Revenue Search: Inside Bittensor

Discover a revolutionary AI-text detector that flags AI-generated content for educational integrity. The founder reveals how it accurately identifies AI-written passages, achieving impressive benchmarks. Key features include plagiarism checks and seamless LMS integrations, catering specifically to universities. The discussion dives into market potential, competitive landscape, and effective B2B strategies. Plus, insights on personalizing outreach to educational institutions and the risks of dual branding in the academic arena.

Captain Taonado, a key figure in the BitTensor community and operator of Subnet 113, shares insights on building a non-custodial privacy mixer. He explains how the Tornado-style mixing works, emphasizing user anonymity through fixed pools and secret withdrawals. Captain discusses incentives for miners who simulate user flows, plans for larger liquidity pools, and strategies for using shielded capital to generate yield. He also addresses societal perceptions of privacy tools, highlighting the importance of privacy-by-default and the evolving landscape of regulatory risks.

Tom Blaise, Co-founder and CEO of BitCast, dives into the innovative world of decentralized marketing. He discusses how BitCast connects brands to creators using AI-verified briefs and outcome-linked rewards, moving beyond traditional PPC methods. The platform tackles creator onboarding with a no-code solution, while Will highlights their influence metrics on X (Twitter) and targeted messaging strategies. Plus, they share exciting upcoming integrations with TikTok and plans to attract large Web2 budgets to revolutionize creator marketing!

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.

Brendan Playford, the innovative founder of Gopher and operator of Subnet 42, dives into the future of cryptographic data infrastructure for developers and finance. He discusses the evolution from Masa to Gopher, focusing on verified, AI-ready data solutions. The conversation highlights their new trading tool, which boasts a 65% win rate, and plans for users to govern revenue within the community. Additionally, Brendan shares insights on the role of miners and the unique mechanics behind Subnet 42's success.

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.

Jill Kenney, CEO of sundae_bar and lead of Subnet 121, brings her experience from Red Bull and Paidia Gaming to the forefront of AI innovation. She discusses how sundae_bar is revolutionizing the AI agent marketplace by connecting developers with businesses for agent deployment. The platform uses a unique BitTensor subnet for feedback-driven development and boasts varied revenue streams. Jill highlights the importance of customization for enterprise needs and shares exciting plans for integrating more builders, ensuring agents are easily accessible for everyone.

Mark and Siam discuss with Austin the challenges of AI alignment, emphasizing the need for wisdom in models, not just knowledge. They dive into Aurelius’s decentralised approach to creating high-quality synthetic alignment data and the innovative use of a constitution for oversight. Austin outlines the market potential for this data, targeting enterprise customers while addressing alignment faking. Plans for publishing datasets and integrating feedback from validators highlight a future where AI not only performs but aligns with human values.