Ventura Labs

Grant sits down with Saim Akif of AKIF CPA at Exploit Summit in Montréal to discuss the tax and accounting questions facing Bittensor participants. Saim shares his approach to staking rewards, mining income, TAO-to-alpha conversions, wallet records, and the limits of using AI for tax preparation.

The conversation also explores government spending, network states, relocation, crypto ATM scams, and the human context behind financial decisions.

Saim on X: https://x.com/OnChainCPA

AKIF CPA: https://akifcpa.com/

Ventura Labs: https://venturalabs.ai/

Chapters

00:00 Saim Akif at Exploit Summit

01:11 Staking rewards and recordkeeping

02:06 Mining income versus staking income

03:56 TAO-to-alpha subnet swaps

05:29 Active versus passive crypto activity

07:21 Privacy, wallet tracking and enforcement

09:44 Why governments collect taxes

11:37 Foreign policy, lobbying and spending

14:23 Network states, relocation and family

17:13 Where to find Saim and AKIF CPA

18:15 Crypto ATMs and educating practitioners

19:40 Can AI replace a crypto CPA?

Michael Parker, co-founder of UMI (Bittensor Subnet 78), explores what it takes for machines to understand human movement, from sign language to the harder question of how robots should respond to people.

In this episode, you’ll learn

• Sign language: Why UMI starts with communication between Deaf and hearing people
• Bittensor competition: How winning models become the next benchmark
• Human intent: The gap between recognizing movement and understanding meaning
• SignRush: How separate signers and interpreters help validate training data
• Product plans: Bitsign, wearable glasses and two-way communication
• Data quality: Why context and authentic behavior matter more than footage alone
• Robotics in society: Assistance, authority and the convenience trade-off
• Human value: What people might still contribute as machines become more capable

Music
Beauty Flow by Kevin MacLeod
Licensed under CC BY 4.0. Edited and mixed for this episode.

In this episode, Ventura Labs interviews Xavier Lyu of Pareton (Subnet 10) about making AI inference faster and cheaper through Bittensor. He covers model quality convergence, GPU optimization, campaign-based competitions, pricing, vertical integration, distribution, and his experience designing dTAO at the Opentensor Foundation.

In this episode, you’ll learn

• Pareton: using permissionless competitions to optimize the software that serves AI models
• AI model convergence: why Xavier believes cost and latency are becoming the competitive edge
• Optimization campaigns: narrowing the search to one model, GPU class, and customer workload
• Hardware beyond NVIDIA: exploring underutilized AMD, Intel, and Chinese GPUs
• Customer requirements: balancing throughput, cost, and the hardware a client can afford
• Pricing: charging for performance gains through savings-sharing or ongoing service contracts
• Vertical integration: owning more of the serving stack and deploying inside client data centers
• Distribution: turning benchmark improvements into paying customers and repeatable revenue
• dTAO: designing the mechanism at OTF, then building a subnet governed by it
• The 54% result: a reported throughput improvement within one week and the challenge of commercializing it
• Agent workloads: why many generated tokens never reach a human reader
• Quant and fundamental analysis: applying simulation, optimization, and business analysis to running a subnet

Music
Beauty Flow by Kevin MacLeod
Licensed under CC BY 4.0. Edited and mixed for this episode.

In this episode, Ventura Labs interviews Cameron Fairchild, CTO of Latent Holdings, in person at Exploit Summit in Montréal about building products for AI agents and Bittensor’s emerging economy. He covers specialist coding agents, inference optimization, agent discovery and payments, subnet profitability, gamma service credits, circular spending, governance, TAO.app, and block-explorer business models.

In this episode, you’ll learn

• Ridges: moving from benchmark performance toward realistic coding workloads and specialist agents
• Agent pricing: evaluating tasks through their cost, usefulness, and commercial value
• Mixture of agents: routing between specialists for linting, database work, and test generation
• Cacheon: optimizing inference kernels without requiring a customer’s proprietary model weights
• Marketing to agents: API discovery, x402 payments, and reaching users through the agents they already use
• Agent-driven development: coding workflows, automated reviews, and expanding engineering coverage
• Revenue vs. profit: negative gross margins, protocol subsidies, and the limits of revenue as a performance metric
• Gamma tokens: proposed service credits, future compute purchases, and redemption risk
• Circular spending: using TAO and alpha to purchase services within Bittensor’s economy
• Governance: decentralizing leadership, avoiding entrenched interests, and separating voting from validation
• TAO.app: the mobile experience and wallet-security considerations
• Block explorers: delegation, advertising, fees, and the cost of maintaining infrastructure

