (CAGR): Based on the projected growth of the underlying AI market (e.g., 44.5% for deepfake detection)
- Discount rate: Reflecting high execution and market risk (e.g., 30%)
- Token emissions rate: Daily issuance to miners (e.g., 2,952 tokens/day)
The output is a fundamental token price, calculated by discounting future OpEx value creation against emissions and supply dynamics.
Multi-year projections incorporate halving events, with prices expected to rise post-halving due to reduced emissions.
A notable omission is the absence of a terminal value, which typically represents a large share of intrinsic value in traditional DCF models. Community feedback suggests this may lead to conservative long-term estimates.
The framework also accounts for Bittensor-native mechanics, including stake-weighted rewards and subnet recycling, where underperforming subnets are gradually outcompeted.
Example Application
The report illustrates the framework using hypothetical or real subnet scenarios. A commonly cited community application focuses on Bitmind Subnet 34 (deepfake detection):
- Inputs: $10M initial OpEx, 44.5% CAGR, 30% discount rate, 2,952 daily miner tokens
- Current fundamental price: ~$9.28 vs. market price of $3.9, implying ~138% undervaluation
- Projections: $13.41 in 1 year, and $80.93 in 4 years post-halving
- Potential returns: Approximately 17x for early staked positions, with value increasingly supported by real revenue as the subnet matures
Related Research
The report references complementary work such as the Subnet Growth Model (SGM) by @here4impact, which focuses more explicitly on growth trajectories and adoption curves, rather than valuation alone.
Conclusions and Implications
The report positions subnet tokens as potentially the “most interesting new asset class since Bitcoin,” enabling decentralized AI monetization without centralized control. This creates a “synaptic economy” where value flows from real utility to $TAO holders, similar to an ETF tracking AI innovation.
A central message is the shift from speculation to fundamentals. Builders, operators, and investors are encouraged to evaluate subnets using operational metrics, not narrative hype. As Bittensor matures post-halving and expands beyond 128 subnets, high-utility subnets could plausibly reach $1B+ market caps, driving network-wide growth.
The framework is intended to guide investment decisions, staking strategies, and subnet design, improving capital allocation across decentralized AI. This rigor may also unlock institutional interest, particularly through vehicles like Yuma Asset Management.
Key caveats remain: regulatory uncertainty, fiat integration challenges, execution risk, and the conservative bias introduced by excluding a terminal value from the DCF model.
This summary is compiled from the report’s title page, original announcement, community DCF applications, critiques (notably around terminal value and token utility), and endorsements from figures such as Barry Silbert and members of the Yuma team. The report is technical yet accessible, and is clearly aimed at maturing the Bittensor ecosystem from speculation toward value-driven analysis.
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