Aurelius is Bittensor subnet 37 (SN37), a project in the AI Alignment and Safety (Other) lane. Simulating moral dilemmas to train AI.
Aurelius turns ethical dilemmas into training data. Miners write moral-conflict scenarios, validators run them through Google DeepMind's Concordia agent simulations, and the resulting transcripts are scored and used to teach language models how to reason about right and wrong.
It is an educational deep dive, not a recommendation to stake. For live price, pool depth, and performance, always open the live subnet page.
What is Aurelius (SN37)?
Aurelius (subnet 37) is a Bittensor subnet built around AI alignment: the problem of getting AI systems to behave in line with human values. Rather than testing a finished model, it produces the raw material for studying moral reasoning. Contributors author structured ethical-dilemma scenarios, simulated characters play them out, and the transcripts are scored and turned into training data.
In plain English: It is like a writers' room for moral dilemmas. People script hard ethical situations (who gets the last dose of medicine, duty versus desire), simulated agents argue their way through them, and the resulting transcripts become study material for teaching AI to handle similar questions.
Centralized reference points: Anthropic alignment research, OpenAI safety, Google DeepMind
How miners and validators work
Like every Bittensor subnet, SN37 is a two-sided market: miners produce work, validators score it, and emissions follow those weights under the network’s dTAO economics.
- Miners serve a library of hand-authored scenario configs. Each one is a premise plus two characters with goals and assigned ethical philosophies (deontology, care ethics, and so on) and a forced choice. The miner returns configs to validators on request, round-robin.
- Validators put each scenario through an eight-stage check, run the accepted ones through a Concordia simulation, score the transcript, and set on-chain weights from the result.
Mechanism and design
The work format is concrete and documented in the subnet's main repo. A miner is a Bittensor axon that serves a directory of scenario JSON files loaded at startup. When a validator queries it with a ScenarioConfigSynapse, the miner returns the next config in its library, signed by its hotkey and stamped with a work_id. Each config follows a fixed schema: a 200 to 2000 character premise, exactly two agents with identities and goals, a tension archetype such as justice versus mercy, and one or more scenes with an optional forced choice. Miners do not generate scenarios on demand; they serve a pre-authored library.
The validator runs an eight-stage pipeline: version check, schema validation, a work-token balance check, per-hotkey rate limiting, a novelty check (using FAISS), a classifier quality gate, the Concordia simulation, and finally a work-token deduction plus an on-chain weight update. The pipeline short-circuits on the first failure, and the work token is only spent if all eight stages pass. Concordia is Google DeepMind's open-source generative-agent simulation framework. Each simulation runs in an ephemeral, resource-capped container with its model egress firewalled to an allowlist; the default model is DeepSeek's deepseek-chat. The transcript it produces is parsed and scored for coherence, and that score determines the miner's weight.
The economics run on work tokens. A validator spends one work token per scenario it accepts for simulation, and a miner has to fund a balance first by depositing TAO to a Central API multisig address. A miner whose balance is zero gets its submissions rejected at stage three before any simulation runs. In plain terms: serving scenarios is not free, which is the design's filter against spam.…
Why the project exists
- The problem it solves: AI systems increasingly make or shape decisions with ethical stakes, and there is no settled way to measure or improve how they reason through them. Aurelius is built to produce structured data aimed at exactly that gap, targeting moral-reasoning benchmarks (the README names MoReBench).
- The opportunity: Alignment data is scarce and expensive to author by hand. A network that crowd-sources dilemma design across many independent authors can cover more moral and cultural ground than a single in-house team.
- The Bittensor advantage: Diverse contributors produce a wider spread of scenarios than a homogeneous lab would, and the scoring pipeline plus a work-token cost is meant to reward quality over raw volume.
- Traction signals: Modest so far. The codebase is actively maintained, with a published Docker path for running validators and miners and a live testnet on subnet 455. At snapshot time the subnet is in a burn-mode state with no active miners and no emission share on-chain (detailed below), and public discussion of it is thin.
What stakers should check before buying alpha
Subnet storytelling is cheap. On-chain and product checks are not. Use the same framework as our subnet metrics guide:
| Check | Why |
|---|---|
| Real product / docs / repo | Separates shipping teams from narrative-only slots |
| Pool depth (TAO) | Your exit door: thin pools amplify slippage |
| Net flows (7d / 30d) | Under flow-based emissions, capital votes with its feet |
| Emission share | Whether the market is funding this subnet right now |
| Ownership concentration | Large holders can move a small alpha market |
| Immunity / deregistration | Post-immunity weak slots can lose the race (see immunity guide) |
Pull the live numbers on Aurelius · SN37 rather than trusting any static screenshot in a blog post.
Risk factors
- No active incentive loop at snapshot: The validator's default burns 100 percent of miner emissions, and on-chain there are no active miners and a 0 percent emission share. Until that changes, the mine-and-reward loop that drives most subnets is not running here.
- Deregistration: With an emission share of 0 percent and well past its four-month immunity, Aurelius is exposed to Bittensor's automatic deregistration, which removes the lowest EMA-price non-immune subnet when a new one registers.
- Single primary developer: Development is driven almost entirely by one contributor. That concentrates both the knowledge and the execution risk.
- Niche demand path: Alignment data is genuinely valuable, but the route from "produces moral-reasoning transcripts" to durable on-chain demand is unproven, and the work-token deposit model asks miners to fund participation up front.
How this fits a TAO portfolio
Many holders keep a root vs subnet split: root for calmer TAO exposure, subnets like Aurelius for thematic upside (and higher variance). If you use TaoScale, you can size SN37 from your wallet without handing over custody. See non-custodial staking.
- Live market: SN37 on TaoScale
- Browse and stake: /invest
- More subnet explainers: /blog/subnets
FAQ
What does Aurelius do on Bittensor?
It is like a writers' room for moral dilemmas. People script hard ethical situations (who gets the last dose of medicine, duty versus desire), simulated agents argue their way through them, and the resulting transcripts become study material for teaching AI to handle similar questions.
What is the SN37 alpha token?
When you stake TAO into subnet 37, you receive that subnet’s alpha. Its price versus TAO moves with the subnet’s pool (see what is dTAO).
How do I research Aurelius before staking?
Read the mechanism above, open the official repo/docs when available, then verify pool depth, flows, and activity on SN37. Cross-check with the metrics checklist.
Is this financial advice?
No. TaoScale publishes educational explainers and live tools. Decisions to stake are yours alone.
Not financial advice. Educational overview of Aurelius (SN37) compiled from public project materials and ecosystem documentation. Mechanisms and market structure change, so verify live data and official sources before acting.