In this episode, Seby Rubino of Instant (Subnet 46) details the pivot of a real estate subnet into an accelerated inference network built around a single in demand model. He discusses the case for a deflationary subnet token, the twelve month arc from Resi to Zipcode to Instant, and why he chose a revenue business over a research bet. Seby also details the miner collateral and alpha slashing design that replaces trusted execution environments with TOPLOC and managed VNet IR verification, the unannounced Zipcode lending product that finances GPU purchases for miners through Delaware SPVs, his read on the compute bubble and the Anthropic and OpenAI endgames, and the model compression and chip level work he expects to reshape inference economics.

In this episode, you’ll learn

• Why Seby is optimizing for burning more tokens than Subnet 46 emits rather than defending token equity
• How Resi became Zipcode and then Instant, and what made the real estate thesis misaligned with Bittensor
• Why accelerated inference is a premium product and how Groq, Cerebras and Taalas shaped the niche
• How miner collateral and alpha slashing make inference verification an economic problem, not just a technical one
• The mechanics of the new Zipcode lending vaults: 50 percent down, custodied GPUs, Delaware SPVs and lender ownership on default
• Where Seby thinks the compute bubble actually breaks, and why one bit and ternary models matter more than bigger racks

Beauty Flow by Kevin MacLeod (incompetech.com).
Licensed under Creative Commons Attribution 4.0:
https://creativecommons.org/licenses/by/4.0/
Source: https://incompetech.com/music/royalty-free/index.html?isrc=USUAN1900008
Excerpt edited, equalized, faded and mixed under narration.

In this episode, Ventura Labs interviews Allan of TAO Galaxy, the quant who brought systematic factor investing to Bittensor, sharing his arc from traditional finance skeptic to running rules-based strategies across the subnet market. He covers his TradFi background and why that industry stayed hostile to crypto, why AI equities outran crypto over the last two years, bull markets and the risk curve, momentum and Bitcoin's 50-day moving average, why institutional strategies are deceivingly simple on the signal side, risk management as the line between retail and sophisticated investors, the 2.5% position and 3% liquidity-pool caps, predicting volatility, new research on market cap as the driver of subnet correlation, the absence of a common market factor in dTAO, allocation by wallet size, comparing TAO to Zcash and NEAR, paid influencers and Bitcast, the Momentum and Carry indexes, and why passive subnet investing does not work yet.

In this episode, you'll learn

• Allan's backstory: traditional finance skeptic, the 2020 lockdown Bitcoin epiphany, discovering Bittensor three years later, mining experiments, and taopilling the friends who became his team
• Why TradFi is anti-crypto: institutional rigor, fear of the new, and his own estimate that more than ninety percent of crypto projects are bullshit
• Bull markets and the risk curve: why capital rotates from mega-cap equities outward, and why dTAO sits at the very end of that tail
• Momentum as an allocation rule: increasing crypto exposure when Bitcoin is above its 50-day moving average and decreasing it when it is not, instead of timing cycles
• What institutional quant actually looks like: simple signals, hard risk models, and why sizing, diversification and correlation are the real work
• TAO Galaxy's explicit risk constraints: never more than 2.5% of the portfolio in one subnet, never more than 3% of a subnet's liquidity pool, and penalties for volatile and illiquid subnets
• Why volatility is far more predictable than returns, using clustering and long-term memory, and why returns are too noisy to forecast
• New correlation research: the closer two subnets are in market cap, the more correlated they are, and why no common market factor exists in dTAO the way the S&P 500 functions in equities
• Why allocations differ by wallet size, with larger wallets moving subnet pools and smaller wallets more exposed to fixed staking fees
• The case against complaining: why comparing TAO to a decade-old Zcash or a VC-funded NEAR misreads what an organic project looks like
• Paid influencers in crypto, and why Bitcast functions as Bittensor's own marketing network
• TAO Galaxy's products: the Momentum and Carry indexes, non-custodial delegation through the Mentat Minds and Trusted Stake proxies, whitelisted access while liquidity is thin, a capped retail offering coming, and the portfolio app at taogalaxy.com
• Why the subnet market is not mature enough for a passive approach, and why holding the top ten subnets for a 40% APY can be wiped out in a single day

Beauty Flow by Kevin MacLeod (incompetech.com).
Licensed under Creative Commons Attribution 4.0:
https://creativecommons.org/licenses/by/4.0/
Source: https://incompetech.com/music/royalty-free/index.html?isrc=USUAN1900008
Excerpt edited, equalized, faded and mixed under narration.

