A live, on-location Revenue Search featuring LeadPoet, a Bittensor-powered subnet/product that automates outbound sales by crowdsourcing high-quality leads from miners, validating them, and selling them to clients—initially via sales agencies for scale. The model gates access by burning alpha, creating a flywheel (more demand → larger reward pool → tougher competition → better data → more demand) and plans to evolve from selling leads to booking meetings. Early pricing spans self-serve subscriptions, volume API, and enterprise deals, with strong emphasis on data quality, anti-gaming validation, and eventual conversion-based miner rewards via CRM integrations. Beta opens in December with an open-source qualification agent; early access targets January, with comparisons to Apollo highlighting fresher, re-validated data and broader coverage through permissionless miners.

After a short hiatus, Revenue Search returns with Crucible Labs: Ala and David explain why Crucible exists: do the unglamorous, high-leverage work the foundation can’t—validate and allocate, build research and investor materials (via Unsupervised Capital), ship a TAO-native wallet with an auto-allocator and Ledger support, and incubate/accelerate stronger subnets. A big theme is governance and speed: DTO changed incentives quickly by design; true decentralization is the destination, but right now rapid, iterative tweaks are vital to keep a permissionless system healthy. Their near-term North Star is onboarding capital and talent through clarity and tooling, not hype: make staking/allocating simpler, abstract complexity, and help investors and builders see where value accrues.

They’re bullish that Bittensor is an “anything-incentive layer,” not just AI—and expect breakout products (e.g., dev-tools like Ridges) to pull mainstream attention and capital far more than explanations of emissions.

This Revenue Search spotlights Subnet 32 “It's AI,” an AI-text detector focused on education. Founder Sergey demos a clean web app that flags AI-written passages, highlights “AI-impactful” tokens, and generates shareable reports; it also offers plagiarism checks, batch scanning, API/Moodle/Zapier integrations, with Canvas coming. Citing a new, large unified benchmark (to be presented at an AI-in-education conference), It's AI claims top average accuracy (AUC ~0.92) versus GPTZero and others. The team targets universities with B2B plans while running low-cost miner inference; early revenue (~$2k/mo) comes mostly from enterprise subscriptions. Hosts push a go-to-market pivot: niche hard into higher-ed, raise enterprise pricing, personalise outreach to ~4k U.S. institutions, and avoid “poacher vs. gamekeeper” branding conflicts with student tools.

This Revenue Search features Subnet 113 (Taonado)—a non-custodial, Tornado-style privacy mixer on the Bittensor EVM. Users deposit fixed denominations (starting with 1 TAO, with 10/100 TAO pools planned), receive a secret note, and later withdraw to a fresh wallet to break linkability; miners simulate realistic flows to deepen the anonymity set and earn the subnet’s alpha. Revenue comes from ~2.5–5% mixing fees (plus gas) and “APY harvesting” by staking idle shielded capital, with an intent to auto buyback-and-burn the alpha. Contracts are a hardened Tornado fork, validation/scoring runs on-chain (no standalone validator), and the team is bootstrapping ~5,000 TAO liquidity to enable larger pools while advocating privacy-by-default across Bittensor.

This Revenue Search jumps into a deep-dive with Bitcast (Tom & Will). Bitcast pitches itself not as an agency but a decentralised ad tooling layer that lets brands brief creators at scale, with AI verifying message-fit and rewards tied to real attention (watch time/eyeballs via official platform data), not vanity metrics. They share traction to date (hundreds of Bittensor videos, ~hundreds of thousands of views, big watch-time) and the blockers they’ve been fixing: a no-code miner (optional, 5% fee) to onboard non-technical creators, a social-proofed website + multilingual outreach, and a scalable “ad read” model where brands pre-fund a budget that creators draw down from—so spend can flex and is linked to measured outcomes. Near term, revenue supports the ALPHA token (currently buyback/burn), with ~40–50 TAO/month cited today and ambitions to tap much larger Web2 budgets.

Will unveils Bitcast’s X (Twitter) integration: map a niche (starting with Bittensor) and compute an influence score using an endorsements graph (quotes/retweets/mentions; PageRank-style). A rolling top ~150 become eligible to mine; entry requires endorsements from those already inside, discouraging bots and low-signal spam. Quality > quantity: limited posts per brief, payouts weighted by who endorses your tweet, and brands can target specific niches/languages (e.g., dev-productivity, infra, other crypto-AI communities). Onboarding is dead simple: paste a wallet, tweet a one-time code, you’re connected. TikTok (different incentive design) comes next, followed by a self-serve ad portal so brands can set budgets, launch, and see results. Q&A covers collusion risks (mitigated by breadth/weights), creator incentives (APY/education; fiat off-ramps likely later), and expansion beyond crypto.

Siam kicks off by revealing a commissioned Bittensor artwork (“Michealeagτao”) he’s gifting to Const, then he and Mark run a no-guest AMA. They cover near-term market timing (expecting the bigger move into late-2025), the risk to compute subnets if TAO fell sharply (miner exodus) versus a healthier ecosystem at higher TAO, and how funding really follows credible, revenue-led plans (examples: Targon, sundae_bar, Shak recycling ~$1.4M into growth). A big chunk focuses on the TAO halving: pools fill more slowly so volatility rises for thinner subnets; historically halvings are “nothing-burgers” day-of, with the impact compounding over time.

