This hosts-only “Revenue Search” episode is a casual catch-up where Siam and Mark answer live chat questions and discuss Bittensor’s bigger picture. They explain that accepting fiat for subnet services doesn’t bypass alpha value—fiat typically routes into TAO and then through liquidity pools—and that long-term alpha appreciation depends on each subnet’s “alphanomics,” mainly revenue-funded buybacks and/or getting miners to lock up alpha (with Chutes and Hippias cited as strong examples). They then talk about why they pitch Bittensor as “not really crypto” to newcomers (it uses blockchain as a coordination/resource-allocation layer for AI), compare Bittensor’s growth vs Bitcoin, and touch on rehypothecation risks as markets mature.

They also cover TaoFlow and subnet churn (registration cadence, deregistration “relegation,” and why they don’t want more than 128 slots yet due to chain bloat and diluted incentives), plus investing views like TAO vs a broad basket of subnets depending on how active you plan to be. A major section focuses on agents: Siam describes building his OpenClaw agent (“Gordy”) with SOPs and tools, and they argue agents will increasingly discover and use Bittensor services. That leads into Handshake as an agent payment/provisioning layer with “providers” (APIs/services) and “skills” (prebuilt workflows), plus efforts to reduce friction like gas issues. They briefly touch on Astrid Arena (agents competing in trading challenges), OTC/Bitstarter deal-making and onboarding new talent, and wrap with a few quick audience questions and upcoming Bittensor social events (London, then San Francisco).

This session begins with Mark and Siam chatting about a proposed new Bittensor “shorting mechanism” aimed at punishing malicious or gaming subnets by letting markets drive their alpha toward zero and trigger deregistration—while flagging obvious risks like self-shorting by subnet owners and the broader concern that changing market rules can make the ecosystem feel less investable. They then bring on Tommi from Subnet 79 (rebranded from “Taos” to “MVTRX”), who explains they’re building a state-of-the-art exchange for Bittensor dTAO/alpha tokens, paired with a sophisticated sandbox simulation framework (C++/Rust) where miners can test trading algorithms under many parallel, realistic limit-order-book simulations before deploying to live trading. The core problem they’re tackling is that alpha markets can be illiquid and risky—especially during “black swan” events—so they want to improve liquidity, reduce slippage, and enable larger players to manage/rebalance portfolios more efficiently using advanced order types and a dynamic incentive model that shifts rewards/fees between makers and takers depending on market conditions (e.g., paying makers more during crash-like imbalances to stabilize markets). On monetization, Tommi outlines two revenue streams: (1) exchange fees from live trading (a steady flow) and (2) selling high-fidelity simulated high-frequency/L3 order book data (chunkier, periodic revenue), with an expectation of beta access for miners and broader user/UI growth later in the year; he also notes revenue would likely be split between buybacks (roughly ~25–50%) and ongoing development rather than burning.

In this Revenue Search session, Mark and Siam open with a quick macro discussion about escalating conflict in the Middle East, the Strait of Hormuz risk, oil spikes, and how that could affect inflation, rate cuts, and global “printing” dynamics—then pivot to why war-time social media becomes a firehose of fake or misleading footage, setting up the day’s guest: Vericore (Subnet 70). Vericore is presented as “community notes on steroids”: miners research specific claims and return an auditable evidence trail with semantic scoring (e.g., supports/refutes a claim, confidence, and left/right leaning), intentionally surfacing varied and even conflicting sources to break echo chambers. The team explains how evidence quality is judged (cross-source corroboration, source weighting/whitelisting for reputable outlets and academic papers, and plans to back-test research once outcomes resolve), and how the product can plug into agent “harnesses” via API/Handshake so bots (including OpenClaw-style agents) can use it as an honesty/unbias layer. On monetization, they discuss charging per API call, building a personalized “Signal” product, lead-gen around prediction markets, and exploring a prediction-market fund/competition where a large share (at least ~50% or potentially all) of profits loops back into subnet token buybacks—while noting current bottlenecks are API speed/performance and structuring the fund, plus balancing building real product vs hype.