In this episode, we are joined by Philipp Koellinger and Christian Roessler of Claims (Subnet 111), who detail the effort to convert 300 million scientific papers into a structured graph of claims and the evidence that supports them. They discuss the incentive failures in academic publishing that make published results difficult to replicate, why retrieval-augmented generation over raw PDFs is not sufficient for precise scientific reasoning, and the difference between extracting what a paper said and establishing what science currently supports. Philipp and Christian also detail the three behaviours the subnet rewards, the Bronze, Silver and Gold data tiers, the use of deliberately injected errors to calibrate language-model judges, and the case for running this work on Bittensor rather than inside a closed company.

In this episode, you'll learn

• Philipp's account of the replication study that no journal would publish, and why both publishing business models, charging readers and charging authors, reward novelty and volume over accuracy
• Why frontier models hallucinate citations at rates that vary sharply by scientific field, and why retrieval-augmented generation over PDFs still leaves AI unable to judge how reliable the underlying evidence is
• The three behaviours Claims pays miners for, coverage, extraction quality, and evidence quality, and why only the first two are live on mainnet today
• How the team defines a substantive claim, why there is no fixed number of claims per paper, and how dilution of quality costs a miner points even when coverage is high
• The Bronze, Silver and Gold data tiers, and the plan to inject known errors into judging tasks so miner consensus can be calibrated against ground truth
• Christian's argument that truth in this system is probabilistic rather than binary, and why conditional logical proofs are becoming easier for AI to verify than empirical findings
• The reasons for building on Bittensor instead of a closed company, covering distributed compute, the information contained in variance between miners, and the incentive language that keeps pushing data quality upward
• The commercial model, from enterprise ontology mapping to the comparison with Palantir Foundry, and why the flood of AI-assisted papers means the work never finishes

References

The essay referenced in this episode, part one of the four-part DeSci Foundation series by Philipp Koellinger, Christian Roessler and Christopher Hill, March 2022:

Why Science is Facing a Credibility Crisis

In this episode, Vincent Rapido, co-founder of Miners Union Validator (MUV) and creator of TaoSwap, covers his full arc in the Bittensor ecosystem — from mining Neural Internet's compute subnet to building two of the ecosystem's most-used infrastructure tools. He discusses the origin of MUV's anti-weight-copying thesis, the sTAO experiment MUV ran before dynamic TAO reached the chain, and the business model tying TaoSwap's zero-fee model to MUV's validator revenue. Rapido also details the mechanics of TaoSwap's Conviction Takeover List, the implications of Root Reborn for validators becoming subnet-investment allocators, and why he has never registered a subnet despite two-plus years in the ecosystem.

In this episode, you'll learn

• The origin of Miners Union Validator: how a group of Bittensor miners discovered that validators were simply copying each other's weights without running subnet code, and co-founded MUV to be the most transparent and equitable validator in the ecosystem
• The sTAO experiment: how MUV implemented stake-weighted subnet voting before dynamic TAO was built, proved that stakers would vote for their own subnets, and delivered the foundation a live chain experiment without requiring an upgrade
• The Christmas sprint: how Rapido discovered a GPU-spoofing exploit on Neural Internet (SN27), spent his entire Christmas vacation fixing it using Hashcat proof-of-work verification, and won a 50 TAO bounty that funded his entry into validator operations
• How MUV's copy-weight tool works and why Rapido argues it doesn't contradict the anti-weight-copying thesis — copying from vetted institutional sources like OTF is fundamentally different from lazy validator mirroring
• TaoSwap's Conviction Takeover List: what the 51% takeover threshold means, how MUV tracks it on-chain, and why the Covenant situation made this tooling necessary
• Root Reborn's implication for validators: how allocating stake to subnets ties APY directly to subnet performance, forcing validators to conduct due diligence rather than extract passive fees — and why Rapido does not expect the pre-dTAO backdoor-deal pattern to return
• The coldkey security gap: only two subnets use on-chain multisig (Substrate's pallet), and the distinction between off-chain tools like Talisman Signet and on-chain protections
• Why Rapido has never launched a subnet: the infrastructure-over-subnets thesis, how TaoSwap can remain neutral by serving all subnets, and the economic reality that validator revenue cannot cover the hardware cost of validating subnets like Chutes