They dig into alphanomics: buyback-and-burn is simple but building a digital asset (alpha) treasury that compounds—and can be borrowed against—is often stronger. Siam runs quick yield math (e.g., Apex’s high APY) to show how accumulating alpha can 4–12× holdings over a few years even before price moves. They expect more “captive demand” models (hold alpha for access), validators and front-ends to package subnet services, and ultimately a power-law leaderboard (S&P-style concentration). DSV’s approach: partner for the long term, avoid short-term rotation, prioritize force-multiplying subnets (e.g., Hippius storage, LeadPoet leads) and real revenue. They close with basics on DSV (min ~$50k; regulated) and promise more Revenue Search sessions.

Victor Teixeira (General TAO Ventures) traces his path from Contango Digital’s $10M blockchain–AI fund to becoming a full-stack Bittensor operator — incubating or advising multiple subnets (e.g., 23, Red Team, TPN), running the Round Table validator, and mining across the stack. He spotlights Subnet 35 (“Cartha”), an FX perp DEX co-built with Taoshi (Subnet 8): miners are either LPs or trader-miners; trading generates fees of which 50% go to LP miners and 40% go to vote-escrowed alpha holders as USDC dividends (weekly), creating aligned “alphanomics” that reduce sell pressure and reward real usage.

The broader discussion centres on revenue-first sustainability in the post-DTAO world. Buyback-and-burn isn’t dismissed, but Victor argues subnets should prioritise tangible earnings, robust VE models, and even prefunding/VC capital to bridge idea→revenue—plus new tools like lending against owner keys. With the TAO halving likely to thin liquidity growth per block, they expect more volatility (especially for smaller pools) and a premium on subnets that can fund OPEX, miner/validator sell-side, and sustain top-leaderboard emissions via real customers. For guest wish-lists to bring fresh eyeballs into BitTensor, Victor suggests Barry Silbert and Ejaz (Bankless)—and generally “everyone,” because mainstream adoption arrives when users benefit from Bittensor under the hood without even noticing.

Gopher (ex-Masa) / Subnet 42 provides cryptographically verified, AI-ready data via a network of ~256 miners running trusted execution environments to scrape and normalise web/X/Reddit/TikTok plus large financial price feeds. Devs use a UI + API (credit-based billing) and a built-in vector DB to aggregate/search topics and power apps. Traction: ~77k users across products (≈54k on AI Insights; ≈20k on a new trading tool), ~$1M ARR, ~1k paying on the trading app launched ~2 weeks ago. That app ingests multi-timeframe price data, generates trade setups, and can execute on Hyperliquid; team claims ~65% win rate with ~1:4 risk-reward on internal accounts and will publish on-chain trading wallets.

Strategy & token alignment: Gopher is migrating Masa to a Cosmos L1 (“Gopher”) focused on data aggregation/apps (Q1 launch target). Subnet 42 remains the data engine; enterprise and app customers pay fiat credits, and usage-based revenue from Gopher’s stack flows to SN42 for the data it supplies. The plan is for Alpha holders on SN42 to govern revenue use (buybacks, treasury, growth, etc.), keeping value with the miners who create it. Near-term focus: expand financial feeds (~100k assets), improve low-latency delivery (~26 ms), add social/news signals into trading, and explore a fund/vaults that trade the signals—while continuing to court market makers, prop funds, and other subnets that need dependable, verifiable data.

Vidaio (Subnet 85) does AI video upscaling (SD → HD/4K) and compression (dramatically smaller files with similar perceived quality). Their consumer web app is live in beta—demo showed ~95% size reduction—with paid tiers coming (think ~$0.05/min, ~75% margins). Beyond creators, the big targets are streamers/broadcasters, legacy libraries, security/medical, and autonomous fleets—anyone drowning in storage/CDN costs. Near-term roadmap adds a streaming pipeline (auto encoding ladders + Hippius storage), plus R&D on colorization and selective video generation/inpainting.

For enterprises needing NDAs and tighter control, Vidaio introduced an Enterprise Track: vetted “Elite miners” execute jobs off-subnet; clients pay fiat split roughly ~75% to miners / ~25% to Vidaio. Miners must post an AlphaBond—locking ~50% of their fiat payout equivalent in ALPHA until client acceptance—creating ALPHA demand/lockups while miners get fiat to cover infra (less sell pressure). Vidaio’s cut first covers OPEX, with surplus flexed between buybacks, product, and growth. Benchmark goals: surpass Topaz Labs (~$8.3M/yr consumer) and Visionular (~$10.5M/yr enterprise) while keeping the subnet as the innovation engine.

AWS went down, so we kicked it old-school on Zoom with Seby (RESI, Subnet 46). Quick refresher: RESI is building a real-estate oracle—unlocking U.S. property data and on-chain intelligence for lenders, DeFi/fractional RE, and proptech. Seby walked through fresh updates: a new white paper and alphanomics, DSV added to their OTC stack, V2 moves from APIs to scraping (cheaper for validators), V3 splits the subnet into data, inference, and storage (with Hippius) so prompts like “price this home from its inspection” fetch comps, analyze reports, and persist results. A public dashboard is rolling out, and go-to-market leans on IDX-style white-label portals for brokerages to drive viral distribution.

On revenue, RESI’s already selling Predict CASA data packs via a paid funnel (Meta ads → two-call close) and tightening the machine with hires and higher spend; the Oracle appraisals target fractionalized/DeFi real estate at $500 setup + $100/mo. Alphanomics is pure-alpha (no new token): stake for pricing tiers, LP with alpha for deeper discounts, and a planned sidechain where gas/wrapped-alpha and LP incentives amplify buy pressure as builders launch on top. TL;DR—Resi’s shipping product, lining up customers, and scaling a sales engine to convert leads into buybacks while turning a locked market into composable, on-chain real-estate intelligence.