This episode, Mark discloses that DSV is already invested in today’s subnet, but they’ll still ask the awkward questions. They bring on Koyuki (“special k”) from San Francisco, who shares her background in AI (web2 + web3), how she joined the Bittensor Foundation/OTF as Head of AI, and then dives into her slides on Subnet 78, Vocence.

Koyuki pitches Vosens as a decentralized “voice intelligence layer” on Bittensor, targeting the rapidly growing voice AI market and competing with incumbents like ElevenLabs by being more open, cheaper, and driven by Bittensor incentives. She shows that Vocence already has a live studio product (TTS/STT, voice cloning/design, text-to-music, API) and outlines how miners submit models that validators score across nine dimensions (script accuracy and naturalness weighted highest), with winning models becoming the new baseline for inference. On revenue, she describes a credit-based SaaS model (consumer + API, with enterprise as the big upside), plans for buybacks into a treasury, and an emissions burn condition if no model clears a defined improvement threshold. The discussion then focuses on the “Turing test” problem for voice agents—latency, filler words, interruptions, and overlapping speech—and Koyuki claims a new “style trajectory TTS” approach will make agents sound truly human soon. Siam offers a $5,000 wager that Vocence can produce a voice agent he can’t detect as AI by the end of the month, and Koyuki accepts, with some talk about testing via a phone-call scenario and adversarial off-script questions. They wrap by noting the prior Vocence slot issues/deregistration risk and arguing this time is different due to stronger leadership, a live product, faster shipping, and early traction.

In this episode, Bob from Subnet 48 (quantum compute) gives a grounded overview of quantum computing: huge long-term promise (materials, batteries, drug simulation), but today’s machines are still “NISQ” (noisy, intermediate-scale, not error-corrected at useful scale). Subnet 48’s pitch is essentially “Airbnb for quantum computers”—miners run real quantum workloads, users submit quantum circuits, and the network executes them cheaper than traditional access. Bob shows OpenQuantum.com as the front-end marketplace, listing multiple hardware providers (IonQ, Rigetti, IQM, AQT) with current machines in the ~20–50 qubit range, and explains that most jobs on OpenQuantum are being executed via Subnet 48.

The conversation then veers into the big scary question: quantum risk to crypto. Bob distinguishes SHA-256 (mining) from elliptic curve cryptography (ownership/signing) and argues the nearer-term threat isn’t quantum “mining Bitcoin faster,” but breaking signature security unless chains migrate to post-quantum schemes. He mentions industry roadmaps and research suggesting timelines could be tighter than people assume, and plugs Subnet 63 (Enigma)—a prize-driven subnet designed to incentivize public breakthroughs in cryptography rather than vague claims.

In this Revenue Search episode, the hosts sit down with Aldo from Subnet 55 (NIOME / “Neural Intelligence in Omics”)—a project tackling one of the messiest problems in biotech: how to make genomic/biodata usable for research and AI without turning it into a privacy and cybersecurity nightmare. Aldo walks through why the status quo is broken, pointing to repeated breaches and misuse across the industry (from direct-to-consumer testing firms to major institutions), and makes the case that “compliance” doesn’t equal “security” when hackers are actively targeting sensitive health data.

NIOME’s approach is twofold. First, through the wider genomes.io ecosystem, individuals can store their DNA data in encrypted “vaults” where the user remains the owner and controls access—rather than handing away rights to hospitals or platforms. Second, the subnet’s core mission is to generate synthetic genomic / biodata at scale—so pharma, biotech, and researchers can train models and run analyses without exposing raw identifiable datasets. The roadmap is built around a structured series of predictive challenges (starting with cystic fibrosis / CFTR), with commercial interest already forming around bespoke challenges, licensing outputs, and data brokerage partnerships (e.g., bringing external datasets into the synthetic pipeline and sharing revenue when that data is used). The big idea: make biodata safe, precise, and scalable and use Bittensor’s open, inspectable “under-the-hood” model development to build trust versus black-box approaches.