In this episode, Charlie Holt of Ninja (Subnet 66) details the development of an agent-harness competition for autonomous coding on Bittensor. He discusses his path from early Bittensor mining and dropping out of college, the Miner Union, Brainlock, and the design of a winner-take-all subnet for improving coding agents. Charlie also details harnesses, GitHub-derived coding tasks, LLM judging, Katana, batch-invariant inference, fully homomorphic encryption, subnet economics, validator accountability, and Bittensor as a classroom for builders.

In this episode, you'll learn

• Charlie's path from discovering Bittensor in 2022 to mining, dropping out of college, organizing with Miner Union, and building on SN66
• How Ninja frames an agent harness as the tool belt around an LLM, and why harnesses matter for agents that search, code, and operate without constant human input
• The mechanics of SN66's winner-take-all competition, where miners submit agent files, validators run task batches, and the winning harness becomes king
• How GitHub commits become coding tasks, why LLM judging is central to the evaluation process, and how Katana fits into the Ninja stack
• Why deterministic inference matters for agent evaluation, including the tradeoffs around temperature, batching, and batch-invariant inference
• What Charlie learned from Brainlock and fully homomorphic encryption, including the gap between cryptographic privacy ideals and practical inference speed
• Charlie's view of subnets as company-like networks, including tokens, voting power, holder accountability, emissions, and the tension between revenue and research
• Why Bittensor can function as a classroom for builders, where contributors learn by competing, shipping, and getting paid for useful work

In this episode, we are joined by Iosif Gershteyn, Harry Crane, and Philip Maymin of Djinn (Subnet 103), who detail the development of a decentralized protocol enabling sharp sports bettors to sell their signals to executing counterparties while preserving signal secrecy through multi-party computation. They discuss the sharp bettor problem of winning bettors getting limited or banned by sportsbooks, the logistical ceiling of personal betting account networks, and how Djinn decouples intelligence from execution by connecting signal generators to the 500 million dormant sports betting accounts worldwide. Iosif, Harry, and Philip also detail the architecture of Djinn's miner and validator layers, the warranty and collateral system for signal quality guarantees, the TLS Notary attestation mechanism for proving line availability, and Djinn's broader applicability to verifiable execution problems beyond sports betting.

In this episode, you'll learn

• The sharp bettor problem — how training profitable sports bettors at analytics.bet led to the discovery that winning bettors get limited or banned, motivating a protocol to separate signal generation from execution
• The protocol architecture of Djinn (Subnet 103), including the roles of geniuses (signal generators), idiots (executors), miners (line attesters via TLS Notary), and validators (secret keepers via multi-party computation)
• The off-chain revenue model drawing from the $1–2 trillion global sports betting market, with a 0.5% protocol fee creating an economic engine independent of Bittensor emissions
• How the warranty and collateral system prevents both-sides shill attacks by requiring signal sellers to put skin in the game, and how Shamir secret sharing enables validators to grade outcomes without learning the true signal index
• The 10-signal audit window mechanics, including USDC reimbursement up to the signal purchase price and Djinn credit refunds beyond, and why bet sizing should match signal notional to maximize warranty coverage
• How TLS Notary enables miners to provide cryptographically verifiable proof that specific betting lines were live and executable at a given time, with peer notarization preventing self-attestation
• Why a majority-stake validator cannot front-run signals — MPC computation treats validators equally regardless of stake, requiring a minimum threshold of independent validators whose shards are combined only to answer whether the true signal was executable
• The broader thesis of Djinn as a protocol for any domain with verifiable execution and proprietary information, including applications in remote robotics and IP-protected manufacturing